def plotgal(xg,yg,final,finalsf,ncl):
ppgplot.pgslw(6)
ppgplot.pgsls(1)
xmin = (-1.*c.pscale[ncl]*(c.xc[ncl]-1))
xmax = (c.pscale[ncl]*(c.xmax[ncl]-c.xc[ncl]))
ymin = (-1.*c.pscale[ncl]*(c.yc[ncl]-1))
ymax = (c.pscale[ncl]*(c.ymax[ncl]-c.yc[ncl]))
ppgplot.pgbox("",0.0,0,"L",0.0,0)
dx=5.
ppgplot.pgenv(xmin-dx,xmax+dx,ymin-dx,ymax+dx,0)
ppgplot.pglab("\gD DEC (\")","\gD RA (\")","")
ppgplot.pgtext(-4,-4,"X")
r = (0.5*c.r200pix[ncl]*c.pscale[ncl])
ppgplot.pgslw(1)
ppgplot.pgsls(2)
ppgplot.pgsfs(2)
ppgplot.pgcirc(0,0,r)
#print "cluster ",ncl," r200: ",c.r200pix[ncl],c.r200Mpc[ncl], " Mpc"
ppgplot.pgslw(3)
ppgplot.pgsls(1)
x = (xg - c.xc[ncl])*c.pscale[ncl]
y = (yg - c.yc[ncl])*c.pscale[ncl]
x = (N.compress((final > 0) & (finalsf < 1), xg) - c.xc[ncl])*c.pscale[ncl]
y = (N.compress((final > 0) & (finalsf < 1), yg) - c.yc[ncl])*c.pscale[ncl]
ppgplot.pgpt(x,y,22)
x = (N.compress((final > 0) & (finalsf > 0), xg) - c.xc[ncl])*c.pscale[ncl]
y = (N.compress((final > 0) & (finalsf > 0), yg) - c.yc[ncl])*c.pscale[ncl]
ppgplot.pgpt(x,y,18)
def plotngalsigmaradcuts():
nr = 1.
nv = 3.
bbJmax = -18.
ppgplot.pgbeg("ngalmhalo-radcut.ps/vcps", 1, 1) #color port.
ppgplot.pgpap(8., 1.25)
ppgplot.pgpage
ppgplot.pgsch(1.2) #font size
ppgplot.pgslw(3) #line width
# 1st panel with symbols w/ stddev errorbars
str1 = "R\dp\u < "
str2 = " R\dv\u"
x1 = .1
x2 = .45
x3 = .6
x4 = .95
y1 = .15
y2 = .425
y3 = .575
y4 = .85
xlabel = 14.25 - 14.
ylabel = 1.14
ppgplot.pgsvp(x1, x2, y3, y4) #sets viewport
g.cutonlbj(bbJmax)
#print "within plotradcuts, after cutonlbj, len(g.x1) = ",len(g.x1)
nr = 1.
c.measurengalcontam(nv, nr, g)
#print "nr = ",nr, " ave contam = ",N.average(c.contam)
sub1plotngalmcl(c.mass, c.membincut, c.obsmembincut)
ppgplot.pgsch(.8)
ppgplot.pgslw(3)
#label="R\dp\u < "+str(nr)+"R\dv\u"
label = str1 + str(nr) + str2
ppgplot.pgtext(xlabel, ylabel, label)
nr = .5
ppgplot.pgsvp(x1, x2, y1, y2) #sets viewport
#ppgplot.pgpanl(1,1)
c.measurengalcontam(nv, nr, g)
#print "nr = ",nr, " ave contam = ",N.average(c.contam)
sub1plotngalmcl(c.mass, c.membincut, c.obsmembincut)
label = str1 + str(nr) + str2
ppgplot.pgsch(.8)
ppgplot.pgslw(3)
ppgplot.pgtext(xlabel, ylabel, label)
ppgplot.pgend()
def plot_report_coversheet(rating_objects):
"""
'rating_objects' is a list of DatabaseRater objects.
"""
ppgplot.pgtext(0,1,"Demo Ratings Report (%s)" % currdatetime.strftime('%c'))
top = 0.9
for r in rating_objects:
formatted_description = ' '.join([line.strip() for line in r.description.splitlines()])
formatted_name = ("%s:" % r.name).ljust(LINELENGTH-1)
text = ("%s %s" % (formatted_name, formatted_description))
lines = []
while text:
if len(text) > LINELENGTH:
index = text.rfind(' ',0, LINELENGTH)
lines.append(text[0:index])
text = text[index+1:]
else:
lines.append(text)
text = ''
if (top - len(lines)*LINESPACING) < 0:
# text will spill over bottom of page
plot_utils.nextpage(vertical=True)
ppgplot.pgtext(0,1,"Demo Ratings Report (%s) (cont'd)" % currdatetime.strftime('%c'))
top = 0.9
for line in lines:
ppgplot.pgtext(0,top, line)
top -= LINESPACING
top -= PARASPACING
def plot_candidate_sheet(rating_objects, pdm_cand_id, cand_ratings, description):
plot_utils.beginplot("cand_rating_report%s(%d).ps" % (currdatetime.strftime('%y%m%d'),pdm_cand_id), vertical=True)
ppgplot.pgtext(0,1,"%d: %s" % (pdm_cand_id, description))
top = 0.5
first = True
for ratobj, rating in zip(rating_objects,cand_ratings):
if (top - PARASPACING) < 0:
if first:
#
# Add P, DM, subints, subbands?
#
pfd = rating_utils.get_pfd_by_cand_id(pdm_cand_id)
prof = rating_utils.prep_profile(pfd)
ppgplot.pgsvp(0.10, 0.90, 0.60, 0.90)
ppgplot.pgswin(0, NUMPHASE, 1.1*np.min(prof), 1.1*np.max(prof))
ppgplot.pgbox('BCNTS', 0.25,5,'BC',0.0,0)
onephase = np.linspace(0,1,prof.size, endpoint=False)
manyphase = np.resize(onephase,prof.size*NUMPHASE)+np.arange(0,NUMPHASE).repeat(prof.size)
ppgplot.pgline(manyphase, np.resize(prof, (NUMPHASE*prof.size,)))
first = False
plot_utils.nextpage(vertical=True)
ppgplot.pgtext(0,1,"%d: %s (cont'd)" % (pdm_cand_id, description))
top = 0.9
ppgplot.pgtext(0,top, '%s: %s' % (ratobj.name, rating))
top -= PARASPACING
if first:
pfd = rating_utils.get_pfd_by_cand_id(pdm_cand_id)
prof = rating_utils.prep_profile(pfd)
ppgplot.pgsvp(0.10, 0.90, 0.60, 0.90)
ppgplot.pgswin(0, NUMPHASE, 1.1*np.min(prof), 1.1*np.max(prof))
ppgplot.pgbox('BCNTS', 0.25,5,'BC',0.0,0)
ppgplot.pgline(np.linspace(0,NUMPHASE,prof.size*NUMPHASE), np.resize(prof, (NUMPHASE*prof.size,)))
def linelabel(xlabel, ylabel, dx, ystep, dxl, dyl, label): #draw key
schdef = ppgplot.pgqch()
ppgplot.pgsch(1.1)
ppgplot.pgtext(xlabel, ylabel, label)
ppgplot.pgsch(1.1)
ylabel = ylabel - 2. * ystep
xs = N.arange(xlabel, (xlabel + dx), .01)
ys = ylabel * N.ones(len(xs), 'd')
ppgplot.pgsls(1)
ppgplot.pgline(xs, ys)
ppgplot.pgtext((xlabel + dxl + dx), (ylabel - dyl), "Memb")
ylabel = ylabel - ystep
ys = ylabel * N.ones(len(xs), 'd')
ppgplot.pgsls(4)
ppgplot.pgline(xs, ys)
ppgplot.pgtext((xlabel + dxl + dx), (ylabel - dyl), "Contam")
ppgplot.pgsch(schdef)
ppgplot.pgsls(1)
def main(args):
with open(os.path.join(os.path.dirname(__file__),
'precisiondata.cpickle')) as filedata:
exptimes, crosspoints, satpoints = pickle.load(filedata)
x_range = [9, 14]
interpcross = interp1d(exptimes, crosspoints, kind='linear')
interpsat = interp1d(exptimes, satpoints, kind='linear')
N = 5
colours = np.arange(2, 2 + N, 1)
exptimes = np.arange(1, N + 1) * 10
if args.besancon:
all_vmags = get_besancon_mag_data()
yhigh = 0.3
title = 'Besancon'
else:
all_vmags = get_nomad_mag_data()
yhigh = 0.4
title = 'NOMAD'
ytot = yhigh * len(all_vmags)
with pgh.open_plot(args.output):
pg.pgvstd()
pg.pgswin(x_range[0], x_range[1], 0, yhigh)
for exptime, colour in zip(exptimes, colours):
satpoint = interpsat(exptime)
crosspoint = interpcross(exptime)
selected = all_vmags[(all_vmags > satpoint) & (all_vmags <=
crosspoint)]
print(exptime, len(selected))
xdata, ydata = cumulative_hist(np.array(selected),
min_val=x_range[0], max_val=x_range[1], norm=len(all_vmags))
ydata /= float(len(all_vmags))
with pgh.change_colour(colour):
pg.pgbin(xdata, ydata, False)
pg.pgbox('bcnst', 0, 0, 'bcnst', 0, 0)
pg.pglab(r'V magnitude', 'High precision fraction', title)
# Label the right hand side
pg.pgswin(x_range[0], x_range[1], 0, ytot)
pg.pgbox('', 0, 0, 'smt', 0, 0)
pg.pgmtxt('r', 2., 0.5, 0.5, 'N')
# Create the legend
pg.pgsvp(0.7, 0.9, 0.1, 0.3)
pg.pgswin(0., 1., 0., 1.)
for i, (exptime, colour) in enumerate(zip(exptimes, colours)):
yval = 0.1 + 0.8 * i / len(exptimes)
with pgh.change_colour(colour):
pg.pgline(np.array([0.2, 0.4]), np.ones(2) * yval)
pg.pgtext(0.5, yval, r'{:d} s'.format(exptime))
for phase in numpy.arange(0.5, 1.51, 0.01):
theta = computeViewingAngle(phase, inclination, beta, phi)
phaseArray.append(phase)
angleArray.append(theta)
mainPlotWindow = ppgplot.pgopen("/xs")
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsci(1)
ppgplot.pgenv(min(phaseArray), max(phaseArray), 0, 180, 0, 0)
ppgplot.pgline(phaseArray, angleArray)
ppgplot.pgsls(2)
ppgplot.pgline([0.5, 1.5], [90, 90])
ppgplot.pglab("orbital phase", "viewing angle",
"Viewing angle \gh as a function of orbital phase.")
ppgplot.pgtext(0.6, 150,
"i:%2.0f \gb:%2.0f \gf:%2.0f" % (inclination, beta, phi))
modelPlotWindow = ppgplot.pgopen("models_i_81_b_40.ps/ps")
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgask(False)
mainFluxMax = 0
mainFluxMin = 1E99
for phase in numpy.arange(0.5, 1.6, 0.1):
ppgplot.pgsci(1)
theta = computeViewingAngle(phase, inclination, beta, phi)
intensities = ["i", "i_0", "i_1"]
lineStyles = [1, 2, 3]
index = 0
def gotoit():
nbin = 10
#c=Cluster()
#g=Galaxy()
clusterfile = "clusters.spec.dat"
print "reading in cluster file to get cluster parameters"
c.creadfiles(clusterfile)
print "got ", len(c.z), " clusters"
c.convarray()
c.Kcorr()
go2 = [] #combined arrays containing all galaxies
gsf = [] #combined arrays containing all galaxies
gsig5 = []
gsig10 = []
gsig52r200 = [] #spec catalogs extended out to 2xR200
gsig102r200 = [] #spec catalogs extended out to 2xR200
gsig5phot = []
gsig10phot = []
sgo2 = [] #combined arrays containing all galaxies
sgha = [] #combined arrays containing all galaxies
sgsf = [] #combined arrays containing all galaxies
sgsig5 = []
sgsig10 = []
sgsig52r200 = [] #spec catalogs extended out to 2xR200
sgsig102r200 = [] #spec catalogs extended out to 2xR200
sgsig5phot = []
sgsig10phot = []
if (mode < 1):
c.getsdssphotcats()
c.getsdssspeccats()
gr = [] #list of median g-r colors
psplotinit('summary.ps')
x1 = .1
x2 = .45
x3 = .6
x4 = .95
y1 = .15
y2 = .45
y3 = .55
y4 = .85
ppgplot.pgsch(1.2) #font size
ppgplot.pgslw(2)
#for i in range(len(c.z)):
cl = [10]
(xl, xu, yl, yu) = ppgplot.pgqvp(0)
print "viewport = ", xl, xu, yl, yu
complall = []
for i in range(len(c.z)):
#for i in cl:
gname = "g" + str(i)
gname = Galaxy()
gspecfile = "abell" + str(c.id[i]) + ".spec.dat"
gname.greadfiles(gspecfile, i)
print "number of members = ", len(gname.z)
if len(gname.z) < 10:
print "less than 10 members", len(gname.z)
continue
gname.convarray()
#gname.cullmembers()
#gname.getmemb()#get members w/in R200
#gr.append(N.average(gname.g-gname.r))
gspec2r200file = "abell" + str(c.id[i]) + ".spec2r200.dat"
gname.greadspecfiles(gspec2r200file, c.dL[i], c.kcorr[i], i)
print i, c.id[i], " getnearest, first call", len(gname.ra), len(
gname.sra), sum(gname.smemb)
#gname.getnearest(i)
(gname.sig52r200, gname.sig102r200) = gname.getnearestgen(
gname.ra, gname.dec, gname.sra, gname.sdec, i
) #measure distances from ra1, dec1 to members in catalog ra2, dec2
sig52r200 = N.compress(gname.memb > 0, gname.sig52r200)
gsig52r200[len(gsig5phot):] = sig52r200
sig102r200 = N.compress(gname.memb > 0, gname.sig102r200)
gsig102r200[len(gsig10phot):] = sig102r200
gphotfile = "abell" + str(c.id[i]) + ".phot.dat"
gname.greadphotfiles(gphotfile, c.dL[i], c.kcorr[i])
gname.getnearest(i)
#print "len of local density arrays = ",len(gname.sig5),len(gname.sig5phot)
#print gspecfile, c.z[i],c.kcorr[i]
(ds5, ds10) = gname.gwritefiles(gspecfile, i)
o2 = N.compress(gname.memb > 0, gname.o2)
go2[len(go2):] = o2
sf = N.compress(gname.memb > 0, gname.sf)
gsf[len(gsf):] = sf
sig5 = N.compress(gname.memb > 0, gname.sig5)
gsig5[len(gsig5):] = sig5
sig10 = N.compress(gname.memb > 0, gname.sig10)
gsig10[len(gsig10):] = sig10
sig5phot = N.compress(gname.memb > 0, gname.sig5phot)
gsig5phot[len(gsig5phot):] = sig5phot
sig10phot = N.compress(gname.memb > 0, gname.sig10phot)
gsig10phot[len(gsig10phot):] = sig10phot
ds5 = N.array(ds5, 'f')
ds10 = N.array(ds10, 'f')
#print len(ds5),len(ds10)
#ppgplot.pgsvp(xl,xu,yl,yu)
ppgplot.pgsvp(0.1, .9, .08, .92)
ppgplot.pgslw(7)
label = 'Abell ' + str(
c.id[i]) + ' (z=%5.2f, \gs=%3.0f km/s)' % (c.z[i], c.sigma[i])
ppgplot.pgtext(0., 1., label)
ppgplot.pgslw(2)
ppgplot.pgsvp(x1, x2, y1, y2) #sets viewport
#ppgplot.pgbox("",0.0,0,"",0.0)
ppgplot.pgswin(-1., 3., -1., 3.) #axes limits
ppgplot.pgbox('bcnst', 1, 2, 'bcvnst', 1, 2) #tickmarks and labeling
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5,
"\gS\d10\u(phot) (gal/Mpc\u2\d)") #xlabel
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, "\gS\d10\u(spec) (gal/Mpc\u2\d)")
x = N.arange(-5., 10., .1)
y = x
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgline(x, y)
x = N.log10(sig10phot)
y = N.log10(sig10)
ppgplot.pgsch(.7)
ppgplot.pgpt(x, y, 17)
xp = N.array([-0.5], 'f')
yp = N.array([2.5], 'f')
ppgplot.pgpt(xp, yp, 17)
ppgplot.pgtext((xp + .1), yp, 'spec(1.2xR200) vs phot')
ppgplot.pgsci(4)
xp = N.array([-0.5], 'f')
yp = N.array([2.2], 'f')
ppgplot.pgpt(xp, yp, 21)
ppgplot.pgtext((xp + .1), yp, 'spec(2xR200) vs phot')
y = N.log10(sig102r200)
ppgplot.pgsch(.9)
ppgplot.pgpt(x, y, 21)
ppgplot.pgsch(1.2)
ppgplot.pgslw(2) #line width
ppgplot.pgsci(1)
#ppgplot.pgenv(-200.,200.,-1.,20.,0,0)
#ppgplot.pgsci(2)
#ppgplot.pghist(len(ds5),ds5,-200.,200.,30,1)
#ppgplot.pgsci(4)
#ppgplot.pghist(len(ds10),ds10,-200.,200.,30,1)
#ppgplot.pgsci(1)
#ppgplot.pglab("\gD\gS","Ngal",gspecfile)
#ppgplot.pgpanl(1,2)
g = N.compress(gname.memb > 0, gname.g)
r = N.compress(gname.memb > 0, gname.r)
V = N.compress(gname.memb > 0, gname.V)
dmag = N.compress(gname.memb > 0, gname.dmagnearest)
dnearest = N.compress(gname.memb > 0, gname.nearest)
dz = N.compress(gname.memb > 0, gname.dz)
#ppgplot.pgsvp(x3,x4,y1,y2) #sets viewport
#ppgplot.pgenv(-.5,3.,-1.,5.,0,0)
#ppgplot.pgpt((g-V),(g-r),17)
#ppgplot.pgsci(1)
#ppgplot.pglab("g - M\dV\u",'g-r',gspecfile)
ppgplot.pgsvp(x1, x2, y3, y4) #sets viewport
#ppgplot.pgbox("",0.0,0,"",0.0)
ppgplot.pgswin(
(c.ra[i] + 2. * c.r200deg[i] / N.cos(c.dec[i] * N.pi / 180.)),
(c.ra[i] - 2 * c.r200deg[i] / N.cos(c.dec[i] * N.pi / 180.)),
(c.dec[i] - 2. * c.r200deg[i]), (c.dec[i] + 2. * c.r200deg[i]))
ppgplot.pgbox('bcnst', 0.0, 0.0, 'bcvnst', 0.0,
0.0) #tickmarks and labeling
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "RA") #xlabel
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, "Dec")
#ppgplot.pglab("RA",'Dec',gspecfile)
ppgplot.pgsfs(2)
ppgplot.pgcirc(c.ra[i], c.dec[i], c.r200deg[i])
ppgplot.pgsls(4)
ppgplot.pgcirc(c.ra[i], c.dec[i], 1.2 * c.r200deg[i])
ppgplot.pgsls(1)
#ppgplot.pgcirc(c.ra[i],c.dec[i],c.r200deg[i]/N.cos(c.dec[i]*N.pi/180.))
ppgplot.pgsci(2)
ppgplot.pgpt(gname.ra, gname.dec, 17)
ppgplot.pgsci(4)
ppgplot.pgpt(gname.photra, gname.photdec, 21)
ppgplot.pgsci(1)
#calculate completeness w/in R200
dspec = N.sqrt((gname.ra - c.ra[i])**2 + (gname.dec - c.dec[i])**2)
dphot = N.sqrt((gname.photra - c.ra[i])**2 +
(gname.photdec - c.dec[i])**2)
nphot = 1. * len(N.compress(dphot < c.r200deg[i], dphot))
nspec = 1. * len(N.compress(dspec < c.r200deg[i], dspec))
s = "Completeness for cluster Abell %s = %6.2f (nspec=%6.1f,nphot= %6.1f)" % (
str(c.id[i]), float(nspec / nphot), nspec, nphot)
print s
complall.append(float(nspec / nphot))
ppgplot.pgsvp(x3, x4, y3, y4) #sets viewport
#ppgplot.pgsvp(x1,x2,y3,y4) #sets viewport
#ppgplot.pgbox("",0.0,0,"",0.0)
ppgplot.pgswin(-0.005, .05, -1., 1.)
ppgplot.pgbox('bcnst', .02, 2, 'bcvnst', 1, 4) #tickmarks and labeling
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5,
"Dist to nearest phot neighbor (deg)") #xlabel
ppgplot.pgsch(1.2)
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'M\dV\u(phot) - M\dV\u(spec)')
ppgplot.pgsci(2)
ppgplot.pgpt(dnearest, dmag, 17)
ppgplot.pgsci(1)
x = N.arange(-30., 30., 1.)
y = 0 * x
ppgplot.pgsci(1)
ppgplot.pgsls(2)
ppgplot.pgline(x, y)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
dm = N.compress(dnearest < 0.01, dmag)
std = '%5.3f (%5.3f)' % (pylab.mean(dm), pylab.std(dm))
#ppgplot.pgslw(7)
#label='Abell '+str(c.id[i])
#ppgplot.pgtext(0.,1.,label)
ppgplot.pgslw(2)
label = '\gDM\dV\u(err) = ' + std
ppgplot.pgsch(.9)
ppgplot.pgtext(0., .8, label)
#label = "z = %5.2f"%(c.z[i])
#ppgplot.pgtext(0.,.8,label)
ppgplot.pgsch(1.2)
#ppgplot.pgsvp(x3,x4,y3,y4) #sets viewport
#ppgplot.pgenv(-.15,.15,-3.,3.,0,0)
#ppgplot.pgsci(2)
#ppgplot.pgpt(dz,dmag,17)
#ppgplot.pgsci(1)
#ppgplot.pglab("z-z\dcl\u",'\gD Mag',gspecfile)
ppgplot.pgsvp(x3, x4, y1, y2) #sets viewport
ppgplot.pgswin(-3., 3., -1., 1.)
ppgplot.pgbox('bcnst', 1, 2, 'bcvnst', 1, 4) #tickmarks and labeling
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "\gDv/\gs") #xlabel
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'M\dV\u(phot) - M\dV\u(spec)')
ppgplot.pgsci(2)
dv = dz / (1 + c.z[i]) * 3.e5 / c.sigma[i]
ppgplot.pgpt(dv, dmag, 17)
ppgplot.pgsci(1)
x = N.arange(-30., 30., 1.)
y = 0 * x
ppgplot.pgsci(1)
ppgplot.pgsls(2)
ppgplot.pgline(x, y)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
#ppgplot.pgsvp(x1,x2,y1,y2) #sets viewport
#ppgplot.pgenv(0.,3.5,-3.,3.,0,0)
#ppgplot.pgsci(4)
#ppgplot.pgpt((g-r),dmag,17)
#ppgplot.pgsci(1)
#ppgplot.pglab("g-r",'\gD Mag',gspecfile)
#ppgplot.pgsvp(x1,x2,y1,y2) #sets viewport
#ppgplot.pgenv(-25.,-18.,-1.,1.,0,0)
#ppgplot.pgsci(4)
#ppgplot.pgpt((V),dmag,17)
#x=N.arange(-30.,30.,1.)
#y=0*x
#ppgplot.pgsci(1)
#ppgplot.pgsls(2)
#ppgplot.pgline(x,y)
#ppgplot.pgsls(1)
#ppgplot.pgsci(1)
#ppgplot.pglab("M\dV\u(spec)",'M\dV\u(phot) - M\dV\u(spec)',gspecfile)
#ppgplot.pgpage()
#ppgplot.pgpage()
#combine galaxy data
ppgplot.pgpage()
(sssig5,
sssig10) = gname.getnearestgen(gname.sra, gname.sdec, gname.sra,
gname.sdec,
i) #get spec-spec local density
(spsig5,
spsig10) = gname.getnearestgen(gname.sra, gname.sdec, gname.photra,
gname.photdec,
i) #get spec-phot local density
o2 = N.compress(gname.smemb > 0, gname.so2)
sgo2[len(sgo2):] = o2
ha = N.compress(gname.smemb > 0, gname.sha)
sgha[len(sgha):] = ha
sf = N.compress(gname.smemb > 0, gname.ssf)
sgsf[len(sgsf):] = sf
sig5 = N.compress(gname.smemb > 0, sssig5)
sgsig5[len(sgsig5):] = sig5
sig10 = N.compress(gname.smemb > 0, sssig10)
sgsig10[len(sgsig10):] = sig10
sig5phot = N.compress(gname.smemb > 0, spsig5)
sgsig5phot[len(sgsig5phot):] = sig5phot
sig10phot = N.compress(gname.smemb > 0, spsig10)
sgsig10phot[len(sgsig10phot):] = sig10phot
#gr=N.array(gr,'f')
#c.assigncolor(gr)
#for i in range(len(c.z)):
# print c.id[i],c.z[i],c.r200[i],c.r200deg[i]
print "Average Completeness w/in R200 = ", N.average(N.array(
complall, 'f'))
print "sig o2", len(gsig10), len(gsig10phot), len(go2)
print "sig o2 large", len(sgsig10), len(sgsig10phot), len(sgo2)
plotsigo2all(gsig10, gsig10phot, go2, 'o2vsig10spec', nbin)
#plotsigo2(gsig5phot,-1*go2,'o2vsig5phot',nbin)
plotsigsff(gsig5, gsf, 'sffvsig5spec', nbin) #sf frac versus sigma
plotsigsff(gsig5phot, gsf, 'sffvsig5phot', nbin) #sf frac versus sigma
plotsigsffall(gsig5, gsig5phot, gsf, 'sffvsig5all',
nbin) #sf frac versus sigma
plotsig10sffall(gsig10, gsig10phot, gsf, 'sffvsig10all',
nbin) #sf frac versus sigma
#plotsighaall(gsig10,gsig10phot,gha,'havsig10spec',20)
#plotsigo2all(sgsig10,sgsig10phot,sgo2,'o2vsig10spec.large',30)
plotsighaall(sgsig10, sgsig10phot, sgha, 'havsig10spec.large', 10)
#plotsigsffall(sgsig5,sgsig5phot,sgsf,'sffvsig5.large',nbin)#sf frac versus sigma
#plotsig10sffall(sgsig10,sgsig10phot,sgsf,'sffvsig10.large',nbin)#sf frac versus sigma
psplotinit('one2one.ps')
ppgplot.pgenv(-1.5, 2.5, -1.5, 2.5, 0)
ppgplot.pglab("\gS\d10\u(phot) (gal/Mpc\u2\d)",
"\gS\d10\u(spec) (gal/Mpc\u2\d)", "")
x = N.arange(-5., 10., .1)
y = x
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgline(x, y)
x = N.log10(gsig10phot)
y = N.log10(gsig10)
ppgplot.pgsch(.7)
ppgplot.pgpt(x, y, 17)
ppgplot.pgsch(1.)
ppgplot.pgsci(1)
ppgplot.pgend()
superMax = numpy.ma.max(superPixel)
superMin = numpy.ma.min(superPixel)
variance = numpy.ma.var(superPixel)
numPixels = numpy.ma.count(superPixel)
print(
"[%d, %d] \tmax: %d \tmin: %d \t variance: %f \t variance/pixel: %f"
% (position[1], position[0], superMax, superMin, variance,
variance / numPixels))
ppgplot.pgslct(spPreview['pgplotHandle'])
boostedPreview = generalUtils.percentiles(superPixel, 20, 99)
ppgplot.pggray(boostedPreview, 0, superPixelSize - 1, 0,
superPixelSize - 1, 0, 255,
imagePlot['pgPlotTransform'])
ppgplot.pgsci(0)
ppgplot.pgsch(5)
ppgplot.pgtext(1, 2, "max: %d" % superMax)
ppgplot.pgslct(imagePlot['pgplotHandle'])
pointingObject = {'x': position[1], 'y': position[0]}
pointings.append(pointingObject)
ppgplot.pgsfs(2)
ppgplot.pgsci(5)
ppgplot.pgcirc(position[1], position[0], radius)
xpts = [
startX, startX, startX + superPixelSize,
startX + superPixelSize
]
ypts = [
startY, startY + superPixelSize, startY + superPixelSize,
startY
]
ppgplot.pgsfs(2)
superMax = numpy.ma.max(superPixel)
superMin = numpy.ma.min(superPixel)
variance = numpy.ma.var(superPixel)
numPixels = numpy.ma.count(superPixel)
print (
"[%d, %d] \tmax: %d \tmin: %d \t variance: %f \t variance/pixel: %f"
% (position[1], position[0], superMax, superMin, variance, variance / numPixels)
)
ppgplot.pgslct(spPreview["pgplotHandle"])
boostedPreview = generalUtils.percentiles(superPixel, 20, 99)
ppgplot.pggray(
boostedPreview, 0, superPixelSize - 1, 0, superPixelSize - 1, 0, 255, imagePlot["pgPlotTransform"]
)
ppgplot.pgsci(0)
ppgplot.pgsch(5)
ppgplot.pgtext(1, 2, "max: %d" % superMax)
ppgplot.pgslct(imagePlot["pgplotHandle"])
pointingObject = {"x": position[1], "y": position[0]}
pointings.append(pointingObject)
ppgplot.pgsfs(2)
ppgplot.pgsci(5)
ppgplot.pgcirc(position[1], position[0], radius)
xpts = [startX, startX, startX + superPixelSize, startX + superPixelSize]
ypts = [startY, startY + superPixelSize, startY + superPixelSize, startY]
ppgplot.pgsfs(2)
ppgplot.pgsci(4)
ppgplot.pgpoly(xpts, ypts)
tempBitmap = numpy.zeros(numpy.shape(imageCopy))
tempBitmap = gridCircle(position[0], position[1], radius, tempBitmap)
additionalMask = numpy.ma.make_mask(tempBitmap)
booleanMask = numpy.ma.mask_or(booleanMask, additionalMask)
def plot_rating_sheet(rating):
"""
Plot a fact sheet on the ratings in the database corresponding to 'rating'.
'rating' is a dictionary of information from the MySQL database (as returned
by 'get_all_rating_types()'.
"""
plot_utils.beginplot("rating_report%s.ps" % currdatetime.strftime('%y%m%d'), vertical=True)
ch0 = ppgplot.pgqch()
ppgplot.pgsch(0.5)
ch = ppgplot.pgqch()
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 1 of 2" % (rating["name"], currdatetime.strftime('%c')))
ppgplot.pgsch(ch)
# Plot Histograms
all_ratings = get_ratings(rating["rating_id"])
range = xmin,xmax = np.min(all_ratings), np.max(all_ratings)
ppgplot.pgsci(1)
ppgplot.pgslw(1)
#===== Total/Classified/Unclassified
# Get data
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
(tot_counts, tot_left_edges)=np.histogram(all_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(tot_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, tot_counts)
(clsfd_counts, clsfd_left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3,4,5,6,7)), bins=NUMBINS, range=range)
ppgplot.pgsci(3) # plot classified in green
ppgplot.pgbin(tot_left_edges, clsfd_counts)
unclsfd_counts = tot_counts-clsfd_counts
ppgplot.pgsci(2) # plot unclassified in red
ppgplot.pgbin(tot_left_edges, unclsfd_counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Counts","")
#===== Class 1/2/3
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
(counts, left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3)), bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Class 1/2/3","")
#===== RFI
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
rfi_ratings = get_ratings(rating["rating_id"], human_classification=(4,))
(counts, left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI","")
#===== Noise
ppgplot.pgsvp(0.1, 0.9, 0.3, 0.45)
noise_ratings = get_ratings(rating["rating_id"], human_classification=(5,))
(counts, left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Noise","")
#===== Known/Harmonic
ppgplot.pgsvp(0.1, 0.9, 0.15, 0.3)
known_ratings = get_ratings(rating["rating_id"], human_classification=(6,7))
(counts, left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known/Harmonic","")
#===== Second page for differential histograms
plot_utils.nextpage(vertical=True)
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 2 of 2" % (rating["name"], currdatetime.strftime('%c')))
#===== RFI - Known
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
if rfi_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - known_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
#===== RFI - Noise
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
if noise_ratings.size==0 or rfi_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
#===== Known - Noise
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
if noise_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
# Y-axis label is taken care of outside of if/else (below)
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = known_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgswin(xmin,xmax,0,1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("NTS",0,5,"",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known - Noise","")
ppgplot.pgsch(ch0) # reset character height
# Plot theta as a function of orbital phase for these parameters
for phase in numpy.arange(0.5, 1.51, 0.01):
theta = computeViewingAngle(phase, inclination, beta, phi)
phaseArray.append(phase)
angleArray.append(theta)
mainPlotWindow = ppgplot.pgopen("/xs")
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgsci(1)
ppgplot.pgenv(min(phaseArray), max(phaseArray), 0, 180, 0, 0)
ppgplot.pgline(phaseArray, angleArray)
ppgplot.pgsls(2)
ppgplot.pgline([0.5, 1.5], [90, 90])
ppgplot.pglab("orbital phase", "viewing angle", "Viewing angle \gh as a function of orbital phase.")
ppgplot.pgtext(0.6, 150, "i:%2.0f \gb:%2.0f \gf:%2.0f"%(inclination, beta, phi))
modelPlotWindow = ppgplot.pgopen("models_i_81_b_40.ps/ps")
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgask(False)
mainFluxMax = 0
mainFluxMin = 1E99
for phase in numpy.arange(0.5, 1.6, 0.1):
ppgplot.pgsci(1)
theta = computeViewingAngle(phase, inclination, beta, phi)
intensities = ["i", "i_0", "i_1"]
lineStyles = [1, 2, 3]
def plothistsfr():
DATAMIN = -4.
DATAMAX = 15.
NBIN = int((DATAMAX-DATAMIN)*2.)
#print "ngal = ",len(g0.sfr)
ppgplot.pgbox("",0.0,0,"L",0.0,0)
ppgplot.pgenv(DATAMIN,DATAMAX,0,45,0)
ppgplot.pglab("SFR (h\d100\u\u-2\d M\d\(2281)\u yr\u-1 \d)","Number of Galaxies","")
ppgplot.pgsls(1)#dotted
ppgplot.pgslw(4) #line width
#ppgplot.pgsci(4)
#x=N.compress((abs(g0.ew) > ewmin),g0.sfr)
x=N.compress((g0.final > 0),g0.sfrc)
ppgplot.pghist(len(x),x,DATAMIN,DATAMAX,NBIN,5)
xlabel = 6.5
ylabel = 38.
ystep = 3.
dy=.4
dxl=3
dxr=.5
ppgplot.pgslw(deflw) #line width
ppgplot.pgtext(xlabel,ylabel,"CL1040")
xlin = N.array([xlabel-dxl,xlabel-dxr],'f')
ylin = N.array([ylabel+dy,ylabel+dy],'f')
ppgplot.pgslw(4) #line width
ppgplot.pgline(xlin,ylin)
ppgplot.pgslw(5)
ppgplot.pgsls(3)#dot-dash-dot-dash
#ppgplot.pgsci(3)
#x=N.compress((abs(g1.ew) > ewmin),g1.sfr)
x=N.compress((g1.final > 0),g1.sfrc)
ppgplot.pghist(len(x),x,DATAMIN,DATAMAX,NBIN,5)
ylabel = ylabel - ystep
xlin = N.array([xlabel-dxl,xlabel-dxr],'f')
ylin = N.array([ylabel+dy,ylabel+dy],'f')
ppgplot.pgline(xlin,ylin)
ppgplot.pgsls(1)
ppgplot.pgslw(deflw)
ppgplot.pgtext(xlabel,ylabel,"CL1054-12")
ppgplot.pgsls(1)#dot-dash-dot-dash
#ppgplot.pgsci(2)
ppgplot.pgslw(2) #line width
#x=N.compress((abs(g2.ew) > ewmin),g2.sfr)
x=N.compress((g2.final > 0),g2.sfrc)
ppgplot.pghist(len(x),x,DATAMIN,DATAMAX,NBIN,5)
ylabel = ylabel - ystep
ppgplot.pgslw(deflw) #line width
ppgplot.pgtext(xlabel,ylabel,"CL1216")
xlin = N.array([xlabel-dxl,xlabel-dxr],'f')
ylin = N.array([ylabel+dy,ylabel+dy],'f')
ppgplot.pgslw(2) #line width
ppgplot.pgline(xlin,ylin)
#print "Number in g2.ratios = ",len(g2.ratio)
#for ratio in g2.ratio:
# print ratio
#drawbinned(x,y,5)
ppgplot.pgsci(1)
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()
extendPhases = copy.deepcopy(phases)
for p in phases:
extendPhases.append(p + 1.0)
phases = extendPhases
mag.extend(mag)
err.extend(err)
# print phases
magMax = numpy.max(mag) + err[numpy.argmax(mag)]
magMin = numpy.min(mag) - err[numpy.argmin(mag)]
meanError = numpy.mean(err)
if extraColumn: ppgplot.pgsch(1.8)
ppgplot.pgsch(1.6)
ppgplot.pgenv(0. ,2.0 , magMax + meanError*2, magMin - meanError*2, 0, 0)
# ppgplot.pglab("Phase", "CRTS mag", "Phase plot: %s [%d]"%(o.id, len(phases)/2) )
ppgplot.pglab("Phase", "CRTS mag", "WD%s"%o.id)
ppgplot.pgtext(0.1, magMax + meanError*2 - 0.01, "%.2f d"%(o.ephemeris.Period))
ppgplot.pgsch(1.0)
ppgplot.pgpt(phases, mag)
ppgplot.pgerrb(2, phases, mag, err, 0)
ppgplot.pgerrb(4, phases, mag, err, 0)
matplotlib.pyplot.xlabel("Phase", size = labelSize)
matplotlib.pyplot.ylabel('CRTS magnitude', size = labelSize)
matplotlib.pyplot.errorbar(phases, mag, color='k', yerr=err, fmt = '.', ecolor='0.75', capsize=0)
if extraColumn:
additionalData = o.getColumn(extraColumnName)
additionalData.extend(additionalData)
dataRange = numpy.max(additionalData) - numpy.min(additionalData)
ppgplot.pgsch(1.8)
ppgplot.pgenv(0, 2, numpy.min(additionalData), numpy.max(additionalData), 0, 0)
ppgplot.pglab("Phase", extraColumnName, "")
day = sys.argv[4]#ARGV[3] # print "TODAY year month day\n " # Read LMST ######################################################## #calendar_buf = `echo m 1 year | skycalendar` #calendar_buf =~ m/month day\//g #(calendar) = split (/\n/, ') (buf, buf,buf, jdmid, hour,min,sec,ss_hour, ss_min, twie_hour,twie_min, twib_hour, twib_min, sr_hour, sr_min) = calendar.split()#/\s+/, calendar) ppgplot.pgsch(0.8) # character height (size) ppgplot.pgslw(2) # line width ppgplot.pgtext(6.5,0.80,filename) ppgplot.pgsch(0.6) # character height (size) ppgplot.pgtext(-10.32,0.80,str(year)) ppgplot.pgtext(-9.5,0.80,str(month)) ppgplot.pgtext(-8.9,0.80,str(day)) ppgplot.pgtext(-8,0.80,"LMST Midnight") ppgplot.pgtext(-4.8,0.80,"hour") ppgplot.pgtext(-4.25,0.80,"min") ppgplot.pgtext(-3.75,0.80,"sec") ppgplot.pgtext(-8,0.84,"Sun Set") ppgplot.pgtext(-4.8,0.84,"ss_hour") ppgplot.pgtext(-4.25,0.84,"ss_min")
labx = 'Fourier Frequency Offset (bins)'
laby = 'Fourier Frequency Derivative (bins)'
contours = num.asarray([0.1, 0.3, 0.5, 0.7, 0.9])
imfract = 0.65
margin = 0.08
ppgplot.pgopen(device)
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgpage()
# Give z and w values and power change
ppgplot.pgsvp(margin + imfract, 1.0 - margin / 2, margin + imfract,
1.0 - margin / 2)
ppgplot.pgswin(0.0, 1.0, 0.0, 1.0)
ppgplot.pgtext(0.1, 0.8, "Frac Recovered" % frp)
ppgplot.pgtext(0.2, 0.65, "Power = %.3f" % frp)
ppgplot.pgtext(0.1, 0.4, "signal z = %.1f" % z)
ppgplot.pgtext(0.1, 0.25, "signal w = %.1f" % w)
# freq cut
ppgplot.pgsvp(margin, margin + imfract, margin + imfract, 1.0 - margin / 2)
ppgplot.pgswin(min(rs), max(rs), -0.1, 1.1)
ppgplot.pgbox("BCST", 0.0, 0, "BCNST", 0.0, 0)
ppgplot.pgline(rs, freqcut)
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, "Relative Power")
#fdot cut
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)
device='/XWIN'
labx='Fourier Frequency Offset (bins)'
laby='Fourier Frequency Derivative (bins)'
contours = num.asarray([0.1, 0.3, 0.5, 0.7, 0.9])
imfract = 0.65
margin = 0.08
ppgplot.pgopen(device)
ppgplot.pgpap(0.0, 1.0)
ppgplot.pgpage()
# Give z and w values and power change
ppgplot.pgsvp(margin+imfract, 1.0-margin/2, margin+imfract, 1.0-margin/2)
ppgplot.pgswin(0.0, 1.0, 0.0, 1.0)
ppgplot.pgtext(0.1, 0.8, "Frac Recovered" % frp)
ppgplot.pgtext(0.2, 0.65, "Power = %.3f" % frp)
ppgplot.pgtext(0.1, 0.4, "signal z = %.1f" % z)
ppgplot.pgtext(0.1, 0.25, "signal w = %.1f" % w)
# freq cut
ppgplot.pgsvp(margin, margin+imfract, margin+imfract, 1.0-margin/2)
ppgplot.pgswin(min(rs), max(rs), -0.1, 1.1)
ppgplot.pgbox("BCST", 0.0, 0, "BCNST", 0.0, 0)
ppgplot.pgline(rs, freqcut)
ppgplot.pgmtxt("L", 2.0, 0.5, 0.5, "Relative Power");
#fdot cut
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)
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)
k = 1.2807e-16 #erg K
l = N.arange(2000., 50000., 1000.) #wavelength in A
l = l * 1.e-8 #convert to cm
T = 40000. #K
B = 2. * h * c**2 / (l**5) / (N.exp(h * c / (l * k * T)) - 1) / 1.e14
l = l * 1.e4
xmin = 1.15 * min(l)
xmax = max(l)
ymin = min(B)
ymax = 1.2 * max(B)
my.psplotinit("blackbody.ps")
ppgplot.pgbox("", 0.0, 0, "", 0.0, 0)
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0, 0)
ppgplot.pglab("Wavelength", "Energy Output/second", "")
ppgplot.pgsci(4)
ppgplot.pgline(l, B)
ppgplot.pgtext(.6, 3., 'Star A')
ppgplot.pgtext(.6, -.5, 'Blue')
#T=20000.#K
#B=2.*h*c**2/(l**5)/(N.exp(h*c/(l*k*T))-1)
l = (l * 1.e-4 + 20000e-8) * 1.e4
ppgplot.pgsci(2)
ppgplot.pgline(l, B)
ppgplot.pgtext(2.6, 3., 'Star B')
ppgplot.pgtext(3.6, -.5, 'Red')
ppgplot.pgsci(1)
ppgplot.pgend()