import pandas as pd

Rebuild strict-known ledger (Z=1-118) quickly

rows_known = [
(“H (1)”, 7, 2), (“He (2)”, 9, 2), (“Li (3)”, 11, 2), (“Be (4)”, 12, 1), (“B (5)”, 13, 2), (“C (6)”, 15, 2),
(“N (7)”, 16, 2), (“O (8)”, 17, 3), (“F (9)”, 18, 1), (“Ne (10)”, 19, 3), (“Na (11)”, 20, 1), (“Mg (12)”, 22, 3),
(“Al (13)”, 22, 1), (“Si (14)”, 23, 3), (“P (15)”, 23, 1), (“S (16)”, 24, 4), (“Cl (17)”, 24, 2), (“Ar (18)”, 24, 3),
(“K (19)”, 24, 2), (“Ca (20)”, 24, 6), (“Sc (21)”, 25, 1), (“Ti (22)”, 26, 5), (“V (23)”, 26, 1), (“Cr (24)”, 26, 4),
(“Mn (25)”, 26, 1), (“Fe (26)”, 28, 4), (“Co (27)”, 29, 1), (“Ni (28)”, 31, 5), (“Cu (29)”, 29, 2), (“Zn (30)”, 30, 5),
(“Ga (31)”, 31, 2), (“Ge (32)”, 32, 5), (“As (33)”, 33, 1), (“Se (34)”, 30, 6), (“Br (35)”, 31, 2), (“Kr (36)”, 32, 6),
(“Rb (37)”, 32, 1), (“Sr (38)”, 34, 4), (“Y (39)”, 32, 1), (“Zr (40)”, 34, 5), (“Nb (41)”, 34, 1), (“Mo (42)”, 35, 7),
(“Tc (43)”, 36, 0), (“Ru (44)”, 37, 7), (“Rh (45)”, 35, 1), (“Pd (46)”, 36, 6), (“Ag (47)”, 38, 2), (“Cd (48)”, 39, 8),
(“In (49)”, 39, 2), (“Sn (50)”, 40, 10), (“Sb (51)”, 36, 2), (“Te (52)”, 38, 8), (“I (53)”, 37, 1), (“Xe (54)”, 40, 9),
(“Cs (55)”, 39, 1), (“Ba (56)”, 40, 7), (“La (57)”, 39, 1), (“Ce (58)”, 40, 4), (“Pr (59)”, 39, 1), (“Nd (60)”, 41, 5),
(“Pm (61)”, 39, 0), (“Sm (62)”, 41, 7), (“Eu (63)”, 40, 2), (“Gd (64)”, 41, 7), (“Tb (65)”, 39, 1), (“Dy (66)”, 40, 7),
(“Ho (67)”, 39, 1), (“Er (68)”, 40, 6), (“Tm (69)”, 39, 1), (“Yb (70)”, 41, 7), (“Lu (71)”, 40, 1), (“Hf (72)”, 36, 5),
(“Ta (73)”, 37, 1), (“W (74)”, 35, 5), (“Re (75)”, 39, 1), (“Os (76)”, 35, 7), (“Ir (77)”, 34, 2), (“Pt (78)”, 35, 6),
(“Au (79)”, 36, 1), (“Hg (80)”, 38, 7), (“Tl (81)”, 39, 2), (“Pb (82)”, 43, 4), (“Bi (83)”, 41, 0), (“Po (84)”, 42, 0),
(“At (85)”, 39, 0), (“Rn (86)”, 39, 0), (“Fr (87)”, 34, 0), (“Ra (88)”, 34, 0), (“Ac (89)”, 33, 0), (“Th (90)”, 31, 1),
(“Pa (91)”, 29, 0), (“U (92)”, 28, 0), (“Np (93)”, 20, 0), (“Pu (94)”, 20, 0), (“Am (95)”, 17, 0), (“Cm (96)”, 19, 0),
(“Bk (97)”, 21, 0), (“Cf (98)”, 20, 0), (“Es (99)”, 18, 0), (“Fm (100)”, 19, 0), (“Md (101)”, 16, 0), (“No (102)”, 13, 0),
(“Lr (103)”, 16, 0), (“Rf (104)”, 18, 0), (“Db (105)”, 16, 0), (“Sg (106)”, 14, 0), (“Bh (107)”, 15, 0), (“Hs (108)”, 15, 0),
(“Mt (109)”, 13, 0), (“Ds (110)”, 15, 0), (“Rg (111)”, 11, 0), (“Cn (112)”, 9, 0), (“Nh (113)”, 9, 0), (“Fl (114)”, 6, 0),
(“Mc (115)”, 4, 0), (“Lv (116)”, 4, 0), (“Ts (117)”, 2, 0), (“Og (118)”, 1, 0)
]
df_known = pd.DataFrame(rows_known, columns=[“Element (Z)”, “Isotopes Known”, “Stable”])
df_known[“Unstable”] = df_known[“Isotopes Known”] – df_known[“Stable”]

Load predicted ledger

pred = pd.read_csv(“/mnt/data/isotope_predicted_only_Z1-118.csv”)

Merge

df_master = pd.merge(df_known, pred[[“Element (Z)”, “Predicted Isotopes (est.)”]], on=”Element (Z)”, how=”left”)

Add gap column

df_master[“Gap (Predicted – Known)”] = df_master[“Predicted Isotopes (est.)”] – df_master[“Isotopes Known”]

Save outputs

master_csv_gap = “/mnt/data/isotope_master_table_known_vs_predicted_with_gap.csv”
master_md_gap = “/mnt/data/isotope_master_table_known_vs_predicted_with_gap.md”
df_master.to_csv(master_csv_gap, index=False)

md_text = df_master.to_markdown(index=False)
with open(master_md_gap, “w”, encoding=”utf-8″) as f:
f.write(“# Master Isotope Table (Known vs Predicted + Gap, Z = 1 → 118)\n\n”)
f.write(md_text + “\n\n”)
f.write(“## Totals\n”)
f.write(f”- Total elements: {len(df_master)}\n”)
f.write(f”- Known isotopes: {int(df_master[‘Isotopes Known’].sum())}\n”)
f.write(f”- Stable isotopes (strict): {int(df_master[‘Stable’].sum())}\n”)
f.write(f”- Unstable isotopes: {int(df_master[‘Unstable’].sum())}\n”)
f.write(f”- Predicted isotopes: {int(df_master[‘Predicted Isotopes (est.)’].sum())}\n”)
f.write(f”- Total gap (Predicted – Known): {int(df_master[‘Gap (Predicted – Known)’].sum())}\n”)

from caas_jupyter_tools import display_dataframe_to_user
display_dataframe_to_user(“Master Isotope Table (Known vs Predicted + Gap, Z = 1–118)”, df_master)

int(df_master[‘Gap (Predicted – Known)’].sum()), master_csv_gap, master_md_gap