File size: 4,527 Bytes
0ec8fd6 | 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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | #!/usr/bin/env python3
"""Code-preserving parse of copernicus-marine-notebook-gallery notebooks.
Reuses the extract_code.py pattern: verbatim markdown + verbatim ```python
code cells + trimmed ```text outputs; classify recipe_kinds via regex.
Writes parsed/<name>/<notebook_id>.md and prints a JSON manifest to stdout.
"""
import json, re, sys
from pathlib import Path
REPO = Path("/Users/dmpantiu/copernicus_mcp/notebook_harvest/repos/copernicus-marine-notebook-gallery")
OUT = Path("/Users/dmpantiu/copernicus_mcp/notebook_harvest/parsed/copernicus-marine-notebook-gallery")
ROOT = Path("/Users/dmpantiu/copernicus_mcp")
DOWNLOAD_RE = re.compile(
r"cdsapi|\.retrieve\(|copernicusmarine|\bcm\.(subset|get|open_dataset)|"
r"motuclient|--service-id|--product-id|!?\bwget\b|urlretrieve|requests\.get|\.hda\b|EO:",
re.I)
ANALYZE_RE = re.compile(
r"\bimport xarray|\bxr\.|\.open_dataset|\.open_mfdataset|\bimport pandas|\bpd\.|"
r"\bimport numpy|\bnp\.|\bscipy|nc\.Dataset|netCDF4|\.groupby\(|\.resample\(|"
r"\.mean\(|\.sel\(|\.isel\(", re.I)
PLOT_RE = re.compile(
r"\bmatplotlib|\bplt\.|\bcartopy|\bccrs\b|\bcmocean|\.plot\(|\.plot\.|seaborn|\bsns\.|pcolor|contourf",
re.I)
def _src(cell):
s = cell.get("source", "")
return "".join(s) if isinstance(s, list) else s
def code_line_count(code):
n = 0
for ln in code.splitlines():
st = ln.strip()
if st and not st.startswith("#"):
n += 1
return n
def text_outputs(cell):
out = []
for o in cell.get("outputs", []):
ot = o.get("output_type"); s = None
if ot == "stream":
t = o.get("text", ""); s = "".join(t) if isinstance(t, list) else t
elif ot in ("execute_result", "display_data"):
tp = (o.get("data") or {}).get("text/plain")
if tp is not None:
s = "".join(tp) if isinstance(tp, list) else tp
if not s:
continue
s = s.strip()
if not s or re.fullmatch(r"<[^>]+>", s) or s.startswith("<Figure"):
continue
lines = [ln for ln in s.splitlines()
if "%|" not in ln and "it/s]" not in ln and "B/s]" not in ln]
s = "\n".join(lines).strip()
if len(s) >= 8:
out.append(s[:1500])
return out
def classify(code):
kinds = []
if DOWNLOAD_RE.search(code): kinds.append("download")
if ANALYZE_RE.search(code): kinds.append("analyze")
if PLOT_RE.search(code): kinds.append("plot")
return kinds or ["other"]
def slug(path):
return re.sub(r"[^A-Za-z0-9._-]+", "-", path.stem).strip("-")
def extract(path):
nb = json.loads(path.read_text(encoding="utf-8", errors="replace"))
parts = []; n_cells = 0; n_lines = 0; kinds = set(); title = ""
for cell in nb.get("cells", []):
ct = cell.get("cell_type")
if ct == "markdown":
txt = _src(cell).strip()
if txt:
parts.append(txt)
if not title:
for ln in txt.splitlines():
if ln.startswith("# "):
title = ln[2:].strip(); break
elif ct == "code":
src = _src(cell).rstrip()
if not src.strip():
continue
n_cells += 1; n_lines += code_line_count(src)
kinds.update(classify(src))
parts.append("```python\n" + src + "\n```")
for to in text_outputs(cell):
parts.append("```text\n" + to + "\n```")
return {"md": "\n\n".join(parts).strip(), "title": title or path.stem,
"n_code_cells": n_cells, "n_code_lines": n_lines,
"recipe_kinds": sorted(kinds)}
def main():
OUT.mkdir(parents=True, exist_ok=True)
recs = []
for nb in sorted(REPO.rglob("*.ipynb")):
if ".ipynb_checkpoints" in nb.parts:
continue
nid = slug(nb)
ex = extract(nb)
if ex["n_code_cells"] == 0:
print(f"SKIP prose-only: {nid}", file=sys.stderr); continue
md = OUT / f"{nid}.md"
md.write_text(ex["md"], encoding="utf-8")
recs.append({
"notebook_id": nid, "title": ex["title"],
"src_path": str(nb.relative_to(REPO)),
"md_path": str(md.relative_to(ROOT)),
"n_code_cells": ex["n_code_cells"], "n_code_lines": ex["n_code_lines"],
"recipe_kinds": ex["recipe_kinds"],
})
print(json.dumps(recs, indent=2))
if __name__ == "__main__":
main()
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