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)
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)