-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmake_obj_space_hist_plot.py
169 lines (150 loc) · 4.39 KB
/
make_obj_space_hist_plot.py
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
import os
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rcParams
from adjustText import adjust_text
thisdir = os.path.dirname(os.path.abspath(__file__))
volc_param_path = os.path.join(thisdir, "..", "..", "data", "raw_volc_m_b.csv")
RAW_VOLC_PARAMS = pd.read_csv(volc_param_path).to_numpy()
CANDIDATES_TO_LABEL = [
"Hf$_\mathregular{1}$Cr",
"Zr$_\mathregular{1}$Cr",
"Ti$_\mathregular{1}$Fe",
"Au$_\mathregular{1}$Re",
"Ag$_\mathregular{1}$Re",
]
HHI = np.loadtxt("HHI_CANDIDATES.txt")
SEG_ENER = np.loadtxt("SEG_CANDIDATES.txt")
dGN = np.loadtxt("dGN_CANDIDATES.txt")
HHI_init = np.loadtxt("HHI_INIT.txt")
SEG_ENER_init = np.loadtxt("SEG_INIT.txt")
dGN_init = np.loadtxt("dGN_INIT.txt")
with open("FORMULAS.json", "r") as f:
formulas = json.load(f)
def volcano(dG):
strong_leg = RAW_VOLC_PARAMS[0, 1] + RAW_VOLC_PARAMS[0, 0] * dG
weak_leg = RAW_VOLC_PARAMS[1, 1] + RAW_VOLC_PARAMS[1, 0] * dG
return np.minimum(strong_leg, weak_leg)
plt.style.use("seaborn-v0_8-ticks")
rcParams.update(
{
"font.family": "sans-serif",
"axes.labelsize": 18,
"xtick.labelsize": 14,
"ytick.labelsize": 14,
"xtick.major.size": 7,
"ytick.major.size": 7,
"xtick.major.width": 2.0,
"ytick.major.width": 2.0,
"font.size": 20,
"axes.linewidth": 2.0,
"lines.dashed_pattern": (5, 2.5),
"lines.markersize": 10,
"lines.linewidth": 3,
"lines.markeredgewidth": 2,
"lines.markeredgecolor": "k",
"legend.fontsize": 16,
"legend.frameon": True,
}
)
fig, (axins, ax) = plt.subplots(1, 2, figsize=(16, 6))
cmap = mpl.cm.autumn_r
ax.scatter(HHI_init, SEG_ENER_init, c="grey", alpha=0.5)
ax.scatter(HHI, SEG_ENER, c=range(len(HHI)), cmap=cmap, ec="k")
ax.set_xlabel("Normalized HHI")
ax.set_ylabel("Segregation Energy (eV)")
ax.text(
0.3,
0.6,
"Stable",
c="k",
rotation=90,
bbox=dict(boxstyle="larrow,pad=0.3", lw=2, fc="mediumseagreen"),
transform=ax.transAxes,
)
ax.text(
0.3,
0.9,
"Economical",
c="k",
bbox=dict(boxstyle="rarrow,pad=0.3", lw=2, fc="mediumseagreen"),
transform=ax.transAxes,
)
xlims = ax.get_xlim()
ylims = ax.get_ylim()
ax.scatter(
[0.9807015550000001],
[-2.1695],
marker="*",
s=500,
c="mediumseagreen",
ec="k",
lw=1.5,
)
ax.set_xlim(0.08526725125, 1.0233412837500002)
ax.set_ylim(-2.3449750000000003, 1.5154750000000001)
x = np.linspace(-2.5, 1.0, 3000)
axins.plot(x, volcano(x), c="k")
axins.scatter(dGN, volcano(dGN), zorder=100, ec="k", c=range(len(dGN)), cmap=cmap)
axins.scatter(dGN_init, volcano(dGN_init), zorder=50, c="grey", alpha=0.5)
axins.set_xlim(-2.5, 1)
axins.set_ylim(-1.75, -0.5)
axins.set_ylabel("U$_\mathrm{L}$ (V)")
axins.set_xlabel("$\Delta G_{\mathrm{N}}$ (eV)")
axins.axvspan(-1.1767, -0.5767, facecolor="mediumseagreen", alpha=0.3)
norm = mpl.colors.Normalize(vmin=1, vmax=len(dGN))
fig.colorbar(
mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, label="Iteration Count"
)
# Volcano Annotation
volcano_texts = []
for idx, name in enumerate(formulas):
if name in CANDIDATES_TO_LABEL:
volcano_texts.append(
axins.text(
s=" " + name,
x=dGN[idx],
y=volcano(dGN[idx]),
fontsize="x-small",
fontweight="heavy",
),
)
adjust_text(
volcano_texts,
x=x,
y=volcano(x),
expand_text=(1.5, 1.5),
# expand_points=(2.4, 2.4),
expand_points=(3.0, 3.0),
ax=axins,
force_points=(0.0005, 0.0005),
arrowprops=dict(arrowstyle="-|>", color="k", lw=1.5),
)
# HHI vs SEG annotation
hhi_seg_texts = []
for idx, name in enumerate(formulas):
if name in CANDIDATES_TO_LABEL:
hhi_seg_texts.append(
ax.text(
s=" " + name,
x=HHI[idx],
y=SEG_ENER[idx],
fontsize="x-small",
fontweight="heavy",
),
)
adjust_text(
hhi_seg_texts,
x=np.concatenate((HHI, HHI_init)),
y=np.concatenate((SEG_ENER, SEG_ENER_init)),
expand_points=(2.8, 3.0),
expand_text=(1.5, 3.5),
ax=ax,
force_points=(0.1, 0.01),
force_text=(0.7, 1.0),
arrowprops=dict(arrowstyle="-|>", color="k", lw=1.5),
)
plt.savefig("search_trajectory.png", bbox_inches="tight", dpi=200)