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)
def plot2d(z, x=None, y=None, title=None, rangex=None, rangey=None, \
rangez=None, labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', image=ppgplot_palette_, contours=None, \
logx=0, logy=0, logx2=0, logy2=0, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, labels=ppgplot_labels_, \
labelint=ppgplot_labelint_, labelmin=ppgplot_labelmin_, \
font=ppgplot_font_, id=0, noscale=0, aspect=1, \
fontsize=ppgplot_font_size_, ticks='out', panels=[1,1], \
device=ppgplot_device_):
"""
plot2d(z, ...)
An interface to make various 2D plots using PGPLOT.
'z' is the 2D Numpy array to be plotted.
The optional entries are:
x: x values (default = 0, 1, ...)
y: y values (default = 0, 1, ...)
title: graph title (default = None)
rangex: range for the x-axis (default = automatic)
rangey: range for the y-axis (default = automatic)
rangez: range for the z-axis (default = automatic)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: range for 2nd x-axis (default = None)
rangey2: range for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
image: color palette for image (default = 'rainbow')
contours: list of contour values (default = None)
line: contour line style (default = 1 (solid))
width: contour line width (default = 1 (thin))
color: contour line color (default = 'white')
labels: color of contour labels (default = None)
labelint: contour label spacing (default = 20)
labelmin: min contour label spacing (default = 20)
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: show ID line on plot (default = 0 (no))
noscale: turn off auto scaling (default = 0 (no))
aspect: Aspect ratio (default = 1 (square))
ticks: Ticks point in or out (default = 'out')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is a 2D array
z = Num.asarray(z);
if not len(z.shape)==2:
print 'Input data array must be 2 dimensional.'
return
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, pgpalette
# Define the X and Y axis limits if needed
if x is None: x=Num.arange(z.shape[1], dtype='f')
else: x = Num.asarray(x)
if y is None: y=Num.arange(z.shape[0], dtype='f')
else: y = Num.asarray(y)
# Determine the scaling to use for the axes
if rangex is None: rangex=[Num.minimum.reduce(x), \
Num.maximum.reduce(x)]
if rangey is None: rangey=[Num.minimum.reduce(y), \
Num.maximum.reduce(y)]
if rangez is None: rangez=[Num.minimum.reduce(Num.ravel(z)), \
Num.maximum.reduce(Num.ravel(z))]
# Prep the plotting device...
if (not ppgplot_dev_prep_):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, logx, logy, \
logx2, logy2, font, fontsize, id, aspect, \
ticks, panels, device=device)
if image is not None:
# Set the color indices and the color table
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
lo_col_ind = lo_col_ind + 2
ppgplot.pgscir(lo_col_ind, hi_col_ind)
pgpalette.setpalette(image)
ppgplot.pgctab(pgpalette.l,pgpalette.r,pgpalette.g,pgpalette.b)
# Construct the image
ppgplot.pgimag_s(z, 0.0, 0.0, rangex[0], rangey[0], \
rangex[1], rangey[1])
reset_colors()
if contours is not None:
contours = Num.asarray(contours)
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
# Choose the line color for the contourlines
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Construct the contours
ppgplot.pgcont_s(z, len(contours), contours, rangex[0], \
rangey[0], rangex[1], rangey[1])
# Label the contours if requested
if labels is not None:
# Choose the line color for the contourlines
if type(labels) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[labels])
else:
ppgplot.pgsci(labels)
for i in range(len(contours)):
ppgplot.pgconl_s(z, contours[i], str(contours[i]),
labelint, labelmin)
def plot2d(z, x=None, y=None, title=None, rangex=None, rangey=None, \
rangez=None, labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', image=ppgplot_palette_, contours=None, \
logx=0, logy=0, logx2=0, logy2=0, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, labels=ppgplot_labels_, \
labelint=ppgplot_labelint_, labelmin=ppgplot_labelmin_, \
font=ppgplot_font_, id=0, noscale=0, aspect=1, \
fontsize=ppgplot_font_size_, ticks='out', panels=[1,1], \
device=ppgplot_device_):
"""
plot2d(z, ...)
An interface to make various 2D plots using PGPLOT.
'z' is the 2D Numpy array to be plotted.
The optional entries are:
x: x values (default = 0, 1, ...)
y: y values (default = 0, 1, ...)
title: graph title (default = None)
rangex: range for the x-axis (default = automatic)
rangey: range for the y-axis (default = automatic)
rangez: range for the z-axis (default = automatic)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: range for 2nd x-axis (default = None)
rangey2: range for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
image: color palette for image (default = 'rainbow')
contours: list of contour values (default = None)
line: contour line style (default = 1 (solid))
width: contour line width (default = 1 (thin))
color: contour line color (default = 'white')
labels: color of contour labels (default = None)
labelint: contour label spacing (default = 20)
labelmin: min contour label spacing (default = 20)
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: show ID line on plot (default = 0 (no))
noscale: turn off auto scaling (default = 0 (no))
aspect: Aspect ratio (default = 1 (square))
ticks: Ticks point in or out (default = 'out')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is a 2D array
z = Num.asarray(z)
if not len(z.shape) == 2:
print 'Input data array must be 2 dimensional.'
return
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, pgpalette
# Define the X and Y axis limits if needed
if x is None: x = Num.arange(z.shape[1], dtype='f')
else: x = Num.asarray(x)
if y is None: y = Num.arange(z.shape[0], dtype='f')
else: y = Num.asarray(y)
# Determine the scaling to use for the axes
if rangex is None:
dx = x[-1] - x[-2]
rangex = [x[0], x[-1] + dx]
if rangey is None:
dy = y[-1] - y[-2]
rangey = [y[0], y[-1] + dy]
if rangez is None: rangez=[Num.minimum.reduce(Num.ravel(z)), \
Num.maximum.reduce(Num.ravel(z))]
# Prep the plotting device...
if (not ppgplot_dev_prep_):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, logx, logy, \
logx2, logy2, font, fontsize, id, aspect, \
ticks, panels, device=device)
if image is not None:
# Set the color indices and the color table
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
lo_col_ind = lo_col_ind + 2
ppgplot.pgscir(lo_col_ind, hi_col_ind)
pgpalette.setpalette(image)
ppgplot.pgctab(pgpalette.l, pgpalette.r, pgpalette.g, pgpalette.b)
# Construct the image
ppgplot.pgimag_s(z, 0.0, 0.0, rangex[0], rangey[0], \
rangex[1], rangey[1])
reset_colors()
if contours is not None:
contours = Num.asarray(contours)
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
# Choose the line color for the contourlines
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Construct the contours
ppgplot.pgcont_s(z, len(contours), contours, rangex[0], \
rangey[0], rangex[1], rangey[1])
# Label the contours if requested
if labels is not None:
# Choose the line color for the contourlines
if type(labels) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[labels])
else:
ppgplot.pgsci(labels)
for i in range(len(contours)):
ppgplot.pgconl_s(z, contours[i], str(contours[i]), labelint,
labelmin)
ppgplot.pgsvp(margin + imfract, 1.0 - margin / 2, margin, margin + imfract)
ppgplot.pgswin(-0.1, 1.1, min(zs), max(zs))
ppgplot.pgbox("BCNST", 0.0, 0, "BCST", 0.0, 0)
ppgplot.pgline(fdotcut, zs)
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, "Relative Power")
# f-fdot image
ppgplot.pgsvp(margin, margin + imfract, margin, margin + imfract)
ppgplot.pgswin(min(rs), max(rs), min(zs), max(zs))
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, labx)
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, laby)
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
lo_col_ind = lo_col_ind + 2
ppgplot.pgscir(lo_col_ind, hi_col_ind)
pgpalette.setpalette(image)
ppgplot.pgctab(pgpalette.l, pgpalette.r, pgpalette.g, pgpalette.b)
ppgplot.pgimag_s(pffdot, 0.0, 0.0, rgx[0], rgy[0], rgx[1], rgy[1])
ppgplot.pgsci(1)
ppgplot.pgcont_s(pffdot, len(contours), contours, rgx[0], rgy[0], rgx[1],
rgy[1])
ppgplot.pgbox("BCST", 0.0, 0, "BCST", 0.0, 0)
ppgplot.pgsci(1)
ppgplot.pgbox("N", 0.0, 0, "N", 0.0, 0)
# gray axes
ppgplot.pgscr(1, 0.5, 0.5, 0.5)
ppgplot.pgsci(1)
ppgplot.pgslw(2)
ppgplot.pgline(rgx, num.asarray([0.0, 0.0]))
ppgplot.pgline(num.asarray([0.0, 0.0]), rgy)
ppgplot.pgsvp(margin+imfract, 1.0-margin/2, margin, margin+imfract)
ppgplot.pgswin(-0.1, 1.1, min(zs), max(zs))
ppgplot.pgbox("BCNST", 0.0, 0, "BCST", 0.0, 0)
ppgplot.pgline(fdotcut, zs)
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, "Relative Power");
# f-fdot image
ppgplot.pgsvp(margin, margin+imfract, margin, margin+imfract)
ppgplot.pgswin(min(rs), max(rs), min(zs), max(zs))
ppgplot.pgmtxt("B", 2.4, 0.5, 0.5, labx);
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, laby);
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
lo_col_ind = lo_col_ind + 2
ppgplot.pgscir(lo_col_ind, hi_col_ind)
pgpalette.setpalette(image)
ppgplot.pgctab(pgpalette.l, pgpalette.r, pgpalette.g, pgpalette.b)
ppgplot.pgimag_s(pffdot, 0.0, 0.0, rgx[0], rgy[0], rgx[1], rgy[1])
ppgplot.pgsci(1)
ppgplot.pgcont_s(pffdot, len(contours), contours, rgx[0], rgy[0], rgx[1], rgy[1])
ppgplot.pgbox("BCST", 0.0, 0, "BCST", 0.0, 0)
ppgplot.pgsci(1)
ppgplot.pgbox("N", 0.0, 0, "N", 0.0, 0)
# gray axes
ppgplot.pgscr(1, 0.5, 0.5, 0.5)
ppgplot.pgsci(1)
ppgplot.pgslw(2)
ppgplot.pgline(rgx, num.asarray([0.0, 0.0]))
ppgplot.pgline(num.asarray([0.0, 0.0]), rgy)
ppgplot.pgclos()
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()