def run(basename, prof):
read_scale()
# read in half light radius, Nu0[munit/Rscale^2], Nu0[munit/pc^2], munit
print(gp.files.get_scale_file(0))
A = np.loadtxt(gp.files.get_scale_file(0), unpack=False, skiprows=1)
gp.rstarhalf = A[0] # halflight radius (all tracer part.s), stored first ("0")
# read in radial bins
radii, binmin, binmax, nudat1, nuerr1 = gh.readcol5(gp.files.nufiles[0])
gp.xipol = radii*gp.rstarhalf # [pc]
maxr = max(radii)
radii = np.hstack([radii, 2*maxr, 4*maxr, 8*maxr])
gp.xepol = radii*gp.rstarhalf
models = read_models()
# use physical representation for profiles
profs = []
for i in range(len(models)):
M = models[i]
tmp_rho = M[0:gp.nepol]
off = gp.nipol
tmp_nu = M[off:off+gp.nepol]
off += gp.nipol
tmp_beta = M[off:off+gp.nbeta]
# TODO: include handling of N populations
try:
tmp_prof = physical(gp.xepol, prof, pop, tmp_rho, tmp_nu, tmp_beta)
profs.append(tmp_prof)
except Exception as detail:
print('handling error in profile', detail)
continue
sortedprofs = gh.sort_profiles_binwise(np.array(profs).transpose())
Mmin, M95lo, M68lo, Mmedi, \
M68hi, M95hi, Mmax = gh.get_median_1_2_sig(sortedprofs)
# plot ranges
if prof=='rho' or prof=='nu':
gpl.startlog(); gpl.xscale('log')
else:
gpl.start()
plot_labels(prof, pop)
if prof=='nr':
gpl.xscale('log')
# gpl.xlim([0.,2.*gp.xipol[-1]])
gp.xipol = np.hstack([gp.xipol[0]/2., gp.xipol, gp.rinfty*gp.xipol[-1]])
if prof=='nu' or prof=='sig':
plot_data(gp.xipol, prof, pop)
plot_analytic(gp.xepol, prof, pop)
radfill = gp.xepol
if prof == 'sig':
radfill = gp.xipol
fill_nice(radfill, M95lo, M68lo, Mmedi, M68hi, M95hi)
gpl.savefig(basename+'/prof_'+prof+'_'+str(pop)+'.png')
gpl.ion(); gpl.show(); gpl.ioff()
return
gamma_DM = 1 # cusp
return r_star1, r_star2
print('input: ', basename)
M = np.loadtxt(basename+'prof'+prof+str(pop),skiprows=0,unpack=False)
print('len (M) = ',len(M))
radii = M[0]
profs = M[1:] # all saved profiles
#profs = M[-10:]
#profs = M[-10000::10] # only the last 1e5 profiles, thinned by 10
Mprofbins = np.transpose(profs)
newMprofbins = gh.sort_profiles_binwise(Mprofbins)
Mmin, M95lo, M68lo, Mmedi, M68hi, M95hi, Mmax = gh.get_median_1_2_sig(newMprofbins)
if prof == 'nu':
Mmax=rho_INT_Rho(radii, Mmax)
M95hi=rho_INT_Rho(radii, M95hi)
M68hi=rho_INT_Rho(radii, M68hi)
Mmedi=rho_INT_Rho(radii, Mmedi)
M68lo=rho_INT_Rho(radii, M68lo)
M95lo=rho_INT_Rho(radii, M95lo)
Mmin=rho_INT_Rho(radii, Mmin)
# def extralast(rad, prof):
# tck = splrep(rad[:-1],np.log(prof[:-1]),k=2,s=0.1)
# prof = np.hstack([prof[:-1],np.exp(splev(rad[-1],tck))])
