{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3d. Calculate fO2 including uncertainties\n",
"\n",
"This time, we'll include measurement uncertainties in the calculations. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python set-up\n",
"You need to install VolFe once on your machine, if you haven't yet. Then we need to import a few Python packages (including VolFe). "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Install VolFe on your machine. Don't remove the # from this line!\n",
"# pip install VolFe # Remove the first # in this line if you have not installed VolFe on your machine before.\n",
"\n",
"# import python packages\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import VolFe as vf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import data\n",
"\n",
"We'll use the examples_marianas_wT csv in files and use all the default options again. \n",
"\n",
"The data in this file are from Brounce et al. (2014) and Kelley & Cottrell (2012) with updated values for Fe3+/FeT from Cottrell et al. (2021) where available."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Read csv to define melt composition\n",
"my_analyses = pd.read_csv(\"../files/example_marianas_wT.csv\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the calculation\n",
"\n",
"Here we'll use a Monte Carlo approach using the uncertainties on the inputs to calculate the undertainties on the calculation outputs. To include the uncertainties there needs to be a column with one standard deviation included (e.g., SiO2_sd). The uncertainties are either given as relative (R, fraction) or absolute (A - i.e., same units as the parameter), indicated with a sd_type column (e.g., SiO2_sd_type) - if no type is given, its assumed to be absolute. If no column is present, it is assumed the uncertainty is 0. The number of iterations is how many random compositions within error are used for each calculation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 51/51 [3:32:03<00:00, 249.48s/it] \n"
]
}
],
"source": [
"# runs the calculation\n",
"results = vf.calc_comp_error_function(my_analyses,iterations=100,function='calc_melt_S_oxybarometer')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we can plot these against the volatile content.\n",
"This shows that the pressure is mostly controlled by CO2 and H2O content, which are correlated."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4500.0, 0.0)"
]
},
"execution_count": 4,
"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=(4,4))\n",
"\n",
"# Plotting data\n",
"\n",
"data = results[results['fO2_DFMQ_sulf'] != \"\"] # all calculations\n",
"\n",
"ax1.errorbar(data['fO2_DFMQ_sulf'], data['P_bar_sulf'], xerr=data['fO2_DFMQ_sulf_sd'], yerr=data['P_bar_sulf_sd'], fmt='o')\n",
"\n",
"ax1.set_xlabel('DFMQ')\n",
"ax1.set_ylabel('P (bar)')\n",
"ax1.set_ylim([4500, 0])"
]
}
],
"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": 2
}