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read_models_Ti.py
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275 lines (249 loc) · 15.1 KB
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from nugridpy import nugridse as mp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from collections import OrderedDict
import presolargrains as psg
## range of isotopic ratios
isotopes_x = ['Ti-46','Ti-48']
isotopes_y = ['Ti-47','Ti-48']
#isotopes_y = ['Ti-49','Ti-48']
#isotopes_y = ['Ti-50','Ti-48']
## References in data
# 'Alexander ApJ 1999', 'Alexander GCA 1993',
# 'Alexander GCA 1994', 'Alexander GCA 1995', 'Amari ApJ 1992',
# 'Amari ApJ 1999', 'Amari ApJ 2001', 'Amari ApJ 2001 (nova)',
# 'Amari unpublished (2003)', 'Amari unpublished 1992',
# 'Avila ApJ 2012', 'Barzyk MAPS 2007', 'Barzyk MAPS 2008',
# 'Besmehn GCA 2003', 'Besmehn Thesis 2001', 'Bose GCA 2012',
# 'Busemann E&P Ltrs 2009', 'Croat Astron. J. 2010',
# 'Floss MAPS 2012', 'Fujiya ApJ Letters 2011',
# 'Fujiya ApJ Letters 2013', 'Gao LPSC 1997', 'Gyngard LPSC 2006',
# 'Gyngard MetSoc 2006', 'Gyngard MetSoc 2010',
# 'Gyngard unpublished 2009', 'Heck ApJ 2007', 'Heck ApJ 2009',
# 'Henkel MAPS 2007', 'Hoppe ApJ 1994', 'Hoppe ApJ 1997',
# 'Hoppe ApJ 2001', 'Hoppe ApJ 2009', 'Hoppe ApJ 2010',
# 'Hoppe ApJ Letters 2012', 'Hoppe GCA 1996', 'Hoppe LPSC 1996',
# 'Hoppe MAPS 2000', 'Hoppe Science 1996', 'Hoppe unpublished',
# 'Hoppe unpublished ', 'Huss GCA 1997', 'Huss MAPS 2007',
# 'Hynes LPSC 2006', 'Hynes LPSC 2008', 'Ireland ApJ 1991',
# 'Jennings LPSC 2002', 'Lin ApJ 2002', 'Liu ApJ 2014', 'Liu ApJ 2015',
# 'Lyon LPSC 2006', 'Marhas ApJ 2008', 'Marhas LPSC 2004',
# 'Marhas LPSC 2005', 'Marhas LPSC 2007', 'Marhas MAPS 2007',
# 'Marhas Met Soc 2005', 'Nguyen GCA 2018', 'Nicolussi GCA 1998',
# 'Nicolussi Phys Rev Ltrs 1998', 'Nicolussi Science 1997',
# 'Nittler ApJ 1996', 'Nittler ApJ 2005', 'Nittler GCA 2003',
# 'Nittler Met Soc 2006', 'Nittler Thesis 1996',
# 'Orthous-Daunay LPSC 2012', 'Pellin LPSC 2000', 'Pellin LPSC 2006',
# 'Reference', 'Savina GCA 2003', 'Savina Science 2004',
# 'Stadermann GCA 2006', 'Stroud Met Soc 2004', 'Trappitsch GCA 2018'
# 'Virag GCA 1992', 'Wopenka LPSC 2010', 'Xu Metsoc 2012',
# 'Yada MAPS 2008','Zhao ApJ 2013', 'Zinner GCA 2007', 'Zinner LPSC 2010',
# 'Zinner MAPS 2003'
reference = 'Nguyen GCA 2018'
reference1 = 'Gyngard GCA 2018'
## class of presolar grain
# '', 'AB', 'C', 'HM only', 'M', 'MS', 'N', 'Type', 'U', 'U/C', 'X',
# 'X ', 'Y', 'Z', 'uncl.', 'unclass'
clas_1 = 'M'
clas_6 = 'AB'
#run2=mp.se('../mppnp/z3m2/m2z3m2/ll22/H5_surf','surf.h5')
#run3=mp.se('../mppnp/z3m2/m2z3m2/2ll22/H5_surf','surf.h5')
#run6=mp.se('../mppnp/z2m2/m3z2m2/Fe56/H5_surf','surf.h5')
run1=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m2z1m2/H5_surf','surf.h5')
run2=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m2z2m2/H5_surf','surf.h5')
run3=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m2z3m2/H5_surf','surf.h5')
run5=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m3z1m2/H5_surf','surf.h5')
run6=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m3z2m2/H5_surf','surf.h5')
run7=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m3z3m2/H5_surf','surf.h5')
#run8=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m3z2m2_hCBM_TDUx4p3/H5_surf','surf.h5')
#run9=mp.se('/run/media/s1785107/Seagate Backup Plus Drive/eddie3/NuclearPhysics/NuPPN/frames/mppnp/RUN_set1upd_m3z3m2_hCBM_TDUx4p3/H5_surf','surf.h5')
#run5=mp.se('../mppnp/z1m2/m1p65z1m2/H5_surf','surf.h5')
#run6=mp.se('../mppnp/z2m2/m1p65z2m2/H5_surf','surf.h5')
##run6=mp.se('../mppnp/z1m2/m3z1m2/kadon1/H5_surf','surf.h5')
##run7=mp.se('../mppnp/z1m2/m3z1m2/kadon1_Ni/H5_surf','surf.h5')
#run8=mp.se('../mppnp/z2m2/m3z2m2/As09/H5_surf','surf.h5')
#run9=mp.se('../mppnp/z2m2/m3z2m2/2Ni58/H5_surf','surf.h5')
#run10=mp.se('../mppnp/z2m2/m3z2m2/0.5Ni58/H5_surf','surf.h5')
#run11=mp.se('../mppnp/z2m2/m3z2m2/As05/H5_surf','surf.h5')
##run12=mp.se('../mppnp/z1m2/m3z1m2/kadon1_FeNi/H5_surf','surf.h5')
#run2=mp.se('../mppnp/z1m2/m2z1m2_he07/H5_surf','surf.h5')
#run3=mp.se('../mppnp/z1m2/m2z1m2/H5_surf_critter','surf.h5')
#run4=mp.se('../mppnp/z2m2/m2z2m2_he07/H5_surf','surf.h5')
#run5=mp.se('../mppnp/z2m2/m2z2m2/H5_surf_critter','surf.h5')
#run6=mp.se('../mppnp/z1m2/m3z1m2_he07/H5_surf','surf.h5')
#run7=mp.se('../mppnp/z1m2/m3z1m2/H5_surf_critter','surf.h5')
#run8=mp.se('../mppnp/z2m2/m3z2m2_he07/H5_surf','surf.h5')
#run9=mp.se('../mppnp/z2m2/m3z2m2_he07/CNSi/H5_surf','surf.h5')
#run9=mp.se('../mppnp/z2m2/m3z2m2/H5_surf_critter','surf.h5')
#run1=mp.se('../mppnp/z2m2/m3z2m2_he07/H5_surf','surf.h5')
#run2=mp.se('../mppnp/z2m2/m3z2m2_he07/ll13/H5_surf','surf.h5')
#run3=mp.se('../mppnp/z2m2/m3z2m2_he07/ll22/H5_surf','surf.h5')
#run4=mp.se('../mppnp/z2m2/m3z2m2_he07/ll31/H5_surf','surf.h5')
#run_mass=[run1,run2,run3,run4,run5,run6,run7,run8,run9,run10,run11,run12,run13]
run_mass=[run1,run2,run3,run5,run6,run7]
metallicity_label=[#'$M1p65.z1m2$','$M1p65.z2m2$']
#'$M3.z1m2$']#,'$M3.z1m2$ $KADoNiS$ $1.0$','$M3.z1m2$ $KADoNiS$ $1.0,$ $Ni$','$M3.z1m2$ $KADoNiS$ $1.0,$ $FeNi$']
#'$M3.z2m2$ $KADoNiS$ $0.3$ $Asplund$ $2009$','$M3.z2m2$ $KADoNiS$ $0.3$ $2xNi58$ $iniab$','$M3.z2m2$ $KADoNiS$ $0.3$ $0.5xNi58$ $iniab$','$M3.z2m2$ $KADoNiS$ $0.3$ $Asplund$ $2005$']
'$M2.z1m2$','$M2.z2m2$','$M2.z3m2$','$M3.z1m2$','$M3.z2m2$','$M3.z3m2$']
#'$M2.z6m3\_hCBM$','$M3.z6m3\_hCBM$','$M2.z1m2\_hCBM$','$M2.z2m2\_hCBM$','$M3.z2m2\_hCBM$']
#'$M1p86.z2m2\_hCBM$','$M1p86.z2m2\_hCBM,$ $Ne22$','$M1p86.z2m2,$ $Ne22,$ $3x$ $Fe56(n,\gamma)$','$M2.z2m2$','$M2.z2m2\_hCBM$']
#'$M2.z1m2$','$M2.z2m2,$ $Ne22$ $\uparrow(\\alpha,\gamma)\downarrow(\\alpha,n)$','$M2.z1m2,$ $Ne22,$ $3x$ $Fe56(n,\gamma)$','$M2.z1m2,$ $Ne22,$ $2x$ $Fe56(n,\gamma)$','$M2.z1m2,$ $Ne22,$ $1.5x$ $Fe56(n,\gamma)$',\
#'$M2.z1m2\_hCBM$','$M2.z1m2\_hCBM,$ $Ne22$ $\uparrow(\\alpha,\gamma)\downarrow(\\alpha,n)$','$M2.z1m2\_hCBM,$ $Ne22,$ $3x$ $Fe56(n,\gamma)$','$M2.z1m2\_hCBM,$ $Ne22,$ $2x$ $Fe56(n,\gamma)$','$M2.z1m2\_hCBM,$ $Ne22,$ $`1.5x$ $Fe56(n,\gamma)$']
#'$M2.z1m2,$ $Ne22,$ $1.5x$ $Fe56(n,\gamma)$','$M2.z1m2,$ $Ne22,$ $2x$ $Fe56(n,\gamma)$','$M2.z1m2,$ $1.15x$ $Fe56(n,\gamma)$','$M2.z1m2,$ $Ne22,$ $3x$ $Fe56(n,\gamma)$']
#'$M3.z2m2\_he07$','$M3.z2m2\_he07,$ $Longland$ $13$','$M3.z2m2\_he07,$ $Longland$ $22$','$M3.z2m2\_he07,$ $Longland$ $31$']
#'$M2.z3m2$','$M2.z3m2$ $Longland$ $22$','$M2.z3m2$ $Longland$ $2x22/2$']
#'$M1p65.z2m2$','$M2.z2m2$','$M3.z2m2$']
#'$M2.z2m2$','$M2.z2m2,$ $Ritter$ $et$ $al.$ $2017$']
#['$M1p65.z2m2$','$M1p65.z2m2,$ $Ritter$ $et$ $al.$ $2017$','$M3.z1m2\_lowres$','$M3.z1m2$','$M3.z1m2,$ $Ritter$ $et$ $al.$ $2017$']
#['$M2.z1m2\_he07$','$M2.z1m2\_R17$','$M2.z2m2\_he07$','$M2.z2m2\_R17$','$M3.z1m2\_he07$','$M3.z1m2\_R17$','$M3.z2m2\_he07$','$M3.z2m2\_R17$'] ## legend labels
sparcity_sindex=2000 ## sparcity to adopt reading s-process index data
sparcity_isoratio=2000 ## sparcity to adopt reading isotopic ratio data
markers=['k','tab:blue', 'tab:cyan', 'tab:green', 'tab:olive', 'tab:gray', 'tab:brown', 'y', 'tab:purple', 'tab:pink', 'tab:orange','tab:red']## markers to use while plotting s-process indices
symbols=['h','s','^','>','D','<','p','d','^','s','o'] ## linestyles to use while plotting s-process indices
line=['solid','solid','solid','solid','solid','solid','dashed','dotted']
## IF ISO_RATIO IS TRUE THIS SETS THE ISOTOPIC RATIOS YOU WANT TO PLOT
a = [''.join( isotopes_x[0].split('-')[::-1] ),''.join( isotopes_x[1].split('-')[::-1] )]
b = '/'.join(a)
c = str('d()')
iso_ratio_1 = c[:2] + b + c[2:]
d = [''.join( isotopes_y[0].split('-')[::-1] ),''.join( isotopes_y[1].split('-')[::-1] )]
e = '/'.join(d)
iso_ratio_2 = c[:2] + e + c[2:]
# load data
df = pd.read_csv('Ti_grains.txt', sep='\t',usecols=[1,2,16,17,18,19,20,21,22,23],engine='python')
df = df.replace(r'\s+', np.nan, regex=True)
df = df.fillna(value=0.0)
df_plot10 = df[((df['GRP']==clas_6))]
df1 = pd.read_csv('Ti-agb-grains.txt', sep='\s*',skiprows=[0,1,3,242,243,244],engine='python')
df_plot1 = df1[((df1['Type']==clas_1))]
df_plot11 = df1[((df1['Type']==clas_6))]
# data sets
x = df_plot10[iso_ratio_1].values
y = df_plot10[iso_ratio_2].values
xerr = df_plot10['Error['+iso_ratio_1+']'].values
yerr = df_plot10['Error['+iso_ratio_2+']'].values
x1 = df_plot1[iso_ratio_1].values
y1 = df_plot1[iso_ratio_2].values
xerr1 = df_plot1['Error['+iso_ratio_1+']'].values
yerr1 = df_plot1['Error['+iso_ratio_2+']'].values
x11 = df_plot11[iso_ratio_1].values
y11 = df_plot11[iso_ratio_2].values
xerr11 = df_plot11['Error['+iso_ratio_1+']'].values
yerr11 = df_plot11['Error['+iso_ratio_2+']'].values
# adding in LaTex
aa = ['}}$'.join( isotopes_x[0].split('-')[::-1] ),'}}$'.join( isotopes_x[1].split('-')[::-1] )]
bb = '/'.join(aa)
cc = str('d(${^{)')
iso_ratio_11 = (cc[:6] + bb + cc[6:]).replace('/','/${^{')
dd = ['}}$'.join( isotopes_y[0].split('-')[::-1] ),'}}$'.join( isotopes_y[1].split('-')[::-1] )]
ee = '/'.join(dd)
iso_ratio_22 = (cc[:6] + ee + cc[6:]).replace('/','/${^{')
if iso_ratio_11[0] == 'd':
iso_ratio_111=iso_ratio_11.replace('d','$\delta$')
if iso_ratio_22[0] == 'd':
iso_ratio_222=iso_ratio_22.replace('d','$\delta$')
# get initial value
initial_isotopic_ratio_x = []
initial_isotopic_ratio_y = []
# sparcity for cycles I am looking at.
sparsity = sparcity_isoratio
isotopic_ratio_x = []
isotopic_ratio_y = []
isotopic_ratio_x_tps = []
isotopic_ratio_y_tps = []
isotopic_ratio_x_tps_co = []
isotopic_ratio_y_tps_co = []
k = 0
for i in run_mass:
initial_isotopic_ratio_x.append(float(i.se.get(min(i.se.cycles),'iso_massf',isotopes_x[0]))/float(i.se.get(min(i.se.cycles),'iso_massf',isotopes_x[1])))
initial_isotopic_ratio_y.append(float(i.se.get(min(i.se.cycles),'iso_massf',isotopes_y[0]))/float(i.se.get(min(i.se.cycles),'iso_massf',isotopes_y[1])))
dum_isotopic_ratio_x = []
dum_isotopic_ratio_y = []
dumdum_isotopic_ratio_x = []
dumdum_isotopic_ratio_y = []
dum_isotopic_ratio_x_tps = []
dum_isotopic_ratio_y_tps = []
dum_isotopic_ratio_x_tps_co = []
dum_isotopic_ratio_y_tps_co = []
co_ratio=[]
for j in i.se.cycles[0::sparsity]:
print(j)
dum_isotopic_ratio_x.append(float(i.se.get(j,'iso_massf',isotopes_x[0]))/float(i.se.get(j,'iso_massf',isotopes_x[1])))
dum_isotopic_ratio_y.append(float(i.se.get(j,'iso_massf',isotopes_y[0]))/float(i.se.get(j,'iso_massf',isotopes_y[1])))
co_ratio.append((float(i.se.get(j,'iso_massf','C-12')*4.))/(float(i.se.get(j,'iso_massf','O-16'))*3))
if (len(co_ratio)>1):
if (co_ratio[len(co_ratio)-1]>(co_ratio[len(co_ratio)-2]+0.02)):
dum_isotopic_ratio_x_tps.append(float(i.se.get(j,'iso_massf',isotopes_x[0]))/float(i.se.get(j,'iso_massf',isotopes_x[1])))
dum_isotopic_ratio_y_tps.append(float(i.se.get(j,'iso_massf',isotopes_y[0]))/float(i.se.get(j,'iso_massf',isotopes_y[1])))
if (co_ratio[len(co_ratio)-1]>1.):
dum_isotopic_ratio_x_tps_co.append(float(i.se.get(j,'iso_massf',isotopes_x[0]))/float(i.se.get(j,'iso_massf',isotopes_x[1])))
dum_isotopic_ratio_y_tps_co.append(float(i.se.get(j,'iso_massf',isotopes_y[0]))/float(i.se.get(j,'iso_massf',isotopes_y[1])))
dumdum_isotopic_ratio_x=(np.array(dum_isotopic_ratio_x)/initial_isotopic_ratio_x[-1:]-1.)*1000.
dumdum_isotopic_ratio_y=(np.array(dum_isotopic_ratio_y)/initial_isotopic_ratio_y[-1:]-1.)*1000.
dumdum_isotopic_ratio_x_tps=(np.array(dum_isotopic_ratio_x_tps)/initial_isotopic_ratio_x[-1:]-1.)*1000.
dumdum_isotopic_ratio_y_tps=(np.array(dum_isotopic_ratio_y_tps)/initial_isotopic_ratio_y[-1:]-1.)*1000.
dumdum_isotopic_ratio_x_tps_co=(np.array(dum_isotopic_ratio_x_tps_co)/initial_isotopic_ratio_x[-1:]-1.)*1000.
dumdum_isotopic_ratio_y_tps_co=(np.array(dum_isotopic_ratio_y_tps_co)/initial_isotopic_ratio_y[-1:]-1.)*1000.
isotopic_ratio_x.append(dumdum_isotopic_ratio_x)
isotopic_ratio_y.append(dumdum_isotopic_ratio_y)
isotopic_ratio_x_tps.append(dumdum_isotopic_ratio_x_tps)
isotopic_ratio_y_tps.append(dumdum_isotopic_ratio_y_tps)
isotopic_ratio_x_tps_co.append(dumdum_isotopic_ratio_x_tps_co)
isotopic_ratio_y_tps_co.append(dumdum_isotopic_ratio_y_tps_co)
k = k+1
mass_label =[]
for i in run_mass:
mass_label.append(float(i.se.get('mini')))
array_to_plot_x_tps = isotopic_ratio_x_tps
array_to_plot_y_tps = isotopic_ratio_y_tps
array_to_plot_x_tps_co = isotopic_ratio_x_tps_co
array_to_plot_y_tps_co = isotopic_ratio_y_tps_co
plt.rcParams['text.usetex'] = True
#plt.rcParams['axes.linewidth'] = 5
plt.rcParams['xtick.major.size'] = 20
plt.rcParams['xtick.major.width'] = 2#3
plt.rcParams['ytick.major.size'] = 20
plt.rcParams['ytick.major.width'] = 2#3
plt.rcParams['xtick.labelsize'] = 30
plt.rcParams['ytick.labelsize'] = 30
plt.tick_params(axis='both',pad=5,direction='in')
plt.axhline(y=0.,linewidth=2, color='k')
plt.axvline(x=0.,linewidth=2, color='k')
plt.errorbar(x.astype(np.float64),y.astype(np.float64),xerr=2*xerr.astype(np.float64),yerr=2*yerr.astype(np.float64),marker=symbols[-2],c=markers[-2],ls='None',markersize=8.,label=reference+', '+clas_6)
plt.errorbar(x1.astype(np.float64),y1.astype(np.float64),xerr=2*xerr1.astype(np.float64),yerr=2*yerr1.astype(np.float64),marker=symbols[-1],c=markers[-1],ls='None',markersize=8.,label=reference1+', '+clas_1)
plt.errorbar(x11.astype(np.float64),y11.astype(np.float64),xerr=2*xerr11.astype(np.float64),yerr=2*yerr11.astype(np.float64),marker=symbols[-3],c=markers[-3],ls='None',markersize=8.,label=reference1+', '+clas_6)
for k in range(0,len(array_to_plot_x_tps)):
plt.plot(array_to_plot_x_tps[k],array_to_plot_y_tps[k],c=markers[k],ls=line[k],markersize=10.,linewidth=2.)
plt.plot(array_to_plot_x_tps_co[k],array_to_plot_y_tps_co[k],marker=symbols[k],c=markers[k],ls=line[k],markersize=20.,linewidth=2.,label=str(metallicity_label[k]))
plt.xlabel(iso_ratio_111, fontsize=40)
plt.ylabel(iso_ratio_222, fontsize=40)
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys(),prop={'size':25})
plt.show()
labels = df_plot10['GRAIN'].values
labels1 = df_plot1['Label'].values
labels2 = df_plot11['Label'].values
for label, x, y in zip(labels, x, y):
plt.annotate(
label,
xy=(x, y), xytext=(-20, 20),
textcoords='offset points', #ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='white', alpha=0.5),
arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0'))
for label2, x1, y1 in zip(labels1, x1, y1):
plt.annotate(
label2,
xy=(x1, y1), xytext=(-20, 20),
textcoords='offset points', #ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='white', alpha=0.5),
arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0'))
for label2, x11, y11 in zip(labels2, x11, y11):
plt.annotate(
label2,
xy=(x11, y11), xytext=(-20, 20),
textcoords='offset points', #ha='right', va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='white', alpha=0.5),
arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0'))
#plt.show()