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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 26 00:50:46 2024
@author: chingchen
"""
import pandas as pd
import numpy as np
import numpy.ma as ma
# from matplotlib import cm
# import matplotlib as mpl
# from scipy.misc import derivative
import matplotlib.pyplot as plt
labelsize = 20
bwith = 3
### PATH ###
path = '/Users/chingchen/Desktop/data/'
workpath = '/Users/chingchen/Desktop/StagYY_Works/thermal_evolution_v2/'
modelpath = '/Users/chingchen/Desktop/model/'
figpath = '/Users/chingchen/Desktop/figure/'
colors=['#282130','#3CB371','#4682B4','#CD5C5C','#97795D','#414F67','#4198B9','#3CB371']
header_list = ['time_Gyr','Prad','Ptidal','Fcore','Pint','Hint','conv',
'melt','P','zbot','%vol','Tbot','Tm','Fbot','Ftop','dlid','T_core']
fig,(ax3,ax4) = plt.subplots(1,2,figsize=(17,7))
label_list=['vol = 0.%','vol = 1.5%','vol = 3.0%']
# ---------------------------------------- figure 3 --------------------------------
# model_list = ['Europa-tidal1_eta1.0d14_P1.2TW_0.0wt%-NH3',
# 'Europa-tidal1_eta1.0d14_P1.2TW_1.5wt%-NH3',
# 'Europa-tidal1_eta1.0d14_P1.2TW_3.0wt%-NH3',
# ]
#
# for i, model in enumerate(model_list):
# data = pd.read_csv(workpath+model+'_Hvar_2_thermal-evolution.dat',
# header=None,names=header_list,delim_whitespace=True)[2:].reset_index(drop=True)
# data = data.replace('D','e',regex=True).astype(float) # data convert to float
# x = data.time_Gyr
# mask_cond = data.conv
# mask_conv = ~ma.array(data.Tm, mask = data.conv).mask
# zbot_cond = ma.array(data.Tm, mask = mask_cond)
# zbot_conv = ma.array(data.Tm, mask = mask_conv)
# ax3.plot(x,zbot_conv,color=colors[i],label=label_list[i],lw=3)
# ax3.plot(x,zbot_cond,color='darkred')
# ---------------------------------------- figure 3 dash --------------------------------
# model_list = ['Europa-tidal1_eta1.0d14_P0.8TW_0.0wt%-NH3',
# 'Europa-tidal1_eta1.0d14_P0.8TW_1.5wt%-NH3',
# 'Europa-tidal1_eta1.0d14_P0.8TW_3.0wt%-NH3',
# ]
# for i, model in enumerate(model_list):
# data = pd.read_csv(workpath+model+'_Hvar_2_thermal-evolution.dat',
# header=None,names=header_list,delim_whitespace=True)[2:].reset_index(drop=True)
# data = data.replace('D','e',regex=True).astype(float) # data convert to float
# x = data.time_Gyr
# mask_cond = data.conv
# mask_conv = ~ma.array(data.Tm, mask = data.conv).mask
# zbot_cond = ma.array(data.Tm, mask = mask_cond)
# zbot_conv = ma.array(data.Tm, mask = mask_conv)
# ax3.plot(x,zbot_conv,color=colors[i],lw=3,linestyle='dashed')
# ax3.plot(x,zbot_cond,color='darkred')
# print(zbot_conv)
# ---------------------------------------- figure 3 dash 1.0 TW--------------------------------
model_list = ['Europa-tidal1_eta1.0d14_P1.0TW_0.0wt%-NH3',
'Europa-tidal1_eta1.0d14_P1.0TW_1.5wt%-NH3',
'Europa-tidal1_eta1.0d14_P1.0TW_3.0wt%-NH3',
]
for i, model in enumerate(model_list):
data = pd.read_csv(workpath+model+'_Hvar_2_thermal-evolution.dat',
header=None,names=header_list,delim_whitespace=True)[2:].reset_index(drop=True)
data = data.replace('D','e',regex=True).astype(float) # data convert to float
x = data.time_Gyr
mask_cond = data.conv
mask_conv = ~ma.array(data.Tm, mask = data.conv).mask
zbot_cond = ma.array(data.Tm, mask = mask_cond)
zbot_conv = ma.array(data.Tm, mask = mask_conv)
ax3.plot(x,zbot_conv,color=colors[i],lw=3,label=label_list[i])
ax3.plot(x,zbot_cond,color='darkred')
print(np.max(data.Tm[data.time_Gyr>2.5]),np.mean(data.Tm[data.time_Gyr>2.5]),np.min(data.Tm[data.time_Gyr>2.5]))
# ---------------------------------------- figure 4 --------------------------------
# model_list = ['Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P1.2TW_0.0wt%_D5.0km-NH3',# composition
# 'Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P1.2TW_1.5wt%_D5.0km-NH3',
# 'Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P1.2TW_3.0wt%_D5.0km-NH3',
# ]
# for i, model in enumerate(model_list):
# data = pd.read_csv(workpath+model+'_Hvar_2_thermal-evolution.dat',
# header=None,names=header_list,delim_whitespace=True)[2:].reset_index(drop=True)
# data = data.replace('D','e',regex=True).astype(float) # data convert to float
# x = data.time_Gyr
# mask_cond = data.conv
# mask_conv = ~ma.array(data.Tm, mask = data.conv).mask
# zbot_cond = ma.array(data.Tm, mask = mask_cond)
# zbot_conv = ma.array(data.Tm, mask = mask_conv)
# ax4.plot(x,zbot_conv,color=colors[i],label=label_list[i],lw=3)
# ax4.plot(x,zbot_cond,color='darkred')
# print(np.max(data.zbot[data.time_Gyr>2.5]),np.mean(data.zbot[data.time_Gyr>2.5]),np.min(data.zbot[data.time_Gyr>2.5]))
# ---------------------------------------- figure 4 dashed --------------------------------
# model_list = ['Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P0.8TW_0.0wt%_D5.0km-NH3',# composition
# 'Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P0.8TW_1.5wt%_D5.0km-NH3',
# 'Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P0.8TW_3.0wt%_D5.0km-NH3',
# ]
# for i, model in enumerate(model_list):
# data = pd.read_csv(workpath+model+'_Hvar_2_thermal-evolution.dat',
# header=None,names=header_list,delim_whitespace=True)[2:].reset_index(drop=True)
# data = data.replace('D','e',regex=True).astype(float) # data convert to float
# x = data.time_Gyr
# mask_cond = data.conv
# mask_conv = ~ma.array(data.Tm, mask = data.conv).mask
# zbot_cond = ma.array(data.Tm, mask = mask_cond)
# zbot_conv = ma.array(data.Tm, mask = mask_conv)
# ax4.plot(x,zbot_conv,color=colors[i],linestyle='dashed',lw=1)
# ax4.plot(x,zbot_cond,color='darkred',linestyle='dashed')
# print(np.max(data.zbot[data.time_Gyr>2.5]),np.mean(data.zbot[data.time_Gyr>2.5]),np.min(data.zbot[data.time_Gyr>2.5]))
# ---------------------------------------- figure 4 dashed 1.0 TW--------------------------------
model_list = ['Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P1.0TW_0.0wt%_D5.0km-NH3',# composition
'Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P1.0TW_1.5wt%_D5.0km-NH3',
'Europa-tidal5_period0.14Gyr_emx10%_eta1.0d14_P1.0TW_3.0wt%_D5.0km-NH3',
]
for i, model in enumerate(model_list):
data = pd.read_csv(workpath+model+'_Hvar_2_thermal-evolution.dat',
header=None,names=header_list,delim_whitespace=True)[2:].reset_index(drop=True)
data = data.replace('D','e',regex=True).astype(float) # data convert to float
x = data.time_Gyr
mask_cond = data.conv
mask_conv = ~ma.array(data.Tm, mask = data.conv).mask
zbot_cond = ma.array(data.Tm, mask = mask_cond)
zbot_conv = ma.array(data.Tm, mask = mask_conv)
ax4.plot(x,zbot_conv,color=colors[i],lw=2)
ax4.plot(x,zbot_cond,color='darkred')
print(np.max(data.Tm[data.time_Gyr>2.5]),np.mean(data.Tm[data.time_Gyr>2.5]),np.min(data.Tm[data.time_Gyr>2.5]))
# ------------------------------ figure setting ------------------------------
ax3.legend(fontsize=labelsize)
ax3.set_xlabel('Time (Gyr)',fontsize=labelsize)
ax4.set_xlabel('Time (Gyr)',fontsize=labelsize)
for aa in [ax3,ax4]:
aa.set_ylim(265,275)
aa.set_ylabel('T$_m$',fontsize = labelsize)
aa.minorticks_on()
# aa.tick_params(which='minor', length=5, width=2, direction='in')
aa.tick_params(labelsize=labelsize,width=3,length=10,right=True, top=True,direction='in',pad=15)
aa.set_xlim(0,4.55)
aa.grid()
for axis in ['top','bottom','left','right']:
aa.spines[axis].set_linewidth(bwith)
ax3.set_title('Viscosity = 10$^{14}$ with constant total power = 1.0 TW',fontsize=labelsize-2)
ax4.set_title('Viscosity = 10$^{14}$ with varying total power = 1.0 TW ',fontsize=labelsize-2)
# fig.savefig('/Users/chingchen/Desktop/StagYY_Works/paper_europa_ice_shell/figureSSS1_v2_tm.pdf')