File size: 4,739 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | #!/usr/bin/env python
"""EPC comparison plot for slides - APS style.
2 subplots:
1. AO (HPRO) vs ML (E3_AO)
2. DFT vs ML (E3_AO)
Each subplot colors points by transition type:
occ-occ, occ-cond (mixed), cond-cond
xlim/ylim start from 0 (plotting |g| magnitudes).
Output: pictures_ml/deep_h_epc.png
"""
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_PATH = '/home/apolyukhin/git/aps_slides/random_slides/pictures_ml/deep_h_epc.png'
N_OCC = 4
NK = 216
# =============================================================================
# APS slides style
# =============================================================================
marp_text_color = "#575279"
color_vv = "mediumseagreen" # occ-occ (pbe)
color_vc = "#b4637a" # occ-cond (hse)
color_cc = "#ea9d34" # cond-cond (kcw)
alpha = 0.4
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
out_dft_dir = os.path.join(DISP_DIR, 'out_dft')
out_hpro_dir = os.path.join(DISP_DIR, 'out_hpro_ao')
out_e3_dir = os.path.join(DISP_DIR, 'out_e3_ao')
print('Loading EPC data...')
g_dft = parse_epc_dir(out_dft_dir)
g_hpro = parse_epc_dir(out_hpro_dir)
g_e3 = parse_epc_dir(out_e3_dir)
print(f' DFT: {len(g_dft)} HPRO: {len(g_hpro)} E3: {len(g_e3)}')
# Use optical modes (nu >= 4) with non-negligible DFT value
optical_keys = [k for k in g_dft if k[3] >= 4 and abs(g_dft[k]) > 1e-4]
g_dft_arr = np.array([g_dft[k] for k in optical_keys]) * 1000 # meV
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
# Transition type masks — standard (for AO vs ML panel)
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_ao_ml = [
('occ-occ', is_vv, color_vv),
('occ-cond', is_vc, color_vc),
('cond-cond',is_cc, color_cc),
]
# Transition type masks — DFT vs ML: cond-cond restricted to 1st cond band only
N_OCC1 = N_OCC + 1
is_cc1 = np.array([k[1] == N_OCC1 and k[2] == N_OCC1 for k in optical_keys])
cats_dft_ml = [
('occ-occ', is_vv, color_vv),
('occ-cond', is_vc, color_vc),
('1st cond-cond', is_cc1, color_cc),
]
# =============================================================================
# Plot
# =============================================================================
def plot_panel(ax, g_x, g_y, label_x, label_y, cats):
x_abs = np.abs(g_x)
y_abs = np.abs(g_y)
lim = max(x_abs.max(), y_abs.max()) * 1.05
for cat_label, mask, color in cats:
if not mask.any():
continue
mae = np.mean(np.abs(g_y[mask] - g_x[mask]))
ax.scatter(x_abs[mask], y_abs[mask], s=4, alpha=alpha, color=color,
label=f'{cat_label} (MAE={mae:.1f} meV)', rasterized=True)
ax.plot([0, lim], [0, lim], '--', color=marp_text_color, lw=1.0, alpha=0.6)
ax.set_xlim(0, lim)
ax.set_ylim(0, lim)
ax.set_aspect('equal')
ax.set_xlabel(f'|g| {label_x} (meV)')
ax.set_ylabel(f'|g| {label_y} (meV)')
ax.legend(loc='upper left', framealpha=legend_alpha, fontsize=0.65*fontsize)
fig, axes = plt.subplots(1, 2, figsize=(14, 6), facecolor='none')
for ax in axes:
ax.set_facecolor('none')
plot_panel(axes[0], g_hpro_arr, g_e3_arr, 'AO', 'ML', cats_ao_ml)
plot_panel(axes[1], g_dft_arr, g_e3_arr, 'DFT', 'ML', cats_dft_ml)
plt.tight_layout()
plt.savefig(OUT_PATH, dpi=300, transparent=True, bbox_inches='tight')
print(f'Saved: {OUT_PATH}')
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