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Plotting Calibration datasets
This notebook shows how to plot the calibration dataset of each model. You can then overlay your data to check the cali range!
[1]:
import Thermobar as pt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import PySulfSat as ss
ss.__version__
[1]:
'0.0.17'
To use the TASPlot you will need this
[1]:
!pip install git+https://bitbucket.org/jsteven5/tasplot.git
import tasplot
Collecting git+https://bitbucket.org/jsteven5/tasplot.git
Cloning https://bitbucket.org/jsteven5/tasplot.git to c:\users\penny\appdata\local\temp\pip-req-build-ohpyhqxq
Resolved https://bitbucket.org/jsteven5/tasplot.git to commit dcd1d9e1f57d8b8554e6d76f6ab83ccc299903e2
Preparing metadata (setup.py): started
Preparing metadata (setup.py): finished with status 'done'
Requirement already satisfied: matplotlib in c:\users\penny\anaconda3\lib\site-packages (from TASplot==0.1) (3.7.1)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (1.0.5)
Requirement already satisfied: cycler>=0.10 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (4.25.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (1.4.4)
Requirement already satisfied: numpy>=1.20 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (1.24.2)
Requirement already satisfied: packaging>=20.0 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (23.0)
Requirement already satisfied: pillow>=6.2.0 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (9.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (2.8.2)
Requirement already satisfied: importlib-resources>=3.2.0 in c:\users\penny\anaconda3\lib\site-packages (from matplotlib->TASplot==0.1) (5.2.0)
Requirement already satisfied: zipp>=3.1.0 in c:\users\penny\anaconda3\lib\site-packages (from importlib-resources>=3.2.0->matplotlib->TASplot==0.1) (3.11.0)
Requirement already satisfied: six>=1.5 in c:\users\penny\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib->TASplot==0.1) (1.16.0)
Running command git clone --filter=blob:none --quiet https://bitbucket.org/jsteven5/tasplot.git 'C:\Users\penny\AppData\Local\Temp\pip-req-build-ohpyhqxq'
warning: filtering not recognized by server, ignoring
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Lets load SCSS models
[2]:
df_S2017=ss.return_cali_dataset(model='S2017_SCSS')
df_LZ2022=ss.return_cali_dataset(model='LiZhang2022_SCSS')
df_Liu21=ss.return_cali_dataset(model='Liu2021_SCSS')
df_Blan=ss.return_cali_dataset(model='B2021_SCSS')
df_F2015=ss.return_cali_dataset(model='F2015_SCSS')
Have a look at what you get!
[3]:
df_LZ2022.head()
[3]:
| Sample | P_kbar | T_K | SiO2_Liq | TiO2_Liq | Al2O3_Liq | FeOt_Liq | MgO_Liq | CaO_Liq | Na2O_Liq | ... | S_Liq_ppm | logfO2 | Fe_sulf_wt% | Cu_sulf_wt% | Ni_sulf_wt% | S_sulf_wt | Fe_FeNiCu_sulf | Cu_FeNiCu_sulf | Ni_FeNiCu_sulf | T_C | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | B-4 | 2.89 | 1323.15 | 44.58 | 4.54 | 12.84 | 13.11 | 5.47 | 9.48 | 2.58 | ... | NaN | -10.260196 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1050.0 |
| 1 | B-11 | 3.12 | 1323.15 | 44.22 | 4.67 | 12.94 | 12.97 | 5.43 | 9.37 | 2.61 | ... | NaN | -10.044314 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1050.0 |
| 2 | B-12 | 3.12 | 1323.15 | 43.86 | 4.69 | 12.77 | 14.29 | 5.57 | 9.47 | 2.59 | ... | NaN | -10.044314 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1050.0 |
| 3 | B-13 | 3.12 | 1323.15 | 43.82 | 4.63 | 12.74 | 14.36 | 5.43 | 9.51 | 2.54 | ... | NaN | -10.044314 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1050.0 |
| 4 | B-16 | 2.99 | 1323.15 | 46.55 | 3.55 | 14.74 | 12.97 | 4.26 | 8.83 | 3.16 | ... | NaN | -10.253291 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1050.0 |
5 rows × 23 columns
Lets plot on a TAS diagram
[4]:
figure_mosaic="""
AA
BC
"""
fig, axes=plt.subplot_mosaic(mosaic=figure_mosaic, figsize=(8,8))
axes['A'].set_title('SCSS calibration datasets')
import tasplot
import matplotlib.pyplot as plt
tasplot.add_LeMaitre_fields(axes['A'])
for t in axes['A'].texts[:]:
t.remove()
ms=3
axes['A'].plot(df_Blan['SiO2_Liq'],
df_Blan['Na2O_Liq']+df_Blan['K2O_Liq'], 'pc', mfc='cyan', ms=3,
label='Blanchard et al. (2019)')
axes['A'].plot(df_S2017['SiO2_Liq'],
df_S2017['Na2O_Liq']+df_S2017['K2O_Liq'], 'or', mfc='red', ms=3,
label='Smythe et al. (2017)')
axes['A'].plot(df_LZ2022['SiO2_Liq'],
df_LZ2022['Na2O_Liq']+df_LZ2022['K2O_Liq'], 'sk', mfc='w', ms=3,
label='Liu and Zhang (2022)')
axes['A'].plot(df_Liu21['SiO2_Liq'],
df_Liu21['Na2O_Liq']+df_Liu21['K2O_Liq'], '<b', mfc='w', ms=4,
label='Liu et al. (2021')
axes['A'].plot(df_F2015['SiO2_Liq'],
df_F2015['Na2O_Liq']+df_F2015['K2O_Liq'], 'o',
mec='cornflowerblue', mfc='None', ms=4, alpha=0.6,
label='Fontin et al. (2015)')
axes['A'].set_xlim([30, 80])
x2='P_kbar'
y2='T_C'
axes['B'].plot(df_Blan[x2],
df_Blan[y2], 'pc', mfc='cyan', ms=3)
axes['B'].plot(df_S2017[x2],
df_S2017[y2], 'or', mfc='red', ms=3)
axes['B'].plot(df_F2015[x2],
df_F2015[y2], 'o', mec='cornflowerblue',
mfc='None', ms=4, alpha=0.6)
axes['B'].plot(df_LZ2022[x2],
df_LZ2022[y2], 'sk', mfc='w', ms=3)
axes['B'].plot(df_Liu21[x2],
df_Liu21[y2], '<b', mfc='w', ms=4)
# axes['B'].set_xlim([1000, 2000])
# axes['B'].set_ylim([-0.5, 21])
x3='MgO_Liq'
y3='FeOt_Liq'
axes['C'].plot(df_Blan[x3],
df_Blan[y3], 'pc', mfc='cyan', ms=3)
axes['C'].plot(df_S2017[x3],
df_S2017[y3], 'or', mfc='red', ms=3)
axes['C'].plot(df_F2015[x3],
df_F2015[y3], 'o', mec='cornflowerblue',
mfc='None', ms=4, alpha=0.6)
axes['C'].plot(df_LZ2022[x3],
df_LZ2022[y3], 'sk', mfc='w', ms=3)
axes['C'].plot(df_Liu21[x3],
df_Liu21[y3], '<b', mfc='w', ms=4)
axes['A'].set_xlabel('SiO$_2$ Liq (wt%)')
axes['A'].set_ylabel('Na$_2$O + K$_2$O Liq (wt%)')
axes['B'].set_xlabel('P (kbar)')
axes['B'].set_ylabel('T (K)')
axes['C'].set_xlabel('MgO Liq (wt%)')
axes['C'].set_ylabel('FeO$_T$ Liq (wt%)')
axes['A'].legend(loc='upper left', fontsize=7)
fig.savefig('cali_datasets_SCSS.png', dpi=200)
SCAS models
[5]:
df_CD2019=ss.return_cali_dataset(model="CD2019_SCAS")
df_ZT2022=ss.return_cali_dataset(model='ZT2022_SCAS')
df_MK_2015=ss.return_cali_dataset(model='MK_2015')
Tas diagram
[6]:
figure_mosaic="""
AA
BC
"""
fig, axes=plt.subplot_mosaic(mosaic=figure_mosaic, figsize=(8,8))
axes['A'].set_title('SCAS calibration datasets')
import tasplot
import matplotlib.pyplot as plt
tasplot.add_LeMaitre_fields(axes['A'])
for t in axes['A'].texts[:]:
t.remove()
ms=3
axes['A'].plot(df_ZT2022['SiO2_Liq'],
df_ZT2022['Na2O_Liq']+df_ZT2022['K2O_Liq'],
'ok', mfc='c', ms=5, alpha=0.6, label='Zajacz and Tsay (2022)')
axes['A'].plot(df_MK_2015['SiO2_Liq'],
df_MK_2015['Na2O_Liq']+df_MK_2015['K2O_Liq'],
'.k', ms=2, label='Masotta & Keppler (2015)')
axes['A'].plot(df_CD2019['SiO2_Liq'],
df_CD2019['Na2O_Liq']+df_CD2019['K2O_Liq'],
'+r', mfc='red', ms=3, label='Chowdhury and Dasgupta (2019)')
axes['A'].set_xlim([30, 90])
y2='P_kbar'
x2='T_C'
axes['B'].plot(df_ZT2022[x2],
df_ZT2022[y2], 'ok', mfc='c', ms=5, alpha=0.6)
axes['B'].plot(df_MK_2015[x2],
df_MK_2015[y2], '.k', ms=2)
axes['B'].plot(df_CD2019[x2],
df_CD2019[y2], '+r', mfc='red', ms=3)
x3='MgO_Liq'
y3='FeOt_Liq'
axes['C'].plot(df_ZT2022[x3],
df_ZT2022[y3], 'ok', mfc='c', ms=5, alpha=0.6)
axes['C'].plot(df_MK_2015[x3],
df_MK_2015[y3], '.k', ms=2)
axes['C'].plot(df_CD2019[x3],
df_CD2019[y3], '+r', mfc='red', ms=3)
axes['A'].set_xlabel('SiO$_2$ Liq (wt%)')
axes['A'].set_ylabel('Na$_2$O + K$_2$O Liq (wt%)')
axes['B'].set_ylabel('P (kbar)')
axes['B'].set_xlabel('T (°C)')
axes['C'].set_xlabel('MgO Liq (wt%)')
axes['C'].set_ylabel('FeO$_T$ Liq (wt%)')
axes['A'].legend(fontsize=7)
fig.savefig('cali_datasets_SCAS.pdf', dpi=200)
Lets add some of your data!
[7]:
Pin=pd.read_excel('Pinatubo_data.xlsx')
Pin
[7]:
| Sample | T_C | P(bar) | P_kbar | CaO | T_K | SiO2_Liq | TiO2_Liq | Al2O3_Liq | FeOt_Liq | MgO_Liq | CaO_Liq | Na2O_Liq | K2O_Liq | H2O_Liq | * Data from Masotta supplement | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Pinatubo 1991 | 850 | 2000 | 2 | 0.5 | 1123.15 | 68.580974 | 0.471167 | 17.119068 | 4.711670 | 2.209250 | 0.5 | 4.659318 | 1.748553 | 5 | NaN |
| 1 | Pinatubo 1991 | 850 | 2000 | 2 | 1.0 | 1123.15 | 68.236346 | 0.468799 | 17.033042 | 4.687993 | 2.198148 | 1.0 | 4.635904 | 1.739766 | 5 | NaN |
| 2 | Pinatubo 1991 | 850 | 2000 | 2 | 1.5 | 1123.15 | 67.891718 | 0.466432 | 16.947017 | 4.664317 | 2.187046 | 1.5 | 4.612491 | 1.730980 | 5 | NaN |
| 3 | Pinatubo 1991 | 850 | 2000 | 2 | 2.0 | 1123.15 | 67.547090 | 0.464064 | 16.860991 | 4.640640 | 2.175944 | 2.0 | 4.589077 | 1.722193 | 5 | NaN |
| 4 | Pinatubo 1991 | 850 | 2000 | 2 | 2.5 | 1123.15 | 67.202462 | 0.461696 | 16.774966 | 4.616963 | 2.164843 | 2.5 | 4.565663 | 1.713406 | 5 | NaN |
[8]:
figure_mosaic="""
AA
BC
"""
fig, axes=plt.subplot_mosaic(mosaic=figure_mosaic, figsize=(8,8))
axes['A'].set_title('SCAS calibration datasets')
import tasplot
import matplotlib.pyplot as plt
tasplot.add_LeMaitre_fields(axes['A'])
for t in axes['A'].texts[:]:
t.remove()
ms=3
axes['A'].plot(df_ZT2022['SiO2_Liq'],
df_ZT2022['Na2O_Liq']+df_ZT2022['K2O_Liq'],
'ok', mfc='c', ms=5, alpha=0.6, label='Zajacz and Tsay (2022)')
axes['A'].plot(df_MK_2015['SiO2_Liq'],
df_MK_2015['Na2O_Liq']+df_MK_2015['K2O_Liq'],
'.k', ms=2, label='Masotta & Keppler (2015)')
axes['A'].plot(df_CD2019['SiO2_Liq'],
df_CD2019['Na2O_Liq']+df_CD2019['K2O_Liq'],
'+r', mfc='red', ms=3, label='Chowdhury and Dasgupta (2019)')
axes['A'].plot(Pin['SiO2_Liq'],
Pin['Na2O_Liq']+Pin['K2O_Liq'] ,
'*k', mfc='yellow', ms=12, label='mydata')
axes['A'].set_xlim([30, 90])
y2='P_kbar'
x2='T_C'
axes['B'].plot(df_ZT2022[x2],
df_ZT2022[y2], 'ok', mfc='c', ms=5, alpha=0.6)
axes['B'].plot(df_MK_2015[x2],
df_MK_2015[y2], '.k', ms=2)
axes['B'].plot(df_CD2019[x2],
df_CD2019[y2], '+r', mfc='red', ms=3)
axes['B'].plot(Pin[x2],
Pin[y2] ,
'*k', mfc='yellow', ms=12)
x3='MgO_Liq'
y3='FeOt_Liq'
axes['C'].plot(df_ZT2022[x3],
df_ZT2022[y3], 'ok', mfc='c', ms=5, alpha=0.6)
axes['C'].plot(df_MK_2015[x3],
df_MK_2015[y3], '.k', ms=2)
axes['C'].plot(df_CD2019[x3],
df_CD2019[y3], '+r', mfc='red', ms=3)
axes['C'].plot(Pin[x3],
Pin[y3] ,
'*k', mfc='yellow', ms=12)
axes['A'].set_xlabel('SiO$_2$ Liq (wt%)')
axes['A'].set_ylabel('Na$_2$O + K$_2$O Liq (wt%)')
axes['B'].set_ylabel('P (kbar)')
axes['B'].set_xlabel('T (°C)')
axes['C'].set_xlabel('MgO Liq (wt%)')
axes['C'].set_ylabel('FeO$_T$ Liq (wt%)')
axes['A'].legend(fontsize=7)
fig.savefig('cali_datasets_SCAS.pdf', dpi=200)
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