pressure_loop_thermal_boundary_layer_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64c675b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "from matplotlib.tri import Triangulation\n",
    "import matplotlib.pyplot as plt\n",
    "# These are just cosmetics\n",
    "plt.style.use('seaborn-white')\n",
    "#plt.style.use('ggplot')\n",
    "plt.rc('font', size=16) #controls default text size\n",
    "plt.rc('axes', titlesize=16) #fontsize of the title\n",
    "plt.rc('axes', labelsize=16) #fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=16) #fontsize of the x tick labels\n",
    "plt.rc('ytick', labelsize=16) #fontsize of the y tick labels\n",
    "plt.rc('legend', fontsize=14) #fontsize of the legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e368d394",
   "metadata": {},
   "outputs": [],
   "source": [
    "import V2RhoT_gibbs_lib as lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5abc2f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "DMM_no_atten = np.loadtxt('./databases/DMM_HP',comments='#')\n",
    "# correction using grain size = 10 mm and oscillatio period of 75 seconds.\n",
    "# Attenuation model of Jackson and Faul 2010\n",
    "# Function: lib.atten_correction (T (oC),P (Pascal),Vp (km/s),Vs (km/s),oscilation period (s), grain size (mm))\n",
    "DMM_atten_corrected = np.copy(DMM_no_atten)\n",
    "for i in range(len(DMM_atten_corrected)):\n",
    "    DMM_atten_corrected[i,3],DMM_atten_corrected[i,4] = lib.atten_correction(DMM_atten_corrected[i,0],DMM_atten_corrected[i,1]*1e5,\n",
    "                                                         DMM_atten_corrected[i,3],DMM_atten_corrected[i,4],75,10)\n",
    "\n",
    "# correction for melts\n",
    "# These are relations from lab experiments. More details in Afonso et al., 2016 III\n",
    "# Function: lib.velocity_melt_correction_mantle (T (oC),P (GPa),VP (km/s),Vs (km/s)\n",
    "DMM_atten_melt_corrected = np.copy(DMM_atten_corrected)\n",
    "melt = np.zeros_like(DMM_atten_melt_corrected[:,0])\n",
    "for i in range(len(DMM_atten_melt_corrected)):\n",
    "    DMM_atten_melt_corrected[i,3],DMM_atten_melt_corrected[i,4],melt[i] = lib.velocity_melt_correction_mantle(DMM_atten_melt_corrected[i,0]-273.15,\n",
    "                                                                                             DMM_atten_melt_corrected[i,1]/1e4,\n",
    "                                                             DMM_atten_melt_corrected[i,3],DMM_atten_melt_corrected[i,4])\n",
    "# append melt to the table \n",
    "DMM_atten_melt_corrected[:,5]=melt[:]\n",
    "Composition_1 =DMM_atten_melt_corrected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "84bc00cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b1bbf01b48>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(melt,DMM_atten_melt_corrected[:,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb798d36",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'temp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-893031979bdd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'temp' is not defined"
     ]
    }
   ],
   "source": [
    "np.shape(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0db2e65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### lithostatic pressure \n",
    "def lithostatic_pressure(depth,density):\n",
    "    Pressure_lith = np.zeros_like(depth)\n",
    "    # Check is the first index is at zero depth or not\n",
    "    # If it is not then pressures is set equal rho*g*h\n",
    "    # else is it set to 0\n",
    "    if depth[0] != 0:\n",
    "        Pressure_lith[0] = density[0] * depth[0]* 9.8 * 1e3 * 1e-5\n",
    "    else:\n",
    "        #thickness = (depth[1]-depth[0]) #*1e3\n",
    "        Pressure_lith[0] = 0\n",
    "    # Now looping through depths\n",
    "    for j in range(len(depth)-2):\n",
    "        thickness = (depth[j+2] - depth [j+1]) *1e3\n",
    "        Pressure_lith[j+1]=Pressure_lith[j] + thickness*density[j+1] *9.8*1e-5\n",
    "        \n",
    "    # fixing last index\n",
    "    Pressure_lith[-1] = Pressure_lith[-2] + (depth[-1] - depth [-2])*1e3 * density[-2]*9.8*1e-5 \n",
    "    #print(depth[:],density[:],Pressure_lith[:])\n",
    "\n",
    "    p_func = interpolate.interp1d(depth,Pressure_lith)\n",
    "    return p_func\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff584d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "### lithostatic pressure \n",
    "def lithostatic_pressure_depth(depth,density):\n",
    "    Pressure = 0\n",
    "    # Check is the first index is at zero depth or not\n",
    "    # If it is not then pressures is set equal rho*g*h\n",
    "    # else is it set to 0\n",
    "    if np.size(depth) > 1:\n",
    "        Pressure_lith = np.zeros_like(depth)\n",
    "        if depth[0] != 0:\n",
    "            Pressure_lith[0] = density[0] * depth[0]* 9.8 * 1e3 * 1e-5\n",
    "        else:\n",
    "            #thickness = (depth[1]-depth[0]) #*1e3\n",
    "            Pressure_lith[0] = 0\n",
    "            # Now looping through depths\n",
    "        for j in range(len(depth)-2):\n",
    "            thickness = (depth[j+2] - depth [j+1]) *1e3\n",
    "            Pressure_lith[j+1]=Pressure_lith[j] + thickness*density[j+1] *9.8*1e-5\n",
    "        Pressure = Pressure_lith[-1]\n",
    "    elif depth !=0:\n",
    "        Pressure = (depth-0)*density* 9.8 * 1e3 * 1e-5\n",
    "    else:\n",
    "        Pressure = 0\n",
    "    #print(Pressure)\n",
    "    return Pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723b017d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70d17ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tomo_NA_stack=np.loadtxt('./data_tomo/NA_age_vel_stack.dat',comments='#')\n",
    "# Just formating the data required\n",
    "data=tomo_NA_stack[:,0];\n",
    "data=np.column_stack((data,tomo_NA_stack[:,0]))\n",
    "data=np.column_stack((data,tomo_NA_stack[:,1]))\n",
    "data=np.column_stack((data,tomo_NA_stack[:,2]))\n",
    "data[:,1]=0\n",
    "X = np.unique(data[:,0])\n",
    "Y = np.unique(data[:,1])\n",
    "Z = np.unique(data[:,2])\n",
    "nx,ny,nz = np.meshgrid(X,Y,Z)\n",
    "V= np.zeros_like(nz)\n",
    "Composition = np.zeros_like(nz)+1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "000dea5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
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      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n",
      "Data is not a 3D volume!!\n"
     ]
    }
   ],
   "source": [
    "m,n,p = np.shape(nz)\n",
    "for i in range(m):\n",
    "    for j in range(n):\n",
    "        for k in range(p):\n",
    "            ind = np.where( (data[:,0] == nx[i][j][k]) & (data[:,1] == ny[i][j][k]) & (data[:,2] == nz[i][j][k]))\n",
    "            #print(nx[i][j][k],ny[i][j][k],nz[i][j][k])\n",
    "            try:\n",
    "                V[i][j][k] = data[ind[0],3]\n",
    "            except:\n",
    "                print('Data is not a 3D volume!!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21d72deb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a73f14a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vel_to_temp_P_loop(nx,ny,nz,Vs,Table):\n",
    "    Temperature_out = np.zeros_like(nx)\n",
    "    Density_out     = np.zeros_like(nx) #+3300\n",
    "    diff_Vs         = np.zeros_like(nx)\n",
    "    P_out           = np.zeros_like(nx)\n",
    "    melt_out        = np.zeros_like(nx)\n",
    "    Vp_out          = np.zeros_like(nx)#np.zeros_like(tomo[:,1])\n",
    "    Vs_out          = np.zeros_like(nx)#np.zeros_like(tomo[:,1])\n",
    "    m,n,p=np.shape(nx)\n",
    "    # looping over all the geospatial nodes\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            #P_func=lithostatic_pressure(nz[i][j][:],Density_out[i][j][:])\n",
    "            # Starting with ak135 pressure profile\n",
    "            P_func=lib.pressure_inter\n",
    "            # setting positive difference to set the while loop\n",
    "            diff = 10\n",
    "            p_counter  = 0\n",
    "            while diff > 0:\n",
    "                #print(f'{p_counter} Pressure counter {threshhold}.')\n",
    "                p_counter = p_counter+1\n",
    "                # loop over all depths to get the density and temperature\n",
    "                for k in range(p):\n",
    "                    P  = P_func(nz[i][j][k])\n",
    "                    Vs_in = Vs[i][j][k]\n",
    "                    P_table,temp,dens,vp,vs,melt=lib.lookup_vs_P_accurate_prop(Vs_in,P.tolist(),Table)\n",
    "                    P_out[i][j][k] = P_table \n",
    "                    Temperature_out[i][j][k]=temp\n",
    "                    Density_out[i][j][k]=dens\n",
    "                    Vs_out[i][j][k]=vs\n",
    "                    Vp_out[i][j][k]=vp\n",
    "                    melt_out[i][j][k]=melt\n",
    "                # calulcating new pressure profile\n",
    "                P_func_new=lithostatic_pressure(nz[i][j][:],Density_out[i][j][:])\n",
    "                # RMS difference between initial and updated pressure profile\n",
    "                diff = np.sqrt(((P_func_new(nz[i][j][:]) - P_func(nz[i][j][:])) ** 2).mean())\n",
    "                print('Pressure counter',p_counter)\n",
    "                print(f'{p_counter} Pressure updated {diff}.')\n",
    "                print(diff)\n",
    "                # update the pressure profile\n",
    "                P_func = P_func_new\n",
    "    return P_out,Temperature_out,Density_out,Vp_out,Vs_out,melt_out \n",
    "\n",
    "def vel_to_temp(nx,ny,nz,Vs,Table):\n",
    "    Temperature_out = np.zeros_like(nx)\n",
    "    Density_out     = np.zeros_like(nx)#+3300\n",
    "    diff_Vs         = np.zeros_like(nx)\n",
    "    P_out           = np.zeros_like(nx)\n",
    "    melt_out        = np.zeros_like(nx)\n",
    "    Vp_out          = np.zeros_like(nx)#np.zeros_like(tomo[:,1])\n",
    "    Vs_out          = np.zeros_like(nx)#np.zeros_like(tomo[:,1])\n",
    "    m,n,p=np.shape(nx)\n",
    "    # looping over all the geospatial nodes\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            P_func=lib.pressure_inter\n",
    "            for k in range(p):\n",
    "                P  = P_func(nz[i][j][k])\n",
    "                Vs_in = Vs[i][j][k]\n",
    "                P_table,temp,dens,vp,vs,melt=lib.lookup_vs_P_accurate_prop(Vs_in,P.tolist(),Table)\n",
    "                P_out[i][j][k] = P_table \n",
    "                Temperature_out[i][j][k]=temp\n",
    "                Density_out[i][j][k]=dens\n",
    "                Vs_out[i][j][k]=vs\n",
    "                Vp_out[i][j][k]=vp\n",
    "                melt_out[i][j][k]=melt\n",
    "    return P_out,Temperature_out,Density_out,Vp_out,Vs_out,melt_out "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34edda93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vel_to_temp_P_loop(nx,ny,nz,Vs,Table):\n",
    "    Temperature_out = np.zeros_like(nx)\n",
    "    Density_out     = np.zeros_like(nx)\n",
    "    diff_Vs         = np.zeros_like(nx)\n",
    "    P_out           = lib.pressure_inter(nz) #np.zeros_like(nx)\n",
    "    melt_out        = np.zeros_like(nx)\n",
    "    Vp_out          = np.zeros_like(nx)#np.zeros_like(tomo[:,1])\n",
    "    Vs_out          = np.zeros_like(nx)#np.zeros_like(tomo[:,1])\n",
    "    m,n,p=np.shape(nx)\n",
    "    # looping over all the geospatial nodes\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            P_func=lithostatic_pressure(nz[i][j][:],Density_out[i][j][:])\n",
    "            # Starting with ak135 pressure profile\n",
    "            #P_func=lib.pressure_inter\n",
    "            # setting positive difference to set the while loop\n",
    "                #print(f'{p_counter} Pressure counter {threshhold}.')\n",
    "                # loop over all depths to get the density and temperature\n",
    "                #P_new=np.zeros_like(nz[i][j][:])\n",
    "            for k in range(p):\n",
    "                    Vs_in = Vs[i][j][k]\n",
    "                    #  = P_func(nz[i][j][k])\n",
    "                    diff = 100\n",
    "                    p_counter  = 0\n",
    "                    #P =lib.pressure_inter(nz[i][j][k])\n",
    "                    while  diff > 0:\n",
    "                        #P =  (nz[i][j][k]-0)*Density_out[i][j][k]* 9.8 * 1e3 * 1e-5\n",
    "                        if k==0:\n",
    "                            P =   lithostatic_pressure_depth(nz[i][j][0],Density_out[i][j][0])\n",
    "                        else:\n",
    "                            P =   lithostatic_pressure_depth(nz[i][j][0:k],Density_out[i][j][0:k])\n",
    "                        \n",
    "                        P_table,temp,dens,vp,vs,melt=lib.lookup_vs_P_accurate_prop(Vs_in,P,Table)\n",
    "                        Density_out[i][j][k]=dens\n",
    "                        if k==0:\n",
    "                            P_new =   lithostatic_pressure_depth(nz[i][j][0],Density_out[i][j][0])\n",
    "                        else:\n",
    "                            P_new =   lithostatic_pressure_depth(nz[i][j][0:k],Density_out[i][j][0:k])\n",
    "                        #P_new=P_table\n",
    "                        \n",
    "                        #if k==0:\n",
    "                        #    P_new =   (nz[i][j][k]-0)*Density_out[i][j][k]* 9.8 * 1e3 * 1e-5 #lithostatic_pressure_depth(nz[i][j][0],Density_out[i][j][0])\n",
    "                        #else:\n",
    "                        #    P_new =   (nz[i][j][k]-0)*Density_out[i][j][k]* 9.8 * 1e3 * 1e-5 #lithostatic_pressure_depth(nz[i][j][0:k],Density_out[i][j][0:k])\n",
    "                        #P_new=P_table\n",
    "                        diff = abs(P_new-P)\n",
    "                        p_counter = p_counter+1\n",
    "                        print('Depth is',nz[i][j][k])\n",
    "                        print(f'{p_counter} Pressure updated {diff}.')\n",
    "                        P = P_new\n",
    "                        P_out[i][j][k] = P \n",
    "                    Temperature_out[i][j][k]=temp\n",
    "                    Vs_out[i][j][k]=vs\n",
    "                    Vp_out[i][j][k]=vp\n",
    "                    melt_out[i][j][k]=melt\n",
    "                # calulcating new pressure profile\n",
    "                #P_func_new=lithostatic_pressure(nz[i][j][:],Density_out[i][j][:])\n",
    "                #P_func_new = interpolate.interp1d(nz[i][j][:],P_new[:])\n",
    "                # RMS difference between initial and updated pressure profile\n",
    "                #diff = np.sqrt(((P_func_new(nz[i][j][:]) - P_func(nz[i][j][:])) ** 2).mean())\n",
    "                #print(f'{p_counter} Pressure updated {diff}.')\n",
    "                # update the pressure profile\n",
    "                #P_func = P_func_new\n",
    "    return P_out,Temperature_out,Density_out,Vp_out,Vs_out,melt_out \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92f2e741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pressure counter 1\n",
      "1 Pressure updated 1959.565653349907.\n",
      "1959.565653349907\n",
      "Pressure counter 2\n",
      "2 Pressure updated 95.99526017517351.\n",
      "95.99526017517351\n",
      "Pressure counter 3\n",
      "3 Pressure updated 3.9277045882507835.\n",
      "3.9277045882507835\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.4694835473696602.\n",
      "0.4694835473696602\n",
      "Pressure counter 5\n",
      "5 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1940.9655986326054.\n",
      "1940.9655986326054\n",
      "Pressure counter 2\n",
      "2 Pressure updated 95.43357926669529.\n",
      "95.43357926669529\n",
      "Pressure counter 3\n",
      "3 Pressure updated 4.0336172688589045.\n",
      "4.0336172688589045\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1908.9708088794948.\n",
      "1908.9708088794948\n",
      "Pressure counter 2\n",
      "2 Pressure updated 91.2337609685515.\n",
      "91.2337609685515\n",
      "Pressure counter 3\n",
      "3 Pressure updated 3.937343035685562.\n",
      "3.937343035685562\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1891.2888403791449.\n",
      "1891.2888403791449\n",
      "Pressure counter 2\n",
      "2 Pressure updated 88.34598296981652.\n",
      "88.34598296981652\n",
      "Pressure counter 3\n",
      "3 Pressure updated 3.9744583006073038.\n",
      "3.9744583006073038\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.7145497558873768.\n",
      "0.7145497558873768\n",
      "Pressure counter 5\n",
      "5 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1862.319207616127.\n",
      "1862.319207616127\n",
      "Pressure counter 2\n",
      "2 Pressure updated 87.37915901796953.\n",
      "87.37915901796953\n",
      "Pressure counter 3\n",
      "3 Pressure updated 2.6806848843829076.\n",
      "2.6806848843829076\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1831.2023691853567.\n",
      "1831.2023691853567\n",
      "Pressure counter 2\n",
      "2 Pressure updated 91.46315021377639.\n",
      "91.46315021377639\n",
      "Pressure counter 3\n",
      "3 Pressure updated 3.5003465294379628.\n",
      "3.5003465294379628\n",
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      "1 Pressure updated 1775.6489597314837.\n",
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      "Pressure counter 2\n",
      "2 Pressure updated 84.54822148888336.\n",
      "84.54822148888336\n",
      "Pressure counter 3\n",
      "3 Pressure updated 2.8998249834133234.\n",
      "2.8998249834133234\n",
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      "4 Pressure updated 0.0.\n",
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      "1 Pressure updated 1721.8302609284215.\n",
      "1721.8302609284215\n",
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      "2 Pressure updated 79.86289256820953.\n",
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      "3 Pressure updated 2.2419744884166226.\n",
      "2.2419744884166226\n",
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      "4 Pressure updated 0.0.\n",
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      "1 Pressure updated 1685.1686154743552.\n",
      "1685.1686154743552\n",
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      "2 Pressure updated 77.34804341523285.\n",
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      "3 Pressure updated 2.262701011653732.\n",
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      "4 Pressure updated 0.0.\n",
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      "1 Pressure updated 1647.6213468501728.\n",
      "1647.6213468501728\n",
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      "2 Pressure updated 73.56188763114116.\n",
      "73.56188763114116\n",
      "Pressure counter 3\n",
      "3 Pressure updated 4.968796649987545.\n",
      "4.968796649987545\n",
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      "4 Pressure updated 0.132300000001851.\n",
      "0.132300000001851\n",
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      "5 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1610.812185877481.\n",
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      "2 Pressure updated 69.91049675277185.\n",
      "69.91049675277185\n",
      "Pressure counter 3\n",
      "3 Pressure updated 2.436993198804636.\n",
      "2.436993198804636\n",
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      "4 Pressure updated 0.3229538897512945.\n",
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      "5 Pressure updated 0.0.\n",
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      "1 Pressure updated 1540.4353122519467.\n",
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      "2 Pressure updated 71.21892576737113.\n",
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      "3 Pressure updated 1.9534711306753094.\n",
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      "1 Pressure updated 1490.0539657075844.\n",
      "1490.0539657075844\n",
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      "2 Pressure updated 66.54699424490539.\n",
      "66.54699424490539\n",
      "Pressure counter 3\n",
      "3 Pressure updated 1.4041901741935956.\n",
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      "4 Pressure updated 0.0.\n",
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      "1 Pressure updated 1403.487806596603.\n",
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      "2 Pressure updated 58.53285846912101.\n",
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      "3 Pressure updated 3.576557441362685.\n",
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      "4 Pressure updated 0.0.\n",
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      "1 Pressure updated 1389.4503457597789.\n",
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      "2 Pressure updated 59.713683431320106.\n",
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      "3 Pressure updated 0.7859260520931272.\n",
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      "1 Pressure updated 1375.6601795397955.\n",
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      "2 Pressure updated 51.99867562981898.\n",
      "51.99867562981898\n",
      "Pressure counter 3\n",
      "3 Pressure updated 0.7771413902817226.\n",
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      "1 Pressure updated 1360.9205546005837.\n",
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      "2 Pressure updated 52.941236741203284.\n",
      "52.941236741203284\n",
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      "3 Pressure updated 1.764075093453896.\n",
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      "4 Pressure updated 0.0.\n",
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      "1 Pressure updated 1338.0184417164255.\n",
      "1338.0184417164255\n",
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      "2 Pressure updated 53.135073657718365.\n",
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      "3 Pressure updated 1.3327596914891024.\n",
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      "1 Pressure updated 1319.0320153757475.\n",
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      "2 Pressure updated 46.67162486836271.\n",
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      "3 Pressure updated 1.2107630060744798.\n",
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      "4 Pressure updated 0.5134123921172374.\n",
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      "1 Pressure updated 1299.104655075843.\n",
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      "2 Pressure updated 48.54613118945025.\n",
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      "3 Pressure updated 0.9328931180085327.\n",
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      "4 Pressure updated 0.5823933953928084.\n",
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      "1 Pressure updated 1288.8296305160193.\n",
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      "3 Pressure updated 1.8242704359686555.\n",
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      "2 Pressure updated 45.616100599456594.\n",
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      "3 Pressure updated 2.525742953445832.\n",
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      "3 Pressure updated 3.191670113707312.\n",
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      "1 Pressure updated 1259.97989573981.\n",
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      "2 Pressure updated 44.97772077670385.\n",
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      "3 Pressure updated 1.4514081536395445.\n",
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      "3 Pressure updated 1.5336672220853387.\n",
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      "1 Pressure updated 1247.6329830346722.\n",
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      "2 Pressure updated 44.099804447883926.\n",
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      "3 Pressure updated 1.6961970583898727.\n",
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      "1 Pressure updated 1211.8695146793093.\n",
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      "2 Pressure updated 41.25832845182743.\n",
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      "3 Pressure updated 1.0713869889114371.\n",
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      "1 Pressure updated 1192.0945996462842.\n",
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      "2 Pressure updated 39.839192568181964.\n",
      "39.839192568181964\n",
      "Pressure counter 3\n",
      "3 Pressure updated 1.4558420234357266.\n",
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      "1 Pressure updated 1242.4023647123788.\n",
      "1242.4023647123788\n",
      "Pressure counter 2\n",
      "2 Pressure updated 43.08207733560471.\n",
      "43.08207733560471\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pressure counter 3\n",
      "3 Pressure updated 1.3413524016368694.\n",
      "1.3413524016368694\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.0.\n",
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      "Pressure counter 1\n",
      "1 Pressure updated 1210.441680228536.\n",
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      "Pressure counter 2\n",
      "2 Pressure updated 40.606567966951104.\n",
      "40.606567966951104\n",
      "Pressure counter 3\n",
      "3 Pressure updated 0.5850895469869655.\n",
      "0.5850895469869655\n",
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      "Pressure counter 1\n",
      "1 Pressure updated 1211.4765342248588.\n",
      "1211.4765342248588\n",
      "Pressure counter 2\n",
      "2 Pressure updated 41.14085989676861.\n",
      "41.14085989676861\n",
      "Pressure counter 3\n",
      "3 Pressure updated 0.5653674274220298.\n",
      "0.5653674274220298\n",
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      "4 Pressure updated 0.0.\n",
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      "Pressure counter 1\n",
      "1 Pressure updated 1231.6143990564822.\n",
      "1231.6143990564822\n",
      "Pressure counter 2\n",
      "2 Pressure updated 39.819134804753006.\n",
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      "Pressure counter 3\n",
      "3 Pressure updated 1.5587621755036367.\n",
      "1.5587621755036367\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1258.1534092600718.\n",
      "1258.1534092600718\n",
      "Pressure counter 2\n",
      "2 Pressure updated 45.24119619304086.\n",
      "45.24119619304086\n",
      "Pressure counter 3\n",
      "3 Pressure updated 2.4595013728780146.\n",
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      "1 Pressure updated 1263.2320272038594.\n",
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      "2 Pressure updated 47.052166463299685.\n",
      "47.052166463299685\n",
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      "3 Pressure updated 4.271886727767173.\n",
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      "1 Pressure updated 1271.59072780565.\n",
      "1271.59072780565\n",
      "Pressure counter 2\n",
      "2 Pressure updated 46.10182652186459.\n",
      "46.10182652186459\n",
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      "3 Pressure updated 1.668736258476925.\n",
      "1.668736258476925\n",
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      "4 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1297.3924752761911.\n",
      "1297.3924752761911\n",
      "Pressure counter 2\n",
      "2 Pressure updated 49.11196536573687.\n",
      "49.11196536573687\n",
      "Pressure counter 3\n",
      "3 Pressure updated 1.3509516661670369.\n",
      "1.3509516661670369\n",
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      "Pressure counter 1\n",
      "1 Pressure updated 1301.5447354553553.\n",
      "1301.5447354553553\n",
      "Pressure counter 2\n",
      "2 Pressure updated 47.67499812635484.\n",
      "47.67499812635484\n",
      "Pressure counter 3\n",
      "3 Pressure updated 1.3346566141331275.\n",
      "1.3346566141331275\n",
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      "4 Pressure updated 0.5410493710193774.\n",
      "0.5410493710193774\n",
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      "Pressure counter 1\n",
      "1 Pressure updated 1308.953613806877.\n",
      "1308.953613806877\n",
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      "2 Pressure updated 47.900539689345926.\n",
      "47.900539689345926\n",
      "Pressure counter 3\n",
      "3 Pressure updated 2.4780592187541814.\n",
      "2.4780592187541814\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.0.\n",
      "0.0\n",
      "Pressure counter 1\n",
      "1 Pressure updated 1343.8441894398745.\n",
      "1343.8441894398745\n",
      "Pressure counter 2\n",
      "2 Pressure updated 51.19060030852666.\n",
      "51.19060030852666\n",
      "Pressure counter 3\n",
      "3 Pressure updated 1.1643413957783593.\n",
      "1.1643413957783593\n",
      "Pressure counter 4\n",
      "4 Pressure updated 0.0.\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "P_P_loop,T_P_loop,D_P_loop,Vp_P_loop,Vs_P_loop,melt_P_loop=vel_to_temp_P_loop(nx,ny,nz,V,Composition_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6562387",
   "metadata": {},
   "outputs": [],
   "source": [
    "P,T,D,Vp,Vs,melt=vel_to_temp(nx,ny,nz,V,DMM_atten_melt_corrected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ffea203e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'P loop')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tri = Triangulation(nx.flatten(),-nz.flatten())\n",
    "fig, ax = plt.subplots(1, 2,sharex=False, sharey=False,figsize=(12,6))\n",
    "T_gibbs = ax[0].tricontourf(tri, T.flatten(),cmap='hot')#,levels=np.arange(200,1500,100))\n",
    "T_gibbs_P = ax[1].tricontourf(tri, T_P_loop.flatten(),cmap='hot')#,levels=np.arange(200,1500,100))\n",
    "for a in ax:\n",
    "        #a.set_aspect('equal')\n",
    "        a.set_ylim([-400,0])\n",
    "        a.set_xlabel('Age (Ma)')\n",
    "        a.set_ylabel('Depth (km)')\n",
    "        a.set_title('')\n",
    "fig.colorbar(T_gibbs, ax=ax[0], label='T (degree/celsius)', orientation='horizontal')\n",
    "fig.colorbar(T_gibbs_P, ax=ax[1], label='T (degree/celsius)', orientation='horizontal')\n",
    "ax[0].set_title('No P loop')\n",
    "ax[1].set_title('P loop')\n",
    "\n",
    "#fig.colorbar(m_t_out, ax=ax[2], label='Temperature (Celcius)', orientation='horizontal')\n",
    "#fig.colorbar(m_dens_out, ax=ax[3], label='Density (g/cm3)', orientation='horizontal')\n",
    "#fig.suptitle(title, fontsize=16)\n",
    "#ax.set_ylim([-400,0])\n",
    "#ax.set_xlabel('Distance (km)')\n",
    "#ax.set_ylabel('Depth (km)')\n",
    "#fig.colorbar(m_v_in_gibbs, ax=ax[0], label='Temperature (degree/celcius)', orientation='vertical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22aa956f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'P loop')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tri = Triangulation(nx.flatten(),-nz.flatten())\n",
    "fig, ax = plt.subplots(1, 2,sharex=False, sharey=False,figsize=(20,5))\n",
    "D_gibbs = ax[0].tricontourf(tri, D.flatten())\n",
    "D_gibbs_P = ax[1].tricontourf(tri, D_P_loop.flatten())\n",
    "for a in ax:\n",
    "        #a.set_aspect('equal')\n",
    "        a.set_ylim([-400,0])\n",
    "        a.set_xlabel('Age (Ma)')\n",
    "        a.set_ylabel('Depth (km)')\n",
    "        a.set_title('')\n",
    "fig.colorbar(D_gibbs, ax=ax[0], label='Density (kg/m3)', orientation='horizontal')\n",
    "fig.colorbar(D_gibbs_P, ax=ax[1], label='Density (kg/m3)', orientation='horizontal')\n",
    "ax[0].set_title('No P loop')\n",
    "ax[1].set_title('P loop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "48dbd7a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'P loop')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qSZMmXO4OAABwB8hgAADAVZgyVfTq1Uu//vqrBg0apI0bN2rixIlauXKl+vfvb5nTt29fbdq0SWPGjNHGjRs1dOhQ7d69W3379nVi5QAAAOZFBgMAAK7ClA2t2rVra9asWUpOTtaAAQO0YcMGTZw4UR07drTMadWqld5++21t27ZNAwYM0MGDBzVjxgxFRkY6sXIAAADzIoMBAABX4WXkfB2NB0tJSVHbtm11zy+/yCcz09nlAACAQpZVrJgu1KihxMREhYSEOLsc/BcZDAAA9+bIDGbKK7QAAAAAAADguWhoAQAAAAAAwFSK2Tvx9OnT+v7773X8+HFduXJF99xzjypUqKBmzZqpXLlyjqwRAADAY5HBAAAAciuwoZWQkKB58+Zp165dMgxDgYGBKlmypC5fvqxr167J29tbERER6t27t9q0aVMUNQMAALg9MhgAAEDe8mxoHT16VK+88oqSk5PVoUMHDRkyRHXr1pWfn59lzpUrV/TDDz9oy5YtGjVqlKpUqaJJkyapevXqRVI8AACAuyGDAQAAFCzPbzns0KGD+vXrp06dOsnHx6fAHWVkZGjFihX68MMPtW7dukIv1JH4hh0AANybmb7lkAwGAADchSMzWJ5XaK1cuVIlSpSwe0fFixdXly5d1Llz50IpDAAAwBORwQAAAAqW57cc3k6QKoyfAwAAABkMAADAHnZ9y+H58+c1depU7dmzR5cvX8613cvLSwkJCYVeHAAAgCcjgwEAANhmV0PrlVde0ZYtW9SxY0cFBQU5uCQAAABIZDAAAIC82NXQ2rp1q8aNG6fHHnvM0fUAAADgv8hgAAAAtuV5D61blS9f3uqrogEAAOB4ZDAAAADb7GpovfTSS3rvvfe0c+dOXb9+3dE1AQAAQGQwAACAvNj1kcMaNWooPT1d3bp1y3PO/v37C60oAAAAkMEAAADyYldDa8SIEUpPT1efPn1Urlw5R9cEAAAAkcEAAADyYldD6+DBg3rvvffUpk0bR9cDAACA/yKDAQAA2GbXPbSqVq2q9PR0R9cCAACAW5DBAAAAbLPrCq1hw4bp9ddfl7e3t+rXr2/z23aCgoIKuzYAAACPRgYDAACwza6G1tChQ3X16lUNGTIkzznckBQAAKBwkcEAAABss/sKLS8vL0fXAgAAgFuQwQAAAGyzq6HVuXNnFSuW99SdO3cWWkEAAAC4iQwGAABgm103he/Xr59u3LiRa/zSpUsaNWqUunfvXuiFAQAAeDoyGAAAgG12NbT27dun3r17Ky0tzTK2fPlyPfTQQ/r666/1v//7vw4rEAAAwFORwQAAAGyzq6H18ccf68SJE3r22Wf1ww8/qHv37ho5cqQaN26s1atXq1+/fo6uEwAAwOOQwQAAAGyzq6FVtWpVffLJJ0pPT9czzzyjs2fPas6cOZo2bZoqVqzo6BoBAAA8EhkMAADAtjwbWhcvXrT6V7x4cU2fPl116tSRYRiqWLGi1XYAAADcPTIYAABAwfL82pymTZva/JpowzAkSY888ojV+P79+wu5NAAAAM9DBgMAAChYng2tCRMm2AxTAAAAcBwyGAAAQMHybGg98cQTRVkHAAAARAYDAACwR5730Prb3/6mffv23dbO9uzZo+7du991UQAAAJ6KDAYAAFCwPK/QevbZZ9W/f3/VqFFDjzzyiNq1a6cyZcrkmnfmzBn961//0hdffKGjR4/q9ddfd2jBAAAA7owMBgAAULA8G1qxsbGKiorSggUL9M4772j06NGqWLGiKlSooJIlS+rKlSs6ffq0Tp06pTJlyqh79+6aPXu2AgICirJ+AAAAt0IGAwAAKFieDS1J8vf314ABA/T8888rKSlJW7du1fHjx5WamqpKlSqpcePGiomJUXR0tHx9fYuqZgAAALdGBgMAAMhfvg2tHL6+vmrVqpVatWrl6HoAAADwX2QwAAAA2/K8Kbwz/fDDD+revbuioqLUokULDRs2TOfOnbOas2PHDnXp0kUNGzZUXFycli1blms/CQkJ6tSpkxo0aKDOnTtr/fr1RXUIAAAApkMGAwAAZuFyDa3Dhw+rZ8+e8vPz0zvvvKPhw4frhx9+UK9evZSRkWGZ07t3b4WEhGjatGmKjY3VqFGjtGbNGst+kpKSNHDgQEVHR2v69OkKCwvTgAEDtHv3bicdGQAAgOsigwEAADOx6yOHRWnx4sUKDg7WtGnTVLx4cUlS1apV1aVLF23ZskWtWrVSfHy8KlWqpClTpsjLy0stW7bUhQsXNGPGDHXs2FGSNGPGDDVr1kxjxoyRJLVs2VInTpzQ7NmzNXv2bKcdHwAAgCsigwEAADNxuSu0atWqpeeee84SpCSpRo0akqSUlBRJ0pYtWxQbGysvLy/LnHbt2unQoUM6ffq00tPTtWvXLrVp08Zq323btlVSUpKysrKK4EgAAADMgwwGAADMxOWu0HrmmWdyjX377beSboaqtLQ0nTlzRlWrVrWaU7lyZUnSkSNHVLZsWWVmZtqck56erpMnTyokJMRBRwAAAGA+ZDAAAGAmdje0NmzYoK1bt+ry5cvKzs622ubl5aUJEyYUuI+MjAwdO3Ysz+333nuvAgMDrcZOnjypt99+W+Hh4WratKnOnj0rSfLz87Oal/M4NTXV8vXV+c0BAAAwAzIYAABAbnY1tGbPnq333ntPgYGBKl++vLy9rT+peOtl5/k5ffq0Hn744Ty3jxw5Uj179rQ8PnnypHr27Kns7Gy9++678vLykmEY+T6nt7e3XXMAAABcHRkMAADANrsaWkuWLNFTTz2lsWPHysfH546fLCQkRAcPHrRr7qFDh9SnTx9lZmZq3rx5qlKliiTJ399fknT16lWr+TmPAwICFBAQUOAcAAAAV0cGAwAAsM2uhtaVK1f0yCOP3FWQuh0//vijevfurYCAAC1cuFDVqlWzbPPz81NwcLCSk5OtfibncfXq1eXn5ydvb2+bc0qXLq3y5cs7/BgAAADuFhkMAADANruu+46OjtaOHTscXYukm4GnT58+uvfee/XJJ59YBakcMTExWr9+vdU35SQkJCg0NFTlypVTyZIlFRkZqYSEBKufS0xMVJMmTbjcHQAAmAIZDAAAwLY8r9Bat26d5X83bdpU7733ns6dO6eIiAiVKlUq1/y4uLhCKWjChAlKTU3Vq6++qpMnT+rkyZOWbRUrVtR9992nXr166c9//rMGDRqkLl26aMuWLVq5cqWmTp1qmdu3b189//zzGjNmjNq1a6dVq1Zp9+7dWrx4caHUCQAA4AhkMAAAgIJ5GTl37/yD2rVr278TLy/t37//rovJyMhQRESEMjMzbW4fNmyYevXqJUn67rvvNHnyZP3yyy+qWLGi+vbtqyeeeMJq/ldffaWZM2fqxIkTql69ul588UXFxsbm2m9KSoratm2re375RT55PDcAADCvrGLFdKFGDSUmJiokJMTZ5eSLDAYAANyFIzNYng2t48eP39aOKlWqVCgFOQNhCgAA92amhhYZDAAAuAtHZrA8b2RQqVIly7/ly5erWLFiVmM5/yRp/vz5hVoUAACApyKDAQAAFCzPhtbFixd18eJFXbhwQTNmzNDhw4ctY7f+27x5s5YuXVqUNQMAALgtMhgAAEDB8rwp/Msvv6zNmzdbHufcN8GWFi1aFG5VAAAAHooMBgAAULA8G1rjx4/Xli1bZBiGXnnlFfXr109VqlSxmuPt7a0yZcooJibG4YUCAAB4AjIYAABAwfJsaJUvX17/8z//I+nmN+i0atVKZcuWLbLCAAAAPBEZDAAAoGB5NrRu9T//8z/KyMjQihUrtHPnTl26dElly5ZV06ZNFRcXJ2/vPG/FBQAAgDtEBgMAALDNrobW8ePH9eyzzyo5OVmVK1dW2bJltX//fn366aeqW7eu5s+fr8DAQEfXCgAA4FHIYAAAALbZ1dAaN26cJGnlypX605/+ZBk/cOCABg4cqIkTJ2rSpEmOqRAAAMBDkcEAAABss+s69W3btunll1+2ClKSVLt2bQ0ePFjffvutQ4oDAADwZGQwAAAA2+xqaAUFBSktLS3P7b6+voVWEAAAAG4igwEAANhmV0Nr8ODBmjx5sjZv3mw1/tNPP2nKlCl6+eWXHVIcAACAJyODAQAA2OZlGIZR0KROnTrp+PHjunbtmgICAhQcHKyLFy/q/PnzkqRSpUr9vkMvL+3cudNxFTtASkqK2rZtq3t++UU+mZnOLgcAABSyrGLFdKFGDSUmJiokJMTZ5diNDAYAAMzMkRnMrpvCd+jQoVCfFAAAAAUjgwEAANhmV0NrwIABjq4DAAAAf0AGAwAAsM2uhpYkXblyRYsWLVJSUpLOnTun999/X+vXr1edOnX04IMPOrJGAAAAj0UGAwAAyM2um8KnpKSoU6dOWrBggfz9/XXkyBHduHFDBw8e1AsvvKCNGzc6uk4AAACPQwYDAACwza4rtMaPH6/g4GAtWLBAJUqUUHh4uCTpnXfeUWZmpmbOnKlWrVo5tFAAAABPQwYDAACwza4rtL7//nv17dtXfn5+8vLystr29NNP69ChQw4pDgAAwJORwQAAAGyzq6Hl6+ur69ev29x28eJF+fr6FmpRAAAAIIMBAADkxa6GVqtWrfTee+/pyJEjljEvLy9dvHhR8fHxatGihaPqAwAA8FhkMAAAANvsamiNGDFCvr6+evTRR9W5c2dJ0qhRo9S+fXtduXJFw4YNc2iRAAAAnogMBgAAYJtdN4UvW7asvvzySy1fvlzbtm1T+fLl5e/vr8cff1xPPvmk/P39HV0nAACAxyGDAQAA2GZXQ0uSSpQooaefflpPP/20I+sBAADALchgAAAAuRXY0Lpy5YrWrFmjH374Qb/99psk6f7771ejRo3Uvn17+fn5ObxIAAAAT0MGAwAAyFu+Da3Vq1frtdde0+XLl+Xj46OgoCBJ0pYtW7R06VJNmjRJr7/+ujp06FAUtQIAAHgEMhgAAED+8mx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      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tri = Triangulation(nx.flatten(),-nz.flatten())\n",
    "fig, ax = plt.subplots(1, 2,sharex=False, sharey=False,figsize=(20,5))\n",
    "D_gibbs   = ax[0].tricontourf(tri, melt.flatten(),cmap='hot')#,levels=np.arange(0,2,0.2))\n",
    "D_gibbs_P = ax[1].tricontourf(tri, melt_P_loop.flatten(),cmap='hot')#,levels=np.arange(0,2,0.2))\n",
    "for a in ax:\n",
    "        #a.set_aspect('equal')\n",
    "        a.set_ylim([-400,0])\n",
    "        a.set_xlabel('Age (Ma)')\n",
    "        a.set_ylabel('Depth (km)')\n",
    "        a.set_title('')\n",
    "fig.colorbar(D_gibbs, ax=ax[0], label='Melts', orientation='horizontal')\n",
    "fig.colorbar(D_gibbs_P, ax=ax[1], label='Melts', orientation='horizontal')\n",
    "ax[0].set_title('No P loop')\n",
    "ax[1].set_title('P loop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25ef2d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'P loop')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tri = Triangulation(nx.flatten(),-nz.flatten())\n",
    "fig, ax = plt.subplots(1, 2,sharex=False, sharey=False,figsize=(12,6))\n",
    "T_gibbs = ax[0].tricontourf(tri, T.flatten(),cmap='hot')#,levels=np.arange(200,1500,100))\n",
    "T_gibbs_P = ax[1].tricontourf(tri, T_P_loop.flatten(),cmap='hot')#,levels=np.arange(200,1500,100))\n",
    "for a in ax:\n",
    "        #a.set_aspect('equal')\n",
    "        a.set_ylim([-400,0])\n",
    "        a.set_xlabel('Age (Ma)')\n",
    "        a.set_ylabel('Depth (km)')\n",
    "        a.set_title('')\n",
    "fig.colorbar(T_gibbs, ax=ax[0], label='T (degree/celsius)', orientation='horizontal')\n",
    "fig.colorbar(T_gibbs_P, ax=ax[1], label='T (degree/celsius)', orientation='horizontal')\n",
    "ax[0].set_title('No P loop')\n",
    "ax[1].set_title('P loop')\n",
    "\n",
    "#fig.colorbar(m_t_out, ax=ax[2], label='Temperature (Celcius)', orientation='horizontal')\n",
    "#fig.colorbar(m_dens_out, ax=ax[3], label='Density (g/cm3)', orientation='horizontal')\n",
    "#fig.suptitle(title, fontsize=16)\n",
    "#ax.set_ylim([-400,0])\n",
    "#ax.set_xlabel('Distance (km)')\n",
    "#ax.set_ylabel('Depth (km)')\n",
    "#fig.colorbar(m_v_in_gibbs, ax=ax[0], label='Temperature (degree/celcius)', orientation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "979f4f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'P loop')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tri = Triangulation(nx.flatten(),-nz.flatten())\n",
    "fig, ax = plt.subplots(1, 2,sharex=False, sharey=False,figsize=(20,5))\n",
    "D_gibbs = ax[0].tricontourf(tri, D.flatten())\n",
    "D_gibbs_P = ax[1].tricontourf(tri, D_P_loop.flatten())\n",
    "for a in ax:\n",
    "        #a.set_aspect('equal')\n",
    "        a.set_ylim([-400,0])\n",
    "        a.set_xlabel('Age (Ma)')\n",
    "        a.set_ylabel('Depth (km)')\n",
    "        a.set_title('')\n",
    "fig.colorbar(D_gibbs, ax=ax[0], label='Density (kg/m3)', orientation='horizontal')\n",
    "fig.colorbar(D_gibbs_P, ax=ax[1], label='Density (kg/m3)', orientation='horizontal')\n",
    "ax[0].set_title('No P loop')\n",
    "ax[1].set_title('P loop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89e0e4b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ntri = Triangulation(nx.flatten(),-nz.flatten())\\nfig, ax = plt.subplots(1, 2,sharex=False, sharey=False,figsize=(20,5))\\nD_gibbs   = ax[0].tricontourf(tri, melt.flatten(),cmap='hot')#,levels=np.arange(0,2,0.2))\\nD_gibbs_P = ax[1].tricontourf(tri, melt_P_loop.flatten(),cmap='hot')#,levels=np.arange(0,2,0.2))\\nfor a in ax:\\n        #a.set_aspect('equal')\\n        a.set_ylim([-400,0])\\n        a.set_xlabel('Age (Ma)')\\n        a.set_ylabel('Depth (km)')\\n        a.set_title('')\\nfig.colorbar(D_gibbs, ax=ax[0], label='Melts', orientation='horizontal')\\nfig.colorbar(D_gibbs_P, ax=ax[1], label='Melts', orientation='horizontal')\\nax[0].set_title('No P loop')\\nax[1].set_title('P loop')\\n\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "tri = Triangulation(nx.flatten(),-nz.flatten())\n",
    "fig, ax = plt.subplots(1, 2,sharex=False, sharey=False,figsize=(20,5))\n",
    "D_gibbs   = ax[0].tricontourf(tri, melt.flatten(),cmap='hot')#,levels=np.arange(0,2,0.2))\n",
    "D_gibbs_P = ax[1].tricontourf(tri, melt_P_loop.flatten(),cmap='hot')#,levels=np.arange(0,2,0.2))\n",
    "for a in ax:\n",
    "        #a.set_aspect('equal')\n",
    "        a.set_ylim([-400,0])\n",
    "        a.set_xlabel('Age (Ma)')\n",
    "        a.set_ylabel('Depth (km)')\n",
    "        a.set_title('')\n",
    "fig.colorbar(D_gibbs, ax=ax[0], label='Melts', orientation='horizontal')\n",
    "fig.colorbar(D_gibbs_P, ax=ax[1], label='Melts', orientation='horizontal')\n",
    "ax[0].set_title('No P loop')\n",
    "ax[1].set_title('P loop')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19ab5ddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adiabatic gradient (/km): 0.31752\n"
     ]
    }
   ],
   "source": [
    "T_mantle = 1350 # oC\n",
    "alpha    = 3e-5 # oC-1\n",
    "cp       = 1250 # J/kg/oC \n",
    "g        = 9.8 #m/s2\n",
    "dT_dZ    = ((T_mantle*alpha*g)/cp)*1e3 # oC/km\n",
    "print('Adiabatic gradient (/km):',dT_dZ)\n",
    "#T = T_mantle + Z*dT_dZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e58b41a",
   "metadata": {},
   "outputs": [],
   "source": [
    "m,n,p=np.shape(nx)\n",
    "Thermal_boundary = np.zeros((m,n))\n",
    "for i in range(m):\n",
    "    for j in range(n):\n",
    "        T_mantle    = 1200 #T_P_loop[i][j][-1] #1600 # oC\n",
    "        #T_mantle    = T_P_loop[i][j][-1] #1600 # oC\n",
    "        #print(T_mantle)\n",
    "        alpha       = 3e-5 # oC-1\n",
    "        cp          = 1250 # J/kg/oC \n",
    "        g           = 9.8 #m/s2\n",
    "        dT_dZ       = ((T_mantle*alpha*g)/cp)*1e3 # oC/km\n",
    "        adiabat     = T_mantle + nz[i][j][:]*dT_dZ\n",
    "        temperature = T_P_loop[i][j][:]\n",
    "        dist        = np.array((adiabat-temperature)**2);\n",
    "        index       = dist.argmin()\n",
    "        Thermal_boundary[i][j]=nz[i][j][index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "78b1af98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age (Ma)')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JTK7l0qtXL7zxxhvKxAIAPXv2BADcvHkTAJCVlYXQ0FCV7WlGjBiBK1euoKSkBAqFAjk5ORg+fLjKucPCwpCdnY3a2loD3AkRkeXSuuVy9OhRnDx5Eg8ePEBdXZ1KmUQiwapVq0QJaPz48WrHvvvuOwCPk0xlZSVKS0vh7u6uUsfNzQ0AkJeXB0dHR9TU1DRYR6FQoLi4GK6urqLES0RE6rRKLikpKdiwYQMcHBzg7OwMqVS1waPtBpfV1dXIz8/XWN6pUyc4ODioHCsuLsbf/vY3+Pj4YNCgQbh9+zYAwNbWVqVe/We5XK5cc9NYHSIi0h+tksuuXbvw+uuvY9myZbCysmr2xUpKSvDiiy9qLF+0aBEmT56s/FxcXIzJkyejrq4O//u//wuJRKLc8l9TQpNKpVrVISIi/dEquZSXl2P06NE6JRYAcHV1RW5urlZ1r1y5gunTp6Ompgbbtm1D9+7dATzeNQAAKioqVOrXf7a3t1duQdNYHSIi0h+t/gsfEBCAM2fO6DsWpR9//BHjx4+HlZUVdu3aBW9vb2WZra0tnJycUFBQoPKd+s8eHh5wc3ODVCptsE67du3g7Oys/5sgIrJgGlsuR44cUf590KBB2LBhA+7cuQNfX1+0bdtWrX5ERIQoARUUFGD69Ono1KkT0tLSGkwEgYGByMzMxJw5c5StKZlMBk9PT3Ts2BEA4OfnB5lMhrFjxyq/l5GRgYEDB7JbjIhIzzQml/j4eLVjX375Jb788ku14xKJRLTXHK9atQpyuRxLly5FcXExiouLlWUuLi7o3Lkzpk6ditdeew1z5sxRrn/Zv38/EhMTlXXj4uIwY8YMLFmyBCNGjMCBAwdw/vx57Ny5U5Q4iYhIM4lQP/r9B4WFhU91om7duukcTHV1NXx9fVFTU9Ng+YIFCzB16lQAwPHjx7F27Vpcu3YNLi4uiIuLU3t75r59+7Bp0yYUFRXBw8MD8+bNQ2hoqNp5b968ibCwMGRkZHCKMhGRlhr77dTYcnkyWWzcuBGxsbENdlEVFhZi+/btWLx4sc6BWltb4+eff9aqbnBwMIKDgxutExMTg5iYGJ3jIiKip6Nx8OHevXu4d++ecluVq1evKo89+eeHH37A7t27DRkzERGZOI0tl/nz5+OHH35Qfq7vjmrIkCFDxI2KiIhaNI3J5aOPPkJWVhYEQcAHH3yAmTNnKtea1JNKpWjfvj0CAwP1HigREbUcGpOLs7MzXn75ZQCPZ4OFhITA0dHRYIEREVHLpdUK/ZdffhnV1dXYu3cvzp49i/v378PR0RGDBg1CREQE140QEZEKrZJLYWEhpkyZgoKCAri5ucHR0RGXLl3CP/7xD/Tu3Rvbt29X23CSiIgsl1bJZcWKFQCA/fv349lnn1Uev3z5MuLj4/Hxxx9j9erV+omQiIhaHK36s06dOoX58+erJBYA8Pb2xty5c5XvWyEiIgK0TC4dOnRAZWWlxvL696cQEREBWiaXuXPnYu3atSrrXgDg4sWLWL9+PebPn6+X4IiIqGXSaszls88+Q2VlJaZNmwZ7e3s4OTnh3r17uHv3LgDgww8/xIcffgjg8bTls2fP6i9iIiIyeVoll5EjR+o7DiIiMiNaJZfZs2frOw4iIjIjWiUX4PGrjnfs2IHs7GzcuXMHn3zyCTIzM/Hcc881uTsxERFZFq0G9G/evImoqCikpaXBzs4OeXl5ePToEXJzc/Hmm2/i+++/13ecRETUgmjVcvnoo4/g5OSEtLQ0tGnTBj4+PgCAdevWoaamBps2bUJISIheAyUiopZDq5bLiRMnEBcXB1tbW0gkEpWycePG4cqVK3oJjoiIWiatkkvr1q1RVVXVYNm9e/e4iJKIiFRolVxCQkKwYcMG5OXlKY9JJBLcu3cPqampfFkYERGp0Cq5LFy4EK1bt8ZLL72E6OhoAEBCQgLCw8NRXl6OBQsW6DVIIiJqWbQa0Hd0dMS//vUv7NmzB6dOnYKzszPs7OwwZswYvPrqq7Czs9N3nERE1IJovc6lTZs2GDduHMaNG6fPeIiIyAw0mVzKy8tx6NAhnDt3Dr///jsAoEuXLujfvz/Cw8Nha2ur9yCJiKhlaTS5HDx4EH/961/x4MEDWFlZoUOHDgCArKws7N69G6tXr8by5cu59xgREanQmFyys7Px7rvvwt/fH7NmzUJAQACk0sfj/48ePcLp06eRmpqKd999F05OTujfv7/BgiYiItOmcbbYtm3bMHDgQOzYsQODBg1SJhbg8bqXwYMH4/PPP4e/vz8+++wzgwRLREQtg8bkcvHiRa0G719//XX8+OOPogZFREQtm8bkUl5eDicnpyZP4OzsjPv374saFBERtWwak0ttbS2sra2bPEGrVq1QV1cnalBERNSyabVCn4iI6Gk0OhV5zZo1sLe3b/QE5eXlogZEREQtn8bk8sILLwAAKioqGj2BVCqFv7+/uFEREVGLpjG57Nixw5BxEBGRGeGYCxERiY7JhYiIRMfkQkREomNyISIi0TG5EBGR6JhciIhIdEwuREQkOiYXIiISnUkml2PHjuHVV1+Fr68vIiIisGPHDgiCoFLnzJkziI2NRb9+/RAREYH09HS188hkMkRFRaFv376Ijo5GZmamoW6BiMiimVxyycnJwcyZM+Hp6YlNmzYhNjYWq1evxueff66sc/XqVUybNg2urq5ISkpCaGgoEhIScOjQIWWd7OxsxMfHIyAgABs3boSXlxdmz56N8+fPG+GuiIgsS6MbVxpDWloaevXqhVWrVkEikSAoKAjXrl3Drl27MHnyZABAamoqunXrhvXr10MikWDo0KEoKytDcnIyIiMjAQDJyckICgrCkiVLAABDhw5FUVERUlJSkJKSYqzbIyKyCCbXclm4cKEyadSztrbGo0ePlJ+zsrIQGhqqUmfEiBG4cuUKSkpKoFAokJOTg+HDh6ucOywsDNnZ2aitrdX/jRARWTCTa7l07dpV+fcHDx7gu+++w969ezFz5kwAQGVlJUpLS+Hu7q7yPTc3NwBAXl4eHB0dUVNT02AdhUKB4uJiuLq66vlOiIgsl0GTS3V1NfLz8zWWd+rUCQ4ODgCAwsJCZcvDx8cHf/7znwEAcrkcAGBra6vy3frPcrkcrVu3brIOERHpj0GTS0lJCV588UWN5YsWLVKOq9jZ2eHzzz/HnTt3kJiYiLFjx2Lv3r3KWWNPdok9SSqValWHiIj0x6DJxdXVFbm5uVrVdXBwwKBBgwAAzz77LKKjo3H48GGEh4cDUH+JWf1ne3t75dszG6tDRET6Y3L/hZfJZLhw4YLKMU9PT1hbW6O0tBS2trZwcnJCQUGBSp36zx4eHnBzc4NUKm2wTrt27eDs7KzfmyAisnAml1xSU1Px97//XeXYiRMnUF1dDU9PTwBAYGAgMjMzVWZ9yWQyeHp6omPHjrCxsYGfnx9kMpnKeTIyMjBw4EB2ixER6ZnJ/cq++eabOHXqFJYuXYrs7Gx88cUXeOeddxAQEICQkBAAwNSpU3H9+nXMmTMH33//PT7++GPs378fs2bNUp4nLi4Ox44dw5IlS/D999/jvffew/nz5xEXF2esWyMishgS4Y/7qpiAjIwMbNq0Cb/99hvs7e0xevRozJ07F23btlXWOX78ONauXYtr167BxcUFcXFxeOWVV1TOs2/fPmzatAlFRUXw8PDAvHnzEBoaqna9mzdvIiwsDBkZGZyiTESkpcZ+O00yuRgakwsR0dNr7LfT5LrFiIio5WNyISIi0TG5EBGR6JhciIhIdEwuREQkOiYXIiISHZMLERGJjsmFiIhEx+RCRESiY3IhIjJTpQ8UeP3TbJSWK5pVrgsmFyIiM/VJxq84nXcXn8h+bVa5Lgz6sjAiItI/r8XfoqqmTvl558l87DyZjzatpMhdOarJcjGw5UJEZGaOLxiGaF8X2Fg//om3sZYixtcFx98fplW5GJhciIjMTOf2NrBv0wpVNXVo00qKqpo62Ldphc72NlqVi4HdYkREZuiOvArjB7rjLwHd8cWpfNz+w6B9U+W6YnIhIjJDn070V/595Rifpy7XFbvFiIhIdEwuREQkOiYXIiISHZMLERGJjsmFzJo+t7cgIs2YXMis6XN7CyLSjFORySwZYnsLItKMLRcyS4bY3oKINGNyIbNkiO0tiEgzJhcyW/XbW+x5azDGD3THbXmVsUMiUmHOE0445kJmS9/bWxDp6skJJytf7mPscETF5EJEZGCWMOGE3WJERAZmCRNOmFyIiAzMEiacsFuMiMgI9P0+FWNjciEiMgJzn3DCbjEiIhIdkwsREYmOyYWIiETH5EJERKJjciEiItExuRARkeiYXIiISHRMLkREJqqpXZNNeVdlJhciIhPV1Gu6Tfk13lyhT0RkYpraNbkl7Kps8i2XjRs3wsvLS+34mTNnEBsbi379+iEiIgLp6elqdWQyGaKiotC3b19ER0cjMzPTECETEemkqV2TW8KuyiadXK5cuYKUlBS141evXsW0adPg6uqKpKQkhIaGIiEhAYcOHVLWyc7ORnx8PAICApQJavbs2Th//rwB74CI6Ok1tWtyS9hV2WS7xWpra/HBBx/A0dERJSUlKmWpqano1q0b1q9fD4lEgqFDh6KsrAzJycmIjIwEACQnJyMoKAhLliwBAAwdOhRFRUVISUlpMGEREZmSpnZNNvVdlU02uaSlpaGiogITJkzAunXrVMqysrIQHR0NiUSiPDZixAjs378fJSUlcHBwQE5ODhISElS+FxYWhsTERNTW1sLKysog90FE1BxN7Zps6rsqm2RyuXHjBpKSkrBlyxb89NNPKmWVlZUoLS2Fu7u7ynE3NzcAQF5eHhwdHVFTU9NgHYVCgeLiYri6uiqP19bWAgBu3bqlj9shIjJL9b+Z9b+hTzJocqmurkZ+fr7G8k6dOqF9+/ZYvHgxYmJi4O/vr5Zc5HI5AMDW1lbleP1nuVyO1q1bN1nnSbdv3wYAjB8//mlviYjI4t2+fVvtP/MGTS4lJSV48cUXNZYvWrQIbdq0wY0bN7B58+YG6wiCAAAqXWJPkkqlWtV5ko+PD3bt2gUnJyd2lxERaam2tha3b9+Gj496t5xBk4urqytyc3M1lhcXF2P06NH4+OOPYWNjg5qaGmWiqKmpgVQqhZ2dHQCgoqJC5bv1n+3t7WFvb99knSfZ2NjA398fRET0dP7YYqlnUmMu2dnZqKioQHx8vFrZ888/j9mzZ+Ptt9+Gk5MTCgoKVMrrP3t4eMDW1hZSqbTBOu3atYOzs7P+boKIiExrncuwYcOQnp6u8mfKlCkAgPT0dLz++usAgMDAQGRmZqoMIslkMnh6eqJjx46wsbGBn58fZDKZyvkzMjIwcOBAtW6x5tq9ezciIiLQt29fjB07Fjk5OaKc1xyVlZXBy8tL7U/9fyQEQcDmzZsRGhqKfv36YcqUKbh69aqRozYNGRkZ8PPzUzmmzfN69OgRVq1ahcGDB8PPzw/x8fFq0/otQUPP76effmrw3+OaNWuUdfj8dCSYuO3btwuenp4qxy5duiQ8//zzwqxZs4SjR48Kq1atEjw9PYVvv/1WWefo0aOCp6ensHjxYuHo0aPC/Pnzhd69ewvnzp0TJa5//etfgre3t5CUlCQcPXpUmDp1quDn5yfk5+eLcn5zk5WVJXh6egr/+c9/hJycHOWf69evC4IgCElJSUKfPn2Ezz//XJDJZMKrr74qDBkyRHjw4IFxAzeys2fPCn5+foKvr6/KcW2e18KFC4WAgADh66+/Fr799lshPDxciI6OFmpqagx9G0aj6fn985//FHx9fVX+Lebk5AiFhYXKOnx+ummRyUUQBOHYsWNCdHS04OPjI0RERAhff/21Wp29e/cKERERgo+PjxAVFSVkZmaKElNdXZ0wbNgwYenSpcpjjx49EoYPHy6sWLFClGuYm+3btwtBQUENlpWXlwu+vr7Cp59+qjx27949wc/PT9i2bZuhQjQpVVVVQmpqqvD8888LL7zwgsqPozbP68aNG4K3t7fwzTffKOtcv35d8PLyEg4fPmy4GzGSxp6fIAjCypUrhdjYWI3ft/TnJwaT6hZryOTJkxucBBAcHIx9+/bh4sWLOHz4MF555RW1OjExMTh8+DAuXryI/fv3IzQ0VJSYbty4gcLCQgwfPlx5zNraGqGhoTh+/Lgo1zA3ubm5De4RBwA//vgjKisrERYWpjzm4OCAgIAAi32ex44dQ2pqKhYsWIAJEyaolGnzvE6cOAEAKv/me/TogWeffdYinmljzw9o/N8jwOcnBpNPLqYoLy8PgPosCTc3N+Tn5ze4oMjS5ebm4uHDhxg3bhz69OmDoUOHYsuWLRAEQfk86xfC1nN1dVWWWZo+ffogIyMDkyZNUptSr83zun79Ojp16oR27dpprGPOGnt+wON9C4uLixETEwMfHx+Eh4djz549ynJLf35iMKnZYi1FYws56+rq8PDhQ+WUaXo8F/7q1ato27Yt3n//fbi4uODo0aNYt24dFAoFrK2t0bp1a+Xi13q2trZqC14tRWMzGusXCjf2vCoqKtT+fdbXsYSdKBp7fiUlJSgrK8ONGzcwb948ODg44MCBA1i4cCEkEgnGjBlj8c9PDEwuzSA0sUhT03FLlpKSAhcXF2Vrb+DAgaisrMSWLVvw5ptv8lk+BUEQmnxe2tSxVA4ODti6dSs8PT3RuXNnAEBQUBBKS0uxceNGjBkzhs9PBOwWa4bGFmlaWVk1+D8eS2ZlZYXAwEC1bsTg4GA8fPgQbdu2xaNHj1BdXa1SXlFRobbglR7/+2vqednZ2an9+/xjHUtlY2ODIUOGKBNLveDgYBQUFKCiooLPTwRMLs1Q/yPZ0CLNHj16GCEi01ZSUoKvvvoKd+/eVTleVVUF4PH/JAVBwM2bN1XKb968CQ8PD4PF2VK4u7s3+bx69OiBO3fuQKFQaKxjqa5fv44vvvgCjx49UjleVVUFGxsbtGvXjs9PBEwuzdCjRw907dpVZZFmdXU1jh49isDAQCNGZpoePXqEpUuXYv/+/SrHDx8+jB49eiA8PBxt2rRReZ7379/HqVOn+Dwb4Ofn1+TzCgwMRG1tLb777jtlnby8PPz6668W/0xLSkqwfPlyfP/998pjgiDgyJEj8Pf3h0Qi4fMTAcdcmkEikWD69OlYsWIFHBwc0L9/f+zcuRNlZWWYPHmyscMzOW5ubnjppZeQmJgIiUSCZ555BocOHcKRI0eQnJwMW1tbTJgwAYmJiZBKpejRowdSUlJgZ2eH2NhYY4dvcrR5Xt27d0dkZCSWLFkCuVyO9u3bY/369fDy8sKIESOMfAfG9cILL2DAgAFYtmwZ7t+/DycnJ+zevRu5ubn48ssvAfD5icJ4S2xavq1btwohISFC3759hbFjx4q2+t8cPXz4UFi3bp0wbNgwwcfHR4iJiRGOHDmiLK+urhb+/ve/C0FBQYKvr68wZcoU4bfffjNixKbjk08+UVsEqM3zqqioEBYvXiy88MILwoABA4S3335buHXrliFDNwkNPb+ysjJhyZIlQnBwsNCnTx9h7NixwunTp1Xq8PnpRiII/3/qExERkUg45kJERKJjciEiItExuRARkeiYXIiISHRMLkREJDomFyIiEh2TC9FTGjNmDLy8vHDhwgWDX3vNmjVISkoCACQlJcHLywtBQUGoq6trsP6kSZPg5eWFrVu3an2Ny5cv46WXXlLbHoXoaTC5ED2FK1eu4PLly+jVqxf++c9/GvTaFy5cwIEDBzB16lTlMYlEgt9//x1nzpxRq6/peFO8vb3h4+OD5ORkneIly8bkQvQU9uzZA29vb8TGxuKbb75BZWWlwa69du1a/OUvf1F5gVXbtm3h7e2NI0eOqNU/cuQIevXq1axrTZ8+Hdu2bcPt27ebHS9ZNiYXIi3V1tbiwIEDCA4OxqhRo/Dw4UMcPHhQpc7ly5cxadIk+Pr6IiwsDPv27UN4eLiyKwt43KJYsGABAgIC4OfnhzfffFNth+0/+uWXX3Dy5EmMHj1arSwiIgIymQx/3Gzj0KFDGDVqlFr9CxcuYPr06fD394ePjw9GjhyJf/zjHyp1nnnmGXh4eGDnzp1NPheihjC5EGkpKysLpaWliIqKgrOzMwIDA5Genq4sv3PnDiZNmoSqqiqsX78e06dPx0cffYTi4mJlHYVCgUmTJuHs2bNYvHgx/va3v+HOnTuYMGEC7t+/r/HaBw4cgJeXF7p3765WNnLkSBQXF+PixYvKY3fv3sXp06cRGRmpUreoqAiTJk1Cu3btkJiYiOTkZHh4eGDZsmXIzc1VqRsREYFvvvnmqZ8TEcDkQqS1vXv3onfv3vD09AQAxMTEICcnB7/99hsAYMeOHairq8Nnn32G4cOHY9y4cVixYoXKS7327t2L69evIzU1FdHR0QgPD0daWhoqKyuxY8cOjdc+efIknn/++QbLevXqhWeeeUala6y+S+yP7x759ddf4evri7Vr12Lw4MEICQnBmjVrAACnT59Wqdu7d28UFBSgqKjoKZ4S0WNMLkRakMvlyMjIQHh4OB48eIAHDx5g0KBBaNu2rXJg/+TJkwgICED79u2V3xsxYgRatfrvmy1OnjwJd3d3uLu7o6amBjU1NbCxscGAAQNw4sQJjdcvLCxEly5dNJZHRETg3//+t/LzoUOH1FotABASEoK0tDTU1dXh8uXLOHToED799FMAUJsd5uLiorw20dPi+1yItHD48GE8fPgQiYmJSExMVCnbt28f3n33XZSVlakNoFtZWeFPf/qT8vO9e/dw7dq1Blshjb3FVC6Xo23bthrLIyMjsXnzZuTm5sLJyQmnT5/G0qVL1erV1tZi9erV+Oqrr1BdXY3u3bvD398fANTGbOqvV15ervG6RJowuRBpYe/evejTpw/ee+89leO//fYbPvzwQ8hkMjg7O6u9yrmurg737t1Tfra3t4e3tzdWrlypdo3WrVtrvH6HDh0a/ZH39vZG9+7d8e9//xvOzs545pln0LNnT7V6mzdvxu7du7FmzRqEhISgXbt2ePjwocrYUb36MaAOHTpovC6RJkwuRE0oKirC6dOnkZCQgIEDB6qU+fv7Y/PmzUhPT4e/vz/+7//+D3K5HHZ2dgCAY8eOqYy59O/fHz/88AO6desGR0dHAI9bDPPnz4enpye8vLwajKFLly64detWo3HWzxpzcnJqsEsMAM6fPw8fHx+VWWTHjx9XxvGk0tJSAP/tHiN6GhxzIWrCvn37IJFIMHLkSLUyKysrjBo1CtnZ2Xj11VchlUoxY8YMZGZm4uuvv8bixYsBPF7sCACvvfYaOnTogDfeeAMHDx5EVlYW5s6di4MHD8Lb21tjDIGBgTh//nyjcY4cORKXLl1Cdna2xuTSp08f/Pjjj9i5cydOnTqFrVu3YuHChZBIJFAoFCp1c3Jy0LNnz0bHeog0YXIhasK+ffvQv39/dO7cucHyqKgo1NXVIT09Hdu2bUNdXR3i4+OxadMmLFq0CMDj994DgJ2dHXbt2oWePXvir3/9K9566y0UFRVh06ZNCAkJ0RhDeHg48vPzkZ+fr7FO37594eLiAg8Pjwa7xABgxowZGDNmDDZu3Ii4uDgcOHAAS5cuxeDBg5GTk6NS94cffkBERESjz4ZIE77mmEgkOTk5UCgUCAwMVB67fv06IiMjsWnTJoSFhel0/okTJ6J///545513dA21ST///DP+/Oc/IyMjA05OTnq/HpkftlyIRJKfn6/cNuX06dP49ttvMXfuXPTo0QNDhgzR+fxz585Feno65HK5CNE2bvv27ZgwYQITCzUbWy5EIkpLS8NXX32FwsJC2NraYvDgwXjvvffg7OwsyvlXrVoFW1tbzJkzR5TzNeTSpUuYN28e9uzZAxsbG71dh8wbkwsREYmO3WJERCQ6JhciIhIdkwsREYmOyYWIiETH5EJERKJjciEiItH9P7gj4FLWj1cZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,-Thermal_boundary.flatten(),'*')\n",
    "plt.ylim([-400,0])\n",
    "plt.ylabel('Depth (km)')\n",
    "plt.xlabel('Age (Ma)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ea76c663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2183a16d688>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=25\n",
    "T_mantle    = 1300 # oC\n",
    "#T_mantle    = T_P_loop[0][index][-1]\n",
    "alpha       = 3e-5 # oC-1\n",
    "cp          = 1250 # J/kg/oC \n",
    "g           = 9.8 #m/s2\n",
    "dT_dZ       = ((T_mantle*alpha*g)/cp)*1e3 # oC/km\n",
    "        \n",
    "plt.plot(T[0][index][:],-nz[0][index][:],'*',label=str(np.unique(nx[0][index][:]))+'Ma')\n",
    "plt.plot(T_mantle + nz[0][index][:]*dT_dZ,-nz[0][index][:])\n",
    "#plt.plot(T_mantle + nz[0][index][:]*dT_dZ,-nz[0][index][:],'*')\n",
    "\n",
    "#plt.ylim([-400,0])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91ef8789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2183a87f088>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=35\n",
    "T_mantle    = 1300 # oC\n",
    "#T_mantle    = T_P_loop[0][index][-1]\n",
    "alpha       = 3e-5 # oC-1\n",
    "cp          = 1250 # J/kg/oC \n",
    "g           = 9.8 #m/s2\n",
    "dT_dZ       = ((T_mantle*alpha*g)/cp)*1e3 # oC/km\n",
    "        \n",
    "plt.plot(T[0][index][:],-nz[0][index][:],'*',label=str(np.unique(nx[0][index][:]))+'Ma')\n",
    "plt.plot(T_mantle + nz[0][index][:]*dT_dZ,-nz[0][index][:])\n",
    "#plt.plot(T_mantle + nz[0][index][:]*dT_dZ,-nz[0][index][:],'*')\n",
    "\n",
    "#plt.ylim([-400,0])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b9954a",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}