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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pipeline/sb_iso.py
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pipeline/sb_iso.py
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"""Step 2: isophote fitting of the NGC 5128 luminance master.
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Pass A free-centre fits on the dust-free outer body -> adopted centre
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Pass B fixed-centre fit, stars AND the dust lane masked -> PRIMARY table
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Pass C the same fit on a de-reddened image, where the
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extinction is estimated from the B-R colour excess and
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calibrated against the Pass B model over 150<a<600 px -> inner extension
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Outputs: sb-isophotes.csv, sb-isophotes-dereddened.csv,
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_isoB.npz, _isoC.npz, _geom.npy, _extcal.npy
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"""
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import numpy as np
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import time
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from astropy.io import fits
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from photutils.isophote import Ellipse, EllipseGeometry
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from sb_common import *
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import sb_model
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SMA_MIN, SMA_MAX, STEP = 8.0, 1750.0, 0.11
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L = load('Luminance')
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starmask = fits.getdata(path('sb-mask-stars.fits')).astype(bool)
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dustmask = fits.getdata(path('sb-mask-dust.fits')).astype(bool)
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exc = np.load(path('sb-colour-excess.npy'))
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def table(iso):
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k = [i for i in iso if i.sma > 0 and np.isfinite(i.intens)]
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def g(f):
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return np.array([(f(i) if f(i) is not None else np.nan) for i in k], float)
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return dict(sma=g(lambda i: i.sma), intens=g(lambda i: i.intens),
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int_err=g(lambda i: i.int_err), rms=g(lambda i: i.rms),
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eps=g(lambda i: i.eps), eps_err=g(lambda i: i.ellip_err),
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pa=np.degrees(g(lambda i: i.pa)) % 180.,
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pa_err=np.degrees(g(lambda i: i.pa_err)),
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ndata=g(lambda i: i.ndata), nflag=g(lambda i: i.nflag),
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stop=g(lambda i: i.stop_code))
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def fit(img, mask, x0, y0, label):
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arr = np.ma.masked_array(img, mask=mask)
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g = EllipseGeometry(x0=x0, y0=y0, sma=300., eps=0.15, pa=np.radians(150.))
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g.fix_center = True
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t = time.time()
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iso = Ellipse(arr, geometry=g).fit_image(
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sma0=300., minsma=SMA_MIN, maxsma=SMA_MAX, step=STEP, linear=False,
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nclip=3, sclip=3.0, fix_center=True)
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print('%s: %d isophotes in %.0f s' % (label, len(iso), time.time() - t))
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return table(iso)
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# ------------------------------------------------------------------- Pass A
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arr = np.ma.masked_array(L, mask=starmask | dustmask)
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cen = []
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for s in [350., 500., 650., 800., 1000.]:
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try:
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it = Ellipse(arr, geometry=EllipseGeometry(
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x0=X0, y0=Y0, sma=s, eps=0.18, pa=np.radians(148.))
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).fit_image(sma0=s, minsma=s * 0.98, maxsma=s * 1.02, step=0.1,
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nclip=3, sclip=3.)
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for i in it:
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if np.isfinite(i.x0):
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cen.append((i.x0, i.y0))
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except Exception as e:
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print(' passA sma=%.0f: %s' % (s, e))
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cen = np.array(cen)
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XC, YC = float(np.median(cen[:, 0])), float(np.median(cen[:, 1]))
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off = np.hypot(XC - X0, YC - Y0)
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print('Pass A: outer-isophote centre %.2f, %.2f (scatter %.1f, %.1f px, n=%d)'
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% (XC, YC, cen[:, 0].std(), cen[:, 1].std(), len(cen)))
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print(' WCS/Gaia nucleus %.2f, %.2f -> offset %.1f px = %.1f arcsec'
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% (X0, Y0, off, off * PIXSCALE))
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np.save(path('_geom.npy'), np.array([XC, YC]))
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del arr
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# ------------------------------------------------------------------- Pass B
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tB = fit(L, starmask | dustmask, XC, YC, 'Pass B (stars+dust masked)')
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np.savez(path('_isoB.npz'), **tB)
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# ---------------------------------------------------------------- extinction
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# A_L from the Pass B model, used only where that model is directly constrained
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good = np.isfinite(tB['intens']) & (tB['ndata'] > 150) & (tB['sma'] > 90)
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tBg = {k: v[good] for k, v in tB.items()}
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modB, aB = sb_model.build(L.shape, XC, YC, tBg, block=4)
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with np.errstate(all='ignore'):
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A_L = -2.5 * np.log10(np.clip(L, 1e-3, None) / np.clip(modB, 1e-3, None))
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cal = (dustmask & ~starmask & (aB > 150) & (aB < 600) & np.isfinite(exc)
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& (exc > 0.05) & np.isfinite(A_L) & (A_L > -0.5) & (A_L < 4.0))
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cal &= exc > 0.15 # restrict to a well-measured colour excess
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x, y = exc[cal].astype(float), A_L[cal].astype(float)
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# robust slope through the origin: median of the per-pixel ratios
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k_ratio = float(np.median(y / x))
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scatter = float(np.median(np.abs(y - k_ratio * x)) * 1.4826)
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print('extinction calibration on %d px: A_L = %.3f * E(B-R), scatter %.3f mag'
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% (cal.sum(), k_ratio, scatter))
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print(' (least-squares through origin for comparison: %.3f)'
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% (np.sum(x * y) / np.sum(x * x)))
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np.save(path('_extcal.npy'), np.array([k_ratio, scatter, cal.sum()]))
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E = np.clip(np.nan_to_num(exc, nan=0.0), 0.0, None)
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A_est = np.clip(k_ratio * E, 0.0, 2.5) # >2.5 mag is unreliable
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heavy = (k_ratio * E) > 2.5
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Lc = (L * 10 ** (0.4 * A_est)).astype(np.float32)
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print('de-reddening: median A_L inside the lane mask %.2f mag; %d px above the '
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'2.5 mag cap (masked in Pass C)' % (np.median(A_est[dustmask]), heavy.sum()))
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del modB, aB, A_L, A_est, E
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# ------------------------------------------------------------------- Pass C
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tC = fit(Lc, starmask | heavy, XC, YC, 'Pass C (de-reddened)')
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np.savez(path('_isoC.npz'), **tC)
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del Lc, L
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# ------------------------------------------------------------------- CSVs
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write_isophote_csv(tB, 'sb-isophotes.csv')
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write_isophote_csv(tC, 'sb-isophotes-dereddened.csv')
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for name, t in [('Pass B (primary, dust masked)', tB), ('Pass C (de-reddened)', tC)]:
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print('')
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print(name)
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print(' sma_px arcsec mu eps PA ndata nflag stop')
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for i in range(len(t['sma'])):
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if t['sma'][i] > 30 and i % 3:
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continue
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print('%7.1f %7.1f %6.2f %6.3f %6.1f %6d %5d %4d'
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% (t['sma'][i], t['sma'][i] * PIXSCALE, mu(t['intens'][i]),
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t['eps'][i], t['pa'][i], t['ndata'][i], t['nflag'][i], t['stop'][i]))
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