Move the processing code under pipeline/
Preparing to merge this repository into a combined astrophotography repo. session-scripts/ becomes pipeline/ because the scripts import layout.py from their own directory and must stay together, and because 'pipeline' says what it is rather than how it came about. observing/ stays at the top level: observing plans are not processing code.
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"""
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gc-complete.py -- step 3. Artificial star tests.
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Why this exists: NGC 5128's light makes the noise rise steeply toward the
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nucleus, so the SAME cluster is harder to detect at r=2' than at r=20'. Without
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correcting for that, the measured radial density profile is the true profile
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MULTIPLIED by an unknown, radially-decreasing completeness -- which flattens or
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even inverts a real central concentration. So we measure the completeness
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directly: inject fake point sources of known magnitude at known positions into
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the real luminance image, re-run the identical detection and selection chain,
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and count how many come back, as a function of magnitude and projected radius.
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The PSF is empirical, built by median-stacking bright isolated stars from the
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image itself.
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Output: _gc_complete.npz (recovery grid), and the PSF stamp.
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"""
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import os, time, numpy as np, sep, warnings
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from astropy.io import fits
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from astropy.wcs import WCS
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import layout
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warnings.filterwarnings("ignore")
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S = layout.SESSION
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BW, FW, THRESH, MINAREA, APR = 16, 3, 3.0, 5, 5.0
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ZP_L = 27.941
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PIXSCALE = 0.5376
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NUC_RA, NUC_DEC = 201.365063, -43.019113
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NRUN = 6 # injection runs
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NPER = 2500 # fakes per run
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MAGS = (17.5, 22.5)
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HALF = 15 # PSF stamp half-size, px
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RNG = np.random.default_rng(20260721)
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def gk(fwhm=5.0):
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sig = fwhm / 2.3548
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n = int(2 * round(3 * sig) + 1)
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r = np.arange(n) - n // 2
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xx, yy = np.meshgrid(r, r)
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k = np.exp(-(xx ** 2 + yy ** 2) / (2 * sig ** 2))
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return (k / k.sum()).astype(np.float32)
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def build_psf(data, cat):
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"""median stack of bright, isolated, unsaturated stars"""
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m = (cat["base"] & cat["is_star"] & (cat["mag"] > 14.5) & (cat["mag"] < 17.0)
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& (cat["r_arcmin"] > 8) & (cat["fwhm"] < 5.6))
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x, y = cat["x"][m], cat["y"][m]
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# isolation: no other detection within 20 px
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ax, ay = cat["x"], cat["y"]
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keep = []
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for i in range(len(x)):
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dd = np.hypot(ax - x[i], ay - y[i])
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if (dd < 20).sum() <= 1:
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keep.append(i)
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x, y = x[keep], y[keep]
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print(" PSF from %d isolated bright stars" % len(x))
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stamps = []
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for xi, yi in zip(x, y):
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ix, iy = int(round(xi)), int(round(yi))
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if ix < HALF or iy < HALF or ix >= data.shape[1] - HALF or iy >= data.shape[0] - HALF:
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continue
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st = data[iy - HALF:iy + HALF + 1, ix - HALF:ix + HALF + 1].astype(np.float64)
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st = st - np.median(st[[0, -1], :])
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s = st.sum()
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if s > 0:
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stamps.append(st / s)
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psf = np.median(np.array(stamps), axis=0)
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psf /= psf.sum()
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# normalise so that a source of total flux F injected as F*psf measures
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# F_ap through the r=5 px aperture; we need the aperture correction
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yy, xx = np.mgrid[-HALF:HALF + 1, -HALF:HALF + 1]
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apfrac = psf[np.hypot(xx, yy) <= APR].sum()
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print(" aperture fraction inside r=%.0f px: %.4f" % (APR, apfrac))
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return psf.astype(np.float32), apfrac
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def main():
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t0 = time.time()
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hdr = fits.getheader(layout.path("master-Luminance.fit"))
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w = WCS(hdr)
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ny, nx = hdr["NAXIS2"], hdr["NAXIS1"]
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cx, cy = [float(v) for v in w.all_world2pix(NUC_RA, NUC_DEC, 0)]
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cat = dict(np.load(layout.path("_gc_cat.npz"), allow_pickle=True))
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FW_MIN, FW_MAX = float(cat["FW_MIN"]), float(cat["FW_MAX"])
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base_img = np.ascontiguousarray(
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fits.getdata(layout.path("master-Luminance.fit")).astype(np.float32))
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psf, apfrac = build_psf(base_img, cat)
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np.save(layout.path("_gc_psf.npy"), psf)
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sep.set_extract_pixstack(1000000)
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K = gk()
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rec_mag, rec_rad, rec_ok = [], [], []
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for run in range(NRUN):
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img = base_img.copy()
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# positions: uniform over the frame, min 40 px apart from each other
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xs = RNG.uniform(HALF + 5, nx - HALF - 5, NPER)
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ys = RNG.uniform(HALF + 5, ny - HALF - 5, NPER)
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mags = RNG.uniform(*MAGS, NPER)
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# total flux such that the r=5px aperture measures the intended mag
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ftot = 10 ** (-0.4 * (mags - ZP_L)) / apfrac
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for xi, yi, f in zip(xs, ys, ftot):
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ix, iy = int(round(xi)), int(round(yi))
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img[iy - HALF:iy + HALF + 1, ix - HALF:ix + HALF + 1] += (f * psf)
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b = sep.Background(img, bw=BW, bh=BW, fw=FW, fh=FW)
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ds = img - b.back()
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rm = b.rms()
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o = sep.extract(ds, THRESH, err=rm, minarea=MINAREA, filter_kernel=K,
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filter_type="matched", deblend_nthresh=32,
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deblend_cont=0.005, clean=True)
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fl, fe, afl = sep.sum_circle(ds, o["x"], o["y"], APR, err=rm, subpix=5)
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fl2, _, _ = sep.sum_circle(ds, o["x"], o["y"], 2 * APR, err=rm, subpix=5)
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rh, _ = sep.flux_radius(ds, o["x"], o["y"], np.full(len(o), 6 * APR), 0.5,
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normflux=fl2, subpix=5)
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egain = float(hdr.get("EGAIN", 0.2467))
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fet = np.sqrt(fe ** 2 + np.clip(fl, 0, None) / (egain * 12.0))
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snr = fl / np.clip(fet, 1e-9, None)
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omag = -2.5 * np.log10(np.clip(fl, 1e-9, None)) + ZP_L
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ofwhm = 2.0 * rh
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oelong = o["a"] / np.clip(o["b"], 1e-6, None)
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ok_sel = ((snr >= 5.0) & ((o["flag"].astype(int) & 0b1110) == 0)
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& (ofwhm > FW_MIN) & (ofwhm < FW_MAX) & (oelong < 2.0)
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& (omag > 17.5) & (omag < 21.5) & (fl > 0))
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ox, oy = o["x"][ok_sel], o["y"][ok_sel]
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# match each injected star to the surviving detections, within 2 px
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for xi, yi, mg in zip(xs, ys, mags):
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dd = np.hypot(ox - xi, oy - yi)
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hit = (dd.min() < 2.0) if len(dd) else False
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rec_mag.append(mg)
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rec_rad.append(np.hypot(xi - cx, yi - cy) * PIXSCALE / 60.0)
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rec_ok.append(bool(hit))
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print(" run %d/%d done (%.0f s)" % (run + 1, NRUN, time.time() - t0), flush=True)
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del img, b, ds, rm, o
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rec_mag = np.array(rec_mag); rec_rad = np.array(rec_rad); rec_ok = np.array(rec_ok)
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np.savez(layout.path("_gc_complete.npz"), mag=rec_mag, rad=rec_rad,
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ok=rec_ok, apfrac=apfrac)
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print("\ninjected %d, recovered %d (%.1f%%)" % (len(rec_ok), rec_ok.sum(), 100 * rec_ok.mean()))
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print("\ncompleteness grid (rows = mag, cols = radius arcmin):")
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rb = [0, 2, 4, 6, 9, 13, 18, 30]
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mb = np.arange(17.5, 22.6, 0.5)
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print(" " + "".join("%7s" % ("%d-%d" % (rb[i], rb[i + 1])) for i in range(len(rb) - 1)))
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for j in range(len(mb) - 1):
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row = "%4.1f " % mb[j]
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for i in range(len(rb) - 1):
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s = ((rec_mag >= mb[j]) & (rec_mag < mb[j + 1]) &
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(rec_rad >= rb[i]) & (rec_rad < rb[i + 1]))
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row += "%7s" % ("%.2f" % rec_ok[s].mean() if s.sum() > 20 else "-")
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print(row)
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if __name__ == "__main__":
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main()
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