Add resumable all-sky 2MASS acquisition

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wyj committed 2026-08-26 20:04:08 -04:00
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@@ -1,3 +1,5 @@
/build /build
/output /output
/scripts/__pycache__ /scripts/__pycache__
assets/2mass/processed/all_sky/*.csv
assets/2mass/processed/all_sky/.done/
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@@ -50,5 +50,29 @@ python3 scripts/process_2mass_psc.py \
The resulting M44 CSV contains 7,802 sources after the documented three-band The resulting M44 CSV contains 7,802 sources after the documented three-band
photometry and `rd_flg` selection. photometry and `rd_flg` selection.
## Full-sky acquisition
`scripts/download_2mass_psc_all_sky.py` provides a resumable full-sky
acquisition without asking IRSA for a cone larger than 1 degree. Inspect its
storage plan before starting the long download:
```sh
python3 scripts/download_2mass_psc_all_sky.py --plan
python3 scripts/download_2mass_psc_all_sky.py --download --workers 2
```
It covers each latitude band with integer-RA cone sectors, wider at high
latitudes, while outputs are cleaned and retained by unique 1-degree RA/Dec
ownership cells. Cone overlap cannot create duplicate stars. Temporary raw
tables and cleaning intermediates are stored under `/tmp/2mass_psc_all_sky/`
and deleted after each successful cover. The generated full-sky catalogue has
one CSV per ownership cell under `processed/all_sky/`, named
`tile_raRRR_decDDD.csv`. `RRR` is
`floor(RA_deg mod 360)` and `DDD` is `min(179, floor(Dec_deg + 90))`; the
corresponding range is `[RRR, RRR+1) x [DDD-90, DDD-89)` degrees. It is
intentionally not a single CSV because that would duplicate a roughly 15--17
GiB result during merging.
See that directory's README for restart and raw-retention behavior.
Provenance: [IRSA Gator program interface](https://irsa.ipac.caltech.edu/docs/howto/gator_prog_interface.html), Provenance: [IRSA Gator program interface](https://irsa.ipac.caltech.edu/docs/howto/gator_prog_interface.html),
[2MASS PSC column descriptions](https://irsa.ipac.caltech.edu/2MASS/download/allsky/format_psc.html). [2MASS PSC column descriptions](https://irsa.ipac.caltech.edu/2MASS/download/allsky/format_psc.html).
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@@ -13,11 +13,14 @@ the source-sky direction.
## Quality selection ## Quality selection
All three J/H/Ks magnitudes must be finite and every character of `rd_flg` must RA/Dec must be finite and inside their ICRS ranges; all three J/H/Ks magnitudes
be `1`, `2`, or `3`. These are the 2MASS default-magnitude origins that must be finite; and every character of `rd_flg` must be `1`, `2`, or `3`.
generally indicate the best detections, photometry, and astrometry. Thus the These are the 2MASS default-magnitude origins that generally indicate the best
script rejects nondetections/upper limits (`0`), poor aperture photometry (`4`), detections, photometry, and astrometry. Thus the script rejects
inconsistent band deblends (`6`), and missing brightness estimates (`9`). nondetections/upper limits (`0`), poor aperture photometry (`4`), inconsistent
band deblends (`6`), and missing brightness estimates (`9`).
The IPAC literal `null` is treated as a missing numeric value, the same as a
blank field, and is therefore rejected by these finite-value checks.
## Two-parameter fit ## Two-parameter fit
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# Full-sky 2MASS PSC output
This directory is generated, not versioned. Create it with:
```sh
python3 scripts/download_2mass_psc_all_sky.py --plan
python3 scripts/download_2mass_psc_all_sky.py --download --workers 2
```
The acquisition uses fixed final integer indices: `ra_index = 0..359` and
`dec_index = 0..179`. Each ownership cell is `[ra_index, ra_index + 1) x
[dec_index - 90, dec_index - 89)` in ICRS degrees, except that `dec_index=179`
also owns Dec `+90`. To avoid polar over-fetch, each latitude band is acquired
through wider integer-RA cover sectors. A cover owns a disjoint run of final
tiles and its one-degree cone contains every point it owns; cone overlap is
therefore discarded by ownership rather than a whole-sky de-duplication table.
Raw responses and cleaning intermediates are in `/tmp/2mass_psc_all_sky/` and
are deleted after each successful cover. `.done/` makes reruns resume after
completed final tiles.
Downloads stay serial to be considerate of IRSA, while `--workers N` cleans up
to `N` already-downloaded covers in parallel. The queue is bounded to `N`, so
temporary raw/intermediate storage cannot grow without bound. Start with
`--workers 2`; increase it only if CPU and `/tmp` headroom remain available.
Each atomic output CSV uses the renderer's four-column CSV format and has a
name such as `tile_ra129_dec109.csv`, which means RA `[129, 130)` and Dec
`[19, 20)` degrees. For any coordinate, use
`ra_index = floor(RA_deg mod 360)` and
`dec_index = min(179, floor(Dec_deg + 90))` to select the filename directly.
It applies the same three-band `rd_flg`/photometry selection and blackbody fit
as the sample processor. The downloader fails if an IRSA response reaches
`--outrows`, so crowded fields cannot be silently truncated. It deletes raw
tile tables after successful cleaning unless `--keep-raw` is passed.
The two existing fields imply roughly 15--17 GiB of cleaned CSV for the full
PSC. Their raw response rows imply 70--75 GiB if every source appeared once.
The cover grid uses substantially fewer cone areas than the old fixed-grid
fetcher, but retaining every overlapping raw response would still need roughly
190--210 GiB. The default bounded pipeline only needs up to `--workers` raw
responses and intermediates in addition to final output.
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#!/usr/bin/env python3
"""Resumable 2MASS PSC acquisition into fixed one-degree output tiles.
The renderer-facing layout is always ``tile_raRRR_decDDD.csv``. For download
efficiency, one declination band is covered by wider integer-RA sectors at high
latitude. Every sector is queried by one <= 1 degree IRSA cone and owns a
disjoint run of final one-degree tiles, so overlap between cones cannot produce
duplicate output records. Raw tables and photometry intermediates live under
``/tmp/2mass_psc_all_sky`` and are removed after a successful cover.
"""
from __future__ import annotations
import argparse
import csv
import math
import re
import shutil
import subprocess
import sys
import tempfile
from concurrent.futures import FIRST_COMPLETED, Future, ProcessPoolExecutor, wait
from dataclasses import dataclass
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
OUTPUT_ROOT = ROOT / "assets/2mass/processed/all_sky"
DONE_ROOT = OUTPUT_ROOT / ".done"
TMP_ROOT = Path("/tmp/2mass_psc_all_sky")
PROCESSOR = ROOT / "scripts/process_2mass_psc.py"
RADIUS_DEG = 1.0
@dataclass(frozen=True)
class Tile:
dec_index: int
ra_index: int
@property
def name(self) -> str:
return f"ra{self.ra_index:03d}_dec{self.dec_index:03d}"
@property
def output_name(self) -> str:
return f"tile_ra{self.ra_index:03d}_dec{self.dec_index:03d}.csv"
@dataclass(frozen=True)
class Cover:
"""A cone and its disjoint, integer-degree RA ownership interval."""
dec_index: int
cover_index: int
ra_start: int
ra_end: int
@property
def name(self) -> str:
return f"dec{self.dec_index:03d}_cover{self.cover_index:03d}_ra{self.ra_start:03d}_{self.ra_end:03d}"
@property
def center_ra(self) -> float:
return (self.ra_start + self.ra_end) * 0.5
@property
def center_dec(self) -> float:
return self.dec_index - 89.5
def tile_indices(ra: float, dec: float) -> tuple[int, int]:
"""Return directly-computable (ra_index, dec_index) for ICRS degrees."""
if not math.isfinite(ra) or not math.isfinite(dec) or dec < -90.0 or dec > 90.0:
raise ValueError("expected finite ICRS RA and Dec with -90 <= Dec <= 90")
return int(math.floor(ra % 360.0)), min(179, int(math.floor(dec + 90.0)))
def covers_for_band(dec_index: int) -> list[Cover]:
"""Partition a band into maximum safe integer-degree RA cover sectors."""
if not 0 <= dec_index < 180:
raise ValueError("dec_index must be in 0..179")
dec_min = dec_index - 90.0
dec_max = dec_min + 1.0
# Start from a tangent-plane bound using the edge farther from the pole,
# then prove the exact spherical four-corner condition below.
edge_cosine = max(math.cos(math.radians(dec_min)), math.cos(math.radians(dec_max)))
width = min(360, max(1, int(math.floor(math.sqrt(3.0) / edge_cosine))))
def corners_fit(candidate: int) -> bool:
center_ra = candidate * 0.5
center_dec = math.radians(dec_index - 89.5)
for ra in (0.0, float(candidate)):
for dec in (dec_min, dec_max):
source_dec = math.radians(dec)
cosine_distance = (math.sin(center_dec) * math.sin(source_dec) +
math.cos(center_dec) * math.cos(source_dec) *
math.cos(math.radians(ra - center_ra)))
if math.degrees(math.acos(max(-1.0, min(1.0, cosine_distance)))) > RADIUS_DEG:
return False
return True
while width > 1 and not corners_fit(width):
width -= 1
return [Cover(dec_index, number, start, min(start + width, 360))
for number, start in enumerate(range(0, 360, width))]
def done_path(tile: Tile) -> Path:
return DONE_ROOT / (tile.name + ".done")
def tile_is_done(tile: Tile) -> bool:
return done_path(tile).exists()
def query_cover(cover: Cover, raw_path: Path, outrows: int) -> None:
"""Fetch and validate one IPAC table without exposing partial downloads."""
raw_path.parent.mkdir(parents=True, exist_ok=True)
with tempfile.NamedTemporaryFile(prefix=cover.name + "_", suffix=".tbl",
dir=raw_path.parent, delete=False) as temporary:
temporary_path = Path(temporary.name)
command = [
"curl", "--fail", "--silent", "--show-error", "--location", "--get",
"https://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-query",
"--data-urlencode", "catalog=fp_psc",
"--data-urlencode", "spatial=cone",
"--data-urlencode", f"objstr={cover.center_ra:.10f} {cover.center_dec:.10f}",
"--data-urlencode", f"radius={RADIUS_DEG}",
"--data-urlencode", "radunits=deg",
"--data-urlencode", "outfmt=1",
"--data-urlencode", "selcols=designation,ra,dec,j_m,h_m,k_m,rd_flg",
"--data-urlencode", f"outrows={outrows}", "-o", str(temporary_path),
]
try:
subprocess.run(command, check=True)
text = temporary_path.read_text(encoding="ascii")
header = re.search(r"^\|\s*designation\s*\|", text, re.MULTILINE)
if "2MASS All-Sky Point Source Catalog" not in text or header is None:
raise RuntimeError(f"{cover.name}: IRSA returned no valid PSC IPAC table")
row_count = re.search(r"^\\RowsRetrieved\s*=\s*(\d+)\s*$", text, re.MULTILINE)
if row_count is not None and int(row_count.group(1)) >= outrows:
raise RuntimeError(f"{cover.name}: reached --outrows={outrows}; rerun with a larger cap")
temporary_path.replace(raw_path)
except Exception:
temporary_path.unlink(missing_ok=True)
raise
def write_tile(tile: Tile, rows: list[dict[str, str]]) -> None:
"""Atomically write one complete final tile and only then mark it done."""
output_path = OUTPUT_ROOT / tile.output_name
with tempfile.NamedTemporaryFile(prefix=tile.name + "_", suffix=".csv",
dir=OUTPUT_ROOT, mode="w", encoding="ascii",
newline="", delete=False) as temporary:
writer = csv.DictWriter(temporary,
fieldnames=("ra_deg", "dec_deg", "temperature_K", "amplitude_sr"),
lineterminator="\n")
writer.writeheader()
writer.writerows(rows)
temporary_path = Path(temporary.name)
temporary_path.replace(output_path)
done_path(tile).write_text(f"retained_rows={len(rows)}\n", encoding="ascii")
def process_cover(cover: Cover, raw_path: Path, allowed_ra: set[int] | None = None) -> int:
"""Clean a cone then distribute its owned rows into final 1-degree tiles."""
with tempfile.NamedTemporaryFile(prefix=cover.name + "_", suffix=".csv",
dir=TMP_ROOT, delete=False) as temporary:
intermediate = Path(temporary.name)
try:
subprocess.run([sys.executable, str(PROCESSOR), "--input", str(raw_path),
"--output", str(intermediate)], check=True)
buckets: dict[int, list[dict[str, str]]] = {}
with intermediate.open(newline="", encoding="ascii") as source:
for row in csv.DictReader(source):
try:
ra_index, dec_index = tile_indices(float(row["ra_deg"]), float(row["dec_deg"]))
except ValueError:
continue
if dec_index != cover.dec_index or not cover.ra_start <= ra_index < cover.ra_end:
continue
if allowed_ra is not None and ra_index not in allowed_ra:
continue
tile = Tile(dec_index, ra_index)
if not tile_is_done(tile):
buckets.setdefault(ra_index, []).append(row)
completed = 0
for ra_index in range(cover.ra_start, cover.ra_end):
if allowed_ra is not None and ra_index not in allowed_ra:
continue
tile = Tile(cover.dec_index, ra_index)
if not tile_is_done(tile):
write_tile(tile, buckets.get(ra_index, []))
completed += 1
return completed
finally:
intermediate.unlink(missing_ok=True)
def cover_has_work(cover: Cover, allowed_ra: set[int] | None = None) -> bool:
return any(not tile_is_done(Tile(cover.dec_index, ra_index))
and (allowed_ra is None or ra_index in allowed_ra)
for ra_index in range(cover.ra_start, cover.ra_end))
def download_cover(cover: Cover, outrows: int, keep_raw: bool,
allowed_ra: set[int] | None = None) -> int:
"""Acquire one cover, distribute it, then reclaim its temporary raw table."""
raw_path = TMP_ROOT / "raw" / (cover.name + ".tbl")
if not raw_path.exists():
query_cover(cover, raw_path, outrows)
completed = process_cover(cover, raw_path, allowed_ra)
if not keep_raw:
raw_path.unlink()
print(f"completed={cover.name} final_tiles={completed}", flush=True)
return completed
def reap_completed_covers(pending: dict[Future[int], tuple[Cover, Path]],
keep_raw: bool) -> None:
"""Collect at least one worker; only delete raw data after success."""
completed, _ = wait(pending, return_when=FIRST_COMPLETED)
for future in completed:
cover, raw_path = pending.pop(future)
final_tiles = future.result()
if not keep_raw:
raw_path.unlink(missing_ok=True)
print(f"processed={cover.name} final_tiles={final_tiles}", flush=True)
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--plan", action="store_true", help="print cover count and exit")
parser.add_argument("--download", action="store_true", help="perform the IRSA acquisition")
parser.add_argument("--outrows", type=int, default=1_000_000,
help="per-cone IRSA row cap; cap hits are fatal (default: %(default)s)")
parser.add_argument("--keep-raw", action="store_true", help="retain raw IPAC tables under /tmp")
parser.add_argument("--limit", type=int, default=None, help="only process this many unfinished covers")
parser.add_argument("--workers", type=int, default=2,
help="parallel cleaning workers; downloads remain serial (default: %(default)s)")
args = parser.parse_args()
covers = [cover for dec_index in range(180) for cover in covers_for_band(dec_index)]
print(f"final_tiles=64800 covers={len(covers)} radius_deg={RADIUS_DEG:g} tmp_root={TMP_ROOT}")
print("estimated_final_cleaned_csv=15-17_GiB; raw/intermediate files are reclaimed per cover")
if args.plan:
return
if not args.download:
parser.error("choose --plan or --download")
if args.workers < 1:
parser.error("--workers must be at least 1")
if shutil.which("curl") is None:
parser.error("curl is required")
OUTPUT_ROOT.mkdir(parents=True, exist_ok=True)
DONE_ROOT.mkdir(parents=True, exist_ok=True)
TMP_ROOT.mkdir(parents=True, exist_ok=True)
scheduled = 0
pending: dict[Future[int], tuple[Cover, Path]] = {}
# One serial producer downloads covers. At most --workers downloaded
# covers await cleaning, bounding /tmp usage while overlapping the network
# wait with CPU-bound blackbody fitting and CSV distribution.
with ProcessPoolExecutor(max_workers=args.workers) as executor:
for cover in covers:
if not cover_has_work(cover):
continue
if args.limit is not None and scheduled >= args.limit:
break
while len(pending) >= args.workers:
reap_completed_covers(pending, args.keep_raw)
raw_path = TMP_ROOT / "raw" / (cover.name + ".tbl")
if not raw_path.exists():
query_cover(cover, raw_path, args.outrows)
print(f"downloaded={cover.name}; queued_cleaning={len(pending) + 1}/{args.workers}",
flush=True)
pending[executor.submit(process_cover, cover, raw_path)] = (cover, raw_path)
scheduled += 1
while pending:
reap_completed_covers(pending, args.keep_raw)
if __name__ == "__main__":
main()
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@@ -23,6 +23,7 @@ OUTPUT = ROOT / "assets/2mass/processed/2mass_psc_m31_0p5deg_stars.csv"
WAVELENGTH_M = np.array([1.235, 1.662, 2.159]) * 1e-6 WAVELENGTH_M = np.array([1.235, 1.662, 2.159]) * 1e-6
ZERO_POINT_JY = np.array([1594.0, 1024.0, 666.7]) ZERO_POINT_JY = np.array([1594.0, 1024.0, 666.7])
GOOD_RD_FLAGS = frozenset("123") GOOD_RD_FLAGS = frozenset("123")
MISSING_NUMERIC_TOKENS = frozenset(("", "null"))
MIN_TEMPERATURE_K = 300.0 MIN_TEMPERATURE_K = 300.0
MAX_TEMPERATURE_K = 100000.0 MAX_TEMPERATURE_K = 100000.0
@@ -62,7 +63,8 @@ def load_required_columns(path: Path) -> dict[str, np.ndarray]:
result: dict[str, np.ndarray] = {} result: dict[str, np.ndarray] = {}
for name in ("ra", "dec", "j_m", "h_m", "k_m"): for name in ("ra", "dec", "j_m", "h_m", "k_m"):
result[name] = np.array( result[name] = np.array(
[float(value) if value else math.nan for value in values[name]], dtype=float [float(value) if value.lower() not in MISSING_NUMERIC_TOKENS else math.nan
for value in values[name]], dtype=float
) )
result["rd_flg"] = np.array(values["rd_flg"], dtype="U3") result["rd_flg"] = np.array(values["rd_flg"], dtype="U3")
return result return result
@@ -123,11 +125,14 @@ def main() -> None:
columns = load_required_columns(args.input) columns = load_required_columns(args.input)
magnitudes = np.column_stack((columns["j_m"], columns["h_m"], columns["k_m"])) magnitudes = np.column_stack((columns["j_m"], columns["h_m"], columns["k_m"]))
valid_photometry = np.isfinite(magnitudes).all(axis=1) valid_photometry = np.isfinite(magnitudes).all(axis=1)
valid_position = (np.isfinite(columns["ra"]) & np.isfinite(columns["dec"]) &
(columns["ra"] >= 0.0) & (columns["ra"] < 360.0) &
(columns["dec"] >= -90.0) & (columns["dec"] <= 90.0))
valid_rd_flag = np.array( valid_rd_flag = np.array(
[len(flag) == 3 and all(value in GOOD_RD_FLAGS for value in flag) [len(flag) == 3 and all(value in GOOD_RD_FLAGS for value in flag)
for flag in columns["rd_flg"]] for flag in columns["rd_flg"]]
) )
keep = valid_photometry & valid_rd_flag keep = valid_position & valid_photometry & valid_rd_flag
if not np.any(keep): if not np.any(keep):
raise ValueError("no sources retain valid J/H/Ks photometry") raise ValueError("no sources retain valid J/H/Ks photometry")
@@ -145,6 +150,7 @@ def main() -> None:
f"{amplitude:.9e}")) f"{amplitude:.9e}"))
print(f"input_rows={len(keep)}") print(f"input_rows={len(keep)}")
print(f"discarded_invalid_position={np.count_nonzero(~valid_position)}")
print(f"discarded_invalid_photometry={np.count_nonzero(~valid_photometry)}") print(f"discarded_invalid_photometry={np.count_nonzero(~valid_photometry)}")
print(f"discarded_rd_flg_not_123={np.count_nonzero(~valid_rd_flag)}") print(f"discarded_rd_flg_not_123={np.count_nonzero(~valid_rd_flag)}")
print(f"retained_rows={np.count_nonzero(keep)}") print(f"retained_rows={np.count_nonzero(keep)}")