File size: 4,998 Bytes
e9e349d | 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 | #!/usr/bin/env python
"""EPC relative error distribution for slides - APS style.
Two separate figures:
1. AO vs ML: deep_h_epc_distribution.png
2. DFT vs ML: deep_h_epc_distribution_dft_ml.png
x-axis: reduced index (sorted by |g_ref| descending)
y-axis: relative error |g_ml - g_ref| / |g_ref|
Points colored by transition type: occ-occ, occ-cond, cond-cond
"""
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
DISP_DIR = os.path.join(SCRIPT_DIR, 'displacements')
OUT_AO_ML = '/home/apolyukhin/git/aps_slides/random_slides/pictures_ml/deep_h_epc_distribution.png'
OUT_DFT_ML = '/home/apolyukhin/git/aps_slides/random_slides/pictures_ml/deep_h_epc_distribution_dft_ml.png'
N_OCC = 4
NK = 216
# =============================================================================
# APS slides style
# =============================================================================
marp_text_color = "#575279"
color_vv = "mediumseagreen"
color_vc = "#b4637a"
color_cc = "#ea9d34"
alpha = 0.3
legend_alpha = 0.5
fontsize = 22
plt.rcParams.update({
'font.size': fontsize,
'mathtext.fontset': 'cm',
'text.color': marp_text_color,
'axes.labelcolor': marp_text_color,
'xtick.color': marp_text_color,
'ytick.color': marp_text_color,
'axes.edgecolor': marp_text_color,
'axes.labelpad': 10,
})
# =============================================================================
# Data loading
# =============================================================================
def parse_epc_dir(dir_path, nk=NK):
g = {}
for ik in range(1, nk + 1):
fn = os.path.join(dir_path, f'comparison_{ik}_1.txt')
if not os.path.isfile(fn):
continue
with open(fn) as f:
for line in f:
cols = line.split()
if len(cols) < 8:
continue
i, j, nu = int(cols[0]), int(cols[1]), int(cols[2])
g[(ik, i, j, nu)] = float(cols[7])
return g
print('Loading EPC data...')
g_dft = parse_epc_dir(os.path.join(DISP_DIR, 'out_dft'))
g_hpro = parse_epc_dir(os.path.join(DISP_DIR, 'out_hpro_ao'))
g_e3 = parse_epc_dir(os.path.join(DISP_DIR, 'out_e3_ao'))
# Optical modes with non-negligible DFT value, up to first conduction band
N_OCC1 = N_OCC + 1
optical_keys = [k for k in g_dft if k[3] >= 4 and abs(g_dft[k]) > 1e-4
and k[1] <= N_OCC1 and k[2] <= N_OCC1]
g_dft_arr = np.array([g_dft[k] for k in optical_keys]) * 1000
g_hpro_arr = np.array([g_hpro.get(k, 0.0) for k in optical_keys]) * 1000
g_e3_arr = np.array([g_e3.get(k, 0.0) for k in optical_keys]) * 1000
is_vv = np.array([k[1] <= N_OCC and k[2] <= N_OCC for k in optical_keys])
is_cc = np.array([k[1] > N_OCC and k[2] > N_OCC for k in optical_keys])
is_vc = ~is_vv & ~is_cc
cats = [
('occ-occ', is_vv, color_vv),
('occ-cond', is_vc, color_vc),
('cond-cond', is_cc, color_cc),
]
# =============================================================================
# Plot
# =============================================================================
def make_plot(g_ref, g_ml, label_ref, label_ml, out_path,
clip_pct=99, rect_x_min=50.0, rect_face_alpha=0.15, rect_edge_lw=1.5):
abs_err = np.abs(g_ml - g_ref) / np.abs(g_ref) * 100
clip_val = np.percentile(abs_err, clip_pct)
x = np.abs(g_ref)
y = np.minimum(abs_err, clip_val)
fig, ax = plt.subplots(figsize=(10, 5.5), facecolor='none')
ax.set_facecolor('none')
ax.scatter(x, y, s=2, alpha=alpha, color=marp_text_color, rasterized=True)
# Colored rectangle: x >= rect_x_min, height = max error for those points
x_max = x.max() * 1.02
mask_sig = x >= rect_x_min
P = abs_err[mask_sig].max()
from matplotlib.patches import Rectangle
import matplotlib.colors as mcolors
rgb = mcolors.to_rgb(color_vv)
rect = Rectangle((rect_x_min, 0), x_max - rect_x_min, P,
linewidth=rect_edge_lw,
edgecolor=(*rgb, 1.0),
facecolor=(*rgb, rect_face_alpha),
zorder=0,
label=f'$\\forall\\,|g|>{rect_x_min:.0f}$ meV, $|\\delta g|<{P:.1f}\\%$')
ax.add_patch(rect)
ax.set_xlabel(f'$|g_{{\\rm {label_ref}}}|$ (meV)')
ax.set_ylabel(f'$|g_{{\\rm {label_ml}}}-g_{{\\rm {label_ref}}}|/|g_{{\\rm {label_ref}}}|$ (%)')
ax.set_xlim(0, x_max)
ax.set_ylim(0, clip_val * 1.05)
ax.legend(loc='upper right', framealpha=legend_alpha, fontsize=0.7*fontsize)
plt.tight_layout()
plt.savefig(out_path, dpi=300, transparent=True, bbox_inches='tight')
plt.close(fig)
print(f'Saved: {out_path}')
make_plot(g_hpro_arr, g_e3_arr, 'AO', 'ML', OUT_AO_ML)
make_plot(g_dft_arr, g_e3_arr, 'DFT', 'ML', OUT_DFT_ML,
rect_face_alpha=0.18, rect_edge_lw=2.5)
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