laurence
5286a2e81b
The scripts that processed the NGC 5128 session of 2026-07-21 previously lived inside the data directory and addressed it with absolute paths. Code and data are now separated: the code lives here, and a session is located at runtime through the ASTRO_SESSION environment variable. layout.py is what makes that work. It maps a FILENAME to the subdirectory that file belongs in, using the same rules the session directories are organised with, so a script can go on asking for 'master-Red.fit' or '_stars.npz' without any call site knowing the directory structure. Anything unrecognised resolves to the session root, which is visible and correctable rather than silently wrong. restructure.py reorganises a flat session directory into that layout. It is idempotent and dry-run by default. The 50 session scripts are kept as they were run rather than tidied into a library. They were written in sequence as the work went along, several of them by parallel agents, and they show it - but they are the honest provenance of a published set of results, and the productionised pipeline should be able to reproduce those results exactly. Verified before committing: all 51 files compile without warnings, and verify_core.py, closeup.py and triptych.py were run end to end against the reorganised session, correctly finding inputs across calibrated/, stacks/masters/ and final/ and writing outputs back to the right places.
156 lines
6.8 KiB
Python
156 lines
6.8 KiB
Python
"""
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gc-validate.py -- step 5. External validation against published catalogues.
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Two independent truth sets fall inside the field:
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* SIMBAD, object type GlC -- confirmed/catalogued Cen A clusters
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* SCABS (Taylor et al. 2017, MNRAS 469, 3444) -- deep DECam GC candidates,
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with V magnitudes, so it can be binned in brightness
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Neither is complete, especially in the inner few arcmin where the galaxy swamps
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even professional data, so a candidate that matches nothing is UNCONFIRMED, not
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false. Produces NGC5128-gc-recovery.png and prints the numbers used in the notes.
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"""
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import os, numpy as np, warnings
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from astropy.io import fits
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from astropy.wcs import WCS
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from astropy.coordinates import SkyCoord
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import layout
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warnings.filterwarnings("ignore")
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S = layout.SESSION
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PIXSCALE = 0.5376
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NUC_RA, NUC_DEC = 201.365063, -43.019113
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C_CAND, C_EXT, C_KNOWN, C_ACC = "#2563eb", "#e8710a", "#127a5a", "#8b5cf6"
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C_INK, C_MUTED, C_GRID = "#1a1a1a", "#5c5c5c", "#d8d8d4"
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SURF = "#fcfcfb"
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plt.rcParams.update({
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"figure.facecolor": SURF, "axes.facecolor": SURF, "axes.edgecolor": C_MUTED,
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"text.color": C_INK, "xtick.color": C_MUTED, "ytick.color": C_MUTED,
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"axes.grid": True, "grid.color": C_GRID, "grid.linewidth": 0.6,
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"axes.axisbelow": True, "font.size": 10, "axes.spines.top": False,
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"axes.spines.right": False, "legend.frameon": False, "axes.labelcolor": C_INK})
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def main():
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d = dict(np.load(layout.path("_gc_cat.npz"), allow_pickle=True))
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hdr = fits.getheader(layout.path("master-Luminance.fit"))
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w = WCS(hdr)
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cx, cy = [float(v) for v in w.all_world2pix(NUC_RA, NUC_DEC, 0)]
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cat = SkyCoord(d["ra"], d["dec"], unit="deg")
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nuc = SkyCoord(NUC_RA, NUC_DEC, unit="deg")
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# ---- reference sets restricted to the frame -----------------------------
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def infield(ra, dec):
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x, y = w.all_world2pix(ra, dec, 0)
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return (x > 30) & (x < 4758) & (y > 30) & (y < 3164), x, y
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z = np.load(layout.path("_simbad.npz"), allow_pickle=True)
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m = z["otype"].astype(str) == "GlC"
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sra, sdec = z["ra"][m].astype(float), z["dec"][m].astype(float)
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inf, _, _ = infield(sra, sdec)
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sim = SkyCoord(sra[inf], sdec[inf], unit="deg")
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r = np.load(layout.path("_cena_gc_ref.npz"))
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infr, _, _ = infield(r["ra"], r["dec"])
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good_p = infr & (r["prob"] >= 0.9) & np.isfinite(r["vmag"])
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sca = SkyCoord(r["ra"][good_p], r["dec"][good_p], unit="deg")
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scav = r["vmag"][good_p]
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print("in-field reference objects: SIMBAD GlC %d ; SCABS p>=0.9 with V %d"
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% (len(sim), len(sca)))
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cand = d["cand"]
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ccand = cat[cand]
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call = cat[d["base"]]
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def recov(ref):
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i, s, _ = ref.match_to_catalog_sky(ccand)
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j, s2, _ = ref.match_to_catalog_sky(call)
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return s.arcsec < 2.0, s2.arcsec < 2.0
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hit_sim, det_sim = recov(sim)
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hit_sca, det_sca = recov(sca)
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print("SIMBAD GlC: detected at all %d/%d (%.0f%%), kept as candidate %d/%d (%.0f%%)"
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% (det_sim.sum(), len(sim), 100 * det_sim.mean(),
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hit_sim.sum(), len(sim), 100 * hit_sim.mean()))
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print("SCABS p>=0.9: detected %d/%d (%.0f%%), kept %d/%d (%.0f%%)"
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% (det_sca.sum(), len(sca), 100 * det_sca.mean(),
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hit_sca.sum(), len(sca), 100 * hit_sca.mean()))
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# ---- purity -------------------------------------------------------------
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ci, cs, _ = ccand.match_to_catalog_sky(sim)
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matched_sim = cs.arcsec < 2.0
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ci2, cs2, _ = ccand.match_to_catalog_sky(
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SkyCoord(r["ra"][infr], r["dec"][infr], unit="deg"))
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matched_sca = cs2.arcsec < 2.0
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any_match = matched_sim | matched_sca
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print("\nPURITY: %d candidates; %d (%.0f%%) match SIMBAD GlC or SCABS; "
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"%d (%.0f%%) unconfirmed"
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% (cand.sum(), any_match.sum(), 100 * any_match.mean(),
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(~any_match).sum(), 100 * (~any_match).mean()))
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st = d["simbad_type"][cand]
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bad = np.isin(st, ["*", "PM*", "V*", "RR*", "EB*", "LP*"])
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print(" candidates matching a SIMBAD STAR-type object: %d (%.1f%%)"
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% (bad.sum(), 100 * bad.mean()))
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print(" matching a SIMBAD galaxy: %d ; Cepheid: %d ; X-ray/LXB: %d"
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% ((st == "G").sum(), (st == "Ce*").sum(), np.isin(st, ["X", "LXB"]).sum()))
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rcand = d["r_arcmin"][cand]
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print(" unconfirmed fraction inside 8': %.0f%% ; outside 8': %.0f%%"
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% (100 * (~any_match)[rcand < 8].mean(), 100 * (~any_match)[rcand >= 8].mean()))
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# ---- figure -------------------------------------------------------------
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fig, ax = plt.subplots(1, 2, figsize=(12.6, 5.0))
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a = ax[0]
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vb = np.arange(17.0, 22.6, 0.5)
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vc = 0.5 * (vb[1:] + vb[:-1])
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fd, fk, nn = [], [], []
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for j in range(len(vb) - 1):
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s = (scav >= vb[j]) & (scav < vb[j + 1])
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nn.append(s.sum())
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fd.append(det_sca[s].mean() if s.sum() > 4 else np.nan)
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fk.append(hit_sca[s].mean() if s.sum() > 4 else np.nan)
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a.plot(vc, fd, lw=2.2, marker="o", ms=7, color=C_KNOWN,
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label="detected by our pipeline")
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a.plot(vc, fk, lw=2.2, marker="s", ms=7, color=C_CAND,
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label="detected AND kept as a candidate")
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for x, y, n in zip(vc, fd, nn):
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if np.isfinite(y) and n > 4:
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a.annotate("%d" % n, (x, y), xytext=(0, 8), textcoords="offset points",
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ha="center", fontsize=7.5, color=C_MUTED)
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a.set_xlabel("SCABS V magnitude"); a.set_ylabel("fraction recovered")
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a.set_ylim(0, 1.05)
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a.set_title("a) recovery of published clusters vs brightness", fontsize=10.5, loc="left")
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a.legend(fontsize=9, labelcolor=C_INK)
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a.text(0.98, 0.9, "numbers = reference objects per bin", transform=a.transAxes,
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ha="right", fontsize=8, color=C_MUTED)
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b = ax[1]
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rsca = sca.separation(nuc).arcmin
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rb = np.array([0, 2, 4, 6, 9, 12, 16, 21, 27])
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rc = 0.5 * (rb[1:] + rb[:-1])
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for arr, c, lab, mk in ((det_sca, C_KNOWN, "detected", "o"),
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(hit_sca, C_CAND, "kept as candidate", "s")):
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f, nnr = [], []
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for j in range(len(rb) - 1):
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s = (rsca >= rb[j]) & (rsca < rb[j + 1])
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nnr.append(s.sum())
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f.append(arr[s].mean() if s.sum() > 4 else np.nan)
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b.plot(rc, f, lw=2.2, marker=mk, ms=7, color=c, label=lab)
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b.set_xlabel("projected radius (arcmin)"); b.set_ylabel("fraction recovered")
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b.set_ylim(0, 1.05)
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b.set_title("b) the same, vs radius (SCABS p$\\geq$0.9)", fontsize=10.5, loc="left")
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b.legend(fontsize=9, labelcolor=C_INK)
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fig.suptitle("External validation against SCABS (Taylor et al. 2017) and SIMBAD",
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fontsize=12.5, x=0.008, ha="left")
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fig.tight_layout()
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fig.savefig(layout.path("NGC5128-gc-recovery.png"), dpi=115, bbox_inches="tight",
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facecolor=SURF)
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plt.close(fig)
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print("wrote NGC5128-gc-recovery.png")
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if __name__ == "__main__":
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main()
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