{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14c0d7bd-b360-412b-9c1b-6952be1ff3af",
   "metadata": {},
   "source": [
    "# Example from La Palma\n",
    "- This notebook shows how to convert the Raman-based FI densities from Dayton et al. (2023) into pressures and depths in the crust\n",
    "- This assumes +-50K error in entrapment temperature, and an error in CO2 density of 0.002925 g/cm3\n",
    "- Depth is calculated using a 2-step density profile, with 2.8 g/cm3 above the Moho, and 3.1 g/cm3 below\n",
    "- You can download the excel spreadsheet here. \n",
    "- https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/Dayton_et_al_2023_LaPalma_Example.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102b92e2",
   "metadata": {},
   "source": [
    "### Install DiadFit if you havent already! You might also have to install CoolProp if you want to use Span and Wagner EOS - the error message will give you instructions, else reach out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6031844",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --upgrade DiadFit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f31fa32-ec74-4136-a6d6-578c3544d75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0.91'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import DiadFit as pf\n",
    "pf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c00252-8e94-474d-bb74-c5b2a1b22f2b",
   "metadata": {},
   "source": [
    "## Lets load in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5f0e52e-8f00-4bf5-85ac-7b4a64ef9587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAMPLE</th>\n",
       "      <th>FileName</th>\n",
       "      <th>Density (g/cm^3)</th>\n",
       "      <th>Comment (EPMA Data Point)</th>\n",
       "      <th>Na2O</th>\n",
       "      <th>MgO</th>\n",
       "      <th>SiO2</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>P2O5</th>\n",
       "      <th>K2O</th>\n",
       "      <th>CaO</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>FeO</th>\n",
       "      <th>MnO</th>\n",
       "      <th>Cr2O3</th>\n",
       "      <th>NiO</th>\n",
       "      <th>Total</th>\n",
       "      <th>Fo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>0.875343</td>\n",
       "      <td>LM0_G1_RIM</td>\n",
       "      <td>0.009036</td>\n",
       "      <td>42.12351</td>\n",
       "      <td>39.90642</td>\n",
       "      <td>0.018331</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.005965</td>\n",
       "      <td>0.213926</td>\n",
       "      <td>0.034901</td>\n",
       "      <td>17.91823</td>\n",
       "      <td>0.292019</td>\n",
       "      <td>0.078602</td>\n",
       "      <td>0.190220</td>\n",
       "      <td>100.7912</td>\n",
       "      <td>0.807341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>06 LM0 G2 FI1</td>\n",
       "      <td>0.780430</td>\n",
       "      <td>LM0_G2_CENTER</td>\n",
       "      <td>0.009698</td>\n",
       "      <td>44.42279</td>\n",
       "      <td>39.70696</td>\n",
       "      <td>0.008494</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.322745</td>\n",
       "      <td>0.022287</td>\n",
       "      <td>15.26419</td>\n",
       "      <td>0.225410</td>\n",
       "      <td>0.018465</td>\n",
       "      <td>0.194827</td>\n",
       "      <td>100.1963</td>\n",
       "      <td>0.838388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>17 LM0 G3 FI3</td>\n",
       "      <td>0.936785</td>\n",
       "      <td>LM0_G3_CENTER</td>\n",
       "      <td>0.011004</td>\n",
       "      <td>45.31343</td>\n",
       "      <td>40.64921</td>\n",
       "      <td>0.016474</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.003951</td>\n",
       "      <td>0.241455</td>\n",
       "      <td>0.018161</td>\n",
       "      <td>14.36721</td>\n",
       "      <td>0.216594</td>\n",
       "      <td>0.065003</td>\n",
       "      <td>0.260763</td>\n",
       "      <td>101.1633</td>\n",
       "      <td>0.848989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>11 LM0 G3 FI1 (CRR)</td>\n",
       "      <td>0.928828</td>\n",
       "      <td>LM0_G3_CENTER</td>\n",
       "      <td>0.011004</td>\n",
       "      <td>45.31343</td>\n",
       "      <td>40.64921</td>\n",
       "      <td>0.016474</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.003951</td>\n",
       "      <td>0.241455</td>\n",
       "      <td>0.018161</td>\n",
       "      <td>14.36721</td>\n",
       "      <td>0.216594</td>\n",
       "      <td>0.065003</td>\n",
       "      <td>0.260763</td>\n",
       "      <td>101.1633</td>\n",
       "      <td>0.848989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>19 LM0 G3 FI4</td>\n",
       "      <td>0.928514</td>\n",
       "      <td>LM0_G3_CENTER</td>\n",
       "      <td>0.011004</td>\n",
       "      <td>45.31343</td>\n",
       "      <td>40.64921</td>\n",
       "      <td>0.016474</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.003951</td>\n",
       "      <td>0.241455</td>\n",
       "      <td>0.018161</td>\n",
       "      <td>14.36721</td>\n",
       "      <td>0.216594</td>\n",
       "      <td>0.065003</td>\n",
       "      <td>0.260763</td>\n",
       "      <td>101.1633</td>\n",
       "      <td>0.848989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SAMPLE             FileName  Density (g/cm^3) Comment (EPMA Data Point)  \\\n",
       "0       0        03 LM0 G1 FI1          0.875343                LM0_G1_RIM   \n",
       "1       0        06 LM0 G2 FI1          0.780430             LM0_G2_CENTER   \n",
       "2       0        17 LM0 G3 FI3          0.936785             LM0_G3_CENTER   \n",
       "3       0  11 LM0 G3 FI1 (CRR)          0.928828             LM0_G3_CENTER   \n",
       "4       0        19 LM0 G3 FI4          0.928514             LM0_G3_CENTER   \n",
       "\n",
       "       Na2O       MgO      SiO2     Al2O3      P2O5       K2O       CaO  \\\n",
       "0  0.009036  42.12351  39.90642  0.018331  0.000023  0.005965  0.213926   \n",
       "1  0.009698  44.42279  39.70696  0.008494  0.000023  0.000412  0.322745   \n",
       "2  0.011004  45.31343  40.64921  0.016474  0.000023  0.003951  0.241455   \n",
       "3  0.011004  45.31343  40.64921  0.016474  0.000023  0.003951  0.241455   \n",
       "4  0.011004  45.31343  40.64921  0.016474  0.000023  0.003951  0.241455   \n",
       "\n",
       "       TiO2       FeO       MnO     Cr2O3       NiO     Total        Fo  \n",
       "0  0.034901  17.91823  0.292019  0.078602  0.190220  100.7912  0.807341  \n",
       "1  0.022287  15.26419  0.225410  0.018465  0.194827  100.1963  0.838388  \n",
       "2  0.018161  14.36721  0.216594  0.065003  0.260763  101.1633  0.848989  \n",
       "3  0.018161  14.36721  0.216594  0.065003  0.260763  101.1633  0.848989  \n",
       "4  0.018161  14.36721  0.216594  0.065003  0.260763  101.1633  0.848989  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get from here: https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/Dayton_et_al_2023_LaPalma_Example.xlsx\n",
    "data=pd.read_excel('Dayton_et_al_2023_LaPalma_Example.xlsx',\n",
    "                   sheet_name='Sheet1')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c537eb2d-226e-494e-8994-fc9fb4a77b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# of meas')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Density (g/cm^3)'], ec='k')\n",
    "plt.xlabel('CO$_2$ density (g/cm^3)')\n",
    "plt.ylabel('# of meas')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef9cd0c-3963-47a2-bd32-3463709f3b2a",
   "metadata": {},
   "source": [
    "## Now lets propagate uncertainty in each fluid inclusion\n",
    "- Here we use a temperature of 1150 K, with a +-50 K (i.e. an absolute uncertainty) distributed normally\n",
    "- We say the error in CO2 density (from repeated Raman measurements) is 0.002925 g/cm3 (i.e. an absolute uncertainty) distributed normally \n",
    "- We want to use 2 step crustal density model, with 2800 kg/m3 above 14km depth, and 3100kg/m3 below. Right now, we are not propagating uncertainty in this. \n",
    "- The figure shows us the simulation file the 1st file (file_i=0). For the Nth file, enter file_i=N-1 as python counting starts at 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01b41d4c-8842-4fe5-b5e8-3de7aabfc5e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are not using multiprocessing based on your selected EOS. You can override this by setting multiprocess=True in the function, but for SP94 and SW96 it might actually be slower\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing: 100%|██████████| 115/115 [00:01<00:00, 76.97it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>CO2_dens_gcm3</th>\n",
       "      <th>SingleCalc_D_km</th>\n",
       "      <th>SingleCalc_P_kbar</th>\n",
       "      <th>Mean_MC_P_kbar</th>\n",
       "      <th>Med_MC_P_kbar</th>\n",
       "      <th>std_dev_MC_P_kbar</th>\n",
       "      <th>std_dev_MC_P_kbar_from_percentile</th>\n",
       "      <th>Mean_MC_D_km</th>\n",
       "      <th>Med_MC_D_km</th>\n",
       "      <th>std_dev_MC_D_km</th>\n",
       "      <th>std_dev_MC_D_km_from_percentile</th>\n",
       "      <th>T_K_input</th>\n",
       "      <th>error_T_K</th>\n",
       "      <th>CO2_dens_gcm3_input</th>\n",
       "      <th>error_CO2_dens_gcm3</th>\n",
       "      <th>crust_dens_kgm3_input</th>\n",
       "      <th>error_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>EOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>0.875343</td>\n",
       "      <td>21.140399</td>\n",
       "      <td>6.016987</td>\n",
       "      <td>6.031485</td>\n",
       "      <td>6.026854</td>\n",
       "      <td>0.222039</td>\n",
       "      <td>0.219125</td>\n",
       "      <td>21.188075</td>\n",
       "      <td>21.172847</td>\n",
       "      <td>0.730127</td>\n",
       "      <td>0.720544</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.875343</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06 LM0 G2 FI1</td>\n",
       "      <td>0.780430</td>\n",
       "      <td>16.834446</td>\n",
       "      <td>4.707503</td>\n",
       "      <td>4.714673</td>\n",
       "      <td>4.712628</td>\n",
       "      <td>0.187193</td>\n",
       "      <td>0.188277</td>\n",
       "      <td>16.858021</td>\n",
       "      <td>16.851298</td>\n",
       "      <td>0.615543</td>\n",
       "      <td>0.619108</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.780430</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17 LM0 G3 FI3</td>\n",
       "      <td>0.936785</td>\n",
       "      <td>24.412263</td>\n",
       "      <td>7.011993</td>\n",
       "      <td>7.020079</td>\n",
       "      <td>7.023037</td>\n",
       "      <td>0.259320</td>\n",
       "      <td>0.266165</td>\n",
       "      <td>24.438851</td>\n",
       "      <td>24.448577</td>\n",
       "      <td>0.852716</td>\n",
       "      <td>0.875226</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.936785</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11 LM0 G3 FI1 (CRR)</td>\n",
       "      <td>0.928828</td>\n",
       "      <td>23.965558</td>\n",
       "      <td>6.876146</td>\n",
       "      <td>6.870309</td>\n",
       "      <td>6.867972</td>\n",
       "      <td>0.247936</td>\n",
       "      <td>0.246916</td>\n",
       "      <td>23.946364</td>\n",
       "      <td>23.938680</td>\n",
       "      <td>0.815284</td>\n",
       "      <td>0.811931</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.928828</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19 LM0 G3 FI4</td>\n",
       "      <td>0.928514</td>\n",
       "      <td>23.948123</td>\n",
       "      <td>6.870844</td>\n",
       "      <td>6.869369</td>\n",
       "      <td>6.874090</td>\n",
       "      <td>0.253193</td>\n",
       "      <td>0.259509</td>\n",
       "      <td>23.943273</td>\n",
       "      <td>23.958798</td>\n",
       "      <td>0.832571</td>\n",
       "      <td>0.853339</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.928514</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Filename  CO2_dens_gcm3  SingleCalc_D_km  SingleCalc_P_kbar  \\\n",
       "0        03 LM0 G1 FI1       0.875343        21.140399           6.016987   \n",
       "1        06 LM0 G2 FI1       0.780430        16.834446           4.707503   \n",
       "2        17 LM0 G3 FI3       0.936785        24.412263           7.011993   \n",
       "3  11 LM0 G3 FI1 (CRR)       0.928828        23.965558           6.876146   \n",
       "4        19 LM0 G3 FI4       0.928514        23.948123           6.870844   \n",
       "\n",
       "   Mean_MC_P_kbar  Med_MC_P_kbar  std_dev_MC_P_kbar  \\\n",
       "0        6.031485       6.026854           0.222039   \n",
       "1        4.714673       4.712628           0.187193   \n",
       "2        7.020079       7.023037           0.259320   \n",
       "3        6.870309       6.867972           0.247936   \n",
       "4        6.869369       6.874090           0.253193   \n",
       "\n",
       "   std_dev_MC_P_kbar_from_percentile  Mean_MC_D_km  Med_MC_D_km  \\\n",
       "0                           0.219125     21.188075    21.172847   \n",
       "1                           0.188277     16.858021    16.851298   \n",
       "2                           0.266165     24.438851    24.448577   \n",
       "3                           0.246916     23.946364    23.938680   \n",
       "4                           0.259509     23.943273    23.958798   \n",
       "\n",
       "   std_dev_MC_D_km  std_dev_MC_D_km_from_percentile  T_K_input  error_T_K  \\\n",
       "0         0.730127                         0.720544    1423.15         50   \n",
       "1         0.615543                         0.619108    1423.15         50   \n",
       "2         0.852716                         0.875226    1423.15         50   \n",
       "3         0.815284                         0.811931    1423.15         50   \n",
       "4         0.832571                         0.853339    1423.15         50   \n",
       "\n",
       "   CO2_dens_gcm3_input  error_CO2_dens_gcm3 crust_dens_kgm3_input  \\\n",
       "0             0.875343             0.002925                  None   \n",
       "1             0.780430             0.002925                  None   \n",
       "2             0.936785             0.002925                  None   \n",
       "3             0.928828             0.002925                  None   \n",
       "4             0.928514             0.002925                  None   \n",
       "\n",
       "   error_crust_dens_kgm3     model   EOS  \n",
       "0                    0.0  two-step  SW96  \n",
       "1                    0.0  two-step  SW96  \n",
       "2                    0.0  two-step  SW96  \n",
       "3                    0.0  two-step  SW96  \n",
       "4                    0.0  two-step  SW96  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MC_Av, MC_All, fig=pf.propagate_FI_uncertainty(\n",
    "T_K=1150+273.15,\n",
    "error_T_K=50, \n",
    "error_type_T_K='Abs', \n",
    "error_dist_T_K='normal',\n",
    "CO2_dens_gcm3=data['Density (g/cm^3)'],\n",
    "error_CO2_dens=0.002925, error_type_CO2_dens='Abs', error_dist_CO2_dens='normal',\n",
    "sample_ID=data['FileName'],\n",
    "model='two-step', d1=14, rho1=2800, rho2=3100,\n",
    "N_dup=1000, fig_i=0, plot_figure=True)\n",
    "MC_Av.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f029063-850a-4a58-9b9b-9bd60dfac0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>CO2_dens_gcm3</th>\n",
       "      <th>SingleCalc_D_km</th>\n",
       "      <th>SingleCalc_P_kbar</th>\n",
       "      <th>Mean_MC_P_kbar</th>\n",
       "      <th>Med_MC_P_kbar</th>\n",
       "      <th>std_dev_MC_P_kbar</th>\n",
       "      <th>std_dev_MC_P_kbar_from_percentile</th>\n",
       "      <th>Mean_MC_D_km</th>\n",
       "      <th>Med_MC_D_km</th>\n",
       "      <th>std_dev_MC_D_km</th>\n",
       "      <th>std_dev_MC_D_km_from_percentile</th>\n",
       "      <th>T_K_input</th>\n",
       "      <th>error_T_K</th>\n",
       "      <th>CO2_dens_gcm3_input</th>\n",
       "      <th>error_CO2_dens_gcm3</th>\n",
       "      <th>crust_dens_kgm3_input</th>\n",
       "      <th>error_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>EOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>0.875343</td>\n",
       "      <td>21.140399</td>\n",
       "      <td>6.016987</td>\n",
       "      <td>6.031485</td>\n",
       "      <td>6.026854</td>\n",
       "      <td>0.222039</td>\n",
       "      <td>0.219125</td>\n",
       "      <td>21.188075</td>\n",
       "      <td>21.172847</td>\n",
       "      <td>0.730127</td>\n",
       "      <td>0.720544</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.875343</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06 LM0 G2 FI1</td>\n",
       "      <td>0.780430</td>\n",
       "      <td>16.834446</td>\n",
       "      <td>4.707503</td>\n",
       "      <td>4.714673</td>\n",
       "      <td>4.712628</td>\n",
       "      <td>0.187193</td>\n",
       "      <td>0.188277</td>\n",
       "      <td>16.858021</td>\n",
       "      <td>16.851298</td>\n",
       "      <td>0.615543</td>\n",
       "      <td>0.619108</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.780430</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17 LM0 G3 FI3</td>\n",
       "      <td>0.936785</td>\n",
       "      <td>24.412263</td>\n",
       "      <td>7.011993</td>\n",
       "      <td>7.020079</td>\n",
       "      <td>7.023037</td>\n",
       "      <td>0.259320</td>\n",
       "      <td>0.266165</td>\n",
       "      <td>24.438851</td>\n",
       "      <td>24.448577</td>\n",
       "      <td>0.852716</td>\n",
       "      <td>0.875226</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.936785</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11 LM0 G3 FI1 (CRR)</td>\n",
       "      <td>0.928828</td>\n",
       "      <td>23.965558</td>\n",
       "      <td>6.876146</td>\n",
       "      <td>6.870309</td>\n",
       "      <td>6.867972</td>\n",
       "      <td>0.247936</td>\n",
       "      <td>0.246916</td>\n",
       "      <td>23.946364</td>\n",
       "      <td>23.938680</td>\n",
       "      <td>0.815284</td>\n",
       "      <td>0.811931</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.928828</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19 LM0 G3 FI4</td>\n",
       "      <td>0.928514</td>\n",
       "      <td>23.948123</td>\n",
       "      <td>6.870844</td>\n",
       "      <td>6.869369</td>\n",
       "      <td>6.874090</td>\n",
       "      <td>0.253193</td>\n",
       "      <td>0.259509</td>\n",
       "      <td>23.943273</td>\n",
       "      <td>23.958798</td>\n",
       "      <td>0.832571</td>\n",
       "      <td>0.853339</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>50</td>\n",
       "      <td>0.928514</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>two-step</td>\n",
       "      <td>SW96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Filename  CO2_dens_gcm3  SingleCalc_D_km  SingleCalc_P_kbar  \\\n",
       "0        03 LM0 G1 FI1       0.875343        21.140399           6.016987   \n",
       "1        06 LM0 G2 FI1       0.780430        16.834446           4.707503   \n",
       "2        17 LM0 G3 FI3       0.936785        24.412263           7.011993   \n",
       "3  11 LM0 G3 FI1 (CRR)       0.928828        23.965558           6.876146   \n",
       "4        19 LM0 G3 FI4       0.928514        23.948123           6.870844   \n",
       "\n",
       "   Mean_MC_P_kbar  Med_MC_P_kbar  std_dev_MC_P_kbar  \\\n",
       "0        6.031485       6.026854           0.222039   \n",
       "1        4.714673       4.712628           0.187193   \n",
       "2        7.020079       7.023037           0.259320   \n",
       "3        6.870309       6.867972           0.247936   \n",
       "4        6.869369       6.874090           0.253193   \n",
       "\n",
       "   std_dev_MC_P_kbar_from_percentile  Mean_MC_D_km  Med_MC_D_km  \\\n",
       "0                           0.219125     21.188075    21.172847   \n",
       "1                           0.188277     16.858021    16.851298   \n",
       "2                           0.266165     24.438851    24.448577   \n",
       "3                           0.246916     23.946364    23.938680   \n",
       "4                           0.259509     23.943273    23.958798   \n",
       "\n",
       "   std_dev_MC_D_km  std_dev_MC_D_km_from_percentile  T_K_input  error_T_K  \\\n",
       "0         0.730127                         0.720544    1423.15         50   \n",
       "1         0.615543                         0.619108    1423.15         50   \n",
       "2         0.852716                         0.875226    1423.15         50   \n",
       "3         0.815284                         0.811931    1423.15         50   \n",
       "4         0.832571                         0.853339    1423.15         50   \n",
       "\n",
       "   CO2_dens_gcm3_input  error_CO2_dens_gcm3 crust_dens_kgm3_input  \\\n",
       "0             0.875343             0.002925                  None   \n",
       "1             0.780430             0.002925                  None   \n",
       "2             0.936785             0.002925                  None   \n",
       "3             0.928828             0.002925                  None   \n",
       "4             0.928514             0.002925                  None   \n",
       "\n",
       "   error_crust_dens_kgm3     model   EOS  \n",
       "0                    0.0  two-step  SW96  \n",
       "1                    0.0  two-step  SW96  \n",
       "2                    0.0  two-step  SW96  \n",
       "3                    0.0  two-step  SW96  \n",
       "4                    0.0  two-step  SW96  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This returns 2 dataframes, one showing the mean and standard deviation of the simulation for each fluid inclusion\n",
    "MC_Av.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d788a1a-f0f6-4fbe-bf3c-c68de16ea182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Pressure (kbar)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Depth (km)</th>\n",
       "      <th>MC_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>MC_T_K</th>\n",
       "      <th>MC_CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>6.121520</td>\n",
       "      <td>612.151979</td>\n",
       "      <td>21.484133</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1456.265617</td>\n",
       "      <td>0.872784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>5.747624</td>\n",
       "      <td>574.762434</td>\n",
       "      <td>20.254659</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1364.131195</td>\n",
       "      <td>0.874644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>6.452705</td>\n",
       "      <td>645.270453</td>\n",
       "      <td>22.573163</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1508.484791</td>\n",
       "      <td>0.879484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>6.292424</td>\n",
       "      <td>629.242358</td>\n",
       "      <td>22.046114</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1482.444938</td>\n",
       "      <td>0.876493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03 LM0 G1 FI1</td>\n",
       "      <td>6.435249</td>\n",
       "      <td>643.524923</td>\n",
       "      <td>22.515765</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1517.705758</td>\n",
       "      <td>0.875919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Filename  Pressure (kbar)  Pressure (MPa)  Depth (km)  \\\n",
       "0  03 LM0 G1 FI1         6.121520      612.151979   21.484133   \n",
       "1  03 LM0 G1 FI1         5.747624      574.762434   20.254659   \n",
       "2  03 LM0 G1 FI1         6.452705      645.270453   22.573163   \n",
       "3  03 LM0 G1 FI1         6.292424      629.242358   22.046114   \n",
       "4  03 LM0 G1 FI1         6.435249      643.524923   22.515765   \n",
       "\n",
       "  MC_crust_dens_kgm3     model       MC_T_K  MC_CO2_dens_gcm3  \n",
       "0               None  two-step  1456.265617          0.872784  \n",
       "1               None  two-step  1364.131195          0.874644  \n",
       "2               None  two-step  1508.484791          0.879484  \n",
       "3               None  two-step  1482.444938          0.876493  \n",
       "4               None  two-step  1517.705758          0.875919  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The second output shows every single simulation for each FI. So the first N rows are for the first FI, then next N rows for the next, etc. \n",
    "MC_All.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4b93fe-09b7-4edc-9ed2-1cf416af2c2d",
   "metadata": {},
   "source": [
    "### Lets segment for the eruption sample\n",
    "- These are 'Logicals' e.g. a list of True and False statements, these allow us to splice up the dataframe for each sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b65bc8b-28e5-43ea-9630-ee78a6501d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sam0=data['SAMPLE']==0 # this gives a list of true and false for sample = 0\n",
    "sam1=data['SAMPLE']==2\n",
    "sam4=data['SAMPLE']==4\n",
    "sam6=data['SAMPLE']==6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "89b36417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAMPLE</th>\n",
       "      <th>FileName</th>\n",
       "      <th>Density (g/cm^3)</th>\n",
       "      <th>Comment (EPMA Data Point)</th>\n",
       "      <th>Na2O</th>\n",
       "      <th>MgO</th>\n",
       "      <th>SiO2</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>P2O5</th>\n",
       "      <th>K2O</th>\n",
       "      <th>CaO</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>FeO</th>\n",
       "      <th>MnO</th>\n",
       "      <th>Cr2O3</th>\n",
       "      <th>NiO</th>\n",
       "      <th>Total</th>\n",
       "      <th>Fo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>6</td>\n",
       "      <td>01 LM6 G1 FI1</td>\n",
       "      <td>0.833430</td>\n",
       "      <td>LM6_G1_CORE2</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>41.97921</td>\n",
       "      <td>39.79742</td>\n",
       "      <td>0.016172</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.244621</td>\n",
       "      <td>0.039359</td>\n",
       "      <td>18.16324</td>\n",
       "      <td>0.289626</td>\n",
       "      <td>0.025383</td>\n",
       "      <td>0.200139</td>\n",
       "      <td>100.7552</td>\n",
       "      <td>0.804681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6</td>\n",
       "      <td>07 LM6 G1 FI4</td>\n",
       "      <td>0.831903</td>\n",
       "      <td>LM6_G1_CORE3</td>\n",
       "      <td>0.007265</td>\n",
       "      <td>41.50474</td>\n",
       "      <td>40.45826</td>\n",
       "      <td>0.021972</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>0.254588</td>\n",
       "      <td>0.019733</td>\n",
       "      <td>17.99747</td>\n",
       "      <td>0.307189</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>0.176755</td>\n",
       "      <td>100.7648</td>\n",
       "      <td>0.804335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>6</td>\n",
       "      <td>03 LM6 G1 FI2</td>\n",
       "      <td>0.879573</td>\n",
       "      <td>LM6_G1_NEARMI</td>\n",
       "      <td>0.019585</td>\n",
       "      <td>41.74017</td>\n",
       "      <td>39.96414</td>\n",
       "      <td>0.049670</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.004542</td>\n",
       "      <td>0.251532</td>\n",
       "      <td>0.040087</td>\n",
       "      <td>18.45561</td>\n",
       "      <td>0.240858</td>\n",
       "      <td>0.020140</td>\n",
       "      <td>0.172209</td>\n",
       "      <td>100.9586</td>\n",
       "      <td>0.801251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>6</td>\n",
       "      <td>05 LM6 G1 FI3</td>\n",
       "      <td>0.796166</td>\n",
       "      <td>LM6_G1_NEARMI</td>\n",
       "      <td>0.019585</td>\n",
       "      <td>41.74017</td>\n",
       "      <td>39.96414</td>\n",
       "      <td>0.049670</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.004542</td>\n",
       "      <td>0.251532</td>\n",
       "      <td>0.040087</td>\n",
       "      <td>18.45561</td>\n",
       "      <td>0.240858</td>\n",
       "      <td>0.020140</td>\n",
       "      <td>0.172209</td>\n",
       "      <td>100.9586</td>\n",
       "      <td>0.801251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>6</td>\n",
       "      <td>72 LM6 G10 FI1</td>\n",
       "      <td>0.848642</td>\n",
       "      <td>LM6_G10_CENTER</td>\n",
       "      <td>0.045207</td>\n",
       "      <td>42.88505</td>\n",
       "      <td>40.72833</td>\n",
       "      <td>0.171445</td>\n",
       "      <td>0.019677</td>\n",
       "      <td>0.014681</td>\n",
       "      <td>0.371840</td>\n",
       "      <td>0.060140</td>\n",
       "      <td>17.00363</td>\n",
       "      <td>0.246182</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>0.212640</td>\n",
       "      <td>101.7597</td>\n",
       "      <td>0.818041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SAMPLE        FileName  Density (g/cm^3) Comment (EPMA Data Point)  \\\n",
       "90       6   01 LM6 G1 FI1          0.833430              LM6_G1_CORE2   \n",
       "91       6   07 LM6 G1 FI4          0.831903              LM6_G1_CORE3   \n",
       "92       6   03 LM6 G1 FI2          0.879573             LM6_G1_NEARMI   \n",
       "93       6   05 LM6 G1 FI3          0.796166             LM6_G1_NEARMI   \n",
       "94       6  72 LM6 G10 FI1          0.848642            LM6_G10_CENTER   \n",
       "\n",
       "        Na2O       MgO      SiO2     Al2O3      P2O5       K2O       CaO  \\\n",
       "90  0.000013  41.97921  39.79742  0.016172  0.000023  0.000012  0.244621   \n",
       "91  0.007265  41.50474  40.45826  0.021972  0.000023  0.001032  0.254588   \n",
       "92  0.019585  41.74017  39.96414  0.049670  0.000023  0.004542  0.251532   \n",
       "93  0.019585  41.74017  39.96414  0.049670  0.000023  0.004542  0.251532   \n",
       "94  0.045207  42.88505  40.72833  0.171445  0.019677  0.014681  0.371840   \n",
       "\n",
       "        TiO2       FeO       MnO     Cr2O3       NiO     Total        Fo  \n",
       "90  0.039359  18.16324  0.289626  0.025383  0.200139  100.7552  0.804681  \n",
       "91  0.019733  17.99747  0.307189  0.015767  0.176755  100.7648  0.804335  \n",
       "92  0.040087  18.45561  0.240858  0.020140  0.172209  100.9586  0.801251  \n",
       "93  0.040087  18.45561  0.240858  0.020140  0.172209  100.9586  0.801251  \n",
       "94  0.060140  17.00363  0.246182  0.000881  0.212640  101.7597  0.818041  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## For example, lets get the data for sample 6\n",
    "data.loc[sam6].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb5b4ed-77d7-484e-b665-f34e5ce9ab6e",
   "metadata": {},
   "source": [
    "## Lets plot each FI depth and its error bar, colored by sample (as in Dayton et al. 2023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8c064a8-fdab-42ed-a543-ca117b53688d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Pressure (kbar)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1, 1, figsize=(6,5))\n",
    "\n",
    "\n",
    "ms=7\n",
    "# This plots a symbol with its error bar for sample 0\n",
    "ax1.errorbar(data['Fo'].loc[sam0], \n",
    "             MC_Av['SingleCalc_D_km'].loc[sam0],\n",
    "             xerr=0, yerr=MC_Av['std_dev_MC_D_km_from_percentile'].loc[sam0],\n",
    "             fmt='o', ecolor='k', elinewidth=0.8, mfc='red', ms=ms, mec='k', capsize=5)\n",
    "\n",
    "# This plots a symbol with its error bar for sample 1\n",
    "ax1.errorbar(data['Fo'].loc[sam1], \n",
    "             MC_Av['SingleCalc_D_km'].loc[sam1],\n",
    "             xerr=0, yerr=MC_Av['std_dev_MC_D_km_from_percentile'].loc[sam1],\n",
    "             fmt='^', ecolor='k', elinewidth=0.8, mfc='green', ms=ms, mec='k', capsize=5)\n",
    "\n",
    "# This plots a symbol with its error bar for sample 4\n",
    "ax1.errorbar(data['Fo'].loc[sam4], \n",
    "             MC_Av['SingleCalc_D_km'].loc[sam4],\n",
    "             xerr=0, yerr=MC_Av['std_dev_MC_D_km_from_percentile'].loc[sam4],\n",
    "             fmt='v', ecolor='k', elinewidth=0.8, mfc='blue', ms=ms, mec='k', capsize=5)\n",
    "\n",
    "# This plots a symbol with its error bar for sample 6\n",
    "ax1.errorbar(data['Fo'].loc[sam6], \n",
    "             MC_Av['SingleCalc_D_km'].loc[sam6],\n",
    "             xerr=0, yerr=MC_Av['std_dev_MC_D_km_from_percentile'].loc[sam6],\n",
    "             fmt='s', ecolor='k', elinewidth=0.8, mfc='orange', ms=ms, mec='k', capsize=5)\n",
    "ax1.set_xlabel('Fo content')\n",
    "ax1.set_ylabel('Depth (km)')\n",
    "ax1.invert_yaxis()\n",
    "ax2=ax1.twinx()\n",
    "ax2.invert_yaxis()\n",
    "# This sets the range of pressures you want\n",
    "Plim1=3.8\n",
    "Plim2=8.1\n",
    "ax2.set_ylim([Plim2, Plim1])\n",
    "# This calculates the corresponding depths for those pressures. \n",
    "D_Plim1=pf.convert_pressure_depth_2step(P_kbar=Plim1, d1=14, rho1=2800, rho2=3100, g=9.81)\n",
    "D_Plim2=pf.convert_pressure_depth_2step(P_kbar=Plim2, d1=14, rho1=2800, rho2=3100, g=9.81)\n",
    "ax1.set_ylim([D_Plim2, D_Plim1])\n",
    "ax2.set_ylabel('Pressure (kbar)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ff7c8d-dcaf-465b-ae9c-1c04cd994312",
   "metadata": {},
   "source": [
    "## Complex double axis aligning\n",
    "- The plot above was relatively easy, because we were always working below the density transition from layer 1 to layer 2\n",
    "- Be careful - if your density transition lies in the range, showing the axes is very complicated! \n",
    "- One option is presented here. \n",
    "https://stackoverflow.com/questions/59349185/non-linear-second-axis-in-matplotlib"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}