{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1b. Calculate Pvsat for analyses from a csv file using default options\n", "\n", "This time, instead of creating a dataframe in a cell for the analysis, we'll read it in from a spreadsheet. " ] }, { "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", "### Calculate only for the first row\n", "\n", "There are a few analyses in the csv. First, we will tell the function to stop after the first analysis by saying last_row=1:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sampleT_CP_barSiO2_wtpcTiO2_wtpcAl2O3_wtpcFeOT_wtpcMnO_wtpcMgO_wtpcCaO_wtpc...KHOSg optKOSg optKOSg2 optKCOg optKCOHg optKOCSg optKCOs optcarbonylsulfide optdensity optDate
0TN273-01D-01-011200327.8971357.039561.66173515.5362239.4798990.2402512.9630946.496784...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:16:47.730099
\n", "

1 rows × 173 columns

\n", "
" ], "text/plain": [ "0 sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc \\\n", "0 TN273-01D-01-01 1200 327.89713 57.03956 1.661735 15.536223 9.479899 \n", "\n", "0 MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt \\\n", "0 0.240251 2.963094 6.496784 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "\n", "0 KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt \\\n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "\n", "0 Date \n", "0 2025-02-18 13:16:47.730099 \n", "\n", "[1 rows x 173 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# runs the calculation\n", "vf.calc_Pvsat(my_analyses,last_row=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate for all rows\n", "\n", "To run all the analyses, simply don't tell it the last row you want to run. \n", "\n", "All analyses must be in consequitive rows for this to work!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# runs the calculation\n", "results = vf.calc_Pvsat(my_analyses)" ] }, { "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": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4500.0, 0.0)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "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.plot(data1['CO2T-eq_ppmw'],\n", " data1['P_bar'], 'ok', mfc='blue')\n", "ax2.plot(data1['H2OT-eq_wtpc'],\n", " data1['P_bar'], 'ok', mfc='blue')\n", "ax3.plot(data1['ST_ppmw'],\n", " data1['P_bar'], 'ok', mfc='blue')\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])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate for particular rows\n", "\n", "If we only want to run row 4 (Agr07-4) through 18 (AGR19-02-16) in that spreadsheet, we simply specify the first and last rows we want to run (remembering that row 2 in a spreadsheet - i.e., the first analysis under the headings - is actually row 0 in the dataframe). \n", "\n", "Here it will show the dataframe because we didn't create a new object for the results to go into." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sampleT_CP_barSiO2_wtpcTiO2_wtpcAl2O3_wtpcFeOT_wtpcMnO_wtpcMgO_wtpcCaO_wtpc...KHOSg optKOSg optKOSg2 optKCOg optKCOHg optKOCSg optKCOs optcarbonylsulfide optdensity optDate
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0Agr07-912002243.89799348.255510.76087517.3099129.1505260.1802074.82555112.093912...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:30.113102
0Agr07-15A12002011.08231447.7695320.81084116.74736510.6810750.2202285.66587511.34176...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:31.371052
0Agr07-15B12001512.82918548.0209610.86091416.84787910.431070.1902025.55589611.342036...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:32.264473
0Agr04-1312001795.63538751.0097530.80109516.7428958.8420910.2002744.86665510.173912...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:33.057059
0Agr04-141200837.82129448.7483730.78077518.6184759.3192470.1701695.35531412.071979...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:33.740649
0AGR19-02-412002187.34927247.9419490.65547617.6671369.7297270.2662874.70099413.099285...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:34.500829
0AGR19-02-712001051.38438947.7301390.85286818.5701829.4729250.1726044.40648412.853936...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:35.213903
0AGR19-02-1012002658.55639647.0191980.7458518.1966959.1851930.1532574.68965912.914443...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:36.148222
0AGR19-02-1112001922.60288447.9171690.77797618.0777019.3152370.2149674.72927412.713763...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:36.944427
0AGR19-02-12B12001739.19132347.4352320.74580518.23646110.4004020.234984.13768412.883013...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:37.562294
0AGR19-02-1312001287.55000548.9210790.81688317.18517510.5173680.2348544.34990211.507839...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:38.325082
0AGR19-02-1412002082.78057447.475020.69440817.90142210.0382760.1838145.35102411.907049...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:39.187433
0AGR19-02-1512003106.64006646.7349760.61893917.8883559.5884830.2130774.53550513.697833...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:40.374486
0AGR19-02-1612002643.62035946.2722650.6643417.3231610.7401580.2214475.39524312.149363...Ohmoto97Ohmoto97ONeill22Ohmoto97Ohmoto97Moussallam19Holloway92COSDensityX2025-02-18 13:17:41.435848
\n", "

15 rows × 173 columns

\n", "
" ], "text/plain": [ "0 sample T_C P_bar SiO2_wtpc TiO2_wtpc Al2O3_wtpc FeOT_wtpc \\\n", "0 Agr07-4 1200 3021.75825 45.451495 0.650021 17.270568 10.190335 \n", "0 Agr07-9 1200 2243.897993 48.25551 0.760875 17.309912 9.150526 \n", "0 Agr07-15A 1200 2011.082314 47.769532 0.810841 16.747365 10.681075 \n", "0 Agr07-15B 1200 1512.829185 48.020961 0.860914 16.847879 10.43107 \n", "0 Agr04-13 1200 1795.635387 51.009753 0.801095 16.742895 8.842091 \n", "0 Agr04-14 1200 837.821294 48.748373 0.780775 18.618475 9.319247 \n", "0 AGR19-02-4 1200 2187.349272 47.941949 0.655476 17.667136 9.729727 \n", "0 AGR19-02-7 1200 1051.384389 47.730139 0.852868 18.570182 9.472925 \n", "0 AGR19-02-10 1200 2658.556396 47.019198 0.74585 18.196695 9.185193 \n", "0 AGR19-02-11 1200 1922.602884 47.917169 0.777976 18.077701 9.315237 \n", "0 AGR19-02-12B 1200 1739.191323 47.435232 0.745805 18.236461 10.400402 \n", "0 AGR19-02-13 1200 1287.550005 48.921079 0.816883 17.185175 10.517368 \n", "0 AGR19-02-14 1200 2082.780574 47.47502 0.694408 17.901422 10.038276 \n", "0 AGR19-02-15 1200 3106.640066 46.734976 0.618939 17.888355 9.588483 \n", "0 AGR19-02-16 1200 2643.620359 46.272265 0.66434 17.32316 10.740158 \n", "\n", "0 MnO_wtpc MgO_wtpc CaO_wtpc ... KHOSg opt KOSg opt KOSg2 opt KCOg opt \\\n", "0 0.230008 5.540182 12.320405 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.180207 4.825551 12.093912 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.220228 5.665875 11.34176 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.190202 5.555896 11.342036 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.200274 4.866655 10.173912 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.170169 5.355314 12.071979 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.266287 4.700994 13.099285 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.172604 4.406484 12.853936 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.153257 4.689659 12.914443 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.214967 4.729274 12.713763 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.23498 4.137684 12.883013 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.234854 4.349902 11.507839 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.183814 5.351024 11.907049 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.213077 4.535505 13.697833 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "0 0.221447 5.395243 12.149363 ... Ohmoto97 Ohmoto97 ONeill22 Ohmoto97 \n", "\n", "0 KCOHg opt KOCSg opt KCOs opt carbonylsulfide opt density opt \\\n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "0 Ohmoto97 Moussallam19 Holloway92 COS DensityX \n", "\n", "0 Date \n", "0 2025-02-18 13:17:29.203778 \n", "0 2025-02-18 13:17:30.113102 \n", "0 2025-02-18 13:17:31.371052 \n", "0 2025-02-18 13:17:32.264473 \n", "0 2025-02-18 13:17:33.057059 \n", "0 2025-02-18 13:17:33.740649 \n", "0 2025-02-18 13:17:34.500829 \n", "0 2025-02-18 13:17:35.213903 \n", "0 2025-02-18 13:17:36.148222 \n", "0 2025-02-18 13:17:36.944427 \n", "0 2025-02-18 13:17:37.562294 \n", "0 2025-02-18 13:17:38.325082 \n", "0 2025-02-18 13:17:39.187433 \n", "0 2025-02-18 13:17:40.374486 \n", "0 2025-02-18 13:17:41.435848 \n", "\n", "[15 rows x 173 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vf.calc_Pvsat(my_analyses,first_row=2, last_row=17)" ] } ], "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 }