def getxyfromradec(im, ra, dec):
iraf.imgets(image=im, param='CRVAL1') #get RA of image in deg
t = iraf.imgets.value
RAcenter = float(t)
iraf.imgets(image=im, param='CRVAL2') #get RA of image
t = iraf.imgets.value
DECcenter = float(t)
dra = (RAcenter - ra) * N.cos(
N.pi / 180. * dec) #correct delta ra for cos declination
ddec = (dec - DECcenter)
#ra=ra*3600.#convert to arcsec
#dec=dec*3600.
#dra=N.array(dra,'f')
#ddec=N.array(ddec,'f')
iraf.imgets(image=im, param='CRPIX1') #get x value corresponding to RA
t = float(iraf.imgets.value)
xcenter = t
iraf.imgets(image=im, param='CRPIX2') #get y value corresponding to dec
t = float(iraf.imgets.value)
ycenter = t
iraf.imgets(image=im, param='CD1_1') #get x value corresponding to RA
xplate = abs(float(iraf.imgets.value)) #deg/pixel
iraf.imgets(image=im, param='CD2_2') #get x value corresponding to RA
yplate = abs(float(iraf.imgets.value)) #deg/pixel
#convert offsets to pixels on pisces at 90"
x = dra / xplate + xcenter
y = ddec / yplate + ycenter
return x, y
def xy2rd(self, pos):
"""
This method would apply the WCS keywords to a position to
generate a new sky position.
The algorithm comes directly from 'imgtools.xy2rd'
translate (x,y) to (ra, dec)
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'XY2RD only supported for TAN projections.'
raise TypeError
if isinstance(pos, N.NumArray):
# If we are working with an array of positions,
# point to just X and Y values
posx = pos[:, 0]
posy = pos[:, 1]
else:
# Otherwise, we are working with a single X,Y tuple
posx = pos[0]
posy = pos[1]
xi = self.cd11 * (posx - self.crpix1) + self.cd12 * (posy -
self.crpix2)
eta = self.cd21 * (posx - self.crpix1) + self.cd22 * (posy -
self.crpix2)
xi = DEGTORAD(xi)
eta = DEGTORAD(eta)
ra0 = DEGTORAD(self.crval1)
dec0 = DEGTORAD(self.crval2)
ra = N.arctan((xi / (N.cos(dec0) - eta * N.sin(dec0)))) + ra0
dec = N.arctan(
((eta * N.cos(dec0) + N.sin(dec0)) /
(N.sqrt((N.cos(dec0) - eta * N.sin(dec0))**2 + xi**2))))
ra = RADTODEG(ra)
dec = RADTODEG(dec)
ra = DIVMOD(ra, 360.)
# Otherwise, just return the RA,Dec tuple.
return ra, dec
def xy2rd(filename, x,y,hdu = -1, hms = 0, verbose = 0):
from numarray import sin,cos,arctan2,sqrt
def degtorad(num):
return num/180.0*numarray.pi
def radtodeg(num):
return num/numarray.pi * 180.0
def degtoHMS(num):
mmm = int((num-int(num))*60.0)
sss = ((num-int(num))*60.0-mmm)*60.0
num = `int(num)`+":"+ `mmm`+":"+`sss`
return num
keys = ["CRPIX1","CRPIX2","CRVAL1","CRVAL2","CD1_1","CD1_2","CD2_1","CD2_2"]
CD = read_keys(filename,keys,hdu)
crpix = numarray.zeros((2),numarray.Float)
cd = numarray.zeros((2,2),numarray.Float)
crpix[0] = CD['CRPIX1'][0]
crpix[1] = CD['CRPIX2'][0]
ra0 = CD['CRVAL1'][0]
dec0 = CD['CRVAL2'][0]
cd[0,0] = CD['CD1_1'][0]
cd[0,1] = CD['CD1_2'][0]
cd[1,0] = CD['CD2_1'][0]
cd[1,1] = CD['CD2_2'][0]
xi = cd[0, 0] * (x - crpix[0]) + cd[0, 1] * (y - crpix[1])
eta = cd[1, 0] * (x - crpix[0]) + cd[1, 1] * (y - crpix[1])
xi = degtorad(xi)
eta = degtorad(eta)
ra0 = degtorad(ra0)
dec0 = degtorad(dec0)
ra = arctan2(xi,cos(dec0)-eta*sin(dec0)) + ra0
dec = arctan2(eta*cos(dec0)+sin(dec0),sqrt((cos(dec0)-eta*sin(dec0))**2 + xi**2))
ra = radtodeg(ra)# % 360.0
# if ra < 0: ra = ra + 360.0
dec = radtodeg(dec)
if (hms):
return degtoHMS(ra/15.0),degtoHMS(dec)
else:
return ra,dec
def xy2rd(self,pos):
"""
This method would apply the WCS keywords to a position to
generate a new sky position.
The algorithm comes directly from 'imgtools.xy2rd'
translate (x,y) to (ra, dec)
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'XY2RD only supported for TAN projections.'
raise TypeError
if isinstance(pos,N.NumArray):
# If we are working with an array of positions,
# point to just X and Y values
posx = pos[:,0]
posy = pos[:,1]
else:
# Otherwise, we are working with a single X,Y tuple
posx = pos[0]
posy = pos[1]
xi = self.cd11 * (posx - self.crpix1) + self.cd12 * (posy - self.crpix2)
eta = self.cd21 * (posx - self.crpix1) + self.cd22 * (posy - self.crpix2)
xi = DEGTORAD(xi)
eta = DEGTORAD(eta)
ra0 = DEGTORAD(self.crval1)
dec0 = DEGTORAD(self.crval2)
ra = N.arctan((xi / (N.cos(dec0)-eta*N.sin(dec0)))) + ra0
dec = N.arctan( ((eta*N.cos(dec0)+N.sin(dec0)) /
(N.sqrt((N.cos(dec0)-eta*N.sin(dec0))**2 + xi**2))) )
ra = RADTODEG(ra)
dec = RADTODEG(dec)
ra = DIVMOD(ra, 360.)
# Otherwise, just return the RA,Dec tuple.
return ra,dec
def psf_select(alpha_j, delta_j):
distance = 9999.0
psffile = 'test.fits'
psflist = c.psflist
r = 3.14159265 / 180.0
for element in psflist:
p=pyfits.open(element)
header = p[0].header
if (header.has_key('RA_TARG')):
ra = header['RA_TARG']
if (header.has_key('DEC_TARG')):
dec= header['DEC_TARG']
p.close()
# d = sqrt((ra - alpha_j) ** 2.0 + (dec - delta_j) ** 2.0)
d = n.arccos(n.cos((90.0 - delta_j) * r) * n.cos((90.0 - dec) *\
r) + n.sin((90.0 - delta_j) * r) * n.sin((90.0 - dec) * r) * \
n.cos((alpha_j - ra) * r))
if(d < distance):
psffile = element
distance = d
return psffile, distance
def rd2xy(self, skypos, hour=no):
"""
This method would use the WCS keywords to compute the XY position
from a given RA/Dec tuple (in deg).
NOTE: Investigate how to let this function accept arrays as well
as single positions. WJH 27Mar03
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'RD2XY only supported for TAN projections.'
raise TypeError
det = self.cd11 * self.cd22 - self.cd12 * self.cd21
if det == 0.0:
raise ArithmeticError, "singular CD matrix!"
cdinv11 = self.cd22 / det
cdinv12 = -self.cd12 / det
cdinv21 = -self.cd21 / det
cdinv22 = self.cd11 / det
# translate (ra, dec) to (x, y)
ra0 = DEGTORAD(self.crval1)
dec0 = DEGTORAD(self.crval2)
if hour:
skypos[0] = skypos[0] * 15.
ra = DEGTORAD(skypos[0])
dec = DEGTORAD(skypos[1])
bottom = float(
N.sin(dec) * N.sin(dec0) +
N.cos(dec) * N.cos(dec0) * N.cos(ra - ra0))
if bottom == 0.0:
raise ArithmeticError, "Unreasonable RA/Dec range!"
xi = RADTODEG((N.cos(dec) * N.sin(ra - ra0) / bottom))
eta = RADTODEG((N.sin(dec) * N.cos(dec0) -
N.cos(dec) * N.sin(dec0) * N.cos(ra - ra0)) / bottom)
x = cdinv11 * xi + cdinv12 * eta + self.crpix1
y = cdinv21 * xi + cdinv22 * eta + self.crpix2
return x, y
def autoalign(images):
ra = []
dec = []
xcenter = []
ycenter = []
dx = []
dy = []
xpscale = []
ypscale = []
for im in images:
iraf.imgets(image=im, param='CRVAL1') #get RA of image in deg
t = iraf.imgets.value
ra.append(float(t))
iraf.imgets(image=im, param='CRPIX1') #get pix of RA
t = iraf.imgets.value
xcenter.append(float(t))
iraf.imgets(image=im, param='CD1_1') #get pix of RA
t = iraf.imgets.value
xpscale.append(float(t))
iraf.imgets(image=im, param='CRVAL2') #get RA of image
t = iraf.imgets.value
dec.append(float(t))
iraf.imgets(image=im, param='CRPIX2') #get RA of image
t = iraf.imgets.value
ycenter.append(float(t))
iraf.imgets(image=im, param='CD2_2') #get RA of image
t = iraf.imgets.value
ypscale.append(float(t))
dra = (ra - ra[0]) * N.cos(
N.pi / 180. * dec) #correct delta ra for cos declination
ddec = (dec - dec[0])
dx = dra / xpscale + (xcenter - xcenter[0])
dy = dra / ypscale + (ycenter - ycenter[0])
xshift = dx
yshift = -1. * dy
outfile = open('shifts', 'w')
for i in range(len(xshift)):
s = '%8.3f %8.3f \n' % (xshift[i], yshift[i])
outfile.write(s)
outfile.close()
def rd2xy(self,skypos,hour=no):
"""
This method would use the WCS keywords to compute the XY position
from a given RA/Dec tuple (in deg).
NOTE: Investigate how to let this function accept arrays as well
as single positions. WJH 27Mar03
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'RD2XY only supported for TAN projections.'
raise TypeError
det = self.cd11*self.cd22 - self.cd12*self.cd21
if det == 0.0:
raise ArithmeticError,"singular CD matrix!"
cdinv11 = self.cd22 / det
cdinv12 = -self.cd12 / det
cdinv21 = -self.cd21 / det
cdinv22 = self.cd11 / det
# translate (ra, dec) to (x, y)
ra0 = DEGTORAD(self.crval1)
dec0 = DEGTORAD(self.crval2)
if hour:
skypos[0] = skypos[0] * 15.
ra = DEGTORAD(skypos[0])
dec = DEGTORAD(skypos[1])
bottom = float(N.sin(dec)*N.sin(dec0) + N.cos(dec)*N.cos(dec0)*N.cos(ra-ra0))
if bottom == 0.0:
raise ArithmeticError,"Unreasonable RA/Dec range!"
xi = RADTODEG((N.cos(dec) * N.sin(ra-ra0) / bottom))
eta = RADTODEG((N.sin(dec)*N.cos(dec0) - N.cos(dec)*N.sin(dec0)*N.cos(ra-ra0)) / bottom)
x = cdinv11 * xi + cdinv12 * eta + self.crpix1
y = cdinv21 * xi + cdinv22 * eta + self.crpix2
return x,y
def rd2xy(filename,ra,dec,verbose=0,hdu=-1,hour=0):
from numarray import sin,cos,arctan2,sqrt
def degtorad(num):
return num/180.0*numarray.pi
def radtodeg(num):
return num/numarray.pi * 180.0
keys = ["CRPIX1","CRPIX2","CRVAL1","CRVAL2","CD1_1","CD1_2","CD2_1","CD2_2"]
CD = read_keys(filename,keys,hdu)
crpix = numarray.zeros((2),numarray.Float)
cd = numarray.zeros((2,2),numarray.Float)
cdinv = numarray.zeros((2,2),numarray.Float)
crpix[0] = CD['CRPIX1'][0]
crpix[1] = CD['CRPIX2'][0]
ra0 = CD['CRVAL1'][0]
dec0 = CD['CRVAL2'][0]
cd[0,0] = CD['CD1_1'][0]
cd[0,1] = CD['CD1_2'][0]
cd[1,0] = CD['CD2_1'][0]
cd[1,1] = CD['CD2_2'][0]
det = cd[0,0]*cd[1,1] - cd[0,1]*cd[1,0]
if det == 0:
raise SingularMatrix
cdinv[0,0] = cd[1,1] / det
cdinv[0,1] = -cd[0,1] / det
cdinv[1,0] = -cd[1,0] / det
cdinv[1,1] = cd[0,0] / det
print det,cdinv
ra0 = degtorad(ra0)
dec0 = degtorad(dec0)
if hour: ra=ra*15.0
ra = degtorad(ra)
dec = degtorad(dec)
bottom = sin(dec)*sin(dec0) + cos(dec)*cos(dec0)*cos(ra-ra0)
if bottom == 0:
raise InvalidRaDecRange
xi = cos(dec) * sin(ra-ra0) / bottom
eta = (sin(dec)*cos(dec0) - cos(dec)*sin(dec0)*cos(ra-ra0)) / bottom
xi = radtodeg(xi)
eta = radtodeg(eta)
x = cdinv[0, 0] * xi + cdinv[0, 1] * eta + crpix[0]
y = cdinv[1, 0] * xi + cdinv[1, 1] * eta + crpix[1]
return x,y
def mask(clus_id, line_s):
imagefile = c.imagefile
sex_cata = c.sex_cata
clus_cata = c.out_cata
threshold = c.threshold
thresh_area = c.thresh_area
size = c.size
mask_reg = c.mask_reg
x = n.reshape(n.arange(size*size),(size,size)) % size
x = x.astype(n.Float32)
y = n.reshape(n.arange(size*size),(size,size)) / size
y = y.astype(n.Float32)
values = line_s.split()
mask_file = 'ell_mask_' + str(imagefile)[:6] + '_' + str(clus_id) + '.fits'
xcntr_o = float(values[1]) #x center of the object
ycntr_o = float(values[2]) #y center of the object
xcntr = size / 2.0 + 1.0 + xcntr_o - int(xcntr_o)
ycntr = size / 2.0 + 1.0 + ycntr_o - int(ycntr_o)
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
mag_zero = c.mag_zero #magnitude zero point
sky = float(values[10]) #sky
pos_ang = float(values[11]) - 90.0 #position angle
axis_rat = 1.0 / float(values[12]) #axis ration b/a
major_axis = float(values[14]) #major axis of the object
z = n.zeros((size,size))
for line_j in open(sex_cata,'r'):
try:
values = line_j.split()
xcntr_n = float(values[1]) #x center of the neighbour
ycntr_n = float(values[2]) #y center of the neighbour
mag = float(values[7]) #Magnitude
radius = float(values[9]) #Half light radius
sky = float(values[10]) #sky
pos_ang = float(values[11]) #position angle
axis_rat = 1.0/float(values[12]) #axis ration b/a
si = n.sin(pos_ang * n.pi / 180.0)
co = n.cos(pos_ang * n.pi / 180.0)
area = float(values[13])
maj_axis = float(values[14])#major axis of neighbour
eg = 1.0 - axis_rat
one_minus_eg_sq = (1.0-eg)**2.0
if(abs(xcntr_n - xcntr_o) < size/2.0 and \
abs(ycntr_n - ycntr_o) < size/2.0 and \
xcntr_n != xcntr_o and ycntr_n != ycntr_o):
if((xcntr_o - xcntr_n) < 0):
xn = xcntr + abs(xcntr_n - xcntr_o)
if((ycntr_o - ycntr_n) < 0):
yn = ycntr + abs(ycntr_n - ycntr_o)
if((xcntr_o - xcntr_n) > 0):
xn = xcntr - (xcntr_o -xcntr_n)
if((ycntr_o - ycntr_n) > 0):
yn = ycntr - (ycntr_o -ycntr_n)
tx = (x - xn + 0.5) * co + (y - yn + 0.5) * si
ty = (xn - 0.5 -x) * si + (y - yn + 0.5) * co
R = n.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
z[n.where(R<=mask_reg*maj_axis)] = 1
except:
i=1
hdu = pyfits.PrimaryHDU(z.astype(n.Float32))
hdu.writeto(mask_file)
def HookModel( elevs , c , fsin , fcos ):
return c+ fsin * numarray.sin(elevs * math.pi / 180 ) + fcos * numarray.cos(elevs * math.pi / 180 )
def recenter(self):
"""
Reset the reference position values to correspond to the center
of the reference frame.
Algorithm used here developed by Colin Cox - 27-Jan-2004.
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'WCS.recenter() only supported for TAN projections.'
raise TypeError
# Check to see if WCS is already centered...
if self.crpix1 == self.naxis1 / 2. and self.crpix2 == self.naxis2 / 2.:
# No recentering necessary... return without changing WCS.
return
# This offset aligns the WCS to the center of the pixel, in accordance
# with the 'align=center' option used by 'drizzle'.
#_drz_off = -0.5
_drz_off = 0.
_cen = (self.naxis1 / 2. + _drz_off, self.naxis2 / 2. + _drz_off)
# Compute the RA and Dec for center pixel
_cenrd = self.xy2rd(_cen)
_cd = N.array([[self.cd11, self.cd12], [self.cd21, self.cd22]],
type=N.Float64)
_ra0 = DEGTORAD(self.crval1)
_dec0 = DEGTORAD(self.crval2)
_ra = DEGTORAD(_cenrd[0])
_dec = DEGTORAD(_cenrd[1])
# Set up some terms for use in the final result
_dx = self.naxis1 / 2. - self.crpix1
_dy = self.naxis2 / 2. - self.crpix2
_dE, _dN = DEGTORAD(N.dot(_cd, (_dx, _dy)))
_dE_dN = 1 + N.power(_dE, 2) + N.power(_dN, 2)
_cosdec = N.cos(_dec)
_sindec = N.sin(_dec)
_cosdec0 = N.cos(_dec0)
_sindec0 = N.sin(_dec0)
_n1 = N.power(_cosdec, 2) + _dE * _dE + _dN * _dN * N.power(_sindec, 2)
_dra_dE = (_cosdec0 - _dN * _sindec0) / _n1
_dra_dN = _dE * _sindec0 / _n1
_ddec_dE = -_dE * N.tan(_dec) / _dE_dN
_ddec_dN = (1 / _cosdec) * ((_cosdec0 / N.sqrt(_dE_dN)) -
(_dN * N.sin(_dec) / _dE_dN))
# Compute new CD matrix values now...
_cd11n = _cosdec * (self.cd11 * _dra_dE + self.cd21 * _dra_dN)
_cd12n = _cosdec * (self.cd12 * _dra_dE + self.cd22 * _dra_dN)
_cd21n = self.cd11 * _ddec_dE + self.cd21 * _ddec_dN
_cd22n = self.cd12 * _ddec_dE + self.cd22 * _ddec_dN
_new_orient = RADTODEG(N.arctan2(_cd12n, _cd22n))
# Update the values now...
self.crpix1 = _cen[0]
self.crpix2 = _cen[1]
self.crval1 = RADTODEG(_ra)
self.crval2 = RADTODEG(_dec)
# Keep the same plate scale, only change the orientation
self.rotateCD(_new_orient)
# These would update the CD matrix with the new rotation
# ALONG with the new plate scale which we do not want.
self.cd11 = _cd11n
self.cd12 = _cd12n
self.cd21 = _cd21n
self.cd22 = _cd22n
def recenter(self):
"""
Reset the reference position values to correspond to the center
of the reference frame.
Algorithm used here developed by Colin Cox - 27-Jan-2004.
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'WCS.recenter() only supported for TAN projections.'
raise TypeError
# Check to see if WCS is already centered...
if self.crpix1 == self.naxis1/2. and self.crpix2 == self.naxis2/2.:
# No recentering necessary... return without changing WCS.
return
# This offset aligns the WCS to the center of the pixel, in accordance
# with the 'align=center' option used by 'drizzle'.
#_drz_off = -0.5
_drz_off = 0.
_cen = (self.naxis1/2.+ _drz_off,self.naxis2/2. + _drz_off)
# Compute the RA and Dec for center pixel
_cenrd = self.xy2rd(_cen)
_cd = N.array([[self.cd11,self.cd12],[self.cd21,self.cd22]],type=N.Float64)
_ra0 = DEGTORAD(self.crval1)
_dec0 = DEGTORAD(self.crval2)
_ra = DEGTORAD(_cenrd[0])
_dec = DEGTORAD(_cenrd[1])
# Set up some terms for use in the final result
_dx = self.naxis1/2. - self.crpix1
_dy = self.naxis2/2. - self.crpix2
_dE,_dN = DEGTORAD(N.dot(_cd,(_dx,_dy)))
_dE_dN = 1 + N.power(_dE,2) + N.power(_dN,2)
_cosdec = N.cos(_dec)
_sindec = N.sin(_dec)
_cosdec0 = N.cos(_dec0)
_sindec0 = N.sin(_dec0)
_n1 = N.power(_cosdec,2) + _dE*_dE + _dN*_dN*N.power(_sindec,2)
_dra_dE = (_cosdec0 - _dN*_sindec0)/_n1
_dra_dN = _dE*_sindec0 /_n1
_ddec_dE = -_dE*N.tan(_dec) / _dE_dN
_ddec_dN = (1/_cosdec) * ((_cosdec0 / N.sqrt(_dE_dN)) - (_dN*N.sin(_dec) / _dE_dN))
# Compute new CD matrix values now...
_cd11n = _cosdec * (self.cd11*_dra_dE + self.cd21 * _dra_dN)
_cd12n = _cosdec * (self.cd12*_dra_dE + self.cd22 * _dra_dN)
_cd21n = self.cd11 * _ddec_dE + self.cd21 * _ddec_dN
_cd22n = self.cd12 * _ddec_dE + self.cd22 * _ddec_dN
_new_orient = RADTODEG(N.arctan2(_cd12n,_cd22n))
# Update the values now...
self.crpix1 = _cen[0]
self.crpix2 = _cen[1]
self.crval1 = RADTODEG(_ra)
self.crval2 = RADTODEG(_dec)
# Keep the same plate scale, only change the orientation
self.rotateCD(_new_orient)
# These would update the CD matrix with the new rotation
# ALONG with the new plate scale which we do not want.
self.cd11 = _cd11n
self.cd12 = _cd12n
self.cd21 = _cd21n
self.cd22 = _cd22n
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()