421 lines
12 KiB
Python
421 lines
12 KiB
Python
#!/usr/bin/env python
|
|
from __future__ import print_function
|
|
import os
|
|
import shutil
|
|
from stvid.stio import fourframe, satid, observation
|
|
from stvid.astrometry import is_calibrated
|
|
import numpy as np
|
|
import ppgplot as ppg
|
|
from scipy import optimize, ndimage
|
|
from termcolor import colored
|
|
import datetime
|
|
|
|
|
|
# Gaussian model
|
|
def model(a, nx, ny):
|
|
x, y = np.meshgrid(np.arange(nx), np.arange(ny))
|
|
dx, dy = (x - a[0]) / a[2], (y - a[1]) / a[2]
|
|
arg = -0.5 * (dx**2 + dy**2)
|
|
return a[3] * np.exp(arg) + a[4]
|
|
|
|
|
|
# Residual function
|
|
def residual(a, img):
|
|
ny, nx = img.shape
|
|
mod = model(a, nx, ny)
|
|
return (img - mod).ravel()
|
|
|
|
|
|
# Find peak
|
|
def peakfind(img, w=1.0):
|
|
# Find approximate location
|
|
ny, nx = img.shape
|
|
i = np.argmax(img)
|
|
y0 = int(i / nx)
|
|
x0 = i - y0 * nx
|
|
|
|
# Image properties
|
|
imgavg = np.mean(img)
|
|
imgstd = np.std(img)
|
|
|
|
# Estimate
|
|
a = np.array([x0, y0, w, img[y0, x0] - imgavg, imgavg])
|
|
q, cov_q, infodict, mesg, ier = optimize.leastsq(residual,
|
|
a,
|
|
args=(img),
|
|
full_output=1)
|
|
|
|
# Extract
|
|
xc, yc, w = q[0], q[1], q[2]
|
|
|
|
# Significance
|
|
sigma = (a[3] - imgavg) / (imgstd + 1e-9)
|
|
|
|
return xc, yc, w, sigma
|
|
|
|
|
|
# Plot selection
|
|
def plot_selection(id, x0, y0, dt=2.0, w=10.0):
|
|
dx, dy = id.x1 - id.x0, id.y1 - id.y0
|
|
ang = np.arctan2(dy, dx)
|
|
r = np.sqrt(dx**2 + dy**2)
|
|
drdt = r / (id.t1 - id.t0)
|
|
sa, ca = np.sin(ang), np.cos(ang)
|
|
|
|
dx = np.array([-dt, -dt, dt, dt, -dt]) * drdt
|
|
dy = np.array([w, -w, -w, w, w])
|
|
x = ca * dx - sa * dy + x0
|
|
y = sa * dx + ca * dy + y0
|
|
|
|
ppg.pgsci(7)
|
|
ppg.pgline(x, y)
|
|
|
|
return
|
|
|
|
|
|
# Check if point is inside selection
|
|
def inside_selection(ident, tmid, x0, y0, dt=2.0, w=10.0):
|
|
dx, dy = ident.x1 - ident.x0, ident.y1 - ident.y0
|
|
ang = -np.arctan2(dy, dx)
|
|
r = np.sqrt(dx**2 + dy**2)
|
|
drdt = r / (ident.t1 - ident.t0)
|
|
sa, ca = np.sin(ang), np.cos(ang)
|
|
|
|
xmid = ident.x0 + ident.dxdt * tmid
|
|
ymid = ident.y0 + ident.dydt * tmid
|
|
|
|
dx, dy = x0 - xmid, y0 - ymid
|
|
rm = ca * dx - sa * dy
|
|
wm = sa * dx + ca * dy
|
|
dtm = rm / drdt
|
|
|
|
if (abs(wm) < w) & (abs(dtm) < dt):
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
# Get COSPAR ID
|
|
def get_cospar(norad, nfd):
|
|
f = open(os.path.join(os.getenv("ST_DATADIR"), "data/desig.txt"))
|
|
lines = f.readlines()
|
|
f.close()
|
|
|
|
try:
|
|
cospar = ([line for line in lines if str(norad) in line])[0].split()[1]
|
|
except IndexError:
|
|
t = datetime.datetime.strptime(nfd[:-4], "%Y-%m-%dT%H:%M:%S")
|
|
doy = int(t.strftime("%y%j")) + 500
|
|
cospar = "%sA" % doy
|
|
|
|
return "%2s %s" % (cospar[0:2], cospar[2:])
|
|
|
|
|
|
# IOD position format 2: RA/DEC = HHMMmmm+DDMMmm MX (MX in minutes of arc)
|
|
def format_position(ra, de):
|
|
ram = 60.0 * ra / 15.0
|
|
rah = int(np.floor(ram / 60.0))
|
|
ram -= 60.0 * rah
|
|
|
|
des = np.sign(de)
|
|
dem = 60.0 * np.abs(de)
|
|
ded = int(np.floor(dem / 60.0))
|
|
dem -= 60.0 * ded
|
|
|
|
if des == -1:
|
|
sign = "-"
|
|
else:
|
|
sign = "+"
|
|
|
|
return ("%02d%06.3f%c%02d%05.2f" % (rah, ram, sign, ded, dem)).replace(
|
|
".", "")
|
|
|
|
|
|
# Format IOD line
|
|
def format_iod_line(norad, cospar, site_id, t, ra, de):
|
|
pstr = format_position(ra, de)
|
|
tstr = t.replace("-", "") \
|
|
.replace("T", "") \
|
|
.replace(":", "") \
|
|
.replace(".", "")
|
|
|
|
return "%05d %-9s %04d G %s 17 25 %s 37 S" % (norad, cospar, site_id, tstr,
|
|
pstr)
|
|
|
|
|
|
def store_results(ident, fname, path, iod_line):
|
|
# Find destination
|
|
if ident.catalog.find("classfd.tle") > 0:
|
|
outfname = os.path.join(path, "classfd/classfd.dat")
|
|
dest = os.path.join(path, "classfd")
|
|
color = "blue"
|
|
elif ident.catalog.find("inttles.tle") > 0:
|
|
outfname = os.path.join(path, "classfd/classfd.dat")
|
|
dest = os.path.join(path, "classfd")
|
|
color = "blue"
|
|
elif ident.catalog.find("catalog.tle") > 0:
|
|
outfname = os.path.join(path, "catalog/catalog.dat")
|
|
dest = os.path.join(path, "catalog")
|
|
color = "grey"
|
|
else:
|
|
dest = os.path.join(path, "unid")
|
|
outfname = os.path.join(path, "unid/unid.dat")
|
|
color = "magenta"
|
|
|
|
# Print iod line
|
|
print(colored(iod_line, color))
|
|
|
|
# Copy files
|
|
shutil.copy2(fname, dest)
|
|
shutil.copy2(fname + ".cat", dest)
|
|
shutil.copy2(fname + ".id", dest)
|
|
shutil.copy2(fname + ".png", dest)
|
|
try:
|
|
shutil.move(fname.replace(".fits", "_%05d.png" % ident.norad), dest)
|
|
except Exception:
|
|
pass
|
|
|
|
# Write iodline
|
|
fp = open(outfname, "a")
|
|
fp.write("%s\n" % iod_line)
|
|
fp.close()
|
|
|
|
return
|
|
|
|
|
|
def plot_header(fname, ff, iod_line):
|
|
# ppgplot arrays
|
|
heat_l = np.array([0.0, 0.2, 0.4, 0.6, 1.0])
|
|
heat_r = np.array([0.0, 0.5, 1.0, 1.0, 1.0])
|
|
heat_g = np.array([0.0, 0.0, 0.5, 1.0, 1.0])
|
|
heat_b = np.array([0.0, 0.0, 0.0, 0.3, 1.0])
|
|
|
|
# Plot
|
|
ppg.pgopen(fname)
|
|
ppg.pgpap(0.0, 1.0)
|
|
ppg.pgsvp(0.1, 0.95, 0.1, 0.8)
|
|
|
|
ppg.pgsch(0.8)
|
|
ppg.pgmtxt("T", 6.0, 0.0, 0.0,
|
|
"UT Date: %.23s COSPAR ID: %04d" % (ff.nfd, ff.site_id))
|
|
if is_calibrated(ff):
|
|
ppg.pgsci(1)
|
|
else:
|
|
ppg.pgsci(2)
|
|
ppg.pgmtxt(
|
|
"T", 4.8, 0.0, 0.0, "R.A.: %10.5f (%4.1f'') Decl.: %10.5f (%4.1f'')" %
|
|
(ff.crval[0], 3600.0 * ff.crres[0], ff.crval[1], 3600.0 * ff.crres[1]))
|
|
ppg.pgsci(1)
|
|
ppg.pgmtxt("T", 3.6, 0.0, 0.0, ("FoV: %.2f\\(2218)x%.2f\\(2218) "
|
|
"Scale: %.2f''x%.2f'' pix\\u-1\\d") %
|
|
(ff.wx, ff.wy, 3600.0 * ff.sx, 3600.0 * ff.sy))
|
|
ppg.pgmtxt(
|
|
"T", 2.4, 0.0, 0.0, "Stat: %5.1f+-%.1f (%.1f-%.1f)" %
|
|
(np.mean(ff.zmax), np.std(ff.zmax), ff.zmaxmin, ff.zmaxmax))
|
|
ppg.pgmtxt("T", 0.3, 0.0, 0.0, iod_line)
|
|
|
|
ppg.pgsch(1.0)
|
|
ppg.pgwnad(0.0, ff.nx, 0.0, ff.ny)
|
|
ppg.pglab("x (pix)", "y (pix)", " ")
|
|
ppg.pgctab(heat_l, heat_r, heat_g, heat_b, 5, 1.0, 0.5)
|
|
|
|
|
|
# Extract tracks
|
|
def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin, path):
|
|
# Read four frame
|
|
ff = fourframe(fname)
|
|
|
|
# Skip saturated frames
|
|
if np.sum(ff.zavg > 240.0) / float(ff.nx * ff.ny) > 0.95:
|
|
return
|
|
|
|
# Read satelite IDs
|
|
try:
|
|
f = open(fname + ".id")
|
|
except OSError:
|
|
print("Cannot open", fname + ".id")
|
|
else:
|
|
lines = f.readlines()
|
|
f.close()
|
|
|
|
tr = np.array([-0.5, 1.0, 0.0, -0.5, 0.0, 1.0])
|
|
|
|
# Parse identifications
|
|
idents = [satid(line) for line in lines]
|
|
|
|
# Identify unknowns
|
|
for ident0 in idents:
|
|
if ident0.catalog == "unidentified":
|
|
for ident1 in idents:
|
|
if ident1.catalog == "unidentified":
|
|
continue
|
|
|
|
# Find matches
|
|
p1 = inside_selection(ident1, ident0.t0, ident0.x0, ident0.y0)
|
|
p2 = inside_selection(ident1, ident0.t1, ident0.x1, ident0.y1)
|
|
|
|
# Match found
|
|
if p1 and p2:
|
|
# Copy info
|
|
ident0.norad = ident1.norad
|
|
ident0.catalog = ident1.catalog
|
|
ident0.state = ident1.state
|
|
ident1.state = "remove"
|
|
break
|
|
|
|
# Loop over identifications
|
|
for ident in idents:
|
|
# Skip superseded unknowns
|
|
if ident.state == "remove":
|
|
continue
|
|
|
|
# Skip slow moving objects
|
|
drdt = np.sqrt(ident.dxdt**2 + ident.dydt**2)
|
|
if drdt < drdtmin:
|
|
continue
|
|
|
|
# Extract significant pixels along a track
|
|
x, y, t, sig = ff.significant_pixels_along_track(
|
|
trksig, ident.x0, ident.y0, ident.dxdt, ident.dydt, trkrmin)
|
|
|
|
# Fit tracks
|
|
if len(t) > ntrkmin:
|
|
# Get times
|
|
tmin = np.min(t)
|
|
tmax = np.max(t)
|
|
tmid = 0.5 * (tmax + tmin)
|
|
mjd = ff.mjd + tmid / 86400.0
|
|
|
|
# Skip if no variance in time
|
|
if np.std(t - tmid) == 0.0:
|
|
continue
|
|
|
|
# Very simple polynomial fit; no weighting, no cleaning
|
|
px = np.polyfit(t - tmid, x, 1)
|
|
py = np.polyfit(t - tmid, y, 1)
|
|
|
|
# Extract results
|
|
x0, y0 = px[1], py[1]
|
|
dxdt, dydt = px[0], py[0]
|
|
xmin = x0 + dxdt * (tmin - tmid)
|
|
ymin = y0 + dydt * (tmin - tmid)
|
|
xmax = x0 + dxdt * (tmax - tmid)
|
|
ymax = y0 + dydt * (tmax - tmid)
|
|
|
|
cospar = get_cospar(ident.norad, ff.nfd)
|
|
obs = observation(ff, mjd, x0, y0)
|
|
iod_line = "%s" % format_iod_line(ident.norad, cospar, ff.site_id,
|
|
obs.nfd, obs.ra, obs.de)
|
|
|
|
# Create diagnostic plot
|
|
plot_header(fname.replace(".fits", "_%05d.png/png" % ident.norad),
|
|
ff, iod_line)
|
|
|
|
ppg.pgimag(ff.zmax, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1,
|
|
ff.zmaxmax, ff.zmaxmin, tr)
|
|
ppg.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
|
|
ppg.pgstbg(1)
|
|
|
|
ppg.pgsci(0)
|
|
if ident.catalog.find("classfd.tle") > 0:
|
|
ppg.pgsci(4)
|
|
elif ident.catalog.find("inttles.tle") > 0:
|
|
ppg.pgsci(3)
|
|
|
|
ppg.pgpt(np.array([x0]), np.array([y0]), 4)
|
|
ppg.pgmove(xmin, ymin)
|
|
ppg.pgdraw(xmax, ymax)
|
|
ppg.pgsch(0.65)
|
|
ppg.pgtext(np.array([x0]), np.array([y0]), " %05d" % ident.norad)
|
|
ppg.pgsch(1.0)
|
|
ppg.pgsci(1)
|
|
|
|
ppg.pgend()
|
|
|
|
# Store results
|
|
store_results(ident, fname, path, iod_line)
|
|
|
|
elif ident.catalog.find("classfd.tle") > 0:
|
|
# Track and stack
|
|
t = np.linspace(0.0, ff.texp)
|
|
x, y = ident.x0 + ident.dxdt * t, ident.y0 + ident.dydt * t
|
|
c = (x > 0) & (x < ff.nx) & (y > 0) & (y < ff.ny)
|
|
|
|
# Skip if no points selected
|
|
if np.sum(c) == 0:
|
|
continue
|
|
|
|
# Compute track
|
|
tmid = np.mean(t[c])
|
|
mjd = ff.mjd + tmid / 86400.0
|
|
xmid = ident.x0 + ident.dxdt * tmid
|
|
ymid = ident.y0 + ident.dydt * tmid
|
|
ztrk = ndimage.gaussian_filter(
|
|
ff.track(ident.dxdt, ident.dydt, tmid), 1.0)
|
|
vmin = np.mean(ztrk) - 2.0 * np.std(ztrk)
|
|
vmax = np.mean(ztrk) + 6.0 * np.std(ztrk)
|
|
|
|
# Select region
|
|
xmin = int(xmid - 100)
|
|
xmax = int(xmid + 100)
|
|
ymin = int(ymid - 100)
|
|
ymax = int(ymid + 100)
|
|
if xmin < 0:
|
|
xmin = 0
|
|
if ymin < 0:
|
|
ymin = 0
|
|
if xmax > ff.nx:
|
|
xmax = ff.nx - 1
|
|
if ymax > ff.ny:
|
|
ymax = ff.ny - 1
|
|
|
|
# Find peak
|
|
x0, y0, w, sigma = peakfind(ztrk[ymin:ymax, xmin:xmax])
|
|
x0 += xmin
|
|
y0 += ymin
|
|
|
|
# Skip if peak is not significant
|
|
if sigma < trksig:
|
|
continue
|
|
|
|
# Skip if point is outside selection area
|
|
if inside_selection(ident, tmid, x0, y0) is False:
|
|
continue
|
|
|
|
# Format IOD line
|
|
cospar = get_cospar(ident.norad, ff.nfd)
|
|
obs = observation(ff, mjd, x0, y0)
|
|
iod_line = "%s" % format_iod_line(ident.norad, cospar, ff.site_id,
|
|
obs.nfd, obs.ra, obs.de)
|
|
|
|
# Create diagnostic plot
|
|
plot_header(fname.replace(".fits", "_%05d.png/png" % ident.norad),
|
|
ff, iod_line)
|
|
|
|
ppg.pgimag(ztrk, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1, vmax,
|
|
vmin, tr)
|
|
ppg.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
|
|
ppg.pgstbg(1)
|
|
|
|
plot_selection(ident, xmid, ymid)
|
|
|
|
ppg.pgsci(0)
|
|
if ident.catalog.find("classfd.tle") > 0:
|
|
ppg.pgsci(4)
|
|
elif ident.catalog.find("inttles.tle") > 0:
|
|
ppg.pgsci(3)
|
|
ppg.pgpt(np.array([ident.x0]), np.array([ident.y0]), 17)
|
|
ppg.pgmove(ident.x0, ident.y0)
|
|
ppg.pgdraw(ident.x1, ident.y1)
|
|
ppg.pgpt(np.array([x0]), np.array([y0]), 4)
|
|
ppg.pgsch(0.65)
|
|
ppg.pgtext(np.array([ident.x0]), np.array([ident.y0]),
|
|
" %05d" % ident.norad)
|
|
ppg.pgsch(1.0)
|
|
ppg.pgsci(1)
|
|
|
|
ppg.pgend()
|
|
|
|
# Store results
|
|
store_results(ident, fname, path, iod_line)
|