{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2f. Calculate closed- and open-system degassing paths including isotopic fractionation\n", "\n", "These examples include isotopic fractionation of C, H, and S during closed- and open-system degassing." ] }, { "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": 22, "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\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.0'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# VolFe version\n", "vf.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the inputs\n", "\n", "The following composition is analysis Sari15-04-33 from Brounce et al. (2014) with the updated Fe3+/FeT from Cottrell et al. (2021), with a temperature chosen as 1200 °C." ] }, { "cell_type": "code", "execution_count": 3, "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': 47.89, # wt%\n", " 'TiO2': 0.75, # wt%\n", " 'Al2O3': 16.74, # wt%\n", " 'FeOT': 9.43, # wt%\n", " 'MnO': 0.18, # wt%\n", " 'MgO': 5.92, # wt%\n", " 'CaO': 11.58, # wt%\n", " 'Na2O': 2.14, # wt%\n", " 'K2O': 0.63, # wt%\n", " 'P2O5': 0.17, # wt%\n", " 'H2O': 4.17, # wt%\n", " 'CO2ppm': 1487., # ppm\n", " 'STppm': 1343.5, # ppm\n", " 'Xppm': 0., # ppm\n", " 'Fe3FeT': 0.177}\n", "\n", "# Turn the dictionary into a pandas dataframe, setting the index to 0.\n", "my_analysis = pd.DataFrame(my_analysis, index=[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the concentration and speciation calculation\n", "\n", "### Closed-system" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 98%|█████████▊| 3799.0/3863 [00:24<00:00, 153.72it/s]\n" ] } ], "source": [ "degas_closed = vf.calc_gassing(my_analysis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Open-system" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# choose the options I want - everything else will use the default options\n", "my_models = [['gassing_style','open']]\n", "\n", "# turn to dataframe with correct column headers and indexes \n", "my_models = vf.make_df_and_add_model_defaults(my_models)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 62%|██████▏ | 2406.0/3863 [22:26<13:35, 1.79it/s] \n" ] } ], "source": [ "degas_open = vf.calc_gassing(my_analysis, models=my_models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the isotope calculation\n", "\n", "We use the outputs of the concentration and speciation calculation as inputs to the isotope calculation, specifying the initial isotopic ratio of H, C, and S in delta notation. We will use the default isotopic fractionation factors.\n", "\n", "### Define initial isotopic composition" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# initial isotope composition\n", "R_i = {\"d34S\":0,'d13C':0.,'dD':0.}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Closed-system" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# makes dataframe output from degassing calcualtion into input for isotope calculation\n", "comp1 = degas_closed.reset_index(drop=True) # resets the index\n", "comp1 = comp1[1:] # gets rid of the result at pvsat - isotopes need both melt and vapor to be present at the moment so it doens't work at pvsat currently\n", "comp2 = comp1.reset_index(drop=True) # resets the index again\n", "\n", "# runs the isotope calculation\n", "iso_closed = vf.calc_isotopes_gassing(comp2,R_i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Open-system" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# makes dataframe output from degassing calcualtion into input for isotope calculation\n", "comp1 = degas_open.reset_index(drop=True) # resets the index\n", "comp1 = comp1[1:] # gets rid of the result at pvsat - isotopes need both melt and vapor to be present at the moment so it doens't work at pvsat currently\n", "comp2 = comp1.reset_index(drop=True) # resets the index again\n", "\n", "# runs the isotope calculation\n", "iso_open = vf.calc_isotopes_gassing(comp2,R_i,models=my_models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting\n", "\n", "Here we show the bulk melt (red) and gas (blue) isotopic compositions during closed- (solid) and open- (dashed) system degassing" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4000.0, 0.0)" ] }, "execution_count": 68, "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", "data1 = iso_closed # closed-system degassing\n", "data2 = iso_open # open-system degassing\n", "\n", "# Plotting results\n", "ax1.plot(data1['d13C_m_tot'], data1['P_bar'], '-r')\n", "ax1.plot(data2.loc[0:1130,'d13C_m_tot'], data2.loc[0:1130,'P_bar'], ':r')\n", "ax1.plot(data1['d13C_g_tot'], data1['P_bar'], '-b')\n", "ax1.plot(data2.loc[0:1130,'d13C_g_tot'], data2.loc[0:1130,'P_bar'], ':b')\n", "ax2.plot(data1['dD_m_tot'], data1['P_bar'], '-r')\n", "ax2.plot(data2['dD_m_tot'], data2['P_bar'], ':r')\n", "ax2.plot(data1['dD_g_tot'], data1['P_bar'], '-b')\n", "ax2.plot(data2['dD_g_tot'], data2['P_bar'], ':b')\n", "ax3.plot(data1['d34S_m_tot'], data1['P_bar'], '-r')\n", "ax3.plot(data2.loc[0:2378,'d34S_m_tot'], data2.loc[0:2378,'P_bar'], ':r')\n", "ax3.plot(data1['d34S_g_tot'], data1['P_bar'], '-b')\n", "ax3.plot(data2.loc[0:2378,'d34S_g_tot'], data2.loc[0:2378,'P_bar'], ':b')\n", "\n", "ax1.set_ylabel('P (bar)')\n", "ax1.set_xlabel('d13C (permil)')\n", "ax2.set_xlabel('dD (permil)')\n", "ax3.set_xlabel('d34S (permil)')\n", "ax1.set_ylim([4000,0])\n", "ax2.set_ylim([4000,0])\n", "ax3.set_ylim([4000,0])" ] } ], "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 }