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)