def plot_horizontal_grid(self):
sch, sls, sci = ppgplot.pgqch(), ppgplot.pgqls(), ppgplot.pgqci()
ppgplot.pgsci(15)
ppgplot.pgsls(2)
# Altitudes
for alt in np.arange(0.0, 90.0, 20.0):
az = np.arange(0.0, 360.0)
rx, ry = forward(self.az, self.alt, az, alt * np.ones_like(az))
for i in range(len(rx)):
if i == 0:
ppgplot.pgmove(rx[i], ry[i])
if (np.abs(rx[i]) <= 1.5 * self.w) & (np.abs(ry[i]) <= self.w):
ppgplot.pgdraw(rx[i], ry[i])
else:
ppgplot.pgmove(rx[i], ry[i])
# Azimuths
for az in np.arange(0.0, 360.0, 30.0):
alt = np.arange(0.0, 80.0)
rx, ry = forward(self.az, self.alt, az * np.ones_like(alt), alt)
for i in range(len(rx)):
if i == 0:
ppgplot.pgmove(rx[i], ry[i])
if (np.abs(rx[i]) <= 1.5 * self.w) & (np.abs(ry[i]) <= self.w):
ppgplot.pgdraw(rx[i], ry[i])
else:
ppgplot.pgmove(rx[i], ry[i])
ppgplot.pgsci(1)
ppgplot.pgsls(1)
# Horizon
az = np.arange(0.0, 360.0)
rx, ry = forward(self.az, self.alt, az, np.zeros_like(az))
for i in range(len(rx)):
if i == 0:
ppgplot.pgmove(rx[i], ry[i])
if (np.abs(rx[i]) <= 1.5 * self.w) & (np.abs(ry[i]) <= self.w):
ppgplot.pgdraw(rx[i], ry[i])
else:
ppgplot.pgmove(rx[i], ry[i])
def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin, path,
results_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# Skip superseded unknowns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, results_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:
store_not_seen(ident, fname, results_path)
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:
store_not_seen(ident, fname, results_path)
continue
# Skip if point is outside selection area
if inside_selection(ident, tmid, x0, y0) is False:
store_not_seen(ident, fname, results_path)
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
pngfile = fname.replace(".fits", "_%05d.png" % ident.norad)
plot_header(pngfile + "/png", 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, results_path, iod_line)
def drawLine(self, xLo, yLo, xHi, yHi):
if not self.plotDeviceIsOpened:
raise ValueError("You have not yet opened a PGPLOT device.")
pgplot.pgmove(xLo, yLo)
pgplot.pgdraw(xHi, yHi)
def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin):
# 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()
# ppgplot arrays
tr = np.array([-0.5, 1.0, 0.0, -0.5, 0.0, 1.0])
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])
# Loop over identifications
for line in lines:
# Decode
id = satid(line)
# Skip slow moving objects
drdt = np.sqrt(id.dxdt**2 + id.dydt**2)
if drdt < drdtmin:
continue
# Extract significant pixels
x, y, t, sig = ff.significant(trksig, id.x0, id.y0, id.dxdt, id.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(id.norad)
obs = observation(ff, mjd, x0, y0)
iod_line = "%s" % format_iod_line(id.norad, cospar, ff.site_id,
obs.nfd, obs.ra, obs.de)
print(iod_line)
if id.catalog.find("classfd.tle") > 0:
outfname = "classfd.dat"
elif id.catalog.find("inttles.tle") > 0:
outfname = "inttles.dat"
else:
outfname = "catalog.dat"
f = open(outfname, "a")
f.write("%s\n" % iod_line)
f.close()
# Plot
ppgplot.pgopen(
fname.replace(".fits", "") + "_%05d.png/png" % id.norad)
#ppgplot.pgopen("/xs")
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgsvp(0.1, 0.95, 0.1, 0.8)
ppgplot.pgsch(0.8)
ppgplot.pgmtxt(
"T", 6.0, 0.0, 0.0,
"UT Date: %.23s COSPAR ID: %04d" % (ff.nfd, ff.site_id))
if (3600.0 * ff.crres[0] < 1e-3
) | (3600.0 * ff.crres[1] < 1e-3) | (
ff.crres[0] / ff.sx > 2.0) | (ff.crres[1] / ff.sy > 2.0):
ppgplot.pgsci(2)
else:
ppgplot.pgsci(1)
ppgplot.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]))
ppgplot.pgsci(1)
ppgplot.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))
ppgplot.pgmtxt(
"T", 2.4, 0.0, 0.0, "Stat: %5.1f+-%.1f (%.1f-%.1f)" %
(np.mean(ff.zmax), np.std(ff.zmax), ff.vmin, ff.vmax))
ppgplot.pgmtxt("T", 0.3, 0.0, 0.0, iod_line)
ppgplot.pgsch(1.0)
ppgplot.pgwnad(0.0, ff.nx, 0.0, ff.ny)
ppgplot.pglab("x (pix)", "y (pix)", " ")
ppgplot.pgctab(heat_l, heat_r, heat_g, heat_b, 5, 1.0, 0.5)
ppgplot.pgimag(ff.zmax, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1,
ff.vmax, ff.vmin, tr)
ppgplot.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
ppgplot.pgstbg(1)
ppgplot.pgsci(0)
if id.catalog.find("classfd.tle") > 0:
ppgplot.pgsci(4)
elif id.catalog.find("inttles.tle") > 0:
ppgplot.pgsci(3)
ppgplot.pgpt(np.array([x0]), np.array([y0]), 4)
ppgplot.pgmove(xmin, ymin)
ppgplot.pgdraw(xmax, ymax)
ppgplot.pgsch(0.65)
ppgplot.pgtext(np.array([x0]), np.array([y0]), " %05d" % id.norad)
ppgplot.pgsch(1.0)
ppgplot.pgsci(1)
ppgplot.pgend()
elif id.catalog.find("classfd.tle") > 0:
# Track and stack
t = np.linspace(0.0, ff.texp)
x, y = id.x0 + id.dxdt * t, id.y0 + id.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 = id.x0 + id.dxdt * tmid
ymid = id.y0 + id.dydt * tmid
ztrk = ndimage.gaussian_filter(ff.track(id.dxdt, id.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(id, xmid, ymid, x0, y0) == False:
continue
# Format IOD line
cospar = get_cospar(id.norad)
obs = observation(ff, mjd, x0, y0)
iod_line = "%s" % format_iod_line(id.norad, cospar, ff.site_id,
obs.nfd, obs.ra, obs.de)
print(iod_line)
if id.catalog.find("classfd.tle") > 0:
outfname = "classfd.dat"
elif id.catalog.find("inttles.tle") > 0:
outfname = "inttles.dat"
else:
outfname = "catalog.dat"
f = open(outfname, "a")
f.write("%s\n" % iod_line)
f.close()
# Plot
ppgplot.pgopen(
fname.replace(".fits", "") + "_%05d.png/png" % id.norad)
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgsvp(0.1, 0.95, 0.1, 0.8)
ppgplot.pgsch(0.8)
ppgplot.pgmtxt(
"T", 6.0, 0.0, 0.0,
"UT Date: %.23s COSPAR ID: %04d" % (ff.nfd, ff.site_id))
ppgplot.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]))
ppgplot.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))
ppgplot.pgmtxt(
"T", 2.4, 0.0, 0.0, "Stat: %5.1f+-%.1f (%.1f-%.1f)" %
(np.mean(ff.zmax), np.std(ff.zmax), ff.vmin, ff.vmax))
ppgplot.pgmtxt("T", 0.3, 0.0, 0.0, iod_line)
ppgplot.pgsch(1.0)
ppgplot.pgwnad(0.0, ff.nx, 0.0, ff.ny)
ppgplot.pglab("x (pix)", "y (pix)", " ")
ppgplot.pgctab(heat_l, heat_r, heat_g, heat_b, 5, 1.0, 0.5)
ppgplot.pgimag(ztrk, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1,
vmax, vmin, tr)
ppgplot.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
ppgplot.pgstbg(1)
plot_selection(id, xmid, ymid)
ppgplot.pgsci(0)
if id.catalog.find("classfd.tle") > 0:
ppgplot.pgsci(4)
elif id.catalog.find("inttles.tle") > 0:
ppgplot.pgsci(3)
ppgplot.pgpt(np.array([id.x0]), np.array([id.y0]), 17)
ppgplot.pgmove(id.x0, id.y0)
ppgplot.pgdraw(id.x1, id.y1)
ppgplot.pgpt(np.array([x0]), np.array([y0]), 4)
ppgplot.pgsch(0.65)
ppgplot.pgtext(np.array([id.x0]), np.array([id.y0]),
" %05d" % id.norad)
ppgplot.pgsch(1.0)
ppgplot.pgsci(1)
ppgplot.pgend()