def findnearest(self,ra1,dec1,ra2,dec2,delta):#measure distances from ra1, dec1 to members in catalog ra2, dec2
ramatch=my.findmatch(ra1,ra2,delta)
decmatch=my.findmatch(dec1,dec2,delta)
match=ramatch&decmatch
for i in range(len(ra1)):
angdist=my.DA(c.z[j],h100)#kpc/arcsec
dspec=N.sqrt((ra1[i]-ra2)**2+(dec1[i]-dec2)**2)#sorted array of distances in degrees
dspecsort=N.take(dspec,N.argsort(dspec))
sig5[i]=5./(N.pi)/(dspecsort[5]*3600.*angdist/1000.)**2#convert from deg to arcsec, multiply by DA (kpc/arcsec), divide by 1000 to convert to Mpc, index 5 element b/c 0 is itself
sig10[i]=10./(N.pi)/(dspecsort[10]*3600.*angdist/1000.)**2#convert from deg to arcsec, multiply by DA (kpc/arcsec), divide by 1000 to convert to Mpc, index 5 element b/c 0 is itself
return sig5, sig10
def matchha(self):#match coordinates with ha data
self.haflag=N.zeros(len(self.ra),'f')#flag for halpha
self.hamatch=N.zeros(len(self.ra),'i')
(x1sort,x1index)=my.sortwindex(gha.ra)
(x2sort,x2index)=my.sortwindex(gha.dec)
deltar=100.#matching tolerance in arcsec
deltar=deltar/3600.#convert to degrees
for i in range(len(self.ra)):
temp=[]
(temp,flag)=my.findmatch(self.ra[i],x1sort,deltar)#returns indices of sorted array
if flag < 1:
print i, "No match to Halpha data"
continue
templist1=temp
for j in range(len(temp)):
templist1[j]=x1index[temp[j]]
temp=[]
(temp,flag)=my.findmatch(self.dec[i],x2sort,deltar)
if flag < 1:
print i, "No match to Halpha data"
continue
templist2=temp
for j in range(len(temp)):
templist2[j]=x2index[temp[j]]
a=sets.Set(templist1)
b=sets.Set(templist2)
members=a&b
members=list(members)#contains all galaxies w/in a 4"x4" square
min=100.
for j in members:
d=N.sqrt((self.ra[i]-gha.ra[j])**2+(self.dec[i]-gha.dec[j])**2)
if d < min:
min=d
minindex=j
if min < 2.:
self.haflag[i]=1.
self.hamatch[i]=minindex
def getmemberids(self): #get galaxy members
self.membids = N.zeros(len(self.x1), 'd')
self.membflag = N.ones(len(self.x1),
'f') #flag to indicate if no members are found
self.membincut = N.zeros(len(self.x1), 'd')
self.membids = list(self.membids)
self.membdr = N.zeros(len(self.x1), 'd')
self.membdr = list(self.membdr)
nomemb = 0.
for i in range(len(self.x1)):
delta = nr #tolerance for matching
temp = []
mdv = []
mdr = []
distance = N.sqrt((self.x1[i] - g.x1)**2 + (self.x2[i] - g.x2)**2 +
(self.x3[i] - g.x3)**2) / self.r200[i]
(dsort, dindex) = my.sortwindex(distance)
(temp, matchflag) = my.findmatch(0., dsort, delta)
#templist1=temp
#for j in range(len(temp)):
# templist1[j]=dindex[temp[j]]
#membid=copy.copy(temp)
membid = []
dr = []
#print 'cdtheta = ',cdtheta
#print "temp=",temp
try:
for j in range(len(temp)):
membid.append(dindex[temp[j]])
#print j,temp[j],dindex[temp[j]],'temp=',temp
dr.append(distance[membid[j]])
self.membids[i] = membid
self.membincut[i] = len(membid)
self.membdr[i] = dr
except TypeError:
#if (self.membids[i]+999) < 1.:
nomemb = nomemb + 1.
#print "no members in cluster ",i,self.membids[i]
self.membincut[i] = 0
self.membflag[i] = 0.
continue
print nomemb, " clusters with no members", nomemb / len(self.x1)
def getobsmemberids(self): #get galaxies within nr*dr and nv*dv
#print "within getobsmemberids(), nv,nr,len(x1) = ",nv,nr,len(g.x1)
#print "within measurecontam(), nv,nr,len(x1) = ",nv,nr,len(g.x1),len(self.mass)
#print self.mass
self.obsmembids = N.zeros(len(self.x1), 'd')
self.obsmembflag = N.ones(len(self.x1), 'd')
self.obsmembids = list(self.obsmembids)
self.obsdr = N.zeros(len(self.x1), 'd')
self.obsdr = list(self.obsdr)
self.obsdv = N.zeros(len(self.x1), 'd')
self.obsdv = list(self.obsdv)
self.obsmembincut = N.zeros(len(self.x1), 'd')
nomemb = 0.
vbox = my.vofz(
zbox) #redshift of box center given epoch of observation
#i.e. recession velocity corresponding to z=0.2, 0.4, 0.6, etc of GIF observations
for i in range(len(self.x1)):
#self.obsmembids[i]=[]
vcl = H0 * self.x3[i]
deltar = nr * self.r200[i] #tolerance for matching
deltav = nv * self.sigma[i] #tolerance for matching
#print i,"cluster id, mass, r200, sigma = ",self.id[i],self.mass[i],self.r200[i],self.sigma[i],Mclmin
temp = []
#put cluster in middle of box
x1 = g.x1 - self.x1[i] + 0.5 * simL
#flip coordinates so galaxy x1 ranges from 0 to simL
for j in range(len(x1)):
if x1[j] > simL:
x1[j] = x1[j] - simL
if x1[j] < 0:
x1[j] = x1[j] + simL
x2 = g.x2 - self.x2[i] + 0.5 * simL
for j in range(len(x2)):
if x2[j] > simL:
x2[j] = x2[j] - simL
if x2[j] < 0:
x2[j] = x2[j] + simL
x3 = g.x3 - self.x3[i] + 0.5 * simL
for j in range(len(x3)):
if x3[j] > simL:
x3[j] = x3[j] - simL
if x3[j] < 0:
x3[j] = x3[j] + simL
gvobs = x3 * H0 + g.v3
gvobs = my.relsumv(vbox, gvobs)
gz = my.zofv(gvobs)
(x1sort, x1index) = my.sortwindex(x1)
(x2sort, x2index) = my.sortwindex(x2)
(temp, matchflag1) = my.findmatch(0.5 * simL, x1sort, deltar)
if (matchflag1 > 0):
templist1 = []
for j in range(len(temp)):
templist1.append(x1index[temp[j]])
temp = []
(temp, matchflag2) = my.findmatch(0.5 * simL, x2sort, deltar)
if (matchflag2 > 0):
templist2 = []
for j in range(len(temp)):
templist2.append(x2index[temp[j]])
if (matchflag1 + matchflag2) < 2.:
nomemb = nomemb + 1.
self.obsmembflag[i] = 0.
print "no observed members in cluster ", i
continue
a = sets.Set(templist1)
b = sets.Set(templist2)
members = a & b
members = list(members)
#templist1=N.argsort(templist1)
#templist2=N.argsort(templist2)
subset = []
odv = []
odr = []
ccvobs = my.relsumv(vbox, 0.5 * simL * H0)
ccz = my.zofv(ccvobs)
cdtheta = self.r200[i] / my.DA(ccz, h)
ccx1 = 0.5 * simL
ccx2 = 0.5 * simL
for k in members:
dr = (N.sqrt((x1[k] - ccx1)**2 +
(x2[k] - ccx2)**2)) / self.r200[i]
if dr < nr:
dv = (gvobs[k] - ccvobs) / (1 - gvobs[k] * ccvobs /
(9.e10)) / self.sigma[i]
if abs(dv) <= nv: #deltav:
gdtheta = dr * self.r200[i] / my.DA(gz[k], h)
if gdtheta <= cdtheta: #make sure galaxy is in cone field-of-view
subset.append(k)
odv.append(dv)
odr.append(dr)
self.obsmembids[i] = subset
self.obsdr[i] = odr
self.obsdv[i] = odv
self.obsmembincut[i] = len(subset)
if self.obsmembincut[i] < 1.:
nomemb = nomemb + 1.
self.obsmembflag[i] = 0.
print nomemb, " clusters with no observed members", nomemb / len(
self.x1)
caterrewha = N.array(caterrewha, 'f')
index = N.zeros(len(ra), 'i')
print "number of cluster galaxies = ", len(ra)
ntime = 0
print "sorting catalog ra array"
(catrasort, catraindex) = my.sortwindex(catra)
deltara = .01 #matching tolerance in ra in arcsec
deltara = deltara / 3600. * 15. #convert to degrees
print "matching cluster galaxies against dr2 catalog"
endloop = len(ra)
for i in range(len(ra)):
if 1. * i / 500. > ntime:
print i, "galaxies", mx.DateTime.localtime(), "nomatch = ", nomatch
ntime += 1
match = my.findmatch(
ra[i], catrasort,
deltara) #returns indices of catrasort that lie w/in deltara
#print "matched",i
try: #if more than one match, find closest in terms of ra,dec,z
if len(match) > 1:
diffmin = 1000000.
for j in range(len(match)):
catindex = catraindex[
match[j]] #catindex is for unsorted cat arrays
diff = N.sqrt((catra[catindex] - ra[i])**2 +
(catdec[catindex] - dec[i])**2 +
(catz[catindex] - z[i])**2)
if diff < diffmin:
diffmin = diff
catmatchindex = catindex
index[i] = catmatchindex