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 i in range(gpr.bins):
ind1 = np.argwhere( np.logical_and(rsi > binmin[i], rsi < binmax[i])).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
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
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():
# 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()
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(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():
# 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()
dispvelocity=numpy.zeros((bins,n))
a=numpy.zeros((bins,n))
p_dvlos = numpy.zeros(bins)
p_edvlos= numpy.zeros(bins)
for k in range(n):
rsi = rerror*numpy.random.randn(len(rs))+rs
vlosi = vrerror*numpy.random.randn(len(vlos))+vlos
for i in range(bins):
ind1 = numpy.argwhere(numpy.logical_and(rsi>binmin[i],rsi<binmax[i])).flatten()
a[i][k] = 1.*len(ind1)
vlos1 = vlosi[ind1]
if(len(ind1)<=1):
dispvelocity[i][k] = 0.
else:
dispvelocity[i][k] = meanbiweight(vlos1,ci_perc=68.4,ci_mean=True,ci_std=True)[1]
for i in range(bins):
dispvel = 1.*numpy.sum(dispvelocity[i])/n
ab = 1.*numpy.sum(a[i])/n
if(ab == 0):
dispvelerror = 0.0
else:
dispvelerror = dispvel/numpy.sqrt(ab)
p_dvlos[i] = dispvel
p_edvlos[i] = dispvelerror
maxvlos = max(p_dvlos)
d=open(dir+dwarf+'/velocitydispersionlos.txt','a')
for i in range(bins):
print(rbin[i],p_dvlos[i],p_edvlos[i], file=d)