File size: 2,984 Bytes
64c992d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | """Source of truth for the Table S8 notebook (Dataset Summary Statistics). Edit the cell sources
here, then regenerate:
python3 build_nb_dataset_summary.py si_table_s08_summary
jupyter nbconvert --to notebook --execute --inplace ../si_tables/si_table_s08_summary.ipynb
Regenerating overwrites the .ipynb (clearing its execution outputs). All real code lives in
dataset_summary.py (the counting logic and the PUBLISHED_S8 reference values); the notebook only
runs it over the released data/ files. The notebook lives in analysis/si_tables/ (grouped by role), not here.
Run with no arguments to list the available notebook names.
"""
import os
import sys
from nb_build import md, code, save_notebook as _save
_BOOTSTRAP = r"""
import os, sys
# make the in-repo modules importable (not pip-installed)
REPO = os.path.abspath("../..")
for _p in ("analysis/code", "analysis/code/shared"):
sys.path.insert(0, os.path.join(REPO, _p))
"""
_IMPORTS = r"""
import pandas as pd
import dataset_summary
"""
_SETUP = r"""
DATA_DIR = os.path.join(REPO, "data")
def document_path(name):
os.makedirs("documents", exist_ok=True)
return os.path.join("documents", name)
"""
si_table_s08_summary = [
md(r"""
# Table S8: Dataset Summary Statistics
Molecule and ¹H/¹³C site counts for each training dataset (site counts from each HDF5's
`atomic_numbers`, ¹H=1/¹³C=6; MagNET-Zero combines both sigma-pepper rounds with sigma-concentrate).
"""),
code(_BOOTSTRAP),
code(_IMPORTS),
code(_SETUP),
code(r"""
table_s8 = dataset_summary.summary_table(DATA_DIR)
display(table_s8)
# write the table to this notebook's documents/ folder
out = document_path("si_table_s08_summary.xlsx")
with pd.ExcelWriter(out) as writer:
table_s8.to_excel(writer, sheet_name="Table S8", index=False)
print("wrote", os.path.relpath(out, REPO))
"""),
md("## Exact-reproduction check"),
code(r"""
# every count should match the published SI Table S8 value exactly
for _, row in table_s8.iterrows():
pub = dataset_summary.PUBLISHED_S8[row["dataset"]]
got = (row["molecules"], row["n_1H_sites"], row["n_13C_sites"])
assert got == pub, f"{row['dataset']}: {got} != published {pub}"
print("all rows match the published SI Table S8 exactly")
"""),
]
# name -> (cells, path relative to repo root)
NOTEBOOKS = {
"si_table_s08_summary": (si_table_s08_summary, "analysis/si_tables/si_table_s08_summary.ipynb"),
}
if __name__ == "__main__":
here = os.path.dirname(os.path.abspath(__file__))
repo = os.path.abspath(os.path.join(here, "..", ".."))
names = sys.argv[1:]
if not names:
print("available names:", ", ".join(NOTEBOOKS))
sys.exit(0)
unknown = [n for n in names if n not in NOTEBOOKS]
if unknown:
raise SystemExit(f"unknown notebook name(s): {unknown}; available: {', '.join(NOTEBOOKS)}")
for name in names:
cells, relpath = NOTEBOOKS[name]
_save(cells, os.path.join(repo, relpath))
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