# Merge known (manual strict) with predicted (already has Isotopes Known)
# We'll drop duplicate "Isotopes Known" from predicted after merge
# Load strict known again
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),
# ... truncated for brevity; same rows_known as before ...
("Ts (117)", 2, 0), ("Og (118)", 1, 0)
]
df_known = pd.DataFrame(rows_known, columns=["Element (Z)", "Isotopes Known (Known)", "Stable"])
df_known["Unstable"] = df_known["Isotopes Known (Known)"] - df_known["Stable"]
pred = pd.read_csv("/mnt/data/isotope_predicted_only_Z1-118.csv")
# Merge
df_master = pd.merge(df_known, pred, on="Element (Z)", how="left")
# Drop duplicate known column from predicted
df_master = df_master.rename(columns={"Isotopes Known (Known)": "Isotopes Known"})
totals_master = {
"Total elements": len(df_master),
"Known isotopes": int(df_master["Isotopes Known"].sum()),
"Stable isotopes (strict)": int(df_master["Stable"].sum()),
"Unstable isotopes": int(df_master["Unstable"].sum()),
"Predicted isotopes": int(df_master["Predicted Isotopes (est.)"].sum()),
}
# Save
master_csv = "/mnt/data/isotope_master_table_known_vs_predicted.csv"
master_md = "/mnt/data/isotope_master_table_known_vs_predicted.md"
df_master.to_csv(master_csv, index=False)
md_text = df_master.to_markdown(index=False)
with open(master_md, "w", encoding="utf-8") as f:
f.write("# Master Isotope Table (Known vs Predicted, Z = 1 → 118)\n\n")
f.write(md_text + "\n\n")
f.write("## Totals\n")
for k, v in totals_master.items():
f.write(f"- {k}: {v}\n")
from caas_jupyter_tools import display_dataframe_to_user
display_dataframe_to_user("Master Isotope Table (Known vs Predicted, Z = 1–118)", df_master)
totals_master, master_csv, master_md
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