def run():
print('input:')
print(gpr.fil)
x0,y0,vlos = np.genfromtxt(gpr.fil, skiprows=0, unpack = True,
usecols = (0,1,5))
# use only 3000 random particles:
pdb.set_trace()
ind = np.arange(len(x0))
np.random.shuffle(ind)
ind = ind[:3000]
x0 = x0[ind]; y0 = y0[ind]; vlos = vlos[ind]
x0 *= 1000. # [pc]
y0 *= 1000. # [pc]
# center of mass with means
#com_x, com_y = com_mean(x0,y0) # [TODO]
# shrinking sphere method
com_x, com_y, com_vz = com_shrinkcircle_v_2D(x0,y0, vlos)
print('COM [pc]: ', com_x, com_y)
print('VOM [km/s]', com_vz)
x0 -= com_x; y0 -= com_y # [pc]
vlos -= com_vz #[km/s]
rc = np.sqrt(x0**2+y0**2) # [pc]
rc.sort() # [pc]
for i in range(len(rc)-1):
if rc[i]>rc[i+1]: #[pc]
print('sorting error!')
exit(1)
rhalf = rc[floor(len(rc)/2)] # [pc]
rscale = rhalf # or gpr.r_DM # [pc]
print('rscale = ',rscale,' pc')
print('max(r) = ',max(rc),' pc')
print('last element of r : ',rc[-1],' pc')
print('total number of stars: ',len(rc))
r0 = np.sqrt(x0**2+y0**2)/rscale
sel = (r0<gpr.rprior)
x = x0[sel]/rscale; y = y0[sel]/rscale # [r_scale]
vz=vlos[sel]
m = np.ones(len(x))
r = np.sqrt(x*x+y*y) #[r_scale]
# print("x y z") on first line, to interprete data later on
crscale = open(gpr.get_params_file(0),'w')
print('# rscale in [pc], surfdens_central (=dens0) in [munit/rscale**2], and in [munit/pc**2], and totmass [munit], and max(v_LOS) in [km/s]', file=crscale)
print(rscale, file=crscale)
crscale.close()
print('output: ',gpr.get_com_file(0))
c = open(gpr.get_com_file(0),'w')
print('# x [rscale],','y [rscale],','vLOS [km/s],','rscale = ',rscale,' pc', file=c)
for k in range(len(x)):
print(x[k],y[k],vz[k], file=c) #[rscale], [rscale], [km/s]
c.close()
if not gpr.showplots: return
ion(); ax = subplot(111)
# res = (abs(x)<3)*(abs(y)<3)
# x = x[res]; y = y[res] #[rscale]
en = len(x)
H, xedges, yedges = np.histogram2d(x, y, bins=(30,30), range=[[-3.,3.], [-3.,3.]])
extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]
subplots_adjust(bottom=0.15, left=0.15)
imshow(H, interpolation='bilinear', origin='lower',
cmap=cm.gray, extent=(-3,3,-3,3))
levels = np.logspace(2,4,10)
cset = contour(H, levels, origin='lower', extent=extent) #cmap=cm.gray
clabel(cset, inline=1, fontsize=8, fmt='%1.0i')
for c in cset.collections:
c.set_linestyle('solid')
# scatter(x[:en], y[:en], s=35, vmin=0.95, vmax=1.0, lw=0.0, alpha=0.2)
# xscale('log'); yscale('log')
circ_HL=Circle((0,0), radius=rscale/rscale, fc='None', ec='g', lw=3)
circ_DM=Circle((0,0), radius=gpr.r_DM/rscale, fc='None', ec='r', lw=3)
gca().add_patch(circ_HL); gca().add_patch(circ_DM)
# visible region
axes().set_aspect('equal')
xlabel(r'$x [R_s]$'); ylabel(r'$y [R_s]$')
# title(gpr.fil)
savefig(gpr.get_com_png(0))
if gpr.showplots:
ioff();show();clf()
vznew = (vz-com_vz)*np.sqrt(gp.G1*gp.Mscale/gp.ascale) # [km/s], from conversion from system with L=G=M=1
R0 = np.sqrt(xnew**2+ynew**2) # [pc]
R0.sort() # [pc]
Rhalf = R0[len(R0)/2] # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
print('Rscale = ', Rscale)
# TODO: save Rscale in scale file
xnew /= Rscale; ynew /= Rscale # [rscale]
# only for 0 (all) and 1 (first and only population)
for comp in range(gpr.ncomp):
crscale = open(gpr.get_params_file(comp),'w')
print('# Rscale in [pc],',' surfdens_central (=dens0) in [munit/rscale**2],',\
' and in [munit/pc**2],',' and totmass [munit],',\
' and max(v_LOS) in [km/s]', file=crscale)
print(Rscale, file=crscale)
crscale.close()
print('grh_com: output: ', gpr.fileposcenter[comp])
filepos = open(gpr.fileposcenter[comp],'w')
print('# x [Rscale]','y [Rscale]','vLOS [km/s]', file=filepos)
for k in range(ndm):
print(xnew[k], ynew[k], vznew[k], file=filepos)
filepos.close()
print('')
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():
print('input: ', gpr.fil)
x0,y0,z0,vz0,vb0,Mg0,PM0,comp0 = np.genfromtxt(gpr.fil, skiprows = 0, unpack = True,\
usecols=(0,1,2,11,12,13,19,20),\
dtype="d17",\
converters={0:expDtofloat, # x0 in pc \
1:expDtofloat, # y0 in pc \
2:expDtofloat, # z0 in pc \
11:expDtofloat, # vz0 in km/s\
12:expDtofloat, # vb0(LOS due binary), km/s\
13:expDtofloat, # Mg0 in Angstrom\
19:expDtofloat, # PM0 [1]\
20:expDtofloat}) # comp0 1,2,3(background)
# TODO: use component 6-1 instead of 12-1 for z velocity, to include observational errors
# only use stars which are members of the dwarf: exclude pop3 by construction
pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination, outliers
PM0 = PM0[pm]
comp0 = comp0[pm]; x0=x0[pm]; y0=y0[pm]; z0=z0[pm]; vz0=vz0[pm]; vb0=vb0[pm]; Mg0=Mg0[pm]
pm1 = (comp0 == 1) # will be overwritten below if gp.metalpop
pm2 = (comp0 == 2) # same same
if gp.metalpop:
# drawing of populations based on metallicity
# get parameters from function in pymcmetal.py
import pymcmetal as pmc
p,mu1,sig1,mu2,sig2, M = pmc.bimodal_gauss(Mg0)
pm1, pm2 = pmc.assign_pop(Mg0,p,mu1,sig1,mu2,sig2)
# output: changed pm1, pm2
# cutting pm_i to a maximum of ntracers particles:
from random import shuffle
ind = np.arange(len(x0))
np.random.shuffle(ind)
ind = ind[:gp.files.ntracer]
x0=x0[ind]; y0=y0[ind]; z0=z0[ind]; comp0=comp0[ind]; vz0=vz0[ind]; vb0=vb0[ind]; Mg0=Mg0[ind]
PM0 = PM0[ind]; pm1 = pm1[ind]; pm2 = pm2[ind]; pm = pm1+pm2
# get center of mass with means
#com_x, com_y,com_z = com_mean(x0,y0,z0,PM0) # [TODO], and TODO: z component included if available
# get COM with shrinking sphere method
com_x, com_y, com_z, com_vz = com_shrinkcircle_v(x0, y0, z0, vz0, PM0)
print('COM [pc]: ', com_x, com_y, com_z)
print('VOM [km/s]', com_vz)
# from now on, work with 2D data only; z0 was only used to get center in (x,y) better
x0 -= com_x; y0 -= com_y # [pc]
vz0 -= com_vz #[km/s]
R0 = np.sqrt(x0**2+y0**2) # [pc]
Rc = R0 # [pc]
Rc.sort() # [pc]
for i in range(len(Rc)-1):
if Rc[i]>Rc[i+1]: # [pc]
print('sorting error!')
exit(1)
Rhalf = Rc[floor(len(Rc)/2)] # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
# Rscale = gpr.r_DM # deleted, we only work with data
print('Rscale = ',Rscale,' pc')
print('max(R) = ',max(Rc),' pc')
print('last element of R : ',Rc[-1],' pc')
print('total number of stars: ',len(Rc))
x0 = x0/Rscale; y0 = y0/Rscale # [Rscale]
i = -1
for pmn in [pm,pm1,pm2]:
pmr = (R0<(gpr.rprior*Rscale)) # TODO: read from gl_class_file
pmn = pmn*pmr # [1]
print("fraction of members = ",1.0*sum(pmn)/len(pmn))
i = i+1
x=x0[pmn]; y=y0[pmn]; vz=vz0[pmn]; vb=vb0[pmn]; # [1], [km/s]
Mg=Mg0[pmn]; comp=comp0[pmn]; PMN=PM0[pmn] # [ang], [1], [1]
m = np.ones(len(pmn))
R = np.sqrt(x*x+y*y) # [Rscale]
# print("x y z" on first line, to interprete data later on)
crscale = open(gpr.get_params_file(i),'w')
print('# Rscale in [pc], surfdens_central (=dens0) in [munit/rscale**2], and in [munit/pc**2], and totmass [munit], and max(v_LOS) in [km/s]', file=crscale)
print(Rscale, file=crscale)
crscale.close()
print('output: ',gpr.get_com_file(i))
c = open(gpr.get_com_file(i),'w')
print('# x [Rscale],','y [Rscale],','vLOS [km/s],','Rscale = ',Rscale,' pc', file=c)
for k in range(len(x)):
print(x[k],y[k],vz[k], file=c) # [Rscale], [Rscale], [km/s]
c.close()
if not gpr.showplots: continue
ion(); subplot(111)
res = (abs(x)<3)*(abs(y)<3)
x = x[res]; y = y[res] # [Rscale]
en = len(x)
if en == 0: continue
scatter(x[:en], y[:en], c=pmn[:en], s=35, vmin=0.95, vmax=1.0, lw=0.0, alpha=0.2)
# xscale('log'); yscale('log')
if i == 0: colorbar()
circ_HL=Circle((0,0), radius=Rscale/Rscale, fc='None', ec='g', lw=1)
circ_DM=Circle((0,0), radius=gpr.r_DM/Rscale, fc='None', ec='r', lw=1)
gca().add_patch(circ_HL); gca().add_patch(circ_DM)
# visible region
maxr = max(np.abs(x)); mayr = max(np.abs(y)) #[rscale]
width2 = max([maxr,mayr]) #[rscale]
xlim([-width2,width2]); ylim([-width2,width2])
axes().set_aspect('equal')
xlabel(r'$x [R_s]$'); ylabel(r'$y [R_s]$')
# title(gpr.fil)
savefig(gpr.get_com_png(i))
if gpr.showplots:
ioff();show();clf()
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()
x0 = x0/rscale; y0 = y0/rscale # [r_scale]
i = -1
for pmn in [pm,pm1,pm2,pm3]:
pmr = (r0<(gpr.rprior*rscale)) # TODO: read from gl_class_file
pmn = pmn*pmr # [1]
print("fraction of members = ",1.0*sum(pmn)/len(pmn))
i = i+1
x=x0[pmn]; y=y0[pmn]; z=z0[pmn]; vz=vz0[pmn]; vb=vb0[pmn]; #[1], [km/s]
Mg=Mg0[pmn]; comp=comp0[pmn]; PMN=PM0[pmn] # [ang], [1], [1]
m = np.ones(len(pmn))
r = np.sqrt(x*x+y*y) #[r_scale]
# print "x y z" on first line, to interprete data later on
crscale = open(gpr.get_params_file(i),'w')
print('# rscale in [pc], surfdens_central (=dens0) in [munit/rscale**2], and in [munit/pc**2], and totmass [munit], and max(v_LOS) in [km/s]', file=crscale)
print(rscale, file=crscale)
crscale.close()
print('output: ',gpr.get_com_file(i))
c = open(gpr.get_com_file(i),'w')
print('# x [rscale],','y [rscale],','vLOS [km/s],','rscale = ',rscale,' pc', file=c)
for k in range(len(x)):
print(x[k],y[k],vz[k], file=c) #[rscale], [rscale], [km/s]
c.close()
if not gpr.showplots: continue
ion(); subplot(111)
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():
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():
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()
def run():
print('input:',gpr.fil)
x0, y0, z0, vx, vy, vz = np.transpose(np.loadtxt(gpr.fil))
# cutting pm_i to a maximum of ntracers particles:
from random import shuffle
ind = np.arange(len(x0))
np.random.shuffle(ind)
ind = ind[:gp.files.ntracer]
x0 = x0[ind]; y0 = y0[ind]; z0 = z0[ind]
vx = vx[ind]; vy = vy[ind]; vz = vz[ind]
# get center of mass with means
# com_x, com_y,com_z = com_mean(x0,y0,z0,PM0)
# [TODO], and TODO: z component included if available
# get COM with shrinking sphere method
PM = np.ones(len(x0)) # assign all particles the full probability of membership
com_x, com_y, com_z, com_vz = com_shrinkcircle_v(x0,y0,z0,vz,PM)
print('COM [pc]: ', com_x, com_y, com_z)
print('VOM [km/s]', com_vz)
# from now on, work with 2D data only;
# z0 was only used to get center in (x,y) better
x0 -= com_x; y0 -= com_y # [pc]
vz -= com_vz #[km/s]
R0 = np.sqrt(x0**2+y0**2) # [pc]
Rc = R0 # [pc]
Rc.sort() # [pc]
for i in range(len(Rc)-1):
if Rc[i]>Rc[i+1]: # [pc]
print('sorting error!')
exit(1)
Rhalf = Rc[floor(len(Rc)/2)] # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
print('Rscale = ',Rscale,' pc')
print('max(R) = ',max(Rc),' pc')
print('last element of R : ',Rc[-1],' pc')
print('total number of stars: ',len(Rc))
x0 = x0/Rscale; y0 = y0/Rscale # [Rscale]
i = -1
for comp in range(gp.pops+1): # gp.pops +1 for all components together
pmr = (R0<(gpr.rprior*Rscale)) # TODO: read from gl_class_file
i = i+1
m = np.ones(len(R0))
x = x0; y = y0
R = np.sqrt(x*x+y*y) # [Rscale]
# print("x y z" on first line, to interprete data later on)
crscale = open(gpr.get_params_file(i),'w')
print('# Rscale in [pc], surfdens_central (=dens0) in [munit/rscale**2], and in [munit/pc**2], and totmass [munit], and max(v_LOS) in [km/s]', file=crscale)
print(Rscale, file=crscale)
crscale.close()
print('output: ',gpr.get_com_file(i))
c = open(gpr.get_com_file(i),'w')
print('# x [Rscale],','y [Rscale],','vLOS [km/s],','Rscale = ',Rscale,' pc', file=c)
for k in range(len(x)):
print(x[k], y[k], vz[k], file=c) # [Rscale], [Rscale], [km/s]
c.close()
if not gpr.showplots: continue
figure(1)
subplot(111)
res = (abs(x)<3)*(abs(y)<3)
x = x[res]; y = y[res] # [Rscale]
en = len(x)
if en == 0: continue
scatter(x[:en], y[:en],\
s=35, vmin=0.95, vmax=1.0, lw=0.0, alpha=0.2)
# xscale('log'); yscale('log')
circ_HL=Circle((0,0), radius=Rscale/Rscale, fc='None', ec='g', lw=1)
circ_DM=Circle((0,0), radius=gpr.r_DM/Rscale, fc='None', ec='r', lw=1)
gca().add_patch(circ_HL); gca().add_patch(circ_DM)
# visible region
maxr = max(np.abs(x)); mayr = max(np.abs(y)) #[rscale]
width2 = max([maxr,mayr]) #[rscale]
xlim([-width2,width2]); ylim([-width2,width2])
axes().set_aspect('equal')
xlabel(r'$x [R_s]$'); ylabel(r'$y [R_s]$')
# title(gpr.fil)
savefig(gpr.get_com_png(i))
show(block=True)
