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.
108 lines
4.5 KiB
Python
108 lines
4.5 KiB
Python
"""Shared constants and helpers for the NGC 5128 surface-photometry analysis."""
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import numpy as np, os, warnings
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warnings.filterwarnings('ignore')
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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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DATA = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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PIXSCALE = 0.5376 # arcsec/px (verified from WCS)
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PIXAREA = PIXSCALE**2 # arcsec^2 per pixel
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ZP5 = 27.942 # sep.sum_circle r=5px zero point (verified)
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# curve of growth on 13 isolated field stars: F(r=25)/F(r=5) = 1.2158
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APCOR = +2.5*np.log10(1.2158) # = +0.2121 mag, r=5 -> r=25 ("total")
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ZPTOT = ZP5 + APCOR # 28.154, zero point for total flux
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MU0 = ZPTOT + 2.5*np.log10(PIXAREA) # 26.807; mu = MU0 - 2.5*log10(I_ADU_per_px)
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X0, Y0 = 2397.88, 1592.09 # nucleus, from WCS + Gaia-verified astrometry
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DIST_MPC = 3.8
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KPC_PER_ARCSEC = DIST_MPC*1e3*np.pi/180/3600 # 0.01842 kpc/arcsec
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def path(*p): return layout.path(*p)
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def load(ch):
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"""Load one master as a contiguous native-endian float32 array."""
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d = fits.getdata(path('master-%s.fit' % ch))
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return np.ascontiguousarray(d.astype(np.float32))
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def wcs():
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return WCS(fits.getheader(path('master-Luminance.fit')))
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def mu(I):
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"""Surface brightness (mag/arcsec^2) from intensity in ADU/pixel."""
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I = np.asarray(I, float)
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out = np.full(I.shape, np.nan)
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m = I > 0
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out[m] = MU0 - 2.5*np.log10(I[m])
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return out
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def ell_radius(shape, x0, y0, eps, pa_rad):
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"""Semi-major-axis-equivalent radius map for a fixed ellipse geometry.
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pa_rad measured counter-clockwise from the +x axis (photutils convention)."""
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ny, nx = shape
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y, x = np.mgrid[0:ny, 0:nx].astype(np.float32)
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x -= np.float32(x0); y -= np.float32(y0)
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c, s = np.float32(np.cos(pa_rad)), np.float32(np.sin(pa_rad))
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xp = x*c + y*s
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yp = -x*s + y*c
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del x, y
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return np.sqrt(xp*xp + (yp/np.float32(1.0-eps))**2)
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def sky_pa(pa_deg):
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"""Convert a photutils isophote PA (deg CCW from +x) to sky PA (deg E of N).
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For this frame north lies 0.96 deg CCW of the +x axis and east lies along
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-y, so the two conventions differ by very nearly 90 deg with a flip.
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"""
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cd = wcs().pixel_scale_matrix
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t = np.radians(np.asarray(pa_deg, float))
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xi = cd[0, 0]*np.cos(t) + cd[0, 1]*np.sin(t) # +east
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eta = cd[1, 0]*np.cos(t) + cd[1, 1]*np.sin(t) # +north
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return np.degrees(np.arctan2(xi, eta)) % 180.
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def north_east_pixel():
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"""Unit vectors (dx, dy) pointing north and east in pixel coordinates."""
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cd = wcs().pixel_scale_matrix
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det = cd[0, 0]*cd[1, 1] - cd[0, 1]*cd[1, 0]
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n = np.array([-cd[0, 1], cd[0, 0]])/det
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e = np.array([cd[1, 1], -cd[1, 0]])/det
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return n/np.hypot(*n), e/np.hypot(*e)
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ISO_HDR = ('sma_px,sma_arcsec,sma_arcmin,sma_kpc,intens_adu_px,intens_err,'
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'rms_adu,mu_mag_arcsec2,mu_err,ellipticity,ellipticity_err,'
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'pa_deg_ccw_from_x,pa_err_deg,pa_deg_east_of_north,ndata,nflag,'
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'stop_code')
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def write_isophote_csv(tab, fn):
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"""Write an isophote table to CSV.
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Rows with stop_code != 0 had too little unmasked azimuth for the geometry
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to converge: photutils still measures a valid intensity along the held
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ellipse, but its eps/PA are carried over from the previous isophote and its
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formal errors are meaningless (they come back as values like 1169 and
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24006). Those five geometry columns are therefore blanked to NaN, so the
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file cannot be read as if the geometry had been measured there. The
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intensity columns are kept, because they are real.
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"""
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import os
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a = tab['sma']*PIXSCALE
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conv = tab['stop'] == 0
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blank = lambda v: np.where(conv, v, np.nan)
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with np.errstate(all='ignore'):
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me = 2.5/np.log(10)*tab['int_err']/np.where(tab['intens'] > 0,
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tab['intens'], np.nan)
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out = np.column_stack([tab['sma'], a, a/60., a*KPC_PER_ARCSEC,
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tab['intens'], tab['int_err'], tab['rms'],
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mu(tab['intens']), me,
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blank(tab['eps']), blank(tab['eps_err']),
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blank(tab['pa']), blank(tab['pa_err']),
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blank(sky_pa(tab['pa'])),
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tab['ndata'], tab['nflag'], tab['stop']])
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np.savetxt(path(fn), out, delimiter=',', header=ISO_HDR, comments='',
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fmt='%.5f')
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print('wrote %s (%d rows, %d with converged geometry)'
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% (fn, len(out), conv.sum()))
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