#!/opt/local/bin/python
def run_caustic(self,
data,
gal_mags=None,
gal_memberflag=None,
clus_ra=None,
clus_dec=None,
clus_z=None,
gal_r=None,
gal_v=None,
r200=None,
clus_vdisp=None,
rlimit=4.0,
vlimit=3500,
q=10.0,
H0=100.0,
xmax=6.0,
ymax=5000.0,
cut_sample=True,
gapper=True,
mirror=True,
absflag=False):
self.clus_ra = clus_ra
self.clus_dec = clus_dec
self.clus_z = clus_z
if gal_r == None:
if self.clus_ra == None:
#calculate average ra from galaxies
self.clus_ra = np.average(data[:, 0])
if self.clus_dec == None:
#calculate average dec from galaxies
self.clus_dec = np.average(data[:, 1])
#Reduce data set to only valid redshifts
data_spec = data[np.where((np.isfinite(data[:, 2]))
& (data[:, 2] > 0.0)
& (data[:, 2] < 5.0))]
if self.clus_z == None:
#calculate average z from galaxies
self.clus_z = np.average(data_spec[:, 2])
#calculate angular diameter distance.
#Variable self.ang_d
self.ang_d, self.lum_d = self.zdistance(self.clus_z, H0)
#calculate the spherical angles of galaxies from cluster center.
#Variable self.angle
self.angle = self.findangle(data_spec[:, 0], data_spec[:, 1],
self.clus_ra, self.clus_dec)
self.r = self.angle * self.ang_d
self.v = c * (data_spec[:, 2] - self.clus_z) / (1 + self.clus_z)
else:
data_spec = data[np.where(np.isfinite(gal_v))]
self.r = gal_r
self.v = gal_v
#package galaxy data, USE ASTROPY TABLE HERE!!!!!
if gal_memberflag is None:
self.data_table = np.vstack((self.r, self.v, data_spec.T)).T
else:
self.data_table = np.vstack(
(self.r, self.v, data_spec.T, gal_memberflag)).T
#reduce sample within limits
if cut_sample == True:
self.data_set = self.set_sample(self.data_table,
rlimit=rlimit,
vlimit=vlimit)
else:
self.data_set = self.data_table
#further select sample via shifting gapper
if gapper == True:
self.data_set = self.shiftgapper(self.data_set)
print 'DATA SET SIZE', self.data_set[:, 0].size
'''
#tries to identify double groups that slip through the gapper process
upper_max = np.max(self.data_set[:,1][np.where((self.data_set[:,1]>0.0)&(self.data_set[:,0]<1.0))])
lower_max = np.min(self.data_set[:,1][np.where((self.data_set[:,1]<0.0)&(self.data_set[:,0]<1.0))])
if np.max(np.array([upper_max,-lower_max])) > 1000.0+np.min(np.array([upper_max,-lower_max])):
self.data_set = self.data_set[np.where(np.abs(self.data_set[:,1])<1000.0+np.min(np.array([upper_max,-lower_max])))]
'''
if absflag:
abs_mag = self.data_table[:, 5]
else:
abs_mag = self.data_table[:, 7] - magnitudes.distance_modulus(
self.clus_z, **fidcosmo)
self.Ngal_1mpc = self.r[np.where((abs_mag < -20.55) & (self.r < 1.0)
& (np.abs(self.v) < 3500))].size
if r200 == None:
self.r200 = 0.01 * self.Ngal_1mpc + 0.584 #+np.random.normal(0,0.099)
#vdisp_prelim = astStats.biweightScale(self.data_set[:,1][np.where(self.data_set[:,0]<3.0)],9.0)
#r200_mean_prelim = 0.002*vdisp_prelim + 0.40
#self.r200 = r200_mean_prelim/1.7
'''
#original r200 est
rclip,vclip = self.shiftgapper(np.vstack((self.r[np.where((self.r<3.0) & (np.abs(self.v)<3500.0))],self.v[np.where((self.r<3.0) & (np.abs(self.v)<3500.0))])).T).T
vdisp_prelim_1 = astStats.biweightClipped(vclip,9.0,3.0)['biweightScale']
rclip,vclip = self.shiftgapper(np.vstack((self.r[np.where((self.r<1.5) & (np.abs(self.v)<3500.0))],self.v[np.where((self.r<1.5) & (np.abs(self.v)<3500.0))])).T).T
vdisp_prelim_2 = astStats.biweightClipped(vclip,9.0,3.0)['biweightScale']
if vdisp_prelim_2 < 0.6*vdisp_prelim_1: vdisp_prelim = vdisp_prelim_2
else: vdisp_prelim = vdisp_prelim_1
r200_mean_prelim = 0.002*vdisp_prelim + 0.40
self.r200 = r200_mean_prelim/1.7
'''
if self.r200 > 3.0:
self.r200 = 3.0
if 3.0 * self.r200 < 6.0:
rlimit = 3.0 * self.r200
else:
rlimit = 5.5
else:
self.r200 = r200
print 'Pre_r200=', self.r200
if mirror == True:
print 'Calculating Density w/Mirrored Data'
self.gaussian_kernel(np.append(self.data_set[:, 0],
self.data_set[:, 0]),
np.append(self.data_set[:, 1],
-self.data_set[:, 1]),
self.r200,
normalization=H0,
scale=q,
xmax=xmax,
ymax=ymax,
xres=200,
yres=220)
else:
print 'Calculating Density'
self.gaussian_kernel(self.data_set[:, 0],
self.data_set[:, 1],
self.r200,
normalization=H0,
scale=q,
xmax=xmax,
ymax=ymax,
xres=200,
yres=220)
self.img_tot = self.img / np.max(np.abs(self.img))
self.img_grad_tot = self.img_grad / np.max(np.abs(self.img_grad))
self.img_inf_tot = self.img_inf / np.max(np.abs(self.img_inf))
if clus_vdisp is None:
self.pre_vdisp = 9.15 * self.Ngal_1mpc + 350.32
print 'Pre_vdisp=', self.pre_vdisp
print 'Ngal<1Mpc=', self.Ngal_1mpc
v_cut = self.data_set[:, 1][
np.where((self.data_set[:, 0] < self.r200)
& (np.abs(self.data_set[:, 1]) < 5000.0))]
try:
self.pre_vdisp2 = astStats.biweightScale(v_cut, 9.0)
except:
self.pre_vdisp2 = np.std(v_cut, ddof=1)
print 'Vdisp from galaxies=', self.pre_vdisp2
if self.data_set[:, 0].size < 15:
self.v_unc = 0.35
self.c_unc_sys = 0.75
self.c_unc_int = 0.35
elif self.data_set[:, 0].size < 25 and self.data_set[:,
0].size >= 15:
self.v_unc = 0.30
self.c_unc_sys = 0.55
self.c_unc_int = 0.22
elif self.data_set[:, 0].size < 50 and self.data_set[:,
0].size >= 25:
self.v_unc = 0.23
self.c_unc_sys = 0.42
self.c_unc_int = 0.16
elif self.data_set[:,
0].size < 100 and self.data_set[:,
0].size >= 50:
self.v_unc = 0.18
self.c_unc_sys = 0.34
self.c_unc_int = 0.105
else:
self.v_unc = 0.15
self.c_unc_sys = 0.29
self.c_unc_int = 0.09
if self.pre_vdisp2 > 1.75 * self.pre_vdisp:
self.pre_vdisp_comb = 9.15 * self.Ngal_1mpc + 450.32
else:
self.pre_vdisp_comb = self.pre_vdisp2
'''
if self.data_set[:,1][np.where(self.data_set[:,0]<self.r200)].size >= 10:
self.pre_vdisp_comb = astStats.biweightScale(self.data_set[:,1][np.where(self.data_set[:,0]<self.r200)],9.0)
else:
self.pre_vdisp_comb = np.std(self.data_set[:,1][np.where(self.data_set[:,0]<self.r200)],ddof=1)
#self.pre_vdisp_comb = (self.pre_vdisp*(self.pre_vdisp2*self.v_unc)**2+self.pre_vdisp2*118.14**2)/(118.14**2+(self.pre_vdisp2*self.v_unc)**2)
'''
else:
self.pre_vdisp_comb = clus_vdisp
print 'Combined Vdisp=', self.pre_vdisp_comb
self.beta = 0.5 * self.x_range / (self.x_range + self.r200 / 4.0)
#Identify initial caustic surface and members within the surface
print 'Calculating initial surface'
if gal_memberflag is None:
self.Caustics = CausticSurface(self.data_set,
self.x_range,
self.y_range,
self.img_tot,
r200=self.r200,
halo_vdisp=self.pre_vdisp_comb,
beta=None)
else:
self.Caustics = CausticSurface(self.data_set,
self.x_range,
self.y_range,
self.img_tot,
memberflags=self.data_set[:, -1],
r200=self.r200)
self.caustic_profile = self.Caustics.Ar_finalD
self.caustic_fit = self.Caustics.vesc_fit
self.gal_vdisp = self.Caustics.gal_vdisp
self.memflag = self.Caustics.memflag
#Estimate the mass based off the caustic profile, beta profile (if given), and concentration (if given)
if clus_z is not None:
#self.Mass = MassCalc(self.x_range,self.caustic_profile,self.gal_vdisp,self.clus_z,r200=self.r200,fbr=None,H0=H0)
#self.Mass2 = MassCalc(self.x_range,self.caustic_profile,self.gal_vdisp,self.clus_z,r200=self.r200,fbr=0.65,H0=H0)
self.Mass = MassCalc(self.x_range,
self.caustic_fit,
self.gal_vdisp,
self.clus_z,
r200=self.r200,
fbr=None,
H0=H0)
self.Mass2 = MassCalc(self.x_range,
self.caustic_fit,
self.gal_vdisp,
self.clus_z,
r200=self.r200,
fbr=0.65,
H0=H0)
self.r200_est = self.Mass.r200_est
self.r200_est_fbeta = self.Mass2.r200_est
self.M200_est = self.Mass.M200_est
self.M200_est_fbeta = self.Mass2.M200_est
print 'r200 estimate: ', self.Mass.r200_est
print 'M200 estimate: ', self.Mass.M200_est
self.Ngal = self.data_set[np.where((self.memflag == 1) & (
self.data_set[:, 0] <= self.r200_est))].shape[0]
#calculate velocity dispersion
try:
self.vdisp_gal = astStats.biweightScale(
self.data_set[:, 1][self.memflag == 1], 9.0)
except:
try:
self.vdisp_gal = np.std(self.data_set[:, 1][self.memflag == 1],
ddof=1)
except:
self.vdisp_gal = 0.0
'''
def __init__(self,data,gal_mags=None,gal_memberflag=None,clus_ra=None,clus_dec=None,clus_z=None,gal_r=None,gal_v=None,r200=None,clus_vdisp=None,rlimit=4.0,vlimit=3500,q=10.0,H0=100.0,xmax=6.0,ymax=5000.0,cut_sample=True,gapper=True,mirror=True,absflag=False):
self.clus_ra = clus_ra
self.clus_dec = clus_dec
self.clus_z = clus_z
if gal_r == None:
if self.clus_ra == None:
#calculate average ra from galaxies
self.clus_ra = np.average(data[:,0])
if self.clus_dec == None:
#calculate average dec from galaxies
self.clus_dec = np.average(data[:,1])
#Reduce data set to only valid redshifts
data_spec = data[np.where((np.isfinite(data[:,2])) & (data[:,2] > 0.0) & (data[:,2] < 5.0))]
if self.clus_z == None:
#calculate average z from galaxies
self.clus_z = np.average(data_spec[:,2])
#calculate angular diameter distance.
#Variable self.ang_d
self.ang_d,self.lum_d = self.zdistance(self.clus_z,H0)
#calculate the spherical angles of galaxies from cluster center.
#Variable self.angle
self.angle = self.findangle(data_spec[:,0],data_spec[:,1],self.clus_ra,self.clus_dec)
self.r = self.angle*self.ang_d
self.v = c*(data_spec[:,2] - self.clus_z)/(1+self.clus_z)
else:
data_spec = data[np.where(np.isfinite(gal_v))]
self.r = gal_r
self.v = gal_v
#package galaxy data, USE ASTROPY TABLE HERE!!!!!
if gal_memberflag is None:
self.data_table = np.vstack((self.r,self.v,data_spec.T)).T
else:
self.data_table = np.vstack((self.r,self.v,data_spec.T,gal_memberflag)).T
#reduce sample within limits
if cut_sample == True:
self.data_set = self.set_sample(self.data_table,rlimit=rlimit,vlimit=vlimit)
else:
self.data_set = self.data_table
#further select sample via shifting gapper
if gapper == True:
self.data_set = self.shiftgapper(self.data_set)
print 'DATA SET SIZE',self.data_set[:,0].size
'''
#tries to identify double groups that slip through the gapper process
upper_max = np.max(self.data_set[:,1][np.where((self.data_set[:,1]>0.0)&(self.data_set[:,0]<1.0))])
lower_max = np.min(self.data_set[:,1][np.where((self.data_set[:,1]<0.0)&(self.data_set[:,0]<1.0))])
if np.max(np.array([upper_max,-lower_max])) > 1000.0+np.min(np.array([upper_max,-lower_max])):
self.data_set = self.data_set[np.where(np.abs(self.data_set[:,1])<1000.0+np.min(np.array([upper_max,-lower_max])))]
'''
if absflag:
abs_mag = self.data_table[:,5]
else:
abs_mag = self.data_table[:,7] - magnitudes.distance_modulus(self.clus_z,**fidcosmo)
self.Ngal_1mpc = self.r[np.where((abs_mag < -20.55) & (self.r < 1.0) & (np.abs(self.v) < 3500))].size
if r200 == None:
self.r200 = 0.01*self.Ngal_1mpc+0.584#+np.random.normal(0,0.099)
#vdisp_prelim = astStats.biweightScale(self.data_set[:,1][np.where(self.data_set[:,0]<3.0)],9.0)
#r200_mean_prelim = 0.002*vdisp_prelim + 0.40
#self.r200 = r200_mean_prelim/1.7
'''
#original r200 est
rclip,vclip = self.shiftgapper(np.vstack((self.r[np.where((self.r<3.0) & (np.abs(self.v)<3500.0))],self.v[np.where((self.r<3.0) & (np.abs(self.v)<3500.0))])).T).T
vdisp_prelim_1 = astStats.biweightClipped(vclip,9.0,3.0)['biweightScale']
rclip,vclip = self.shiftgapper(np.vstack((self.r[np.where((self.r<1.5) & (np.abs(self.v)<3500.0))],self.v[np.where((self.r<1.5) & (np.abs(self.v)<3500.0))])).T).T
vdisp_prelim_2 = astStats.biweightClipped(vclip,9.0,3.0)['biweightScale']
if vdisp_prelim_2 < 0.6*vdisp_prelim_1: vdisp_prelim = vdisp_prelim_2
else: vdisp_prelim = vdisp_prelim_1
r200_mean_prelim = 0.002*vdisp_prelim + 0.40
self.r200 = r200_mean_prelim/1.7
'''
if self.r200 > 3.0:
self.r200 = 3.0
if 3.0*self.r200 < 6.0:
rlimit = 3.0*self.r200
else:
rlimit = 5.5
else:
self.r200 = r200
print 'Pre_r200=',self.r200
if mirror == True:
print 'Calculating Density w/Mirrored Data'
self.gaussian_kernel(np.append(self.data_set[:,0],self.data_set[:,0]),np.append(self.data_set[:,1],-self.data_set[:,1]),self.r200,normalization=H0,scale=q,xmax=xmax,ymax=ymax,xres=200,yres=220)
else:
print 'Calculating Density'
self.gaussian_kernel(self.data_set[:,0],self.data_set[:,1],self.r200,normalization=H0,scale=q,xmax=xmax,ymax=ymax,xres=200,yres=220)
self.img_tot = self.img/np.max(np.abs(self.img))
self.img_grad_tot = self.img_grad/np.max(np.abs(self.img_grad))
self.img_inf_tot = self.img_inf/np.max(np.abs(self.img_inf))
if clus_vdisp is None:
self.pre_vdisp = 9.15*self.Ngal_1mpc+350.32
print 'Pre_vdisp=',self.pre_vdisp
print 'Ngal<1Mpc=',self.Ngal_1mpc
v_cut = self.data_set[:,1][np.where((self.data_set[:,0]<self.r200) & (np.abs(self.data_set[:,1])<5000.0))]
try:
self.pre_vdisp2 = astStats.biweightScale(v_cut,9.0)
except:
self.pre_vdisp2 = np.std(v_cut,ddof=1)
print 'Vdisp from galaxies=',self.pre_vdisp2
if self.data_set[:,0].size < 15:
self.v_unc = 0.35
self.c_unc_sys = 0.75
self.c_unc_int = 0.35
elif self.data_set[:,0].size < 25 and self.data_set[:,0].size >= 15:
self.v_unc = 0.30
self.c_unc_sys = 0.55
self.c_unc_int = 0.22
elif self.data_set[:,0].size < 50 and self.data_set[:,0].size >= 25:
self.v_unc = 0.23
self.c_unc_sys = 0.42
self.c_unc_int = 0.16
elif self.data_set[:,0].size < 100 and self.data_set[:,0].size >= 50:
self.v_unc = 0.18
self.c_unc_sys = 0.34
self.c_unc_int = 0.105
else:
self.v_unc = 0.15
self.c_unc_sys = 0.29
self.c_unc_int = 0.09
if self.pre_vdisp2 > 1.75*self.pre_vdisp: self.pre_vdisp_comb = 9.15*self.Ngal_1mpc+450.32
else:
self.pre_vdisp_comb = self.pre_vdisp2
'''
if self.data_set[:,1][np.where(self.data_set[:,0]<self.r200)].size >= 10:
self.pre_vdisp_comb = astStats.biweightScale(self.data_set[:,1][np.where(self.data_set[:,0]<self.r200)],9.0)
else:
self.pre_vdisp_comb = np.std(self.data_set[:,1][np.where(self.data_set[:,0]<self.r200)],ddof=1)
#self.pre_vdisp_comb = (self.pre_vdisp*(self.pre_vdisp2*self.v_unc)**2+self.pre_vdisp2*118.14**2)/(118.14**2+(self.pre_vdisp2*self.v_unc)**2)
'''
else:
self.pre_vdisp_comb = clus_vdisp
print 'Combined Vdisp=',self.pre_vdisp_comb
self.beta = 0.5*self.x_range/(self.x_range + self.r200/4.0)
#Identify initial caustic surface and members within the surface
print 'Calculating initial surface'
if gal_memberflag is None:
self.Caustics = CausticSurface(self.data_set,self.x_range,self.y_range,self.img_tot,r200=self.r200,halo_vdisp=self.pre_vdisp_comb,beta=None)
else:
self.Caustics = CausticSurface(self.data_set,self.x_range,self.y_range,self.img_tot,memberflags=self.data_set[:,-1],r200=self.r200)
self.caustic_profile = self.Caustics.Ar_finalD
self.caustic_fit = self.Caustics.vesc_fit
self.gal_vdisp = self.Caustics.gal_vdisp
self.memflag = self.Caustics.memflag
#Estimate the mass based off the caustic profile, beta profile (if given), and concentration (if given)
if clus_z is not None:
#self.Mass = MassCalc(self.x_range,self.caustic_profile,self.gal_vdisp,self.clus_z,r200=self.r200,fbr=None,H0=H0)
#self.Mass2 = MassCalc(self.x_range,self.caustic_profile,self.gal_vdisp,self.clus_z,r200=self.r200,fbr=0.65,H0=H0)
self.Mass = MassCalc(self.x_range,self.caustic_fit,self.gal_vdisp,self.clus_z,r200=self.r200,fbr=None,H0=H0)
self.Mass2 = MassCalc(self.x_range,self.caustic_fit,self.gal_vdisp,self.clus_z,r200=self.r200,fbr=0.65,H0=H0)
self.r200_est = self.Mass.r200_est
self.r200_est_fbeta = self.Mass2.r200_est
self.M200_est = self.Mass.M200_est
self.M200_est_fbeta = self.Mass2.M200_est
print 'r200 estimate: ',self.Mass.r200_est
print 'M200 estimate: ',self.Mass.M200_est
self.Ngal = self.data_set[np.where((self.memflag==1)&(self.data_set[:,0]<=self.r200_est))].shape[0]
#calculate velocity dispersion
try:
self.vdisp_gal = astStats.biweightScale(self.data_set[:,1][self.memflag==1],9.0)
except:
try:
self.vdisp_gal = np.std(self.data_set[:,1][self.memflag==1],ddof=1)
except:
self.vdisp_gal = 0.0
'''
n_bulge float default -999,r_bulge float default -999,
m_bulge float default -999, ba_bulge float default -999,
pa_bulge float default -999,
r_disk float default -999,m_disk float default -999,
ba_disk float default -999,pa_disk float default -999);""".format(**all_info)
try:
print cmd
cursor.execute(cmd)
except:
pass
for model in all_info['model_list']:
for redshift in np.arange(all_info['z_range']['start'], all_info['z_range']['stop'], all_info['z_range']['scale']):
kpc_scale = distance.angular_diameter_distance(redshift, **all_info['cosmo'])*1000.0*np.pi/(180.0*3600.0) #kpc_per_arcsec
dismod = magnitudes.distance_modulus(redshift, **all_info['cosmo'])
print "redshift:%.2f, scale:%.1f, DM:%.1f" %(redshift, kpc_scale, dismod)
cmd = """insert into {dba}.{out_table} (model, galcount, name,
kpc_per_arcsec, dismod, zeropoint, z, BT, n_bulge, ba_bulge,
pa_bulge, ba_disk, pa_disk) select
'{model}', b.galcount, b.name,
{kpc_scale}, {dismod}, -1.0*c.aa_r-c.kk_r*c.airmass_r, {z},
b.BT, b.n, b.eb,b.bpa+90.0,
b.ed, b.dpa+90.0
from {dba}.CAST as c, {dba}.{in_table} as b, {dba}.DERT as d
where
d.galcount = b.galcount and b.galcount = c.galcount;""".format(model=model,
kpc_scale = kpc_scale, dismod = dismod, z=redshift, **all_info).format(model=model)
print cmd
cursor.execute(cmd)