"""Choose the background mesh size EMPIRICALLY, using the 589 SIMBAD-catalogued
Cen A globular clusters that fall in the field as a truth set. For each mesh
size we measure (a) how many known GCs are recovered, split by projected radius,
and (b) how many total detections there are (a proxy for spurious detections)."""
import os, numpy as np, sep, warnings
from astropy.io import fits
from astropy.wcs import WCS

import layout
warnings.filterwarnings("ignore")
S = layout.SESSION
NUC_RA, NUC_DEC = 201.365063, -43.019113
PIXSCALE = 0.5376


def gk(fwhm=5.0):
    sig = fwhm / 2.3548
    n = int(2 * round(3 * sig) + 1)
    r = np.arange(n) - n // 2
    xx, yy = np.meshgrid(r, r)
    k = np.exp(-(xx ** 2 + yy ** 2) / (2 * sig ** 2))
    return (k / k.sum()).astype(np.float32)


def match(ra1, d1, ra2, d2, tol_as):
    """brute-force nearest match, small catalogues"""
    cd = np.cos(np.radians(d1.mean()))
    idx = np.full(len(ra1), -1)
    sep_as = np.full(len(ra1), 9e9)
    for i in range(len(ra1)):
        dd = np.hypot((ra2 - ra1[i]) * cd, d2 - d1[i]) * 3600.0
        j = np.argmin(dd)
        if dd[j] < tol_as:
            idx[i] = j
            sep_as[i] = dd[j]
    return idx, sep_as


z = np.load(layout.path("_simbad.npz"), allow_pickle=True)
m = z["otype"] == "GlC"
gra, gdec = z["ra"][m].astype(float), z["dec"][m].astype(float)

hdr = fits.getheader(layout.path("master-Luminance.fit"))
w = WCS(hdr)
ny, nx = hdr["NAXIS2"], hdr["NAXIS1"]
gx, gy = w.all_world2pix(gra, gdec, 0)
inf = (gx > 20) & (gx < nx - 20) & (gy > 20) & (gy < ny - 20)
gra, gdec, gx, gy = gra[inf], gdec[inf], gx[inf], gy[inf]
cx, cy = [float(v) for v in w.all_world2pix(NUC_RA, NUC_DEC, 0)]
grad = np.hypot(gx - cx, gy - cy) * PIXSCALE / 60.0
print("known GCs inside the frame: %d  (r range %.2f - %.2f arcmin)"
      % (len(gra), grad.min(), grad.max()))

d = fits.getdata(layout.path("master-Luminance.fit")).astype(np.float32)
d = np.ascontiguousarray(d)
sep.set_extract_pixstack(3000000)
K = gk()

bins = [(0, 2), (2, 4), (4, 8), (8, 14), (14, 30)]
print("\n%6s %7s | %s" % ("mesh", "Ndet", "  ".join("%4.0f-%-4.0f'" % b for b in bins)))
for bw in (12, 16, 24, 32, 48, 64, 128):
    b = sep.Background(d, bw=bw, bh=bw, fw=3, fh=3)
    ds = d - b.back()
    rm = b.rms()
    o = sep.extract(ds, 3.0, err=rm, minarea=5, filter_kernel=K,
                    filter_type="matched", deblend_nthresh=32,
                    deblend_cont=0.005, clean=True)
    ora, odec = w.all_pix2world(o["x"], o["y"], 0)
    idx, sp = match(gra, gdec, ora, odec, 2.0)
    row = []
    for lo, hi in bins:
        s = (grad >= lo) & (grad < hi)
        row.append("%3d/%-3d" % ((idx[s] >= 0).sum(), s.sum()))
    print("%6d %7d | %s" % (bw, len(o), "  ".join(row)))
    del b, ds, rm, o