def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input: ', gpr.fil)
x0, y0, vlos = np.genfromtxt(gpr.fil,
skiprows=0,
unpack=True,
usecols=(0, 1, 5))
# use only 3000 random particles:
ind = np.arange(len(x0))
np.random.shuffle(ind)
ind = ind[:3000]
x0 = x0[ind]
y0 = y0[ind]
vlos = vlos[ind]
x0 *= 1000. # [pc]
y0 *= 1000. # [pc]
# shrinking sphere method
pm = np.ones(len(x0))
com_x, com_y, com_vz = gc.com_shrinkcircle_v_2D(x0, y0, vlos, pm)
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vlos -= com_vz #[km/s]
import gi_file as gf
for pop in range(2):
Rc = np.sqrt(x0**2 + y0**2) # [pc]
Rhalf = np.median(Rc) # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
gp.Xscale.append(Rscale) # [pc]
print('Rscale = ', Rscale, ' pc')
print('max(R) = ', max(Rc), ' pc')
print('total number of stars: ', len(Rc))
R0 = np.sqrt(x0**2 + y0**2) / Rscale
sel = (R0 < gp.maxR)
x = x0[sel] / Rscale
y = y0[sel] / Rscale # [Rscale]
vz = vlos[sel] # [km/s]
m = np.ones(len(x))
R = np.sqrt(x * x + y * y) * Rscale # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), np.median(R))
c = open(gp.files.get_com_file(pop), 'w')
print('# x [Xscale],','y [Xscale],','vLOS [km/s],','Xscale = ', \
Rscale, ' pc', file=c)
for k in range(len(x)):
print(x[k], y[k], vz[k], file=c) #[rscale], [rscale], [km/s]
c.close()
if gpr.showplots:
gpr.show_part_pos(x, y, np.ones(len(x)), Rscale)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gu.G1__pcMsun_1km2s_2 = 1. # as per definition
gp.anM = 1. #
gp.ana = 1. #
print('grh_com: input: ', gpr.simpos)
xall, yall, zall = np.loadtxt(gpr.simpos, skiprows=1, unpack=True) # 3*[gp.ana]
vxall,vyall,vzall= np.loadtxt(gpr.simvel, skiprows=1, unpack=True) # 3*[gp.ana]
nall = len(xall) # [1]
# shuffle and restrict to ntracer random points
ndm = int(min(gp.ntracer[0], nall-1))
trace = random.sample(range(nall), nall)
if gp.pops > 1:
gh.LOG(1, 'implement more than 2 pops for hern')
pdb.set_trace()
PM = [1. for i in trace] # [1]=const, no prob. of membership info in dataset
x = [ xall[i] for i in trace ] # [gp.ana]
y = [ yall[i] for i in trace ] # [gp.ana]
z = [ zall[i] for i in trace ] # [gp.ana]
vz = [ vzall[i] for i in trace ] # [km/s]
PM = np.array(PM); x=np.array(x); y=np.array(y); z=np.array(z); vz=np.array(vz)
com_x, com_y, com_z, com_vz = com_shrinkcircle_v(x,y,z,vz,PM) # 3*[gp.ana], [velocity]
print('COM [gp.ana]: ', com_x, com_y, com_z, com_vz)
xnew = (x-com_x) #*gp.ana # [pc]
ynew = (y-com_y) #*gp.ana # [pc]
#znew = (z-com_z) # *gp.ana # [pc]
vznew = (vz-com_vz) #*1e3*np.sqrt(gu.G1__pcMsun_1km2s_2*gp.anM/gp.ana) # [km/s], from conversion from system with L=G=M=1
R0 = np.sqrt(xnew**2+ynew**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
print('Rscale/pc = ', Rscale)
# only for 0 (all) and 1 (first and only population)
for pop in range(gp.pops+1):
crscale = open(gp.files.get_scale_file(pop),'w')
print('# Rscale in [pc],',' surfdens_central (=dens0) in [Munit/rscale**2],',\
' and totmass_tracers [Munit],',\
' and max(sigma_LOS) in [km/s]', file=crscale)
print(Rscale, file=crscale)
crscale.close()
gh.LOG(2, 'grh_com: output: ', gp.files.get_com_file(pop))
filepos = open(gp.files.get_com_file(pop), 'w')
print('# x [Rscale]','y [Rscale]','vLOS [km/s]', file=filepos)
for k in range(ndm):
print(xnew[k]/Rscale, ynew[k]/Rscale, vznew[k], file=filepos)
filepos.close()
gh.LOG(2, '')
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input: ', gpr.fil)
M0,x0,y0,z0,vx0, vy0, vz0, comp0 = read_data(gpr.fil)
# [Msun], 3*[pc], 3*[km/s], [1]
# assign population
if gp.pops==2:
pm1 = (comp0 == 1) # will be overwritten below if gp.metalpop
pm2 = (comp0 == 2) # same same
elif gp.pops==1:
pm1 = (comp0 < 3)
pm2 = (comp0 == -1) # assign none, but of same length as comp0
# cut to subsets
ind1 = gh.draw_random_subset(x1, gp.ntracer[1-1])
M1, x1, y1, z1, vx1, vy1, vz1, comp1 = select_pm(M1, x1, y1, z1, vx1, vy1, vz1, comp1, ind1)
ind2 = gh.draw_random_subset(x2, gp.ntracer[2-1])
M2, x2, y2, z2, vx2, vy2, vz2, comp2 = select_pm(M2, x2, y2, z2, vx2, vy2, vz2, comp2, ind2)
# use vz for no contamination, or vb for with contamination
M0, x0, y0, z0, vx0, vy0, vz0 = concat_pops(M1, M2, x1, x2, y1, y2, z1, z2, vx1, vx2, vy1, vy2, vz1, vz2, gp)
com_x, com_y, com_z, com_vz = com_shrinkcircle_v(x0, y0, z0, vz0, pm0) # [pc]
print('COM [pc]: ', com_x, com_y, com_z) # [pc]
print('VOM [km/s]', com_vz) # [km/s]
# from now on, work with 2D data only; z0 was only used to get
# center in (x,y) better
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vz0 -= com_vz # [km/s]
R0 = np.sqrt(x0**2+y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] from all tracer points
pop = -1
for pmn in [pm, pm1, pm2]:
pop = pop + 1 # population number
pmr = ( R0 < (gp.maxR*Rscale) ) # read max extension for data
#(rprior*Rscale) from
#gi_params
pmn = pmn*pmr # [1]
print("fraction of members = ", 1.0*sum(pmn)/len(pmn))
x, y, z, comp, vz, vb, Mg, PMN = select_pm(x0, y0, z0, comp0, vz0, vb0, Mg0, PM0, pmn)
R = np.sqrt(x*x+y*y) # [pc]
Rscalei = np.median(R)
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei)
gf.write_data_output(gp.files.get_com_file(pop), x/Rscalei, y/Rscalei, vz, Rscalei)
if gpr.showplots:
gpr.show_part_pos(x, y, pmn, Rscale)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input: ', gpr.fil)
x0,y0,vlos = np.genfromtxt(gpr.fil, skiprows=0, unpack = True,
usecols = (0,1,5))
# use only 3000 random particles:
ind = np.arange(len(x0))
np.random.shuffle(ind)
ind = ind[:3000]
x0 = x0[ind]; y0 = y0[ind]; vlos = vlos[ind]
x0 *= 1000. # [pc]
y0 *= 1000. # [pc]
# shrinking sphere method
pm = np.ones(len(x0))
com_x, com_y, com_vz = gc.com_shrinkcircle_v_2D(x0, y0, vlos, pm)
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vlos -= com_vz #[km/s]
import gi_file as gf
for pop in range(2):
Rc = np.sqrt(x0**2+y0**2) # [pc]
Rhalf = np.median(Rc) # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
gp.Xscale.append(Rscale) # [pc]
print('Rscale = ', Rscale,' pc')
print('max(R) = ', max(Rc),' pc')
print('total number of stars: ', len(Rc))
R0 = np.sqrt(x0**2+y0**2)/Rscale
sel = (R0 < gp.maxR)
x = x0[sel]/Rscale; y = y0[sel]/Rscale # [Rscale]
vz = vlos[sel] # [km/s]
m = np.ones(len(x))
R = np.sqrt(x*x+y*y)*Rscale # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), np.median(R))
c = open(gp.files.get_com_file(pop), 'w')
print('# x [Xscale],','y [Xscale],','vLOS [km/s],','Xscale = ', \
Rscale, ' pc', file=c)
for k in range(len(x)):
print(x[k], y[k], vz[k], file=c) #[rscale], [rscale], [km/s]
c.close()
if gpr.showplots:
gpr.show_part_pos(x, y, np.ones(len(x)), Rscale)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gpr.fil = gpr.dir + "/deBoer/table1.dat"
ALL = np.loadtxt(gpr.fil)
RAh = ALL[:, 0]
RAm = ALL[:, 1]
RAs = ALL[:, 2]
DEd = ALL[:, 3]
DEm = ALL[:, 4]
DEs = ALL[:, 5]
# that's all we read in for now. Crude assumptions: each star belongs to Fornax, and has mass 1Msun
# only use stars which are members of the dwarf
sig = abs(RAh[0]) / RAh[0]
RAh = RAh / sig
xs = 15 * (RAh * 3600 + RAm * 60 + RAs) * sig # [arcsec/15]
sig = abs(DEd[0]) / DEd[0]
DEd = DEd / sig
ys = (DEd * 3600 + DEm * 60 + DEs) * sig # [arcsec]
arcsec = 2. * np.pi / (360. * 60. * 60) # [pc]
kpc = 1000 # [pc]
DL = {
1: lambda x: x * (138), #+/- 8 for Fornax
2: lambda x: x * (101), #+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec * DL) # [pc]
ys *= (arcsec * DL) # [pc]
x0 = np.copy(xs)
y0 = np.copy(ys) # [pc]
com_x, com_y = com_shrinkcircle_2D(x0, y0) # [pc], [km/s]
# from now on, work with 2D data only; z0 was only used to get center in (x,y) better
# x0 -= com_x; y0 -= com_y # [pc]
# vz0 -= com_vz #[km/s]
R0 = np.sqrt(x0**2 + y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] overall
pop = 0
pmr = (R0 < (gp.maxR * Rscale)
) # read max extension for data (rprior*Rscale) from gi_params
x = 1. * x0[pmr]
y = 1. * y0[pmr]
R = np.sqrt(x * x + y * y) # [pc]
Rscalei = np.median(R) # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei) # [pc]
gf.write_data_output(gp.files.get_com_file(pop), x / Rscalei, y / Rscalei,
np.zeros(len(x)), Rscalei) # [pc]
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gpr.fil = gpr.dir+"/deBoer/table1.dat"
ALL = np.loadtxt(gpr.fil)
RAh = ALL[:,0]
RAm = ALL[:,1]
RAs = ALL[:,2]
DEd = ALL[:,3]
DEm = ALL[:,4]
DEs = ALL[:,5]
# that's all we read in for now. Crude assumptions: each star belongs to Fornax, and has mass 1Msun
# only use stars which are members of the dwarf
sig = abs(RAh[0])/RAh[0]
RAh = RAh/sig
xs = 15*(RAh*3600+RAm*60+RAs)*sig # [arcsec/15]
sig = abs(DEd[0])/DEd[0]
DEd = DEd/sig
ys = (DEd*3600+DEm*60+DEs)*sig # [arcsec]
arcsec = 2.*np.pi/(360.*60.*60) # [pc]
kpc = 1000 # [pc]
DL = {1: lambda x: x * (138),#+/- 8 for Fornax
2: lambda x: x * (101),#+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec*DL) # [pc]
ys *= (arcsec*DL) # [pc]
x0 = np.copy(xs)
y0 = np.copy(ys) # [pc]
com_x, com_y = com_shrinkcircle_2D(x0, y0) # [pc], [km/s]
# from now on, work with 2D data only; z0 was only used to get center in (x,y) better
# x0 -= com_x; y0 -= com_y # [pc]
# vz0 -= com_vz #[km/s]
R0 = np.sqrt(x0**2+y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] overall
pop = 0
pmr = (R0<(gp.maxR*Rscale)) # read max extension for data (rprior*Rscale) from gi_params
x=1.*x0[pmr]
y=1.*y0[pmr]
R = np.sqrt(x*x+y*y) # [pc]
Rscalei = np.median(R) # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei) # [pc]
gf.write_data_output(gp.files.get_com_file(pop), x/Rscalei, y/Rscalei, np.zeros(len(x)), Rscalei) # [pc]
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input:', gpr.fil)
x0, y0, z0, vx, vy, vz = np.transpose(np.loadtxt(gpr.fil))
# for purely tangential beta=-0.5 models, have units of kpc instead of pc
if gp.case == 9 or gp.case == 10:
x0 *= 1000. # [pc]
y0 *= 1000. # [pc]
z0 *= 1000. # [pc]
# cutting pm_i to a maximum of ntracers particles:
import gi_helper as gh
ind1 = gh.draw_random_subset(x0, gp.ntracer[1 - 1])
x0, y0, z0, vz0 = select_pm(x0, y0, z0, vz, ind1)
PM = np.ones(
len(x0)) # assign all particles the full probability of membership
import gi_centering as glc
com_x, com_y, com_z, com_vz = glc.com_shrinkcircle_v(x0, y0, z0, vz, PM)
# from now on, work with 2D data only;
# z0 was only used to get center in (x,y) better
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vz -= com_vz # [km/s]
R0 = np.sqrt(x0 * x0 + y0 * y0) # [pc]
Rscale = np.median(R0) # [pc]
import gi_file as gf
for pop in range(gp.pops + 1): # gp.pops +1 for all components together
pmr = (R0 < (gp.maxR * Rscale))
#m = np.ones(len(R0))
x = x0[pmr] # [pc]
y = y0[pmr] # [pc]
R = np.sqrt(x * x + y * y) # [pc]
Rscalei = np.median(R)
# print("x y z" on first line, to interprete data later on)
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei)
gf.write_data_output(gp.files.get_com_file(pop), x / Rscalei,
y / Rscalei, vz, Rscalei)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input:', gpr.fil)
x0, y0, z0, vx, vy, vz = np.transpose(np.loadtxt(gpr.fil))
# for purely tangential beta=-0.5 models, have units of kpc instead of pc
if gp.case == 9 or gp.case == 10:
x0 *= 1000. # [pc]
y0 *= 1000. # [pc]
z0 *= 1000. # [pc]
# cutting pm_i to a maximum of ntracers particles:
import gi_helper as gh
ind1 = gh.draw_random_subset(x0, gp.ntracer[1-1])
x0, y0, z0, vz0 = select_pm(x0, y0, z0, vz, ind1)
PM = np.ones(len(x0)) # assign all particles the full probability of membership
import gi_centering as glc
com_x, com_y, com_z, com_vz = glc.com_shrinkcircle_v(x0, y0, z0, vz, PM)
# from now on, work with 2D data only;
# z0 was only used to get center in (x,y) better
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vz -= com_vz # [km/s]
R0 = np.sqrt(x0*x0+y0*y0) # [pc]
Rscale = np.median(R0) # [pc]
import gi_file as gf
for pop in range(gp.pops+1): # gp.pops +1 for all components together
pmr = (R0<(gp.maxR*Rscale))
#m = np.ones(len(R0))
x = x0[pmr] # [pc]
y = y0[pmr] # [pc]
R = np.sqrt(x*x+y*y) # [pc]
Rscalei = np.median(R)
# print("x y z" on first line, to interprete data later on)
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei)
gf.write_data_output(gp.files.get_com_file(pop), x/Rscalei, y/Rscalei, vz, Rscalei)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
for pop in range(2):
# get radius, used for all binning
print('input: ', gp.files.get_com_file(pop))
if gf.bufcount(gp.files.get_com_file(pop))<2:
return
x,y,vlos = np.loadtxt(gp.files.get_com_file(pop), skiprows=1, unpack=True) #2*[rscale], [km/s]
# totmass_tracers = 1.*len(x) # [Munit], [Munit], where each star is weighted with the same mass
r = np.sqrt(x*x+y*y) # [rscale]
#set binning
#gp.nipol = (max - min)*N^(1/3)/(2*(Q3-Q1)) #(method of wand)
rmin = 0. # [rscale]
rmax = max(r) if gp.maxR < 0 else 1.0*gp.maxR # [rscale]
binmin, binmax, rbin = gh.determine_radius(r, rmin, rmax, gp) # [rscale0]
# offset from the start!
rs = gpr.Rerr*np.random.randn(len(r))+r #[rscale]
vlos = gpr.vrerr*np.random.randn(len(vlos))+vlos #[km/s]
vfil = open(gp.files.sigfiles[pop], 'w')
print('r', 'sigma_r(r)', 'error', file=vfil)
# 30 iterations for drawing a given radius in bin
dispvelocity = np.zeros((gp.nipol,gpr.n))
a = np.zeros((gp.nipol,gpr.n))
p_dvlos = np.zeros(gp.nipol)
p_edvlos = np.zeros(gp.nipol)
for k in range(gpr.n):
rsi = gpr.Rerr*np.random.randn(len(rs))+rs #[rscale]
vlosi = gpr.vrerr*np.random.randn(len(vlos))+vlos #[km/s]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(rsi>binmin[i],rsi<binmax[i])).flatten()
a[i][k] = len(ind1) #[1]
vlos1 = vlosi[ind1] #[km/s]
if(len(ind1)<=1):
dispvelocity[i][k] = dispvelocity[i-1][k]
# attention! should be 0, uses last value
else:
dispvelocity[i][k] = meanbiweight(vlos1,ci_perc=68.4,\
ci_mean=True,ci_std=True)[1]
# [km/s], see BiWeight.py
for i in range(gp.nipol):
dispvel = np.sum(dispvelocity[i])/gpr.n #[km/s]
ab = np.sum(a[i])/(1.*gpr.n) #[1]
if ab == 0:
dispvelerr = p_edvlos[i-1] #[km/s]
# attention! uses last error
else:
dispvelerr = dispvel/np.sqrt(ab) #[km/s]
p_dvlos[i] = dispvel #[km/s]
p_edvlos[i]= dispvelerr #[km/s]
maxsiglos = max(p_dvlos) #[km/s]
print('maxsiglos = ',maxsiglos,'[km/s]')
fpars = open(gp.files.get_scale_file(pop),'a')
print(maxsiglos, file=fpars) #[km/s]
fpars.close()
#import shutil
#shutil.copy2(gp.files.get_scale_file(0), gp.files.get_scale_file(1))
for i in range(gp.nipol):
# [rscale] [maxsiglos] [maxsiglos]
print(rbin[i], binmin[i], binmax[i], np.abs(p_dvlos[i]/maxsiglos),np.abs(p_edvlos[i]/maxsiglos), file=vfil) #/np.sqrt(n))
vfil.close()
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall,zall = np.loadtxt(gp.files.get_com_file(0),skiprows=1,\
usecols=(0,1,2),unpack=True) # 2*[rscale0]
rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)+'_3D')
xall *= rscale0
yall *= rscale0
zall *= rscale0
# calculate 3D
r = np.sqrt(xall**2+yall**2+zall**2) #[pc]
# set number and size of (linearly spaced) bins
rmin = 0. # [pc]
rmax = max(r) if gp.maxR < 0 else 1.0*gp.maxR # [pc]
print('rmax [rscale] = ', rmax)
r = r[(r<rmax)] # [pc]
binmin, binmax, rbin = gh.determine_radius(r, rmin, rmax, gp) # [pc]
vol = volume_spherical_shell(binmin, binmax, gp) # [pc^3]
for pop in range(gp.pops+1):
print('####### working on component ',pop)
print('input: ',gp.files.get_com_file(pop)+'_3D')
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop)+'_3D')<2: continue
x,y,z,v = np.loadtxt(gp.files.get_com_file(pop)+'_3D',\
skiprows=1,usecols=(0,1,2,3),unpack=True)
# 3*[rscale], [km/s]
rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
x *= rscalei
y *= rscalei
z *= rscalei
# calculate 2D radius on the skyplane
r = np.sqrt(x**2+y**2+z**2) # [pc]
# set maximum radius (if gp.maxR is set)
rmax = max(r) if gp.maxR<0 else 1.0*gp.maxR # [pc]
print('rmax [pc] = ', rmax)
sel = (r<=rmax)
x = x[sel]; y = y[sel]; z = z[sel]; v = v[sel]; r = r[sel] # [rscale]
totmass_tracers = 1.*len(x) # [Munit], Munit = 1/star
rs = r # + possible starting offset, [rscale]
vlos = v # + possible starting offset, [km/s]
gf.write_tracer_file(gp.files.get_ntracer_file(pop)+'_3D', totmass_tracers)
de, em = gf.write_headers_3D(gp, pop)
# gpr.n=30 iterations for getting random picked radius values
density = np.zeros((gp.nipol,gpr.n))
a = np.zeros((gp.nipol,gpr.n)) # shared by density, siglos, kappa calcs
for k in range(gpr.n):
rsi = gpr.Rerr * np.random.randn(len(rs)) + rs # [pc]
vlosi = gpr.vrerr*np.random.randn(len(vlos)) + vlos # [km/s]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(rsi>=binmin[i], rsi<binmax[i])).flatten() # [1]
density[i][k] = (1.*len(ind1))/vol[i]*totmass_tracers # [Munit/rscale^2]
vlos1 = vlosi[ind1] # [km/s]
a[i][k] = 1.*len(ind1) # [1]
dens0 = np.sum(density[0])/(1.*gpr.n) # [Munit/rscale^3]
print('dens0 = ',dens0,' [Munit/rscale^3]')
dens0pc = dens0/rscale0**3
gf.write_Sig_scale(gp.files.get_scale_file(pop)+'_3D', dens0pc, totmass_tracers)
tpb0 = np.sum(a[0])/float(gpr.n) # [1] tracers per bin
denserr0 = dens0/np.sqrt(tpb0) # [Munit/rscale^3]
p_dens = np.zeros(gp.nipol)
p_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
dens = np.sum(density[b])/float(gpr.n) # [Munit/rscale^3]
tpb = np.sum(a[b])/float(gpr.n) # [1]
denserr = dens/np.sqrt(tpb) # [Munit/rscale^3]
if(np.isnan(denserr)):
p_dens[b] = p_dens[b-1] # [1]
p_edens[b]= p_edens[b-1] # [1]
else:
p_dens[b] = dens/dens0 # [1]
p_edens[b]= denserr/dens0 # [1] #100/rbin would be artificial guess
print(rbin[b], binmin[b], binmax[b], p_dens[b], p_edens[b], file=de)
# [rscale], 2*[dens0]
indr = (r<binmax[b])
menclosed = float(np.sum(indr))/totmass_tracers # for normalization to 1
# [totmass_tracers]
merr = menclosed/np.sqrt(tpb) # artificial menclosed/10 # [totmass_tracers]
print(rbin[b], binmin[b], binmax[b], menclosed, merr, file=em)
# [rscale], 2*[totmass_tracers]
de.close()
em.close()
if gpr.showplots:
print('plotting for pop ', pop)
#show_plots_dens(rbin, p_dens, p_edens, gp)
mf1 = 0.02 #1/totmass_tracers
mf2 = 0.02
rho_dm, rho_star1, rho_star2 = ga.rho_walk(rbin*rscale0, gp, mf1, mf2)
if pop == 0:
loglog(rbin*rscale0, rho_star1+rho_star2, 'k.-', lw=0.5)
elif pop == 1:
loglog(rbin*rscale0, rho_star1, 'b.-', lw = 0.5)
elif pop == 2:
loglog(rbin*rscale0, rho_star2, 'g.-', lw = 0.5)
loglog(rbin*rscale0, dens0pc*p_dens, 'r.-')
pdb.set_trace()
clf()
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input: ', gpr.fil)
x0,y0,z0,vb0,vz0,Mg0,PM0,comp0 = read_data(gpr.fil)
# [pc], [km/s], [1]
# only use stars which are members of the dwarf: exclude pop3 by
# construction
pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination,
#outliers
x0, y0, z0, comp0, vb0, vz0, Mg0, PM0 = select_pm(x0, y0, z0, comp0, vb0, vz0, Mg0, PM0, pm)
# assign population
if gp.pops==2:
pm1 = (comp0 == 1) # will be overwritten below if gp.metalpop
pm2 = (comp0 == 2) # same same
elif gp.pops==1:
pm1 = (comp0 < 3)
pm2 = (comp0 == -1) # assign none, but of same length as comp0
if gp.metalpop:
# drawing of populations based on metallicity get parameters
# from function in pymcmetal.py
import pickle
fi = open('metalsplit.dat', 'rb')
DATA = pickle.load(fi)
fi.close()
p, mu1, sig1, mu2, sig2, M, pm1, pm2 = DATA
x1, y1, z1, comp1, vb1, vz1, Mg1, PM1 = select_pm(x0, y0, z0, comp0, vb0, vz0, Mg0, PM0, pm1)
x2, y2, z2, comp2, vb2, vz2, Mg2, PM2 = select_pm(x0, y0, z0, comp0, vb0, vz0, Mg0, PM0, pm2)
# cut to subsets
ind1 = gh.draw_random_subset(x1, gp.ntracer[1-1])
x1, y1, z1, comp1, vb1, vz1, Mg1, PM1 = select_pm(x1, y1, z1, comp1, vb1, vz1, Mg1, PM1, ind1)
ind2 = gh.draw_random_subset(x2, gp.ntracer[2-1])
x2, y2, z2, comp2, vb2, vz2, Mg2, PM2 = select_pm(x2, y2, z2, comp2, vb2, vz2, Mg2, PM2, ind2)
# use vz for no contamination, or vb for with contamination
x0, y0, z0, vz0, pm1, pm2, pm = concat_pops(x1, x2, y1, y2, z1, z2, vz1, vz2, gp)
com_x, com_y, com_z, com_vz = com_shrinkcircle_v(x0, y0, z0, vz0, pm) # [pc]
print('COM [pc]: ', com_x, com_y, com_z) # [pc]
print('VOM [km/s]', com_vz) # [km/s]
# from now on, work with 2D data only; z0 was only used to get
# center in (x,y) better
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vz0 -= com_vz # [km/s]
R0 = np.sqrt(x0**2+y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] from all tracer points
pop = -1
for pmn in [pm, pm1, pm2]:
pop = pop + 1 # population number
pmr = ( R0 < (gp.maxR*Rscale) ) # read max extension for data
#(rprior*Rscale) from
#gi_params
pmn = pmn*pmr # [1]
print("fraction of members = ", 1.0*sum(pmn)/len(pmn))
x, y, z, comp, vz, vb, Mg, PMN = select_pm(x0, y0, z0, comp0, vz0, vb0, Mg0, PM0, pmn)
R = np.sqrt(x*x+y*y) # [pc]
Rscalei = np.median(R)
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei)
gf.write_data_output(gp.files.get_com_file(pop), x/Rscalei, y/Rscalei, vz, Rscalei)
if gpr.showplots:
gpr.show_part_pos(x, y, pmn, Rscale)
def run(gp):
pop = 0
import gr_params
gpr = gr_params.grParams(gp)
xall, yall = np.loadtxt(gp.files.get_com_file(0),
skiprows=1,
usecols=(0, 1),
unpack=True)
# 2*[Rscale0]
R = np.sqrt(xall**2 + yall**2) # [Rscale0]
# set number and size of (linearly spaced) bins
Rmin = 0. # [Rscale0]
Rmax = max(R) if gp.maxR < 0 else 1.0 * gp.maxR # [Rscale0]
R = R[(R < Rmax)] # [Rscale0]
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack(
[minr / 8., minr / 4., minr / 2., Rbin, 2 * maxr, 4 * maxr,
8 * maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
Rscale0 = float(gf.read_Xscale(gp.files.get_scale_file(0))) # [pc]
print('####### working on component ', pop)
print('input: ', gp.files.get_com_file(pop))
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop)) < 2:
return
# only read in data if needed: pops = 1: reuse data from pop=0 part
x, y = np.loadtxt(gp.files.get_com_file(pop),
skiprows=1,
usecols=(0, 1),
unpack=True)
# [Rscalei], [Rscalei]
# calculate 2D radius on the skyplane
R = np.sqrt(x**2 + y**2) #[Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set maximum radius (if gp.maxR is set)
Rmax = max(R) if gp.maxR < 0 else 1.0 * gp.maxR # [Rscale0]
print('Rmax [Rscale0] = ', Rmax)
sel = (R * Rscalei <= Rmax * Rscale0)
x = x[sel] # [Rscalei]
y = y[sel] # [Rscalei]
R = R[sel] # [Rscalei]
totmass_tracers = float(len(x)) # [Munit], Munit = 1/star
Rs = R # + possible starting offset, [Rscalei]
tr = open(gp.files.get_ntracer_file(pop), 'w')
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
Sig_phot = np.zeros((gp.nipol, gpr.n))
# particle selections, shared by density, siglos, kappa and zeta calculations
tpb = np.zeros((gp.nipol, gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi * Rscalei >= Binmin[i] * Rscale0, \
Rsi * Rscalei < Binmax[i] * Rscale0)).flatten() # [1]
tpb[i][k] = float(len(ind1)) #[1]
Sig_phot[i][k] = float(
len(ind1)) * totmass_tracers / Vol[i] # [Munit/rscale^2]
# do the following for all populations
Sig0 = np.sum(Sig_phot[0]) / float(gpr.n) # [Munit/Rscale^2]
Sig0pc = Sig0 / Rscale0**2 # [munis/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Sig0pc, totmass_tracers)
# calculate density and mass profile, store it
# ----------------------------------------------------------------------
P_dens = np.zeros(gp.nipol)
P_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
Sig = np.sum(Sig_phot[b]) / (1. * gpr.n) # [Munit/Rscale^2]
tpbb = np.sum(tpb[b]) / float(
gpr.n) # [1], mean number of tracers in bin
Sigerr = Sig / np.sqrt(tpbb) # [Munit/Rscale^2], Poissonian error
# compare data and analytic profile <=> get stellar
# density or mass ratio from Matt Walker
if (np.isnan(Sigerr)):
P_dens[b] = P_dens[b - 1] # [1]
P_edens[b] = P_edens[b - 1] # [1]
else:
P_dens[b] = Sig / Sig0 # [1]
P_edens[b] = Sigerr / Sig0 # [1]
print(Rbin[b], Binmin[b], Binmax[b], P_dens[b], P_edens[b], file=f_Sig)
# 3*[rscale], [dens0], [dens0]
indr = (R < Binmax[b])
Menclosed = float(
np.sum(indr)
) / totmass_tracers # for normalization to 1#[totmass_tracers]
Merr = Menclosed / np.sqrt(
tpbb) # or artificial Menclosed/10 #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], Menclosed, Merr,
file=f_mass) # [Rscale0], 2* [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin * Rscalei, Sig0pc * P_dens, gp)
#numin = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens-P_edens), gp)
numax = gip.Sig_INT_rho(Rbin * Rscalei, Sig0pc * (P_dens + P_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax - numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b],\
numedi[b]/nu0pc, nuerr[b]/nu0pc, \
file = f_nu)
f_nu.close()
# write dummy sig scale, not to be used later on
maxsiglos = -1. #[km/s]
fpars = open(gp.files.get_scale_file(pop), 'a')
print(maxsiglos, file=fpars) #[km/s]
fpars.close()
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gu.G1__pcMsun_1km2s_2 = 1. # as per definition
gp.anM = 1. #
gp.ana = 1. #
print('grh_com: input: ', gpr.simpos)
xall, yall, zall = np.loadtxt(gpr.simpos, skiprows=1,
unpack=True) # 3*[gp.ana]
vxall, vyall, vzall = np.loadtxt(gpr.simvel, skiprows=1,
unpack=True) # 3*[gp.ana]
nall = len(xall) # [1]
# shuffle and restrict to ntracer random points
ndm = int(min(gp.ntracer[0], nall - 1))
trace = random.sample(range(nall), nall)
if gp.pops > 1:
gh.LOG(1, 'implement more than 2 pops for hern')
pdb.set_trace()
PM = [1.
for i in trace] # [1]=const, no prob. of membership info in dataset
x = [xall[i] for i in trace] # [gp.ana]
y = [yall[i] for i in trace] # [gp.ana]
z = [zall[i] for i in trace] # [gp.ana]
vz = [vzall[i] for i in trace] # [km/s]
PM = np.array(PM)
x = np.array(x)
y = np.array(y)
z = np.array(z)
vz = np.array(vz)
com_x, com_y, com_z, com_vz = com_shrinkcircle_v(
x, y, z, vz, PM) # 3*[gp.ana], [velocity]
print('COM [gp.ana]: ', com_x, com_y, com_z, com_vz)
xnew = (x - com_x) #*gp.ana # [pc]
ynew = (y - com_y) #*gp.ana # [pc]
#znew = (z-com_z) # *gp.ana # [pc]
vznew = (
vz - com_vz
) #*1e3*np.sqrt(gu.G1__pcMsun_1km2s_2*gp.anM/gp.ana) # [km/s], from conversion from system with L=G=M=1
R0 = np.sqrt(xnew**2 + ynew**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
print('Rscale/pc = ', Rscale)
# only for 0 (all) and 1 (first and only population)
for pop in range(gp.pops + 1):
crscale = open(gp.files.get_scale_file(pop), 'w')
print('# Rscale in [pc],',' surfdens_central (=dens0) in [Munit/rscale**2],',\
' and totmass_tracers [Munit],',\
' and max(sigma_LOS) in [km/s]', file=crscale)
print(Rscale, file=crscale)
crscale.close()
gh.LOG(2, 'grh_com: output: ', gp.files.get_com_file(pop))
filepos = open(gp.files.get_com_file(pop), 'w')
print('# x [Rscale]', 'y [Rscale]', 'vLOS [km/s]', file=filepos)
for k in range(ndm):
print(xnew[k] / Rscale, ynew[k] / Rscale, vznew[k], file=filepos)
filepos.close()
gh.LOG(2, '')
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gpr.fil = gpr.dir+"/data/tracers.dat"
A = np.loadtxt(gpr.fil, skiprows=25)
RAh,RAm,RAs,DEd,DEm,DEs,Vlos,e_Vlos,Teff,e_Teff,logg,e_logg,Fe,e_Fe,Nobs = A.T
# only use stars which have Mg measurements
pm = (Nobs>0) # (PM>=0.95)*
print("f_members = ", gh.pretty(1.*sum(pm)/len(pm)))
RAh=RAh[pm]
RAm=RAm[pm]
RAs=RAs[pm]
DEd=DEd[pm]
DEm=DEm[pm]
DEs=DEs[pm]
Vlos=Vlos[pm]
e_Vlos=e_Vlos[pm]
Teff=Teff[pm]
e_Teff=e_Teff[pm]
logg=logg[pm]
e_logg=e_logg[pm]
Fe=Fe[pm]
e_Fe=e_Fe[pm]
Nobs = Nobs[pm]
sig = abs(RAh[0])/RAh[0]
#print('RAh: signum = ',gh.pretty(sig))
RAh = RAh/sig
xs = 15*(RAh*3600+RAm*60+RAs)*sig # [arcsec/15]
sig = abs(DEd[0])/DEd[0]
#print('DEd: signum = ', gh.pretty(sig))
DEd = DEd/sig
ys = (DEd*3600+DEm*60+DEs)*sig # [arcsec]
arcsec = 2.*np.pi/(360.*60.*60) # [pc]
kpc = 1000 # [pc]
DL = {1: lambda x: x * (138),#+/- 8 for Fornax
2: lambda x: x * (101),#+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec*DL) # [pc]
ys *= (arcsec*DL) # [pc]
x0 = np.copy(xs)
y0 = np.copy(ys) # [pc]
vz0 = np.copy(Vlos) # [km/s]
Fe0 = np.copy(Fe)
# only use stars which are members of the dwarf: exclude pop3 by construction
#pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination, outliers
#x0, y0, vz0, Mg0, PM0 = select_pm(x0, y0, vz0, Mg0, PM0, pm)
# assign population
if gp.pops == 2:
# drawing of populations based on metallicity
# get parameters from function in pymcmetal.py
#[p, mu1, sig1, mu2, sig2] = np.loadtxt(gp.files.dir+'metalsplit.dat')
#[pm1, pm2] = np.loadtxt(gp.files.dir+'metalsplit_assignment.dat')
popass = np.loadtxt(gp.files.dir+'popass')
pm1 = (popass==1)
pm2 = (popass==2)
elif gp.pops == 1:
pm1 = (Teff >= 0)
pm2 = (Teff < 0) # assign none, but of same length as xs
x1, y1, vz1, Fe1, PM1 = select_pm(x0, y0, vz0, Fe, pm, pm1)
x2, y2, vz2, Fe2, PM2 = select_pm(x0, y0, vz0, Fe, pm, pm2)
# cutting pm_i to a maximum of ntracers_i particles each:
ind1 = np.arange(len(x1))
np.random.shuffle(ind1) # random.shuffle already changes ind
ind1 = ind1[:gp.ntracer[1-1]]
ind2 = np.arange(len(x2))
np.random.shuffle(ind2) # random.shuffle already changes ind
ind2 = ind2[:gp.ntracer[2-1]]
x1, y1, vz1, Fe1, PMS1 = select_pm(x1, y1, vz1, Fe1, PM1, ind1)
x2, y2, vz2, Fe2, PMS2 = select_pm(x2, y2, vz2, Fe2, PM2, ind2)
x0, y0, vz0, pm1, pm2, pm = concat_pops(x1, x2, y1, y2, vz1, vz2, gp)
# optimum: get 3D center of mass with means
# com_x, com_y, com_z = com_mean(x0,y0,z0,PM0) # 3*[pc], z component included if available
com_x, com_y, com_vz = com_shrinkcircle_v_2D(x0, y0, vz0, pm) # [pc], [km/s]
# from now on, work with 2D data only; z0 was only used to get center in (x,y) better
# x0 -= com_x; y0 -= com_y # [pc]
# vz0 -= com_vz #[km/s]
R0 = np.sqrt(x0**2+y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] overall
pop = -1
for pmn in [pm, pm1, pm2]:
pop = pop+1
pmr = (R0<(gp.maxR*Rscale)) # read max extension for data
# (rprior*Rscale) from gi_params
pmn = pmn*pmr # [1]
print("fraction of members = ", 1.0*sum(pmn)/len(pmn))
x, y, vz, Fe, PMN = select_pm(x0, y0, vz0, Fe0, pm, pmn)
R = np.sqrt(x*x+y*y) # [pc]
Rscalei = np.median(R) # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei) # [pc]
gf.write_data_output(gp.files.get_com_file(pop), x/Rscalei, y/Rscalei, vz, Rscalei) # [pc]
if gpr.showplots:
gpr.show_part_pos(x, y, pmn, Rscale)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall,zall = np.loadtxt(gp.files.get_com_file(0),skiprows=1,\
usecols=(0,1,2),unpack=True) # 2*[rscale0]
rscale0 = gf.read_Xscale(gp.files.get_scale_file(0) + '_3D')
xall *= rscale0
yall *= rscale0
zall *= rscale0
# calculate 3D
r = np.sqrt(xall**2 + yall**2 + zall**2) #[pc]
# set number and size of (linearly spaced) bins
rmin = 0. # [pc]
rmax = max(r) if gp.maxR < 0 else 1.0 * gp.maxR # [pc]
print('rmax [rscale] = ', rmax)
r = r[(r < rmax)] # [pc]
binmin, binmax, rbin = gh.determine_radius(r, rmin, rmax, gp) # [pc]
vol = volume_spherical_shell(binmin, binmax, gp) # [pc^3]
for pop in range(gp.pops + 1):
print('####### working on component ', pop)
print('input: ', gp.files.get_com_file(pop) + '_3D')
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop) + '_3D') < 2: continue
x,y,z,v = np.loadtxt(gp.files.get_com_file(pop)+'_3D',\
skiprows=1,usecols=(0,1,2,3),unpack=True)
# 3*[rscale], [km/s]
rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
x *= rscalei
y *= rscalei
z *= rscalei
# calculate 2D radius on the skyplane
r = np.sqrt(x**2 + y**2 + z**2) # [pc]
# set maximum radius (if gp.maxR is set)
rmax = max(r) if gp.maxR < 0 else 1.0 * gp.maxR # [pc]
print('rmax [pc] = ', rmax)
sel = (r <= rmax)
x = x[sel]
y = y[sel]
z = z[sel]
v = v[sel]
r = r[sel] # [rscale]
totmass_tracers = 1. * len(x) # [Munit], Munit = 1/star
rs = r # + possible starting offset, [rscale]
vlos = v # + possible starting offset, [km/s]
gf.write_tracer_file(
gp.files.get_ntracer_file(pop) + '_3D', totmass_tracers)
de, em = gf.write_headers_3D(gp, pop)
# gpr.n=30 iterations for getting random picked radius values
density = np.zeros((gp.nipol, gpr.n))
a = np.zeros(
(gp.nipol, gpr.n)) # shared by density, siglos, kappa calcs
for k in range(gpr.n):
rsi = gpr.Rerr * np.random.randn(len(rs)) + rs # [pc]
vlosi = gpr.vrerr * np.random.randn(len(vlos)) + vlos # [km/s]
for i in range(gp.nipol):
ind1 = np.argwhere(
np.logical_and(rsi >= binmin[i],
rsi < binmax[i])).flatten() # [1]
density[i][k] = (
1. *
len(ind1)) / vol[i] * totmass_tracers # [Munit/rscale^2]
vlos1 = vlosi[ind1] # [km/s]
a[i][k] = 1. * len(ind1) # [1]
dens0 = np.sum(density[0]) / (1. * gpr.n) # [Munit/rscale^3]
print('dens0 = ', dens0, ' [Munit/rscale^3]')
dens0pc = dens0 / rscale0**3
gf.write_Sig_scale(
gp.files.get_scale_file(pop) + '_3D', dens0pc, totmass_tracers)
tpb0 = np.sum(a[0]) / float(gpr.n) # [1] tracers per bin
denserr0 = dens0 / np.sqrt(tpb0) # [Munit/rscale^3]
p_dens = np.zeros(gp.nipol)
p_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
dens = np.sum(density[b]) / float(gpr.n) # [Munit/rscale^3]
tpb = np.sum(a[b]) / float(gpr.n) # [1]
denserr = dens / np.sqrt(tpb) # [Munit/rscale^3]
if (np.isnan(denserr)):
p_dens[b] = p_dens[b - 1] # [1]
p_edens[b] = p_edens[b - 1] # [1]
else:
p_dens[b] = dens / dens0 # [1]
p_edens[
b] = denserr / dens0 # [1] #100/rbin would be artificial guess
print(rbin[b],
binmin[b],
binmax[b],
p_dens[b],
p_edens[b],
file=de)
# [rscale], 2*[dens0]
indr = (r < binmax[b])
menclosed = float(
np.sum(indr)) / totmass_tracers # for normalization to 1
# [totmass_tracers]
merr = menclosed / np.sqrt(
tpb) # artificial menclosed/10 # [totmass_tracers]
print(rbin[b], binmin[b], binmax[b], menclosed, merr, file=em)
# [rscale], 2*[totmass_tracers]
de.close()
em.close()
if gpr.showplots:
print('plotting for pop ', pop)
#show_plots_dens(rbin, p_dens, p_edens, gp)
mf1 = 0.02 #1/totmass_tracers
mf2 = 0.02
rho_dm, rho_star1, rho_star2 = ga.rho_walk(rbin * rscale0, gp, mf1,
mf2)
if pop == 0:
loglog(rbin * rscale0, rho_star1 + rho_star2, 'k.-', lw=0.5)
elif pop == 1:
loglog(rbin * rscale0, rho_star1, 'b.-', lw=0.5)
elif pop == 2:
loglog(rbin * rscale0, rho_star2, 'g.-', lw=0.5)
loglog(rbin * rscale0, dens0pc * p_dens, 'r.-')
pdb.set_trace()
clf()
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
global Nsample, split, e_split, PM, split_min, split_max
gpr.fil = gpr.dir + "data/tracers.dat"
# number of measured tracer stars
Nsample = bufcount(gpr.fil)
delim = [0, 22, 3, 3, 6, 4, 3, 5, 6, 6, 7, 5, 6, 5, 6, 5, 6]
#ID = np.genfromtxt(gpr.fil,skiprows=29,unpack=True,usecols=(0,1),delimiter=delim)
if gp.case == 5:
RAh, RAm, RAs, DEd, DEm, DEs, VHel, e_VHel, Teff, e_Teff, logg, e_logg, Fe, e_Fe, N = np.loadtxt(
gpr.fil, skiprows=25, unpack=True)
PM = np.ones(len(RAh))
split = logg
e_split = e_logg
else:
RAh, RAm, RAs, DEd, DEm, DEs, Vmag, VI, VHel, e_VHel, SigFe, e_SigFe, Mg, Mg_err, PM = np.genfromtxt(
gpr.fil,
skiprows=29,
unpack=True,
usecols=tuple(range(2, 17)),
delimiter=delim,
filling_values=-1)
split = Mg
e_split = Mg_err
if gp.case == 5:
sel = (N > 0)
else:
sel = (Mg > -1) # exclude missing data on Mg
RAh = RAh[sel]
RAm = RAm[sel]
RAs = RAs[sel]
DEd = DEd[sel]
DEm = DEm[sel]
DEs = DEs[sel]
#Vmag = Vmag[sel]
#VI = VI[sel]
VHel = VHel[sel]
e_VHel = e_VHel[sel]
if gp.case < 5:
Mg = Mg[sel]
Mg_err = Mg_err[sel]
elif gp.case == 5:
Teff = Teff[sel]
e_Teff = e_Teff[sel]
logg = logg[sel]
e_logg = e_logg[sel]
Fe = Fe[sel]
e_Fe = e_Fe[sel]
N = N[sel]
split = split[sel]
e_split = e_split[sel]
PM = PM[sel]
split_min = min(split) # -3, 3 if according to WalkerPenarrubia2011
split_max = max(split)
# easiest way for visualization: use histogram to show data
#hist(split, np.sqrt(len(split))/2, normed=True)
# but: it's not as easy as that
# we have datapoints with errors and probability of membership weighting
# thus, we need to smear the values out using a Gaussian of width = split_err
# and add them up afterwards after scaling with probability PM
x = np.array(np.linspace(split_min, split_max, 100))
splitdf = np.zeros(100)
for i in range(len(split)):
splitdf += PM[i] * gh.gauss(x, split[i], e_split[i])
splitdf /= sum(PM)
#plot(x, Mgdf, 'g', lw=2)
# only then we want to compare to Gaussians
n_dims = 1 + gp.pops * 2
#Nsample = 10*n_dims
pymultinest.run(
myloglike,
myprior,
n_dims, # nest_ndims
n_dims + 1, # nest_totPar
n_dims, # separate modes on nest_nCdims
# the rho parameters only (gp.nrho in this case)
[gp.pops, gp.nipol, gp.nrho],
True, # nest_IS = INS enabled
True, #nest_mmodal = # separate modes
True, # nest_ceff = use const sampling efficiency
Nsample, # nest_nlive =
0.0, # nest_tol = 0 to keep working infinitely
0.8, # nest_ef =
10000, # nest_updInt = output after this many iterations
1., # null_log_evidence separate modes if
#logevidence > this param.
Nsample, # maxClst =
-1.e30, # nest_Ztol = mode tolerance in the
#case where no special value exists: highly negative
gp.files.outdir, # outputfiles_basename =
-1, # seed =
True, # nest_fb =
False, # nest_resume =
0, # context =
True, # nest_outfile =
-999999, # nest_logZero = points with log L < log_zero will be
1000, # nest_maxIter =
False, # initMPI = use MPI
None) #dump_callback =
import os
os.system(
'cd ' + gp.files.outdir +
'; grep -n6 Maximum stats.dat|tail -5|cut -d " " -f8 > metalmaxL.dat;')
os.system("cd " + gp.files.outdir +
"; sed -i 's/\\([0-9]\\)-\\([0-9]\\)/\\1E-\\2/g' metalmaxL.dat")
os.system("cd " + gp.files.outdir +
"; sed -i 's/\\([0-9]\\)+\\([0-9]\\)/\\1E+\\2/g' metalmaxL.dat")
cubeML = np.loadtxt(gp.files.outdir + 'metalmaxL.dat')
cubeMLphys = cubeML #myprior(cubeML, 1+gp.pops*2, 1+gp.pops*2)
#myloglike(cubeMLphys, 1+gp.pops*2, 1+gp.pops*2)
pML, mu1ML, sig1ML, mu2ML, sig2ML = cubeMLphys
#g1 = pML*gh.gauss(x, mu1ML, sig1ML)
#g2 = (1-pML)*gh.gauss(x, mu2ML, sig2ML)
#gtot = g1+g2
#plot(x, pML*g1, 'white')
#plot(x, (1-pML)*g2, 'white')
#plot(x, gtot, 'r')
#xlabel('Mg')
#ylabel('pdf')
#pdb.set_trace()
sig = abs(RAh[0]) / RAh[0]
RAh = RAh / sig
xs = 15 * (RAh * 3600 + RAm * 60 + RAs) * sig # [arcsec/15]
sig = abs(DEd[0]) / DEd[0]
DEd = DEd / sig
ys = (DEd * 3600 + DEm * 60 + DEs) * sig # [arcsec]
arcsec = 2. * np.pi / (360. * 60. * 60) # [pc]
kpc = 1000 # [pc]
DL = {
1: lambda x: x * (138), #+/- 8 for Fornax
2: lambda x: x * (101), #+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec * DL) # [pc]
ys *= (arcsec * DL) # [pc]
# alternative: get center of photometric measurements by deBoer
# for Fornax, we have
if gp.case == 1:
com_x = 96203.736358393697
com_y = -83114.080684733024
xs = xs - com_x
ys = ys - com_y
else:
# determine com_x, com_y from shrinking sphere
import gi_centering as grc
com_x, com_y = grc.com_shrinkcircle_2D(xs, ys)
# instantiate different samplings, store half-light radii (2D)
coll_R1half = []
coll_R2half = []
coll_popass = []
print('drawing 1000 assignments of stars to best fitting Gaussians')
import numpy.random as npr
#import gi_project as gip
for kl in range(1000):
# get a sample assignment:
popass = []
for i in range(sum(sel)):
# random assignment, wrong
#if npr.rand() <= 0.5:
# popass.append(1)
#else:
# popass.append(2)
spl = split[i]
ppop1 = pML * gh.gauss(spl, mu1ML, sig1ML)
ppop2 = (1 - pML) * gh.gauss(spl, mu2ML, sig2ML)
if npr.rand() <= ppop1 / (ppop1 + ppop2):
popass.append(1)
else:
popass.append(2)
popass = np.array(popass)
coll_popass.append(popass)
sel1 = (popass == 1)
sel2 = (popass == 2)
# radii of all stellar tracers from pop 1 and 2
R1 = np.sqrt((xs[sel1])**2 + (ys[sel1])**2)
R2 = np.sqrt((xs[sel2])**2 + (ys[sel2])**2)
R1.sort()
R2.sort()
for pop in np.arange(2) + 1:
if pop == 1:
R0 = R1 # [pc]
Rhalf = R1[len(R1) / 2]
coll_R1half.append(Rhalf)
co = 'blue'
else:
R0 = R2 # [pc]
Rhalf = R2[len(R2) / 2]
coll_R2half.append(Rhalf)
co = 'red'
coll_R1half = np.array(coll_R1half)
coll_R2half = np.array(coll_R2half)
coll_Rdiffhalf = np.abs(coll_R1half - coll_R2half)
# select 3 assignments: one for median, one for median-1sigma, one for median+1sigma
med_Rdiff = np.median(coll_Rdiffhalf)
stdif = np.std(coll_Rdiffhalf)
min1s_Rdiff = med_Rdiff - stdif
max1s_Rdiff = med_Rdiff + stdif
#clf()
#hist(coll_Rdiffhalf, np.sqrt(len(coll_Rdiffhalf))/2)
#xlabel(r'$\Delta R/pc$')
#ylabel('count')
#axvline(med_Rdiff, color='r')
#axvline(min1s_Rdiff, color='g')
#axvline(max1s_Rdiff, color='g')
kmed = np.argmin(abs(coll_Rdiffhalf - med_Rdiff))
kmin1s = np.argmin(abs(coll_Rdiffhalf - min1s_Rdiff))
kmax1s = np.argmin(abs(coll_Rdiffhalf - max1s_Rdiff))
print('saving median, lower 68%, upper 68% stellar assignments')
np.savetxt(gpr.dir + 'data/popass_median', coll_popass[kmed])
np.savetxt(gpr.dir + 'data/popass_min1s', coll_popass[kmin1s])
np.savetxt(gpr.dir + 'data/popass_max1s', coll_popass[kmax1s])
print('finished')
ax.set_ylim(-1, 1.5)
show()
## \fn show_metallicity(Fe, Fe_err, Mg, Mg_err)
# show ellipses with error bars for each star's Fe and Mg
# @param Fe iron abundance
# @param Fe_err error on it
# @param Mg Magnesium abundance
# @param Mg_err error on it
import gi_params
gp = gi_params.Params()
import gr_params
gpr = gr_params.grParams(gp)
DL = {
1: lambda x: x * (138), #+/- 8 for Fornax
2: lambda x: x * (101), #+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](gu.kpc__pc)
k2 = {
1: lambda x: x * (339), #+/-36 for Fornax
2: lambda x: x * (137), #+/-22 for Carina
3: lambda x: x * (94), #+/-26 for Sculptor
4: lambda x: x * (294), #+/-38 for Sextans
5: lambda x: x * (-1) # TODO: look up for Draco
}[gp.case](1)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall = np.loadtxt(gp.files.get_com_file(0), skiprows=1, usecols=(0,1), unpack=True)
# 2*[Rscale0]
R = np.sqrt(xall**2+yall**2) # [Rscale0]
# set number and size of (linearly spaced) bins
Rmin = 0. #[Rscale0]
Rmax = max(R) if gp.maxR < 0 else 1.0*gp.maxR # [Rscale0]
R = R[(R<Rmax)] # [Rscale0]
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack([minr/8., minr/4., minr/2., Rbin, 2*maxr, 4*maxr, 8*maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
Rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)) # [pc]
for pop in range(gp.pops+1):
print('####### working on component ',pop)
print('input: ', gp.files.get_com_file(pop))
# exclude second condition if self-consistent approach wished
if gp.investigate == "obs" and gp.case==1 and pop==0:
# for Fornax, overwrite first Sigma with deBoer data
import gr_MCMCbin_for
gr_MCMCbin_for.run(gp)
continue
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop))<2:
continue
# only read in data if needed: pops = 1: reuse data from pop=0 part
if (gp.pops == 1 and pop < 1 or gp.pops == 2) or gp.investigate == 'obs':
x,y,v = np.loadtxt(gp.files.get_com_file(pop), skiprows=1,usecols=(0,1,2),unpack=True)
# [Rscalei], [Rscalei], [km/s]
# calculate 2D radius on the skyplane
R = np.sqrt(x**2+y**2) #[Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set maximum radius (if gp.maxR is set)
Rmax = max(R) if gp.maxR<0 else 1.0*gp.maxR # [Rscale0]
print('Rmax [Rscale0] = ', Rmax)
#pdb.set_trace()
#from pylab import clf, hist, axvline, xlim
#clf()
#hist(np.log10(R*Rscalei), 40)
#for i in range(len(Rbin)):
# axvline(np.log10(Rbin[i]*Rscale0))
#xlim([np.log10(min(gp.xepol*Rscale0)), np.log10(max(gp.xepol*Rscale0))])
sel = (R * Rscalei <= Rmax * Rscale0)
x = x[sel]
y = y[sel]
v = v[sel]
R = R[sel] # [Rscalei]
totmass_tracers = float(len(x)) # [Munit], Munit = 1/star
Rs = R # + possible starting offset, [Rscalei]
vlos = v # + possible starting offset, [km/s]
tr = open(gp.files.get_ntracer_file(pop),'w')
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
if (gp.pops == 1 and pop < 1) or gp.pops == 2 or gp.investigate == 'obs':
Sig_kin = np.zeros((gp.nipol, gpr.n))
siglos = np.zeros((gp.nipol, gpr.n))
if gp.usekappa:
kappa = np.zeros((gp.nipol, gpr.n))
if gp.usezeta:
v2 = np.zeros((gp.nipol, gpr.n))
v4 = np.zeros((gp.nipol, gpr.n))
Ntot = np.zeros(gpr.n)
zetaa = np.zeros(gpr.n)
zetab = np.zeros(gpr.n)
# particle selections, shared by density, siglos, kappa and zeta calculations
tpb = np.zeros((gp.nipol,gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
vlosi = gh.add_errors(vlos, gpr.vrerr) # [km/s]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi * Rscalei >= Binmin[i] * Rscale0, Rsi * Rscalei < Binmax[i] * Rscale0)).flatten() # [1]
tpb[i][k] = float(len(ind1)) # [1]
Sig_kin[i][k] = float(len(ind1))*totmass_tracers/Vol[i] # [Munit/rscale**2]
if(len(ind1)<=1):
siglos[i][k] = siglos[i-1][k]
print('### using last value, missing data')
if gp.usekappa:
kappa[i][k] = kappa[i-1][k]
# attention! should be 0, uses last value
if gp.usezeta:
v2[i][k] = v2[i-1][k]
v4[i][k] = v4[i-1][k]
else:
siglos[i][k] = meanbiweight(vlosi[ind1], ci_perc=68.4, \
ci_mean=True, ci_std=True)[1]
# [km/s], see BiWeight.py
if gp.usekappa:
kappa[i][k] = kurtosis(vlosi[ind1], axis=0, \
fisher=False, bias=False) # [1]
if gp.usezeta:
ave, adev, sdev, var, skew, curt = gh.moments(vlosi[ind1])
v2[i][k] = var
v4[i][k] = (curt+3)*var**2
Sigma = Sig_kin[:,k]
if gp.usezeta:
pdb.set_trace()
Ntot[k] = gh.Ntot(Rbin, Sigma, gp)
zetaa[k] = gh.starred(Rbin, v4[:,k], Sigma, Ntot[k], gp)
v2denom = (gh.starred(Rbin, v2[:,k], Sigma, Ntot[k], gp))**2
zetaa[k] /= v2denom
zetab[k] = gh.starred(Rbin, v4[:,k]*Rbin**2, Sigma, Ntot[k], gp)
zetab[k] /= v2denom
zetab[k] /= (gh.starred(Rbin, Rbin, Sigma, Ntot[k], gp))**2
if gp.investigate == 'obs' and gp.case < 5:
Sig_phot = obs_Sig_phot(Binmin, Binmax, Rscale0, Sig_kin, gp, gpr)
else:
Sig_phot = Sig_kin
# do the following for all populations
Sig0 = np.sum(Sig_phot[0])/float(gpr.n) # [Munit/Rscale^2]
Sig0pc = Sig0/Rscale0**2 # [munis/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Sig0pc, totmass_tracers)
# calculate density and mass profile, store it
# ----------------------------------------------------------------------
#tpb0 = np.sum(tpb[0])/float(gpr.n) # [1]
#Sigerr0 = Sig0/np.sqrt(tpb0) # [Munit/Rscale^2]
P_dens = np.zeros(gp.nipol)
P_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
Sig = np.sum(Sig_kin[b])/(1.*gpr.n) # [Munit/Rscale^2]
tpbb = np.sum(tpb[b])/float(gpr.n) # [1], mean number of tracers in bin
Sigerr = Sig/np.sqrt(tpbb) # [Munit/Rscale^2], Poissonian error
# compare data and analytic profile <=> get stellar
# density or mass ratio from Matt Walker
if(np.isnan(Sigerr)):
P_dens[b] = P_dens[b-1] # [1]
P_edens[b]= P_edens[b-1] # [1]
else:
P_dens[b] = Sig/Sig0 # [1]
P_edens[b]= Sigerr/Sig0 # [1]
print(Rbin[b], Binmin[b], Binmax[b], P_dens[b], P_edens[b], file=f_Sig)
# 3*[rscale], [dens0], [dens0]
indr = (R<Binmax[b])
Menclosed = float(np.sum(indr))/totmass_tracers # for normalization to 1#[totmass_tracers]
Merr = Menclosed/np.sqrt(tpbb) # or artificial Menclosed/10 #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], Menclosed, Merr, file=f_mass) # [Rscale0], 2* [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*P_dens, gp)
#numin = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens-P_edens), gp)
numax = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens+P_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax-numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], numedi[b]/nu0pc, nuerr[b]/nu0pc, file = f_nu)
f_nu.close()
# calculate and output siglos
# --------------------------------------------
p_dvlos = np.zeros(gp.nipol)
p_edvlos = np.zeros(gp.nipol)
for b in range(gp.nipol):
sig = np.sum(siglos[b])/gpr.n #[km/s]
tpbb = np.sum(tpb[b])/float(gpr.n) #[1]
if tpbb == 0:
sigerr = p_edvlos[b-1] #[km/s]
# attention! uses last error
else:
# Poisson error with measurement errors
#sigerr = sig/np.sqrt(tpbb)
#sigerr = np.sqrt(sigerr**2+2**2) # 2km/s
# standard deviation
#sigerr = stddevbiweight(siglos[b])
# Poisson error, first guess
sigerr = sig/np.sqrt(tpbb) #[km/s]
p_dvlos[b] = sig #[km/s]
p_edvlos[b]= sigerr #[km/s]
maxsiglos = max(p_dvlos) #[km/s]
print('maxsiglos = ', maxsiglos, '[km/s]')
fpars = open(gp.files.get_scale_file(pop),'a')
print(maxsiglos, file=fpars) #[km/s]
fpars.close()
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], np.abs(p_dvlos[b]/maxsiglos),\
np.abs(p_edvlos[b]/maxsiglos), file=f_sig)
# 3*[rscale], 2*[maxsiglos]
f_sig.close()
# calculate and output kurtosis kappa
# --------------------------------------------
if gp.usekappa:
p_kappa = np.zeros(gp.nipol) # needed for plotting later
p_ekappa = np.zeros(gp.nipol)
for b in range(gp.nipol):
kappavel = np.sum(kappa[b])/gpr.n #[1]
tpbb = np.sum(tpb[b])/float(gpr.n) #[1]
if tpbb == 0:
kappavelerr = p_edvlos[b-1] #[1]
# attention! uses last error
else:
kappavelerr = np.abs(kappavel/np.sqrt(tpbb)) #[1]
p_kappa[b] = kappavel
p_ekappa[b] = kappavelerr
print(Rbin[b], Binmin[b], Binmax[b], \
kappavel, kappavelerr, file=f_kap)
# [rscale], 2*[1]
f_kap.close()
# output zetas
# -------------------------------------------------------------
if gp.usezeta:
print(np.median(zetaa), np.median(zetab), file=f_zeta)
f_zeta.close()
if gpr.showplots:
gpr.show_plots_dens_2D(Rbin*Rscalei, P_dens, P_edens, Sig0pc)
gpr.show_plots_sigma(Rbin*Rscalei, p_dvlos, p_edvlos)
if gp.usekappa:
gpr.show_plots_kappa(Rbin*Rscalei, p_kappa, p_ekappa)
# overwrite Sig profile if photometric data is used
if gp.investigate == 'obs' and gp.case==1 and pop==1 and not gp.selfconsistentnu:
import os
os.system('cp '+gp.files.get_scale_file(0)+' '+gp.files.get_scale_file(1))
# replace last line with actual maxsiglos from tracer particles
os.system("sed -i '$s/^.*/"+str(maxsiglos)+"/' "+gp.files.get_scale_file(1))
os.system('cp '+gp.files.Sigfiles[0]+' '+gp.files.Sigfiles[1])
continue
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input: ', gpr.fil)
x0, y0, z0, vb0, vz0, Mg0, PM0, comp0 = read_data(gpr.fil)
# [pc], [km/s], [1]
# only use stars which are members of the dwarf: exclude pop3 by
# construction
pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination,
#outliers
x0, y0, z0, comp0, vb0, vz0, Mg0, PM0 = select_pm(x0, y0, z0, comp0, vb0,
vz0, Mg0, PM0, pm)
# assign population
if gp.pops == 2:
pm1 = (comp0 == 1) # will be overwritten below if gp.metalpop
pm2 = (comp0 == 2) # same same
elif gp.pops == 1:
pm1 = (comp0 < 3)
pm2 = (comp0 == -1) # assign none, but of same length as comp0
if gp.metalpop:
# drawing of populations based on metallicity get parameters
# from function in pymcmetal.py
import pickle
fi = open('metalsplit.dat', 'rb')
DATA = pickle.load(fi)
fi.close()
p, mu1, sig1, mu2, sig2, M, pm1, pm2 = DATA
x1, y1, z1, comp1, vb1, vz1, Mg1, PM1 = select_pm(x0, y0, z0, comp0, vb0,
vz0, Mg0, PM0, pm1)
x2, y2, z2, comp2, vb2, vz2, Mg2, PM2 = select_pm(x0, y0, z0, comp0, vb0,
vz0, Mg0, PM0, pm2)
# cut to subsets
ind1 = gh.draw_random_subset(x1, gp.ntracer[1 - 1])
x1, y1, z1, comp1, vb1, vz1, Mg1, PM1 = select_pm(x1, y1, z1, comp1, vb1,
vz1, Mg1, PM1, ind1)
ind2 = gh.draw_random_subset(x2, gp.ntracer[2 - 1])
x2, y2, z2, comp2, vb2, vz2, Mg2, PM2 = select_pm(x2, y2, z2, comp2, vb2,
vz2, Mg2, PM2, ind2)
# use vz for no contamination, or vb for with contamination
x0, y0, z0, vz0, pm1, pm2, pm = concat_pops(x1, x2, y1, y2, z1, z2, vz1,
vz2, gp)
com_x, com_y, com_z, com_vz = com_shrinkcircle_v(x0, y0, z0, vz0,
pm) # [pc]
print('COM [pc]: ', com_x, com_y, com_z) # [pc]
print('VOM [km/s]', com_vz) # [km/s]
# from now on, work with 2D data only; z0 was only used to get
# center in (x,y) better
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vz0 -= com_vz # [km/s]
R0 = np.sqrt(x0**2 + y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] from all tracer points
pop = -1
for pmn in [pm, pm1, pm2]:
pop = pop + 1 # population number
pmr = (R0 < (gp.maxR * Rscale)) # read max extension for data
#(rprior*Rscale) from
#gi_params
pmn = pmn * pmr # [1]
print("fraction of members = ", 1.0 * sum(pmn) / len(pmn))
x, y, z, comp, vz, vb, Mg, PMN = select_pm(x0, y0, z0, comp0, vz0, vb0,
Mg0, PM0, pmn)
R = np.sqrt(x * x + y * y) # [pc]
Rscalei = np.median(R)
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei)
gf.write_data_output(gp.files.get_com_file(pop), x / Rscalei,
y / Rscalei, vz, Rscalei)
if gpr.showplots:
gpr.show_part_pos(x, y, pmn, Rscale)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
## read input measurements
print('input: ', gpr.fil)
x0,y0,z0,vb0,vz0,Mg0,PM0,comp0=np.genfromtxt(gpr.fil,skiprows=0,unpack=True,\
usecols=(0, 1, 2, 5, 11, 13, 19, 20),\
dtype="d17",\
converters={0:expDtofloat, # x0 in pc \
1:expDtofloat, # y0 in pc \
2:expDtofloat, # z0 in pc \
5:expDtofloat, # vz0 in km/s\
12:expDtofloat, # vb0(LOS due binary), km/s\
13:expDtofloat, # Mg0 in Angstrom\
19:expDtofloat, # PM0 [1]\
20:expDtofloat}) # comp0 1,2,3(background)
# use component 12-1 instead of 6-1 for z velocity, to exclude observational errors
# only use stars which are members of the dwarf: exclude pop3 by construction
pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination, outliers
PM0 = PM0[pm]
comp0 = comp0[pm]
x0 = x0[pm]
y0 = y0[pm]
z0 = z0[pm]
vz0 = vz0[pm]; vb0 = vb0[pm]; Mg0 = Mg0[pm]
pm1 = (comp0 == 1) # will be overwritten below if gp.metalpop
pm2 = (comp0 == 2) # same same
pm3 = (comp0 == 3)
if gp.metalpop:
# drawing of populations based on metallicity
# get parameters from function in pymcmetal.py
import pickle
fi = open('metalsplit.dat', 'rb')
DATA = pickle.load(fi)
fi.close()
p, mu1, sig1, mu2, sig2, M, pm1, pm2 = DATA
# cutting pm_i to a maximum of ntracers particles:
ind = np.arange(len(x0))
np.random.shuffle(ind)
ind = ind[:np.sum(gp.ntracer)]
x0 = x0[ind]; y0 = y0[ind]; z0 = z0[ind]; comp0 = comp0[ind]
vz0 = vz0[ind]; vb0=vb0[ind]; Mg0 = Mg0[ind]
PM0 = PM0[ind]; pm1 = pm1[ind]; pm2 = pm2[ind]; pm3 = pm3[ind];
pm = pm1+pm2+pm3
# get COM with shrinking sphere method
com_x, com_y, com_z = com_shrinkcircle(x0,y0,z0,PM0)
print('COM [pc]: ', com_x, com_y, com_z)
com_vz = np.sum(vz0*PM0)/np.sum(PM0) # [km/s]
print('VOM [km/s]', com_vz)
# from now on, continue to work with 3D data. store to different files
x0 -= com_x; y0 -= com_y; z0 -= com_z # [pc]
vz0 -= com_vz #[km/s]
# but still get the same radii as from 2D method, to get comparison of integration routines right
r0 = np.sqrt(x0*x0+y0*y0+z0*z0) # [pc]
rhalf = np.median(r0) # [pc]
rscale = rhalf # or gpr.r_DM # [pc]
print('rscale = ', rscale, ' pc')
print('max(R) = ', max(r0) ,' pc')
print('last element of R : ',r0[-1],' pc')
print('total number of stars: ',len(r0))
pop = -1
for pmn in [pm, pm1, pm2]:
pmr = (r0<(gp.maxR*rscale)) # [1] based on [pc]
pmn = pmn*pmr # [1]
print("fraction of members = ", 1.0*sum(pmn)/len(pmn))
pop = pop + 1
x = x0[pmn]; y = y0[pmn]; z = z0[pmn]; vz = vz0[pmn]; vb = vb0[pmn]; # [pc], [km/s]
Mg = Mg0[pmn]; comp = comp0[pmn]; PMN = PM0[pmn] # [ang], [1], [1]
m = np.ones(len(pmn))
rscalei = np.median(np.sqrt(x*x+y*y+z*z))
# print("x y z" on first line, to interprete data later on)
crscale = open(gp.files.get_scale_file(pop)+'_3D','w')
print('# rscale in [pc], surfdens_central (=dens0) in [Munit/rscale0^2], and in [Munit/pc^2], and totmass_tracers [Munit], and max(sigma_LOS) in [km/s]', file=crscale)
print(rscalei, file=crscale) # use 3 different half-light radii
crscale.close()
# store recentered positions and velocity
print('output: ',gp.files.get_com_file(pop)+'_3D')
c = open(gp.files.get_com_file(pop)+'_3D','w')
print('# x [rscale],','y [rscale],', 'z [rscale]','vLOS [km/s],','rscale = ',rscalei,' pc', file=c)
for k in range(len(x)):
print(x[k]/rscalei, y[k]/rscalei, z[k]/rscalei, vz[k], file=c) # 3* [pc], [km/s]
c.close()
if gpr.showplots and False:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#res = (abs(x)<3*rscalei)*(abs(y)<3*rscalei)
#x = x[res]; y = y[res]; z = z[res]
en = len(x)
ax.scatter3D(x[:en], y[:en], z[:en], c=pmn[:en], s=35, \
vmin=0.95, vmax=1.0, lw=0.0, alpha=0.2)
#circ_HL=Circle((0,0), radius=rscalei, fc='None', ec='b', lw=1)
#gca().add_patch(circ_HL)
#circ_DM=Circle((0,0), radius=gpr.r_DM, fc='None', ec='r', lw=1)
#gca().add_patch(circ_DM)
pdb.set_trace()
gpr.show_part_pos(x, y, pmn, rscalei)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gpr.fil = gpr.dir + "/data/tracers.dat"
delim = [0, 22, 3, 3, 6, 4, 3, 5, 6, 6, 7, 5, 6, 5, 6, 5, 6]
ID = np.genfromtxt(gpr.fil,
skiprows=29,
unpack=True,
usecols=(0, 1),
delimiter=delim)
RAh, RAm, RAs, DEd, DEm, DEs, Vmag, VI, VHel, e_VHel, SigFe, e_SigFe, SigMg, e_SigMg, PM = np.genfromtxt(
gpr.fil,
skiprows=29,
unpack=True,
usecols=tuple(range(2, 17)),
delimiter=delim,
filling_values=-1)
# only use stars which have Mg measurements
pm = (SigMg > -1) * (PM >= 0.95)
print("f_members = ", gh.pretty(1. * sum(pm) / len(pm)))
ID = ID[1][pm]
RAh = RAh[pm]
RAm = RAm[pm]
RAs = RAs[pm]
DEd = DEd[pm]
DEm = DEm[pm]
DEs = DEs[pm]
Vmag = Vmag[pm]
VI = VI[pm]
VHel = VHel[pm]
e_VHel = e_VHel[pm]
SigFe = SigFe[pm]
e_SigFe = e_SigFe[pm]
SigMg = SigMg[pm]
e_SigMg = e_SigMg[pm]
PM = PM[pm]
Mg0 = SigMg
sig = abs(RAh[0]) / RAh[0]
RAh = RAh / sig
xs = 15 * (RAh * 3600 + RAm * 60 + RAs) * sig # [arcsec/15]
sig = abs(DEd[0]) / DEd[0]
DEd = DEd / sig
ys = (DEd * 3600 + DEm * 60 + DEs) * sig # [arcsec]
arcsec = 2. * np.pi / (360. * 60. * 60) # [pc]
kpc = 1000 # [pc]
DL = {
1: lambda x: x * (138), #+/- 8 for Fornax
2: lambda x: x * (101), #+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec * DL) # [pc]
ys *= (arcsec * DL) # [pc]
PM0 = np.copy(PM)
x0 = np.copy(xs)
y0 = np.copy(ys) # [pc]
vz0 = np.copy(VHel) # [km/s]
# only use stars which are members of the dwarf: exclude pop3 by construction
#pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination, outliers
#x0, y0, vz0, Mg0, PM0 = select_pm(x0, y0, vz0, Mg0, PM0, pm)
# assign population
if gp.pops == 2:
# drawing of populations based on metallicity
# get parameters from function in pymcmetal.py
#[p, mu1, sig1, mu2, sig2] = np.loadtxt(gp.files.dir+'metalsplit.dat')
#[pm1, pm2] = np.loadtxt(gp.files.dir+'metalsplit_assignment.dat')
popass = np.loadtxt(gp.files.dir + 'popass')
pm1 = (popass == 1)
pm2 = (popass == 2)
elif gp.pops == 1:
pm1 = (PM >= 0)
pm2 = (PM < 0) # assign none, but of same length as xs
x1, y1, vz1, Mg1, PM1 = select_pm(x0, y0, vz0, Mg0, PM0, pm1)
x2, y2, vz2, Mg2, PM2 = select_pm(x0, y0, vz0, Mg0, PM0, pm2)
# cutting pm_i to a maximum of ntracers_i particles each:
ind1 = np.arange(len(x1))
np.random.shuffle(ind1) # random.shuffle already changes ind
ind1 = ind1[:gp.ntracer[1 - 1]]
ind2 = np.arange(len(x2))
np.random.shuffle(ind2) # random.shuffle already changes ind
ind2 = ind2[:gp.ntracer[2 - 1]]
x1, y1, vz1, Mg1, PMS1 = select_pm(x1, y1, vz1, Mg1, PM1, ind1)
x2, y2, vz2, Mg2, PMS2 = select_pm(x2, y2, vz2, Mg2, PM2, ind2)
x0, y0, vz0, pm1, pm2, pm = concat_pops(x1, x2, y1, y2, vz1, vz2, gp)
# optimum: get 3D center of mass with means
# com_x, com_y, com_z = com_mean(x0,y0,z0,PM0) # 3*[pc], z component included if available
com_x, com_y, com_vz = com_shrinkcircle_v_2D(x0, y0, vz0,
pm) # [pc], [km/s]
# from now on, work with 2D data only; z0 was only used to get center in (x,y) better
# x0 -= com_x; y0 -= com_y # [pc]
# vz0 -= com_vz #[km/s]
R0 = np.sqrt(x0**2 + y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] overall
pop = -1
for pmn in [pm, pm1, pm2]:
pop = pop + 1
pmr = (R0 < (gp.maxR * Rscale)) # read max extension for data
# (rprior*Rscale) from gi_params
pmn = pmn * pmr # [1]
print("fraction of members = ", 1.0 * sum(pmn) / len(pmn))
x, y, vz, Mg, PMN = select_pm(x0, y0, vz0, Mg0, PM0, pmn)
R = np.sqrt(x * x + y * y) # [pc]
Rscalei = np.median(R) # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei) # [pc]
gf.write_data_output(gp.files.get_com_file(pop), x / Rscalei,
y / Rscalei, vz, Rscalei) # [pc]
if gpr.showplots:
gpr.show_part_pos(x, y, pmn, Rscale)
def run(gp, pop):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall = np.loadtxt(gp.files.get_com_file(0), skiprows=1, \
usecols=(0,1), unpack=True)
# 2*[Rscale0]
R = np.sqrt(xall**2+yall**2) # [Rscale0]
# set number and size of (linearly spaced) bins
Rmin = 0. #[Rscale0]
Rmax = max(R) if gp.maxR < 0 else 1.0*gp.maxR # [Rscale0]
R = R[(R<Rmax)] # [Rscale0]
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack([minr/8., minr/4., minr/2., Rbin, 2*maxr, 4*maxr, 8*maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
Rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)) # [pc]
print('####### working on component ',pop)
print('input: ', gp.files.get_com_file(pop))
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop))<2:
return
# only read in data if needed: pops = 1: reuse data from pop=0 part
x,y = np.loadtxt(gp.files.get_com_file(pop), skiprows=1, usecols=(0,1), unpack = True)
# [Rscalei], [Rscalei]
# calculate 2D radius on the skyplane
R = np.sqrt(x**2+y**2) #[Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set maximum radius (if gp.maxR is set)
Rmax = max(R) if gp.maxR<0 else 1.0*gp.maxR # [Rscale0]
print('Rmax [Rscale0] = ', Rmax)
sel = (R * Rscalei <= Rmax * Rscale0)
x = x[sel] # [Rscalei]
y = y[sel] # [Rscalei]
R = R[sel] # [Rscalei]
totmass_tracers = float(len(x)) # [Munit], Munit = 1/star
Rs = R # + possible starting offset, [Rscalei]
tr = open(gp.files.get_ntracer_file(pop),'w')
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
Sig_phot = np.zeros((gp.nipol, gpr.n))
# particle selections, shared by density, siglos, kappa and zeta calculations
tpb = np.zeros((gp.nipol,gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi * Rscalei >= Binmin[i] * Rscale0, \
Rsi * Rscalei < Binmax[i] * Rscale0)).flatten() # [1]
tpb[i][k] = float(len(ind1)) #[1]
Sig_phot[i][k] = float(len(ind1))*totmass_tracers/Vol[i] # [Munit/rscale^2]
# do the following for all populations
Sig0 = np.sum(Sig_phot[0])/float(gpr.n) # [Munit/Rscale^2]
Sig0pc = Sig0/Rscale0**2 # [munis/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Sig0pc, totmass_tracers)
# calculate density and mass profile, store it
# ----------------------------------------------------------------------
P_dens = np.zeros(gp.nipol)
P_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
Sig = np.sum(Sig_phot[b])/(1.*gpr.n) # [Munit/Rscale^2]
tpbb = np.sum(tpb[b])/float(gpr.n) # [1], mean number of tracers in bin
Sigerr = Sig/np.sqrt(tpbb) # [Munit/Rscale^2], Poissonian error
# compare data and analytic profile <=> get stellar
# density or mass ratio from Matt Walker
if(np.isnan(Sigerr)):
P_dens[b] = P_dens[b-1] # [1]
P_edens[b]= P_edens[b-1] # [1]
else:
P_dens[b] = Sig/Sig0 # [1]
P_edens[b]= Sigerr/Sig0 # [1]
print(Rbin[b], Binmin[b], Binmax[b], P_dens[b], P_edens[b], file=f_Sig)
# 3*[rscale], [dens0], [dens0]
indr = (R<Binmax[b])
Menclosed = float(np.sum(indr))/totmass_tracers # for normalization to 1#[totmass_tracers]
Merr = Menclosed/np.sqrt(tpbb) # or artificial Menclosed/10 #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], Menclosed, Merr, file=f_mass) # [Rscale0], 2* [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*P_dens, gp)
#numin = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens-P_edens), gp)
numax = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens+P_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax-numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b],\
numedi[b]/nu0pc, nuerr[b]/nu0pc, \
file = f_nu)
f_nu.close()
# write dummy sig scale, not to be used later on
maxsiglos = -1. #[km/s]
fpars = open(gp.files.get_scale_file(pop),'a')
print(maxsiglos, file=fpars) #[km/s]
fpars.close()
def read(Rdiff, gp):
if Rdiff != 'median' and Rdiff != 'min1s' and Rdiff != 'max1s':
print('run grd_metalsplit.py to get the split by metallicity done before reading it in for GravImage')
exit(1)
import gr_params
gpr = gr_params.grParams(gp)
global Nsample, split, e_split, PM, split_min, split_max
gpr.fil = gpr.dir+"data/tracers.dat"
# number of measured tracer stars
Nsample = bufcount(gpr.fil)
delim = [0,22,3,3,6,4,3,5,6,6,7,5,6,5,6,5,6]
#ID = np.genfromtxt(gpr.fil,skiprows=29,unpack=True,usecols=(0,1),delimiter=delim)
if gp.case==5:
RAh,RAm,RAs,DEd,DEm,DEs,VHel,e_VHel,Teff,e_Teff,logg,e_logg,Fe,e_Fe,N=np.loadtxt(gpr.fil, skiprows=25, unpack=True)
PM = np.ones(len(RAh))
split = logg
e_split = e_logg
sel = (N>0)
else:
RAh,RAm,RAs,DEd,DEm,DEs,Vmag,VI,VHel,e_VHel,SigFe,e_SigFe, Mg,Mg_err,PM = np.genfromtxt(gpr.fil, skiprows=29, unpack=True, usecols=tuple(range(2,17)), delimiter=delim, filling_values=-1)
split = Mg
e_split = Mg_err
sel = (Mg>-1) # exclude missing data on Mg
RAh = RAh[sel]
RAm = RAm[sel]
RAs = RAs[sel]
DEd = DEd[sel]
DEm = DEm[sel]
DEs = DEs[sel]
#Vmag = Vmag[sel]
#VI = VI[sel]
VHel = VHel[sel]
e_VHel = e_VHel[sel]
if gp.case < 5:
Mg = Mg[sel]
Mg_err = Mg_err[sel]
elif gp.case == 5:
Teff = Teff[sel]
e_Teff = e_Teff[sel]
logg = logg[sel]
e_logg = e_logg[sel]
Fe = Fe[sel]
e_Fe = e_Fe[sel]
N = N[sel]
split = split[sel]
e_split = e_split[sel]
PM = PM[sel]
split_min = min(split) # -3, 3 if according to WalkerPenarrubia2011
split_max = max(split)
# but: it's not as easy as that
# we have datapoints with errors and probability of membership weighting
# thus, we need to smear the values out using a Gaussian of width = split_err
# and add them up afterwards after scaling with probability PM
x = np.array(np.linspace(split_min, split_max, 100))
splitdf = np.zeros(100)
for i in range(len(split)):
splitdf += PM[i]*gh.gauss(x, split[i], e_split[i])
splitdf /= sum(PM)
sig = abs(RAh[0])/RAh[0]
RAh = RAh/sig
xs = 15*(RAh*3600+RAm*60+RAs)*sig # [arcsec/15]
sig = abs(DEd[0])/DEd[0]
DEd = DEd/sig
ys = (DEd*3600+DEm*60+DEs)*sig # [arcsec]
arcsec = 2.*np.pi/(360.*60.*60) # [pc]
kpc = 1000 # [pc]
DL = {1: lambda x: x * (138),#+/- 8 for Fornax
2: lambda x: x * (101),#+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec*DL) # [pc]
ys *= (arcsec*DL) # [pc]
# alternative: get center of photometric measurements by deBoer
# for Fornax, we have
if gp.case == 1:
com_x = 96203.736358393697
com_y = -83114.080684733024
xs = xs-com_x
ys = ys-com_y
else:
# determine com_x, com_y from shrinking sphere
import gi_centering as grc
com_x, com_y = grc.com_shrinkcircle_2D(xs, ys)
popass = np.loadtxt(gpr.dir+'data/popass_'+Rdiff)
sel1 = (popass==1)
sel2 = (popass==2)
# radii of all stellar tracers from pop 1 and 2
R1 = np.sqrt((xs[sel1])**2 + (ys[sel1])**2)
R2 = np.sqrt((xs[sel2])**2 + (ys[sel2])**2)
R1.sort()
R2.sort()
R0 = np.hstack([R1, R2])
R0.sort()
for pop in np.arange(2)+1:
if pop == 1:
Rhalf = R1[len(R1)/2]
co = 'blue'
else:
Rhalf = R2[len(R2)/2]
co = 'red'
Rmin = min(R0) # [pc]
Rmax = max(R0) # [pc]
Binmin, Binmax, Rbin = gh.determine_radius(R0, Rmin, Rmax, gp) # [pc]
gp.xipol = Rbin # [pc]
minr = min(Rbin)# [pc]
maxr = max(Rbin)# [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [pc^2]
totmass_tracers = float(len(x))
Rsi = gh.add_errors(R0, gpr.Rerr) # [pc], gpr.Rerr was in
tpb = np.zeros(gp.nipol)
Sig_phot = np.zeros(gp.nipol)
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi >= Binmin[i], Rsi < Binmax[i])).flatten() # [1]
tpb[i] = float(len(ind1)) # [1]
Sig_phot[i] = float(len(ind1))*totmass_tracers/Vol[i] # [Munit/pc^2]
#loglog(gp.xipol, Sig_phot, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\Sigma(R)$')
#pdb.set_trace()
# deproject to get 3D nu profiles
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol =np.hstack([minr/8.,minr/4.,minr/2.,Rbin,2*maxr,4*maxr,8*maxr])#[pc]
gp.xfine = introduce_points_in_between(gp.xepol, gp)
#pdb.set_trace()
#Sigdatnu, Sigerrnu = gh.complete_nu(Rbin, Sig_phot, Sig_phot/10., gp.xfine)
#dummyx,nudatnu,nuerrnu,Mrnu = gip.Sig_NORM_rho(gp.xfine,Sigdatnu,Sigerrnu,gp)
#nudat = gh.linipollog(gp.xfine, nudatnu, gp.xipol)
#nuerr = gh.linipollog(gp.xfine, nuerrnu, gp.xipol)
#loglog(gp.xipol, nudat, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\nu(R)$')
#plum = 100*gh.plummer(gp.xipol, Rhalf, len(R0))
#loglog(gp.xipol, plum, color=co, linestyle='--')
#ylim([min(plum), max(plum)])
#pdb.set_trace()
return
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print("scalefile: ", gp.files.get_scale_file(0))
Rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)) # [pc]
print("input: ", gp.files.get_com_file(0))
# start from data centered on COM already:
x, y, v = np.loadtxt(
gp.files.get_com_file(0), skiprows=1, usecols=(0, 1, 2), unpack=True
) # [Rscalei], [Rscalei], [km/s]
for pop in range(2):
# calculate 2D radius on the skyplane
R = np.sqrt(x ** 2 + y ** 2) # [Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set number and size of bins
Rmin = 0.0 # [rscale]
Rmax = max(R) if gp.maxR < 0 else float(gp.maxR) # [Rscale0]
sel = R * Rscalei < Rmax * Rscale0
x = x[sel]
y = y[sel]
v = v[sel] # [rscale]
totmass_tracers = 1.0 * len(x) # [munit], munit = 1/star
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack([minr / 8.0, minr / 4.0, minr / 2.0, Rbin, 2 * maxr, 4 * maxr, 8 * maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
# rs = gpr.Rerr*np.random.randn(len(r))+r
Rs = R # [Rscale] # if no initial offset is whished
tr = open(gp.files.get_ntracer_file(pop), "w")
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
# 30 iterations for getting random picked radius values
Density = np.zeros((gp.nipol, gpr.n))
tpb = np.zeros((gp.nipol, gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
for j in range(gp.nipol):
ind1 = np.argwhere(
np.logical_and(Rsi * Rscalei >= Binmin[j] * Rscale0, Rsi * Rscalei < Binmax[j] * Rscale0)
).flatten() # [1]
Density[j][k] = float(len(ind1)) / Vol[j] * totmass_tracers # [munit/Rscale0^2]
tpb[j][k] = float(len(ind1)) # [1]
Dens0 = np.sum(Density[0]) / float(gpr.n) # [Munit/Rscale0^2]
Dens0pc = Dens0 / Rscale0 ** 2 # [Munit/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Dens0pc, totmass_tracers)
tpbb0 = np.sum(tpb[0]) / float(gpr.n) # [1]
Denserr0 = Dens0 / np.sqrt(tpbb0) # [Munit/rscale^2]
p_dens = np.zeros(gp.nipol)
p_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
Dens = np.sum(Density[b]) / float(gpr.n) # [Munit/rscale^2]
tpbb = np.sum(tpb[b]) / float(gpr.n) # [1]
Denserr = Dens / np.sqrt(tpbb) # [Munit/rscale^2]
if np.isnan(Denserr):
p_dens[b] = p_dens[b - 1] # [1]
p_edens[b] = p_edens[b - 1] # [1]
else:
p_dens[b] = Dens / Dens0 # [1]
p_edens[b] = Denserr / Dens0 # [1] #100/rbin would be artificial guess
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], p_dens[b], p_edens[b], file=f_Sig)
# [rscale], [dens0], [dens0]
indr = R < Binmax[b]
menclosed = float(np.sum(indr)) / totmass_tracers
# /totmass_tracers for normalization to 1 at last bin #[totmass_tracers]
merr = menclosed / np.sqrt(tpbb) # artificial menclosed/10 gives good approximation #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], menclosed, merr, file=f_mass)
# [rscale], [totmass_tracers], [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin * Rscalei, Dens0pc * p_dens, gp)
numin = gip.Sig_INT_rho(Rbin * Rscalei, Dens0pc * (p_dens - p_edens), gp)
numax = gip.Sig_INT_rho(Rbin * Rscalei, Dens0pc * (p_dens + p_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax - numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], numedi[b] / nu0pc, nuerr[b] / nu0pc, file=f_nu)
f_nu.close()
if gpr.showplots:
gpr.show_plots_dens_2D(Rbin * Rscalei, p_dens, p_edens, Dens0pc)
def run(gp):
global K, C, D, F, zth, zp_kz, zmin, zmax, z0, z02
# Set up simple population here using analytic formulae:
zmin = 100.0 # [pc], first bin center
zmax = 1300.0 # [pc], last bin center
# get Stuetzpunkte for theoretical profiles (not yet stars, finer spacing in real space)
nth = gp.nipol # [1] number of bins
zth = 1.0 * np.arange(nth) * (zmax - zmin) / (nth - 1.0) + zmin # [pc] bin centers
z0 = 240.0 # [pc], scaleheight of first population
z02 = 200.0 # [pc], scaleheight of second population
D = 250.0 # [pc], scaleheight of all stellar tracers
K = 1.65
F = 1.65e-4
C = 17.0 ** 2.0 # [km/s] integration constant in sig
# Draw mock data from exponential disk:
nu_zth = np.exp(-zth / z0) # [nu0] = [Msun/A/pc] 3D tracer density
Kz_zth = -(K * zth / np.sqrt(zth ** 2.0 + D ** 2.0) + 2.0 * F * zth)
if gp.adddarkdisc:
DD = 600 # [pc] scaleheight of dark disc
KD = 0.15 * 1.650
Kz_zth = Kz_zth - KD * zth / np.sqrt(zth ** 2.0 + DD ** 2.0)
# calculate sig_z^2
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i] * nu_zth[:i], zth[:i])
sigzth = np.sqrt((inti + C) / nu_zth)
# project back to positions of stars
ran = npr.uniform(size=int(gp.ntracer[1 - 1])) # [1]
zstar = -z0 * np.log(1.0 - ran) # [pc] stellar positions, exponential falloff
sigzstar = gh.ipol(zth, sigzth, zstar)
# > 0 ((IDL, Justin)) stellar velocity dispersion
# assign [0,1] * maxsig
ran2 = npr.normal(size=int(gp.ntracer[1 - 1])) # [1]
vzstar = ran2 * sigzstar # [km/s]
# Add second population [thick-disc like]:
if gp.pops == 2:
nu_zth2 = gp.ntracer[2 - 1] / gp.ntracer[1 - 1] * np.exp(-zth / z02)
# [nu0,2] = [Msun/A/pc], 3D tracer density, exponentially falling
# no normalization to 1 done here
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i] * nu_zth2[:i], zth[:i])
sigzth2 = np.sqrt((inti + C) / nu_zth2) # same integration constant
ran = npr.uniform(-1.0, 1.0, gp.ntracer[2 - 1]) # [1]
zstar2 = -z02 * np.log(1.0 - ran) # [pc]
# zstarobs = np.hstack([zstar, zstar2]) # concat pop1, pop2 for all stars
sigzstar2 = gh.ipol(zth, sigzth2, zstar2)
ran2 = npr.normal(-1.0, 1, gp.ntracer[2 - 1]) # [1]
vzstar2 = ran2 * sigzstar2 # [(km/2)^2]
# enforce observational cut on zmax:
sel = zstar < zmax
print("fraction of z<zmax selected elements: ", 1.0 * sum(sel) / (1.0 * len(sel)))
z_dat1 = zstar[sel]
vz_dat1 = vzstar[sel]
# throw away velocities of value zero (unstable?):
sel = abs(vz_dat1) > 0
print("fraction of vz_dat>0 selected elements: ", 1.0 * sum(sel) / (1.0 * len(sel)))
z_dat1 = z_dat1[sel]
vz_dat1 = vz_dat1[sel]
# Calulate binned data (for plots/binned anal.). old way, linear spacings, no const #particles/bin
binmin1, binmax1, z_dat_bin1, sig_dat_bin1, count_bin1 = gh.binsmooth(z_dat1, vz_dat1, zmin, zmax, gp.nipol, 0.0)
sig_dat_err_bin1 = np.sqrt(sig_dat_bin1) # Poisson errors
nu_dat_bin1, nu_dat_err_bin1 = gh.bincount(z_dat1, binmax1)
nu_dat_bin1 /= binmax1 - binmin1
nu_dat_err_bin1 /= binmax1 - binmin1
import gr_params
gpr = gr_params.grParams(gp)
if gpr.showplots:
nuscaleb = nu_zth[np.argmin(np.abs(zth - z0))]
plt.loglog(zth, nu_zth / nuscaleb, "b.-")
nuscaler = nu_dat_bin1[np.argmin(np.abs(zth - z0))]
plt.loglog(zth, nu_dat_bin1 / nuscaler, "r.-")
# pdb.set_trace()
Sig_dat_bin1 = np.cumsum(nu_dat_bin1)
Sig_dat_err_bin1 = np.sqrt(Sig_dat_bin1)
Mrdat1 = np.cumsum(Sig_dat_bin1)
Mrerr1 = Mrdat1 * Sig_dat_err_bin1 / Sig_dat_bin1
scales = [[], [], []]
scales[1].append(z0) # [pc]
scales[1].append(Sig_dat_bin1[0])
scales[1].append(Mrdat1[-1])
scales[1].append(nu_dat_bin1[0])
scales[1].append(max(sig_dat_bin1))
# start analysis of "all stars" with only component 1,
# append to it later if more populations required
z_dat0 = z_dat1 # [pc]
vz_dat0 = vz_dat1 # [km/s]
if gp.pops == 2:
# enforce observational constraints on z<z_max
sel = zstar2 < zmax
z_dat2 = zstar2[sel]
vz_dat2 = vzstar2[sel]
# cut zero velocities:
sel = abs(vz_dat2) > 0
z_dat2 = z_dat2[sel]
vz_dat2 = vz_dat2[sel]
# Calulate binned data (for plots/binned analysis):
binmin2, binmax2, z_dat_bin2, sig_dat_bin2, count_bin2 = gh.binsmooth(
z_dat2, vz_dat2, zmin, zmax, gp.nipol, 0.0
)
sig_dat_err_bin2 = np.sqrt(sig_dat_bin2) # Poissonian errors
nu_dat_bin2, nu_dat_err_bin2 = gh.bincount(z_dat2, binmax2)
nu_dat_bin2 /= binmax2 - binmin2
nu_dat_err_bin2 /= binmax2 - binmin2
Sig_dat_bin2 = np.cumsum(nu_dat_bin2)
Sig_dat_err_bin2 = np.sqrt(Sig_dat_bin2)
Mrdat2 = np.cumsum(nu_dat_bin2)
Mrerr2 = np.sqrt(Mrdat2)
scales[2].append(z02) # [pc]
scales[2].append(Sig_dat_bin2[0])
scales[2].append(Mrdat2[-1])
scales[2].append(nu_dat_bin2[0]) # normalize by max density of first bin, rather
scales[2].append(max(sig_dat_bin2))
# calculate properties for all pop together with stacked values
z_dat0 = np.hstack([z_dat1, z_dat2])
vz_dat0 = np.hstack([vz_dat1, vz_dat2])
# Calulate binned data (for plots/binned anal.). old way, linear spacings, no const #particles/bin
binmin0, binmax0, z_dat_bin0, sig_dat_bin0, count_bin0 = gh.binsmooth(z_dat0, vz_dat0, zmin, zmax, gp.nipol, 0.0)
sig_dat_err_bin0 = np.sqrt(sig_dat_bin0)
# binmin, binmax, z_dat_bin = gh.bin_r_const_tracers(z_dat, gp.nipol)
nu_dat_bin0, nu_dat_err_bin0 = gh.bincount(z_dat0, binmax0)
nu_dat_bin0 /= binmax0 - binmin0
nu_dat_err_bin0 /= binmax0 - binmin0
Sig_dat_bin0 = np.cumsum(nu_dat_bin0)
Sig_dat_err_bin0 = np.sqrt(Sig_dat_bin0)
# renorm0 = max(nu_dat_bin0)
xip = np.copy(z_dat_bin0) # [pc]
Mrdat0 = K * xip / np.sqrt(xip ** 2.0 + D ** 2.0) / (2.0 * np.pi * gu.G1__pcMsun_1km2s_2)
Mrerr0 = Mrdat0 * nu_dat_err_bin0 / nu_dat_bin0
scales[0].append(D) # [pc]
scales[0].append(Sig_dat_bin0[0])
scales[0].append(Mrdat0[-1])
scales[0].append(nu_dat_bin0[0])
scales[0].append(max(sig_dat_bin0))
rmin = binmin0 / scales[0][0] # [pc]
rbin = xip / scales[0][0] # [pc]
rmax = binmax0 / scales[0][0] # [pc]
# store parameters for output
# normalized by scale values
nudat = []
nudat.append(nu_dat_bin0 / scales[0][3]) # [Msun/pc^3]
nudat.append(nu_dat_bin1 / scales[1][3])
if gp.pops == 2:
nudat.append(nu_dat_bin2 / scales[2][3])
nuerr = []
nuerr.append(nu_dat_err_bin0 / scales[0][3]) # [Msun/pc^3]
nuerr.append(nu_dat_err_bin1 / scales[1][3])
if gp.pops == 2:
nuerr.append(nu_dat_err_bin2 / scales[2][3])
Mrdat = []
Mrdat.append(Mrdat0 / scales[0][2]) # [Msun]
Mrdat.append(Mrdat1 / scales[1][2])
if gp.pops == 2:
Mrdat.append(Mrdat2 / scales[2][2])
Mrerr = []
Mrerr.append(Mrerr0 / scales[0][2]) # [Msun]
Mrerr.append(Mrerr1 / scales[1][2])
if gp.pops == 2:
Mrerr.append(Mrerr2 / scales[2][2])
Sigdat = []
Sigdat.append(Sig_dat_bin0 / scales[0][1])
Sigdat.append(Sig_dat_bin1 / scales[1][1])
if gp.pops == 2:
Sigdat.append(Sig_dat_bin2 / scales[2][1])
Sigerr = []
Sigerr.append(Sig_dat_err_bin0 / scales[0][1])
Sigerr.append(Sig_dat_err_bin1 / scales[1][1])
if gp.pops == 2:
Sigerr.append(Sig_dat_err_bin2 / scales[2][1])
sigdat = []
sigdat.append(sig_dat_bin0 / scales[0][4]) # [km/s]
sigdat.append(sig_dat_bin1 / scales[1][4])
if gp.pops == 2:
sigdat.append(sig_dat_bin2 / scales[2][4])
sigerr = []
sigerr.append(sig_dat_err_bin0 / scales[0][4]) # [km/s]
sigerr.append(sig_dat_err_bin1 / scales[1][4])
if gp.pops == 2:
sigerr.append(sig_dat_err_bin2 / scales[2][4])
write_disc_output_files(rbin, rmin, rmax, nudat, nuerr, Sigdat, Sigerr, Mrdat, Mrerr, sigdat, sigerr, scales, gp)
return gp.dat
def read(Rdiff, gp):
if Rdiff != 'median' and Rdiff != 'min1s' and Rdiff != 'max1s':
print(
'run grd_metalsplit.py to get the split by metallicity done before reading it in for GravImage'
)
exit(1)
import gr_params
gpr = gr_params.grParams(gp)
global Nsample, split, e_split, PM, split_min, split_max
gpr.fil = gpr.dir + "data/tracers.dat"
# number of measured tracer stars
Nsample = bufcount(gpr.fil)
delim = [0, 22, 3, 3, 6, 4, 3, 5, 6, 6, 7, 5, 6, 5, 6, 5, 6]
#ID = np.genfromtxt(gpr.fil,skiprows=29,unpack=True,usecols=(0,1),delimiter=delim)
if gp.case == 5:
RAh, RAm, RAs, DEd, DEm, DEs, VHel, e_VHel, Teff, e_Teff, logg, e_logg, Fe, e_Fe, N = np.loadtxt(
gpr.fil, skiprows=25, unpack=True)
PM = np.ones(len(RAh))
split = logg
e_split = e_logg
sel = (N > 0)
else:
RAh, RAm, RAs, DEd, DEm, DEs, Vmag, VI, VHel, e_VHel, SigFe, e_SigFe, Mg, Mg_err, PM = np.genfromtxt(
gpr.fil,
skiprows=29,
unpack=True,
usecols=tuple(range(2, 17)),
delimiter=delim,
filling_values=-1)
split = Mg
e_split = Mg_err
sel = (Mg > -1) # exclude missing data on Mg
RAh = RAh[sel]
RAm = RAm[sel]
RAs = RAs[sel]
DEd = DEd[sel]
DEm = DEm[sel]
DEs = DEs[sel]
#Vmag = Vmag[sel]
#VI = VI[sel]
VHel = VHel[sel]
e_VHel = e_VHel[sel]
if gp.case < 5:
Mg = Mg[sel]
Mg_err = Mg_err[sel]
elif gp.case == 5:
Teff = Teff[sel]
e_Teff = e_Teff[sel]
logg = logg[sel]
e_logg = e_logg[sel]
Fe = Fe[sel]
e_Fe = e_Fe[sel]
N = N[sel]
split = split[sel]
e_split = e_split[sel]
PM = PM[sel]
split_min = min(split) # -3, 3 if according to WalkerPenarrubia2011
split_max = max(split)
# but: it's not as easy as that
# we have datapoints with errors and probability of membership weighting
# thus, we need to smear the values out using a Gaussian of width = split_err
# and add them up afterwards after scaling with probability PM
x = np.array(np.linspace(split_min, split_max, 100))
splitdf = np.zeros(100)
for i in range(len(split)):
splitdf += PM[i] * gh.gauss(x, split[i], e_split[i])
splitdf /= sum(PM)
sig = abs(RAh[0]) / RAh[0]
RAh = RAh / sig
xs = 15 * (RAh * 3600 + RAm * 60 + RAs) * sig # [arcsec/15]
sig = abs(DEd[0]) / DEd[0]
DEd = DEd / sig
ys = (DEd * 3600 + DEm * 60 + DEs) * sig # [arcsec]
arcsec = 2. * np.pi / (360. * 60. * 60) # [pc]
kpc = 1000 # [pc]
DL = {
1: lambda x: x * (138), #+/- 8 for Fornax
2: lambda x: x * (101), #+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec * DL) # [pc]
ys *= (arcsec * DL) # [pc]
# alternative: get center of photometric measurements by deBoer
# for Fornax, we have
if gp.case == 1:
com_x = 96203.736358393697
com_y = -83114.080684733024
xs = xs - com_x
ys = ys - com_y
else:
# determine com_x, com_y from shrinking sphere
import gi_centering as grc
com_x, com_y = grc.com_shrinkcircle_2D(xs, ys)
popass = np.loadtxt(gpr.dir + 'data/popass_' + Rdiff)
sel1 = (popass == 1)
sel2 = (popass == 2)
# radii of all stellar tracers from pop 1 and 2
R1 = np.sqrt((xs[sel1])**2 + (ys[sel1])**2)
R2 = np.sqrt((xs[sel2])**2 + (ys[sel2])**2)
R1.sort()
R2.sort()
R0 = np.hstack([R1, R2])
R0.sort()
for pop in np.arange(2) + 1:
if pop == 1:
Rhalf = R1[len(R1) / 2]
co = 'blue'
else:
Rhalf = R2[len(R2) / 2]
co = 'red'
Rmin = min(R0) # [pc]
Rmax = max(R0) # [pc]
Binmin, Binmax, Rbin = gh.determine_radius(R0, Rmin, Rmax, gp) # [pc]
gp.xipol = Rbin # [pc]
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [pc^2]
totmass_tracers = float(len(x))
Rsi = gh.add_errors(R0, gpr.Rerr) # [pc], gpr.Rerr was in
tpb = np.zeros(gp.nipol)
Sig_phot = np.zeros(gp.nipol)
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi >= Binmin[i],
Rsi < Binmax[i])).flatten() # [1]
tpb[i] = float(len(ind1)) # [1]
Sig_phot[i] = float(
len(ind1)) * totmass_tracers / Vol[i] # [Munit/pc^2]
#loglog(gp.xipol, Sig_phot, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\Sigma(R)$')
#pdb.set_trace()
# deproject to get 3D nu profiles
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack(
[minr / 8., minr / 4., minr / 2., Rbin, 2 * maxr, 4 * maxr,
8 * maxr]) #[pc]
gp.xfine = introduce_points_in_between(gp.xepol, gp)
#pdb.set_trace()
#Sigdatnu, Sigerrnu = gh.complete_nu(Rbin, Sig_phot, Sig_phot/10., gp.xfine)
#dummyx,nudatnu,nuerrnu,Mrnu = gip.Sig_NORM_rho(gp.xfine,Sigdatnu,Sigerrnu,gp)
#nudat = gh.linipollog(gp.xfine, nudatnu, gp.xipol)
#nuerr = gh.linipollog(gp.xfine, nuerrnu, gp.xipol)
#loglog(gp.xipol, nudat, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\nu(R)$')
#plum = 100*gh.plummer(gp.xipol, Rhalf, len(R0))
#loglog(gp.xipol, plum, color=co, linestyle='--')
#ylim([min(plum), max(plum)])
#pdb.set_trace()
return
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
global Nsample, split, e_split, PM, split_min, split_max
gpr.fil = gpr.dir+"data/tracers.dat"
# number of measured tracer stars
Nsample = bufcount(gpr.fil)
delim = [0,22,3,3,6,4,3,5,6,6,7,5,6,5,6,5,6]
#ID = np.genfromtxt(gpr.fil,skiprows=29,unpack=True,usecols=(0,1),delimiter=delim)
if gp.case==5:
RAh,RAm,RAs,DEd,DEm,DEs,VHel,e_VHel,Teff,e_Teff,logg,e_logg,Fe,e_Fe,N=np.loadtxt(gpr.fil, skiprows=25, unpack=True)
PM = np.ones(len(RAh))
split = logg
e_split = e_logg
else:
RAh,RAm,RAs,DEd,DEm,DEs,Vmag,VI,VHel,e_VHel,SigFe,e_SigFe, Mg,Mg_err,PM = np.genfromtxt(gpr.fil, skiprows=29, unpack=True, usecols=tuple(range(2,17)), delimiter=delim, filling_values=-1)
split = Mg
e_split = Mg_err
if gp.case == 5:
sel = (N>0)
else:
sel = (Mg>-1) # exclude missing data on Mg
RAh = RAh[sel]
RAm = RAm[sel]
RAs = RAs[sel]
DEd = DEd[sel]
DEm = DEm[sel]
DEs = DEs[sel]
#Vmag = Vmag[sel]
#VI = VI[sel]
VHel = VHel[sel]
e_VHel = e_VHel[sel]
if gp.case < 5:
Mg = Mg[sel]
Mg_err = Mg_err[sel]
elif gp.case == 5:
Teff = Teff[sel]
e_Teff = e_Teff[sel]
logg = logg[sel]
e_logg = e_logg[sel]
Fe = Fe[sel]
e_Fe = e_Fe[sel]
N = N[sel]
split = split[sel]
e_split = e_split[sel]
PM = PM[sel]
split_min = min(split) # -3, 3 if according to WalkerPenarrubia2011
split_max = max(split)
# easiest way for visualization: use histogram to show data
#hist(split, np.sqrt(len(split))/2, normed=True)
# but: it's not as easy as that
# we have datapoints with errors and probability of membership weighting
# thus, we need to smear the values out using a Gaussian of width = split_err
# and add them up afterwards after scaling with probability PM
x = np.array(np.linspace(split_min, split_max, 100))
splitdf = np.zeros(100)
for i in range(len(split)):
splitdf += PM[i]*gh.gauss(x, split[i], e_split[i])
splitdf /= sum(PM)
#plot(x, Mgdf, 'g', lw=2)
# only then we want to compare to Gaussians
n_dims = 1+gp.pops*2
#Nsample = 10*n_dims
pymultinest.run(myloglike, myprior, n_dims, # nest_ndims
n_dims+1, # nest_totPar
n_dims, # separate modes on nest_nCdims
# the rho parameters only (gp.nrho in this case)
[ gp.pops, gp.nipol, gp.nrho],
True, # nest_IS = INS enabled
True, #nest_mmodal = # separate modes
True, # nest_ceff = use const sampling efficiency
Nsample, # nest_nlive =
0.0, # nest_tol = 0 to keep working infinitely
0.8, # nest_ef =
10000, # nest_updInt = output after this many iterations
1., # null_log_evidence separate modes if
#logevidence > this param.
Nsample, # maxClst =
-1.e30, # nest_Ztol = mode tolerance in the
#case where no special value exists: highly negative
gp.files.outdir, # outputfiles_basename =
-1, # seed =
True, # nest_fb =
False, # nest_resume =
0, # context =
True, # nest_outfile =
-999999, # nest_logZero = points with log L < log_zero will be
1000, # nest_maxIter =
False, # initMPI = use MPI
None) #dump_callback =
import os
os.system('cd '+gp.files.outdir+'; grep -n6 Maximum stats.dat|tail -5|cut -d " " -f8 > metalmaxL.dat;')
os.system("cd "+gp.files.outdir+"; sed -i 's/\\([0-9]\\)-\\([0-9]\\)/\\1E-\\2/g' metalmaxL.dat")
os.system("cd "+gp.files.outdir+"; sed -i 's/\\([0-9]\\)+\\([0-9]\\)/\\1E+\\2/g' metalmaxL.dat")
cubeML = np.loadtxt(gp.files.outdir+'metalmaxL.dat')
cubeMLphys = cubeML #myprior(cubeML, 1+gp.pops*2, 1+gp.pops*2)
#myloglike(cubeMLphys, 1+gp.pops*2, 1+gp.pops*2)
pML, mu1ML, sig1ML, mu2ML, sig2ML = cubeMLphys
#g1 = pML*gh.gauss(x, mu1ML, sig1ML)
#g2 = (1-pML)*gh.gauss(x, mu2ML, sig2ML)
#gtot = g1+g2
#plot(x, pML*g1, 'white')
#plot(x, (1-pML)*g2, 'white')
#plot(x, gtot, 'r')
#xlabel('Mg')
#ylabel('pdf')
#pdb.set_trace()
sig = abs(RAh[0])/RAh[0]
RAh = RAh/sig
xs = 15*(RAh*3600+RAm*60+RAs)*sig # [arcsec/15]
sig = abs(DEd[0])/DEd[0]
DEd = DEd/sig
ys = (DEd*3600+DEm*60+DEs)*sig # [arcsec]
arcsec = 2.*np.pi/(360.*60.*60) # [pc]
kpc = 1000 # [pc]
DL = {1: lambda x: x * (138),#+/- 8 for Fornax
2: lambda x: x * (101),#+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec*DL) # [pc]
ys *= (arcsec*DL) # [pc]
# alternative: get center of photometric measurements by deBoer
# for Fornax, we have
if gp.case == 1:
com_x = 96203.736358393697
com_y = -83114.080684733024
xs = xs-com_x
ys = ys-com_y
else:
# determine com_x, com_y from shrinking sphere
import gi_centering as grc
com_x, com_y = grc.com_shrinkcircle_2D(xs, ys)
# instantiate different samplings, store half-light radii (2D)
coll_R1half = []
coll_R2half = []
coll_popass = []
print('drawing 1000 assignments of stars to best fitting Gaussians')
import numpy.random as npr
#import gi_project as gip
for kl in range(1000):
# get a sample assignment:
popass = []
for i in range(sum(sel)):
# random assignment, wrong
#if npr.rand() <= 0.5:
# popass.append(1)
#else:
# popass.append(2)
spl = split[i]
ppop1 = pML*gh.gauss(spl, mu1ML, sig1ML)
ppop2 = (1-pML)*gh.gauss(spl, mu2ML, sig2ML)
if npr.rand() <= ppop1/(ppop1+ppop2):
popass.append(1)
else:
popass.append(2)
popass = np.array(popass)
coll_popass.append(popass)
sel1 = (popass==1)
sel2 = (popass==2)
# radii of all stellar tracers from pop 1 and 2
R1 = np.sqrt((xs[sel1])**2 + (ys[sel1])**2)
R2 = np.sqrt((xs[sel2])**2 + (ys[sel2])**2)
R1.sort()
R2.sort()
for pop in np.arange(2)+1:
if pop == 1:
R0 = R1 # [pc]
Rhalf = R1[len(R1)/2]
coll_R1half.append(Rhalf)
co = 'blue'
else:
R0 = R2 # [pc]
Rhalf = R2[len(R2)/2]
coll_R2half.append(Rhalf)
co = 'red'
coll_R1half = np.array(coll_R1half)
coll_R2half = np.array(coll_R2half)
coll_Rdiffhalf = np.abs(coll_R1half-coll_R2half)
# select 3 assignments: one for median, one for median-1sigma, one for median+1sigma
med_Rdiff = np.median(coll_Rdiffhalf)
stdif = np.std(coll_Rdiffhalf)
min1s_Rdiff = med_Rdiff-stdif
max1s_Rdiff = med_Rdiff+stdif
#clf()
#hist(coll_Rdiffhalf, np.sqrt(len(coll_Rdiffhalf))/2)
#xlabel(r'$\Delta R/pc$')
#ylabel('count')
#axvline(med_Rdiff, color='r')
#axvline(min1s_Rdiff, color='g')
#axvline(max1s_Rdiff, color='g')
kmed = np.argmin(abs(coll_Rdiffhalf-med_Rdiff))
kmin1s = np.argmin(abs(coll_Rdiffhalf-min1s_Rdiff))
kmax1s = np.argmin(abs(coll_Rdiffhalf-max1s_Rdiff))
print('saving median, lower 68%, upper 68% stellar assignments')
np.savetxt(gpr.dir+'data/popass_median', coll_popass[kmed])
np.savetxt(gpr.dir+'data/popass_min1s', coll_popass[kmin1s])
np.savetxt(gpr.dir+'data/popass_max1s', coll_popass[kmax1s])
print('finished')