def calcavesky():
input = open("noise.dat", 'r')
aperture = []
counts = []
area = []
j = 0
for line in input:
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('mosaic_minus') > -1: #skip lines with '#' in them
j = 0
continue
j = j + 1
if (j > 3):
#print j, line
t = line.split()
aperture.append(float(t[0]))
counts.append(float(t[1]))
area.append(float(t[2]))
input.close()
aperture = N.array(aperture, 'f')
counts = N.array(counts, 'f')
area = N.array(area, 'f')
ap = N.zeros(npoints, 'f')
aparea = N.zeros(nap, 'f')
aveap = N.zeros(nap, 'f')
aveaperr = N.zeros(nap, 'f')
avearea = N.zeros(nap, 'f')
aveareaerr = N.zeros(nap, 'f')
#for i in range(len(ap)):
for i in range(nap):
#print i, len(ap),aperture[i],aperture[i+1]
if (i < (nap - 1)):
ap = N.compress(
(aperture >= aperture[i]) & (aperture < aperture[i + 1]),
counts)
aparea = N.compress(
(aperture >= aperture[i]) & (aperture < aperture[i + 1]), area)
else:
ap = N.compress((aperture >= aperture[i]) & (aperture < 20.),
counts)
aparea = N.compress((aperture >= aperture[i]) & (aperture < 20.),
area)
#print ap
#aparea=N.compress((aperture >= aperture[i]) & (aperture < aperture[i+1]),area)
aveap[i] = N.average(ap)
aveaperr[i] = scipy.stats.std(ap)
avearea[i] = N.average(aparea)
aveareaerr[i] = scipy.stats.std(aparea)
print "ave sky = %8.4f +/- %8.4f" % (N.average(ap),
scipy.stats.std(ap))
print "ave area = %8.4f +/- %8.4f" % (N.average(aparea),
scipy.stats.std(aparea))
return aveap, aveaperr, avearea, aveareaerr
def binitave(x, y, n): #bin arrays x, y into n bins, returning xbin,ybin
nx = len(x)
y = N.take(y, N.argsort(x)) #sort y according to x rankings
x = N.take(x, N.argsort(x))
xbin = N.zeros(n, 'f')
ybin = N.zeros(n, 'f')
ybinerr = N.zeros(n, 'f')
for i in range(n):
nmin = i * int(float(nx) / float(n))
nmax = (i + 1) * int(float(nx) / float(n))
#xbin[i]=pylab.median(x[nmin:nmax])
#ybin[i]=pylab.median(y[nmin:nmax])
xbin[i] = N.average(x[nmin:nmax])
ybin[i] = N.average(y[nmin:nmax])
ybinerr[i] = scipy.stats.std(y[nmin:nmax])
return xbin, ybin, ybinerr
def binitbins(xmin, xmax, nbin, x, y): #use equally spaced bins
dx = float((xmax - xmin) / (nbin))
xbin = N.arange(xmin, (xmax), dx) + dx / 2.
#print "within binitbins"
#print "xbin = ",xbin
#print "dx = ",dx
#print "xmax = ",xmax
#print "xmin = ",xmin
ybin = N.zeros(len(xbin), 'd')
ybinerr = N.zeros(len(xbin), 'd')
xbinnumb = N.array(len(x), 'd')
x1 = N.compress((x >= xmin) & (x <= xmax), x)
y1 = N.compress((x >= xmin) & (x <= xmax), y)
x = x1
y = y1
xbinnumb = ((x - xmin) * nbin / (xmax - xmin)
) #calculate x bin number for each point
j = -1
for i in range(len(xbin)):
ydata = N.compress(abs(xbinnumb - float(i)) < .5, y)
try:
ybin[i] = N.average(ydata)
#ybin[i]=pylab.median(ydata)
ybinerr[i] = pylab.std(ydata) / N.sqrt(float(len(ydata)))
except ZeroDivisionError:
ybin[i] = 0.
ybinerr[i] = 0.
return xbin, ybin, ybinerr
def azmr(self):
x=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -18.),self.Mabs)
y=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -18.),self.ar)
x1=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -20.38),self.Mabs)
y1=N.compress((self.mpaflag > 0.1) & (self.ew > 4.) & (self.Mabs < -20.38),self.ar)
y=2.5*N.log10(y)
#pylab.plot(x,y,'k.',markersize=0.1,zorder=1)
print "average Ar for Mr < -20.38 = %5.2f +/- %5.2f"%(N.average(y1),pylab.std(y1))
(xbin,ybin)=my.binit(x1,y1,20)
#(xbin,ybin,ybinerr)=my.biniterr(x,y,20)
for i in range(len(xbin)):
print i,xbin[i],ybin[i]
print "Average of binned values = ",N.average(ybin)
print "average Ar for Mr < -20.38 = %5.2f +/- %5.2f"%(N.average(N.log10(y1)),pylab.std(N.log10(y1)))
#pylab.axis([-26.,-12.,0.1,30.])
pylab.xlabel(r'$\rm{M_r}$',fontsize=28.)
pylab.ylabel(r'$\rm{A_r}$',fontsize=28.)
(xbin,ybin)=my.binit(x,y,20)
#(xbin,ybin,ybinerr)=my.biniterr(x,y,20)
for i in range(len(xbin)):
print i,xbin[i],ybin[i]
pylab.plot(xbin,ybin,'r-',lw=5)
ax=pylab.gca()
xmin=-24.
xmax=-18.
ymin=-1.
ymax=3.
my.contourf(x,y,xmin,xmax,ymin,ymax)
pylab.axvline(x=-20.6,linewidth=3,ls='--',c='g')
xl=N.arange(-23.,-20.5,.2)
yl=0.76*N.ones(len(xl),'f')
pylab.plot(xl,yl,'b-',lw=3)
pylab.axis([-24.,-18,-1.,2.4])
#ax.set_yscale('log')
#pylab.show()
pylab.savefig('armr.eps')
print "fraction w/MPA stellar mass and Az = ",N.sum(self.mpaflag)/(1.*len(self.mpaflag))
def biniterr(
x, y, n
): #bin arrays x, y into n equally-populated bins, returning xbin,ybin
nx = len(x)
#x=N.array(x,'f')
#y=N.array(y,'f')
y = N.take(y, N.argsort(x))
x = N.take(x, N.argsort(x))
xbin = N.zeros(n, 'f')
xbin = N.zeros(n, 'f')
ybin = N.zeros(n, 'f')
ybinerr = N.zeros(n, 'f')
for i in range(n):
nmin = i * int(float(nx) / float(n))
nmax = (i + 1) * int(float(nx) / float(n))
#xbin[i]=scipy.stats.stats.median(x[nmin:nmax])
#ybin[i]=scipy.stats.stats.median(y[nmin:nmax])
xbin[i] = N.average(x[nmin:nmax])
ybin[i] = N.average(y[nmin:nmax])
ybinerr[i] = pylab.std(y[nmin:nmax]) / N.sqrt(1. * (nmax - nmin))
return xbin, ybin, ybinerr
def calcCorrelationHelper(s1p, s2p):
# if the traits share less than six strains, then we don't
# bother with the correlations
if len(s1p) < 6:
return 0.0
# subtract by x-bar and y-bar elementwise
#oldS1P = s1p.copy()
#oldS2P = s2p.copy()
s1p = (s1p - numarray.average(s1p)).astype(numarray.Float64)
s2p = (s2p - numarray.average(s2p)).astype(numarray.Float64)
# square for the variances
s1p_2 = numarray.sum(s1p**2)
s2p_2 = numarray.sum(s2p**2)
try:
corr = (numarray.sum(s1p*s2p)/
numarray.sqrt(s1p_2 * s2p_2))
except ZeroDivisionError:
corr = 0.0
return corr
def binitsumequal(x, y, n): #bin arrays x, y into n bins, returning xbin,ybin
nx = len(x)
#x=N.array(x,'f')
#y=N.array(y,'f')
y = N.take(y, N.argsort(x))
x = N.take(x, N.argsort(x))
xbin = N.zeros(n, 'f')
ybin = N.zeros(n, 'f')
#ybinerr=N.zeros(n,'f')
for i in range(n):
nmin = i * int(float(nx) / float(n))
nmax = (i + 1) * int(float(nx) / float(n))
xbin[i] = N.average(x[nmin:nmax])
ybin[i] = N.sum(y[nmin:nmax])
#xbin[i]=N.average(x[nmin:nmax])
#ybin[i]=N.average(y[nmin:nmax])
#ybinerr[i]=scipy.stats.std(y[nmin:nmax])
return xbin, ybin #, ybinerr
def getsdssphotcats(self): #get photometric sources within 2R200
print "elapsed time = ",time.clock()-starttime
self.mcut=N.zeros(len(self.z),'f')
cl=N.arange(17,len(self.z),1)
self.nphot=N.zeros(len(self.z),'f')
self.nspec=N.zeros(len(self.z),'f')
for i in range(len(self.z)):
#for i in cl:
dL = self.dL[i]
print "getting phot cat for cluster abell",self.id[i]
r200arcmin=self.r200deg[i]*60.
#drsearch=2.*r200arcmin#2xR200 in arcmin for sdss query
drsearch=1.*r200arcmin#2xR200 in arcmin for sdss query
#Vg=0.3556-0.7614*((self.avegr)-0.6148)#(V-g) from Blanton et al 2003
mr=mabscut - 0.1331 + 5.*N.log10(dL)+25.+self.kcorr[i]
print i, self.z[i], dL, mr
self.mcut[i]=mr
print "ra, dec, dr, mr = %12.8f %12.8f %8.3f %5.2f" % (self.ra[i],self.dec[i],drsearch,mr)
#query="select g.ra, g.dec, g.u, g.g, g.r, g.i, g.z, g.plate_ID, g.MJD, from galaxy g, dbo.fGetNearbyObjEq(%12.8f,%12.8f,%8.3f) n where g.objID = n.objID and (g.g < %5.2f) and ((0.384*g.g + 0.716*g.r)< %5.2f)" % (self.ra[i],self.dec[i],drsearch,(mr+1.5),mr)
query="select g.ra, g.dec, g.u, g.g, g.r, g.i, g.z, g.objid, g.specObjID,g.extinction_u, g.extinction_g, g.extinction_r, g.extinction_i, g.extinction_z from galaxy g, dbo.fGetNearbyObjEq(%12.8f,%12.8f,%8.3f) n where g.objID = n.objID and (g.g < %5.2f) and (g.PrimTarget & 0x00000040) > 0 " % (self.ra[i],self.dec[i],drsearch,(mr))
lines=sqlcl.query(query).readlines()
#print query
print "got number+1 phot objects = ",len(lines)
#print lines
self.nphot[i]=1.*len(lines)
query="select g.ra, g.dec, g.u, g.g, g.r, g.i, g.z, g.objid, g.specObjID,g.extinction_u, g.extinction_g, g.extinction_r, g.extinction_i, g.extinction_z, l.ew, l.ewErr, l2.ew, l2.ewErr from galaxy g, specobj s, SpecLine l, SpecLine l2, dbo.fGetNearbyObjEq(%12.8f,%12.8f,%8.3f) n where g.objID = n.objID and g.objID = s.bestobjid and s.specobjID=l.specobjID and s.specobjID=l2.specobjID and (g.g < %5.2f) and (g.PrimTarget & 0x00000040) > 0 and l.LineID = 3727 and l2.LineID = 6565" % (self.ra[i],self.dec[i],drsearch,(mr))
lines=sqlcl.query(query).readlines()
#print query
print "got number+1 spec objects w/in R200= ",len(lines)
#print lines
self.nspec[i]=1.*len(lines)
self.compl=self.nspec/self.nphot
print "average completeness of sdss spectroscopy is = ",N.average(self.compl), pylab.std(self.compl)
def customCmp(traitPair, traitPair2):
magAvg1 = numarray.average(map(abs, traitPair[1]))
magAvg2 = numarray.average(map(abs, traitPair2[1]))
# invert the sign to get descending order
return -cmp(magAvg1, magAvg2)
import numarray as N
#check translation of ediscs ra and dec
era = []
edec = []
gra = []
gdec = []
infile1 = open('cl1018radec.dat', 'r')
for line in infile1:
t = line.split()
era.append(float(t[0]))
edec.append(float(t[1]))
infile1.close()
era = N.array(era, 'd')
edec = N.array(edec, 'd')
infile1 = open('cl1018-GMOSradec.dat', 'r')
for line in infile1:
t = line.split()
gra.append(float(t[0]))
gdec.append(float(t[1]))
infile1.close()
gra = N.array(gra, 'd')
gdec = N.array(gdec, 'd')
dr = N.sqrt((gra - era)**2 + (gdec - edec)**2)
dr = dr * 3600.
for i in range(len(dr)):
print dr[i]
print "Average difference = ", N.average(dr), max(dr), min(dr)
z = []
fHa = []
infile = open('targets', 'r')
for line in infile:
if line.find('#') > -1:
continue
t = line.split()
id.append(t[0])
z.append(float(t[6]))
fHa.append(float(t[5]))
z = N.array(z, 'f')
fHa = N.array(fHa, 'd') * 1.e-16 #f(HA) 1e-16 erg/s/cm2
dL = N.zeros(len(z), 'd')
for i in range(len(dL)):
dL[i] = my.dLcm(z[i], h)
LHa = fHa * 4. * N.pi * dL**2
sfr = LHa * 7.9e-42
print "id z SFR SFR/dL^2 f/(1Msun/yr@z=0.35) expt Ncycles (70um)"
for i in range(len(z)):
r = 1.e6 * sfr[i] / (my.dL(z[i], h)**2)
r2 = r / (3.12 / 11.5 * 10.)
exp = 1 / N.sqrt(r2)
print "%s %5.4f %5.1f %5.2f %5.2f %5.2f %2i" % (
id[i], z[i], sfr[i], r, r2, exp, round(10. * exp))
zmin = min(z)
zmax = max(z)
r = (my.dL(zmax, h) / my.dL(zmin, h))**2
print "(max dL/min dL)^2 = ", r
print "Average SFR = ", N.average(sfr)
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()
