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.

session-scripts/sb_residual.py

@@ -1,77 +0,0 @@
-"""Step 4: smooth elliptical model, model-subtracted residual, and renders.
-
-The model is the Pass B isophote table (stars and dust lane masked) turned into
-a 2-D image with sb_model.build, with a Sersic extrapolation inside a = 62 px
-where no dust-free azimuth exists.  Subtracting it leaves everything that is
-not a smooth ellipse: the dust lane, foreground stars, and any shell, tidal
-feature or halo asymmetry.
-
-Outputs
-  sb-model.fits          the smooth model
-  sb-residual.fits       luminance minus model
-  NGC5128-sb-model-residual.png  4-panel: data / model / residual / binned deep residual
-  NGC5128-sb-residual-deep.png   heavily binned residual alone, for shell hunting
-"""
-import numpy as np
-from astropy.io import fits
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-from scipy.ndimage import gaussian_filter
-from sb_common import *
-import sb_model
-
-XC, YC = np.load(path('_geom.npy'))
-P = np.load(path('_profile.npz'))
-tB = dict(np.load(path('_isoB.npz')))
-star = fits.getdata(path('sb-mask-stars.fits')).astype(bool)
-dust = fits.getdata(path('sb-mask-dust.fits')).astype(bool)
-A_IN, A_OUT = float(P['a_in']), float(P['a_out'])
-mue, re_as, nser = P['sersic']
-
-
-def sersic_mu(a_as):
-    bn = 2 * nser - 1 / 3. + 0.009876 / nser
-    return mue + 2.5 * bn / np.log(10) * ((a_as / re_as) ** (1. / nser) - 1.)
-
-
-# ---- build a profile that is defined at every radius -----------------------
-ac = P['a']
-Lp = P['Luminance'].copy()
-inner = ac < A_IN
-Lp[inner] = 10 ** ((MU0 - sersic_mu(ac[inner] * PIXSCALE)) / 2.5)
-okp = np.isfinite(Lp) & (ac < 1500)
-tab = dict(sma=ac[okp], intens=Lp[okp],
-           eps=np.interp(ac[okp], tB['sma'], np.where(np.isfinite(tB['eps']),
-                                                      tB['eps'], 0.15)),
-           pa=np.interp(ac[okp], tB['sma'], np.where(np.isfinite(tB['pa']),
-                                                     tB['pa'], 150.)))
-model, a_map = sb_model.build((3194, 4788), XC, YC, tab, block=2)
-fits.PrimaryHDU(model).writeto(path('sb-model.fits'), overwrite=True)
-
-PEDL = float(P['ped'])
-L = load('Luminance')
-res = (L - model).astype(np.float32)
-fits.PrimaryHDU(res).writeto(path('sb-residual.fits'), overwrite=True)
-print('model built; residual rms inside a<600 px: %.2f ADU/px'
-      % res[(a_map < 600) & ~star & ~dust].std())
-
-
-def binned(img, mask, B):
-    """Masked block mean, returning NaN where a block is mostly masked."""
-    H, W = img.shape
-    h, w = H // B, W // B
-    a = img[:h * B, :w * B].reshape(h, B, w, B)
-    m = (~mask)[:h * B, :w * B].reshape(h, B, w, B)
-    n = m.sum(axis=(1, 3))
-    s = np.where(m, a, 0).sum(axis=(1, 3))
-    return np.where(n > 0.35 * B * B, s / np.maximum(n, 1), np.nan)
-
-
-
-
-# ------------------------------------------------------------------ renders
-import sb_render_tail
-sb_render_tail.run(dict(res=res, model=model, a_map=a_map, L=L, star=star,
-                        dust=dust, XC=XC, YC=YC, PEDL=PEDL, A_OUT=A_OUT,
-                        tab=tab, binned=binned))