Fig10_SubSampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d21affcd-2b70-4a8c-993d-7588ff26cce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib import cm\n",
    "import warnings as w\n",
    "pd.options.display.max_columns = None\n",
    "w.simplefilter(action=\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cde94dc6-cc1f-42cd-9210-63f1ebc598a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Uncomment once per computer\n",
    "#!pip install Thermobar\n",
    "#!pip install \"https://github.com/PennyWieser/Thermobar_onnx/archive/refs/tags/0.02.zip\"\n",
    "import Thermobar as pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c11c135-aa43-4738-84db-3ce286497234",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn-colorblind\")\n",
    "plt.rcParams[\"font.family\"] = 'arial'\n",
    "plt.rcParams[\"font.size\"] =12\n",
    "plt.rcParams[\"mathtext.default\"] = \"regular\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"dejavusans\"\n",
    "plt.rcParams['patch.linewidth'] = 1\n",
    "plt.rcParams['axes.linewidth'] = 1\n",
    "plt.rcParams[\"xtick.direction\"] = \"in\"\n",
    "plt.rcParams[\"ytick.direction\"] = \"in\"\n",
    "plt.rcParams[\"ytick.direction\"] = \"in\"\n",
    "plt.rcParams[\"xtick.major.size\"] = 6 # Sets length of ticks\n",
    "plt.rcParams[\"ytick.major.size\"] = 4 # Sets length of ticks\n",
    "plt.rcParams[\"ytick.labelsize\"] = 12 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"xtick.labelsize\"] = 12 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"axes.titlesize\"] = 14 # Overall title\n",
    "plt.rcParams[\"axes.labelsize\"] = 14 # Axes labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ab6621-5515-4b08-9111-ac10b24f920d",
   "metadata": {},
   "source": [
    "## Loading in Krawzinski 1 sigma microprobe error data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c00f6061-5bad-4c67-92a9-bb93eb727209",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kraw_07=pd.read_excel('Supporting_Data_3.xlsx', \n",
    "                      sheet_name='Kraw13_042907_Analyses')\n",
    "Kraw_06=pd.read_excel('Supporting_Data_3.xlsx', \n",
    "                      sheet_name='Kraw13_083006_Analyses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0851183e-9359-4daa-8832-763cac6245a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kraw_07_Cpx=Kraw_07.loc[(Kraw_07['Phase']==\"cpx\")]\n",
    "Kraw_06_Cpx=Kraw_06.loc[(Kraw_06['Phase']==\"cpx\")]\n",
    "\n",
    "Kraw_07_Cpx_Na=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"Na2O\"]\n",
    "Kraw_06_Cpx_Na=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"Na2O\"]\n",
    "\n",
    "Kraw_07_Cpx_Al=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"Al2O3\"]\n",
    "Kraw_06_Cpx_Al=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"Al2O3\"]\n",
    "\n",
    "Kraw_07_Cpx_Si=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"SiO2\"]\n",
    "Kraw_06_Cpx_Si=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"SiO2\"]\n",
    "\n",
    "Kraw_07_Cpx_Ti=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"TiO2\"]\n",
    "Kraw_06_Cpx_Ti=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"TiO2\"]\n",
    "\n",
    "Kraw_07_Cpx_Mn=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"MnO\"]\n",
    "Kraw_06_Cpx_Mn=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"MnO\"]\n",
    "\n",
    "Kraw_07_Cpx_Mg=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"MgO\"]\n",
    "Kraw_06_Cpx_Mg=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"MgO\"]\n",
    "\n",
    "Kraw_07_Cpx_Fe=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"FeO\"]\n",
    "Kraw_06_Cpx_Fe=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"FeO\"]\n",
    "\n",
    "Kraw_07_Cpx_Ca=Kraw_07_Cpx.loc[Kraw_07_Cpx['Element']==\"CaO\"]\n",
    "Kraw_06_Cpx_Ca=Kraw_06_Cpx.loc[Kraw_06_Cpx['Element']==\"CaO\"]\n",
    "\n",
    "\n",
    "Kraw_07_Amp=Kraw_07.loc[Kraw_07['Phase']==\"amp\"]\n",
    "Kraw_06_Amp=Kraw_06.loc[Kraw_06['Phase']==\"amp\"]\n",
    "\n",
    "Kraw_07_Amp_Na=Kraw_07_Amp.loc[Kraw_07_Amp['Element']==\"Na2O\"]\n",
    "Kraw_06_Amp_Na=Kraw_06_Amp.loc[Kraw_06_Amp['Element']==\"Na2O\"]\n",
    "\n",
    "Kraw_06_Cali=pd.read_excel('Supporting_Data_3.xlsx', \n",
    "                           sheet_name='Kraw13_083006_Cali')\n",
    "Kraw_06_Cali_Na5=Kraw_06_Cali.loc[Kraw_06_Cali['Element']==\"Na5-1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb45c245-7b11-430b-8898-956e9f3d2b74",
   "metadata": {},
   "source": [
    "## Real compositions from Mikes B1038 experiments\n",
    "- 8 kbar, 1050 C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29235714-83b8-428e-a1f4-06bc8fa64f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Exp_Cond=pd.read_excel('Supporting_Data_2.xlsx',  sheet_name='Exp_Cond')\n",
    "out_Cpx_B1038=pt.import_excel('Supporting_Data_2.xlsx',\n",
    "                              sheet_name='B1038', suffix=\"_Cpx\")\n",
    "Cpx_B1038_all=out_Cpx_B1038['Cpxs']\n",
    "Cpx_B1038_input_all=out_Cpx_B1038['my_input']\n",
    "\n",
    "# As has all minerals, need to filter out things that arent Cpx\n",
    "IsCpx_B1038=Cpx_B1038_input_all['phase']==\"cpx\"\n",
    "Cpx_B1038=Cpx_B1038_all.loc[IsCpx_B1038].reset_index(drop=True)\n",
    "Cpx_B1038_input=Cpx_B1038_input_all.loc[IsCpx_B1038].reset_index(drop=True)\n",
    "H2O_B1038=Exp_Cond.loc[Exp_Cond['Experiment']==\"B1038\", 'H2O_Liq'].iloc[0]\n",
    "Cpx_B1038_MeanNa=Cpx_B1038.copy()\n",
    "Cpx_B1038_MeanNa['Na2O_Cpx']=np.mean(Cpx_B1038_MeanNa['Na2O_Cpx'])\n",
    "Cpx_B1038_MeanAl=Cpx_B1038.copy()\n",
    "Cpx_B1038_MeanAl['Al2O3_Cpx']=np.mean(Cpx_B1038_MeanAl['Al2O3_Cpx'])\n",
    "\n",
    "Cpx_B1038_Calc=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038, H2O_Liq=H2O_B1038)\n",
    "Cpx_B1038_Calc_MeanNa=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MeanNa, H2O_Liq=H2O_B1038)\n",
    "Cpx_B1038_Calc_MeanAl=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MeanAl, H2O_Liq=H2O_B1038)\n",
    "\n",
    "\n",
    "out_Liq_B1038=pt.import_excel('Supporting_Data_2.xlsx',  sheet_name='B1038_Glass', suffix=\"_Liq\")\n",
    "Liq_B1038_all=out_Liq_B1038['Liqs']\n",
    "Liq_B1038_input_all=out_Liq_B1038['my_input']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c42b2c-64ac-4b94-9440-0406375e7c0c",
   "metadata": {},
   "source": [
    "## Making MC for Cpx - B1038 using the Analytical 1 sigma values from 2006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b4f0226-6111-4f38-8eab-b0c08734593c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kraw_06_B1038_cpx2=Kraw_06.loc[Kraw_06['Name']==\"B1038-cpx2\"]\n",
    "B1038_Cpx2=Cpx_B1038.mean(axis=0).to_frame().T\n",
    "\n",
    "B1038_Cpx_Err1=pd.DataFrame(data={'SiO2_Cpx_Err': Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"SiO2\", 'Error_Per'].values,\n",
    "                            'TiO2_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"TiO2\", 'Error_Per'].values,\n",
    "                             'Al2O3_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"Al2O3\", 'Error_Per'].values,\n",
    "                             'FeOt_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"FeO\", 'Error_Per'].values,\n",
    "                            'MnO_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"MnO\", 'Error_Per'].values,\n",
    "                            'MgO_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"MgO\", 'Error_Per'].values,\n",
    "                            'CaO_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"CaO\", 'Error_Per'].values,\n",
    "                            'Na2O_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"Na2O\", 'Error_Per'].values,\n",
    "                            'Cr2O3_Cpx_Err':Kraw_06_B1038_cpx2.loc[Kraw_06_B1038_cpx2['Element']==\"Cr2O3\", 'Error_Per'].values,\n",
    "                               'K2O_Cpx_Err': 0}, index=[0])\n",
    "B1038_CpxAv_noise1=pt.add_noise_sample_1phase(phase_comp=B1038_Cpx2, phase_err=B1038_Cpx_Err1,\n",
    "                                             phase_err_type=\"Perc\", duplicates=5000, err_dist=\"normal\", positive=False)\n",
    "B1038_CpxAv_noise1_N500=pt.add_noise_sample_1phase(phase_comp=B1038_Cpx2, phase_err=B1038_Cpx_Err1,\n",
    "                                             phase_err_type=\"Perc\", duplicates=500, err_dist=\"normal\", positive=False)\n",
    "Cpx_B1038_Calc_Syn=pt.calculate_cpx_only_press_all_eqs(cpx_comps=B1038_CpxAv_noise1, H2O_Liq=H2O_B1038)\n",
    "Cpx_B1038_Calc=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038, H2O_Liq=H2O_B1038)\n",
    "Cpx_B1038_Calc_Av=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038.mean(axis=0).to_frame().T, H2O_Liq=H2O_B1038)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501180cf-0ec6-47df-b528-15be444f045e",
   "metadata": {},
   "source": [
    "## Making MC for liquids fro Krawzinkski et al- Using errors from 2007, as dont have from 2006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9dc2c68a-305d-4a32-bfae-c0743270bc02",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "Kraw_06_B1038_Liq2=Kraw_07.loc[Kraw_07['Name']==\"B1038 Glass\"]\n",
    "B1038_Liq2=pd.DataFrame(data={'SiO2_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"SiO2\", 'Ox-Wt%'].values,\n",
    "                            'TiO2_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"TiO2\", 'Ox-Wt%'].values,\n",
    "                             'Al2O3_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"Al2O3\", 'Ox-Wt%'].values,\n",
    "                             'FeOt_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"FeO\", 'Ox-Wt%'].values,\n",
    "                            'MnO_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"MnO\", 'Ox-Wt%'].values,\n",
    "                            'MgO_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"MgO\", 'Ox-Wt%'].values,\n",
    "                            'CaO_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"CaO\", 'Ox-Wt%'].values,\n",
    "                            'Na2O_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"Na2O\", 'Ox-Wt%'].values,\n",
    "                              'K2O_Liq': Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"K2O\", 'Ox-Wt%'].values,\n",
    "                            'Cr2O3_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"Cr2O3\", 'Ox-Wt%'].values,\n",
    "                              'P2O5_Liq':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"P2O5\", 'Ox-Wt%'].values,\n",
    "                            'H2O_Liq': H2O_B1038\n",
    "                             }, index=[0])\n",
    "B1038_Liq2['Sample_ID_Liq']=\"B1038_Liq2\"\n",
    "\n",
    "B1038_Liq_Err2=pd.DataFrame(data={'SiO2_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"SiO2\", 'Error_Per'].values,\n",
    "                            'TiO2_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"TiO2\", 'Error_Per'].values,\n",
    "                             'Al2O3_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"Al2O3\", 'Error_Per'].values,\n",
    "                             'FeOt_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"FeO\", 'Error_Per'].values,\n",
    "                            'MnO_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"MnO\", 'Error_Per'].values,\n",
    "                            'MgO_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"MgO\", 'Error_Per'].values,\n",
    "                            'CaO_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"CaO\", 'Error_Per'].values,\n",
    "                            'Na2O_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"Na2O\", 'Error_Per'].values,\n",
    "                              'K2O_Liq_Err': Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"K2O\", 'Error_Per'].values,\n",
    "                            'Cr2O3_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"Cr2O3\", 'Error_Per'].values,\n",
    "                              'P2O5_Liq_Err':Kraw_06_B1038_Liq2.loc[Kraw_06_B1038_Liq2['Element']==\"Cr2O3\", 'Error_Per'].values,\n",
    "                            'H2O_Liq_Err':0,\n",
    "                             }, index=[0])\n",
    "\n",
    "\n",
    "\n",
    "B1038_LiqAv_noise1=pt.add_noise_sample_1phase(phase_comp=Liq_B1038_all.mean(axis=0).to_frame().T, \n",
    "                                              phase_err=B1038_Liq_Err2,\n",
    "                                             phase_err_type=\"Perc\", duplicates=5000, err_dist=\"normal\", positive=False)\n",
    "\n",
    "B1038_LiqAv_noise1_N500=pt.add_noise_sample_1phase(phase_comp=Liq_B1038_all.mean(axis=0).to_frame().T,\n",
    "                                                   phase_err=B1038_Liq_Err2,\n",
    "                                             phase_err_type=\"Perc\", duplicates=500, err_dist=\"normal\", positive=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c11da3-c195-4c4e-a3e3-5952d0d971e8",
   "metadata": {},
   "source": [
    "## Calculating PT for mean Cpx and mean liquid composition. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5db4c9be-2dbb-499d-9486-eadd6f039735",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering N=1 Cpx & N=1 Liqs, which is a total of N=1 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=1 Cpx-Liq matches using the specified filter. N=1 Cpx out of the N=1 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=1 Cpx & N=1 Liqs, which is a total of N=1 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=1 Cpx-Liq matches using the specified filter. N=1 Cpx out of the N=1 Cpx that you input matched to 1 or more liquids\n"
     ]
    }
   ],
   "source": [
    "# Mean liquid, Mean Cpx\n",
    "B1038_Cpx_Liq_N17_eq33_Liq_Mean=pt.calculate_cpx_liq_press_temp_matching(liq_comps=Liq_B1038_all.mean(axis=0).to_frame().T, \n",
    "                                                                         cpx_comps=Cpx_B1038.mean(axis=0).to_frame().T,\n",
    "       H2O_Liq=H2O_B1038, equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\", return_all_pairs=True\n",
    "                                                                  ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_Liq_30_eq33_Liq_Mean=pt.calculate_cpx_liq_press_temp_matching(liq_comps=Liq_B1038_all.mean(axis=0).to_frame().T,\n",
    "                                                                        cpx_comps=Cpx_B1038.mean(axis=0).to_frame().T,\n",
    "       H2O_Liq=H2O_B1038, equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq30\", return_all_pairs=True\n",
    "                                                                 ).get('All_PTs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5b1f2c-10af-4b77-acfb-e9fd6caaa71d",
   "metadata": {},
   "source": [
    "## Kraw-2013 - Calculating all Cpx-Liq matches (measured)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7f66012-9688-4ae7-a4e5-20d4d417ca6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering N=18 Cpx & N=20 Liqs, which is a total of N=360 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=360 Cpx-Liq matches using the specified filter. N=18 Cpx out of the N=18 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=18 Cpx & N=20 Liqs, which is a total of N=360 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=360 Cpx-Liq matches using the specified filter. N=18 Cpx out of the N=18 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=18 Cpx & N=20 Liqs, which is a total of N=360 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=360 Cpx-Liq matches using the specified filter. N=18 Cpx out of the N=18 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=18 Cpx & N=20 Liqs, which is a total of N=360 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=360 Cpx-Liq matches using the specified filter. N=18 Cpx out of the N=18 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=18 Cpx & N=20 Liqs, which is a total of N=360 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Youve selected a P-independent function\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator StandardScaler from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreeRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreesRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator StandardScaler from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreeRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Youve selected a T-independent function\n",
      "Youve selected a T-independent function\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=360 Cpx-Liq matches using the specified filter. N=18 Cpx out of the N=18 Cpx that you input matched to 1 or more liquids\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreesRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# all possible Liq- Cpx matches\n",
    "B1038_Cpx_Liq_N17_eq33_Liq=pt.calculate_cpx_liq_press_temp_matching(liq_comps=Liq_B1038_all,\n",
    "                                                                    cpx_comps=Cpx_B1038,\n",
    "       H2O_Liq=H2O_B1038, equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\", return_all_pairs=True\n",
    "                                                                  ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_Liq_30_eq33_Liq=pt.calculate_cpx_liq_press_temp_matching(liq_comps=Liq_B1038_all, cpx_comps=Cpx_B1038,\n",
    "       H2O_Liq=H2O_B1038, equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq30\", return_all_pairs=True\n",
    "                                                                 ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_Liq_31_eq33_Liq=pt.calculate_cpx_liq_press_temp_matching(liq_comps=Liq_B1038_all, cpx_comps=Cpx_B1038,\n",
    "       H2O_Liq=H2O_B1038, equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\", return_all_pairs=True,\n",
    "                                                        ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_Liq_32c_eq33_Liq=pt.calculate_cpx_liq_press_temp_matching(liq_comps=Liq_B1038_all, cpx_comps=Cpx_B1038,\n",
    "       H2O_Liq=H2O_B1038, equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\", return_all_pairs=True,\n",
    "                                                        ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_Liq_Pet_Liq=pt.calculate_cpx_liq_press_temp_matching(liq_comps=Liq_B1038_all, cpx_comps=Cpx_B1038,\n",
    "       H2O_Liq=H2O_B1038, equationT=\"T_Petrelli2020_Cpx_Liq\", equationP=\"P_Petrelli2020_Cpx_Liq\", return_all_pairs=True,\n",
    "                                                        ).get('All_PTs')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db408309-61c5-4d89-886c-48367a345a63",
   "metadata": {},
   "source": [
    "## Kraw-2013 - Using mean liquid composition, analytical noise in Cpx only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f44b8207-ffad-4ac7-86d2-db255779f89e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering N=5000 Cpx & N=1 Liqs, which is a total of N=5000 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=5000 Cpx-Liq matches using the specified filter. N=5000 Cpx out of the N=5000 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=5000 Cpx & N=1 Liqs, which is a total of N=5000 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=5000 Cpx-Liq matches using the specified filter. N=5000 Cpx out of the N=5000 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=5000 Cpx & N=1 Liqs, which is a total of N=5000 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=5000 Cpx-Liq matches using the specified filter. N=5000 Cpx out of the N=5000 Cpx that you input matched to 1 or more liquids\n",
      "Considering N=5000 Cpx & N=1 Liqs, which is a total of N=5000 Liq-Cpx pairs, be patient if this is >>1 million!\n",
      "No equilibrium filters applied\n",
      "Youve selected a P-independent function\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator StandardScaler from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreeRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreesRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Youve selected a T-independent function\n",
      "Youve selected a T-independent function\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator StandardScaler from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreeRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator ExtraTreesRegressor from version 1.0.2 when using version 1.1.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n",
      "Done!!! I found a total of N=5000 Cpx-Liq matches using the specified filter. N=5000 Cpx out of the N=5000 Cpx that you input matched to 1 or more liquids\n"
     ]
    }
   ],
   "source": [
    "# Using analytical error for Cpx, but not for Liquid. \n",
    "B1038_Liq2_Mean=Liq_B1038_all.mean(axis=0).to_frame().T\n",
    "B1038_Cpx_AvLiq_N17_eq33_Liq_Syn=pt.calculate_cpx_liq_press_temp_matching(cpx_comps=B1038_CpxAv_noise1,\n",
    "                                                                liq_comps=B1038_Liq2_Mean,\n",
    "                                                                         H2O_Liq=H2O_B1038,\n",
    "                                                               equationT=\"T_Put2008_eq33\",\n",
    "                                                               equationP=\"P_Neave2017\",\n",
    "                                                    return_all_pairs=True\n",
    "                                                               ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_AvLiq_31_eq33_Liq_Syn=pt.calculate_cpx_liq_press_temp_matching(cpx_comps=B1038_CpxAv_noise1,\n",
    "                                                                liq_comps=B1038_Liq2_Mean,\n",
    "                                                                        H2O_Liq=H2O_B1038,\n",
    "                                                               equationT=\"T_Put2008_eq33\",\n",
    "                                                               equationP=\"P_Put2008_eq31\",\n",
    "                                                    return_all_pairs=True\n",
    "                                                               ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_AvLiq_30_eq33_Liq_Syn=pt.calculate_cpx_liq_press_temp_matching(cpx_comps=B1038_CpxAv_noise1,\n",
    "                                                                liq_comps=B1038_Liq2_Mean,\n",
    "                                                                        H2O_Liq=H2O_B1038,\n",
    "                                                               equationT=\"T_Put2008_eq33\",\n",
    "                                                               equationP=\"P_Put2008_eq30\",\n",
    "                                                    return_all_pairs=True\n",
    "                                                               ).get('All_PTs')\n",
    "\n",
    "B1038_Cpx_AvLiq_Pet_Liq_Syn=pt.calculate_cpx_liq_press_temp_matching(cpx_comps=B1038_CpxAv_noise1,\n",
    "                                                                liq_comps=B1038_Liq2_Mean,\n",
    "                                                                        H2O_Liq=H2O_B1038,\n",
    "                                                               equationT=\"T_Petrelli2020_Cpx_Liq\",\n",
    "                                                               equationP=\"P_Petrelli2020_Cpx_Liq\",\n",
    "                                                    return_all_pairs=True\n",
    "                                                               ).get('All_PTs')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa690c8a-d594-49b7-af2b-f7734ee4b8fa",
   "metadata": {},
   "source": [
    "# Neave et al. 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc138bfc-1869-4e80-9343-0478300ce9c0",
   "metadata": {},
   "source": [
    "## 1. Loading in analytical imprecision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60083552-02ae-48ea-9853-79664ba17ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Neave_18=pd.read_excel('Supporting_Data_3.xlsx', sheet_name='Neave19_20180718_Python')\n",
    "#Neave_19=pd.read_excel('Kraw_2013_Calibration_Errors.xlsx', sheet_name='083006_Analyses')\n",
    "\n",
    "Neave_sigma=pt.import_excel('Supporting_Data_3.xlsx', sheet_name='Neave19_20180718_Python', suffix=\"_Cpx\")\n",
    "Neave_sigma_in=Neave_sigma['my_input']\n",
    "Neave_sigma_Cpx=Neave_sigma['Cpxs']\n",
    "Neave_conv=pt.convert_oxide_percent_to_element_weight_percent(df=Neave_sigma_Cpx, suffix='_Cpx', without_oxygen=True)\n",
    "\n",
    "Pec_Err_Neave_Na=100*Neave_sigma_in['Na_sig']/Neave_conv['Na_wt_noO2']\n",
    "Pec_Err_Neave_Al=100*Neave_sigma_in['Al_sig']/Neave_conv['Al_wt_noO2']\n",
    "Pec_Err_Neave_Ti=100*Neave_sigma_in['Ti_sig']/Neave_conv['Ti_wt_noO2']\n",
    "Pec_Err_Neave_Ca=100*Neave_sigma_in['Ca_sig']/Neave_conv['Ca_wt_noO2']\n",
    "Pec_Err_Neave_Fe=100*Neave_sigma_in['Fe_sig']/Neave_conv['Fe_wt_noO2']\n",
    "Pec_Err_Neave_Mn=100*Neave_sigma_in['Mn_sig']/Neave_conv['Mn_wt_noO2']\n",
    "Pec_Err_Neave_Mg=100*Neave_sigma_in['Mg_sig']/Neave_conv['Mg_wt_noO2']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4beb2fd4-56d5-485f-8358-493c5acb2106",
   "metadata": {},
   "source": [
    "##  2. Loading in Cpx compositions themselves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "be01639f-a48b-4847-917a-ecf0e83b51cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_2nd_NExp=pt.import_excel('Supporting_Data_5.xlsx', sheet_name='Neave_Cpx', suffix=\"_Cpx\") \n",
    "Cpxs_2nd_NExp=out_2nd_NExp['Cpxs']\n",
    "input_2nd_NExp=out_2nd_NExp['my_input']\n",
    "cpx_comp_2nd_NExp=pt.calculate_clinopyroxene_components(cpx_comps=Cpxs_2nd_NExp)\n",
    "All_NExp=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpxs_2nd_NExp)\n",
    "All_NExp['P_kbar_x']=input_2nd_NExp['Pressure(MPa)']/100\n",
    "All_NExp_9=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0188-9\"]\n",
    "All_NExp_2=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0201-2\"]\n",
    "All_NExp_8=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0187-8\"]\n",
    "All_NExp_4=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0183-4\"]\n",
    "All_NExp_1=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0167-1\"]\n",
    "All_NExp_10=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0184-10\"]\n",
    "All_NExp_Y2=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0205-2\"]\n",
    "All_NExp_Y3=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0200-3\"]\n",
    "All_NExp_Y1=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0272-1\"]\n",
    "All_NExp_Y7=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0287-1\"]\n",
    "All_NExp_043=All_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0204-3\"]\n",
    "\n",
    "Cpxs_Exp9=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0188-9\"]\n",
    "Cpxs_Exp2=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0201-2\"]\n",
    "Cpxs_Exp8=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0187-8\"]\n",
    "Cpxs_Exp4=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0183-4\"]\n",
    "Cpxs_Exp1=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0167-1\"]\n",
    "Cpxs_Exp10=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"B0184-10\"]\n",
    "Cpxs_ExpY2=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0205-2\"]\n",
    "Cpxs_ExpY3=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0200-3\"]\n",
    "Cpxs_ExpY1=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0272-1\"]\n",
    "Cpxs_ExpY27=Cpxs_2nd_NExp.loc[input_2nd_NExp['ExperimentID']==\"Y0272-2\"]\n",
    "\n",
    "out_av_glass=pt.import_excel('Supporting_Data_5.xlsx', sheet_name='Neave_Liqs', suffix=\"_Liq\")\n",
    "av_all=out_av_glass['my_input']\n",
    "av_glass=out_av_glass['Liqs']\n",
    "# Doesnt exist\n",
    "Glass_ExpY1=av_glass.loc[av_all['Comment']==\"Y272-1_gl01\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d5850a-f58b-41c9-9751-7bd1b959c1ac",
   "metadata": {},
   "source": [
    "## 3. Create average compositions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20b9cb04-8529-4a9b-9ecf-caf49e9ed93b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell averages the composition of Cpxs from different experiments of Neave, \n",
    "# and calculates their standard deviation and precision. \n",
    "Cpxs_Exp9_Mean=Cpxs_Exp9.mean(axis=0).to_frame().T\n",
    "Cpxs_Exp9_Std=Cpxs_Exp9.std(axis=0).to_frame().T\n",
    "Prec_Exp9=100*Cpxs_Exp9_Std/Cpxs_Exp9_Mean\n",
    "\n",
    "Cpxs_Exp2_Mean=Cpxs_Exp2.mean(axis=0).to_frame().T\n",
    "Cpxs_Exp2_Std=Cpxs_Exp2.std(axis=0).to_frame().T\n",
    "Prec_Exp2=100*Cpxs_Exp2_Std/Cpxs_Exp2_Mean\n",
    "\n",
    "Cpxs_Exp8_Mean=Cpxs_Exp8.mean(axis=0).to_frame().T\n",
    "Cpxs_Exp8_Std=Cpxs_Exp8.std(axis=0).to_frame().T\n",
    "Prec_Exp8=100*Cpxs_Exp8_Std/Cpxs_Exp8_Mean\n",
    "\n",
    "Cpxs_Exp4_Mean=Cpxs_Exp4.mean(axis=0).to_frame().T\n",
    "Cpxs_Exp4_Std=Cpxs_Exp4.std(axis=0).to_frame().T\n",
    "Prec_Exp4=100*Cpxs_Exp4_Std/Cpxs_Exp4_Mean\n",
    "\n",
    "Cpxs_Exp1_Mean=Cpxs_Exp1.mean(axis=0).to_frame().T\n",
    "Cpxs_Exp1_Std=Cpxs_Exp1.std(axis=0).to_frame().T\n",
    "Prec_Exp1=100*Cpxs_Exp1_Std/Cpxs_Exp1_Mean\n",
    "\n",
    "Cpxs_Exp10_Mean=Cpxs_Exp10.mean(axis=0).to_frame().T\n",
    "Cpxs_Exp10_Std=Cpxs_Exp10.std(axis=0).to_frame().T\n",
    "Prec_Exp10=100*Cpxs_Exp10_Std/Cpxs_Exp10_Mean\n",
    "\n",
    "Cpxs_ExpY2_Mean=Cpxs_ExpY2.mean(axis=0).to_frame().T\n",
    "Cpxs_ExpY2_Std=Cpxs_ExpY2.std(axis=0).to_frame().T\n",
    "Prec_ExpY2=100*Cpxs_ExpY2_Std/Cpxs_ExpY2_Mean\n",
    "\n",
    "Cpxs_ExpY3_Mean=Cpxs_ExpY3.mean(axis=0).to_frame().T\n",
    "Cpxs_ExpY3_Std=Cpxs_ExpY3.std(axis=0).to_frame().T\n",
    "Prec_ExpY3=100*Cpxs_ExpY3_Std/Cpxs_ExpY3_Mean\n",
    "\n",
    "Cpxs_ExpY1_Mean=Cpxs_ExpY1.mean(axis=0).to_frame().T\n",
    "Cpxs_ExpY1_Std=Cpxs_ExpY1.std(axis=0).to_frame().T\n",
    "Prec_ExpY1=100*Cpxs_ExpY1_Std/Cpxs_ExpY1_Mean#\n",
    "\n",
    "Cpxs_ExpY27_Mean=Cpxs_ExpY27.mean(axis=0).to_frame().T\n",
    "Cpxs_ExpY27_Std=Cpxs_ExpY27.std(axis=0).to_frame().T\n",
    "Prec_ExpY27=100*Cpxs_ExpY27_Std/Cpxs_ExpY27_Mean\n",
    "\n",
    "# This cell compiles those precision estaimtes\n",
    "Neave_Exp_Prec=pd.concat([Prec_Exp9, Prec_Exp2, Prec_Exp8, Prec_Exp4, Prec_Exp1, Prec_Exp10,\n",
    "                          Prec_ExpY2, Prec_ExpY3, Prec_ExpY1])\n",
    "Neave_Exp_Mean=pd.concat([Cpxs_Exp9_Mean, Cpxs_Exp2_Mean, Cpxs_Exp8_Mean, Cpxs_Exp4_Mean, Cpxs_Exp1_Mean,\n",
    "                          Cpxs_Exp10_Mean, Cpxs_ExpY2_Mean, Cpxs_ExpY3_Mean, Cpxs_ExpY1_Mean])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67dc543f-f499-4766-bc7a-20482bf56363",
   "metadata": {},
   "source": [
    "### 4. Calculating Neave Cpx Errors for 1 specific expermient used here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db69b8a7-1d48-4774-a102-048552e9e8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Neave_sigma_in_ExpY1=Neave_sigma_in.loc[Neave_sigma_in['Exp']==\"Y272-1\"]\n",
    "Neave_conv_ExpY1=Neave_conv.loc[Neave_sigma_in['Exp']==\"Y272-1\"]\n",
    "\n",
    "Pec_Err_Neave_Na_ExpY1=100*Neave_sigma_in_ExpY1['Na_sig']/Neave_conv_ExpY1['Na_wt_noO2']\n",
    "Pec_Err_Neave_Si_ExpY1=100*Neave_sigma_in_ExpY1['Si_sig']/Neave_conv_ExpY1['Si_wt_noO2']\n",
    "Pec_Err_Neave_Cr_ExpY1=100*Neave_sigma_in_ExpY1['Cr_sig']/Neave_conv_ExpY1['Cr_wt_noO2']\n",
    "Pec_Err_Neave_Al_ExpY1=100*Neave_sigma_in_ExpY1['Al_sig']/Neave_conv_ExpY1['Al_wt_noO2']\n",
    "Pec_Err_Neave_Ti_ExpY1=100*Neave_sigma_in_ExpY1['Ti_sig']/Neave_conv_ExpY1['Ti_wt_noO2']\n",
    "Pec_Err_Neave_Ca_ExpY1=100*Neave_sigma_in_ExpY1['Ca_sig']/Neave_conv_ExpY1['Ca_wt_noO2']\n",
    "Pec_Err_Neave_Fe_ExpY1=100*Neave_sigma_in_ExpY1['Fe_sig']/Neave_conv_ExpY1['Fe_wt_noO2']\n",
    "Pec_Err_Neave_Mn_ExpY1=100*Neave_sigma_in_ExpY1['Mn_sig']/Neave_conv_ExpY1['Mn_wt_noO2']\n",
    "Pec_Err_Neave_Mg_ExpY1=100*Neave_sigma_in_ExpY1['Mg_sig']/Neave_conv_ExpY1['Mg_wt_noO2']\n",
    "\n",
    "Pec_Err_Neave_Cr_ExpY1.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "\n",
    "ExpY1_Mean_Cpx=Cpxs_ExpY1.mean(axis=0).to_frame().T\n",
    "\n",
    "ExpY1_Cpx_Err=pd.DataFrame(data={'SiO2_Cpx_Err': np.nanmean(Pec_Err_Neave_Si_ExpY1),\n",
    "                            'TiO2_Cpx_Err':np.nanmean(Pec_Err_Neave_Ti_ExpY1),\n",
    "                             'Al2O3_Cpx_Err':np.nanmean(Pec_Err_Neave_Al_ExpY1),\n",
    "                             'FeOt_Cpx_Err':np.nanmean(Pec_Err_Neave_Fe_ExpY1),\n",
    "                            'MnO_Cpx_Err':np.nanmean(Pec_Err_Neave_Mn_ExpY1),\n",
    "                            'MgO_Cpx_Err':np.nanmean(Pec_Err_Neave_Mg_ExpY1),\n",
    "                            'CaO_Cpx_Err':np.nanmean(Pec_Err_Neave_Ca_ExpY1),\n",
    "                            'Na2O_Cpx_Err':np.nanmean(Pec_Err_Neave_Na_ExpY1),\n",
    "                            'Cr2O3_Cpx_Err':np.nanmean(Pec_Err_Neave_Cr_ExpY1),\n",
    "                               'K2O_Cpx_Err': 0}, index=[0])\n",
    "ExpY1_noise1=pt.add_noise_sample_1phase(phase_comp=ExpY1_Mean_Cpx, phase_err=ExpY1_Cpx_Err,\n",
    "                                             phase_err_type=\"Perc\", duplicates=5000, \n",
    "                                        err_dist=\"normal\", positive=False)\n",
    "ExpY1_noise1_500=pt.add_noise_sample_1phase(phase_comp=ExpY1_Mean_Cpx, phase_err=ExpY1_Cpx_Err,\n",
    "                                             phase_err_type=\"Perc\", duplicates=500, \n",
    "                                        err_dist=\"normal\", positive=False)\n",
    "ExpY1_noise1_500_JustNa=pt.add_noise_sample_1phase(phase_comp=ExpY1_Mean_Cpx, variable=\"Na2O\",\n",
    "                                                   variable_err=Pec_Err_Neave_Na_ExpY1,\n",
    "                                             variable_err_type=\"Perc\", duplicates=500, \n",
    "                                        err_dist=\"normal\", positive=False)\n",
    "Cpx_ExpY1_Syn=pt.calculate_cpx_only_press_all_eqs(cpx_comps=ExpY1_noise1)\n",
    "Cpx_ExpY1_Calc=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpxs_ExpY1.reset_index(drop=True))\n",
    "Cpx_ExpY1_Calc_100=pt.calculate_cpx_only_press_all_eqs(cpx_comps=ExpY1_noise1_500)\n",
    "Cpx_ExpY1_Calc_100_JustNa=pt.calculate_cpx_only_press_all_eqs(cpx_comps=ExpY1_noise1_500_JustNa)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4a051f-4444-42b4-bb6a-77680a44a0db",
   "metadata": {},
   "source": [
    "## C1: Calculating Cascade for Cpx-only from Neave et al. (2019) \n",
    "- Takes 5000 synthetic Cpxs generated using analytical errors only, randomly samples 100 of these, then calculates average composition, then from the averages for different N calculates the P. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "18050044-bc3c-4a0e-a85f-05363060611d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Made it to averaging step\n",
      "Made it to N=10\n"
     ]
    }
   ],
   "source": [
    "SampleDF_Cpx=ExpY1_noise1\n",
    "loops2=np.linspace(0, 700, 701)\n",
    "#Mean_Cut2_Cpxs_Calcs=np.empty(len(loops), np.shape(Mean_Cut2_Cpxs_Calcs)[1], dtype=float)\n",
    "for i in range(0, len(loops2)):\n",
    "\n",
    "# Or can cascade down the whole way\n",
    "    RandNeave_100Cpxs=SampleDF_Cpx.sample(n=100)\n",
    "    Mean_RandNeave_100Cpxs=RandNeave_100Cpxs.mean(axis=0).to_frame().T \n",
    "    \n",
    "    RandNeave_40Cpxs=RandNeave_100Cpxs.sample(n=40)\n",
    "    Mean_RandNeave_40Cpxs=RandNeave_40Cpxs.mean(axis=0).to_frame().T \n",
    "\n",
    "    RandNeave_30Cpxs=RandNeave_40Cpxs.sample(n=30)\n",
    "    Mean_RandNeave_30Cpxs=RandNeave_30Cpxs.mean(axis=0).to_frame().T \n",
    "    \n",
    "    RandNeave_20Cpxs=RandNeave_30Cpxs.sample(n=20)\n",
    "    Mean_RandNeave_20Cpxs=RandNeave_20Cpxs.mean(axis=0).to_frame().T \n",
    "\n",
    "    RandNeave_15Cpxs=RandNeave_20Cpxs.sample(n=15)\n",
    "    Mean_RandNeave_15Cpxs=RandNeave_15Cpxs.mean(axis=0).to_frame().T \n",
    "    \n",
    "    RandNeave_10Cpxs=RandNeave_15Cpxs.sample(n=10)\n",
    "    Mean_RandNeave_10Cpxs=RandNeave_10Cpxs.mean(axis=0).to_frame().T   \n",
    "    \n",
    "    RandNeave_7Cpxs=RandNeave_10Cpxs.sample(n=7)\n",
    "    Mean_RandNeave_7Cpxs=RandNeave_7Cpxs.mean(axis=0).to_frame().T    \n",
    "    \n",
    "    RandNeave_5Cpxs=RandNeave_7Cpxs.sample(n=5)\n",
    "    Mean_RandNeave_5Cpxs=RandNeave_5Cpxs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    RandNeave_4Cpxs=RandNeave_5Cpxs.sample(n=4)\n",
    "    Mean_RandNeave_4Cpxs=RandNeave_4Cpxs.mean(axis=0).to_frame().T    \n",
    "    \n",
    "    RandNeave_3Cpxs=RandNeave_4Cpxs.sample(n=3)\n",
    "    Mean_RandNeave_3Cpxs=RandNeave_3Cpxs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    \n",
    "    RandNeave_2Cpxs=RandNeave_3Cpxs.sample(n=2)\n",
    "    Mean_RandNeave_2Cpxs=RandNeave_2Cpxs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    RandNeave_1Cpxs=RandNeave_2Cpxs.sample(n=1)\n",
    "    Mean_RandNeave_1Cpxs=RandNeave_1Cpxs.mean(axis=0).to_frame().T  \n",
    "    \n",
    "    if i==0:\n",
    "        Neave_1Cpxs=Mean_RandNeave_1Cpxs\n",
    "        Neave_2Cpxs=Mean_RandNeave_2Cpxs\n",
    "        Neave_3Cpxs=Mean_RandNeave_3Cpxs \n",
    "        Neave_4Cpxs=Mean_RandNeave_4Cpxs\n",
    "        Neave_5Cpxs=Mean_RandNeave_5Cpxs\n",
    "        Neave_7Cpxs=Mean_RandNeave_7Cpxs\n",
    "        Neave_10Cpxs=Mean_RandNeave_10Cpxs\n",
    "        Neave_15Cpxs=Mean_RandNeave_15Cpxs\n",
    "        Neave_20Cpxs=Mean_RandNeave_20Cpxs  \n",
    "        Neave_30Cpxs=Mean_RandNeave_30Cpxs \n",
    "        Neave_40Cpxs=Mean_RandNeave_40Cpxs\n",
    "        Neave_100Cpxs=Mean_RandNeave_100Cpxs\n",
    "    else:\n",
    "        Neave_1Cpxs=Neave_1Cpxs.append(Mean_RandNeave_1Cpxs).reset_index(drop=True)\n",
    "        Neave_2Cpxs=Neave_2Cpxs.append(Mean_RandNeave_2Cpxs).reset_index(drop=True)\n",
    "        Neave_3Cpxs=Neave_3Cpxs.append(Mean_RandNeave_3Cpxs).reset_index(drop=True)\n",
    "        Neave_4Cpxs=Neave_4Cpxs.append(Mean_RandNeave_4Cpxs).reset_index(drop=True)\n",
    "        Neave_5Cpxs=Neave_5Cpxs.append(Mean_RandNeave_5Cpxs).reset_index(drop=True)\n",
    "        Neave_7Cpxs=Neave_7Cpxs.append(Mean_RandNeave_7Cpxs).reset_index(drop=True)\n",
    "        Neave_10Cpxs=Neave_10Cpxs.append(Mean_RandNeave_10Cpxs).reset_index(drop=True)\n",
    "        Neave_15Cpxs=Neave_15Cpxs.append(Mean_RandNeave_15Cpxs).reset_index(drop=True)\n",
    "        Neave_20Cpxs=Neave_20Cpxs.append(Mean_RandNeave_20Cpxs).reset_index(drop=True)    \n",
    "        Neave_30Cpxs=Neave_30Cpxs.append(Mean_RandNeave_30Cpxs).reset_index(drop=True)    \n",
    "        Neave_40Cpxs=Neave_40Cpxs.append(Mean_RandNeave_40Cpxs).reset_index(drop=True) \n",
    "        Neave_100Cpxs=Neave_100Cpxs.append(Mean_RandNeave_100Cpxs).reset_index(drop=True) \n",
    "print('Made it to averaging step')\n",
    "Neave_1Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_1Cpxs)\n",
    "Neave_2Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_2Cpxs)\n",
    "Neave_3Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_3Cpxs)\n",
    "Neave_4Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_4Cpxs)\n",
    "Neave_5Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_5Cpxs)\n",
    "Neave_7Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_7Cpxs)\n",
    "Neave_10Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_10Cpxs)\n",
    "print('Made it to N=10')\n",
    "Neave_15Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_15Cpxs)\n",
    "Neave_20Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_20Cpxs)\n",
    "Neave_30Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_30Cpxs)\n",
    "Neave_40Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_40Cpxs)\n",
    "Neave_100Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Neave_100Cpxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351ff8aa-cfac-4e2f-bceb-ba9423a84e9b",
   "metadata": {},
   "source": [
    "## C2. Calculating Cascade for actual measured Cpxs (18 of them from Kraw2013)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cb7ed71f-5ac7-48f7-ac9a-bb27070ffd74",
   "metadata": {},
   "outputs": [],
   "source": [
    "SampleDF_Cpx=Cpx_B1038\n",
    "loops2=np.linspace(0, 700, 701)\n",
    "#Mean_Cut2_Cpxs_Calcs=np.empty(len(loops), np.shape(Mean_Cut2_Cpxs_Calcs)[1], dtype=float)\n",
    "for i in range(0, len(loops2)):\n",
    "\n",
    "# Or can cascade down the whole way\n",
    "  \n",
    "    RandKrawMeas_18Cpxs=SampleDF_Cpx\n",
    "    Mean_RandKrawMeas_18Cpxs=RandKrawMeas_18Cpxs.mean(axis=0).to_frame().T \n",
    "\n",
    "    RandKrawMeas_15Cpxs=RandKrawMeas_18Cpxs.sample(n=15)\n",
    "    Mean_RandKrawMeas_15Cpxs=RandKrawMeas_15Cpxs.mean(axis=0).to_frame().T \n",
    "    \n",
    "    RandKrawMeas_10Cpxs=RandKrawMeas_18Cpxs.sample(n=10)\n",
    "    Mean_RandKrawMeas_10Cpxs=RandKrawMeas_10Cpxs.mean(axis=0).to_frame().T   \n",
    "    \n",
    "    RandKrawMeas_7Cpxs=RandKrawMeas_18Cpxs.sample(n=7)\n",
    "    Mean_RandKrawMeas_7Cpxs=RandKrawMeas_7Cpxs.mean(axis=0).to_frame().T    \n",
    "    \n",
    "    RandKrawMeas_5Cpxs=RandKrawMeas_18Cpxs.sample(n=5)\n",
    "    Mean_RandKrawMeas_5Cpxs=RandKrawMeas_5Cpxs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    RandKrawMeas_4Cpxs=RandKrawMeas_18Cpxs.sample(n=4)\n",
    "    Mean_RandKrawMeas_4Cpxs=RandKrawMeas_4Cpxs.mean(axis=0).to_frame().T    \n",
    "    \n",
    "    RandKrawMeas_3Cpxs=RandKrawMeas_18Cpxs.sample(n=3)\n",
    "    Mean_RandKrawMeas_3Cpxs=RandKrawMeas_3Cpxs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    \n",
    "    RandKrawMeas_2Cpxs=RandKrawMeas_18Cpxs.sample(n=2)\n",
    "    Mean_RandKrawMeas_2Cpxs=RandKrawMeas_2Cpxs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    RandKrawMeas_1Cpxs=RandKrawMeas_18Cpxs.sample(n=1)\n",
    "    Mean_RandKrawMeas_1Cpxs=RandKrawMeas_1Cpxs.mean(axis=0).to_frame().T  \n",
    "    \n",
    "    if i==0:\n",
    "        KrawMeas_1Cpxs=Mean_RandKrawMeas_1Cpxs\n",
    "        KrawMeas_2Cpxs=Mean_RandKrawMeas_2Cpxs\n",
    "        KrawMeas_3Cpxs=Mean_RandKrawMeas_3Cpxs \n",
    "        KrawMeas_4Cpxs=Mean_RandKrawMeas_4Cpxs\n",
    "        KrawMeas_5Cpxs=Mean_RandKrawMeas_5Cpxs\n",
    "        KrawMeas_7Cpxs=Mean_RandKrawMeas_7Cpxs\n",
    "        KrawMeas_10Cpxs=Mean_RandKrawMeas_10Cpxs\n",
    "        KrawMeas_15Cpxs=Mean_RandKrawMeas_15Cpxs\n",
    "        KrawMeas_18Cpxs=Mean_RandKrawMeas_18Cpxs\n",
    "\n",
    "    else:\n",
    "        KrawMeas_1Cpxs=KrawMeas_1Cpxs.append(Mean_RandKrawMeas_1Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_2Cpxs=KrawMeas_2Cpxs.append(Mean_RandKrawMeas_2Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_3Cpxs=KrawMeas_3Cpxs.append(Mean_RandKrawMeas_3Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_4Cpxs=KrawMeas_4Cpxs.append(Mean_RandKrawMeas_4Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_5Cpxs=KrawMeas_5Cpxs.append(Mean_RandKrawMeas_5Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_7Cpxs=KrawMeas_7Cpxs.append(Mean_RandKrawMeas_7Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_10Cpxs=KrawMeas_10Cpxs.append(Mean_RandKrawMeas_10Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_15Cpxs=KrawMeas_15Cpxs.append(Mean_RandKrawMeas_15Cpxs).reset_index(drop=True)\n",
    "        KrawMeas_18Cpxs=KrawMeas_18Cpxs.append(Mean_RandKrawMeas_18Cpxs).reset_index(drop=True)    \n",
    "\n",
    "KrawMeas_1Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_1Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_2Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_2Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_3Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_3Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_4Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_4Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_5Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_5Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_7Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_7Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_10Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_10Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_15Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_15Cpxs, H2O_Liq=H2O_B1038)\n",
    "KrawMeas_18Cpxs_CompAv=pt.calculate_cpx_only_press_all_eqs(cpx_comps=KrawMeas_18Cpxs, H2O_Liq=H2O_B1038)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33977624-41d0-4967-b892-981bd7548431",
   "metadata": {},
   "source": [
    "## C4: Lets not cheat, lets re-allocate pressures and temps after splitting into cpx and liq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d2da955b-e1a6-41ef-ac13-5442e74fa861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, lets sample\n",
    "loops2=np.linspace(0, 700, 701)\n",
    "for i in range(0, len(loops2)):\n",
    "    nliq=10\n",
    "# This takes all possible Cpx-Liq pairs from B1038_Cpx_Liq_N17_eq33_Liq(although exact equation is arbitrary).\n",
    "# It then finds all rows which matched to Cpx number 1. Of these rows, it randomly takes 10 of them, and averages.\n",
    "# So you have Cpx 1 matching to the average of 10 liquid compositions. \n",
    "    Cpx_Liq_1_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[0]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_2_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[1]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_3_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[2]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_4_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[3]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_5_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[4]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_6_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[5]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_7_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[6]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_8_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[7]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_9_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[8]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_10_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[9]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    \n",
    "    Cpx_Liq_11_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[10]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_12_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[11]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_13_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[12]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_14_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[13]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_15_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[14]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_16_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[15]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_17_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[16]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "    Cpx_Liq_18_Sim=B1038_Cpx_Liq_N17_eq33_Liq.loc[B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx']==\n",
    "                                   B1038_Cpx_Liq_N17_eq33_Liq['Sample_ID_Cpx'].unique()[17]].sample(n=nliq).mean(axis=0).to_frame().T\n",
    "  \n",
    "    # Now we combine all these Cpx-Liqs into a single dataframe. \n",
    "    Combo_Cpx=pd.concat([Cpx_Liq_1_Sim, Cpx_Liq_2_Sim, Cpx_Liq_3_Sim, Cpx_Liq_4_Sim,\n",
    "                        Cpx_Liq_5_Sim, Cpx_Liq_6_Sim, Cpx_Liq_7_Sim,\n",
    "                        Cpx_Liq_8_Sim, Cpx_Liq_9_Sim, Cpx_Liq_10_Sim,\n",
    "                        Cpx_Liq_11_Sim, Cpx_Liq_12_Sim, Cpx_Liq_13_Sim, Cpx_Liq_14_Sim,\n",
    "                        Cpx_Liq_15_Sim, Cpx_Liq_16_Sim, Cpx_Liq_17_Sim,\n",
    "                        Cpx_Liq_18_Sim])\n",
    "# Now, I consider what if we sampled all these Cpx-Liq Matches\n",
    "    RandKraw_18Cpxs_Liqs=Combo_Cpx\n",
    "    Mean_RandKraw_18Cpxs_Liqs=RandKraw_18Cpxs_Liqs.mean(axis=0).to_frame().T \n",
    "\n",
    "    RandKraw_15Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=15)\n",
    "    Mean_RandKraw_15Cpxs_Liqs=RandKraw_15Cpxs_Liqs.mean(axis=0).to_frame().T \n",
    "    \n",
    "    RandKraw_10Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=10)\n",
    "    Mean_RandKraw_10Cpxs_Liqs=RandKraw_10Cpxs_Liqs.mean(axis=0).to_frame().T   \n",
    "    \n",
    "    RandKraw_7Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=7)\n",
    "    Mean_RandKraw_7Cpxs_Liqs=RandKraw_7Cpxs_Liqs.mean(axis=0).to_frame().T    \n",
    "    \n",
    "    RandKraw_5Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=5)\n",
    "    Mean_RandKraw_5Cpxs_Liqs=RandKraw_5Cpxs_Liqs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    RandKraw_4Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=4)\n",
    "    Mean_RandKraw_4Cpxs_Liqs=RandKraw_4Cpxs_Liqs.mean(axis=0).to_frame().T    \n",
    "    \n",
    "    RandKraw_3Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=3)\n",
    "    Mean_RandKraw_3Cpxs_Liqs=RandKraw_3Cpxs_Liqs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    \n",
    "    RandKraw_2Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=2)\n",
    "    Mean_RandKraw_2Cpxs_Liqs=RandKraw_2Cpxs_Liqs.mean(axis=0).to_frame().T    \n",
    "\n",
    "    RandKraw_1Cpxs_Liqs=RandKraw_18Cpxs_Liqs.sample(n=1)\n",
    "    Mean_RandKraw_1Cpxs_Liqs=RandKraw_1Cpxs_Liqs# RandKraw_1Cpxs_Liqs.mean(axis=0).to_frame().T  \n",
    "    \n",
    "    if i==0:\n",
    "        Kraw_1Cpxs_Liqs=Mean_RandKraw_1Cpxs_Liqs\n",
    "        Kraw_2Cpxs_Liqs=Mean_RandKraw_2Cpxs_Liqs\n",
    "        Kraw_3Cpxs_Liqs=Mean_RandKraw_3Cpxs_Liqs \n",
    "        Kraw_4Cpxs_Liqs=Mean_RandKraw_4Cpxs_Liqs\n",
    "        Kraw_5Cpxs_Liqs=Mean_RandKraw_5Cpxs_Liqs\n",
    "        Kraw_7Cpxs_Liqs=Mean_RandKraw_7Cpxs_Liqs\n",
    "        Kraw_10Cpxs_Liqs=Mean_RandKraw_10Cpxs_Liqs\n",
    "        Kraw_15Cpxs_Liqs=Mean_RandKraw_15Cpxs_Liqs\n",
    "        Kraw_18Cpxs_Liqs=Mean_RandKraw_18Cpxs_Liqs\n",
    "    else:\n",
    "        Kraw_1Cpxs_Liqs=Kraw_1Cpxs_Liqs.append(Mean_RandKraw_1Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_2Cpxs_Liqs=Kraw_2Cpxs_Liqs.append(Mean_RandKraw_2Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_3Cpxs_Liqs=Kraw_3Cpxs_Liqs.append(Mean_RandKraw_3Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_4Cpxs_Liqs=Kraw_4Cpxs_Liqs.append(Mean_RandKraw_4Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_5Cpxs_Liqs=Kraw_5Cpxs_Liqs.append(Mean_RandKraw_5Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_7Cpxs_Liqs=Kraw_7Cpxs_Liqs.append(Mean_RandKraw_7Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_10Cpxs_Liqs=Kraw_10Cpxs_Liqs.append(Mean_RandKraw_10Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_15Cpxs_Liqs=Kraw_15Cpxs_Liqs.append(Mean_RandKraw_15Cpxs_Liqs).reset_index(drop=True)\n",
    "        Kraw_18Cpxs_Liqs=Kraw_18Cpxs_Liqs.append(Mean_RandKraw_18Cpxs_Liqs).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11755810-4c78-41ef-8c90-390b4bb7a6c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Fe3FeT from input file to calculate Kd Fe-Mg\n",
      "Using Fe3FeT from input file to calculate Kd Fe-Mg\n"
     ]
    }
   ],
   "source": [
    "# First allocate a\n",
    "Kraw_1Cpxs_Liqs['N']=1\n",
    "Kraw_2Cpxs_Liqs['N']=2\n",
    "Kraw_3Cpxs_Liqs['N']=3\n",
    "Kraw_4Cpxs_Liqs['N']=4\n",
    "Kraw_5Cpxs_Liqs['N']=5\n",
    "Kraw_7Cpxs_Liqs['N']=7\n",
    "Kraw_10Cpxs_Liqs['N']=10\n",
    "Kraw_15Cpxs_Liqs['N']=15\n",
    "Kraw_18Cpxs_Liqs['N']=18\n",
    "\n",
    "df_ideal_liq = pd.DataFrame(columns=['SiO2_Liq', 'TiO2_Liq', 'Al2O3_Liq',\n",
    "'FeOt_Liq', 'MnO_Liq', 'MgO_Liq', 'CaO_Liq', 'Na2O_Liq', 'K2O_Liq',\n",
    "'Cr2O3_Liq', 'P2O5_Liq', 'H2O_Liq', 'Fe3Fet_Liq', 'NiO_Liq', 'CoO_Liq',\n",
    " 'CO2_Liq'])\n",
    "\n",
    "\n",
    "df_ideal_cpx = pd.DataFrame(columns=['SiO2_Cpx', 'TiO2_Cpx', 'Al2O3_Cpx',\n",
    "'FeOt_Cpx','MnO_Cpx', 'MgO_Cpx', 'CaO_Cpx', 'Na2O_Cpx', 'K2O_Cpx',\n",
    "'Cr2O3_Cpx'])\n",
    "\n",
    "Kraw_Av=pd.concat([Kraw_1Cpxs_Liqs, Kraw_2Cpxs_Liqs, Kraw_3Cpxs_Liqs, Kraw_4Cpxs_Liqs, Kraw_5Cpxs_Liqs, Kraw_7Cpxs_Liqs,\n",
    "                  Kraw_10Cpxs_Liqs, Kraw_15Cpxs_Liqs, Kraw_18Cpxs_Liqs]).reset_index(drop=True)\n",
    "Kraw_Av_Liq=Kraw_Av.reindex(df_ideal_liq.columns, axis=1).fillna(0)\n",
    "Kraw_Av_Liq['Sample_ID_Liq']=Kraw_Av['N']\n",
    "Kraw_Av_Cpx=Kraw_Av.reindex(df_ideal_cpx.columns, axis=1).fillna(0)\n",
    "Kraw_Av_Cpx['Sample_ID_Cpx']=Kraw_Av['N']\n",
    "\n",
    "B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av=pt.calculate_cpx_liq_press_temp(cpx_comps=Kraw_Av_Cpx,\n",
    "                                                                liq_comps=Kraw_Av_Liq,\n",
    "                                                                         H2O_Liq=H2O_B1038,\n",
    "                                                               equationT=\"T_Put2008_eq33\",\n",
    "                                                               equationP=\"P_Neave2017\", eq_tests=True\n",
    "                                                    \n",
    "                                                               )\n",
    "B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']=Kraw_Av_Cpx['Sample_ID_Cpx']\n",
    "\n",
    "B1038_Cpx_Liq_30_eq33_Loop_Comp_Av=pt.calculate_cpx_liq_press_temp(cpx_comps=Kraw_Av_Cpx,\n",
    "                                                                liq_comps=Kraw_Av_Liq,\n",
    "                                                                         H2O_Liq=H2O_B1038,\n",
    "                                                               equationT=\"T_Put2008_eq33\",\n",
    "                                                               equationP=\"P_Put2008_eq30\",eq_tests=True\n",
    "                                                    \n",
    "                                                               )\n",
    "B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']=Kraw_Av_Cpx['Sample_ID_Cpx']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7c23bf-771e-4c81-826c-0f475636783e",
   "metadata": {},
   "source": [
    "## Big 6 part figure, # of analyses. Using analytical error Neave, actual measured Cpxs from Kraw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fddfe482-dc8a-4a94-8443-9f8a6e9183e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax2, ax1), (ax6, ax5), (ax3, ax4)) = plt.subplots(3, 2, figsize=(10,13))\n",
    "ms2=12\n",
    "Av_Cpx1=np.mean(Cpx_ExpY1_Syn['P_Put_Teq32d_Peq32b'])\n",
    "ax2.plot(Neave_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+1, np.abs(Neave_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+2,  np.abs(Neave_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+3,  np.abs(Neave_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+4,  np.abs(Neave_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+5,  np.abs(Neave_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+7,  np.abs(Neave_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+10,  np.abs(Neave_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+15,  np.abs(Neave_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax2.plot(Neave_100Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+20,  np.abs(Neave_100Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')#ax2.plot(Neave_30Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+30, (Neave_30Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.k')\n",
    "\n",
    "ms1=7\n",
    "ax2.plot(1,  np.mean(np.abs(Neave_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(2,  np.mean(np.abs(Neave_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)),  '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(3,  np.mean(np.abs(Neave_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)),  '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(4,  np.mean(np.abs(Neave_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)),  '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(5,  np.mean(np.abs(Neave_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)),  '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(7,  np.mean(np.abs(Neave_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)),  '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(10,  np.mean(np.abs(Neave_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)),  '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(15,  np.mean(np.abs(Neave_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)),  '*k', mfc='blue', ms=ms2)\n",
    "ax2.plot(20,  np.mean(np.abs(Neave_100Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "\n",
    "\n",
    "ax2.plot(1,  np.nanpercentile(np.abs(Neave_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(2,  np.nanpercentile(np.abs(Neave_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(3,  np.nanpercentile(np.abs(Neave_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(4,  np.nanpercentile(np.abs(Neave_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(5,  np.nanpercentile(np.abs(Neave_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(7,  np.nanpercentile(np.abs(Neave_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(10,  np.nanpercentile(np.abs(Neave_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(15,  np.nanpercentile(np.abs(Neave_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax2.plot(20,  np.nanpercentile(np.abs(Neave_100Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "\n",
    "ax2.set_ylabel('| Calc P$_{N}$ - Exp P$_{All \\ Sim}$ | kbar \\n Putirka (2008) eq32d-32b')\n",
    "ax2.set_xlabel('# of Cpx analyses averaged')\n",
    "\n",
    "Av_Cpx1=np.mean(Cpx_ExpY1_Syn['P_Wang21_eq1'])\n",
    "\n",
    "ax1.plot(Neave_1Cpxs_CompAv['P_Wang21_eq1']*0+1, np.abs(Neave_1Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_2Cpxs_CompAv['P_Wang21_eq1']*0+2,  np.abs(Neave_2Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_3Cpxs_CompAv['P_Wang21_eq1']*0+3,  np.abs(Neave_3Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_4Cpxs_CompAv['P_Wang21_eq1']*0+4,  np.abs(Neave_4Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_5Cpxs_CompAv['P_Wang21_eq1']*0+5,  np.abs(Neave_5Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_7Cpxs_CompAv['P_Wang21_eq1']*0+7,  np.abs(Neave_7Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_10Cpxs_CompAv['P_Wang21_eq1']*0+10,  np.abs(Neave_10Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_15Cpxs_CompAv['P_Wang21_eq1']*0+15,  np.abs(Neave_15Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax1.plot(Neave_100Cpxs_CompAv['P_Wang21_eq1']*0+20,  np.abs(Neave_100Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')#ax1.plot(Neave_30Cpxs_CompAv['P_Wang21_eq1']*0+30, (Neave_30Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.k')\n",
    "# ax1.plot(Neave_40Cpxs_CompAv['P_Wang21_eq1']*0+40, (Neave_40Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.k')\n",
    "# ax1.plot(Neave_100Cpxs_CompAv['P_Wang21_eq1']*0+100, (Neave_100Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.k')\n",
    "\n",
    "ms1=7\n",
    "ax1.plot(1,  np.mean(np.abs(Neave_1Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(2,  np.mean(np.abs(Neave_2Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(3,  np.mean(np.abs(Neave_3Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(4,  np.mean(np.abs(Neave_4Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(5,  np.mean(np.abs(Neave_5Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(7,  np.mean(np.abs(Neave_7Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(10,  np.mean(np.abs(Neave_10Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(15,  np.mean(np.abs(Neave_15Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax1.plot(20,  np.mean(np.abs(Neave_100Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "\n",
    "\n",
    "ax1.plot(1,  np.nanpercentile(np.abs(Neave_1Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(2,  np.nanpercentile(np.abs(Neave_2Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(3,  np.nanpercentile(np.abs(Neave_3Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(4,  np.nanpercentile(np.abs(Neave_4Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(5,  np.nanpercentile(np.abs(Neave_5Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(7,  np.nanpercentile(np.abs(Neave_7Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(10,  np.nanpercentile(np.abs(Neave_10Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(15,  np.nanpercentile(np.abs(Neave_15Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax1.plot(20,  np.nanpercentile(np.abs(Neave_100Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "\n",
    "\n",
    "\n",
    "ax1.set_ylabel('| Calc P$_{N}$ - Exp P$_{All \\ Sim}$ | kbar \\n Wang et al. (2021)')\n",
    "ax1.set_xlabel('# of Cpx analyses averaged')\n",
    "\n",
    "## This looks at subsampling the 18 measured Cpxs for Cpx-only from Kraw2013\n",
    "\n",
    "Av_Cpx1=np.mean(Cpx_B1038_Calc['P_Put_Teq32d_Peq32b'])\n",
    "ax6.plot(KrawMeas_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+1, np.abs(KrawMeas_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax6.plot(KrawMeas_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+2,  np.abs(KrawMeas_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax6.plot(KrawMeas_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+3,  np.abs(KrawMeas_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax6.plot(KrawMeas_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+4,  np.abs(KrawMeas_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax6.plot(KrawMeas_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+5,  np.abs(KrawMeas_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax6.plot(KrawMeas_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+7,  np.abs(KrawMeas_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax6.plot(KrawMeas_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+10,  np.abs(KrawMeas_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "ax6.plot(KrawMeas_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']*0+15,  np.abs(KrawMeas_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), '.r')\n",
    "\n",
    "ms1=7\n",
    "ax6.plot(1,  np.mean(np.abs(KrawMeas_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax6.plot(2,  np.mean(np.abs(KrawMeas_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax6.plot(3,  np.mean(np.abs(KrawMeas_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax6.plot(4,  np.mean(np.abs(KrawMeas_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax6.plot(5,  np.mean(np.abs(KrawMeas_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax6.plot(7,  np.mean(np.abs(KrawMeas_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax6.plot(10,  np.mean(np.abs(KrawMeas_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax6.plot(15,  np.mean(np.abs(KrawMeas_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "\n",
    "\n",
    "ax6.plot(1,  np.nanpercentile(np.abs(KrawMeas_1Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax6.plot(2,  np.nanpercentile(np.abs(KrawMeas_2Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax6.plot(3,  np.nanpercentile(np.abs(KrawMeas_3Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax6.plot(4,  np.nanpercentile(np.abs(KrawMeas_4Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax6.plot(5,  np.nanpercentile(np.abs(KrawMeas_5Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax6.plot(7,  np.nanpercentile(np.abs(KrawMeas_7Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax6.plot(10,  np.nanpercentile(np.abs(KrawMeas_10Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax6.plot(15,  np.nanpercentile(np.abs(KrawMeas_15Cpxs_CompAv['P_Put_Teq32d_Peq32b']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "\n",
    "ax6.set_ylabel('| Calc P$_{N}$ - Exp P$_{All \\ Sim}$ | kbar \\n Putirka (2008) eq32d-32b')\n",
    "ax6.set_xlabel('# of Cpx analyses averaged')\n",
    "\n",
    "Av_Cpx1=np.mean(Cpx_B1038_Calc['P_Wang21_eq1'])\n",
    "\n",
    "ax5.plot(KrawMeas_1Cpxs_CompAv['P_Wang21_eq1']*0+1, np.abs(KrawMeas_1Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax5.plot(KrawMeas_2Cpxs_CompAv['P_Wang21_eq1']*0+2,  np.abs(KrawMeas_2Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax5.plot(KrawMeas_3Cpxs_CompAv['P_Wang21_eq1']*0+3,  np.abs(KrawMeas_3Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax5.plot(KrawMeas_4Cpxs_CompAv['P_Wang21_eq1']*0+4,  np.abs(KrawMeas_4Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax5.plot(KrawMeas_5Cpxs_CompAv['P_Wang21_eq1']*0+5,  np.abs(KrawMeas_5Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax5.plot(KrawMeas_7Cpxs_CompAv['P_Wang21_eq1']*0+7,  np.abs(KrawMeas_7Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax5.plot(KrawMeas_10Cpxs_CompAv['P_Wang21_eq1']*0+10,  np.abs(KrawMeas_10Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "ax5.plot(KrawMeas_15Cpxs_CompAv['P_Wang21_eq1']*0+15,  np.abs(KrawMeas_15Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.r')\n",
    "# ax5.plot(KrawMeas_40Cpxs_CompAv['P_Wang21_eq1']*0+40, (KrawMeas_40Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.k')\n",
    "# ax5.plot(KrawMeas_100Cpxs_CompAv['P_Wang21_eq1']*0+100, (KrawMeas_100Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), '.k')\n",
    "\n",
    "ms1=7\n",
    "ax5.plot(1,  np.mean(np.abs(KrawMeas_1Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax5.plot(2,  np.mean(np.abs(KrawMeas_2Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax5.plot(3,  np.mean(np.abs(KrawMeas_3Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax5.plot(4,  np.mean(np.abs(KrawMeas_4Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax5.plot(5,  np.mean(np.abs(KrawMeas_5Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax5.plot(7,  np.mean(np.abs(KrawMeas_7Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax5.plot(10,  np.mean(np.abs(KrawMeas_10Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax5.plot(15,  np.mean(np.abs(KrawMeas_15Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "\n",
    "\n",
    "ax5.plot(1,  np.nanpercentile(np.abs(KrawMeas_1Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax5.plot(2,  np.nanpercentile(np.abs(KrawMeas_2Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax5.plot(3,  np.nanpercentile(np.abs(KrawMeas_3Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax5.plot(4,  np.nanpercentile(np.abs(KrawMeas_4Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax5.plot(5,  np.nanpercentile(np.abs(KrawMeas_5Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax5.plot(7,  np.nanpercentile(np.abs(KrawMeas_7Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax5.plot(10,  np.nanpercentile(np.abs(KrawMeas_10Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax5.plot(15,  np.nanpercentile(np.abs(KrawMeas_15Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "\n",
    "\n",
    "\n",
    "ax5.set_ylabel('| Calc P$_{N}$ - Exp P$_{All \\ Sim}$ | kbar \\n Wang et al. (2021)')\n",
    "ax5.set_xlabel('# of Cpx analyses averaged')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Mike Cpx-liq\n",
    "\n",
    "\n",
    "Av_Cpx1=B1038_Cpx_Liq_N17_eq33_Liq_Mean['P_kbar_calc'][0]\n",
    "\n",
    "ax3.plot(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N'], \n",
    "         np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc']-Av_Cpx1),\n",
    "         '.', color='grey')\n",
    "\n",
    "ax3.plot(1,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==1]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(2,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==2]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(3,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==3]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(4,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==4]\n",
    "                         -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(5,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==5]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(7,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==7]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(10,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==10]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(15,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==15]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(18,  np.mean(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==18]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax3.plot(1,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==1]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(2,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==2]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(3,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==3]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(4,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==4]\n",
    "                         -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(5,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==5]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(7,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==7]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(10,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==10]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(15,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==15]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax3.plot(18,  np.nanpercentile(np.abs(B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==18]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Av_Cpx1=B1038_Cpx_Liq_30_eq33_Liq_Mean['P_kbar_calc'][0]\n",
    "\n",
    "ax4.plot(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N'], \n",
    "         np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc']-Av_Cpx1),\n",
    "       '.', color='grey')\n",
    "\n",
    "ax4.plot(1,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==1]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(2,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==2]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(3,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==3]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(4,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==4]\n",
    "                         -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(5,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==5]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(7,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==7]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(10,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==10]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(15,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==15]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(18,  np.mean(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==18]\n",
    "                            -Av_Cpx1)), '*k', mfc='blue', ms=ms2)\n",
    "ax4.plot(1,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==1]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(2,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==2]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(3,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==3]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(4,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==4]\n",
    "                         -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(5,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==5]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(7,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==7]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(10,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==10]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(15,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==15]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "ax4.plot(18,  np.nanpercentile(np.abs(B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_30_eq33_Loop_Comp_Av['N']==18]\n",
    "                            -Av_Cpx1),  q=95, interpolation='linear'), '*k', mfc='yellow', ms=ms2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax3.set_ylabel('| Calc P$_{N}$ - Exp P$_{All \\ Meas}$ | kbar \\n Neave and Putirka (2017)- Putirka (2008) eq33')\n",
    "\n",
    "ax4.set_ylabel('| Calc P$_{N}$ - Exp P$_{All \\ Meas}$ | kbar \\n Putirka (2008) eq33-30')\n",
    "ax3.set_xlabel('# of Cpx analyses averaged')\n",
    "ax4.set_xlabel('# of Cpx analyses averaged')\n",
    "ax2.annotate(\"a) Cpx-Only, Analytical imprecision \\n Neave et al. (2019) Y0272-1\", xy=(0.25, 0.88), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax1.annotate(\"b) Cpx-Only, Analytical imprecision \\n Neave et al. (2019) Y0272-1\", xy=(0.25, 0.88), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax5.annotate(\"d) Cpx-only, Experimental imprecision \\n Krawczyinski et al. (2012) B1038\", xy=(0.18, 0.88), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax6.annotate(\"c) Cpx-only, Experimental imprecision \\n Krawczyinski et al. (2012) B1038\", xy=(0.18, 0.88), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax3.annotate(\"e) Cpx-Liq, Experimental imprecision \\n Krawczyinski et al. (2012) B1038\", xy=(0.20, 0.88), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax4.annotate(\"f) Cpx-Liq, Experimental imprecision \\n Krawczyinski et al. (2012) B1038\", xy=(0.20, 0.88), xycoords=\"axes fraction\", fontsize=13)\n",
    "\n",
    "ax2.set_ylim([0, 4])\n",
    "ax1.set_ylim([0, 4])\n",
    "ax3.set_ylim([0, 10])\n",
    "ax4.set_ylim([0, 10])\n",
    "ax5.set_ylim([0, 7])\n",
    "ax6.set_ylim([0, 7])\n",
    "ax1.set_xticks(np.linspace(0, 15, 16))\n",
    "ax2.set_xticks(np.linspace(0, 15, 16))\n",
    "ax3.set_xticks(np.linspace(0, 18, 19))\n",
    "ax4.set_xticks(np.linspace(0, 18, 19))\n",
    "ax5.set_xticks(np.linspace(0, 15, 16))\n",
    "ax6.set_xticks(np.linspace(0, 15, 16))\n",
    "fig.tight_layout()\n",
    "fig.savefig('simulating_nos_of_analyses.png', dpi=200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63b551ce-d2c1-4651-92b5-96308b47af2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7874571614631325"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanpercentile(np.abs(KrawMeas_15Cpxs_CompAv['P_Wang21_eq1']-Av_Cpx1), q=95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f44ff6-b586-4d7a-9695-e01a79ea90a7",
   "metadata": {},
   "source": [
    "## Big 6 part figure -  Change in pressure vs N cpxs\n",
    "- Using analytical error for both Cpx-only, experimental error for Mike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "667b7465-0c2f-42ba-b853-dcff329bf0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.599892901105582"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.abs((B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['P_kbar_calc'].loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==1]\n",
    "                            -Av_Cpx1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e866612d-ce50-4eed-805e-2486fce6071d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2, 2, figsize=(12,10), sharex=True)\n",
    "\n",
    "\n",
    "N1=B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av.loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==1]\n",
    "N2=B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av.loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==2]\n",
    "N4=B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av.loc[B1038_Cpx_Liq_N17_eq33_Loop_Comp_Av['N']==4]\n",
    "\n",
    "# plt.plot(N1['Jd'], N1['P_kbar_calc'], '.r')\n",
    "# plt.plot(N2['Jd'], N2['P_kbar_calc'], '.b')\n",
    "ax0.plot(N1['Jd'], N1['Na2O_Cpx'], '.r')\n",
    "x='Jd'\n",
    "N1JdAl=N1.loc[N1['Jd_from 0=Na, 1=Al']==1]\n",
    "N2JdAl=N2.loc[N2['Jd_from 0=Na, 1=Al']==1]\n",
    "N1JdNa=N1.loc[N1['Jd_from 0=Na, 1=Al']==0]\n",
    "N2JdNa=N2.loc[N2['Jd_from 0=Na, 1=Al']==0]\n",
    "N4JdAl=N4.loc[N4['Jd_from 0=Na, 1=Al']==0]\n",
    "N4JdAl=N4.loc[N4['Jd_from 0=Na, 1=Al']==1]\n",
    "\n",
    "ax1.plot(N1[x], N1['P_kbar_calc'], '.r')\n",
    "ax2.plot(N2[x], N2['P_kbar_calc'], '.r')\n",
    "ax3.plot(N4[x], N4['P_kbar_calc'], '.r')\n",
    "\n",
    "ax2.yaxis.set_tick_params(which='both', labelbottom=True)\n",
    "ax3.yaxis.set_tick_params(which='both', labelbottom=True)\n",
    "ax0.xaxis.set_tick_params(which='both', labelbottom=True)\n",
    "ax1.xaxis.set_tick_params(which='both', labelbottom=True)\n",
    "\n",
    "ax1.set_ylabel('Calculated P (Neave and Putirka, 2017, \\n Putirka 2008 eq 33')\n",
    "ax2.set_ylabel('Calculated P (Neave and Putirka, 2017, \\n Putirka 2008 eq 33')\n",
    "ax3.set_ylabel('Calculated P (Neave and Putirka, 2017, \\n Putirka 2008 eq 33')\n",
    "ax0.set_ylabel('Na$_2$O Cpx (wt%)')\n",
    "\n",
    "ax1.set_xlabel('Jd')\n",
    "ax2.set_xlabel('Jd')\n",
    "ax3.set_xlabel('Jd')\n",
    "ax0.set_xlabel('Jd')\n",
    "ax0.annotate(\"a) Cpx compositions\", xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax1.annotate(\"b) Cpx-Liq pairs, no Cpx-averaging \", xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax2.annotate(\"c) Cpx-Liq pairs, Averaging 2 Cpx\", xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=13)\n",
    "ax3.annotate(\"d) Cpx-Liq pairs, Averaging 4 Cpx\", xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=13)\n",
    "\n",
    "ax1.set_ylim([-20, 4])\n",
    "ax2.set_ylim([-20, 4])\n",
    "ax3.set_ylim([-20, 4])\n",
    "fig.savefig('Suplement_Jd_averaging.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b677b19-9064-45df-9342-d9ea65dde0bf",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Jd_from 0=Na, 1=Al'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3629\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3628\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3629\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3630\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Jd_from 0=Na, 1=Al'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [24]\u001b[0m, in \u001b[0;36m<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# plt.plot(N1['Jd'], N1['P_kbar_calc'], '.r')\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# plt.plot(N2['Jd'], N2['P_kbar_calc'], '.b')\u001b[39;00m\n\u001b[0;32m      8\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJd\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 9\u001b[0m N1JdAl\u001b[38;5;241m=\u001b[39mN1\u001b[38;5;241m.\u001b[39mloc[\u001b[43mN1\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mJd_from 0=Na, 1=Al\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     10\u001b[0m N2JdAl\u001b[38;5;241m=\u001b[39mN2\u001b[38;5;241m.\u001b[39mloc[N2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJd_from 0=Na, 1=Al\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     11\u001b[0m N1JdNa\u001b[38;5;241m=\u001b[39mN1\u001b[38;5;241m.\u001b[39mloc[N1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJd_from 0=Na, 1=Al\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py:958\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    955\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[0;32m    957\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m--> 958\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key):\n\u001b[0;32m    961\u001b[0m     \u001b[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[0;32m    962\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    963\u001b[0m         \u001b[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py:1069\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1066\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[0;32m   1068\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1069\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1070\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_get_values_for_loc(\u001b[38;5;28mself\u001b[39m, loc, label)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3631\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3629\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3630\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3631\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3632\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3633\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3634\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3635\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3636\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Jd_from 0=Na, 1=Al'"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "N1=Neave_1Cpxs_CompAv['Jd']\n",
    "N2=Neave_2Cpxs_CompAv['Jd']\n",
    "N4=Neave_4Cpxs_CompAv['Jd']\n",
    "\n",
    "# plt.plot(N1['Jd'], N1['P_kbar_calc'], '.r')\n",
    "# plt.plot(N2['Jd'], N2['P_kbar_calc'], '.b')\n",
    "\n",
    "x='Jd'\n",
    "N1JdAl=N1.loc[N1['Jd_from 0=Na, 1=Al']==1]\n",
    "N2JdAl=N2.loc[N2['Jd_from 0=Na, 1=Al']==1]\n",
    "N1JdNa=N1.loc[N1['Jd_from 0=Na, 1=Al']==0]\n",
    "N2JdNa=N2.loc[N2['Jd_from 0=Na, 1=Al']==0]\n",
    "N4JdAl=N4.loc[N4['Jd_from 0=Na, 1=Al']==0]\n",
    "N4JdAl=N4.loc[N4['Jd_from 0=Na, 1=Al']==1]\n",
    "\n",
    "ax1.plot(N1[x], N1['P_kbar_calc'], '.r')\n",
    "ax2.plot(N2[x], N2['P_kbar_calc'], '.r')\n",
    "ax3.plot(N4[x], N4['P_kbar_calc'], '.r')\n",
    "\n",
    "ax2.yaxis.set_tick_params(which='both', labelbottom=True)\n",
    "ax1.set_ylabel('Calculated P (Neave and Putirka, 2017, Putirka 2008 eq 33')\n",
    "ax1.set_xlabel('Jd')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f44d641-3619-422c-a6d2-d94f19795b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(N2JdAl[x], N2JdAl['T_K_calc'], '.b')\n",
    "plt.plot(N1JdAl[x], N1JdAl['T_K_calc'], '.r')\n",
    "plt.plot(N2Jd[x], N2JdNa['T_K_calc'], '.k')\n",
    "#plt.plot(N1JdAl[x], N1JdNa['T_K_calc'], '.r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f89095a7-49bf-454d-9507-79f0f8eceed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "N2JdAl['Al2O3_Cpx']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e20e0ae8-e38d-48ed-84f6-ba3d0cfc88d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "ax1.plot(N2['Al_VI_cat_6ox'], N2['Jd'], '.b')\n",
    "ax1.plot(B1038_Cpx_Liq_N17_eq33_Liq['Al_VI_cat_6ox'], B1038_Cpx_Liq_N17_eq33_Liq['Jd'], 'ok', mfc='red', ms=5)\n",
    "\n",
    "ax2.plot(N2['Jd'], N2['P_kbar_calc'], '.b')\n",
    "ax2.plot(B1038_Cpx_Liq_N17_eq33_Liq['Jd'], B1038_Cpx_Liq_N17_eq33_Liq['P_kbar_calc'], 'ok', mfc='red', ms=5)\n",
    "\n",
    "#plt.plot(N1JdAl['Jd'], N1JdAl['P_kbar_calc'], '.r')\n",
    "#plt.plot(Cpx_B1038_Calc['Jd'], Cpx_B1038_Calc['Al2O3_Cpx'], 'or')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8974f82-524d-43bb-84e5-7fa4c7fe08eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cpx_B1038_Calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d0c860e-c81d-4207-9841-2d6354e1be88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5f4f6ff-aeb2-40bf-b0e1-c408adf24275",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a592e78b-1ffb-4801-8e9a-b941dc7e4747",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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