Recompute predicted-only ledger and output

# Recompute predicted-only ledger and output the final batch (Z = 101–118),
# also re-saving the full predicted CSV so subsequent batches stay available.

import pandas as pd

strict_path = "/mnt/data/isotope_master_summary_Z1-118_strict.csv"
df = pd.read_csv(strict_path)

KNOWN_TOTAL = int(df["Isotopes Known"].sum())  # expected 3269
TARGET_TOTAL = 7759
scale = TARGET_TOTAL / KNOWN_TOTAL

pred = df[["Element (Z)", "Isotopes Known"]].copy()
pred["Predicted Isotopes (est.)"] = (pred["Isotopes Known"] * scale).round().astype(int)

# Adjust any rounding drift to hit TARGET_TOTAL exactly
drift = TARGET_TOTAL - int(pred["Predicted Isotopes (est.)"].sum())
if drift != 0:
    # Pick the row with largest positive fractional part to add 1, or most negative to subtract 1
    frac = (df["Isotopes Known"] * scale) - (pred["Predicted Isotopes (est.)"])
    idx = frac.idxmax() if drift > 0 else frac.idxmin()
    pred.loc[idx, "Predicted Isotopes (est.)"] += drift

# Save
pred_csv = "/mnt/data/isotope_predicted_only_Z1-118_neufcourt2020_scaled.csv"
pred_md  = "/mnt/data/isotope_predicted_only_Z1-118_neufcourt2020_scaled.md"
pred.to_csv(pred_csv, index=False)
with open(pred_md, "w", encoding="utf-8") as f:
    f.write(pred.to_markdown(index=False))

# Extract final batch
def Z_of(label):
    return int(label.split("(")[1].split(")")[0])

pred["Z"] = pred["Element (Z)"].apply(Z_of)
batch6 = pred[pred["Z"].between(101, 118)][["Element (Z)", "Predicted Isotopes (est.)"]]

from caas_jupyter_tools import display_dataframe_to_user
display_dataframe_to_user("Predicted-only Ledger (Batch Z=101–118)", batch6)

scale, int(pred["Predicted Isotopes (est.)"].sum()), pred_csv, pred_md, batch6

Rebuild the full Predicted-only Ledger Z=1–118 and export as Markdown and CSV

Build Master Table (Known vs Predicted, Z=1–118)

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