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.
283 lines
12 KiB
Python
283 lines
12 KiB
Python
"""Figure: the moving-object search, its sensitivity, and its failure mode.
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The blind search found nothing, so the figure has to show that the search
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worked rather than showing a discovery. Four things are plotted.
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Rows 1-2 are postage-stamp strips across the twelve luminance subs, cut at a
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sky position that is FIXED on the sky (the object's position in the first sub).
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A real moving object drifts across the strip. So does a hot pixel, because
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registration holds the sky still and therefore drags anything fixed to the
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detector in the opposite direction. The two are indistinguishable here, which
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is the whole problem.
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Rows 3-4 are the same two objects, cut instead at a FIXED DETECTOR position.
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Now they separate cleanly: the real object still moves, the hot pixel does not
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move at all. This is the cut that took the search from 141 confident false
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detections to zero.
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The bottom panels are the sensitivity: recovery fraction of synthetic movers as
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a function of magnitude and apparent rate, from three independent injection
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runs (twelve injections per cell), and a slice through it.
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"""
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import os
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import numpy as np
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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 matplotlib.colors import LinearSegmentedColormap
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from scipy.spatial import cKDTree
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import mo_common as C
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HALF = 13
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OUTPNG = os.path.join(C.OUT, "NGC5128-mo-moving-object-search.png")
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INJ_MAG = 18.5
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INJ_RATE = 25.0 # px/hr = 13.4 arcsec/hr
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def load_grids():
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mags = rates = None
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grids = []
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for seed in (1, 2, 3):
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p = os.path.join(C.OUT, f"_mo_sensitivity_{seed}.npz")
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if not os.path.exists(p):
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continue
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z = np.load(p)
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mags, rates = z["mags"], z["rates"]
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grids.append(z["grid"])
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return mags, rates, np.mean(grids, axis=0), len(grids) * int(
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np.load(os.path.join(C.OUT, "_mo_sensitivity_1.npz"))["nrep"])
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# A detector defect verified by hand: reproducing the search with the
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# detector-frame cuts switched off yields 141 tracklets, and this one is
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# typical. Its detected centroid sits at native pixel (224.00, 787.00) in
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# every one of subs 002-009 - not merely close, but the same pixel centre to
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# 0.02 px - while its registered position walks 18.6 px across the sequence
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# at 15.9 arcsec/hr with a brightness stable to 0.06 mag. It is a hot pixel.
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HOT_NATIVE = np.array([224.00, 787.00])
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HOT_REF_KEY = "Luminance_002"
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def find_hot_pixel(tforms, keys):
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return HOT_NATIVE, 8
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def stamps(inj_ref0, inj_v, hot_native, tforms, keys, times):
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"""Cut all four strips in a single pass over the subs."""
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out = {"inj_sky": [], "inj_det": [], "hot_sky": [], "hot_det": []}
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hot_ref0 = tforms[keys[0]](hot_native[None, :])[0]
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inj_native0 = tforms[keys[0]].inverse(inj_ref0[None, :])[0]
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def cut(img, x, y):
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xi, yi = int(round(x)), int(round(y))
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return img[yi - HALF:yi + HALF + 1, xi - HALF:xi + HALF + 1].copy()
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for i, (key, path) in enumerate(C.lum_frames()):
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with fits.open(path, memmap=False) as hd:
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img = hd[0].data.astype(np.float32)
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tf = tforms[key]
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inv = tf.inverse
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lin = np.linalg.inv(tf.params[:2, :2])
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# Inject the synthetic at its true position in this sub.
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true_ref = inj_ref0 + inj_v * times[i]
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tx, ty = inv(true_ref[None, :])[0]
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objs, ap, _ = C.detect(img)
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mobjs, map_, _, _, _ = MASTER
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zp, _ = C.frame_zeropoint(objs["x"], objs["y"], ap, mobjs, map_, tf)
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d = lin @ (inj_v * 300.0 / 3600.0)
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C.add_source(img, tx, ty, 10 ** ((zp - INJ_MAG) / 2.5) / C.APFRAC,
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d[0], d[1])
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# Strip 1: cut at the sky position the object had at t=0.
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sx, sy = inv(inj_ref0[None, :])[0]
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out["inj_sky"].append(cut(img, sx, sy))
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# Strip 2: cut at the detector position it had at t=0.
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out["inj_det"].append(cut(img, inj_native0[0], inj_native0[1]))
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# Strip 3: hot pixel, cut at its t=0 sky position.
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hx, hy = inv(hot_ref0[None, :])[0]
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out["hot_sky"].append(cut(img, hx, hy))
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# Strip 4: hot pixel, cut at its fixed detector position.
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out["hot_det"].append(cut(img, hot_native[0], hot_native[1]))
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del img
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print(f" stamps from {key}")
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return out
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def show(ax, s, vlo, vhi, edge="#7a7f87"):
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ax.imshow(s, origin="lower", cmap="gray", vmin=vlo, vmax=vhi,
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interpolation="nearest")
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n = s.shape[0]
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c = (n - 1) / 2.0
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# A faint crosshair marks the centre of the stamp, i.e. the position the
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# cut is held fixed at. Whether the source stays on it is the whole point.
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ax.plot([c, c], [c - 0.30 * n, c - 0.12 * n], color="#f0c05a", lw=0.8)
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ax.plot([c - 0.30 * n, c - 0.12 * n], [c, c], color="#f0c05a", lw=0.8)
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ax.set_xlim(-0.5, n - 0.5)
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ax.set_ylim(-0.5, n - 0.5)
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ax.set_xticks([])
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ax.set_yticks([])
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for sp in ax.spines.values():
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sp.set_color(edge)
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sp.set_linewidth(0.8)
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def main():
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global MASTER
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MASTER = C.master_sources()
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times = C.lum_times()
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keys = [k for k, _ in C.lum_frames()]
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tforms = C.frame_transforms()
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hot, hotn = find_hot_pixel(tforms, keys)
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print(f"hot pixel at detector ({hot[0]:.1f}, {hot[1]:.1f}), "
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f"present in {hotn + 1}/12 subs")
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ang = np.radians(35.0)
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inj_v = INJ_RATE * np.array([np.cos(ang), np.sin(ang)])
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mobjs = MASTER[0]
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mtree = cKDTree(np.column_stack([mobjs["x"], mobjs["y"]]))
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rng = np.random.default_rng(11)
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for _ in range(50000):
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p0 = np.array([rng.uniform(600, 4200), rng.uniform(600, 2600)])
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track = np.array([p0 + inj_v * t for t in times])
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if np.hypot(*(p0 - np.array([2394.0, 1597.0]))) < 1100:
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continue
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d, _ = mtree.query(track, distance_upper_bound=30.0)
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if np.all(~np.isfinite(d)):
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inj_ref0 = p0
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break
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else:
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inj_ref0 = np.array([1500.0, 2500.0])
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print(f"synthetic injected at reference pixel "
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f"({inj_ref0[0]:.0f}, {inj_ref0[1]:.0f})")
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st = stamps(inj_ref0, inj_v, hot, tforms, keys, times)
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mags, rates, grid, nrep = load_grids()
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from astropy.stats import sigma_clipped_stats
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fig = plt.figure(figsize=(15.0, 11.4))
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gs = fig.add_gridspec(
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5, 12, height_ratios=[0.78, 0.78, 0.78, 0.78, 2.5],
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hspace=0.16, wspace=0.06,
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left=0.175, right=0.985, top=0.878, bottom=0.08)
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labels = [
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("inj_sky", "synthetic mover", "held at fixed SKY position",
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"#2f7d54", "moves -> could be real"),
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("hot_sky", "hot pixel", "held at fixed SKY position",
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"#b5484f", "also moves -> looks identical"),
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("inj_det", "synthetic mover", "held at fixed DETECTOR position",
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"#2f7d54", "still moves -> REAL"),
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("hot_det", "hot pixel", "held at fixed DETECTOR position",
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"#b5484f", "does not move -> DEFECT"),
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]
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for r, (kk, who, how, col, verdict) in enumerate(labels):
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arr = np.array(st[kk], dtype=float)
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med, _, sd = sigma_clipped_stats(arr, sigma=3.0)
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vlo, vhi = med - 1.5 * sd, med + 14.0 * sd
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for c in range(12):
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ax = fig.add_subplot(gs[r, c])
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show(ax, st[kk][c], vlo, vhi, edge=col)
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if r == 0:
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ax.set_title(f"{times[c] * 60:.0f} min", fontsize=8.5,
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color="#3b3f45", pad=4)
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if c == 0:
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bb = ax.get_position()
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fig.text(0.170, bb.y0 + bb.height * 0.80, who, fontsize=10.5,
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color=col, ha="right", va="center", weight="bold")
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fig.text(0.170, bb.y0 + bb.height * 0.50, how, fontsize=8.4,
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color="#5a6068", ha="right", va="center")
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fig.text(0.170, bb.y0 + bb.height * 0.18, verdict,
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fontsize=8.6, color=col, ha="right", va="center",
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style="italic")
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fig.text(0.02, 0.962,
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"Moving-object search: 12 x 300 s luminance subs of NGC 5128, "
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"2026-07-21 08:56-10:00 UTC",
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fontsize=14, color="#1b1e23", weight="bold", ha="left")
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fig.text(0.02, 0.937,
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"Each strip is a 14 x 14 arcsec cutout, one per sub, with the "
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"cut position held fixed (yellow crosshair). Registration holds "
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"the sky still, so it drags anything fixed to the",
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fontsize=9.4, color="#4a4f57", ha="left")
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fig.text(0.02, 0.918,
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"detector across the registered frame - a hot pixel therefore "
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"mimics a minor planet perfectly. Cutting at a fixed detector "
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"position separates them. Synthetic source: G = "
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f"{INJ_MAG}, {INJ_RATE * C.SCALE:.1f} arcsec/hr.",
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fontsize=9.4, color="#4a4f57", ha="left")
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# ---- sensitivity heat map ----
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axg = fig.add_subplot(gs[4, 0:6])
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cmap = LinearSegmentedColormap.from_list(
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"rec", ["#f5f6f8", "#cfe0ec", "#7fb0cd", "#3d7ba6", "#17456b"])
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im = axg.imshow(grid, origin="lower", aspect="auto", cmap=cmap,
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vmin=0, vmax=1, interpolation="nearest")
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axg.set_xticks(range(len(rates)))
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axg.set_xticklabels([f"{r * C.SCALE:.1f}" for r in rates], fontsize=8.5)
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axg.set_yticks(range(len(mags)))
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axg.set_yticklabels([f"{m:g}" for m in mags], fontsize=9)
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axg.set_xlabel("apparent rate (arcsec/hr)", fontsize=10)
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axg.set_ylabel("G magnitude", fontsize=10)
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axg.set_title(f"recovery of injected synthetic movers "
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f"({nrep} per cell, 3 independent runs)", fontsize=10.5,
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color="#1b1e23", pad=8)
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for i in range(len(mags)):
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for j in range(len(rates)):
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v = grid[i, j]
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axg.text(j, i, f"{v:.2f}".lstrip("0") if v else ".",
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ha="center", va="center", fontsize=7.0,
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color="white" if v > 0.55 else "#585d65")
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cb = fig.colorbar(im, ax=axg, fraction=0.032, pad=0.014)
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cb.set_label("recovered", fontsize=9)
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cb.ax.tick_params(labelsize=8)
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# ---- slices ----
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axs = fig.add_subplot(gs[4, 7:12])
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band = (rates * C.SCALE >= 8) & (rates * C.SCALE <= 40)
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prof = grid[:, band].mean(axis=1)
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axs.plot(mags, prof, "-o", color="#2e6f9e", lw=2.2, ms=5.5,
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label="8-40 arcsec/hr (main-belt range)")
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prof2 = grid[:, rates * C.SCALE > 60].mean(axis=1)
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axs.plot(mags, prof2, "-s", color="#b5484f", lw=1.8, ms=4.5,
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label="> 60 arcsec/hr (trailing losses)")
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axs.axhline(0.5, color="#9aa0a8", ls=":", lw=1.2)
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axs.text(mags[0] + 0.02, 0.53, "50% recovery", fontsize=8,
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color="#6b7079")
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axs.set_xlabel("G magnitude", fontsize=10)
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axs.set_ylabel("fraction recovered", fontsize=10)
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axs.set_ylim(-0.03, 1.08)
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axs.set_title("sensitivity vs magnitude", fontsize=10.5,
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color="#1b1e23", pad=8)
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axs.legend(fontsize=8.5, frameon=False, loc="lower left")
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axs.grid(alpha=0.25, lw=0.6)
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for sp in ("top", "right"):
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axs.spines[sp].set_visible(False)
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order = np.argsort(prof)
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g50 = float(np.interp(0.5, prof[order], mags[order]))
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print(f"50% recovery for main-belt rates at G = {g50:.2f}")
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fig.text(0.02, 0.024,
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"Result: 0 candidates from the blind search. Time-permuted "
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"control on the real residuals: 0 tracklets in 60 trials. "
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f"50% recovery at G = {g50:.1f} for main-belt rates. The only "
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"catalogued minor planet within 30' of the pointing,",
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fontsize=9, color="#4a4f57", ha="left")
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fig.text(0.02, 0.006,
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"(427494) 2002 BK26 at V = 21.7, fell 2.4 arcmin outside the "
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"frame - the field's long axis runs along declination, not right "
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"ascension.",
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fontsize=9, color="#4a4f57", ha="left")
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fig.savefig(OUTPNG, dpi=125, facecolor="white")
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print(f"wrote {OUTPNG}")
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MASTER = None
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if __name__ == "__main__":
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main()
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