{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparison of isobar calculation with VESIcal - Dixon\n",
"\n",
"We compare isobars created using VESIcal and the Dixon model (i.e., simplification of the Dixon, 1997, model as implemented in VolatileCal, Newman & Lowenstern, 2002) with the closest match of model-dependent variables possible in VolFe."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python set-up\n",
"First we need to import a few Python packages (including VolFe). You need to install VolFe (and the other packages such as VESIcal) once on your machine. If you haven't yet, uncomment the line below (remove the # for the lines beginning !pip)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install VolFe and Thermobar on your machine if you have not done so already. Remove the # from lines below to do this (don't remove the # from this line!).\n",
"# !pip install VolFe\n",
"# !pip install VESIcal"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\ehughes\\AppData\\Local\\miniforge3\\envs\\volfe-dev\\Lib\\site-packages\\VESIcal\\calculate_classes.py:7: UserWarning: \n",
"\n",
" WARNING: Thermoengine is not installed. MagmaSat model will not function. A model name must be given when performing any calculation as model='some-model-name'.\n",
" from VESIcal.models import magmasat\n"
]
}
],
"source": [
"# import python packages\n",
"import pandas as pd\n",
"import numpy as np\n",
"import VolFe as vf\n",
"import VESIcal as vc\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check versions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.4.1'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# VolFe\n",
"vf.__version__"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.2.10'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# VESIcal\n",
"vc.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define inputs\n",
"\n",
"In this example it is read from a dataframe, which is from Brounce et al. (2014) with the updated Fe3+/FeT from Cottrell et al. (2021).\n",
"\n",
"Note the volatile content of the melt is not used in this calculation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VolFe inputs"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# Define the melt composition, fO2 estimate, and T as a dictionary.\n",
"my_analysis = {'Sample': 'Sari15-04-33',\n",
" 'T_C': 1200., # Temperature in 'C\n",
" 'SiO2': 43.97, # wt%\n",
" 'TiO2': 0.70, # wt%\n",
" 'Al2O3': 19.09, # wt%\n",
" 'FeOT': 9.36, # wt%\n",
" 'MnO': 0.22, # wt%\n",
" 'MgO': 6.76, # wt%\n",
" 'CaO': 13.28, # wt%\n",
" 'Na2O': 1.46, # wt%\n",
" 'K2O': 0.37, # wt%\n",
" 'P2O5': 0.11, # wt%\n",
" 'Fe3FeT': 0.177}\n",
"\n",
"# Turn the dictionary into a pandas dataframe, setting the index to 0.\n",
"vf_input = pd.DataFrame(my_analysis, index=[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VESIcal input\n",
"\n",
"As a default, VESIcal does not normalise the melt composition. The Dixon (1997) CO2 solubility function equires SiO2 (wt%): VolFe does normalise SiO2 to the volatile-free melt composition. This makes a big difference - we'll calculate it for both in VESIcal-VolatileCalc."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ehughes\\AppData\\Local\\Temp\\ipykernel_46120\\1771545535.py:15: RuntimeWarning: FeOT oxide found. Using FeOT for FeO value.\n",
" vc_input_nn = vc.Sample(my_analysis)\n"
]
}
],
"source": [
"# Define the melt composition, fO2 estimate, and T as a dictionary.\n",
"my_analysis = {'Temp': 1200., # Temperature in 'C\n",
" 'SiO2': 43.97, # wt%\n",
" 'TiO2': 0.70, # wt%\n",
" 'Al2O3': 19.09, # wt%\n",
" 'FeOT': 9.36, # wt%\n",
" 'MnO': 0.22, # wt%\n",
" 'MgO': 6.76, # wt%\n",
" 'CaO': 13.28, # wt%\n",
" 'Na2O': 1.46, # wt%\n",
" 'K2O': 0.37, # wt%\n",
" 'P2O5': 0.11, # wt%\n",
" 'Fe3FeT': 0.177}\n",
"\n",
"vc_input_nn = vc.Sample(my_analysis)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ehughes\\AppData\\Local\\Temp\\ipykernel_46120\\1988291726.py:5: RuntimeWarning: FeOT oxide found. Using FeOT for FeO value.\n",
" vc_input_n = vc.Sample(my_analysis)\n"
]
}
],
"source": [
"sum = my_analysis['SiO2']+my_analysis['TiO2']+my_analysis['Al2O3']+my_analysis['FeOT']+my_analysis['MnO']+my_analysis['MgO']+my_analysis['CaO']+my_analysis['Na2O']+my_analysis['K2O']+my_analysis['P2O5']\n",
"for i in ['SiO2','TiO2','Al2O3','FeOT','MnO','MgO','CaO','Na2O','K2O','P2O5']:\n",
" my_analysis[i] = 100.*my_analysis[i]/sum\n",
"\n",
"vc_input_n = vc.Sample(my_analysis)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate isobars\n",
"\n",
"### VolFe\n",
"\n",
"Model options are chosen to be as close to the Dixon model as implemented in VESIcal.\n",
"- Only CO2 and H2O species are present in the melt and vapor, as in VESIcal\n",
"- The CO2 solubility function is 'NorthArchBasalt_Dixon97' (eq. 8 from Dixon, 1997), which is the same model as the Dixon model of VESIcal\n",
"- The fugacity models of CO2 and H2O are the Flower's (1979) correction of Holloway (1977), which is the same model as in VESIcal\n",
"\n",
"The key difference is that VolFe only cosiders total H2O (H2OT), whereas the Dixon model in VESIcal considers a regular solution of OH- and H2Omol. Therefore, the water models are quite different."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# choose the options I want - everything else will use the default options\n",
"my_models = [[\"carbon dioxide\", \"NorthArchBasalt_Dixon97\"], [\"COH_species\", 'H2O-CO2 only'],['y_CO2','Flowers79'],['y_H2O','Flowers79']]\n",
"\n",
"# turn to dataframe with correct column headers and indexes\n",
"my_models = vf.make_df_and_add_model_defaults(my_models)\n",
"\n",
"# calculate isobars for Ala01-16A which is run 29 in the csv starting at 1000 bars, ending at 4000 bars at 1000 bar intervals\n",
"vf_isobars = vf.calc_isobar(vf_input,models=my_models,initial_P=500.,final_P=2000.,step_P=500.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VESIcal - VolatileCalc"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\ehughes\\AppData\\Local\\miniforge3\\envs\\volfe-dev\\Lib\\site-packages\\VESIcal\\calculate_classes.py:60: RuntimeWarning: pressure exceeds 1000 bar, which Iacono-Marziano et al. (2012) suggest as an upper calibration limit of the Dixon (1997, Pi-SiO2 simpl.) Model, pressure exceeds 1000 bar, which Iacono-Marziano et al. (2012) suggest as an upper calibration limit of the Dixon (1997, Pi-SiO2 simpl.) Model, \n",
" w.warn(self.calib_check, RuntimeWarning)\n"
]
}
],
"source": [
"# no normalisation\n",
"\n",
"vc2_isobars_nn, vc2_isopleths_nn = vc.calculate_isobars_and_isopleths(sample=vc_input_nn,model='Dixon',temperature=my_analysis[\"Temp\"],\n",
" pressure_list=[500.,1000.,1500.,2000.],\n",
" isoplet_list=[0.25,0.50,0.75],\n",
" print_status=True).result\n",
"\n",
"vc2_isobars_nn['CO2_ppm'] = vc2_isobars_nn['CO2_liq']*10000.\n",
"vc2_isobars_nn = vc2_isobars_nn.rename(columns={\"Pressure\":'P_bar','H2O_liq':'H2O_wtpc'})"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\ehughes\\AppData\\Local\\miniforge3\\envs\\volfe-dev\\Lib\\site-packages\\VESIcal\\calculate_classes.py:60: RuntimeWarning: pressure exceeds 1000 bar, which Iacono-Marziano et al. (2012) suggest as an upper calibration limit of the Dixon (1997, Pi-SiO2 simpl.) Model, pressure exceeds 1000 bar, which Iacono-Marziano et al. (2012) suggest as an upper calibration limit of the Dixon (1997, Pi-SiO2 simpl.) Model, \n",
" w.warn(self.calib_check, RuntimeWarning)\n"
]
}
],
"source": [
"# normalised\n",
"\n",
"vc2_isobars_n, vc2_isopleths_n = vc.calculate_isobars_and_isopleths(sample=vc_input_n,model='Dixon',temperature=my_analysis[\"Temp\"],\n",
" pressure_list=[500.,1000.,1500.,2000.],\n",
" isoplet_list=[0.25,0.50,0.75],\n",
" print_status=True).result\n",
"\n",
"vc2_isobars_n['CO2_ppm'] = vc2_isobars_n['CO2_liq']*10000.\n",
"vc2_isobars_n = vc2_isobars_n.rename(columns={\"Pressure\":'P_bar','H2O_liq':'H2O_wtpc'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting\n",
"\n",
"Finally, we can plot all everything!\n",
"\n",
"The VolFe (blue solid) results agree well for CO2 solubility when Dixon in VESIcal uses the normalised composition (black dashed) but are very different when both use the inputted composition as is (i.e., no normalisation - black dashed), highlighting the importance of understanding whether normalisation has been applied or not. The water solubility models are different and diverge more and more with higher pressure. The shapes of the curves are similar given these differences, suggesting the calculations are consistent."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'CO2 (ppmw)')"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1) = plt.subplots(1, 1, figsize=(5,5))\n",
"\n",
"# Plotting results\n",
"\n",
"for tool in ['vf_isobars','vc2_isobars_n','vc2_isobars_nn']:\n",
" if tool == 'vf_isobars':\n",
" line = '-b'\n",
" elif tool == 'vc2_isobars_n':\n",
" line = '-k'\n",
" elif tool == 'vc2_isobars_nn':\n",
" line = '--k'\n",
" data = eval(tool)\n",
" for P in [500,1000,1500,2000]:\n",
" ax1.plot(data[data[\"P_bar\"]==P][\"H2O_wtpc\"], data[data[\"P_bar\"]==P][\"CO2_ppm\"], line)\n",
"\n",
"ax1.set_xlabel('H2O (wt%)')\n",
"ax1.set_ylabel('CO2 (ppmw)')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "volfe-dev",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}