def run():
print('input: ',gpr.get_com_file(0))
# start from data centered on COM already:
x,y,v = np.loadtxt(gpr.get_com_file(0),\
skiprows=1,usecols=(0,1,2),unpack=True) #[rscale], [rscale], [km/s]
# calculate 2D radius on the skyplane
r = np.sqrt(x**2+y**2) #[rscale]
# set number and size of (linearly spaced) bins
rmin = 0. #[rscale]
rmax = max(r) if gpr.rprior<0 else 1.0*gpr.rprior #[rscale]
print('rmax [rscale] = ', rmax)
sel = (r<rmax)
x = x[sel]; y = y[sel]; v = v[sel] #[rscale]
totmass = 1.*len(x) #[munit], munit = 1/star
if gp.lograd:
# space logarithmically in radius
binmin, binmax, rbin = bin_r_log(rmax/gpr.nbins, rmax, gpr.nbins)
elif gp.consttr:
binmin, binmax, rbin = bin_r_const_tracers(r, len(r)/gpr.nbins)
else:
binmin, binmax, rbin = bin_r_linear(rmin, rmax, gpr.nbins)
#volume of a circular bin from binmin to binmax
vol = np.zeros(gpr.nbins)
for k in range(gpr.nbins):
vol[k] = np.pi*(binmax[k]**2-binmin[k]**2) # [rscale**2]
# rs = gpr.rerror*np.random.randn(len(r))+r
rs = r #[rscale] # if no initial offset is whished
print('output: ')
print(gpr.get_ntracer_file(0))
tr = open(gpr.get_ntracer_file(0),'w')
print(totmass, file=tr)
tr.close()
print(gpr.get_dens_file(0))
de = open(gpr.get_dens_file(0),'w')
print(gpr.get_enc_mass_file(0))
em = open(gpr.get_enc_mass_file(0),'w')
print('r','nu(r)/nu(0)','error', file=de)
print('r','M(<r)','error', file=em)
# 30 iterations for getting random picked radius values
density = np.zeros((gpr.nbins,gpr.n))
a = np.zeros((gpr.nbins,gpr.n))
for k in range(gpr.n):
rsi = gpr.rerror * np.random.randn(len(rs)) + rs # [rscale]
for j in range(gpr.nbins):
ind1 = np.argwhere(np.logical_and(rsi>=binmin[j],rsi<binmax[j])).flatten() # [1]
density[j][k] = (1.*len(ind1))/vol[j]*totmass # [munit/rscale**2]
a[j][k] = 1.*len(ind1) #[1]
dens0 = np.sum(density[0])/(1.*gpr.n) # [munit/rscale**2]
print('dens0 = ',dens0,'[munit/rscale**2]')
crscale = open(gpr.get_params_file(0),'r')
rscale = np.loadtxt(crscale, comments='#', skiprows=1, unpack=False)
crscale.close()
cdens = open(gpr.get_params_file(0),'a')
print(dens0, file=cdens) # [munit/rscale**2]
print(dens0/rscale**2, file=cdens) # [munit/pc**2]
print(totmass, file=cdens) # [munit]
cdens.close()
ab0 = np.sum(a[0])/(1.*gpr.n) # [1]
denserr0 = dens0/np.sqrt(ab0) # [munit/rscale**2]
p_dens = np.zeros(gpr.nbins); p_edens = np.zeros(gpr.nbins)
for b in range(gpr.nbins):
dens = np.sum(density[b])/(1.*gpr.n) # [munit/rscale**2]
ab = np.sum(a[b])/(1.*gpr.n) # [1]
denserr = dens/np.sqrt(ab) # [munit/rscale**2]
denserror = np.sqrt((denserr/dens0)**2+(dens*denserr0/(dens0**2))**2) #[1]
if(math.isnan(denserror)):
denserror = 0. # [1]
## [PS]: TODO: change bin sizes to include same number of
## stars in each bin, not assigning wrong density as below
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]= denserror # [1] #100/rbin would be artificial guess
for b in range(gpr.nbins):
print(rbin[b],p_dens[b],p_edens[b], file=de) # [rscale], [dens0], [dens0]
indr = (r<binmax[b])
menclosed = 1.0*np.sum(indr)/totmass # /totmass for normalization to 1 at last bin #[totmass]
merror = menclosed/np.sqrt(ab) # artificial menclosed/10 gives good approximation #[totmass]
print(rbin[b],menclosed,merror, file=em) # [rscale], [totmass], [totmass]
# TODO: check: take rbinmax for MCMC?
de.close()
em.close()
if not gpr.showplots: return
ion(); subplot(111)
print('rbin = ',rbin)
print('p_dens = ',p_dens)
print('p_edens = ',p_edens)
plot(rbin,p_dens,'b',linewidth=3)
lbound = p_dens-p_edens; lbound[lbound<1e-6] = 1e-6
ubound = p_dens+p_edens;
fill_between(rbin,lbound,ubound,alpha=0.5,color='r')
# xscale('log');
yscale('log')
xlim([np.min(rbin),np.max(rbin)])
ylim([np.min(lbound),np.max(ubound)])
# ylim([1e-3,3.])#ylim([1e-6,2*np.max(p_dens)])
# ylim([0,1])
xlabel(r'$r [r_c]$')
ylabel(r'$\nu(r)/\nu(0)$')
# plt.legend(['\rho','\rho'],'lower left')
# title(fil)
# axes().set_aspect('equal')
savefig(gpr.get_dens_png(0))
if gpr.showplots:
ioff(); show(); clf()
def run():
xall, yall = np.loadtxt(gpr.get_com_file(0), skiprows=1, usecols=(0, 1), unpack=True) # 2*[Rscale]
# calculate 2D radius on the skyplane
R = np.sqrt(xall ** 2 + yall ** 2) # [Rscale]
# set number and size of (linearly spaced) bins
Rmin = 0.0 # [Rscale]
Rmax = max(r) if gpr.rprior < 0 else 1.0 * gpr.rprior # [Rscale]
print("Rmax [Rscale] = ", Rmax)
R = R[(R < Rmax)]
# determine radius once and for all
# this must not be changed between readout and gravlite run
# if you wish to change: set gp.getnewdata = True in gl_params.py
if gp.lograd:
print(gpr.nbins, " bins in log spacings")
Binmin, Binmax, Rbin = bin_r_log(Rmax / gpr.nbins, Rmax, gpr.nbins)
elif gp.consttr:
print(len(R) / gpr.nbins, " particles per bin")
Binmin, Binmax, Rbin = bin_r_const_tracers(R, len(R) / gpr.nbins)
else:
print(gpr.nbins, " bins in linear spacings")
Binmin, Binmax, Rbin = bin_r_linear(Rmin, Rmax, gpr.nbins)
# volume of a circular ring from binmin to binmax
vol = np.zeros(gpr.nbins)
for k in range(gpr.nbins):
vol[k] = 4.0 * np.pi / 3.0 * (Binmax[k] ** 3 - Binmin[k] ** 3) # [Rscale^3]
for comp in range(gpr.ncomp):
print("####### working on component ", comp)
print("input: ", gpr.get_com_file(comp) + "_3D")
# start from data centered on COM already:
if gfile.bufcount(gpr.get_com_file(comp) + "_3D") < 2:
continue
x, y, z, v = np.loadtxt(
gpr.get_com_file(comp) + "_3D", skiprows=1, usecols=(0, 1, 2, 3), unpack=True
) # 3*[Rscale], [km/s]
# calculate 2D radius on the skyplane
r = np.sqrt(x ** 2 + y ** 2) # [Rscale]
# set maximum radius (if gpr.rprior is set)
rmax = max(r) if gpr.rprior < 0 else 1.0 * gpr.rprior # [Rscale]
print("rmax [Rscale] = ", rmax)
sel = r <= rmax
x = x[sel]
y = y[sel]
z = z[sel]
v = v[sel]
r = r[sel] # [Rscale]
totmass = 1.0 * len(x) # [munit], munit = 1/star
rs = r # + possible starting offset, [Rscale]
vlos = v # + possible starting offset, [km/s]
print("output density: ")
print(gpr.get_ntracer_file(comp) + "_3D")
tr = open(gpr.get_ntracer_file(comp) + "_3D", "w")
print(totmass, file=tr)
tr.close()
print(gpr.get_dens_file(comp) + "_3D")
de = open(gpr.get_dens_file(comp) + "_3D", "w")
print("rbin", "binmin", "binmax", "nu(r)/nu(0)", "error", file=de)
print(gpr.get_enc_mass_file(comp) + "_3D")
em = open(gpr.get_enc_mass_file(comp) + "_3D", "w")
print("rbin", "binmin", "binmax", "M(<r)", "error", file=em)
# gpr.n=30 iterations for getting random picked radius values
density = np.zeros((gpr.nbins, gpr.n))
a = np.zeros((gpr.nbins, gpr.n)) # shared by density, siglos, kappa calcs
for k in range(gpr.n):
rsi = gpr.Rerror * np.random.randn(len(rs)) + rs # [Rscale]
vlosi = gpr.vrerror * np.random.randn(len(vlos)) + vlos # [km/s]
for i in range(gpr.nbins):
ind1 = np.argwhere(np.logical_and(rsi >= Binmin[i], rsi < Binmax[i])).flatten() # [1]
density[i][k] = (1.0 * len(ind1)) / vol[i] * totmass # [munit/Rscale^2]
vlos1 = vlosi[ind1] # [km/s]
a[i][k] = 1.0 * len(ind1) # [1]
# output density
dens0 = np.sum(density[0]) / (1.0 * gpr.n) # [munit/Rscale^3]
print("dens0 = ", dens0, " [munit/Rscale^3]")
crscale = open(gpr.get_params_file(comp) + "_3D", "r")
Rscale = np.loadtxt(crscale, comments="#", skiprows=1, unpack=False)
crscale.close()
cdens = open(gpr.get_params_file(comp) + "_3D", "a")
print(dens0, file=cdens) # [munit/Rscale^3]
print(dens0 / Rscale ** 3, file=cdens) # [munit/pc^3]
print(totmass, file=cdens) # [munit]
cdens.close()
ab0 = np.sum(a[0]) / (1.0 * gpr.n) # [1]
denserr0 = dens0 / np.sqrt(ab0) # [munit/Rscale^3]
p_dens = np.zeros(gpr.nbins)
p_edens = np.zeros(gpr.nbins)
for b in range(gpr.nbins):
dens = np.sum(density[b]) / (1.0 * gpr.n) # [munit/Rscale^3]
ab = np.sum(a[b]) / (1.0 * gpr.n) # [1]
denserr = dens / np.sqrt(ab) # [munit/Rscale^3]
denserror = np.sqrt((denserr / dens0) ** 2 + (dens * denserr0 / (dens0 ** 2)) ** 2) # [1]
if math.isnan(denserror):
denserror = 0.0 # [1]
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] = denserror # [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 = 1.0 * np.sum(indr) / totmass # for normalization to 1 # [totmass]
merror = menclosed / np.sqrt(ab) # artificial menclosed/10 # [totmass]
print(Rbin[b], Binmin[b], Binmax[b], menclosed, merror, file=em) # [rscale], 2*[totmass]
# TODO: check: take rbinmax for MCMC?
de.close()
em.close()
if not gpr.showplots:
continue
# plot density
ion()
subplot(111)
print("rbin = ", Rbin)
print("p_dens = ", p_dens)
print("p_edens = ", p_edens)
plot(Rbin, p_dens, "b", lw=1)
lbound = p_dens - p_edens
lbound[lbound < 1e-6] = 1e-6
ubound = p_dens + p_edens
fill_between(Rbin, lbound, ubound, alpha=0.5, color="r")
yscale("log")
xlim([0, gpr.rprior])
ylim([np.min(lbound), np.max(ubound)])
xlabel(r"$r [r_c]$")
ylabel(r"$\nu(r)/\nu(0)$")
savefig(gpr.get_dens_png(i) + "_3D.png")
ioff()
show()
clf()
def run():
# determine radius once and for all from all tracers
R, Phi, vzall = np.loadtxt(gpr.fileposspherical[0],
comments='#',unpack=True) # 2*[Rscale], [km/s]
# set number and size of (linearly spaced) bins
Rmin = 0. # [Rscale]
Rmax = max(R) if gpr.rprior<0 else 1.0*gpr.rprior # [Rscale]
print('Rmax [Rscale] = ', Rmax) # [Rscale]
R = R[(R<=Rmax)] # [Rscale]
# this must not be changed between readout and gravlite run
# if you wish to change: set gp.getnewdata = True in gl_params.py
if gp.lograd:
Binmin, Binmax, Rbin = bin_r_log(Rmax/gpr.nbins, Rmax, gpr.nbins)
print(gpr.nbins,' bins in log spacings')
elif gp.consttr:
Binmin, Binmax, Rbin = bin_r_const_tracers(R, len(R)/gpr.nbins)
print(len(R)/gpr.nbins,' particles per bin')
else:
Binmin, Binmax, Rbin = bin_r_linear(Rmin, Rmax, gpr.nbins)
print(gpr.nbins, ' bins in linear spacings')
# volume of a circular ring from binmin to binmax
Vol = np.zeros(gpr.nbins)
for k in range(gpr.nbins):
Vol[k] = np.pi*(Binmax[k]**2-Binmin[k]**2) # [Rscale^2]
for comp in range(gpr.ncomp):
print('####### working on component ',comp)
print('grh_MCMCbin: input: ',gpr.fileposspherical[comp])
# start from data centered on COM already:
if gfile.bufcount(gpr.fileposspherical[comp])<2: continue
R, Phi, v = np.loadtxt(gpr.fileposspherical[comp],\
comments='#',unpack=True)
# [Rscale], [1], [km/s]
# set maximum radius (if gpr.rprior is set)
Rmax = max(R) if gpr.rprior<0 else 1.0*gpr.rprior # [Rscale]
print('Rmax [Rscale] = ', Rmax)
sel = (R<=Rmax)
R = R[sel]; v = v[sel] # [Rscale], [km/s]
totmass = 1.*len(R) # [munit], munit = 1/star
Rs = R # + possible starting offset, [Rscale]
vlos = v # + possible starting offset, [km/s]
print('grh_MCMCbin: output density: ')
print(gpr.get_ntracer_file(comp))
tr = open(gpr.get_ntracer_file(comp),'w')
print(totmass, file=tr)
tr.close()
print(gpr.filedenfalloff[comp])
de = open(gpr.filedenfalloff[comp],'w')
print('Rbin [Rscale]','Binmin [Rscale]','Binmax [Rscale]',\
'Nu(R)/Nu(0) [1]','error [1]', file=de)
print(gpr.filemass[comp])
em = open(gpr.filemass[comp],'w')
print('R [Rscale]','Binmin [Rscale]','Binmax [Rscale]',\
'M(<Binmax) [Msun]','error [Msun]', file=em)
print('grh_MCMCbin: output siglos: ',gpr.filesig[comp])
sigfil = open(gpr.filesig[comp],'w')
print('R [Rscale]','Binmin [Rscale]','Binmax [Rscale]',\
'sigma_r(R) [km/s]','error [km/s]', file=sigfil)
print('grh_MCMCbin: output kurtosis: ',gpr.filekappa[comp])
kappafil = open(gpr.filekappa[comp],'w')
print('R [Rscale]','Binmin [Rscale]','Binmax [Rscale]',\
'kappa_los(R) [1]','error [1]', file=kappafil)
# gpr.n=30 iterations for getting random picked radius values
Density = np.zeros((gpr.nbins,gpr.n))
dispvelocity = np.zeros((gpr.nbins,gpr.n))
mom4 = np.zeros((gpr.nbins,gpr.n))
a = np.zeros((gpr.nbins,gpr.n))
# 'a' shared by density, siglos, kappa calcs
for k in range(gpr.n):
Rsi = gpr.Rerror * np.random.randn(len(Rs)) + Rs # [Rscale]
vlosi = gpr.vrerror * np.random.randn(len(vlos)) + vlos # [km/s]
for i in range(gpr.nbins):
ind1 = np.argwhere(np.logical_and(Rsi >= Binmin[i],Rsi<Binmax[i])).flatten() # [1]
Density[i][k] = (1.*len(ind1))/Vol[i]*totmass # [munit/rscale**2]
vlos1 = vlosi[ind1] # [km/s]
if(len(ind1)<=1):
dispvelocity[i][k] = dispvelocity[i-1][k]
mom4[i][k] = mom4[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
mom4[i][k] = kurtosis(vlos1, axis=0, fisher=False, bias=False) # [1]
a[i][k] = 1.*len(ind1) #[1]
# output density
Dens0 = np.sum(Density[0])/(1.*gpr.n) # [munit/Rscale^2]
print('Dens0 = ',Dens0,'[munit/Rscale^2]')
crscale = open(gpr.get_params_file(comp),'r')
Rscale = np.loadtxt(crscale, comments='#', unpack=False) # [pc]
crscale.close()
cdens = open(gpr.get_params_file(comp),'a')
print(Dens0, file=cdens) # [munit/Rscale^2]
print(Dens0*Rscale**2, file=cdens) # [munit/pc^2]
print(totmass, file=cdens) # [munit]
cdens.close()
ab0 = np.sum(a[0])/(1.*gpr.n) # [1]
Denserr0 = Dens0/np.sqrt(ab0) # [munit/Rscale^2]
P_dens = np.zeros(gpr.nbins); P_edens = np.zeros(gpr.nbins)
for b in range(gpr.nbins):
Dens = np.sum(Density[b])/(1.*gpr.n) # [munit/Rscale^2]
ab = np.sum(a[b])/(1.*gpr.n) # [1]
Denserr = Dens/np.sqrt(ab) # [munit/Rscale^2]
Denserror = np.sqrt((Denserr/Dens0)**2+(Dens*Denserr0/(Dens0**2))**2) # [1]
if(math.isnan(Denserror)):
Denserror = 0. # [1]
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]= Denserror # [1] #100/rbin would be artificial guess
print(Rbin[b],Binmin[b],Binmax[b],P_dens[b],P_edens[b], file=de)
# [Rscale], [dens0], [dens0]
indr = (R<Binmax[b])
Menclosed = 1.0*np.sum(indr)/totmass # for normalization to 1 # [totmass]
Merror = Menclosed/np.sqrt(ab) # or artificial Menclosed/10 # [totmass]
print(Rbin[b], Binmin[b], Binmax[b], Menclosed, Merror, file=em)
# [Rscale], 2* [totmass]
de.close()
em.close()
# output siglos
p_dvlos = np.zeros(gpr.nbins); p_edvlos = np.zeros(gpr.nbins)
for b in range(gpr.nbins):
dispvel = np.sum(dispvelocity[b])/gpr.n # [km/s]
ab = np.sum(a[b])/(1.*gpr.n) # [1]
if ab == 0:
dispvelerror = p_edvlos[b-1] # [km/s]
# attention! uses last error
else:
dispvelerror = dispvel/np.sqrt(ab) # [km/s]
p_dvlos[b] = dispvel # [km/s]
p_edvlos[b]= dispvelerror # [km/s]
maxvlos = max(p_dvlos) # [km/s]
print('maxvlos = ',maxvlos,'[km/s]')
fpars = open(gpr.get_params_file(comp),'a')
print(maxvlos, file=fpars) # [km/s]
fpars.close()
for b in range(gpr.nbins):
# [Rscale] [Rscale] [Rscale] [maxvlos] [maxvlos]
print(Rbin[b],Binmin[b],Binmax[b], np.abs(p_dvlos[b]/maxvlos),np.abs(p_edvlos[b]/maxvlos), file=sigfil)
# TODO: check uncommented /np.sqrt(n))
sigfil.close()
# output kurtosis kappa
p_kappa = np.zeros(gpr.nbins) # needed for plotting later
p_ekappa = np.zeros(gpr.nbins)
for b in range(gpr.nbins):
kappavel = np.sum(mom4[b])/gpr.n # [1]
ab = np.sum(a[b])/(1.*gpr.n) # [1]
if ab == 0:
kappavelerror = p_edvlos[b-1] # [1]
# attention! uses last error
else:
kappavelerror = np.abs(kappavel/np.sqrt(ab)) # [1]
p_kappa[b] = kappavel
p_ekappa[b] = kappavelerror
print(Rbin[b],Binmin[b],Binmax[b],\
kappavel, kappavelerror, file=kappafil) # 3*[Rscale], 2*[1]
# TODO: /np.sqrt(n))
kappafil.close()
if not gpr.showplots: continue
# plot density
ion(); subplot(111)
print('Rbin = ',Rbin)
print('P_dens = ',P_dens)
print('P_edens = ',P_edens)
plot(Rbin,P_dens,'b',lw=1)
lbound = P_dens-P_edens; lbound[lbound<1e-6] = 1e-6
ubound = P_dens+P_edens;
fill_between(Rbin, lbound, ubound, alpha=0.5, color='r')
yscale('log')
xlim([0,3.])
ylim([np.min(lbound), np.max(ubound)])
xlabel(r'$R [R_c]$')
ylabel(r'$\nu_{2D}(R)/\nu_{2D}(0)$')
savefig(gpr.get_dens_png(comp))
# from gl_analytic import Sigma_anf
# plot(Rbin, Sigma_anf(Rbin*Rscale)) # Sigma_anf argument in [pc] !
ioff(); show(); clf()
# plot siglos
ion(); subplot(111)
print('Rbin = ',Rbin,' Rscale')
print('p_dvlos = ',p_dvlos,' km/s')
print('p_edvlos = ',p_edvlos, 'km/s')
plot(Rbin,p_dvlos,'b',lw=1)
fill_between(Rbin,p_dvlos-p_edvlos,p_dvlos+p_edvlos,alpha=0.5,color='r') #[rscale],2*[km/s]
xlabel(r'$R [\mathrm{Rscale}]$')
ylabel(r'$\langle\sigma_{\mathrm{LOS}}\rangle [\mathrm{km/s}]$')
ylim([0.,1.5*max(p_dvlos)])
xlim([0,3])
savefig(gpr.get_siglos_png(comp))
# from gl_analytic import sig_los_anf
# plot(Rbin,sig_los_anf(Rbin*Rscale)) # argument must be [pc] !
ioff();show();clf()
# plot kappa
ion(); subplot(111)
print('Rbin = ',Rbin,' Rscale')
print('p_kappa = ',p_kappa)
print('p_ekappa = ',p_ekappa)
plot(Rbin,p_kappa,'b',lw=1)
fill_between(Rbin,p_kappa-p_ekappa,p_kappa+p_ekappa,alpha=0.5,color='r') #[rscale],2*[1]
xlabel(r'$R [\mathrm{Rscale}]$')
ylabel(r'$\langle\kappa_{\mathrm{LOS}}\rangle [1]$')
ylim([0,5])
xlim([0,3])
savefig(gpr.get_kurtosis_png(comp))
ioff();show();clf()
print 'input:'
print gpr.fileposspherical
r,phi = np.loadtxt(gpr.fileposspherical, unpack=True, skiprows=1)
# rs=rerror*np.random.randn(len(r))+r
# rs not changed later on, ever: misplacement, for all realizations. wanted?
# yes, we scatter radii in foo_pool
rs = r
rmin = min(rs); rmax = max(rs)
if gp.lograd:
print gpr.nbins,' bins in log spacings'
binmin, binmax, rbin = bin_r_log(rmax/gpr.nbins, rmax, gpr.nbins)
elif gp.consttr:
print len(r)/gpr.nbins,' particles per bin'
binmin, binmax, rbin = bin_r_const_tracers(rs, len(rs)/gpr.nbins)
else:
print gpr.nbins, ' bins in linear spacings'
binmin, binmax, rbin = bin_r_linear(rmin, rmax, gpr.nbins)
# volume of a bin with height binlength, 2D
vol=np.zeros(gpr.bins)
for i in range(gpr.bins):
vol[i] = np.pi*(binmax[i]**2-binmin[i]**2)
def foo_pool(k):
rsi = gpr.rerror*np.random.randn(len(rs))+rs
locdens = []; loca = []
for i in range(gpr.bins):
ind1 = np.argwhere( np.logical_and(rsi > binmin[i], rsi < binmax[i])).flatten()
def run():
for comp in range(gpr.ncomp):
print('input:')
print(gpr.fileposspherical[comp])
R,Phi,vz = np.loadtxt(gpr.fileposspherical[comp], unpack=True, skiprows=1)
vz /= 0.482126 # [km/s], for G = L = M = 1
Rs = R[:] # gpr.Rerror*np.random.randn(len(r))+r # for initial offset
vzs = vz[:] # gpr.vrerror*np.random.randn(len(vx))+vx # same same
Rmin = min(Rs); Rmax = max(Rs)
if gp.lograd:
Binmin, Binmax, Rbin = bin_r_log(Rmax/gpr.nbins, Rmax, gpr.nbins)
print(gpr.nbins,' bins in log spacings')
elif gp.consttr:
Binmin, Binmax, Rbin = bin_r_const_tracers(Rs, len(Rs)/gpr.nbins)
print(len(R)/gpr.nbins,' particles per bin')
else:
Binmin, Binmax, Rbin = bin_r_linear(Rmin, Rmax, gpr.nbins)
print(gpr.nbins, ' bins in linear spacings')
# if Dispvel is [] still after pool call,
# some error occured inside following function:
def foo_pool(k):
Rsi = gpr.Rerror * np.random.randn(len(Rs))+Rs
vzsi = gpr.vrerror * np.random.randn(len(vzs))+vzs
locdisp=[]; loca = []
for b in range(gpr.bins):
ind1 = np.argwhere( np.logical_and(Rsi > Binmin[b],\
Rsi <= Binmax[b])).flatten()
vz1 = vzsi[ind1]
locdisp.append(meanbiweight(vz1,\
ci_perc=68.4,ci_mean=True,\
ci_std=True)[1])
loca.append(len(ind1))
return locdisp,loca
Dispvel=[]; alog=[]
def log_result(result):
# This is called whenever foo_pool(i) returns a result.
# result_list is modified only by the main process, not the pool workers
dis, alo = result
Dispvel.append(dis)
alog.append(alo)
pool = mp.Pool(processes=gpr.procs)
for k in range(gpr.nit):
pool.apply_async(foo_pool, args = (k, ), callback = log_result)
pool.close()
pool.join()
Sigarr = np.array(Dispvel)
abarr = np.array(alog)
print('output:')
print(gpr.filesig)
filesig = open(gpr.filesig[comp],'w')
print('R [pc]','Binmin [pc]','Binmax [pc]','Sigma_los(R) [km/s]','error [km/s]', file=filesig)
P_Sigma = np.zeros(gpr.bins); P_ESigma = np.zeros(gpr.bins)
for b in range(gpr.bins):
P_Sigma[b] = np.sum(Sigarr[:,b])/gpr.nit
ab = np.sum(abarr[:,b])/gpr.nit
P_ESigma[b] = P_Sigma[b]/np.sqrt(ab)
print(Rbin[b],Binmin[b],Binmax[b],P_Sigma[b],P_ESigma[b], file=filesig)
print(Rbin[b],P_Sigma[b],P_ESigma[b])
filesig.close()
if not gpr.showplots: continue
# plot siglos
ion(); subplot(111)
plot(Rbin,P_Sigma,'b',lw=1)
fill_between(Rbin,P_Sigma-P_ESigma,P_Sigma+P_ESigma,alpha=0.5,color='r') #[rscale],2*[km/s]
xlabel(r'$R [\mathrm{Rscale}]$')
ylabel(r'$\langle\sigma_{\mathrm{LOS}}\rangle [\mathrm{km/s}]$')
# ylim([-5,30])
# xlim([0,3])
savefig(gpr.get_siglos_png(comp))
if gpr.showplots:
ioff();show();clf()
return
def run():
for comp in range(gpr.ncomp):
print('input:',gpr.fileposspherical[comp])
R, Phi, vlos = np.loadtxt(gpr.fileposspherical[comp],\
comments='#', unpack=True)
Totmass = 1.*len(R) # [munit], munit = 1/star
# Rs=gpr.Rerror*np.random.randn(len(r))+r
# Rs not changed later on, ever: misplacement, for all realizations. wanted?
# yes, we scatter radii in foo_pool
Rs = R; Rmin = min(Rs); Rmax = max(Rs)
if gp.lograd:
print(gpr.nbins,' bins in log spacings')
Binmin, Binmax, Rbin = bin_r_log(Rmax/gpr.nbins, Rmax, gpr.nbins)
elif gp.consttr:
Binmin, Binmax, Rbin = bin_r_const_tracers(Rs, len(Rs)/gpr.nbins)
print(len(R)/gpr.nbins,' particles per bin')
else:
print(gpr.nbins, ' bins in linear spacings')
Binmin, Binmax, Rbin = bin_r_linear(Rmin, Rmax, gpr.nbins)
# volume of a bin with height binlength, 2D
Vol = np.zeros(gpr.bins)
for i in range(gpr.bins):
Vol[i] = np.pi*(Binmax[i]**2-Binmin[i]**2)
# gpr.n=30 iterations for getting random picked radius values
Density = np.zeros((gpr.nbins,gpr.n))
A = np.zeros((gpr.nbins,gpr.n)) # shared by density, siglos, kappa calcs
for k in range(gpr.n):
Rsi = gpr.Rerror * np.random.randn(len(Rs)) + Rs # [Rscale]
vlosi = gpr.vrerror * np.random.randn(len(vlos)) + vlos
for i in range(gpr.nbins):
ind1 = np.argwhere(np.logical_and(Rsi>=Binmin[i], Rsi<Binmax[i])).flatten() # [1]
Density[i][k] = (1.*len(ind1))/Vol[i]*Totmass # [munit/Rscale^2]
vlos1 = vlosi[ind1] # [km/s]
A[i][k] = 1.*len(ind1) # [1]
# output density
Dens0 = np.sum(Density[0])/(1.*gpr.n) # [munit/Rscale^3]
print('Dens0 = ',Dens0,' [munit/Rscale^2]')
crscale = open(gpr.get_params_file(comp),'r')
Rscale = np.loadtxt(cscale, comments='#', unpack=False)
crscale.close()
cdens = open(gpr.get_params_file(comp),'a')
print(Dens0,file=cdens) # [munit/Rscale^2]
print(Dens0/Rscale**2,file=cdens) # [munit/pc^2]
print(Totmass,file=cdens) # [munit]
cdens.close()
print(gpr.get_dens_file(comp))
de = open(gpr.get_dens_file(comp),'w')
print('# Rbin [Rscale]','Binmin [Rscale]','Binmax [Rscale]',
'Nu(R)/Nu(0) [1]','error [1]', file=de)
print(gpr.get_enc_mass_file(comp))
em = open(gpr.get_enc_mass_file(comp),'w')
print('# R [Rscale]','Binmin [Rscale]','Binmax [Rscale]',\
'M(<Binmax) [Msun]','error [Msun]', file=em)
AB0 = np.sum(A[0])/(1.*gpr.n) # [1]
Denserr0 = Dens0/np.sqrt(AB0) # [munit/Rscale^3]
P_dens = np.zeros(gpr.nbins); P_edens = np.zeros(gpr.nbins)
for b in range(gpr.nbins):
Dens = np.sum(Density[b])/(1.*gpr.n) # [munit/Rscale^3]
AB = np.sum(A[b])/(1.*gpr.n) # [1]
Denserr = Dens/np.sqrt(AB) # [munit/Rscale^3]
Denserror = np.sqrt((Denserr/Dens0)**2+(Dens*Denserr0/(Dens0**2))**2) #[1]
if(math.isnan(Denserror)):
Denserror = 0. # [1]
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]= Denserror # [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]
# normalization to 1:
indr = (R<Binmax[b])
menclosed = 1.0*np.sum(indr)/Totmass # [Totmass]
merror = menclosed/np.sqrt(AB) # artificial menclosed/10 # [Totmass]
print(Rbin[b], Binmin[b], Binmax[b], menclosed, merror, file=em)
# [rscale], 2*[Totmass]
# TODO: check: take rbinmax for MCMC?
de.close()
em.close()
if not gpr.showplots:
continue
# plot density
ion(); subplot(111)
print('Rbin = ',Rbin)
print('P_dens = ',P_dens)
print('P_edens = ',P_edens)
plot(Rbin,P_dens,'b',lw=1)
lbound = P_dens-P_edens; lbound[lbound<1e-6] = 1e-6
ubound = P_dens+P_edens;
fill_between(Rbin, lbound, ubound, alpha=0.5, color='r')
yscale('log')
# xlim([0,3.])
# ylim([np.min(lbound),np.max(ubound)])
xlabel(r'$R [R_c]$')
ylabel(r'$\nu_{2D}(R)/\nu_{2D}(0)$')
savefig(gpr.get_dens_png(i))
if gpr.showplots:
ioff(); show(); clf()
return
def run():
for comp in range(gpr.ncomp):
print('input:', gpr.fileposspherical[comp])
R,Phi,vlos = np.loadtxt(gpr.fileposspherical[comp],\
comments='#', unpack=True)
Rs = R[:] # gpr.Rerror*np.random.randn(len(r))+r # for initial offset
vloss = vlos[:] # gpr.vrerror*np.random.randn(len(vx))+vx # same same
Rmin = min(Rs); Rmax = max(Rs)
if gp.lograd:
print(gpr.nbins,' bins in log spacings')
Binmin, Binmax, Rbin = bin_r_log(Rmax/gpr.nbins, Rmax, gpr.nbins)
elif gp.consttr:
print(len(R)/gpr.nbins,' particles per bin')
Binmin, Binmax, Rbin = bin_r_const_tracers(Rs, len(Rs)/gpr.nbins)
else:
print(gpr.nbins, ' bins in linear spacings')
Binmin, Binmax, Rbin = bin_r_linear(Rmin, Rmax, gpr.nbins)
# if Dispvel is [] still after pool call,
# some error occured inside following function:
def foo_pool(k):
Rsi = gpr.Rerror * np.random.randn(len(Rs))+Rs
vlossi = gpr.vrerror * np.random.randn(len(vloss))+vloss
lockappa=[]; loca = []
for b in range(gpr.bins):
ind1 = np.argwhere( np.logical_and(Rsi > Binmin[b],\
Rsi <= Binmax[b])).flatten()
vlos1 = vlossi[ind1]
lockappa.append(kurtosis(vlossi, axis=0,\
fisher=False, bias=False))
loca.append(len(ind1))
return lockappa,loca
Kappavel=[]; alog=[]
def log_result(result):
# This is called whenever foo_pool(i) returns a result.
# result_list is modified only by main process, not pool workers.
kappa, alo = result
Kappavel.append(kappa)
alog.append(alo)
pool = mp.Pool(processes=gpr.procs)
for k in range(gpr.nit):
pool.apply_async(foo_pool, args = (k, ), callback = log_result)
pool.close()
pool.join()
Kappaarr = np.array(Kappavel)
abarr = np.array(alog)
print('output:', gpr.filekappa[comp])
filekappa = open(gpr.filekappa[comp],'w')
print('# R [pc]','Binmin [pc]','Binmax [pc]',\
'Kappa_los(R) [1]','error [1]', file=filekappa)
for b in range(gpr.bins):
Kappa = np.sum(Kappaarr[:,b])/gpr.nit
ab = np.sum(abarr[:,b])/gpr.nit
Kappavelerror = Kappa/np.sqrt(ab)
print(Rbin[b], Binmin[b], Binmax[b], \
Kappa, Kappavelerror, file=filekappa)
print(Rbin[b], Kappa, Kappavelerror)
filekappa.close()
return
def run():
xall,yall = np.loadtxt(gpr.get_com_file(0),skiprows=1,usecols=(0,1),unpack=True) # 2*[rcore]
# calculate 2D radius on the skyplane
r = np.sqrt(xall**2+yall**2) #[rcore]
# set number and size of (linearly spaced) bins
rmin = 0. #[rcore]
rmax = max(r) if gpr.rprior<0 else 1.0*gpr.rprior #[rcore]
print 'rmax [rcore] = ', rmax
r = r[(r<rmax)]
# determine radius once and for all
if gp.lograd:
print gpr.nbins,' bins in log spacings'
binmin, binmax, rbin = bin_r_log(rmax/gpr.nbins, rmax, gpr.nbins)
elif gp.consttr:
print len(r)/gpr.nbins,' particles per bin'
binmin, binmax, rbin = bin_r_const_tracers(r, len(r)/gpr.nbins)
else:
print gpr.nbins, ' bins in linear spacings'
binmin, binmax, rbin = bin_r_linear(rmin, rmax, gpr.nbins)
# volume of a circular ring from binmin to binmax
vol = np.zeros(gpr.nbins)
for k in range(gpr.nbins):
vol[k] = np.pi*(binmax[k]**2-binmin[k]**2) # [rcore^2]
for comp in range(gpr.ncomp):
print 'comp = ',comp
print 'input: ',gpr.get_com_file(comp)
# start from data centered on COM already:
if gfile.bufcount(gpr.get_com_file(comp))<2: continue
x,y,vlos = np.loadtxt(gpr.get_com_file(comp),\
skiprows=1,usecols=(0,1,2),unpack=True) #[rcore], [rcore], [km/s]
# calculate 2D radius on the skyplane
r = np.sqrt(x**2+y**2) #[rcore]
# set maximum radius (if gpr.rprior is set)
rmax = max(r) if gpr.rprior<0 else 1.0*gpr.rprior #[rcore]
print 'rmax [rcore] = ', rmax
sel = (r<=rmax)
x = x[sel]; y = y[sel]; vlos = vlos[sel]; r = r[sel] #[rcore]
totmass = 1.*len(x) #[munit], munit = 1/star
rs = r
# no offset from the start!
# rs = gpr.rerror*np.random.randn(len(r))+r #[rcore]
# vlos = gpr.vrerror*np.random.randn(len(vlos))+vlos #[km/s]
print 'output: ',gpr.get_siglos_file(comp)
vfil = open(gpr.get_siglos_file(comp),'w')
print >> vfil,'r','sigma_r(r)','error'
# 30 iterations for drawing a given radius in bin
dispvelocity = np.zeros((gpr.nbins,gpr.n))
a = np.zeros((gpr.nbins,gpr.n))
p_dvlos = np.zeros(gpr.nbins)
p_edvlos = np.zeros(gpr.nbins)
for k in range(gpr.n):
rsi = gpr.rerror*np.random.randn(len(rs))+rs #[rcore]
vlosi = gpr.vrerror*np.random.randn(len(vlos))+vlos #[km/s]
for i in range(gpr.nbins):
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(gpr.nbins):
dispvel = np.sum(dispvelocity[i])/gpr.n #[km/s]
ab = np.sum(a[i])/(1.*gpr.n) #[1]
if ab == 0:
dispvelerror = p_edvlos[i-1] #[km/s]
# attention! uses last error
else:
dispvelerror = dispvel/np.sqrt(ab) #[km/s]
p_dvlos[i] = dispvel #[km/s]
p_edvlos[i]= dispvelerror #[km/s]
maxvlos = max(p_dvlos) #[km/s]
print 'maxvlos = ',maxvlos,'[km/s]'
fpars = open(gpr.get_params_file(comp),'a')
print >> fpars,maxvlos #[km/s]
fpars.close()
for i in range(gpr.nbins):
# [rcore] [maxvlos] [maxvlos]
print >> vfil,rbin[i], np.abs(p_dvlos[i]/maxvlos),np.abs(p_edvlos[i]/maxvlos) #/np.sqrt(n))
vfil.close()
if not gp.testplot_read: continue
ion(); subplot(111)
print 'rbin = ',rbin,' rcore'
print 'p_dvlos = ',p_dvlos,' km/s'
print 'p_edvlos = ',p_edvlos, 'km/s'
plot(rbin,p_dvlos,'b',lw=1)
fill_between(rbin,p_dvlos-p_edvlos,p_dvlos+p_edvlos,alpha=0.5,color='r') #[rcore],[km/s],[km/s]
xlabel(r'$r [\mathrm{rcore}]$')
ylabel(r'$\langle\sigma_{\mathrm{LOS}}\rangle [\mathrm{km/s}]$')
ylim([-5,30])
# xscale('log')
xlim([np.min(rbin),np.max(rbin)])
#plt.legend(['\rho','\rho'],'lower left'); #title(dwarf)
savefig(gpr.get_siglos_png(comp))
if gpr.showplots:
ioff();show();clf()
def run():
# get radius, used for all binning
print('input:')
print(gpr.get_com_file(0))
if gfile.bufcount(gpr.get_com_file(0))<2: return
x,y,vlos = np.loadtxt(gpr.get_com_file(0), skiprows=1, unpack=True) #2*[rscale], [km/s]
totmass = 1.*len(x) # [munit], [Msun], where each star is weighted with the same mass
r = np.sqrt(x*x+y*y) # [rscale]
#set binning
#gpr.nbins = (max - min)*N^(1/3)/(2*(Q3-Q1)) #(method of wand)
rmin = 0. # [rscale]
rmax = max(r) if gpr.rprior<0 else 1.0*gpr.rprior # [rscale]
if gp.lograd:
# space logarithmically in radius
binmin, binmax, rbin = bin_r_log(rmax/gpr.nbins, rmax, gpr.nbins)
elif gp.consttr:
binmin, binmax, rbin = bin_r_const_tracers(r, len(r)/gpr.nbins)
else:
binmin, binmax, rbin = bin_r_linear(rmin, rmax, gpr.nbins)
# offset from the start!
rs = gpr.rerror*np.random.randn(len(r))+r #[rscale]
vlos = gpr.vrerror*np.random.randn(len(vlos))+vlos #[km/s]
print('output: ',gpr.get_siglos_file(0))
vfil = open(gpr.get_siglos_file(0),'w')
print('r','sigma_r(r)','error', file=vfil)
# 30 iterations for drawing a given radius in bin
dispvelocity = np.zeros((gpr.nbins,gpr.n))
a = np.zeros((gpr.nbins,gpr.n))
p_dvlos = np.zeros(gpr.nbins)
p_edvlos = np.zeros(gpr.nbins)
for k in range(gpr.n):
rsi = gpr.rerror*np.random.randn(len(rs))+rs #[rscale]
vlosi = gpr.vrerror*np.random.randn(len(vlos))+vlos #[km/s]
for i in range(gpr.nbins):
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(gpr.nbins):
dispvel = np.sum(dispvelocity[i])/gpr.n #[km/s]
ab = np.sum(a[i])/(1.*gpr.n) #[1]
if ab == 0:
dispvelerror = p_edvlos[i-1] #[km/s]
# attention! uses last error
else:
dispvelerror = dispvel/np.sqrt(ab) #[km/s]
p_dvlos[i] = dispvel #[km/s]
p_edvlos[i]= dispvelerror #[km/s]
maxvlos = max(p_dvlos) #[km/s]
print('maxvlos = ',maxvlos,'[km/s]')
fpars = open(gpr.get_params_file(0),'a')
print(maxvlos, file=fpars) #[km/s]
fpars.close()
import shutil
shutil.copy2(gpr.get_params_file(0), gpr.get_params_file(1))
for i in range(gpr.nbins):
# [rscale] [maxvlos] [maxvlos]
print(rbin[i], np.abs(p_dvlos[i]/maxvlos),np.abs(p_edvlos[i]/maxvlos), file=vfil) #/np.sqrt(n))
vfil.close()
if not gpr.showplots: return
ion(); subplot(111)
print('rbin = ',rbin,' rscale')
print('p_dvlos = ',p_dvlos,' km/s')
print('p_edvlos = ',p_edvlos, 'km/s')
plot(rbin,p_dvlos,'b',linewidth=3)
fill_between(rbin,p_dvlos-p_edvlos,p_dvlos+p_edvlos,alpha=0.5,color='r') #[rscale],[km/s],[km/s]
xlabel(r'$r [rscale]$')
ylabel(r'$\langle\sigma_{LOS}\rangle [km/s]$')
ylim([-5,30])
# xscale('log')
xlim([np.min(rbin),np.max(rbin)])
#plt.legend(['\rho','\rho'],'lower left'); #title(dwarf)
savefig(gpr.get_siglos_png(0))
if gpr.showplots:
ioff();show();clf()