

Improved version of Absorption Spectra v1 Made with help from Chatgpt and Google Colab. Here are the Python script and sample output:
#!/usr/bin/env python3 """ generate_spectra_with_labels.py Generate a 4096×4096-pixel image of the absorption spectra for elements Z=1–128, laid out in 4 columns × 32 rows, using every 24-bit RGB color exactly once. Modifications: 1. Rainbow-style background for known-spectrum tiles goes from purple→red (hue rotated +60°). 2. 1-pixel “white” border around each tile and 2-pixel “white” border around the entire image, all drawn with the lightest available colors. 3. Each tile displays the chemical name (DejaVuSans, 32 px, no anti-aliasing) at its top-left, positioned 2 px from outer & tile borders, using the next-lightest available colors. Labels are drawn by masking, not textboxes, so no clipping or anti-aliasing. 4. The IUPAC systematic names are used for Z > 118. - Absorption lines from NIST (380–780 nm) use the darkest colors (excluding borders & labels). - Known-spectrum background: smooth rotated-hue gradient (violet→red) right-to-left. - Unknown-spectrum background: flat gray. - Every 24-bit RGB color is used exactly once, including borders and labels. Dependencies: pip install astroquery astropy pillow numpy pandas """ import numpy as np from PIL import Image, ImageFont from astroquery.nist import Nist from astropy import units as u import pandas as pd # --- 1. Image geometry --- tile_w, tile_h = 1024, 128 ncols, nrows = 4, 32 width, height = tile_w * ncols, tile_h * nrows # 4096 × 4096 total_pixels = width * height print(f"Total pixels: {total_pixels}") # Precompute flat indices, x/y coords, and tile IDs indices = np.arange(total_pixels, dtype=np.int64) y = indices // width x = indices % width tile_row = y // tile_h tile_col = x // tile_w tile_id = tile_row * ncols + tile_col + 1 # 1-based tile IDs # --- 2. Element names Z=1..128 --- element_names = [ # Z=1–10 "Hydrogen","Helium","Lithium","Beryllium","Boron", "Carbon","Nitrogen","Oxygen","Fluorine","Neon", # Z=11–20 "Sodium","Magnesium","Aluminum","Silicon","Phosphorus", "Sulfur","Chlorine","Argon","Potassium","Calcium", # Z=21–30 "Scandium","Titanium","Vanadium","Chromium","Manganese", "Iron","Cobalt","Nickel","Copper","Zinc", # Z=31–40 "Gallium","Germanium","Arsenic","Selenium","Bromine", "Krypton","Rubidium","Strontium","Yttrium","Zirconium", # Z=41–50 "Niobium","Molybdenum","Technetium","Ruthenium","Rhodium", "Palladium","Silver","Cadmium","Indium","Tin", # Z=51–60 "Antimony","Tellurium","Iodine","Xenon","Cesium", "Barium","Lanthanum","Cerium","Praseodymium","Neodymium", # Z=61–70 "Promethium","Samarium","Europium","Gadolinium","Terbium", "Dysprosium","Holmium","Erbium","Thulium","Ytterbium", # Z=71–80 "Lutetium","Hafnium","Tantalum","Tungsten","Rhenium", "Osmium","Iridium","Platinum","Gold","Mercury", # Z=81–90 "Thallium","Lead","Bismuth","Polonium","Astatine", "Radon","Francium","Radium","Actinium","Thorium", # Z=91–100 "Protactinium","Uranium","Neptunium","Plutonium","Americium", "Curium","Berkelium","Californium","Einsteinium","Fermium", # Z=101–110 "Mendelevium","Nobelium","Lawrencium","Rutherfordium","Dubnium", "Seaborgium","Bohrium","Hassium","Meitnerium","Darmstadtium", # Z=111–118 "Roentgenium","Copernicium","Nihonium","Flerovium","Moscovium", "Livermorium","Tennessine","Oganesson", # Z=119–128 (IUPAC systematic temporary names) "Ununennium","Unbinilium","Unbiunium","Unbibium","Unbitrium", "Unbiquadium","Unbipentium","Unbihexium","Unbiseptium","Unbioctium" ] assert len(element_names) == 128 # --- 3. Build border masks --- # Outer border: 2 px wide at each edge outer_mask = (x < 2) | (x >= width - 2) | (y < 2) | (y >= height - 2) # Tile border: 1 px frame around each tile’s 1024×128 block x_tile = x % tile_w y_tile = y % tile_h tile_border_mask = ( (x_tile == 0) | (x_tile == tile_w - 1) | (y_tile == 0) | (y_tile == tile_h - 1) ) # Combined border mask border_mask = outer_mask | tile_border_mask B_border = int(border_mask.sum()) print(f"Total border pixels (2px outer + 1px/tile): {B_border}") # --- 4. Fetch NIST spectral lines (380–780 nm) for Z=1–118 --- element_symbols = [ 'H','He','Li','Be','B','C','N','O','F','Ne', 'Na','Mg','Al','Si','P','S','Cl','Ar','K','Ca', 'Sc','Ti','V','Cr','Mn','Fe','Co','Ni','Cu','Zn', 'Ga','Ge','As','Se','Br','Kr','Rb','Sr','Y','Zr', 'Nb','Mo','Tc','Ru','Rh','Pd','Ag','Cd','In','Sn', 'Sb','Te','I','Xe','Cs','Ba','La','Ce','Pr','Nd', 'Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb', 'Lu','Hf','Ta','W','Re','Os','Ir','Pt','Au','Hg', 'Tl','Pb','Bi','Po','At','Rn','Fr','Ra','Ac','Th', 'Pa','U','Np','Pu','Am','Cm','Bk','Cf','Es','Fm', 'Md','No','Lr','Rf','Db','Sg','Bh','Hs','Mt','Ds', 'Rg','Cn','Nh','Fl','Mc','Lv','Ts','Og' ] spectral_lines = {} nist = Nist() for Z, sym in enumerate(element_symbols, start=1): try: table = nist.query(380 * u.nm, 780 * u.nm, linename=sym) df = table.to_pandas() if table is not None else pd.DataFrame() if 'Observed' in df.columns: col = df['Observed'] elif 'Ritz' in df.columns: col = df['Ritz'] elif 'Wavelength' in df.columns: col = df['Wavelength'] else: spectral_lines[Z] = [] continue vals = pd.to_numeric(col, errors='coerce').dropna().astype(float) # Convert Å→nm if entries exceed 1000 if len(vals) and vals.max() > 1000: vals /= 10.0 spectral_lines[Z] = [w for w in vals if 380 <= w <= 780] except Exception as e: print(f"Warning Z={Z} ({sym}) fetch error: {e}") spectral_lines[Z] = [] known_ids = [Z for Z in range(1, 129) if spectral_lines.get(Z)] unknown_ids = [Z for Z in range(1, 129) if Z not in known_ids] print(f"Known spectra: {len(known_ids)}, Unknown: {len(unknown_ids)}") # --- 5. Build label mask and list of label pixel indices --- # Load DejaVuSans at 32 px (no anti-aliasing) font = ImageFont.truetype("DejaVuSans.ttf", 32) label_mask = np.zeros((total_pixels,), dtype=bool) label_pixels_list = [] for Z in range(1, 129): name = element_names[Z - 1] mask = font.getmask(name, mode="1") w_text, h_text = mask.size # getmask size returns (width, height) mask_arr = np.array(mask).reshape((h_text, w_text)) # 0 or 255 # Position at 6 px from outer & tile borders: (x0 + 6, y0 + 6) r, c = divmod(Z - 1, ncols) y0, x0 = r * tile_h, c * tile_w for ty in range(h_text): for tx in range(w_text): if mask_arr[ty, tx]: # pixel belongs to glyph global_x = x0 + 6 + tx global_y = y0 + 6 + ty pix_idx = global_y * width + global_x label_pixels_list.append(pix_idx) label_flat = np.unique(np.array(label_pixels_list, dtype=np.int64)) B_labels = int(label_flat.size) print(f"Total label pixels: {B_labels}") label_mask[label_flat] = True # --- 6. Identify raw absorption-line pixels (pre-filter) --- raw_line_pixels = [] for Z in known_ids: r, c = divmod(Z - 1, ncols) y0, x0 = r * tile_h, c * tile_w for wl in spectral_lines[Z]: xi = x0 + int((wl - 380) / 400 * (tile_w - 1)) ys = np.arange(y0, y0 + tile_h) raw_line_pixels.extend((ys * width + xi).tolist()) raw_line_flat = np.unique(np.array(raw_line_pixels, dtype=np.int64)) # Exclude any that fall on a border or a label keep_line_mask = (~border_mask[raw_line_flat]) & (~label_mask[raw_line_flat]) line_flat = raw_line_flat[keep_line_mask] M = int(line_flat.size) print(f"Absorption-line pixels (excluding borders/labels): {M}") # --- 7. Background masks (excluding borders, labels, and lines) --- occupied = border_mask.copy() occupied[line_flat] = True occupied[label_flat] = True mask_unk_bg = (~occupied) & np.isin(tile_id, unknown_ids) G_count = int(mask_unk_bg.sum()) mask_known_bg = (~occupied) & np.isin(tile_id, known_ids) R_count = int(mask_known_bg.sum()) assert (M + G_count + R_count + B_border + B_labels) == total_pixels, ( f"Counts mismatch: M={M}, G={G_count}, R={R_count}, B_border={B_border}, " f"B_labels={B_labels}, sum={M+G_count+R_count+B_border+B_labels}, total={total_pixels}" ) print(f"Counts → Lines: {M}, Gray bg: {G_count}, Rainbow bg: {R_count}, " f"Border: {B_border}, Labels: {B_labels}") # --- 8. Sort all 24-bit colors by brightness --- colors = np.arange(total_pixels, dtype=np.uint32) R_vals = (colors >> 16) & 0xFF G_vals = (colors >> 8) & 0xFF B_vals = colors & 0xFF brightness = 0.299 * R_vals + 0.587 * G_vals + 0.114 * B_vals sorted_idx = np.argsort(brightness) # darkest → lightest # Reserve lightest B_border colors for borders border_pool = sorted_idx[-B_border:] remaining_after_border = sorted_idx[: total_pixels - B_border] # Reserve next-lightest B_labels colors for labels label_pool = remaining_after_border[-B_labels:] remaining_after_border_labels = remaining_after_border[: total_pixels - B_border - B_labels] # Next M darkest → absorption lines line_pool = remaining_after_border_labels[:M] remaining_after_lines = remaining_after_border_labels[M:] # Build grayness metric for the rest (for flat gray background) rem_R = (remaining_after_lines >> 16) & 0xFF rem_G = (remaining_after_lines >> 8) & 0xFF rem_B = remaining_after_lines & 0xFF grayness = ( np.abs(rem_R - rem_G).astype(np.int32) + np.abs(rem_G - rem_B).astype(np.int32) + np.abs(rem_R - rem_B).astype(np.int32) ) g_idx = np.argsort(grayness)[:G_count] gray_pool = remaining_after_lines[g_idx] # The remainder → rainbow background rainbow_pool = np.delete(remaining_after_lines, g_idx) print(f"Pools sizes → Lines: {len(line_pool)}, Gray: {len(gray_pool)}, " f"Rainbow: {len(rainbow_pool)}, Border: {len(border_pool)}, Labels: {len(label_pool)}") # --- 9. Hue-based sorting for rainbow_pool with +60° rotation --- R3 = R_vals[rainbow_pool] / 255.0 G3 = G_vals[rainbow_pool] / 255.0 B3 = B_vals[rainbow_pool] / 255.0 maxc = np.maximum.reduce([R3, G3, B3]) minc = np.minimum.reduce([R3, G3, B3]) diff = maxc - minc hue = np.zeros_like(maxc) maskv = diff > 1e-6 rm = maskv & (maxc == R3) gm = maskv & (maxc == G3) bm = maskv & (maxc == B3) hue[rm] = (60 * ((G3[rm] - B3[rm]) / diff[rm]) + 360) % 360 hue[gm] = (60 * ((B3[gm] - R3[gm]) / diff[gm]) + 120) % 360 hue[bm] = (60 * ((R3[bm] - G3[bm]) / diff[bm]) + 240) % 360 # Rotate by +60° → shift from violet→red hue_rot = (hue + 60.0) % 360.0 hue_rot_norm = hue_rot / 360.0 hue_order = np.argsort(hue_rot_norm) rainbow_sorted = rainbow_pool[hue_order] # --- 10. Assemble final image --- img_flat = np.zeros((total_pixels, 3), dtype=np.uint8) # 10a. Draw borders (lightest colors) border_positions = np.where(border_mask)[0] border_positions.sort() for i, pix in enumerate(border_positions): c = border_pool[i] img_flat[pix] = [(c >> 16) & 0xFF, (c >> 8) & 0xFF, c & 0xFF] # 10b. Draw labels (next-lightest colors) label_positions = label_flat.copy() label_positions.sort() for i, pix in enumerate(label_positions): c = label_pool[i] img_flat[pix] = [(c >> 16) & 0xFF, (c >> 8) & 0xFF, c & 0xFF] # 10c. Draw absorption lines (darkest M colors) line_positions = line_flat.copy() line_positions.sort() for i, pix in enumerate(line_positions): c = line_pool[i] img_flat[pix] = [(c >> 16) & 0xFF, (c >> 8) & 0xFF, c & 0xFF] # 10d. Draw flat gray background for unknown-spectrum tiles unk_positions = np.where(mask_unk_bg)[0] unk_positions.sort() for i, pix in enumerate(unk_positions): c = gray_pool[i] img_flat[pix] = [(c >> 16) & 0xFF, (c >> 8) & 0xFF, c & 0xFF] # 10e. Draw rainbow background for known-spectrum tiles (right→left gradient) k = len(known_ids) n = len(rainbow_sorted) # Compute per-element count of background pixels tile_positions = {} for idx, Z in enumerate(known_ids): count = int(np.count_nonzero(mask_known_bg & (tile_id == Z))) pos = (np.arange(count) * k + idx) % n tile_positions[Z] = pos.tolist() # Resolve any duplicates among the hue indices all_pos = np.concatenate([tile_positions[Z] for Z in known_ids]) unique_pos, counts = np.unique(all_pos, return_counts=True) missing = list(set(np.arange(n)) - set(unique_pos)) def resolve_dupes(tile_positions_dict, counts_map, missing_list): for Z in known_ids: lst = tile_positions_dict[Z] for i, p in enumerate(lst): if counts_map[p] > 1 and missing_list: newp = missing_list.pop() counts_map[p] -= 1 counts_map[newp] = 1 lst[i] = newp if not missing_list: break counts_map = {p: c for p, c in zip(unique_pos, counts)} resolve_dupes(tile_positions, counts_map, missing) # Assign actual rainbow colors per tile for idx, Z in enumerate(known_ids): pos = np.array(tile_positions[Z], dtype=np.int64) cols = rainbow_sorted[pos] local_h = hue_rot_norm[hue_order][pos] grad_idx = np.argsort(local_h) cols_sorted = cols[grad_idx] cols_sorted = cols_sorted[::-1] # reverse for right→left mask_k = mask_known_bg & (tile_id == Z) idxs = np.where(mask_k)[0] order_x = np.argsort(idxs % width) for j, pix in enumerate(idxs[order_x]): c = cols_sorted[j] img_flat[pix] = [(c >> 16) & 0xFF, (c >> 8) & 0xFF, c & 0xFF] # --- 11. Save output --- print("Saving image with labels and borders…") img = Image.fromarray(img_flat.reshape((height, width, 3)), mode='RGB') img.save('absorption_spectra_with_labels.png') print("Done. Output file: absorption_spectra_with_labels.png")
Total pixels: 16777216 Total border pixels (2px outer + 1px/tile): 310636 Warning Z=85 (At) fetch error: Result did not contain a table Warning Z=100 (Fm) fetch error: Result did not contain a table Warning Z=101 (Md) fetch error: Result did not contain a table Warning Z=102 (No) fetch error: Result did not contain a table Warning Z=103 (Lr) fetch error: Result did not contain a table Warning Z=104 (Rf) fetch error: Result did not contain a table Warning Z=105 (Db) fetch error: Result did not contain a table Warning Z=106 (Sg) fetch error: Result did not contain a table Warning Z=107 (Bh) fetch error: Result did not contain a table Warning Z=108 (Hs) fetch error: Result did not contain a table Warning Z=109 (Mt) fetch error: Result did not contain a table Warning Z=110 (Ds) fetch error: Result did not contain a table Warning Z=111 (Rg) fetch error: Result did not contain a table Warning Z=112 (Cn) fetch error: Result did not contain a table Warning Z=113 (Nh) fetch error: Result did not contain a table Warning Z=114 (Fl) fetch error: Result did not contain a table Warning Z=115 (Mc) fetch error: Result did not contain a table Warning Z=116 (Lv) fetch error: Result did not contain a table Warning Z=117 (Ts) fetch error: Result did not contain a table Warning Z=118 (Og) fetch error: Result did not contain a table Known spectra: 98, Unknown: 30 Total label pixels: 136729 Absorption-line pixels (excluding borders/labels): 3054128 Counts → Lines: 3054128, Gray bg: 3818243, Rainbow bg: 9457480, Border: 310636, Labels: 136729 Pools sizes → Lines: 3054128, Gray: 3818243, Rainbow: 9457480, Border: 310636, Labels: 136729 Saving image with labels and borders… Done. Output file: absorption_spectra_with_labels.png
Date | |
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Colors | 16,777,216 |
Pixels | 16,777,216 |
Dimensions | 4,096 × 4,096 |
Bytes | 47,916,834 |