{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1e. Calculate Pvsat 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 [52:24<00:00, 61.66s/it]\n"
]
}
],
"source": [
"# runs the calculation\n",
"results = vf.calc_comp_error_function(my_analyses,iterations=100,function='calc_Pvsat')"
]
},
{
"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": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12,4))\n",
"\n",
"# Plotting data\n",
"\n",
"data1 = results\n",
"\n",
"ax1.errorbar(data1['CO2T-eq_ppmw'], data1['P_bar'], xerr=data1['CO2T-eq_ppmw_sd'], yerr=data1['P_bar_sd'], fmt='o')\n",
"ax2.errorbar(data1['H2OT-eq_wtpc'], data1['P_bar'], xerr=data1['H2OT-eq_wtpc_sd'], yerr=data1['P_bar_sd'], fmt='o')\n",
"ax3.errorbar(data1['ST_ppmw'], data1['P_bar'], xerr=data1['ST_ppmw_sd'], yerr=data1['P_bar_sd'], fmt='o')\n",
"\n",
"ax1.set_xlabel('CO2,T-eq (ppmw)')\n",
"ax2.set_xlabel('H2O,T-eq (wt%)')\n",
"ax3.set_xlabel('ST-eq (ppmw)')\n",
"ax1.set_ylabel('P (bar)')\n",
"ax1.set_ylim([4500, 0])\n",
"ax2.set_ylim([4500, 0])\n",
"ax3.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
}