def process_mock_densdata(options):
print("Using mock Pal 5 data from %s" % options.mockfilename)
# Read and prep data for mocks
xvid= numpy.loadtxt(options.mockfilename)
xv= xvid[:,:6]
xv= xv[numpy.argsort(xvid[:,6])]
XYZ= bovy_coords.galcenrect_to_XYZ(xv[:,0],xv[:,1],xv[:,2],
Xsun=R0,Zsun=0.025)
lbd= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=True)
radec= bovy_coords.lb_to_radec(lbd[:,0],lbd[:,1],degree=True)
xieta= pal5_util.radec_to_pal5xieta(radec[:,0],radec[:,1],degree=True)
# make sure the progenitor is at (0,0)
xieta[:,0]-= numpy.median(xieta[:,0])
xieta[:,1]-= numpy.median(xieta[:,1])
h,e= numpy.histogram(xieta[:,0],range=[0.2,14.3],bins=141)
xdata= numpy.arange(0.25,14.35,0.1)
# Compute power spectrum
tdata= h-0.
pp= Polynomial.fit(xdata,tdata,deg=options.polydeg,w=1./numpy.sqrt(h+1.))
tdata/= pp(xdata)
ll= xdata
py= signal.csd(tdata,tdata,fs=1./(ll[1]-ll[0]),scaling='spectrum',
nperseg=len(ll))[1]
py= py.real
# Also compute the bispectrum
Bspec, Bpx= bispectrum.bispectrum(numpy.vstack((tdata,tdata)).T,
nfft=len(tdata),wind=7,nsamp=1,overlap=0)
ppyr= numpy.fabs(Bspec[len(Bspec)//2+_BISPECIND,len(Bspec)//2:].real)
ppyi= numpy.fabs(Bspec[len(Bspec)//2+_BISPECIND,len(Bspec)//2:].imag)
return (numpy.sqrt(py*(ll[-1]-ll[0])),numpy.sqrt(h+1.)/pp(xdata),
ppyr,ppyi)
def convert_dens_to_obs(sdf_pepper,
apars,
dens,
mO,
dens_smooth,
minxi=0.25,
maxxi=14.35):
"""
NAME:
convert_dens_to_obs
PURPOSE:
Convert track to observed coordinates
INPUT:
sdf_pepper - streampepperdf object
apars - parallel angles
dens - density(apars)
dens_smooth - smooth density(apars)
mO= (None) mean parallel frequency (1D)
[needs to be set to get density on same grid as track]
minxi= (0.25) minimum xi to consider
OUTPUT:
(xi,dens/smooth)
"""
mT = sdf_pepper.meanTrack(apars, _mO=mO, coord='lb')
mradec = bovy_coords.lb_to_radec(mT[0], mT[1], degree=True)
mxieta = pal5_util.radec_to_pal5xieta(mradec[:, 0],
mradec[:, 1],
degree=True)
outll = np.arange(minxi, maxxi, 0.1)
# Interpolate density
ipll = interpolate.InterpolatedUnivariateSpline(mxieta[:, 0], apars)
ipdens = interpolate.InterpolatedUnivariateSpline(apars,
dens / dens_smooth)
return (outll, ipdens(ipll(outll)))
def process_mock_densdata(options):
print ("Using mock Pal 5 data from %s" % options.mockfilename)
# Read and prep data for mocks
xvid = numpy.loadtxt(options.mockfilename)
xv = xvid[:, :6]
xv = xv[numpy.argsort(xvid[:, 6])]
XYZ = bovy_coords.galcenrect_to_XYZ(xv[:, 0], xv[:, 1], xv[:, 2], Xsun=R0, Zsun=0.025)
lbd = bovy_coords.XYZ_to_lbd(XYZ[0], XYZ[1], XYZ[2], degree=True)
radec = bovy_coords.lb_to_radec(lbd[:, 0], lbd[:, 1], degree=True)
xieta = pal5_util.radec_to_pal5xieta(radec[:, 0], radec[:, 1], degree=True)
# make sure the progenitor is at (0,0)
xieta[:, 0] -= numpy.median(xieta[:, 0])
xieta[:, 1] -= numpy.median(xieta[:, 1])
h, e = numpy.histogram(xieta[:, 0], range=[0.2, 14.3], bins=141)
xdata = numpy.arange(0.25, 14.35, 0.1)
# Compute power spectrum
tdata = h - 0.0
pp = Polynomial.fit(xdata, tdata, deg=options.polydeg, w=1.0 / numpy.sqrt(h + 1.0))
tdata /= pp(xdata)
ll = xdata
py = signal.csd(tdata, tdata, fs=1.0 / (ll[1] - ll[0]), scaling="spectrum", nperseg=len(ll))[1]
py = py.real
# Also compute the bispectrum
Bspec, Bpx = bispectrum.bispectrum(numpy.vstack((tdata, tdata)).T, nfft=len(tdata), wind=7, nsamp=1, overlap=0)
ppyr = numpy.fabs(Bspec[len(Bspec) // 2 + _BISPECIND, len(Bspec) // 2 :].real)
ppyi = numpy.fabs(Bspec[len(Bspec) // 2 + _BISPECIND, len(Bspec) // 2 :].imag)
return (numpy.sqrt(py * (ll[-1] - ll[0])), numpy.sqrt(h + 1.0) / pp(xdata), ppyr, ppyi)
def convert_dens_to_obs(sdf_pepper, apars, dens, mO, dens_smooth, minxi=0.25, maxxi=14.35):
"""
NAME:
convert_dens_to_obs
PURPOSE:
Convert track to observed coordinates
INPUT:
sdf_pepper - streampepperdf object
apars - parallel angles
dens - density(apars)
dens_smooth - smooth density(apars)
mO= (None) mean parallel frequency (1D)
[needs to be set to get density on same grid as track]
minxi= (0.25) minimum xi to consider
OUTPUT:
(xi,dens/smooth)
"""
mT = sdf_pepper.meanTrack(apars, _mO=mO, coord="lb")
mradec = bovy_coords.lb_to_radec(mT[0], mT[1], degree=True)
mxieta = pal5_util.radec_to_pal5xieta(mradec[:, 0], mradec[:, 1], degree=True)
outll = numpy.arange(minxi, maxxi, 0.1)
# Interpolate density
ipll = interpolate.InterpolatedUnivariateSpline(mxieta[:, 0], apars)
ipdens = interpolate.InterpolatedUnivariateSpline(apars, dens / dens_smooth)
return (outll, ipdens(ipll(outll)))
def get_star_dx(pepperdf, n=1000, returnapar=False, returnxi=False):
(Omega, angle, dt) = pepperdf.sample(n=n, returnaAdt=True)
RvR = pepperdf._approxaAInv(Omega[0], Omega[1], Omega[2], angle[0],
angle[1], angle[2])
vo = pepperdf._vo
ro = pepperdf._ro
R0 = pepperdf._R0
Zsun = pepperdf._Zsun
vsun = pepperdf._vsun
XYZ = bovy_coords.galcencyl_to_XYZ(RvR[0] * ro,
RvR[5],
RvR[3] * ro,
Xsun=R0,
Zsun=Zsun).T
slbd = bovy_coords.XYZ_to_lbd(XYZ[0], XYZ[1], XYZ[2], degree=True)
sradec = bovy_coords.lb_to_radec(slbd[:, 0], slbd[:, 1], degree=True)
xieta = pal5_util.radec_to_pal5xieta(sradec[:, 0],
sradec[:, 1],
degree=True)
l = slbd[:, 0]
b = slbd[:, 1]
r = slbd[:, 2]
if returnapar:
closesttrackindexes = np.zeros(len(r))
for i in np.arange(len(r)):
closesttrackindexes[i] = pepperdf.find_closest_trackpoint(
RvR[0][i],
RvR[1][i],
RvR[2][i],
RvR[3][i],
RvR[4][i],
RvR[5][i],
interp=True)
starapar = pepperdf._interpolatedThetasTrack[(
closesttrackindexes).astype(int)]
return starapar
if returnxi: return xieta[:, 0]
else: return None
def convert_track_to_obs(apars,mO,coord):
"""
NAME:
convert_track_to_obs
PURPOSE:
Convert track to observed coordinates
INPUT:
apars - parallel angles
mO - mean parallel frequency (1D)
coord - coordinate to convert to (1: eta, 2: distance, 3: vlos, 4: pmll, 5: pmbb)
OUTPUT:
(longitude,(track-smooth)[coord])
"""
mT= sdf_pepper.meanTrack(apars,_mO=mO,coord='lb')
mradec= bovy_coords.lb_to_radec(mT[0],mT[1],degree=True)
mxieta= pal5_util.radec_to_pal5xieta(mradec[:,0],mradec[:,1],degree=True)
mT[0]= mxieta[:,0]
mT[1]= mxieta[:,1]
# Interpolate
ipll= interpolate.InterpolatedUnivariateSpline(mT[0],apars)
ipcoord= interpolate.InterpolatedUnivariateSpline(apars,mT[coord])
outll= numpy.arange(minxi,14.35,0.1)
return (outll,ipcoord(ipll(outll))-smooth_track[coord](smooth_ll(outll)))
def convert_dens_to_obs(apars,dens,dens_smooth,mO,poly_deg=3):
"""
NAME:
convert_dens_to_obs
PURPOSE:
Convert track to observed coordinates
INPUT:
apars - parallel angles
dens - density(apars)
dens_smooth - smooth-stream density(apars)
mO= (None) mean parallel frequency (1D)
[needs to be set to get density on same grid as track]
poly_deg= (3) degree of the polynomial to fit for the 'smooth' stream
OUTPUT:
(xi,dens/smooth)
"""
mT= sdf_pepper.meanTrack(apars,_mO=mO,coord='lb')
mradec= bovy_coords.lb_to_radec(mT[0],mT[1],degree=True)
mxieta= pal5_util.radec_to_pal5xieta(mradec[:,0],mradec[:,1],degree=True)
outll= numpy.arange(minxi,14.35,0.1)
# Interpolate density
ipll= interpolate.InterpolatedUnivariateSpline(mxieta[:,0],apars)
ipdens= interpolate.InterpolatedUnivariateSpline(apars,dens/dens_smooth)
return (outll,ipdens(ipll(outll)))
with open(pepperfilename,'rb') as savefile:
sdf_pepper= pickle.load(savefile)
else:
import simulate_streampepper
timpacts= simulate_streampepper.parse_times('1sampling',9.)
sdf_pepper= pal5_util.setup_pal5model(timpact=timpacts,
hernquist=True)
save_pickles(pepperfilename,sdf_pepper)
# Convert track to xi, eta
trackRADec=\
bovy_coords.lb_to_radec(sdf_smooth._interpolatedObsTrackLB[:,0],
sdf_smooth._interpolatedObsTrackLB[:,1],
degree=True)
trackXiEta=\
pal5_util.radec_to_pal5xieta(trackRADec[:,0],
trackRADec[:,1],degree=True)
smooth_track= []
for coord in range(6):
if coord < 2:
smooth_track.append(\
interpolate.InterpolatedUnivariateSpline(sdf_smooth._interpolatedThetasTrack,
trackXiEta[:,coord]))
else:
smooth_track.append(\
interpolate.InterpolatedUnivariateSpline(sdf_smooth._interpolatedThetasTrack,
sdf_smooth._interpolatedObsTrackLB[:,coord]))
smooth_ll= interpolate.InterpolatedUnivariateSpline(trackXiEta[:,0],
sdf_smooth._interpolatedThetasTrack)
