MPPNPy/read_models_abu.py at master · Bigash94/MPPNPy · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
from nugridpy import nugridse as mp
import matplotlib.pyplot as pl
import numpy as np
from collections import OrderedDict

#run=mp.se('../mppnp/z1m2/m2z1m2_he07/H5_surf','surf.h5')
#run1=mp.se('../mppnp/z1m2/m2z1m2_he07/Si30/H5_surf','surf.h5')
#run2=mp.se('../mppnp/z2m2/m2z2m2_he07/H5_surf','surf.h5')
#run3=mp.se('../mppnp/z2m2/m2z2m2_he07/Si30/H5_surf','surf.h5')
#run4=mp.se('../mppnp/z1m2/m3z1m2_he07/H5_surf','surf.h5')
#run5=mp.se('../mppnp/z1m2/m3z1m2_he07/Si30/H5_surf','surf.h5')
#run6=mp.se('../mppnp/z2m2/m3z2m2_he07/H5_surf','surf.h5')
#run7=mp.se('../mppnp/z2m2/m3z2m2_he07/Si30/H5_surf','surf.h5')

run=mp.se('../mppnp/z1m2/m2z1m2/H5_surf','surf.h5')
run1=mp.se('../mppnp/z2m2/m2z2m2/H5_surf','surf.h5')
run2=mp.se('../mppnp/z3m2/m2z3m2/H5_surf','surf.h5')

run_mass=[run,run1,run2]#,run3,run4,run5,run6,run7]
metallicity_label=['$M2.z1m2$','$M2.z2m2$','$M2.z3m2$']
#'$M2.z1m2\_he07,$ $Guber$ $et$ $al.$ $2003$','$M2.z1m2\_he07,$ $Beer$ $et$ $al.$ $2002$',\
#'$M2.z2m2\_he07,$ $Guber$ $et$ $al.$ $2003$','$M2.z2m2\_he07,$ $Beer$ $et$ $al.$ $2002$',\
#'$M3.z1m2\_he07,$ $Guber$ $et$ $al.$ $2003$','$M3.z1m2\_he07,$ $Beer$ $et$ $al.$ $2002$',\
#'$M3.z2m2\_he07,$ $Guber$ $et$ $al.$ $2003$','$M3.z2m2\_he07,$ $Beer$ $et$ $al.$ $2002$',] ## legend labels
sparcity_sindex=2000   ## sparcity to adopt reading s-process index data
sparcity_isoratio=2000 ## sparcity to adopt reading isotopic ratio data
markers=['gd','ro','b^','b*','kh','k>','r^','ro'] ## markers to use while plotting s-process indices

mg24_ini = []
mg25_ini = []
mg26_ini = []
al27_ini = []
si28_ini = []
si29_ini = []
si30_ini = []
p31_ini = []
s32_ini = []
s33_ini = []
s34_ini = []

for i in run_mass:

    dum_mg24_ini = 0.
    dum_mg25_ini = 0.
    dum_mg26_ini = 0.
    dum_al27_ini = 0.
    dum_si28_ini = 0.
    dum_si29_ini = 0.
    dum_si30_ini = 0.
    dum_p31_ini = 0.
    dum_s32_ini = 0.
    dum_s33_ini = 0.
    dum_s34_ini = 0.
    dum_mg24_ini = float(i.se.get(min(i.se.cycles),'iso_massf','Zr-90'))
    dum_mg25_ini = float(i.se.get(min(i.se.cycles),'iso_massf','Zr-91'))
    dum_mg26_ini = float(i.se.get(min(i.se.cycles),'iso_massf','Zr-92'))
    dum_al27_ini = float(i.se.get(min(i.se.cycles),'iso_massf','Zr-94'))
    dum_si28_ini = float(i.se.get(min(i.se.cycles),'iso_massf','Zr-96'))
    dum_si29_ini = float(i.se.get(min(i.se.cycles),'iso_massf','Si-29'))
    dum_si30_ini = float(i.se.get(min(i.se.cycles),'iso_massf','Si-30'))
    dum_p31_ini = float(i.se.get(min(i.se.cycles),'iso_massf','P-31'))
    dum_s32_ini = float(i.se.get(min(i.se.cycles),'iso_massf','S-32'))
    dum_s33_ini = float(i.se.get(min(i.se.cycles),'iso_massf','S-33'))
    dum_s34_ini = float(i.se.get(min(i.se.cycles),'iso_massf','S-34'))
    mg24_ini.append(dum_mg24_ini)
    mg25_ini.append(dum_mg25_ini)
    mg26_ini.append(dum_mg26_ini)
    al27_ini.append(dum_al27_ini)
    si28_ini.append(dum_si28_ini)
    si29_ini.append(dum_si29_ini)
    si30_ini.append(dum_si30_ini)
    p31_ini.append(dum_p31_ini)
    s32_ini.append(dum_s32_ini)
    s33_ini.append(dum_s33_ini)
    s34_ini.append(dum_s34_ini)

# sparcity for cycles I am looking at.
sparsity = sparcity_sindex
mg24_s=[]
mg25_s=[]
mg26_s=[]
al27_s=[]
si28_s=[]
si29_s=[]
si30_s=[]
p31_s=[]
s32_s=[]
s33_s=[]
s34_s=[]
k = 0

for i in run_mass:
    dum_mg24_s=[]
    dum_mg25_s=[]
    dum_mg26_s=[]
    dum_al27_s=[]
    dum_si28_s=[]
    dum_si29_s=[]
    dum_si30_s=[]
    dum_p31_s=[]
    dum_s32_s=[]
    dum_s33_s=[]
    dum_s34_s=[]

    for j in i.se.cycles[0::sparsity]:
        dum_mg24=0.
        dum_mg25=0.
        dum_mg26=0.
        dum_al27=0.
        dum_si28=0.
        dum_si29=0.
        dum_si30=0.
        dum_p31=0.
        dum_s32=0.
        dum_s33=0.
        dum_s34=0.
        print(j)
        dum_mg24=float(i.se.get(j,'iso_massf','Zr-90'))
        dum_mg25=float(i.se.get(j,'iso_massf','Zr-91'))
        dum_mg26=float(i.se.get(j,'iso_massf','Zr-92'))
        dum_al27=float(i.se.get(j,'iso_massf','Zr-94'))
        dum_si28=float(i.se.get(j,'iso_massf','Zr-96'))
        dum_si29=float(i.se.get(j,'iso_massf','Si-29'))
        dum_si30=float(i.se.get(j,'iso_massf','Si-30'))
        dum_p31=float(i.se.get(j,'iso_massf','P-31'))
        dum_s32=float(i.se.get(j,'iso_massf','S-32'))
        dum_s33=float(i.se.get(j,'iso_massf','S-33'))
        dum_s34=float(i.se.get(j,'iso_massf','S-34'))

    mg24_s.append(dum_mg24/dum_mg24_ini)
    mg25_s.append(dum_mg25/dum_mg25_ini)
    mg26_s.append(dum_mg26/dum_mg26_ini)
    al27_s.append(dum_al27/dum_al27_ini)
    si28_s.append(dum_si28/dum_si28_ini)
    si29_s.append(dum_si29/dum_si29_ini)
    si30_s.append(dum_si30/dum_si30_ini)
    p31_s.append(dum_p31/dum_p31_ini)
    s32_s.append(dum_s32/dum_s32_ini)
    s33_s.append(dum_s33/dum_s33_ini)
    s34_s.append(dum_s34/dum_s34_ini)
    k = k+1

mass_label =[]
for i in run_mass:
    mass_label.append(float(i.se.get('mini')))
params = {'text.usetex': True,
        'xtick.direction': 'in',
        'ytick.direction': 'in',
        'axes.linewidth' : 5,
        'xtick.major.size': 20,
        'ytick.major.size': 20,
        'xtick.labelsize': 30,
        'ytick.labelsize': 30,
        'ytick.major.pad': 5,
        'ytick.major.width': 3,
        'xtick.major.pad': 5,
        'xtick.major.width': 3}
pl.rcParams.update(params)
pl.tick_params(axis='both', pad=5,direction='in')
# Axes object: one row, one column, first plot (one plot!)
fig = pl.figure(1)            # Figure object
ax = fig.add_subplot(1,1,1)

for k in range(len(mg24_s)):
    pl.plot(90,mg24_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    pl.plot(91,mg25_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    pl.plot(92,mg26_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    pl.plot(94,al27_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    pl.plot(96,si28_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    #pl.plot(29,si29_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    #pl.plot(30,si30_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    #pl.plot(31,p31_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    #pl.plot(32,s32_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    #pl.plot(33,s33_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])
    #pl.plot(34,s34_s[k],markers[k],markersize=12.,linewidth=3.,label=metallicity_label[k])

handles, labels = pl.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
pl.legend(by_label.values(), by_label.keys(),prop={'size':25})
pl.xlim(89,97)
pl.xlabel('$A$', fontsize=40)
pl.ylabel('$X/X$_\odot$$', fontsize=40)
pl.show()