{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# This code is a simple introduction to Cpx-Liquid melt matching\n", "- for a more advanced example, see Cpx_MeltMatch2_ScruggsPutirka\n", "- This notebook recreates the method of Gleeson et al. (2020) - JPET - https://doi.org/10.1093/petrology/egaa094" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## This imports all the python things you need" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import sys\n", "sys.path.append(\"..\") # This allows you to put the python file in the folder above. So you can have lots of sub folders\n", "import matplotlib.pyplot as plt\n", "import Thermobar as pt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## This sets plotting parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# This sets some plotting things\n", "plt.rcParams[\"font.family\"] = 'arial'\n", "plt.rcParams[\"font.size\"] =12\n", "plt.rcParams[\"mathtext.default\"] = \"regular\"\n", "plt.rcParams[\"mathtext.fontset\"] = \"dejavusans\"\n", "plt.rcParams['patch.linewidth'] = 1\n", "plt.rcParams['axes.linewidth'] = 1 \n", "plt.rcParams[\"xtick.direction\"] = \"in\"\n", "plt.rcParams[\"ytick.direction\"] = \"in\"\n", "plt.rcParams[\"ytick.direction\"] = \"in\"\n", "plt.rcParams[\"xtick.major.size\"] = 6 # Sets length of ticks\n", "plt.rcParams[\"ytick.major.size\"] = 4 # Sets length of ticks\n", "plt.rcParams[\"ytick.labelsize\"] = 12 # Sets size of numbers on tick marks\n", "plt.rcParams[\"xtick.labelsize\"] = 12 # Sets size of numbers on tick marks\n", "plt.rcParams[\"axes.titlesize\"] = 14 # Overall title\n", "plt.rcParams[\"axes.labelsize\"] = 14 # Axes labels\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading in Data\n", "- In this case, Gleson et al had cpxs in the sheet \"cpxs\" and melts in the \"melts\"\n", "- You column headings need to be \"Sample_ID\", \"SiO2_Liq\", \"MgO_Cpx\" etc. e.g., oxide, then underscore, capital letter for phase" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Loading Liquids\n", "out=pt.import_excel('mn_cp_all_endings.xlsx', sheet_name=\"WR\")\n", "my_input_Liqs=out['my_input']\n", "myLiquids1=out['Liqs']\n", "\n", "out2=pt.import_excel('mn_cp_all_endings.xlsx', sheet_name=\"tenerife_cp_all\")\n", "my_input_Cpxs=out2['my_input']\n", "myCPXs1=out2['Cpxs']\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SiO2_CpxTiO2_CpxAl2O3_CpxFeOt_CpxMnO_CpxMgO_CpxCaO_CpxNa2O_CpxK2O_CpxCr2O3_CpxSample_ID_Cpx
051.600.532.794.300.0716.0424.080.1500.30CF195-GCPX-93_core2
148.960.784.536.210.1414.1723.300.1200.02CF195_GPX_21-rim1
249.870.733.904.970.0814.9423.690.1400.00CF195_GPX_21-rim2
350.180.633.018.010.3313.5623.180.4300.12CF195-GCPX-93_rim2
448.990.644.478.000.1613.4523.660.1600.00CF195-GCPX-111_core1
....................................
13950.670.482.953.950.0616.0723.190.1100.18CF195_CPX_252-m1
14051.660.392.173.790.0916.4423.360.1000.14CF195_CPX_252-m2
14151.530.412.383.810.0916.0523.250.0800.16CF195_CPX_252-m3
14250.410.583.205.490.1415.1122.530.1100.00CF195_CPX_252-core1
14350.000.613.275.560.1215.3722.480.1400.03CF195_CPX_252-core2
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144 rows × 11 columns

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" ], "text/plain": [ " SiO2_Cpx TiO2_Cpx Al2O3_Cpx FeOt_Cpx MnO_Cpx MgO_Cpx CaO_Cpx \\\n", "0 51.60 0.53 2.79 4.30 0.07 16.04 24.08 \n", "1 48.96 0.78 4.53 6.21 0.14 14.17 23.30 \n", "2 49.87 0.73 3.90 4.97 0.08 14.94 23.69 \n", "3 50.18 0.63 3.01 8.01 0.33 13.56 23.18 \n", "4 48.99 0.64 4.47 8.00 0.16 13.45 23.66 \n", ".. ... ... ... ... ... ... ... \n", "139 50.67 0.48 2.95 3.95 0.06 16.07 23.19 \n", "140 51.66 0.39 2.17 3.79 0.09 16.44 23.36 \n", "141 51.53 0.41 2.38 3.81 0.09 16.05 23.25 \n", "142 50.41 0.58 3.20 5.49 0.14 15.11 22.53 \n", "143 50.00 0.61 3.27 5.56 0.12 15.37 22.48 \n", "\n", " Na2O_Cpx K2O_Cpx Cr2O3_Cpx Sample_ID_Cpx \n", "0 0.15 0 0.30 CF195-GCPX-93_core2 \n", "1 0.12 0 0.02 CF195_GPX_21-rim1 \n", "2 0.14 0 0.00 CF195_GPX_21-rim2 \n", "3 0.43 0 0.12 CF195-GCPX-93_rim2 \n", "4 0.16 0 0.00 CF195-GCPX-111_core1 \n", ".. ... ... ... ... \n", "139 0.11 0 0.18 CF195_CPX_252-m1 \n", "140 0.10 0 0.14 CF195_CPX_252-m2 \n", "141 0.08 0 0.16 CF195_CPX_252-m3 \n", "142 0.11 0 0.00 CF195_CPX_252-core1 \n", "143 0.14 0 0.03 CF195_CPX_252-core2 \n", "\n", "[144 rows x 11 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myCPXs1" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SiO2_LiqTiO2_LiqAl2O3_LiqFeOt_LiqMnO_LiqMgO_LiqCaO_LiqNa2O_LiqK2O_LiqCr2O3_LiqP2O5_LiqH2O_LiqFe3Fet_LiqNiO_LiqCoO_LiqCO2_LiqSample_ID_Liq
060.760.4219.093.050.270.231.897.097.1700.030.00.000.00.00
160.770.4319.393.050.270.241.946.827.0800.020.00.000.00.01
263.710.3119.911.630.070.121.556.676.0400.010.00.000.00.02
358.870.4419.892.920.220.321.977.217.1900.040.00.000.00.03
458.860.4319.992.930.220.291.927.157.1700.040.00.000.00.04
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" ], "text/plain": [ " SiO2_Liq TiO2_Liq Al2O3_Liq FeOt_Liq MnO_Liq MgO_Liq CaO_Liq \\\n", "0 60.76 0.42 19.09 3.05 0.27 0.23 1.89 \n", "1 60.77 0.43 19.39 3.05 0.27 0.24 1.94 \n", "2 63.71 0.31 19.91 1.63 0.07 0.12 1.55 \n", "3 58.87 0.44 19.89 2.92 0.22 0.32 1.97 \n", "4 58.86 0.43 19.99 2.93 0.22 0.29 1.92 \n", "\n", " Na2O_Liq K2O_Liq Cr2O3_Liq P2O5_Liq H2O_Liq Fe3Fet_Liq NiO_Liq \\\n", "0 7.09 7.17 0 0.03 0.0 0.0 0 \n", "1 6.82 7.08 0 0.02 0.0 0.0 0 \n", "2 6.67 6.04 0 0.01 0.0 0.0 0 \n", "3 7.21 7.19 0 0.04 0.0 0.0 0 \n", "4 7.15 7.17 0 0.04 0.0 0.0 0 \n", "\n", " CoO_Liq CO2_Liq Sample_ID_Liq \n", "0 0.0 0.0 0 \n", "1 0.0 0.0 1 \n", "2 0.0 0.0 2 \n", "3 0.0 0.0 3 \n", "4 0.0 0.0 4 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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SiO2_CpxTiO2_CpxAl2O3_CpxFeOt_CpxMnO_CpxMgO_CpxCaO_CpxNa2O_CpxK2O_CpxCr2O3_CpxSample_ID_Cpx
051.600.532.794.300.0716.0424.080.1500.30CF195-GCPX-93_core2
148.960.784.536.210.1414.1723.300.1200.02CF195_GPX_21-rim1
249.870.733.904.970.0814.9423.690.1400.00CF195_GPX_21-rim2
350.180.633.018.010.3313.5623.180.4300.12CF195-GCPX-93_rim2
448.990.644.478.000.1613.4523.660.1600.00CF195-GCPX-111_core1
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" ], "text/plain": [ " SiO2_Cpx TiO2_Cpx Al2O3_Cpx FeOt_Cpx MnO_Cpx MgO_Cpx CaO_Cpx \\\n", "0 51.60 0.53 2.79 4.30 0.07 16.04 24.08 \n", "1 48.96 0.78 4.53 6.21 0.14 14.17 23.30 \n", "2 49.87 0.73 3.90 4.97 0.08 14.94 23.69 \n", "3 50.18 0.63 3.01 8.01 0.33 13.56 23.18 \n", "4 48.99 0.64 4.47 8.00 0.16 13.45 23.66 \n", "\n", " Na2O_Cpx K2O_Cpx Cr2O3_Cpx Sample_ID_Cpx \n", "0 0.15 0 0.30 CF195-GCPX-93_core2 \n", "1 0.12 0 0.02 CF195_GPX_21-rim1 \n", "2 0.14 0 0.00 CF195_GPX_21-rim2 \n", "3 0.43 0 0.12 CF195-GCPX-93_rim2 \n", "4 0.16 0 0.00 CF195-GCPX-111_core1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# You can check inputs have read in right using .head()\n", "display(myLiquids1.head())\n", "display(myCPXs1.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Melt matching function\n", "- Initially, we use the default, where all equilibrium criteria are considered, and specify the H2O content of the liquid is 0.5. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Caution, you have selected to use the Kd-Fe-Mg model of Masotta et al. (2013)which is only valid for trachyte and phonolitic magmas. use PutKd=True to use the Kd model of Putirka (2008)\n", "Considering 16704 Liq-Cpx pairs, be patient if this is >>1 million!\n", "Youve selected a P-independent function\n", "Youve selected a P-independent function\n", "171 Matches remaining after initial Kd filter. Now moving onto iterative calculations\n", "Youve selected a P-independent function\n", "Youve selected a T-independent function\n", "Youve selected a T-independent function\n", "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n", "Done!\n" ] } ], "source": [ "melt_match_out=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1, \n", "equationP=\"P_Mas2013_Palk2012\", equationT=\"T_Mas2013_Talk2012\", KdMatch=\"Masotta\",\n", "KdErr=0.05, Fe3Fet_Liq=0.0, sigma=2)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "Avs=melt_match_out['Av_PTs']\n", "rims=Avs.loc[Avs['Sample_ID_Cpx'].str.contains('rim')]\n", "cores=Avs.loc[Avs['Sample_ID_Cpx'].str.contains('core')]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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No. of liquids averagedSample_ID_CpxMean_T_K_calcst_dev_T_K_calcMean_P_kbar_calcst_dev_P_kbar_calcMean_Delta_Kd_Put2008Mean_Delta_Kd_Mas2013Mean_Delta_EnFsMean_Delta_CaTs...Mean_Mgno_Liq_Fe2Mean_DeltaFeMg_WBMean_T_Liq_MinPMean_T_Liq_MaxPMean_Kd_Ideal_PutMean_Kd_Ideal_MasottaMean_DiHd_Pred_MolloMean_EnFs_Pred_MolloMean_CaTs_Pred_P1999Mean_CrCaTS_Pred_P1999
03CF195-GCPX-93_rim21291.7874675.0311041.5649230.2507240.0219770.0237020.0056240.003869...0.4255340.0211441291.7874671291.7874670.2374690.2533880.8325060.0729050.0038690.0
02CF195-GCPX-111_core11304.7253436.9425780.8627900.0267540.0221370.0089590.0121080.028484...0.4401310.0162631304.7253431304.7253430.2406230.2538010.8310850.0850130.0043830.0
03CF195-GCPX-111_core21297.3398454.9242190.9574040.2657740.0252590.0266760.0136700.024821...0.4255340.0226691297.3398451297.3398450.2388250.2559670.8342730.0791840.0041480.0
03CF195-GCPX-111_core31297.8136004.9169900.9610430.2668780.0258640.0272530.0139350.025282...0.4255340.0233821297.8136001297.8136000.2389410.2561870.8333510.0806500.0042370.0
02CF195_GPX_21-core21312.9146127.8677180.9774170.0211210.0131140.0239250.0179410.033159...0.4401310.0188171312.9146121312.9146120.2426080.2575510.8210810.0924150.0048460.0
..................................................................
03CF195-CPX-185_rim21292.7126814.9744701.9065210.2551710.0259390.0276190.0064390.009971...0.4255340.0230251292.7126811292.7126810.2376950.2538180.8251920.0808660.0044220.0
03CF195-CPX-185_rim31290.2988244.9810142.0797750.2507750.0242760.0260970.0071650.002901...0.4255340.0209601290.2988241290.2988240.2371050.2526980.8277370.0758030.0041350.0
03CF195_CPX_138-rim11292.7881885.1863362.0181610.2464660.0215080.0231420.0030210.000343...0.4255340.0214021292.7881881292.7881880.2377140.2538530.8232420.0774950.0042300.0
03CF195_CPX_138-rim21289.8714094.8596361.8615420.2547630.0257980.0276680.0028990.004052...0.4255340.0225871289.8714091289.8714090.2370000.2525000.8293060.0750590.0040520.0
03CF195_CPX_138-rim31288.2741584.9100042.2574490.2507450.0248030.0267580.0036950.002326...0.4255340.0213491288.2741581288.2741580.2366090.2517590.8273930.0734990.0040210.0
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62 rows × 107 columns

\n", "
" ], "text/plain": [ " No. of liquids averaged Sample_ID_Cpx Mean_T_K_calc \\\n", "0 3 CF195-GCPX-93_rim2 1291.787467 \n", "0 2 CF195-GCPX-111_core1 1304.725343 \n", "0 3 CF195-GCPX-111_core2 1297.339845 \n", "0 3 CF195-GCPX-111_core3 1297.813600 \n", "0 2 CF195_GPX_21-core2 1312.914612 \n", ".. ... ... ... \n", "0 3 CF195-CPX-185_rim2 1292.712681 \n", "0 3 CF195-CPX-185_rim3 1290.298824 \n", "0 3 CF195_CPX_138-rim1 1292.788188 \n", "0 3 CF195_CPX_138-rim2 1289.871409 \n", "0 3 CF195_CPX_138-rim3 1288.274158 \n", "\n", " st_dev_T_K_calc Mean_P_kbar_calc st_dev_P_kbar_calc \\\n", "0 5.031104 1.564923 0.250724 \n", "0 6.942578 0.862790 0.026754 \n", "0 4.924219 0.957404 0.265774 \n", "0 4.916990 0.961043 0.266878 \n", "0 7.867718 0.977417 0.021121 \n", ".. ... ... ... \n", "0 4.974470 1.906521 0.255171 \n", "0 4.981014 2.079775 0.250775 \n", "0 5.186336 2.018161 0.246466 \n", "0 4.859636 1.861542 0.254763 \n", "0 4.910004 2.257449 0.250745 \n", "\n", " Mean_Delta_Kd_Put2008 Mean_Delta_Kd_Mas2013 Mean_Delta_EnFs \\\n", "0 0.021977 0.023702 0.005624 \n", "0 0.022137 0.008959 0.012108 \n", "0 0.025259 0.026676 0.013670 \n", "0 0.025864 0.027253 0.013935 \n", "0 0.013114 0.023925 0.017941 \n", ".. ... ... ... \n", "0 0.025939 0.027619 0.006439 \n", "0 0.024276 0.026097 0.007165 \n", "0 0.021508 0.023142 0.003021 \n", "0 0.025798 0.027668 0.002899 \n", "0 0.024803 0.026758 0.003695 \n", "\n", " Mean_Delta_CaTs ... Mean_Mgno_Liq_Fe2 Mean_DeltaFeMg_WB \\\n", "0 0.003869 ... 0.425534 0.021144 \n", "0 0.028484 ... 0.440131 0.016263 \n", "0 0.024821 ... 0.425534 0.022669 \n", "0 0.025282 ... 0.425534 0.023382 \n", "0 0.033159 ... 0.440131 0.018817 \n", ".. ... ... ... ... \n", "0 0.009971 ... 0.425534 0.023025 \n", "0 0.002901 ... 0.425534 0.020960 \n", "0 0.000343 ... 0.425534 0.021402 \n", "0 0.004052 ... 0.425534 0.022587 \n", "0 0.002326 ... 0.425534 0.021349 \n", "\n", " Mean_T_Liq_MinP Mean_T_Liq_MaxP Mean_Kd_Ideal_Put \\\n", "0 1291.787467 1291.787467 0.237469 \n", "0 1304.725343 1304.725343 0.240623 \n", "0 1297.339845 1297.339845 0.238825 \n", "0 1297.813600 1297.813600 0.238941 \n", "0 1312.914612 1312.914612 0.242608 \n", ".. ... ... ... \n", "0 1292.712681 1292.712681 0.237695 \n", "0 1290.298824 1290.298824 0.237105 \n", "0 1292.788188 1292.788188 0.237714 \n", "0 1289.871409 1289.871409 0.237000 \n", "0 1288.274158 1288.274158 0.236609 \n", "\n", " Mean_Kd_Ideal_Masotta Mean_DiHd_Pred_Mollo Mean_EnFs_Pred_Mollo \\\n", "0 0.253388 0.832506 0.072905 \n", "0 0.253801 0.831085 0.085013 \n", "0 0.255967 0.834273 0.079184 \n", "0 0.256187 0.833351 0.080650 \n", "0 0.257551 0.821081 0.092415 \n", ".. ... ... ... \n", "0 0.253818 0.825192 0.080866 \n", "0 0.252698 0.827737 0.075803 \n", "0 0.253853 0.823242 0.077495 \n", "0 0.252500 0.829306 0.075059 \n", "0 0.251759 0.827393 0.073499 \n", "\n", " Mean_CaTs_Pred_P1999 Mean_CrCaTS_Pred_P1999 \n", "0 0.003869 0.0 \n", "0 0.004383 0.0 \n", "0 0.004148 0.0 \n", "0 0.004237 0.0 \n", "0 0.004846 0.0 \n", ".. ... ... \n", "0 0.004422 0.0 \n", "0 0.004135 0.0 \n", "0 0.004230 0.0 \n", "0 0.004052 0.0 \n", "0 0.004021 0.0 \n", "\n", "[62 rows x 107 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Avs" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '#')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(rims['Mean_P_kbar_calc'], label='rims')\n", "plt.hist(cores['Mean_P_kbar_calc'], label='cores')\n", "plt.legend()\n", "plt.xlabel('Pressure (kbar)')\n", "plt.ylabel('#')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KstestResult(statistic=0.2857142857142857, pvalue=0.3310967002469194)\n" ] } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5 wt \" H2O\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Caution, you have selected to use the Kd-Fe-Mg model of Masotta et al. (2013)which is only valid for trachyte and phonolitic magmas. use PutKd=True to use the Kd model of Putirka (2008)\n", "Considering 16704 Liq-Cpx pairs, be patient if this is >>1 million!\n", "Youve selected a P-independent function\n", "Youve selected a P-independent function\n", "203 Matches remaining after initial Kd filter. Now moving onto iterative calculations\n", "Youve selected a P-independent function\n", "Youve selected a T-independent function\n", "Youve selected a T-independent function\n", "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n", "Done!\n" ] } ], "source": [ "melt_match_out_5=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1, \n", "equationP=\"P_Mas2013_Palk2012\", equationT=\"T_Mas2013_Talk2012\", KdMatch=\"Masotta\",\n", "KdErr=0.05, Fe3Fet_Liq=0.0, sigma=2, H2O_Liq=5)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "Avs5=melt_match_out_5['Av_PTs']\n", "rims5=Avs5.loc[Avs5['Sample_ID_Cpx'].str.contains('rim')]\n", "cores5=Avs5.loc[Avs5['Sample_ID_Cpx'].str.contains('core')]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '#')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "All5=melt_match_out_5['All_PTs']\n", "rims5All=All5.loc[All5['Sample_ID_Cpx'].str.contains('rim')]\n", "cores5All=All5.loc[All5['Sample_ID_Cpx'].str.contains('core')]\n", "plt.hist(rims5All['P_kbar_calc'], label='rims')\n", "plt.hist(cores5All['P_kbar_calc'], label='cores')\n", "plt.legend()\n", "plt.xlabel('Pressure (kbar)')\n", "plt.ylabel('#')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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No. of liquids averagedSample_ID_CpxMean_T_K_calcst_dev_T_K_calcMean_P_kbar_calcst_dev_P_kbar_calcMean_Delta_Kd_Put2008Mean_Delta_Kd_Mas2013Mean_Delta_EnFsMean_Delta_CaTs...Mean_Mgno_Liq_Fe2Mean_DeltaFeMg_WBMean_T_Liq_MinPMean_T_Liq_MaxPMean_Kd_Ideal_PutMean_Kd_Ideal_MasottaMean_DiHd_Pred_MolloMean_EnFs_Pred_MolloMean_CaTs_Pred_P1999Mean_CrCaTS_Pred_P1999
03CF195-GCPX-111_core11272.5189044.9823641.0804640.2647660.0242430.0270980.0056130.028734...0.4255340.0206371272.5189041272.5189040.2327320.2444720.8565680.0785180.0041320.0
04CF195-GCPX-111_core21266.7492943.8709851.2167800.3101150.0261490.0357220.0092530.025118...0.4140880.0262861266.7492941266.7492940.2313050.2462720.8606630.0747670.0038510.0
04CF195-GCPX-111_core31267.2047173.8664891.2198740.3115230.0263120.0358950.0094710.025583...0.4140880.0264471267.2047171267.2047170.2314170.2464860.8597040.0761860.0039350.0
02CF195_GPX_21-core21281.3112037.4935131.3475630.0211210.0131570.0095560.0121510.033432...0.4401310.0188171281.3112031281.3112030.2348980.2431810.8492810.0866250.0045740.0
04CF195_CPX_50-core11260.6462833.8686072.2061770.2804990.0260600.0355080.0031030.003483...0.4140880.0262111260.6462831260.6462830.2297900.2434120.8534060.0728760.0039020.0
04CF195_CPX_50-core21257.4787403.8172722.5536200.2717230.0260350.0354390.0029060.002260...0.4140880.0261711257.4787401257.4787400.2290020.2419310.8537490.0688600.0037110.0
03CF195-CPX-85_core11254.2789732.6301612.0706560.2671890.0178150.0313300.0025660.003619...0.4003190.0187401254.2789731254.2789730.2282050.2394520.8617680.0665310.0036190.0
04CF195_CPX_50-core31258.4959783.7083012.2592340.2884890.0294070.0365810.0039100.008199...0.4140880.0272041258.4959781258.4959780.2292550.2424090.8561410.0727640.0039050.0
02CF195-CPX-85_core31254.9784682.1416141.8347540.0757980.0085240.0169350.0022470.003576...0.3880450.0116711254.9784681254.9784680.2283800.2502520.8557210.0683590.0035760.0
02CF195-CPX-85_core41282.2486517.6479031.3826050.0191390.0127510.0161090.0070660.026685...0.4401310.0258091282.2486511282.2486510.2351290.2436080.8458490.0866990.0045850.0
03CF195-CPX-161_core1255.6722222.5133832.4004360.2461390.0174000.0317350.0020630.003865...0.4003190.0189661255.6722221255.6722220.2285530.2400970.8553190.0708230.0039440.0
03CF195_CPX_126-core11254.9665522.5546502.4815110.2432110.0182720.0310740.0049770.003953...0.4003190.0183161254.9665521254.9665520.2283770.2397700.8529940.0707470.0039530.0
04CF195_CPX_126-core21259.2767513.7843912.3301560.2824170.0268710.0360780.0033770.005220...0.4140880.0267471259.2767511259.2767510.2294500.2427720.8540590.0730070.0039310.0
03CF195_CPX_138-core31254.6942682.5301022.3814730.2461450.0170300.0318690.0028800.003791...0.4003190.0193321254.6942681254.6942680.2283090.2396430.8565180.0684750.0037910.0
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14 rows × 107 columns

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" ], "text/plain": [ " No. of liquids averaged Sample_ID_Cpx Mean_T_K_calc \\\n", "0 3 CF195-GCPX-111_core1 1272.518904 \n", "0 4 CF195-GCPX-111_core2 1266.749294 \n", "0 4 CF195-GCPX-111_core3 1267.204717 \n", "0 2 CF195_GPX_21-core2 1281.311203 \n", "0 4 CF195_CPX_50-core1 1260.646283 \n", "0 4 CF195_CPX_50-core2 1257.478740 \n", "0 3 CF195-CPX-85_core1 1254.278973 \n", "0 4 CF195_CPX_50-core3 1258.495978 \n", "0 2 CF195-CPX-85_core3 1254.978468 \n", "0 2 CF195-CPX-85_core4 1282.248651 \n", "0 3 CF195-CPX-161_core 1255.672222 \n", "0 3 CF195_CPX_126-core1 1254.966552 \n", "0 4 CF195_CPX_126-core2 1259.276751 \n", "0 3 CF195_CPX_138-core3 1254.694268 \n", "\n", " st_dev_T_K_calc Mean_P_kbar_calc st_dev_P_kbar_calc \\\n", "0 4.982364 1.080464 0.264766 \n", "0 3.870985 1.216780 0.310115 \n", "0 3.866489 1.219874 0.311523 \n", "0 7.493513 1.347563 0.021121 \n", "0 3.868607 2.206177 0.280499 \n", "0 3.817272 2.553620 0.271723 \n", "0 2.630161 2.070656 0.267189 \n", "0 3.708301 2.259234 0.288489 \n", "0 2.141614 1.834754 0.075798 \n", "0 7.647903 1.382605 0.019139 \n", "0 2.513383 2.400436 0.246139 \n", "0 2.554650 2.481511 0.243211 \n", "0 3.784391 2.330156 0.282417 \n", "0 2.530102 2.381473 0.246145 \n", "\n", " Mean_Delta_Kd_Put2008 Mean_Delta_Kd_Mas2013 Mean_Delta_EnFs \\\n", "0 0.024243 0.027098 0.005613 \n", "0 0.026149 0.035722 0.009253 \n", "0 0.026312 0.035895 0.009471 \n", "0 0.013157 0.009556 0.012151 \n", "0 0.026060 0.035508 0.003103 \n", "0 0.026035 0.035439 0.002906 \n", "0 0.017815 0.031330 0.002566 \n", "0 0.029407 0.036581 0.003910 \n", "0 0.008524 0.016935 0.002247 \n", "0 0.012751 0.016109 0.007066 \n", "0 0.017400 0.031735 0.002063 \n", "0 0.018272 0.031074 0.004977 \n", "0 0.026871 0.036078 0.003377 \n", "0 0.017030 0.031869 0.002880 \n", "\n", " Mean_Delta_CaTs ... Mean_Mgno_Liq_Fe2 Mean_DeltaFeMg_WB \\\n", "0 0.028734 ... 0.425534 0.020637 \n", "0 0.025118 ... 0.414088 0.026286 \n", "0 0.025583 ... 0.414088 0.026447 \n", "0 0.033432 ... 0.440131 0.018817 \n", "0 0.003483 ... 0.414088 0.026211 \n", "0 0.002260 ... 0.414088 0.026171 \n", "0 0.003619 ... 0.400319 0.018740 \n", "0 0.008199 ... 0.414088 0.027204 \n", "0 0.003576 ... 0.388045 0.011671 \n", "0 0.026685 ... 0.440131 0.025809 \n", "0 0.003865 ... 0.400319 0.018966 \n", "0 0.003953 ... 0.400319 0.018316 \n", "0 0.005220 ... 0.414088 0.026747 \n", "0 0.003791 ... 0.400319 0.019332 \n", "\n", " Mean_T_Liq_MinP Mean_T_Liq_MaxP Mean_Kd_Ideal_Put Mean_Kd_Ideal_Masotta \\\n", "0 1272.518904 1272.518904 0.232732 0.244472 \n", "0 1266.749294 1266.749294 0.231305 0.246272 \n", "0 1267.204717 1267.204717 0.231417 0.246486 \n", "0 1281.311203 1281.311203 0.234898 0.243181 \n", "0 1260.646283 1260.646283 0.229790 0.243412 \n", "0 1257.478740 1257.478740 0.229002 0.241931 \n", "0 1254.278973 1254.278973 0.228205 0.239452 \n", "0 1258.495978 1258.495978 0.229255 0.242409 \n", "0 1254.978468 1254.978468 0.228380 0.250252 \n", "0 1282.248651 1282.248651 0.235129 0.243608 \n", "0 1255.672222 1255.672222 0.228553 0.240097 \n", "0 1254.966552 1254.966552 0.228377 0.239770 \n", "0 1259.276751 1259.276751 0.229450 0.242772 \n", "0 1254.694268 1254.694268 0.228309 0.239643 \n", "\n", " Mean_DiHd_Pred_Mollo Mean_EnFs_Pred_Mollo Mean_CaTs_Pred_P1999 \\\n", "0 0.856568 0.078518 0.004132 \n", "0 0.860663 0.074767 0.003851 \n", "0 0.859704 0.076186 0.003935 \n", "0 0.849281 0.086625 0.004574 \n", "0 0.853406 0.072876 0.003902 \n", "0 0.853749 0.068860 0.003711 \n", "0 0.861768 0.066531 0.003619 \n", "0 0.856141 0.072764 0.003905 \n", "0 0.855721 0.068359 0.003576 \n", "0 0.845849 0.086699 0.004585 \n", "0 0.855319 0.070823 0.003944 \n", "0 0.852994 0.070747 0.003953 \n", "0 0.854059 0.073007 0.003931 \n", "0 0.856518 0.068475 0.003791 \n", "\n", " Mean_CrCaTS_Pred_P1999 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "0 0.0 \n", "\n", "[14 rows x 107 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cores5" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '#')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(rims5['Mean_P_kbar_calc'], label='rims')\n", "plt.hist(cores5['Mean_P_kbar_calc'], label='cores')\n", "plt.legend()\n", "plt.xlabel('Pressure (kbar)')\n", "plt.ylabel('#')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '#')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cpx_comp=pt.calculate_clinopyroxene_components(cpx_comps=myCPXs1)\n", "plt.hist(cpx_comp['Mgno_CPX'], color='grey', label='entered')\n", "plt.hist(rims5['Mean_Mgno_CPX'], label='rims with a match')\n", "plt.hist(cores5['Mean_Mgno_CPX'], label='cores with a match')\n", "plt.legend()\n", "plt.xlabel('Mg# Cpx')\n", "plt.ylabel('#')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KstestResult(statistic=0.2435064935064935, pvalue=0.4796410386021369)\n" ] } ], "source": [ "from scipy import stats\n", "test_M_L=stats.ks_2samp(rims5['Mean_P_kbar_calc'], cores5['Mean_P_kbar_calc'])\n", "print(test_M_L)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Are cores and rims matching different liquids?\n", "- No all matches are the same 4 liquids" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(myLiquids1['SiO2_Liq'], myLiquids1['MgO_Liq'], '.k')\n", "plt.plot(rims5All['SiO2_Liq'], rims5All['MgO_Liq'], 'ok', mfc='blue')\n", "plt.plot(cores5All['SiO2_Liq'], cores5All['MgO_Liq'], '*k', mfc='orange')\n", "\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(myLiquids1['MgO_Liq'], myLiquids1['FeOt_Liq'], '.k')\n", "plt.plot(rims5All['MgO_Liq'], rims5All['FeOt_Liq'], 'or')\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(myLiquids1['MgO_Liq'], myLiquids1['TiO2_Liq'], '.k')\n", "plt.plot(rims5All['MgO_Liq'], rims5All['TiO2_Liq'], 'or')\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(myLiquids1['MgO_Liq'], myLiquids1['Na2O_Liq'], '.k')\n", "plt.plot(rims5All['MgO_Liq'], rims5All['Na2O_Liq'], 'or')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## This is weird, so lets look at all matches, and the distribution of equilibrium tests" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Caution, you have selected to use the Kd-Fe-Mg model of Masotta et al. (2013)which is only valid for trachyte and phonolitic magmas. use PutKd=True to use the Kd model of Putirka (2008)\n", "Considering 16704 Liq-Cpx pairs, be patient if this is >>1 million!\n", "Youve selected a P-independent function\n", "Youve selected a P-independent function\n", "16704 Matches remaining after initial Kd filter. Now moving onto iterative calculations\n", "Youve selected a P-independent function\n", "Youve selected a T-independent function\n", "Youve selected a T-independent function\n", "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n", "Done!\n" ] } ], "source": [ "melt_match_out_lots=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1, \n", "equationP=\"P_Mas2013_Palk2012\", equationT=\"T_Mas2013_Talk2012\", KdMatch=\"Masotta\",\n", "KdErr=1, Fe3Fet_Liq=0.0, sigma=100, H2O_Liq=5)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "melt_match_out_lots_all=melt_match_out_lots['All_PTs']" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16704" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# checking you got them all\n", "len(melt_match_out_lots_all)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lets plot a rhodes diagram first" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([1042., 6014., 475., 720., 1486., 1225., 750., 2290., 2234.,\n", " 468.]),\n", " array([0.12670362, 0.14739244, 0.16808127, 0.18877009, 0.20945891,\n", " 0.23014774, 0.25083656, 0.27152539, 0.29221421, 0.31290304,\n", " 0.33359186]),\n", " )" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(melt_match_out_lots_all['Kd_Ideal_Masotta'])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(melt_match_out_lots_all['Kd_Ideal_Masotta'], melt_match_out_lots_all['Na2O_Liq'], 'ok')" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Mg# Cpx')" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "eq_lines_3=pt.calculate_cpx_rhodes_diagram_lines(Min_Mgno=0.3, Max_Mgno=0.7, KdMin=0.15, KdMax=0.3)\n", "eq_lines_3.head()\n", "fig, (ax1) = plt.subplots(1, 1, figsize = (6,5))\n", "#ax1.set_xlim([0.4, 0.7])\n", "#ax1.plot(cpx_comps_Fe3['Mgno_Liq_noFe3'], cpx_comps_Fe3['Mgno_CPX'], '*k', mfc='yellow', ms=8, label=\"No Fe3\")\n", "ax1.plot(melt_match_out_lots_all['Mgno_Liq_Fe2'], melt_match_out_lots_all['Mgno_CPX'], '.k', label=\"20% Fe3\", ms=1)\n", "ax1.plot(eq_lines_3['Mg#_Liq'], eq_lines_3['Eq_Cpx_Mg# (KdMin=0.15)'], ':r', label=\"K$_d$=Put eq 35 + 0.08\")\n", "ax1.plot(eq_lines_3['Mg#_Liq'], eq_lines_3['Eq_Cpx_Mg# (KdMax=0.3)'], ':r', label=\"K$_d$=Put eq 35 - 0.08\")\n", "ax1.set_xlabel('Mg# Liquid')\n", "ax1.set_ylabel('Mg# Cpx')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Delta Kd Masotta')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(melt_match_out_lots_all['Delta_Kd_Mas2013'])\n", "plt.xlabel('Delta Kd Masotta')" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(melt_match_out_lots_all['Delta_EnFs'])\n", "plt.xlabel('Delta EnFs Mollo')\n", "plt.plot([0.05, 0.05], [0, 4000], ':r')" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hqtbsJR0MfB84muyi4UgaBLwUEcNaOWQaMCEi7pB0EjBD0iHAu4ArgMOBF/P9xgHf7oDzMNsuMw7xh7OWllpG9p8DfgjcVdF3DLBJ0uOSFkq6TFIPSf2BwcB0gIh4ANgNOAw4HZgdEWvyi49PAkZ34LmY1WzGIR90wrekVB3ZR8RYAEkfbnHcHOASoBdwH7AOeApYlSfzZiuBfYABwPJW+s0K13PTRgA29vCENEtDXX/pETGl8rGkG4AvAPOAltNoBGwiexcRrfS3qqGh4fX7jY2NNDY21hOqWaum3nkpAKP+9eqSIzErRl3JXtJ5wIKIWNjcBWwAVgB9teXcyX5ko/gV+X1a9LeqqampntDMajL93cPLDsGsUPW+hx0KnC3pbGAnYCwwLSJWSloGjASmSxoObCb7YDeAWZK+AawBGoGZ7YzfrC4z33V82SGYFareefZXAC+RJfGFwJNkH+ICnAOcL2kx8A1gRERszt8FjAceAZaQlXCuaUfsZnXbecN6dt6wvuwwzArjb9Bakprn2NdTs/c3aK2rausbtJ6KYEmaetjJZYdgVigne0vSTw/+QNkhmBXKa+NYknZ/7W/s/trfyg7DrDAe2VuSptx9JeB59pYOJ3tL0q1HnFZ2CGaFcrK3JD006JiyQzArlGv2lqS3/f1l3vb3l8sOw6wwHtlbkibO/Bbgmr2lw8nekjTlqDPLDsGsUE72lqSHDzy67BDMCuWavSVpr1fWstcra8sOw6wwHtlbkr43O1uDzzV7S4WTvSVp4ntGlB2CWaGc7C1Jj+1/RNkhmBXKNXtLUt91a+i7bk3ZYZgVxiN7S9J3fno94Jq9pcPJ3pL0vWNGlR2CWaFqulKVJAG3AYsi4jpJPYDrgRPJ/sO4LiJuzvcdCPwfYE/gFeDjEbEk3zYGuAjoBcwBvhARG1r5eb5SlVkLvkKWVdPWlaqq1uwlHQw8DHy0ovszwEFkFx4/ErhQ0lH5tmnAzRExBLgcmKHMULJr1x4LDAJ6A+PqOiOzdhrwlz8y4C9/LDsMs8LU8gHt58guJn5XRd+ZwK0RsTEi1gLTgdGS+gOD88dExAPAbsBhwOnA7IhYExGbgUnA6A47E7PtcO39E7j2/gllh2FWmKo1+4gYCyDpwxXdA4AXKh6vBA7N+1flybxy2z75tuWt9JsV7jv/49yyQzArVL0f0L4JqCyqC9jUSn9b25r7W9XQ0PD6/cbGRhobG+sM1WxrT+97SNkhmBWq3mS/AuhX8bgf2Uh9BdBXW37CWrmttWNa1dTUVGdoZtXt/2L2p/f7d/jNpaWh3i9VzQLGSOopqTcwCpgZESuBZcBIAEnDgc3AImA2cJqkvfPZPY3AzPaFb1afbz50E9986KaywzArTL0j+4nAAcACYCdgUkQ8lm87B5gi6VJgPTAir+EvlDQeeIRs6uXTwDXtCd6sXt/+wL+VHYJZoWqaZ180z7M325rn2Vs17Zpnb7YjOmjNcg5as7zsMMwK4+USLEnjf34z4LVxLB1O9pakbx4/puwQzArlZG9JWtj3oLJDMCuUa/aWpCF/+j1D/vT7ssMwK4yTvSXpsocnc9nDk8sOw6wwLuNYksaf4OU3LC2eZ29mbfL8/u7D8+zNWjh09e84dPXvyg7DrDBO9pakr/7iFr76i1vKDsOsMK7ZW5Iu+9D5ZYdgVigne0vS7/bar+wQzArlMo4l6fCVz3H4yufKDsOsME72lqSL597OxXNvLzsMs8K4jGNJ+urwsWWHYFYoJ3tLki9HaKlxGceSdPSKRRy9YlHZYZgVpl3JXtL1klZImp+3OyX1kDRB0hJJyySdX7H/QElzJT0raZ6kwe0/BbPtN+6JaYx7YlrZYZgVpr1lnGOAURHxZHOHpAuAg4ChwO7AryT9NiLmAdOACRFxh6STgBmSDql7bQSzOv2vky8sOwSzQtU9spf0ZuAw4GJJiyTdLWlf4Ezg1ojYGBFrgenAaEn9gcH5YyLiAWC3/DnMCvVC7z680LtP2WGYFaY9ZZx+wCPApcChwFPALGBf4IWK/VYC+wADgFURsbmVbVtpaGh4vU2e7KVorWO9b/l83rd8ftlhmBWm7jJORPwXcHLzY0nXAV8DdgEqyzICNpH9x9KyXNO8bStNTU31hmZW1eefnA7AL/cbVm4gZgWpO9lLOhR4d0T8uLIbeIxs1N+sH9kIfgXQV1uuX9y8zaxQ4z7ypbJDMCtUe8o4m4EbJf1z/vizwEKyUs4YST0l9QZGATMjYiWwDBgJIGl4/hye/2aFW73HXqzeY6+ywzArTHvKOIslfR64V1IPshH6OcAq4ABgAbATMCkiHssPOweYIulSYD0wokUN36wQx/7+NwA8tv8RJUfS9ZV5QSBfOKXjtGvqZURMBaa2sunCbey/FDiuPT/TrCN89qm7ACd7S4eXS7Akff60L5cdglmhnOwtSWt2e1vZIZgVymvjWJJOWPY0Jyx7uuwwzArjkb0l6dPzfgLAwwceXXIkZsVwsrckffaMr5QdglmhnOwtSWt3fWvZIZgVyjV7S9Lw559k+PNPVt/RbAfhkb0l6ZO/mQ3AQ4OOKTkSs2I42VuSPn3218oOwaxQTvaWpL+++S1lh2BWKNfsLUkfeW4uH3lubtlhmBXGI3tL0uj/vB+Anx78gZIjMSuGuuLlX7dc8n77lLlCn3UfO29YD8D6XjuXHIl1Rd11tU1JRIRa2+aRvSXJSd5Ss8PV7P86/8GyQ6hbd429O8Z9xjO/4MT7v1t2GHXpjr9v6L5x7yjXwN7hkv0rC7rnHxR039i7Y9yjFjzEOUseLzuMunTH3zd037id7M26sdEjr+Lkt/cvOwyzwpSS7CWdImmhpOcl3SVpjzLisHRt7NGTjWr1cyyzHVLhyV7SXsCtwNkRMQj4PXB10XFY2j66aA7n/X1d2WGYFaaM2TgfBn6dX48WYCKwQNLn6p5vabadPrpoDq+9uo45ZQdiXVLlFO7Vf3i50CndnTXts/B59pIuAfaLiPPzxz2BDcBbI2Jd3uekb2ZWh640z/5NQGvJfFPznW0Fa2Zm9SnjA9oVQL+Kx/2BtRHxtxJiMTNLQhnJ/mfAeyQNzB+fD8wqIQ4zs2QUnuwj4r+BTwIzJD0HHAJ8qdpxtUzX3NY+knpImiBpiaRlks7v6PPqpLh3kXSLpMWSnsnv79IdYm+xzz2Sbiom6vbHLekCSb+V9JykqZLe3NXjzv/GJ0p6Nm/XScXMLa11KrUyt0u6qKKvS78224i71NdmXSKiyzdgL+C/gYH542uAH9S6D3ABcD/ZZxRvA5YAR3WDuK8CfkT2n3IP4D+A8d3hd16xz8XAGuCm7hA3cBbwHPD2/Pd+N3BJN4j7E8Aj+d9JL+DXwIiuEHfef3Ae39+Aiyr6u+xrs0rcpb026z7nsgOo8R/mXOC+isf7AS+Tzyaqtg/w88o/fODrwI3dIO4PAwdVbLsYuL07/M7zx8flL5QrKS7Zt/d3PhP4VMW2vYD+3SDufweeBnYFdgMWAKd2hbjz/puAfwVuY8uk2WVfm1XiLu21WW/rLsslDABeqHi8EtgD2L3GfVrbtk+nRLqldsUdET+LiN8BSHoncCFwV2cGXEtctewjqR/wXbIX1SaK096/lYOAvSU9KGkhWfL5SyfGW0tMtexzG7AW+AOwGlgWEfd2Yry1xPS6iBgbEXfUeHxXeW1uM+6SX5t16S7Jvup0zSr7tNwmiklA7Y0bAElHAI+TjY5/2qERblt7YhfZ29pxEbG6E2JrS3t/572ADwEfAxrIyjnf6OAYW9PeuC8nK5f9E1myfLukqp+FdYBa4t6e47vSa7Oqkl6bdekuyb6W6Zpt7dNyWz+y/8k7W3vjRtIosre6l0TENzs53kp1xw4MAfYHbpA0n2zG1UhJP+zUiNuIaTt+56uAeyJiXUT8A5gKvLeTY64WUy37nAXcEhH/iIiXgduB4zs55mox1XN8V3pttqnE12Zdukuyr2W6Zlv7zALGSOopqTcwiqw229naFbekU4EbgQ9v4y1wZ6o79oj4VUQMiIhhETEMuBm4MyI+1ZXjzu/PAD6Wz7YQcAbZh52drb1x/5bs3QiSegGnAU91asTVY6pFV35tblPJr836lP2hQa0NOJnsQ6fngJ+Svb1uAOa3tU/e3xOYADwDLKXig5YuHvfzZG/N51e073eH2Fs8z9cp6APaDvid9yAriTyb//7/A9ijG8T9jjzWJfnf+Y3ATl0l7op9b2PLDzq79GuzjbhLfW3W07rkNWjNzKxjdZcyjpmZtYOTvZlZApzszcwS4GRvZpYAJ3szswQ42Vunk7RcUuRts6RXJP1S0vDteI7j8uN75o+HSXp/nfF8oiKekLRB0kpJN0ravWK/r0t6Ir+/X77vga0831WSHm3j502VdNs2tn09f94ftbJNklZVnneV83r9d9RWvJYmJ3srypeAvmRf5X8P8EvgPkkfrPP5fgIMakc8q/N4+gIHAo3AScAsSc2vi+vIvpzU2TYAJ0vq0aL/aKBPAT/fElDGZQktTesi4o/5/VXAxZL6At8hu6bB9mrvWu2bK+IB+H+S/i+wGDgTuDsiXmnnz6jVfLL/uI4hW2el2Rlk34ItYrkG28F5ZG9lmgwMbS41SHprfpGIlyX9UdLkyrJKs7xk8k5gSnN5RNJHlF1wZH1+/J3buhjFtkTE88BcsmS/RRlne0h6v6T5kl6VNB2odlGLfwAPAqe26D+D7B1M5XO/Lf+9/EnSOknTJL29hpjqOs52HE72VqZn89sh+e0twJ7A+4FTyEa7t7Vy3Flki2V9CfiipH8mu8jIzcBgYATwL2TrndQT05Cqe22DpL3Ivnr/M2AY2fIFZ9Vw6CwqSkaSBpGtS9/UYr+f5M97KnAC2e/oxzU8f73H2Q7CZRwr08v57e6SDiAbUe8ZES8BSPo4sFzSgMqDIuIlSZvISkMvS9ob+GJETM53WS5pDvCuOmPa6t1EhQWSWq4xshPwZH7/Y8CLwJcjW4vk65I+UsPPvR+4TdLAiFhKNqqfScUyvJIOBY4FhkTEc3nfucASSds812rHRcQzNcRn3ZyTvZWpucyyjuzybwJWaOtLpx5EG+uMR8RSSa9J+t/AULIk/y6yhcHqiWldG9tPJVset9I43viPZQiwKLZcdKoJ2LmtHxoRf5E0l2x0fz1Zsr+0xW4HA39tTtj5cc9LWptv+/M2nr7acU72CXAZx8p0aH67mGzg8TeyUkNlG0iVpXolvZus/DKU7APOfwemtyOmxW1sXxERyyob2Rr+W4TU4vGGGn/2LOBUSX3IzvuxFtvXb+O4HnnblnqPsx2Ik72VaQzwm4j4L7IlY98C9KhIogA38MY7gEqVI+fzgF9GxDkR8YOI+DVZstyuGTv52ubvp32Xl1sMHNZiXvxhNR47C3gf8HGy66NubLF9CVnJ6+DmDklDyH4/z7fxvPUeZzsQJ3sryh6S+kjqK+kQSRPILlTxJYC8xPAg8GNJR+ej9R8B/xStX9rwFWBwPqPkRbJZPUdLGijpeuBI4M1txPOmPJ4+kvaVdDrwAPBwtO/yctPJSjbfkzRI0pepcepkRKwg+8/iUlrMwsm3P0/24e/tko6UdCTZFameiIj5bTxvXcfZjsXJ3opyPdkXmVYBc8hmg/xLRFSWKs4ju4DFz8hKGH8ATt/G890EfAaYQnahjl+SXSLuSWA/4AqyMtC29M3jWU02ur0GmEY+7bJeEbEWGA4cTjZ//ni2b9bLLLKS1s+2sf3fyH5HD+f7PENtX/yq9zjbQfjiJWZmCfDI3swsAU72ZmYJcLI3M0uAk72ZWQKc7M3MEuBkb2aWACd7M7MEONmbmSXAyd7MLAH/H7REwQxrnpYMAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(melt_match_out_lots_all['Delta_DiHd'])\n", "plt.xlabel('Delta DiHd Mollo')\n", "plt.plot([0.06, 0.06], [0, 4000], ':r')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function P_Mas2013_Palk2012 in module Thermobar.clinopyroxene_thermobarometry:\n", "\n", "P_Mas2013_Palk2012(T=None, *, lnK_Jd_liq, H2O_Liq, Na2O_Liq_cat_frac, K2O_Liq_cat_frac, Kd_Fe_Mg_Fet)\n", " Clinopyroxene-liquid barometer of Masotta et al. (2013) for alkaline melts\n", " | SEE=+-1.15 kbar\n", "\n" ] } ], "source": [ "help(pt.P_Mas2013_Palk2012)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(melt_match_out_lots_all['Delta_CaTs'])\n", "plt.xlabel('Delta CaTs Put')\n", "plt.plot([0.03, 0.03], [0, 4000], ':r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clearly, the KD filter is the most restricting... Perhaps set at 0.08, after Putirka" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Caution, you have selected to use the Kd-Fe-Mg model of Masotta et al. (2013)which is only valid for trachyte and phonolitic magmas. use PutKd=True to use the Kd model of Putirka (2008)\n", "Considering 16704 Liq-Cpx pairs, be patient if this is >>1 million!\n", "Youve selected a P-independent function\n", "Youve selected a P-independent function\n", "568 Matches remaining after initial Kd filter. Now moving onto iterative calculations\n", "Youve selected a P-independent function\n", "Youve selected a T-independent function\n", "Youve selected a T-independent function\n", "Finished calculating Ps and Ts, now just averaging the results. Almost there!\n", "Done!\n" ] } ], "source": [ "melt_match_out_5_008=pt.calculate_cpx_liq_press_temp_matching(liq_comps=myLiquids1, cpx_comps=myCPXs1, \n", "equationP=\"P_Mas2013_Palk2012\", equationT=\"T_Mas2013_Talk2012\", KdMatch=\"Masotta\",\n", "KdErr=0.08, Fe3Fet_Liq=0.0, sigma=2, H2O_Liq=5)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '#')" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "All5_008=melt_match_out_5_008['All_PTs']\n", "rims5All_008=All5_008.loc[All5_008['Sample_ID_Cpx'].str.contains('rim')]\n", "cores5All_008=All5_008.loc[All5_008['Sample_ID_Cpx'].str.contains('core')]\n", "plt.hist(rims5All_008['P_kbar_calc'], label='rims')\n", "plt.hist(cores5All_008['P_kbar_calc'], label='cores')\n", "plt.legend()\n", "plt.xlabel('Pressure (kbar)')\n", "plt.ylabel('#')" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(myLiquids1['FeOt_Liq'], myLiquids1['MgO_Liq'], '.k')\n", "plt.plot(rims5All_008['FeOt_Liq'], rims5All_008['MgO_Liq'], 'or')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 4 }