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, 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)
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)
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)
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)
