{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Progapagating uncertainty in melt inclusion vapour bubble reconstructions\n",
    "- This notebook shows how to use Monte Carlo methods to propagate uncertainty in bubble density, bubble volume and melt density to calculate the uncertainty in the equivalent amount of CO2 in the bubble - e.g. how many ppm you add back into the glass\n",
    "- In this instance, we get uncertainty in bubble density from repeated Raman measurements, uncertainty in melt density from the code DensityX, and estimate the uncertainty in bubble volume from optical measurements as ~30-50%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Install DiadFit if you havent already\n",
    "#!pip install DiadFit --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# The code to do the MC is in DiadFit, make sure you cite!\n",
    "import DiadFit as pf\n",
    "pf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario 1 - You have measured x and y using an optical microscope, but you didnt measure Z, so you are assuming it is the average of x and y. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>SampleID</th>\n",
       "      <th>Melt_x_um</th>\n",
       "      <th>Melt_y_um</th>\n",
       "      <th>VB_x_um</th>\n",
       "      <th>VB_y_um</th>\n",
       "      <th>CO2_dens_gcm3</th>\n",
       "      <th>err_CO2_dens_gcm3</th>\n",
       "      <th>melt_dens_kgm3</th>\n",
       "      <th>err_melt_dens_kgm3</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>constant</th>\n",
       "      <th>Vol_melt</th>\n",
       "      <th>Vol_VB</th>\n",
       "      <th>Vol%</th>\n",
       "      <th>CO2_in_melt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Test_melt_1</td>\n",
       "      <td>50</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2601</td>\n",
       "      <td>130.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.18879</td>\n",
       "      <td>35853.426159</td>\n",
       "      <td>981.747704</td>\n",
       "      <td>2.738226</td>\n",
       "      <td>3.158276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test_melt_2</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.020</td>\n",
       "      <td>2603</td>\n",
       "      <td>130.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.18879</td>\n",
       "      <td>36065.483663</td>\n",
       "      <td>65.449847</td>\n",
       "      <td>0.181475</td>\n",
       "      <td>0.278871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Test_melt_3</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2700</td>\n",
       "      <td>135.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.18879</td>\n",
       "      <td>31415.926536</td>\n",
       "      <td>86.393798</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.509259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Test_melt_4</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.020</td>\n",
       "      <td>2606</td>\n",
       "      <td>130.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.18879</td>\n",
       "      <td>7853.981634</td>\n",
       "      <td>10.799225</td>\n",
       "      <td>0.137500</td>\n",
       "      <td>0.189946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Test_melt_5</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.015</td>\n",
       "      <td>2704</td>\n",
       "      <td>135.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.18879</td>\n",
       "      <td>66523.224440</td>\n",
       "      <td>794.822941</td>\n",
       "      <td>1.194805</td>\n",
       "      <td>1.855836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SampleID  Melt_x_um  Melt_y_um  VB_x_um  VB_y_um  CO2_dens_gcm3  \\\n",
       "0  Test_melt_1         50         33       10     15.0          0.030   \n",
       "1  Test_melt_2         40         42        5      5.0          0.040   \n",
       "2  Test_melt_3         30         50        6      5.0          0.050   \n",
       "3  Test_melt_4         20         30        3      2.5          0.036   \n",
       "4  Test_melt_5        100         21       11     12.0          0.042   \n",
       "\n",
       "   err_CO2_dens_gcm3  melt_dens_kgm3  err_melt_dens_kgm3  Unnamed: 9  \\\n",
       "0              0.010            2601              130.05         NaN   \n",
       "1              0.020            2603              130.15         NaN   \n",
       "2              0.010            2700              135.00         NaN   \n",
       "3              0.020            2606              130.30         NaN   \n",
       "4              0.015            2704              135.20         NaN   \n",
       "\n",
       "   constant      Vol_melt      Vol_VB      Vol%  CO2_in_melt  \n",
       "0   4.18879  35853.426159  981.747704  2.738226     3.158276  \n",
       "1   4.18879  36065.483663   65.449847  0.181475     0.278871  \n",
       "2   4.18879  31415.926536   86.393798  0.275000     0.509259  \n",
       "3   4.18879   7853.981634   10.799225  0.137500     0.189946  \n",
       "4   4.18879  66523.224440  794.822941  1.194805     1.855836  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_noZ=pd.read_excel('Melt_inclusions_all_dimensions.xlsx', sheet_name='noZ')\n",
    "df_noZ.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We are assuming the error on the measurements is 1um, but for Z, lets assume the error is half the difference between x and y. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on sample number 0\n"
     ]
    },
    {
     "data": {
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       "</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_eq_in_melt_noMC</th>\n",
       "      <th>mean_MC_CO2_equiv_melt_ppm</th>\n",
       "      <th>med_MC_CO2_equiv_melt_ppm</th>\n",
       "      <th>std_MC_IQR_CO2_equiv_melt_ppm</th>\n",
       "      <th>16th_quantile_melt_ppm</th>\n",
       "      <th>84th_quantile_melt_ppm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Test_melt_1</td>\n",
       "      <td>315.827639</td>\n",
       "      <td>342.152023</td>\n",
       "      <td>305.403692</td>\n",
       "      <td>181.101886</td>\n",
       "      <td>151.279370</td>\n",
       "      <td>513.483142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test_melt_2</td>\n",
       "      <td>27.887058</td>\n",
       "      <td>28.974437</td>\n",
       "      <td>26.647298</td>\n",
       "      <td>16.954758</td>\n",
       "      <td>12.480985</td>\n",
       "      <td>46.390500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Test_melt_3</td>\n",
       "      <td>50.925926</td>\n",
       "      <td>31.805252</td>\n",
       "      <td>48.978685</td>\n",
       "      <td>23.642208</td>\n",
       "      <td>28.854819</td>\n",
       "      <td>76.139236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Test_melt_4</td>\n",
       "      <td>18.994628</td>\n",
       "      <td>19.983244</td>\n",
       "      <td>16.084909</td>\n",
       "      <td>15.292613</td>\n",
       "      <td>5.066951</td>\n",
       "      <td>35.652177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Test_melt_5</td>\n",
       "      <td>185.583647</td>\n",
       "      <td>162.166698</td>\n",
       "      <td>163.250032</td>\n",
       "      <td>149.272392</td>\n",
       "      <td>72.941783</td>\n",
       "      <td>371.486566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Filename  CO2_eq_in_melt_noMC  mean_MC_CO2_equiv_melt_ppm  \\\n",
       "0  Test_melt_1           315.827639                  342.152023   \n",
       "1  Test_melt_2            27.887058                   28.974437   \n",
       "2  Test_melt_3            50.925926                   31.805252   \n",
       "3  Test_melt_4            18.994628                   19.983244   \n",
       "4  Test_melt_5           185.583647                  162.166698   \n",
       "\n",
       "   med_MC_CO2_equiv_melt_ppm  std_MC_IQR_CO2_equiv_melt_ppm  \\\n",
       "0                 305.403692                     181.101886   \n",
       "1                  26.647298                      16.954758   \n",
       "2                  48.978685                      23.642208   \n",
       "3                  16.084909                      15.292613   \n",
       "4                 163.250032                     149.272392   \n",
       "\n",
       "   16th_quantile_melt_ppm  84th_quantile_melt_ppm  \n",
       "0              151.279370              513.483142  \n",
       "1               12.480985               46.390500  \n",
       "2               28.854819               76.139236  \n",
       "3                5.066951               35.652177  \n",
       "4               72.941783              371.486566  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vol_noZ_av, vol_noZ_all, fig=pf.propagate_CO2_in_bubble(sample_ID=df_noZ['SampleID'],  N_dup=1000, vol_perc_bub=None,\n",
    "CO2_bub_dens_gcm3=df_noZ['CO2_dens_gcm3'], melt_dens_kgm3=df_noZ['melt_dens_kgm3'],\n",
    "MI_x=df_noZ['Melt_x_um'], MI_y=df_noZ['Melt_y_um'],VB_x=df_noZ['VB_x_um'], VB_y=df_noZ['VB_y_um'],\n",
    "error_MI_x=1, error_MI_y=1,error_MI_z=0.5*np.abs(df_noZ['Melt_y_um']-df_noZ['Melt_x_um']),error_VB_x=1, error_VB_y=1,\n",
    " error_VB_z=np.abs(df_noZ['VB_y_um']-df_noZ['VB_x_um']),\n",
    "error_type_dimension='Abs', error_dist_dimension='normal',\n",
    "error_CO2_bub_dens_gcm3=df_noZ['err_CO2_dens_gcm3'], error_type_CO2_bub_dens_gcm3='Abs', error_dist_CO2_bub_dens_gcm3='normal',\n",
    "error_melt_dens_kgm3=df_noZ['err_melt_dens_kgm3'], error_type_melt_dens_kgm3='Abs', error_dist_melt_dens_kgm3='normal', neg_values=True)\n",
    "\n",
    "vol_noZ_av.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Run for a single samle\n",
    "- This function lets you investigate for 1 MI how different values propagate to uncertainty in calculated CO2 contents\n",
    "- Here, we show a scenario where you dont use the x-y-z dimensions, you just input the bubble vol% and the error on it (as reported in many supporting datasets)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n"
     ]
    }
   ],
   "source": [
    "#  Which of your samples do you want to look at? here we look at the first one\n",
    "sample_i=0\n",
    "## How many duplicates do you want in the simulation?\n",
    "N_dup=1000\n",
    "\n",
    "######################### VAPOUR BUBBLE VOLUMES ###########################\n",
    "# What is the measured vol% of your bubble\n",
    "vol_perc_bub=5\n",
    "# What is your estimated error on this value? Is this an absolute error, a percentage error - do you think the error is distributed normally or uniformly etc\n",
    "error_vol_perc_bub=40\n",
    "error_type_vol_perc_bub='Perc'\n",
    "error_dist_vol_perc_bub='normal'\n",
    "\n",
    "\n",
    "######################### VAPOUR BUBBLE DENSITIES ###########################\n",
    "# What is your measured bubble density in g/cm3 by Raman or microthermometry?\n",
    "CO2_bub_dens_gcm3=0.02\n",
    "# What is the error on that value? Here we use the absolute error outputted by DiadFit. This is an absolute error and it should follow a normal distribution as its a 1 sigma value\n",
    "error_CO2_bub_dens_gcm3=0.01\n",
    "error_type_CO2_bub_dens_gcm3='Abs'\n",
    "error_dist_CO2_bub_dens_gcm3='normal'\n",
    "\n",
    "######################### SILICATE MELT DENSITIES ###########################\n",
    "# Melt density (e.g. from DensityX)\n",
    "melt_dens_kgm3=2700\n",
    "# Estimated error on that - here we use 200 kg/m3, its absolute, and distributed normally\n",
    "error_melt_dens_kgm3=80\n",
    "error_type_melt_dens_kgm3='Abs'\n",
    "error_dist_melt_dens_kgm3='normal'\n",
    "\n",
    "## Now lets calculate the distribution\n",
    "test_dist=pf.propagate_CO2_in_bubble_ind(sample_i=sample_i,  N_dup=N_dup, \n",
    "vol_perc_bub=vol_perc_bub,\n",
    "error_vol_perc_bub=error_vol_perc_bub, error_type_vol_perc_bub=error_type_vol_perc_bub,\n",
    "error_dist_vol_perc_bub=error_dist_vol_perc_bub,\n",
    "CO2_bub_dens_gcm3=CO2_bub_dens_gcm3, error_CO2_bub_dens_gcm3=error_CO2_bub_dens_gcm3, \n",
    "error_type_CO2_bub_dens_gcm3=error_type_CO2_bub_dens_gcm3,\n",
    "error_dist_CO2_bub_dens_gcm3=error_dist_CO2_bub_dens_gcm3,\n",
    "melt_dens_kgm3=melt_dens_kgm3, error_melt_dens_kgm3=error_melt_dens_kgm3, \n",
    "error_type_melt_dens_kgm3=error_type_melt_dens_kgm3, \n",
    "error_dist_melt_dens_kgm3=error_dist_melt_dens_kgm3, len_loop=1, neg_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "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>CO2_eq_melt_ppm_MC</th>\n",
       "      <th>CO2_eq_melt_ppm_noMC</th>\n",
       "      <th>vol_perc_bub_with_noise</th>\n",
       "      <th>CO2_bub_dens_gcm3_with_noise</th>\n",
       "      <th>melt_dens_kgm3_with_noise</th>\n",
       "      <th>vol_perc_bub</th>\n",
       "      <th>CO2_bub_dens_gcm3</th>\n",
       "      <th>error_type_vol_perc_bub</th>\n",
       "      <th>error_dist_Vol</th>\n",
       "      <th>error_CO2_bub_dens_gcm3</th>\n",
       "      <th>error_type_CO2_bub_dens_gcm3</th>\n",
       "      <th>error_dist_CO2_bub_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>184.027060</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>4.831051</td>\n",
       "      <td>0.010431</td>\n",
       "      <td>2738.272230</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>331.485215</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>7.000886</td>\n",
       "      <td>0.012853</td>\n",
       "      <td>2714.556235</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>558.692180</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>3.619383</td>\n",
       "      <td>0.039636</td>\n",
       "      <td>2567.751507</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>530.544696</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>4.255368</td>\n",
       "      <td>0.034486</td>\n",
       "      <td>2766.049050</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>909.139425</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>8.238086</td>\n",
       "      <td>0.028807</td>\n",
       "      <td>2610.302722</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>166.182144</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>0.018581</td>\n",
       "      <td>2757.999512</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>263.174731</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>3.855987</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>2680.219760</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>344.687962</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>8.222711</td>\n",
       "      <td>0.011141</td>\n",
       "      <td>2657.776998</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>158.276474</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>2.400432</td>\n",
       "      <td>0.017365</td>\n",
       "      <td>2633.646470</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>853.307478</td>\n",
       "      <td>370.37037</td>\n",
       "      <td>5.146933</td>\n",
       "      <td>0.044449</td>\n",
       "      <td>2681.073321</td>\n",
       "      <td>5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Perc</td>\n",
       "      <td>normal</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Abs</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CO2_eq_melt_ppm_MC  CO2_eq_melt_ppm_noMC  vol_perc_bub_with_noise  \\\n",
       "0            184.027060             370.37037                 4.831051   \n",
       "1            331.485215             370.37037                 7.000886   \n",
       "2            558.692180             370.37037                 3.619383   \n",
       "3            530.544696             370.37037                 4.255368   \n",
       "4            909.139425             370.37037                 8.238086   \n",
       "..                  ...                   ...                      ...   \n",
       "995          166.182144             370.37037                 2.466667   \n",
       "996          263.174731             370.37037                 3.855987   \n",
       "997          344.687962             370.37037                 8.222711   \n",
       "998          158.276474             370.37037                 2.400432   \n",
       "999          853.307478             370.37037                 5.146933   \n",
       "\n",
       "     CO2_bub_dens_gcm3_with_noise  melt_dens_kgm3_with_noise  vol_perc_bub  \\\n",
       "0                        0.010431                2738.272230             5   \n",
       "1                        0.012853                2714.556235             5   \n",
       "2                        0.039636                2567.751507             5   \n",
       "3                        0.034486                2766.049050             5   \n",
       "4                        0.028807                2610.302722             5   \n",
       "..                            ...                        ...           ...   \n",
       "995                      0.018581                2757.999512             5   \n",
       "996                      0.018293                2680.219760             5   \n",
       "997                      0.011141                2657.776998             5   \n",
       "998                      0.017365                2633.646470             5   \n",
       "999                      0.044449                2681.073321             5   \n",
       "\n",
       "     CO2_bub_dens_gcm3 error_type_vol_perc_bub error_dist_Vol  \\\n",
       "0                 0.02                    Perc         normal   \n",
       "1                 0.02                    Perc         normal   \n",
       "2                 0.02                    Perc         normal   \n",
       "3                 0.02                    Perc         normal   \n",
       "4                 0.02                    Perc         normal   \n",
       "..                 ...                     ...            ...   \n",
       "995               0.02                    Perc         normal   \n",
       "996               0.02                    Perc         normal   \n",
       "997               0.02                    Perc         normal   \n",
       "998               0.02                    Perc         normal   \n",
       "999               0.02                    Perc         normal   \n",
       "\n",
       "     error_CO2_bub_dens_gcm3 error_type_CO2_bub_dens_gcm3  \\\n",
       "0                       0.01                          Abs   \n",
       "1                       0.01                          Abs   \n",
       "2                       0.01                          Abs   \n",
       "3                       0.01                          Abs   \n",
       "4                       0.01                          Abs   \n",
       "..                       ...                          ...   \n",
       "995                     0.01                          Abs   \n",
       "996                     0.01                          Abs   \n",
       "997                     0.01                          Abs   \n",
       "998                     0.01                          Abs   \n",
       "999                     0.01                          Abs   \n",
       "\n",
       "    error_dist_CO2_bub_dens_gcm3  \n",
       "0                         normal  \n",
       "1                         normal  \n",
       "2                         normal  \n",
       "3                         normal  \n",
       "4                         normal  \n",
       "..                           ...  \n",
       "995                       normal  \n",
       "996                       normal  \n",
       "997                       normal  \n",
       "998                       normal  \n",
       "999                       normal  \n",
       "\n",
       "[1000 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now lets look at the simulation for this one sapmle. The column 'CO2_eq_melt_ppm_noMC' is the same answer as above. The column CO2_eq_melt_ppm_MC gives 1 MonteCarlo result. \n",
    "test_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "370.3703703703703"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets do the basic math just to persuade ourselves this is working\n",
    "vol_perc_bub=5\n",
    "melt_dens_gcm3=2.7\n",
    "bub_dens_gcm3=0.02\n",
    "CO2_eq_bubble=10**4*(bub_dens_gcm3*vol_perc_bub)/melt_dens_gcm3\n",
    "CO2_eq_bubble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1404dfdbad0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets plot some of these outputs to check they look how we expect\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,8))\n",
    "ax1.hist(test_dist['vol_perc_bub_with_noise'], bins=50);\n",
    "ax1.set_xlabel('MC bubble volume (vol%)')\n",
    "ax1.set_ylabel('# of simulations')\n",
    "\n",
    "ax2.hist(test_dist['melt_dens_kgm3_with_noise'], bins=50);\n",
    "ax2.set_xlabel('MC Melt Density (kg/cm3)')\n",
    "\n",
    "ax3.hist(test_dist['CO2_bub_dens_gcm3_with_noise'], bins=50);\n",
    "ax3.set_xlabel('MC Bubble Density (g/cm3)')\n",
    "ax3.set_ylabel('# of simulations')\n",
    "\n",
    "ax4.hist(test_dist['CO2_eq_melt_ppm_MC'], bins=50, color='red');\n",
    "ax4.set_xlabel('CO2 equivalent in the melt held in the bubble (ppm)')\n",
    "# Plotting preferred value - no Monte Carlo (as no negs can skew)\n",
    "ax4.plot([test_dist['CO2_eq_melt_ppm_noMC'].iloc[0],\n",
    "          test_dist['CO2_eq_melt_ppm_noMC'].iloc[0]], [0, N_dup/15], '-k', label='Preferred value');\n",
    "\n",
    "# Plot standard deviation of simulation\n",
    "ax4.plot([test_dist['CO2_eq_melt_ppm_noMC'].iloc[0]+np.std(test_dist['CO2_eq_melt_ppm_MC']), \n",
    "          test_dist['CO2_eq_melt_ppm_noMC'].iloc[0]+np.std(test_dist['CO2_eq_melt_ppm_MC'])], [0, N_dup/15], ':k', label='Preferred+value+1s MC');\n",
    "ax4.plot([test_dist['CO2_eq_melt_ppm_noMC'].iloc[0]-np.std(test_dist['CO2_eq_melt_ppm_MC']), \n",
    "          test_dist['CO2_eq_melt_ppm_noMC'].iloc[0]-np.std(test_dist['CO2_eq_melt_ppm_MC'])], [0, N_dup/15], ':k', label='Preferred-value+1s MC');\n",
    "ax4.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now lets load in some real data\n",
    "- This data has the values for errors as columns for each melt inclusion (a row)\n",
    "- The function outputs 2 things, av is just the mean, and std dev of the simulations. test_dist is all the simulations so you can plot them up!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "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>PEC_amount</th>\n",
       "      <th>Na2O_PEC</th>\n",
       "      <th>Al2O3_PEC</th>\n",
       "      <th>P2O5_PEC</th>\n",
       "      <th>CaO_PEC</th>\n",
       "      <th>K2O_PEC</th>\n",
       "      <th>TiO2_PEC</th>\n",
       "      <th>SiO2_PEC</th>\n",
       "      <th>MgO_PEC</th>\n",
       "      <th>FeOt_PEC</th>\n",
       "      <th>MnO_PEC</th>\n",
       "      <th>H2O_Liq_meas</th>\n",
       "      <th>CO2_Liq_meas</th>\n",
       "      <th>Melt_dens</th>\n",
       "      <th>Melt_dens_err</th>\n",
       "      <th>Vol_%</th>\n",
       "      <th>CO2_dens_gcm3</th>\n",
       "      <th>CO2_dens_gcm3_std_Dev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LL8_613b</td>\n",
       "      <td>16.60</td>\n",
       "      <td>2.469</td>\n",
       "      <td>12.900</td>\n",
       "      <td>0.229</td>\n",
       "      <td>10.702</td>\n",
       "      <td>0.349</td>\n",
       "      <td>2.394</td>\n",
       "      <td>49.770</td>\n",
       "      <td>9.328</td>\n",
       "      <td>11.336</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.240274</td>\n",
       "      <td>28.979265</td>\n",
       "      <td>2.728376</td>\n",
       "      <td>0.081851</td>\n",
       "      <td>2.723415</td>\n",
       "      <td>0.021977</td>\n",
       "      <td>0.000662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LL8_615</td>\n",
       "      <td>30.59</td>\n",
       "      <td>2.131</td>\n",
       "      <td>10.928</td>\n",
       "      <td>0.229</td>\n",
       "      <td>9.435</td>\n",
       "      <td>0.334</td>\n",
       "      <td>1.815</td>\n",
       "      <td>49.676</td>\n",
       "      <td>13.612</td>\n",
       "      <td>11.336</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.238325</td>\n",
       "      <td>37.495154</td>\n",
       "      <td>2.705747</td>\n",
       "      <td>0.081172</td>\n",
       "      <td>4.254140</td>\n",
       "      <td>0.022912</td>\n",
       "      <td>0.006003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LL8_617_a</td>\n",
       "      <td>30.02</td>\n",
       "      <td>2.092</td>\n",
       "      <td>11.323</td>\n",
       "      <td>0.291</td>\n",
       "      <td>9.757</td>\n",
       "      <td>0.324</td>\n",
       "      <td>2.009</td>\n",
       "      <td>49.028</td>\n",
       "      <td>13.349</td>\n",
       "      <td>11.338</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.235590</td>\n",
       "      <td>41.302324</td>\n",
       "      <td>2.716938</td>\n",
       "      <td>0.081508</td>\n",
       "      <td>3.996225</td>\n",
       "      <td>0.029050</td>\n",
       "      <td>0.000521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LL8_623_b</td>\n",
       "      <td>26.68</td>\n",
       "      <td>2.103</td>\n",
       "      <td>11.895</td>\n",
       "      <td>0.299</td>\n",
       "      <td>10.022</td>\n",
       "      <td>0.449</td>\n",
       "      <td>2.448</td>\n",
       "      <td>48.418</td>\n",
       "      <td>12.514</td>\n",
       "      <td>11.331</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.216237</td>\n",
       "      <td>23.837194</td>\n",
       "      <td>2.728426</td>\n",
       "      <td>0.081853</td>\n",
       "      <td>5.110120</td>\n",
       "      <td>0.052225</td>\n",
       "      <td>0.009579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LL8_626</td>\n",
       "      <td>27.81</td>\n",
       "      <td>2.181</td>\n",
       "      <td>11.612</td>\n",
       "      <td>0.209</td>\n",
       "      <td>9.789</td>\n",
       "      <td>0.330</td>\n",
       "      <td>2.027</td>\n",
       "      <td>49.215</td>\n",
       "      <td>12.783</td>\n",
       "      <td>11.331</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.228748</td>\n",
       "      <td>5.660589</td>\n",
       "      <td>2.714561</td>\n",
       "      <td>0.081437</td>\n",
       "      <td>5.645210</td>\n",
       "      <td>0.029996</td>\n",
       "      <td>0.008963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sample  PEC_amount  Na2O_PEC  Al2O3_PEC  P2O5_PEC  CaO_PEC  K2O_PEC  \\\n",
       "0   LL8_613b       16.60     2.469     12.900     0.229   10.702    0.349   \n",
       "1    LL8_615       30.59     2.131     10.928     0.229    9.435    0.334   \n",
       "2  LL8_617_a       30.02     2.092     11.323     0.291    9.757    0.324   \n",
       "3  LL8_623_b       26.68     2.103     11.895     0.299   10.022    0.449   \n",
       "4    LL8_626       27.81     2.181     11.612     0.209    9.789    0.330   \n",
       "\n",
       "   TiO2_PEC  SiO2_PEC  MgO_PEC  FeOt_PEC  MnO_PEC  H2O_Liq_meas  CO2_Liq_meas  \\\n",
       "0     2.394    49.770    9.328    11.336    0.142      0.240274     28.979265   \n",
       "1     1.815    49.676   13.612    11.336    0.151      0.238325     37.495154   \n",
       "2     2.009    49.028   13.349    11.338    0.135      0.235590     41.302324   \n",
       "3     2.448    48.418   12.514    11.331    0.177      0.216237     23.837194   \n",
       "4     2.027    49.215   12.783    11.331    0.172      0.228748      5.660589   \n",
       "\n",
       "   Melt_dens  Melt_dens_err     Vol_%  CO2_dens_gcm3  CO2_dens_gcm3_std_Dev  \n",
       "0   2.728376       0.081851  2.723415       0.021977               0.000662  \n",
       "1   2.705747       0.081172  4.254140       0.022912               0.006003  \n",
       "2   2.716938       0.081508  3.996225       0.029050               0.000521  \n",
       "3   2.728426       0.081853  5.110120       0.052225               0.009579  \n",
       "4   2.714561       0.081437  5.645210       0.029996               0.008963  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download here - https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/CO2_in_Melt_Inclusion_Vapour_Bubbles/Wieser_2021_Kilauea.xlsx\n",
    "df=pd.read_excel('Wieser_2021_Kilauea.xlsx', sheet_name='Sheet2')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on sample number 0\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "working on sample number 20\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "working on sample number 40\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\n",
      "didnt get inside else loop\n",
      "using error on the bubble volume percent, not the entered dimensions, as error_vol_perc_bub was not None\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_eq_in_melt_noMC</th>\n",
       "      <th>mean_MC_CO2_equiv_melt_ppm</th>\n",
       "      <th>med_MC_CO2_equiv_melt_ppm</th>\n",
       "      <th>std_MC_IQR_CO2_equiv_melt_ppm</th>\n",
       "      <th>16th_quantile_melt_ppm</th>\n",
       "      <th>84th_quantile_melt_ppm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LL8_613b</td>\n",
       "      <td>219.365581</td>\n",
       "      <td>217.215534</td>\n",
       "      <td>218.985842</td>\n",
       "      <td>114.200710</td>\n",
       "      <td>101.939183</td>\n",
       "      <td>330.340602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LL8_615</td>\n",
       "      <td>360.243929</td>\n",
       "      <td>358.298977</td>\n",
       "      <td>350.438083</td>\n",
       "      <td>195.562328</td>\n",
       "      <td>161.084693</td>\n",
       "      <td>552.209349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LL8_617_a</td>\n",
       "      <td>427.279031</td>\n",
       "      <td>427.858356</td>\n",
       "      <td>423.736977</td>\n",
       "      <td>217.283396</td>\n",
       "      <td>210.811546</td>\n",
       "      <td>645.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LL8_623_b</td>\n",
       "      <td>978.127362</td>\n",
       "      <td>980.211686</td>\n",
       "      <td>969.344386</td>\n",
       "      <td>529.055224</td>\n",
       "      <td>437.304821</td>\n",
       "      <td>1495.415269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LL8_626</td>\n",
       "      <td>623.806538</td>\n",
       "      <td>611.552947</td>\n",
       "      <td>553.109461</td>\n",
       "      <td>361.732326</td>\n",
       "      <td>256.610321</td>\n",
       "      <td>980.074974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Filename  CO2_eq_in_melt_noMC  mean_MC_CO2_equiv_melt_ppm  \\\n",
       "0   LL8_613b           219.365581                  217.215534   \n",
       "1    LL8_615           360.243929                  358.298977   \n",
       "2  LL8_617_a           427.279031                  427.858356   \n",
       "3  LL8_623_b           978.127362                  980.211686   \n",
       "4    LL8_626           623.806538                  611.552947   \n",
       "\n",
       "   med_MC_CO2_equiv_melt_ppm  std_MC_IQR_CO2_equiv_melt_ppm  \\\n",
       "0                 218.985842                     114.200710   \n",
       "1                 350.438083                     195.562328   \n",
       "2                 423.736977                     217.283396   \n",
       "3                 969.344386                     529.055224   \n",
       "4                 553.109461                     361.732326   \n",
       "\n",
       "   16th_quantile_melt_ppm  84th_quantile_melt_ppm  \n",
       "0              101.939183              330.340602  \n",
       "1              161.084693              552.209349  \n",
       "2              210.811546              645.378337  \n",
       "3              437.304821             1495.415269  \n",
       "4              256.610321              980.074974  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "av, test_dist, fig=pf.propagate_CO2_in_bubble(sample_ID=df['sample'],\n",
    "N_dup=1000, vol_perc_bub=df['Vol_%'],\n",
    "CO2_bub_dens_gcm3=df['CO2_dens_gcm3'], melt_dens_kgm3=df['Melt_dens']*1000,\n",
    "error_vol_perc_bub=50, error_type_vol_perc_bub='Perc', error_dist_vol_perc_bub='normal',\n",
    "error_CO2_bub_dens_gcm3=df['CO2_dens_gcm3_std_Dev'], \n",
    "error_type_CO2_bub_dens_gcm3='Abs', error_dist_CO2_bub_dens_gcm3='normal',\n",
    "error_melt_dens_kgm3=df['Melt_dens_err']*1000, error_type_melt_dens_kgm3='Abs', error_dist_melt_dens_kgm3='normal', \n",
    " neg_values=True)\n",
    "av.head()\n"
   ]
  }
 ],
 "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": 4
}