Processing and analysis code for remote-telescope imaging sessions
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.
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session-scripts/sb_dust.py
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session-scripts/sb_dust.py
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"""Step 5: dust-lane extinction map, with an independent colour-excess check.
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Method
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1. The smooth isophote model of the luminance master gives the light the
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galaxy would show with no dust. A_L = -2.5 log10(observed / model).
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This is a foreground-screen approximation: the lane is a warped disk seen
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nearly edge-on across the near side of the bulge, so the screen assumption
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is good for the lane itself but underestimates the true optical depth
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wherever stars sit in front of the dust.
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2. The same is done for R, G and B on the SAME elliptical isophotes, using
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the sigma-clipped azimuthal median of the dust-free azimuths as each
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channel's unobscured model. E(B-R) = A_B - A_R is then a completely
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independent, model-ratio-based reddening measurement, and A_L / E(B-R)
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is a measured extinction-law ratio rather than an assumed one.
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Outputs
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sb-extinction.fits A_L map (mag), NaN outside the measurable region
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NGC5128-sb-extinction-map.png 4-panel extinction / reddening figure
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NGC5128-sb-extinction-law.png A_L against E(B-R) with the fitted ratio
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"""
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import numpy as np
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from astropy.io import fits
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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 scipy.ndimage import gaussian_filter, median_filter
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from sb_common import *
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XC, YC = np.load(path('_geom.npy'))
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P = np.load(path('_profile.npz'))
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star = fits.getdata(path('sb-mask-stars.fits')).astype(bool)
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dust = fits.getdata(path('sb-mask-dust.fits')).astype(bool)
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a_map = np.load(path('_amap.npy'))
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ac = P['a']
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A = {}
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for ch in ['Luminance', 'Red', 'Green', 'Blue']:
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pr = P[ch]
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ok = np.isfinite(pr) & (pr > 0)
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mdl = np.interp(a_map, ac[ok], pr[ok], left=pr[ok][0], right=np.nan)
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img = gaussian_filter(load(ch), 2.0) # match the colour smoothing
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with np.errstate(all='ignore'):
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A[ch] = np.where((img > 0) & (mdl > 0), -2.5*np.log10(img/mdl),
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np.nan).astype(np.float32)
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del img, mdl
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print('%-10s A map built' % ch)
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AL = A['Luminance']
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EBR = (A['Blue'] - A['Red']).astype(np.float32)
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# region where both are trustworthy: inside the lane, bright enough, not a star
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Lmod = np.interp(a_map, ac[np.isfinite(P['Luminance'])],
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P['Luminance'][np.isfinite(P['Luminance'])])
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valid = (~star) & (a_map < 700) & (Lmod > 60) & np.isfinite(AL) & np.isfinite(EBR)
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lane = valid & dust
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print('valid extinction pixels: %d (%.1f arcmin^2); inside the lane: %d (%.1f arcmin^2)'
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% (valid.sum(), valid.sum()*PIXAREA/3600., lane.sum(), lane.sum()*PIXAREA/3600.))
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x, y = EBR[lane].astype(float), AL[lane].astype(float)
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sel = (x > 0.1) & (x < 2.0) & (y > -0.3) & (y < 3.5)
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k = float(np.median(y[sel]/x[sel]))
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klsq = float(np.sum(x[sel]*y[sel])/np.sum(x[sel]**2))
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print('extinction-law ratio A_L / E(B-R) = %.2f (median of ratios), %.2f (lsq)'
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% (k, klsq))
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ALs = median_filter(np.nan_to_num(AL, nan=0.0), 9)
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ALs[~valid] = np.nan
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pk = np.nanpercentile(ALs[lane], [50, 90, 99, 99.9])
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print('A_L inside the lane mask (9x9 median filtered): '
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'median %.2f, p90 %.2f, p99 %.2f, p99.9 %.2f mag' % tuple(pk))
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iy, ix = np.unravel_index(np.nanargmax(np.where(lane, ALs, np.nan)), ALs.shape)
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w = wcs()
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rr, dd = w.pixel_to_world_values(ix, iy)
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print('peak A_L = %.2f mag at pixel (%d, %d) = %.5f %+.5f deg, %.0f arcsec from '
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'the nucleus' % (ALs[iy, ix], ix, iy, rr, dd,
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np.hypot(ix-XC, iy-YC)*PIXSCALE))
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hdu = fits.PrimaryHDU(np.where(valid, AL, np.nan).astype(np.float32),
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header=w.to_header())
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hdu.header['BUNIT'] = 'mag'
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hdu.header['COMMENT'] = 'A_L = -2.5 log10(observed / smooth isophote model)'
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hdu.writeto(path('sb-extinction.fits'), overwrite=True)
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# obscured light: how much luminance flux the lane removes
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Lraw = load('Luminance')
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lost = float(np.nansum((Lmod - Lraw)[lane]))
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tot = float(np.nansum(Lmod[valid & (a_map < 700)]))
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print('the lane hides %.3e ADU, i.e. %.1f%% of the modelled light inside a=700 px'
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% (lost, 100*lost/tot))
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print(' that is %.2f mag of integrated light removed from the lane region'
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% (-2.5*np.log10(1 - lost/max(np.nansum(Lmod[lane]), 1))))
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# ------------------------------------------------------------------- figure 1
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NV, EV = north_east_pixel()
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CUT = 620
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sl = (slice(int(YC)-CUT, int(YC)+CUT), slice(int(XC)-CUT, int(XC)+CUT))
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ext = [-CUT*PIXSCALE/60, CUT*PIXSCALE/60]*2
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def compass(ax, c='k', x=0.885, y=0.10, Ln=0.075):
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for v, lab in [(NV, 'N'), (EV, 'E')]:
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ax.annotate('', xy=(x+Ln*v[0], y+Ln*v[1]), xytext=(x, y),
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xycoords='axes fraction', textcoords='axes fraction',
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arrowprops=dict(arrowstyle='->', color=c, lw=1.4))
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ax.annotate(lab, xy=(x+1.45*Ln*v[0], y+1.45*Ln*v[1]), color=c,
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xycoords='axes fraction', ha='center', va='center', fontsize=10)
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fig, axs = plt.subplots(2, 2, figsize=(15, 14.4))
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axs = axs.ravel()
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axs[0].imshow(np.arcsinh(np.clip(Lraw[sl], 0, None)/30), origin='lower',
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cmap='gray', extent=ext)
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axs[0].set_title('(a) luminance master')
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compass(axs[0], 'w')
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im = axs[1].imshow(np.where(valid, ALs, np.nan)[sl], origin='lower', cmap='magma_r',
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vmin=0, vmax=2.0, extent=ext)
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axs[1].set_title('(b) extinction $A_L$ from the smooth model [mag]')
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plt.colorbar(im, ax=axs[1], fraction=.046, label='mag')
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compass(axs[1])
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im = axs[2].imshow(np.where(valid, gaussian_filter(np.nan_to_num(EBR), 2), np.nan)[sl],
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origin='lower', cmap='inferno_r', vmin=0, vmax=1.4, extent=ext)
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axs[2].set_title('(c) colour excess E(B-R) from the R and B models [mag]')
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plt.colorbar(im, ax=axs[2], fraction=.046, label='mag')
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compass(axs[2])
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im = axs[3].imshow(np.where(valid, ALs - k*EBR, np.nan)[sl], origin='lower',
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cmap='RdBu_r', vmin=-0.6, vmax=0.6, extent=ext)
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axs[3].set_title('(d) $A_L$ - %.2f E(B-R): agreement of the two methods' % k)
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plt.colorbar(im, ax=axs[3], fraction=.046, label='mag')
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compass(axs[3])
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for a in axs:
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a.set_xlabel('arcmin')
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a.set_ylabel('arcmin')
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fig.suptitle('NGC 5128 dust lane: extinction and reddening '
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'(central %.1f x %.1f arcmin)' % (2*CUT*PIXSCALE/60, 2*CUT*PIXSCALE/60),
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fontsize=14)
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fig.tight_layout()
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fig.savefig(path('NGC5128-sb-extinction-map.png'), dpi=125)
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plt.close(fig)
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# ------------------------------------------------------------------- figure 2
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fig, axs = plt.subplots(1, 2, figsize=(13.5, 5.6))
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h = axs[0].hist2d(x[sel], y[sel], bins=(140, 140), range=[[0, 1.6], [-0.3, 3.0]],
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cmap='viridis', norm=matplotlib.colors.LogNorm())
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plt.colorbar(h[3], ax=axs[0], label='pixels')
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xs = np.linspace(0, 1.6, 50)
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axs[0].plot(xs, k*xs, 'r-', lw=2, label='$A_L$ = %.2f E(B-R) (median ratio)' % k)
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axs[0].plot(xs, klsq*xs, 'w--', lw=1.6, label='least squares: %.2f' % klsq)
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axs[0].set_xlabel('E(B-R) [mag, instrumental]')
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axs[0].set_ylabel('$A_L$ [mag]')
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axs[0].legend(fontsize=9)
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axs[0].set_title('extinction law measured inside the lane (%d px)' % sel.sum())
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bb = np.geomspace(20, 700, 26)
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ib = np.digitize(a_map, bb)
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med, p90 = [], []
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for kk in range(1, len(bb)):
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m = (ib == kk) & lane
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med.append(np.nanmedian(ALs[m]) if m.sum() > 200 else np.nan)
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p90.append(np.nanpercentile(ALs[m], 90) if m.sum() > 200 else np.nan)
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bc = np.sqrt(bb[1:]*bb[:-1])*PIXSCALE
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axs[1].plot(bc, med, 'o-', color='tab:purple', label='median $A_L$ in the lane')
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axs[1].plot(bc, p90, 's--', color='tab:orange', label='90th percentile')
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axs[1].set_xscale('log')
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axs[1].set_xlabel('semi-major axis a [arcsec]')
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axs[1].set_ylabel('$A_L$ [mag]')
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axs[1].grid(alpha=.3)
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axs[1].legend(fontsize=9)
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axs[1].set_title('extinction against radius along the lane')
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fig.tight_layout()
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fig.savefig(path('NGC5128-sb-extinction-law.png'), dpi=140)
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plt.close(fig)
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with open(path('sb-derived-quantities.txt'), 'a') as f:
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wr = lambda t: (f.write(t + chr(10)), print(t))
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wr('')
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wr('Dust lane')
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wr(' lane mask area %.1f arcmin^2' % (dust.sum()*PIXAREA/3600.))
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wr(' measurable extinction area %.1f arcmin^2' % (lane.sum()*PIXAREA/3600.))
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wr(' A_L median / p90 / p99 / p99.9 %.2f / %.2f / %.2f / %.2f mag' % tuple(pk))
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wr(' peak A_L (9x9 median filtered) %.2f mag at RA %.4f Dec %+.4f'
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% (ALs[iy, ix], rr, dd))
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wr(' peak is %.0f arcsec from the nucleus' % (np.hypot(ix-XC, iy-YC)*PIXSCALE))
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wr(' extinction law A_L / E(B-R) %.2f (median of ratios), %.2f (lsq)'
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% (k, klsq))
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wr(' luminance flux hidden by the lane %.1f%% of the modelled light inside a=700 px'
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% (100*lost/tot))
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print('')
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print('wrote sb-extinction.fits, NGC5128-sb-extinction-map.png, NGC5128-sb-extinction-law.png')
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