3_epc/epc_distribution_slides.py

#!/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)