Scraping resilience, metadata tooling, and repository hygiene

Consolidates mosaic and session hardening (login retry, skip processed scans, no retry on 404, started_at), progress reporting (Markdown tables, by-year rollup, rolling-window rate/ETA), and metadata workflow scripts (run_metadata_scan.sh, scan_progress_report.py, export_machine_metadata.py). Adds mosaic reconstruction sample JPEGs referenced by the report. Updates .gitignore for backup/ and .claude/; sample_random_scans helper is documented for branch testing/sample-runs only (see README).
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2026-05-14 19:52:53 -04:00
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#!/usr/bin/env python3
"""
Report mosaic download progress from archives/scans.csv.
Output is formatted as Markdown. Add --by-year for a per-machine ×
per-year breakdown table.
Rate/ETA require two calls at least 60 s apart. Mean mosaic size is
sampled from up to 100 already-downloaded files and cached for 1 hour.
Usage:
python scripts/mosaic_progress_report.py [--archive DIR] [--by-year]
"""
import argparse
import csv
import json
import os
import random
import sys
from collections import Counter
from datetime import datetime, timezone
from pathlib import Path
# Year-based viability model derived from BW1-4 observations:
# pre-2019 → kept on server long-term (~100 %)
# 2019-2022 → purged (~ 0 %)
# 2023+ → recent, mostly available (~82 %)
_R_PRE19 = 1.00
_R_PURGED = 0.00
_R_RECENT = 0.82
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _parse_dt(s: str) -> datetime | None:
try:
return datetime.fromisoformat(s.replace("Z", "+00:00"))
except Exception:
return None
def _fmt_duration(seconds: float) -> str:
if seconds < 0:
return "?"
h = int(seconds // 3600)
m = int((seconds % 3600) // 60)
if h >= 48:
return f"{h // 24}d {h % 24}h"
if h > 0:
return f"{h}h {m:02d}m"
return f"{m}m {int(seconds % 60):02d}s"
def _fmt_size(b: float) -> str:
if b >= 1e12:
return f"{b/1e12:.2f} TB"
if b >= 1e9:
return f"{b/1e9:.2f} GB"
if b >= 1e6:
return f"{b/1e6:.1f} MB"
return f"{b/1e3:.0f} KB"
def _md_table(headers: list[str], rows: list[list[str]], *, align: list[str] | None = None) -> str:
"""Render a Markdown table. align values: 'l', 'r', 'c' (default 'l')."""
if align is None:
align = ["l"] * len(headers)
sep_map = {"l": ":---", "r": "---:", "c": ":---:"}
def row_str(cells: list[str]) -> str:
return "| " + " | ".join(cells) + " |"
lines = [
row_str(headers),
row_str([sep_map.get(a, ":---") for a in align]),
]
for r in rows:
lines.append(row_str(r))
return "\n".join(lines)
def _sample_mean_bytes(rows: list[dict], cache: dict, max_sample: int = 100) -> float | None:
cached_mean = cache.get("mean_bytes")
cached_n = cache.get("sample_n", 0)
cached_ts = _parse_dt(cache.get("size_ts", ""))
now = datetime.now(timezone.utc)
if (
cached_mean and cached_ts
and (now - cached_ts).total_seconds() < 3600
and cached_n >= min(max_sample, len(rows))
):
return float(cached_mean)
sample = random.sample(rows, min(max_sample, len(rows)))
sizes = []
for row in sample:
p = row.get("mosaic_local_path", "")
if p:
try:
sz = os.path.getsize(p)
if sz > 0:
sizes.append(sz)
except OSError:
pass
if not sizes:
return None
mean = sum(sizes) / len(sizes)
cache["mean_bytes"] = mean
cache["sample_n"] = len(sizes)
cache["size_ts"] = now.isoformat()
return mean
def _expected_remaining(pending_rows: list[dict]) -> float:
count = 0.0
for row in pending_rows:
yr = row.get("scan_time", "")[:4]
if yr < "2019":
count += _R_PRE19
elif yr <= "2022":
count += _R_PURGED
else:
count += _R_RECENT
return count
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--archive", default="archives")
parser.add_argument(
"--by-year", action="store_true",
help="Add a per-machine × per-year done/failed breakdown table",
)
args = parser.parse_args()
archive = Path(args.archive)
scans_csv = archive / "scans.csv"
progress_json = archive / ".progress.json"
rate_cache_path = archive / ".mosaic_rate_cache.json"
if not scans_csv.exists():
sys.exit(f"scans.csv not found: {scans_csv}")
# --- Load & deduplicate (last row per machine+scan_id) ---
latest: dict[tuple[str, str], dict] = {}
with open(scans_csv, newline="", encoding="utf-8") as f:
for row in csv.DictReader(f):
key = (row.get("machine", ""), row.get("scan_id", ""))
latest[key] = row
by_machine: dict[str, Counter] = {}
# machine -> year -> Counter(status -> count)
by_machine_year: dict[str, dict[str, Counter]] = {}
total_counts: Counter = Counter()
downloaded_rows: list[dict] = []
pending_rows: list[dict] = []
for (_m, _sid), row in latest.items():
status = row.get("mosaic_download_status", "")
m = row.get("machine", "")
yr = (row.get("scan_time") or "")[:4] or "????"
by_machine.setdefault(m, Counter())[status] += 1
by_machine_year.setdefault(m, {}).setdefault(yr, Counter())[status] += 1
total_counts[status] += 1
if status == "downloaded":
downloaded_rows.append(row)
elif status == "skipped_metadata_only":
pending_rows.append(row)
total = sum(total_counts.values())
downloaded = total_counts["downloaded"]
failed = total_counts["failed"]
zero_skipped = total_counts["skipped_zero_disk_space"]
pending = total_counts["skipped_metadata_only"]
processed = downloaded + failed + zero_skipped
attempted = downloaded + failed
now = datetime.now(timezone.utc)
# --- Elapsed ---
elapsed_str = ""
if progress_json.exists():
try:
data = json.loads(progress_json.read_text())
started_at = _parse_dt(data.get("started_at", ""))
if started_at:
elapsed_str = _fmt_duration((now - started_at).total_seconds())
except Exception:
pass
# --- Rate cache ---
cache: dict = {}
if rate_cache_path.exists():
try:
cache = json.loads(rate_cache_path.read_text())
except Exception:
pass
# Rolling rate: keep up to 60 snapshots; compute rate from the oldest
# snapshot within the last 30 minutes for a smoothed estimate.
snapshots: list[dict] = cache.get("snapshots", [])
# Prune snapshots older than 30 minutes, but keep at least one
cutoff = now.timestamp() - 1800
recent = [s for s in snapshots if s.get("ts", 0) >= cutoff]
if not recent and snapshots:
recent = [snapshots[-1]] # always keep one for continuity
rate_per_sec: float | None = None
rate_window_str = ""
if recent:
oldest = recent[0]
dt = now.timestamp() - oldest["ts"]
dp = processed - oldest["proc"]
if dt >= 60 and dp > 0:
rate_per_sec = dp / dt
window_min = dt / 60
rate_window_str = f"{window_min:.0f}-min avg"
# --- Disk space ---
mean_bytes: float | None = None
size_note = ""
if downloaded_rows:
mean_bytes = _sample_mean_bytes(downloaded_rows, cache)
if mean_bytes and cache.get("sample_n"):
size_note = f"mean {_fmt_size(mean_bytes)} × {cache['sample_n']} sampled files"
dl_bytes: float | None = None
rem_bytes: float | None = None
if mean_bytes:
dl_bytes = downloaded * mean_bytes
rem_bytes = _expected_remaining(pending_rows) * mean_bytes
# Update cache: append new snapshot, keep last 60
recent.append({"ts": now.timestamp(), "proc": processed})
cache["snapshots"] = recent[-60:]
# Keep legacy keys for backwards compat
cache["timestamp"] = now.isoformat()
cache["processed"] = processed
try:
rate_cache_path.write_text(json.dumps(cache))
except Exception:
pass
rate_str = eta_str = ""
if rate_per_sec and rate_per_sec > 0:
rate_str = f"{rate_per_sec * 3600:,.0f} scans/hr ({rate_window_str})"
eta_str = _fmt_duration(pending / rate_per_sec)
# -----------------------------------------------------------------------
# Output — Markdown
# -----------------------------------------------------------------------
ts = datetime.now().strftime("%Y-%m-%d %H:%M")
print(f"# Mosaic download progress — {ts}\n")
print(f"**Archive:** `{archive.resolve()}` ")
meta_parts = []
if elapsed_str:
meta_parts.append(f"**Elapsed:** {elapsed_str}")
if rate_str:
meta_parts.append(f"**Rate:** {rate_str}")
if eta_str:
meta_parts.append(f"**ETA:** {eta_str}")
if meta_parts:
print(" ".join(meta_parts) + " ")
print()
# Summary table
summary_rows = [
["Downloaded", f"{downloaded:,}", f"{100*downloaded/total:.1f}%"],
["Failed", f"{failed:,}", f"{100*failed/total:.1f}%"],
["Skipped (disk=0)",f"{zero_skipped:,}", f"{100*zero_skipped/total:.1f}%"],
["Pending", f"{pending:,}", f"{100*pending/total:.1f}%"],
["**Total**", f"**{total:,}**", ""],
]
if attempted:
summary_rows.append(["**Success rate**", f"**{100*downloaded/attempted:.1f}%**", "*(of attempted)*"])
print(_md_table(["Metric", "Count", ""], summary_rows, align=["l", "r", "l"]))
print()
# Disk space
if dl_bytes is not None and rem_bytes is not None:
total_bytes = dl_bytes + rem_bytes
print(f"### Disk space\n")
print(f"_{size_note}_\n")
ds_rows = [
["Downloaded so far", _fmt_size(dl_bytes), ""],
["Estimated remaining", _fmt_size(rem_bytes), "*(model-based)*"],
["**Grand total**", f"**{_fmt_size(total_bytes)}**", ""],
]
print(_md_table(["", "Size", ""], ds_rows, align=["l", "r", "l"]))
print()
# Per-machine breakdown
print("### Per-machine breakdown\n")
machines = sorted(by_machine)
mc_rows = []
for m in machines:
mc = by_machine[m]
mt = sum(mc.values())
mc_rows.append([
m,
f"{mc['downloaded']:,}",
f"{mc['failed']:,}",
f"{mc['skipped_zero_disk_space']:,}",
f"{mc['skipped_metadata_only']:,}",
f"{mt:,}",
])
mc_rows.append([
"**TOTAL**",
f"**{downloaded:,}**",
f"**{failed:,}**",
f"**{zero_skipped:,}**",
f"**{pending:,}**",
f"**{total:,}**",
])
print(_md_table(
["Machine", "Done", "Failed", "Skip0", "Pending", "Total"],
mc_rows,
align=["l", "r", "r", "r", "r", "r"],
))
# -----------------------------------------------------------------------
# --by-year table
# -----------------------------------------------------------------------
if args.by_year:
print()
print("### Downloads by machine and year\n")
print("*Format: done / failed*\n")
# Only include years that have at least one downloaded or failed scan
all_years = sorted(
yr for yr in set(
yr for m_data in by_machine_year.values() for yr in m_data
)
if any(
by_machine_year[m].get(yr, Counter()).get("downloaded", 0)
+ by_machine_year[m].get(yr, Counter()).get("failed", 0) > 0
for m in by_machine_year
)
)
# Totals per year
yr_totals: dict[str, Counter] = {}
for yr in all_years:
yr_totals[yr] = Counter()
for m in machines:
yr_totals[yr] += by_machine_year.get(m, {}).get(yr, Counter())
year_rows = []
for m in machines:
row_cells = [m]
for yr in all_years:
c = by_machine_year.get(m, {}).get(yr, Counter())
d = c.get("downloaded", 0)
f = c.get("failed", 0)
row_cells.append(f"{d:,} / {f:,}" if (d or f) else "")
year_rows.append(row_cells)
# Totals row
total_cells = ["**TOTAL**"]
for yr in all_years:
d = yr_totals[yr].get("downloaded", 0)
f = yr_totals[yr].get("failed", 0)
total_cells.append(f"**{d:,} / {f:,}**" if (d or f) else "")
year_rows.append(total_cells)
print(_md_table(
["Machine"] + all_years,
year_rows,
align=["l"] + ["r"] * len(all_years),
))
if __name__ == "__main__":
main()