{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling fluid inclusion stretching during stalling\n",
    "- This notebook shows how to model the stretching of CO2 dominated fluid inclusions in olivine using python tool RelaxiFI (DeVitre and Wieser, 2023; EarthArXiv), implemented in DiadFit. Note that this tool is not designed to model FI with fluids other than CO2 at this time, or those in phases other than olivine. It is based on the model of Wanamaker and Evans, 1989.\n",
    "- In this example from the RelaxiFI documentation, based on the work from DeVitre and Wieser, 2023 (EarthArXiv), we model the stretching of two CO2 dominated fluid inclusions with 1 and 20 μm radii, trapped at South Caldera Reservoir (Kilaeua volcano, Hawaii) and stored at Halema'uma'u reservoir for 2 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First, make sure CoolProp is installed if using the Span and Wagner 1996 equation of state\n",
    "- You only need to run this once. If you do not wish to use CoolProp, or face installation problems, please use 'SP94' instead of 'SW96'\n",
    "- If you have Python installed through Anaconda, you may want to install CoolProp through your conda command line 'conda install conda-forge::coolprop'. Else you can use 'pip install CoolProp' as below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install CoolProp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next import the necessary packages, including DiadFit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import DiadFit as pf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's model stretching and evaluate how density, pressure and radius change\n",
    "- We model an FI (5 um) coming from South Caldera (SC) reservoir (4km@ 1300 C) and stalling at Halema'uma'u (HM) reservoir (1km @ 1150 C)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, set the PT conditions of the reservoirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### Establish reservoir PTX conditions\n",
    "\n",
    "## Choose an equation of state and crustal density model for the calculations\n",
    "EOS='SW96'\n",
    "crustal_model_config=pf.config_crustalmodel(model='ryan_lerner') # this configures the crustal model to be used\n",
    "\n",
    "## SC reservoir conditions\n",
    "Trapping_depth = 4 # Average depth of the South Caldera reservoir in km\n",
    "melt_MgO=13 #melt MgO in wt% \n",
    "Trapping_temp=round(21.2*melt_MgO+1017,-1)  # Temperature in C at South Caldera reservoir (MgO thermometry, see DeVitre and Wieser, 2023, Shea 2022)\n",
    "# find pressure @ SC\n",
    "tolerance = 0.001 # How close we want to be to the target pressure\n",
    "Trapping_pressure = pf.find_P_for_kmdepth(target_depth_km=Trapping_depth, crustal_model_config=crustal_model_config, tolerance=tolerance)[0] #This is the calculated pressure at South Caldera reservoir\n",
    "\n",
    "## HM reservoir conditions\n",
    "Storage_depth = 1 # Average depth of the Halema'uma'u reservoir in km\n",
    "melt_MgO=6.5 #melt MgO in wt%\n",
    "Storage_temp=round(21.2*melt_MgO+1017,-1)  # Temperature in C at Halema'uma'u reservoir (MgO thermometry)\n",
    "# find pressure @ HM\n",
    "tolerance = 0.001 # How close we want to be to the target pressure\n",
    "Storage_pressure = pf.find_P_for_kmdepth(target_depth_km=Storage_depth, crustal_model_config=crustal_model_config, tolerance=tolerance)[0] #This is the calculated pressure at Halema'uma'u reservoir\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next, Calculate CO2 density (trapped at SC) and internal pressure adjusted to HM reservoir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### First let's calculate the CO2 density of our 5um FI trapped at SC\n",
    "fi_rho_initial_gcm3=pf.calculate_rho_for_P_T(EOS=EOS,P_kbar=Trapping_pressure,T_K=Trapping_temp+273.15)[0]\n",
    "\n",
    "## Now we move the FI to HM reservoir (1km), Pinternal will change bc T is lower than at SC, we recalculate Pinternal\n",
    "fi_Pi_storage_initial_MPa=pf.calculate_P_for_rho_T(EOS=EOS,CO2_dens_gcm3=fi_rho_initial_gcm3,T_K=Storage_temp+273.15)['P_MPa'][0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With that, set the initial internal pressure for the model and external pressure (HM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pinternal=fi_Pi_storage_initial_MPa # Internal pressure of the FI in MPa\n",
    "Pexternal=Storage_pressure*100 ## External Pressure in the HM reservoir in MPa "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set stalling time (2 years), FI radii (here 1 um and 20 um), and distances from crystal defects (cracks, rims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set stalling time\n",
    "years=2 # choose number of years for stalling\n",
    "totaltime=years*365.25*24*60*60 # this is the totaltime of stalling in s (must be in seconds for model calculations)\n",
    "\n",
    "# Set the FI radius (meters)\n",
    "R0 = 1 * 10 ** -6 # FI radius in m, here 1 micron\n",
    "R1 = 20 * 10 ** -6 # FI radius in m, here 1 micron\n",
    "\n",
    "dist2defect_list=[50,100,250,500] # Distances to crystal defect structures (cracks, rim) in microns\n",
    "\n",
    "R_values = [R0,R1]  # Define R values, if there is more than one R, add them to the list. \n",
    "b_values=[b*10**-6 for b in dist2defect_list] # make sure the bvalues are in meters. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose calculation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "method='RK1' # this is the numerical solver, options are RK1, RK2, RK3 and RK4 (RK1 is the same as Euler method, RK2 same as Heun)\n",
    "steps=1000 #number of steps, more is better but slower"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let's run the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '4.34291339979076e-06' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '4.079746117824357e-06' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '3.911624110397582e-06' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '3.849599542693483e-06' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '3.609313470747111e-05' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '1.123537108771444e-05' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '6.022478879330606e-06' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '63115.2' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n",
      "c:\\users\\penny\\box\\berkeley_new\\diadfit_outer\\src\\DiadFit\\relaxifi.py:518: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '4.868033115812409e-06' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  results.loc[step] = [step * dt_s, step, dt_s, Pexternal_MPa, Pinternal_MPa, dR_dt, R_m * 10 ** 6, b_m * 10 ** 6,\n"
     ]
    }
   ],
   "source": [
    "results_dict = pf.loop_R_b_constant_Pext(R_m_values=R_values, b_m_values=b_values, T_K=Storage_temp+273.15, EOS=EOS, Pinternal_MPa=Pinternal, Pexternal_MPa=Pexternal, \n",
    "                                                    totaltime_s=totaltime, steps=steps, T4endcalc_PD=Trapping_temp,method=method,\n",
    "                                              plotfig=False,crustal_model_config=crustal_model_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally, let's plot our results \n",
    "- Note that the drop in pressure even over 2 years of stalling is not significant for the 1 μm radius FI, and barely so for the 20 μm FI (<10% for both). It is essentially within uncertainty of the method regardless. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b5edf50790>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################## Now let's plot our results ######################\n",
    "\n",
    "time2years=totaltime/years\n",
    "\n",
    "# Define some constants and variables\n",
    "\n",
    "y_col = 'CO2_dens_gcm3'\n",
    "x_col = 'Time(s)'\n",
    "xlabel4plot = 'Time(years)'\n",
    "ylabel4plot = 'CO2 density (g/cm3)'\n",
    "twinlabel4plot = 'Deviation from Entrapment Pressure (%)'\n",
    "linecolor = 'midnightblue'\n",
    "linecolor2 = 'orange'\n",
    "linewidth = 1\n",
    "\n",
    "# Create a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot the data on the primary y-axis\n",
    "\n",
    "ax.plot(results_dict['R0']['b0'][x_col] /time2years, results_dict['R0']['b0'][y_col], color=linecolor2, linestyle=':', linewidth=linewidth, label=str(dist2defect_list[0]))\n",
    "ax.plot(results_dict['R0']['b1'][x_col] /time2years, results_dict['R0']['b1'][y_col], color=linecolor2, linestyle='-.', linewidth=linewidth, label=str(dist2defect_list[1]))\n",
    "ax.plot(results_dict['R0']['b2'][x_col] /time2years, results_dict['R0']['b2'][y_col], color=linecolor2, linestyle='--', linewidth=linewidth, label=str(dist2defect_list[2]))\n",
    "ax.plot(results_dict['R0']['b3'][x_col] /time2years, results_dict['R0']['b3'][y_col], color=linecolor2, linestyle='-', linewidth=linewidth, label=str(dist2defect_list[3]))\n",
    "\n",
    "ax.plot(results_dict['R1']['b0'][x_col] /time2years, results_dict['R1']['b0'][y_col], color=linecolor, linestyle=':', linewidth=linewidth)\n",
    "ax.plot(results_dict['R1']['b1'][x_col] /time2years, results_dict['R1']['b1'][y_col], color=linecolor, linestyle='-.', linewidth=linewidth)\n",
    "ax.plot(results_dict['R1']['b2'][x_col] /time2years, results_dict['R1']['b2'][y_col], color=linecolor, linestyle='--', linewidth=linewidth)\n",
    "ax.plot(results_dict['R1']['b3'][x_col] /time2years, results_dict['R1']['b3'][y_col], color=linecolor, linestyle='-', linewidth=linewidth)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "xlim=([round(min(results_dict['R1']['b0'][x_col]/time2years)),round(max(results_dict['R1']['b0'][x_col]/time2years))])\n",
    "ymin=np.nanmin([np.nanmin(results_dict['R1']['b0'][y_col])])\n",
    "\n",
    "ylim=[ymin,fi_rho_initial_gcm3]\n",
    "\n",
    "ylim_P=pf.calculate_P_for_rho_T(EOS=EOS,CO2_dens_gcm3=pd.Series(ylim),T_K=Trapping_temp+273.15)['P_MPa']\n",
    "\n",
    "def percent_drop(ylim, original_value):\n",
    "    return 100-ylim*100/original_value\n",
    "\n",
    "ax.set_xlim(xlim)\n",
    "ax.set_ylim(ylim)\n",
    "\n",
    "ax2.set_ylim(percent_drop(ylim_P,original_value=Trapping_pressure*100))\n",
    "\n",
    "# Customize axis labels and legends\n",
    "ax.set_title(f\"Stalling at HM reservoir, {linecolor2} FI = {round(R_values[0]*10**6,1)} μm ; {linecolor} FI = {round(R_values[1]*10**6,1)} μm\")\n",
    "\n",
    "ax.set_xlabel(xlabel4plot)\n",
    "ax.set_ylabel(ylabel4plot)\n",
    "ax2.set_ylabel(twinlabel4plot)\n",
    "\n",
    "ax.legend(title='Distance to crystal defect (μm)')\n",
    "\n"
   ]
  }
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