def test_lb_to_radec():
ra, dec= 120, 60.
lb= bovy_coords.radec_to_lb(ra,dec,degree=True,epoch=2000.)
rat, dect= bovy_coords.lb_to_radec(lb[0],lb[1],degree=True,epoch=2000.)
assert numpy.fabs(ra-rat) < 10.**-10., 'lb_to_radec is not the inverse of radec_to_lb'
assert numpy.fabs(dec-dect) < 10.**-10., 'lb_to_radec is not the inverse of radec_to_lb'
# Also test this for degree=False
lb= bovy_coords.radec_to_lb(ra/180.*numpy.pi,dec/180.*numpy.pi,
degree=False,epoch=2000.)
rat, dect= bovy_coords.lb_to_radec(lb[0],lb[1],degree=False,epoch=2000.)
assert numpy.fabs(ra/180.*numpy.pi-rat) < 10.**-10., 'lb_to_radec is not the inverse of radec_to_lb'
assert numpy.fabs(dec/180.*numpy.pi-dect) < 10.**-10., 'lb_to_radec is not the inverse of radec_to_lb'
# And also test this for arrays
os= numpy.ones(2)
lb= bovy_coords.radec_to_lb(os*ra/180.*numpy.pi,os*dec/180.*numpy.pi,
degree=False,epoch=2000.)
ratdect= bovy_coords.lb_to_radec(lb[:,0],lb[:,1],degree=False,epoch=2000.)
rat= ratdect[:,0]
dect= ratdect[:,1]
assert numpy.all(numpy.fabs(ra/180.*numpy.pi-rat) < 10.**-10.), 'lb_to_radec is not the inverse of radec_to_lb'
assert numpy.all(numpy.fabs(dec/180.*numpy.pi-dect) < 10.**-10.), 'lb_to_radec is not the inverse of radec_to_lb'
#Also test for a negative l
l,b= 240., 60.
ra,dec= bovy_coords.lb_to_radec(l,b,degree=True)
lt,bt= bovy_coords.radec_to_lb(ra,dec,degree=True)
assert numpy.fabs(lt-l) < 10.**-10., 'lb_to_radec is not the inverse of radec_to_lb'
assert numpy.fabs(bt-b) < 10.**-10., 'lb_to_radec is not the inverse of radec_to_lb'
return None
def test_radec_to_lb_ngp():
# Test that the NGP is at b=90
ra, dec= 192.25, 27.4
lb= bovy_coords.radec_to_lb(ra,dec,degree=True,epoch=1950.)
assert numpy.fabs(lb[1]-90.) < 10.**-8., 'Galactic latitude of the NGP given in ra,dec is not 90'
# Also test this for degree=False
lb= bovy_coords.radec_to_lb(ra/180.*numpy.pi,dec/180.*numpy.pi,
degree=False,epoch=1950.)
assert numpy.fabs(lb[1]-numpy.pi/2.) < 10.**-8., 'Galactic latitude of the NGP given in ra,dec is not pi/2'
return None
def test_radec_to_lb_ncp():
ra, dec= 180., 90.
lb= bovy_coords.radec_to_lb(ra,dec,degree=True,epoch=1950.)
assert numpy.fabs(lb[0]-123.) < 10.**-8., 'Galactic longitude of the NCP given in ra,dec is not 123'
# Also test this for degree=False
lb= bovy_coords.radec_to_lb(ra/180.*numpy.pi,dec/180.*numpy.pi,
degree=False,epoch=1950.)
assert numpy.fabs(lb[0]-123./180.*numpy.pi) < 10.**-8., 'Galactic longitude of the NCP given in ra,dec is not 123'
# Also test the latter for vector inputs
os= numpy.ones(2)
lb= bovy_coords.radec_to_lb(os*ra/180.*numpy.pi,os*dec/180.*numpy.pi,
degree=False,epoch=1950.)
assert numpy.all(numpy.fabs(lb[:,0]-123./180.*numpy.pi) < 10.**-8.), 'Galactic longitude of the NCP given in ra,dec is not 123'
return None
def phi12_to_lb(phi1,phi2,degree=False):
"""
NAME:
phi12_to_lb
PURPOSE:
Transform (phi1,phi2) to Galactic coordinates (l,b)
INPUT:
phi1 - phi longitude (rad or degree)
phi2 - phi latitude (rad or degree)
degree= (False) if True, input and output are in degrees
OUTPUT:
(l,b) for scalar input
[:,2] array for vector input
HISTORY:
2014-11-04 - Written - Bovy (IAS)
"""
#Convert phi1,phi2 to l,b coordinates
phiXYZ= numpy.array([numpy.cos(phi2)*numpy.cos(phi1),
numpy.cos(phi2)*numpy.sin(phi1),
numpy.sin(phi2)])
eqXYZ= numpy.dot(_TKOP.T,phiXYZ)
#Get ra dec
dec= numpy.arcsin(eqXYZ[2])
ra= numpy.arctan2(eqXYZ[1],eqXYZ[0])
ra[ra<0.]+= 2.*numpy.pi
#Now convert to l,b
return bovy_coords.radec_to_lb(ra,dec)
def convert():
global req_params
#if position given in ra & dec, convert to l & b
if (req_params.get('l') == -9.999):
req_params['l'], req_params['b'] = b_c.radec_to_lb(req_params['ra'],
req_params['dec'],
degree=True,
epoch=2000.0)
#if position given in l & b, convert to ra & dec
if (req_params.get('ra') == -9.999):
req_params['ra'], req_params['dec'] = b_c.lb_to_radec(req_params['l'],
req_params['b'],
degree=True,
epoch=2000.0)
#if proper motion given in pmra & pmdec, convert to pml & pmb
if (req_params.get('pml') == -9.999):
req_params['pml'], req_params['pmb'] = b_c.pmrapmdec_to_pmllpmbb(
req_params['pmra'],
req_params['pmdec'],
req_params['ra'],
req_params['dec'],
degree=True,
epoch=2000.0)
def read_mgiant(self):
dpath = "/Users/htian/Documents/GitHub/rothalo/data/dr4_mgiant.csv"
ra, dec, rv, dist, sn, feh = np.loadtxt(dpath,
skiprows=1,
usecols=(0, 1, 2, 9, 10, 11),
delimiter=',',
unpack=True)
ind_o = (rv > -10000000) & (sn > 10) & (feh > self.min_feh) & (
feh < self.max_feh) & (dist > -1000)
print(len(sn[sn < 10]), len(sn))
print("there are ", len(ra[ind_o]), " stars readout!")
# ra_o = data_halo[ind_o, 1]
# dec_o = data_halo[ind_o, 2]
lb = gub.radec_to_lb(ra, dec, degree=True)
l = lb[:, 0]
b = lb[:, 1]
xyz = gub.lbd_to_XYZ(l, b, dist, degree=True)
self.l_o = l[ind_o]
self.b_o = b[ind_o]
self.feh_o = feh[ind_o]
self.rv_o = rv[ind_o]
self.dist_o = dist[ind_o]
self.Z_o = xyz[ind_o, 2]
self.R_o = np.sqrt((8 - xyz[ind_o, 0])**2 + xyz[ind_o, 1]**2)
self.name = "DR4_mgiant"
def pmradec_to_pmlb(R,ra,dec,vr,vra,vdec):
'''
proper motion in equatorial coordinates to
proper motion in Galactic coordinates to use
in Jo's function to convert to proper motion
in stream coordinates
'''
l,b = bovy_coords.radec_to_lb(ra,dec,degree=False,epoch=2000.0)
dec_NGP = 27.12825 # degrees
ra_NGP = 192.85948 # degrees
dec_NGP *= np.pi/180.
ra_NGP *= np.pi/180.
num1 = np.sin(dec_NGP) - (np.sin(dec) * np.sin(b))
denom1 = np.cos(dec) * np.cos(b)
num2 = np.sin(ra-ra_NGP) * np.cos(dec_NGP)
denom2 = np.cos(b)
cos_phi = num1/denom1
sin_phi = num2/denom2
M = np.zeros((3,3))
M[0,:] = [1,0,0]
M[1,:] = [0,cos_phi,sin_phi]
M[2,:] = [0,-sin_phi,cos_phi]
C = np.array([vr,1./R * vra,np.cos(dec),1./R * vdec])
val = np.dot(M,C)
return val
def phi12_to_lb(phi1,phi2,degree=False):
"""
NAME:
phi12_to_lb
PURPOSE:
Transform (phi1,phi2) to Galactic coordinates (l,b)
INPUT:
phi1 - phi longitude (rad or degree)
phi2 - phi latitude (rad or degree)
degree= (False) if True, input and output are in degrees
OUTPUT:
(l,b) for scalar input
[:,2] array for vector input
HISTORY:
2014-11-04 - Written - Bovy (IAS)
"""
import numpy
_TKOP= numpy.zeros((3,3))
_TKOP[0,:]= [-0.4776303088,-0.1738432154,0.8611897727]
_TKOP[1,:]= [0.510844589,-0.8524449229,0.111245042]
_TKOP[2,:]= [0.7147776536,0.4930681392,0.4959603976]
#Convert phi1,phi2 to l,b coordinates
phiXYZ= numpy.array([numpy.cos(phi2)*numpy.cos(phi1),
numpy.cos(phi2)*numpy.sin(phi1),
numpy.sin(phi2)])
eqXYZ= numpy.dot(_TKOP.T,phiXYZ)
#Get ra dec
dec= numpy.arcsin(eqXYZ[2])
ra= numpy.arctan2(eqXYZ[1],eqXYZ[0])
ra[ra<0.]+= 2.*numpy.pi
#Now convert to l,b
return bovy_coords.radec_to_lb(ra,dec)
def hackGCS(cutbinaries=True,cutvalidfeh=True,cutfeh=True,
casagrande=False,irfm=True):
data= readGCS.readGCS(cutbinaries=cutbinaries,
cutvalidfeh=cutvalidfeh,
cutfeh=cutfeh,
casagrande=casagrande,irfm=irfm)
#To allow for XY pixelization, we will hack these
data= esutil.numpy_util.add_fields(data,[('RC_GALR', float),
('RC_GALPHI', float),
('RA',float),
('DEC',float),
('GLON',float),
('GLAT',float)])
ras= numpy.empty(len(data))
decs= numpy.empty(len(data))
for ii in range(len(data)):
c = SkyCoord(str(data['RAh'][ii])+'h'+str(data['RAm'][ii])+'m'
+str(data['RAs'][ii])+'s',
str(data['DE-'][ii])+str(data['DEd'][ii])+'d'
+str(data['DEm'][ii])+'m'+str(data['DEs'][ii])+'s',
'icrs')
ras[ii]= c.ra.degree
decs[ii]= c.dec.degree
llbb= bovy_coords.radec_to_lb(ras,decs,degree=True)
data['RA']= ras
data['DEC']= decs
data['GLON']= llbb[:,0]
data['GLAT']= llbb[:,1]
data['RC_GALR']= data['Dist']/1000.*numpy.cos(llbb[:,1]/180.*numpy.pi)
data['RC_GALPHI']= llbb[:,0]/180.*numpy.pi
return data
def obs_to_galcen(ra, dec, dist, pmra, pmdec, rv, ro=_R0, vo=_v0, zo=_z0):
vxvv = np.dstack([ra, dec, dist, pmra, pmdec, rv])[0]
ra, dec = vxvv[:, 0], vxvv[:, 1]
lb = bovy_coords.radec_to_lb(ra, dec, degree=True)
pmra, pmdec = vxvv[:, 3], vxvv[:, 4]
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra, pmdec, ra, dec, degree=True)
d, vlos = vxvv[:, 2], vxvv[:, 5]
rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0], lb[:, 1], d, vlos, pmllpmbb[:, 0], pmllpmbb[:, 1], degree=True)
vsolar = np.array([-11.1, 245.7, 7.25])
vsun = vsolar / vo
X = rectgal[:, 0] / ro
Y = rectgal[:, 1] / ro
Z = rectgal[:, 2] / ro
vx = rectgal[:, 3] / vo
vy = rectgal[:, 4] / vo
vz = rectgal[:, 5] / vo
XYZ = np.dstack([X, Y, Z])[0]
vxyz = np.dstack([vx, vy, vz])[0]
Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], Zsun=zo / ro)
vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0], vxyz[:, 1], vxyz[:, 2], Rpz[:, 0], Rpz[:, 1], Rpz[:, 2],
vsun=vsun,
Xsun=1.,
Zsun=zo / ro,
galcen=True)
return XYZ, vxyz, Rpz, vRvTvz
def force_pal5(pot: PotentialType,
dpal5: float,
ro: float = REFR0,
vo: float = REFV0) -> Tuple[float]:
"""Return the force at Pal5.
Parameters
----------
pot: Potential, list
dpal5: float
ro, vo: float
Return
------
force: tuple
[fx, fy, fz]
"""
from galpy import potential
from galpy.util import bovy_coords
# First compute the location based on the distance
l5, b5 = bovy_coords.radec_to_lb(229.018, -0.124, degree=True)
X5, Y5, Z5 = bovy_coords.lbd_to_XYZ(l5, b5, dpal5, degree=True)
R5, p5, Z5 = bovy_coords.XYZ_to_galcencyl(X5, Y5, Z5, Xsun=ro, Zsun=0.025)
args: list = [pot, R5 / ro, Z5 / ro]
kws: dict = {"phi": p5, "use_physical": True, "ro": ro, "vo": vo}
return (
potential.evaluateRforces(*args, **kws),
potential.evaluatezforces(*args, **kws),
potential.evaluatephiforces(*args, **kws),
)
def test_radec_to_lb_otherepochs():
ra, dec= 180., 90.
try:
lb= bovy_coords.radec_to_lb(ra/180.*numpy.pi,dec/180.*numpy.pi,
degree=False,epoch=1975.)
except IOError:
pass
else:
raise AssertionError('radec functions with epoch not equal to 1950 or 2000 did not raise IOError')
def plot_dust_gaia(dist, plotname):
# Load the dust map
green15map = dust.load_combined(dist, nest=True, nside_out=_NSIDE)
dm = dust.dist2distmod(dist)
green15map[green15map == healpy.UNSEEN] = -1.
print "%i are NaN" % (numpy.sum(numpy.isnan(green15map)))
#theta, phi= healpy.pixelfunc.pix2ang(_NSIDE,numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),nest=True)
#print (numpy.pi/2.-theta)[numpy.isnan(green15map)]/numpy.pi*180.
#print phi[numpy.isnan(green15map)]/numpy.pi*180.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,
min=0.,
max=round(10. * (20. - dm - 0.68)) / 10.,
format=r'$%g$',
cmap='gist_yarg',
title="",
unit='$A_G\,(\mathrm{mag})$')
# Plot outline of Marshall et al.
ls = numpy.linspace(-100.125, 100.125, 1001)
healpy.visufunc.projplot(ls, ls * 0. - 10.125, 'k--', lw=1.2, lonlat=True)
healpy.visufunc.projplot(ls, ls * 0. + 10.125, 'k--', lw=1.2, lonlat=True)
bs = numpy.linspace(-10.125, 10.125, 1001)
healpy.visufunc.projplot(bs * 0. - 100.125, bs, 'k--', lw=1.2, lonlat=True)
healpy.visufunc.projplot(bs * 0. + 100.125, bs, 'k--', lw=1.2, lonlat=True)
# Plot boundary of Green et al. map, dec > -30.
ras = numpy.linspace(0., 360., 1001)
decs = ras * 0. - 30.
lbs = bovy_coords.radec_to_lb(ras, decs, degree=True)
ls = lbs[:, 0]
bs = lbs[:, 1]
# rm those within Marshall et al.
keepIndx = True - ((ls > 259.875) * (numpy.fabs(bs) < 10.125) +
(ls < 100.125) * (numpy.fabs(bs) < 10.125))
ls = ls[keepIndx]
bs = bs[keepIndx]
healpy.visufunc.projplot(ls, bs, 'k-.', lw=1.2, lonlat=True)
# Labels
healpy.visufunc.projtext(350.,
-8.,
r'$\mathrm{Marshall\ et\ al.\ (2006)}$',
lonlat=True,
size=13.)
healpy.visufunc.projtext(10.,
-60.,
r'$\mathrm{Drimmel\ et\ al.\ (2003)}$',
lonlat=True,
size=13.)
healpy.visufunc.projtext(160.,
40.,
r'$\mathrm{Green\ et\ al.\ (2015)}$',
lonlat=True,
size=13.)
bovy_plot.bovy_end_print(plotname)
def measure_kinematics_onepop(tgas,twomass,jk,dm,mj,spii,zbins,options,
csvwriter,csvout,maxcovar=30.):
# Compute XYZ
lb= bovy_coords.radec_to_lb(tgas['ra'],tgas['dec'],degree=True,epoch=None)
XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],1./tgas['parallax'],
degree=True)
# Generate vradec and projection matrix
vradec= numpy.array([bovy_coords._K/tgas['parallax']*tgas['pmra'],
bovy_coords._K/tgas['parallax']*tgas['pmdec']])
proj= compute_projection(tgas)
# Sample from the joint (parallax,proper motion) uncertainty distribution
# to get the covariance matrix of the vradec, using MC sims
nmc= 10001
vradec_cov= compute_vradec_cov_mc(tgas,nmc)
# Fit each zbin
if spii == options.start:
startz= options.startz
else:
startz= 0
for ii in tqdm.trange(startz,len(zbins)-1):
indx= (XYZ[:,2] > zbins[ii])\
*(XYZ[:,2] <= zbins[ii+1])\
*(numpy.sqrt(XYZ[:,0]**2.+XYZ[:,1]**2.) < 0.2)
nstar= numpy.sum(indx)
if numpy.sum(indx) < 30: continue
# Basic XD fit
ydata= vradec.T[indx]
ycovar= numpy.zeros_like(vradec.T)[indx]
initamp= numpy.random.uniform(size=options.ngauss)
initamp/= numpy.sum(initamp)
m= numpy.zeros(3)
s= numpy.array([40.,40.,20.])
initmean= []
initcovar= []
for jj in range(options.ngauss):
initmean.append(m+numpy.random.normal(size=3)*s)
initcovar.append(4.*s**2.*numpy.diag(numpy.ones(3)))
initcovar= numpy.array(initcovar)
initmean= numpy.array(initmean)
lnL= extreme_deconvolution(ydata,ycovar,initamp,initmean,initcovar,
projection=proj[indx])
sig2z= combined_sig2(initamp,initmean[:,2],initcovar[:,2,2],
maxcovar=maxcovar)
kurtz= combined_k(initamp,initmean[:,2],initcovar[:,2,2],
maxcovar=maxcovar)
sam= bootstrap(options.nboot,
vradec.T[indx],vradec_cov[indx],proj[indx],
ngauss=options.ngauss,maxcovar=maxcovar)
sig2z_err= 1.4826*numpy.median(numpy.fabs(sam[0]-numpy.median(sam[0])))
kurtz_err= 1.4826*numpy.median(numpy.fabs(sam[1]-numpy.median(sam[1])))
sig2kurtz_corr= numpy.corrcoef(sam)[0,1]
csvwriter.writerow([spii,ii,nstar,
sig2z,sig2z_err,kurtz,kurtz_err,sig2kurtz_corr])
csvout.flush()
return None
def force_pal5(pot,dpal5,ro,vo):
"""Return the force at Pal5"""
# First compute the location based on the distance
l5, b5= bovy_coords.radec_to_lb(229.018,-0.124,degree=True)
X5,Y5,Z5= bovy_coords.lbd_to_XYZ(l5,b5,dpal5,degree=True)
R5,p5,Z5= bovy_coords.XYZ_to_galcencyl(X5,Y5,Z5,Xsun=ro,Zsun=0.025)
return (potential.evaluateRforces(pot,R5/ro,Z5/ro,phi=p5,
use_physical=True,ro=ro,vo=vo),
potential.evaluatezforces(pot,R5/ro,Z5/ro,phi=p5,
use_physical=True,ro=ro,vo=vo),
potential.evaluatephiforces(pot,R5/ro,Z5/ro,phi=p5,
use_physical=True,ro=ro,vo=vo))
def test_coords():
from galpy.util import bovy_coords
ra, dec, dist = 161., 50., 8.5
pmra, pmdec, vlos = -6.8, -10., -115.
# Convert to Galactic and then to rect. Galactic
ll, bb = bovy_coords.radec_to_lb(ra, dec, degree=True)
pmll, pmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra,
pmdec,
ra,
dec,
degree=True)
X, Y, Z = bovy_coords.lbd_to_XYZ(ll, bb, dist, degree=True)
vX, vY, vZ = bovy_coords.vrpmllpmbb_to_vxvyvz(vlos,
pmll,
pmbb,
X,
Y,
Z,
XYZ=True)
# Convert to cylindrical Galactocentric
# Assuming Sun's distance to GC is (8,0.025) in (R,z)
R, phi, z = bovy_coords.XYZ_to_galcencyl(X, Y, Z, Xsun=8., Zsun=0.025)
vR, vT, vz = bovy_coords.vxvyvz_to_galcencyl(vX,
vY,
vZ,
R,
phi,
Z,
vsun=[-10.1, 244., 6.7],
galcen=True)
# 5/12/2016: test weakened, because improved galcen<->heliocen
# transformation has changed these, but still close
print(R, phi, z, vR, vT, vz)
assert numpy.fabs(R - 12.51328515156942
) < 10.**-1., 'Coordinate transformation has changed'
assert numpy.fabs(phi - 0.12177409073433249
) < 10.**-1., 'Coordinate transformation has changed'
assert numpy.fabs(z - 7.1241282354856228
) < 10.**-1., 'Coordinate transformation has changed'
assert numpy.fabs(vR - 78.961682923035966
) < 10.**-1., 'Coordinate transformation has changed'
assert numpy.fabs(vT + 241.49247772351964
) < 10.**-1., 'Coordinate transformation has changed'
assert numpy.fabs(vz + 102.83965442188689
) < 10.**-1., 'Coordinate transformation has changed'
return None
def plot_dust_gaia(dist,plotname):
# Load the dust map
green15map= dust.load_combined(dist,nest=True,nside_out=_NSIDE)
dm= dust.dist2distmod(dist)
green15map[green15map == healpy.UNSEEN]= -1.
print "%i are NaN" % (numpy.sum(numpy.isnan(green15map)))
#theta, phi= healpy.pixelfunc.pix2ang(_NSIDE,numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),nest=True)
#print (numpy.pi/2.-theta)[numpy.isnan(green15map)]/numpy.pi*180.
#print phi[numpy.isnan(green15map)]/numpy.pi*180.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,min=0.,
max=round(10.*(20.-dm-0.68))/10.,
format=r'$%g$',
cmap='gist_yarg',
title="",
unit='$A_G\,(\mathrm{mag})$')
# Plot outline of Marshall et al.
ls= numpy.linspace(-100.125,100.125,1001)
healpy.visufunc.projplot(ls,ls*0.-10.125,'k--',lw=1.2,lonlat=True)
healpy.visufunc.projplot(ls,ls*0.+10.125,'k--',lw=1.2,lonlat=True)
bs= numpy.linspace(-10.125,10.125,1001)
healpy.visufunc.projplot(bs*0.-100.125,bs,'k--',lw=1.2,lonlat=True)
healpy.visufunc.projplot(bs*0.+100.125,bs,'k--',lw=1.2,lonlat=True)
# Plot boundary of Green et al. map, dec > -30.
ras= numpy.linspace(0.,360.,1001)
decs= ras*0.-30.
lbs= bovy_coords.radec_to_lb(ras,decs,degree=True)
ls= lbs[:,0]
bs= lbs[:,1]
# rm those within Marshall et al.
keepIndx= True-((ls > 259.875)*(numpy.fabs(bs) < 10.125)
+(ls < 100.125)*(numpy.fabs(bs) < 10.125))
ls= ls[keepIndx]
bs= bs[keepIndx]
healpy.visufunc.projplot(ls,bs,'k-.',lw=1.2,lonlat=True)
# Labels
healpy.visufunc.projtext(350.,-8.,r'$\mathrm{Marshall\ et\ al.\ (2006)}$',
lonlat=True,size=13.)
healpy.visufunc.projtext(10.,-60.,r'$\mathrm{Drimmel\ et\ al.\ (2003)}$',
lonlat=True,size=13.)
healpy.visufunc.projtext(160.,40.,r'$\mathrm{Green\ et\ al.\ (2015)}$',
lonlat=True,size=13.)
bovy_plot.bovy_end_print(plotname)
def select_targets(outfile, location):
numpy.random.seed(location)
datafilename = '../target_info/SecQuanObj_%03i+00.fits' % location
data = fitsio.read(datafilename)
# De-redden
ak = data['AK']
aj = ak * 2.5
j0 = data['J_M'] - aj
k0 = data['K_M'] - ak
# Cut on jk0,h
indx = ((j0 - k0) > 0.8) * (data['H_M'] > 12.) * (data['H_M'] < 13.)
data = data[indx]
# setup dust map
dmap = mwdust.Marshall06(filter='2MASS H')
# Calculate A_H(7 kpc) according to Marshall et al. (2006)
ahdmap = numpy.zeros(len(data))
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
for ii in range(len(data)):
ahdmap[ii] = dmap(lb[ii, 0], lb[ii, 1], 7.)
# Cut on AH <= 1.4
indx = ahdmap <= 1.4
data = data[indx]
# Match against already drilled targets
data = esutil.numpy_util.add_fields(data, [('FLAG_DONT_OBSERVE', int)])
data['FLAG_DONT_OBSERVE'] = 0
yannyfiles = glob.glob('../target_info/plateHolesSorted-*.par')
for yannyfile in yannyfiles:
drillFile = yanny.yanny(filename=yannyfile, np=True)
drilled = drillFile['STRUCT1']
# spherematch
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
drilled['target_ra'],
drilled['target_dec'],
2. / 3600.,
maxmatch=1)
data['FLAG_DONT_OBSERVE'][m1] = 1
# Write to file
fitsio.write(outfile,
data[numpy.random.permutation(len(data))],
clobber=True)
return None
def test_coords():
from galpy.util import bovy_coords
ra, dec, dist= 161., 50., 8.5
pmra, pmdec, vlos= -6.8, -10., -115.
# Convert to Galactic and then to rect. Galactic
ll, bb= bovy_coords.radec_to_lb(ra,dec,degree=True)
pmll, pmbb= bovy_coords.pmrapmdec_to_pmllpmbb(pmra,pmdec,ra,dec,degree=True)
X,Y,Z= bovy_coords.lbd_to_XYZ(ll,bb,dist,degree=True)
vX,vY,vZ= bovy_coords.vrpmllpmbb_to_vxvyvz(vlos,pmll,pmbb,X,Y,Z,XYZ=True)
# Convert to cylindrical Galactocentric
# Assuming Sun's distance to GC is (8,0.025) in (R,z)
R,phi,z= bovy_coords.XYZ_to_galcencyl(X,Y,Z,Xsun=8.,Zsun=0.025)
vR,vT,vz= bovy_coords.vxvyvz_to_galcencyl(vX,vY,vZ,R,phi,Z,vsun=[-10.1,244.,6.7],galcen=True)
assert numpy.fabs(R-12.51328515156942) < 10.**-4., 'Coordinate transformation has changed'
assert numpy.fabs(phi-0.12177409073433249) < 10.**-4., 'Coordinate transformation has changed'
assert numpy.fabs(z-7.1241282354856228) < 10.**-4., 'Coordinate transformation has changed'
assert numpy.fabs(vR-78.961682923035966) < 10.**-4., 'Coordinate transformation has changed'
assert numpy.fabs(vT+241.49247772351964) < 10.**-4., 'Coordinate transformation has changed'
assert numpy.fabs(vz+102.83965442188689) < 10.**-4., 'Coordinate transformation has changed'
return None
def __call__(self, dist, ra, dec, MJ=None, JK=None):
"""
NAME:
__call__
PURPOSE:
Evaluate the effective selection function
INPUT:
distance - distance in kpc (can be array)
ra, dec - sky coordinates (deg), scalars
MJ= (object-wide default) absolute magnitude in J or an array of samples of absolute magnitudes in J for the tracer population
JK= (object-wide default) J-Ks color or an array of samples of the J-Ks color
OUTPUT
effective selection fraction
HISTORY:
2017-01-18 - Written - Bovy (UofT/CCA)
"""
if isinstance(dist, (int, float)):
dist = numpy.array([dist])
elif isinstance(dist, list):
dist = numpy.array(dist)
MJ, JK = self._parse_mj_jk(MJ, JK)
distmod = 5. * numpy.log10(dist) + 10.
# Extract the distribution of A_J and A_J-A_Ks at this distance
# from the dust map, use twice the radius of a pixel for this
lcen, bcen = bovy_coords.radec_to_lb(ra, dec, degree=True)
pixarea, aj= self._dmap3d.dust_vals_disk(\
lcen,bcen,dist,healpy.nside2resol(_BASE_NSIDE)/numpy.pi*180.)
totarea = numpy.sum(pixarea)
ejk = aj * (1. - 1. / 2.5) # Assume AJ/AK = 2.5
distmod = numpy.tile(distmod, (aj.shape[0], 1))
pixarea = numpy.tile(pixarea, (len(dist), 1)).T
out = numpy.zeros_like(dist)
for mj, jk in zip(MJ, JK):
tj = mj + distmod + aj
tjk = jk + ejk
out += numpy.sum(pixarea * self._tgasSel(tj, tjk, ra, dec), axis=0)
if not self._maxd is None:
out[dist > self._maxd] = 0.
return out / totarea / len(MJ)
def select_targets(outfile,location):
numpy.random.seed(location)
datafilename= '../target_info/SecQuanObj_%03i+00.fits' % location
data= fitsio.read(datafilename)
# De-redden
ak= data['AK']
aj= ak*2.5
j0= data['J_M']-aj
k0= data['K_M']-ak
# Cut on jk0,h
indx= ((j0-k0) > 0.8)*(data['H_M'] > 12.)*(data['H_M'] < 13.)
data= data[indx]
# setup dust map
dmap= mwdust.Marshall06(filter='2MASS H')
# Calculate A_H(7 kpc) according to Marshall et al. (2006)
ahdmap= numpy.zeros(len(data))
lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
for ii in range(len(data)):
ahdmap[ii]= dmap(lb[ii,0],lb[ii,1],7.)
# Cut on AH <= 1.4
indx= ahdmap <= 1.4
data= data[indx]
# Match against already drilled targets
data= esutil.numpy_util.add_fields(data,[('FLAG_DONT_OBSERVE', int)])
data['FLAG_DONT_OBSERVE']= 0
yannyfiles= glob.glob('../target_info/plateHolesSorted-*.par')
for yannyfile in yannyfiles:
drillFile= yanny.yanny(filename=yannyfile,np=True)
drilled= drillFile['STRUCT1']
# spherematch
h=esutil.htm.HTM()
m1,m2,d12 = h.match(data['RA'],data['DEC'],
drilled['target_ra'],drilled['target_dec'],
2./3600.,maxmatch=1)
data['FLAG_DONT_OBSERVE'][m1]= 1
# Write to file
fitsio.write(outfile,data[numpy.random.permutation(len(data))],
clobber=True)
return None
def test_pmllpmbb_to_pmrapmdec():
#This is a random l,b
ll, bb= 132., -20.4
pmll, pmbb= 10., 20.
pmra, pmdec= bovy_coords.pmllpmbb_to_pmrapmdec(pmll,pmbb,
ll,bb,
degree=True,epoch=1950.)
assert numpy.fabs(numpy.sqrt(pmll**2.+pmbb**2.)-numpy.sqrt(pmra**2.+pmdec**2.)) < 10.**-10., 'pmllpmbb_to_pmrapmdec conversion did not work as expected for a random l,b'
# This is close to the NGP
ll,bb= numpy.pi-0.001, numpy.pi/2.-0.001
pmll, pmbb= 10., 20.
os= numpy.ones(2)
pmrapmdec= bovy_coords.pmllpmbb_to_pmrapmdec(os*pmll,os*pmbb,
os*ll,os*bb,
degree=False,epoch=1950.)
pmra= pmrapmdec[:,0]
pmdec= pmrapmdec[:,1]
assert numpy.all(numpy.fabs(numpy.sqrt(pmll**2.+pmbb**2.)-numpy.sqrt(pmra**2.+pmdec**2.)) < 10.**-10.), 'pmllpmbb_to_pmrapmdec conversion did not work as expected close to the NGP'
# This is the NGP
ll,bb= numpy.pi, numpy.pi/2.
pmll, pmbb= 10., 20.
os= numpy.ones(2)
pmrapmdec= bovy_coords.pmllpmbb_to_pmrapmdec(os*pmll,os*pmbb,
os*ll,os*bb,
degree=False,epoch=1950.)
pmra= pmrapmdec[:,0]
pmdec= pmrapmdec[:,1]
assert numpy.all(numpy.fabs(numpy.sqrt(pmll**2.+pmbb**2.)-numpy.sqrt(pmra**2.+pmdec**2.)) < 10.**-10.), 'pmllpmbb_to_pmrapmdec conversion did not work as expected at the NGP'
# This is the NCP
ra, dec= numpy.pi, numpy.pi/2.
ll, bb= bovy_coords.radec_to_lb(ra,dec,degree=False,epoch=1950.)
pmll, pmbb= 10., 20.
pmra, pmdec= bovy_coords.pmllpmbb_to_pmrapmdec(pmll,pmbb,
ll,bb,
degree=False,epoch=1950.)
assert numpy.fabs(numpy.sqrt(pmll**2.+pmbb**2.)-numpy.sqrt(pmra**2.+pmdec**2.)) < 10.**-10., 'pmllpmbb_to_pmrapmdec conversion did not work as expected at the NCP'
return None
def comove_coords(t, lit_gaia):
###could add other outputs like Vr, pred, in addition to sep,sep3d,and Vtan off
ra = t.target_df.squeeze()['ra'] * u.deg
dec = t.target_df.squeeze()['dec'] * u.deg
distance = (1000.0 / t.target_df.squeeze()['parallax']) * u.pc
radvel = t.target_df.squeeze(
)['dr2_radial_velocity'] * u.kilometer / u.second
pmra = t.target_df.squeeze()['pmra'] * u.mas / u.year
pmdec = t.target_df.squeeze()['pmdec'] * u.mas / u.year
Pcoord = SkyCoord( ra=ra, dec=dec, \
distance=distance, frame='icrs' , \
radial_velocity=radvel , \
pm_ra_cosdec= pmra , pm_dec= pmdec )
# # Query Gaia with search radius and parallax cut
# # Note, a cut on parallax_error was added because searches at low galactic latitude
# # return an overwhelming number of noisy sources that scatter into the search volume - ALK 20210325
# print('Querying Gaia for neighbors')
# if (searchradpc < Pcoord.distance):
# sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE CONTAINS( \
# POINT('ICRS',gaiaedr3.gaia_source.ra,gaiaedr3.gaia_source.dec), \
# CIRCLE('ICRS'," + str(Pcoord.ra.value) +","+ str(Pcoord.dec.value) +","+ str(searchraddeg.value) +"))\
# =1 AND parallax>" + str(minpar.value) + " AND parallax_error<0.5;"
# if (searchradpc >= Pcoord.distance):
# sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE parallax>" + str(minpar.value) + " AND parallax_error<0.5;"
# print('Note, using all-sky search')
# if verbose == True:
# print(sqltext)
# print()
# job = Gaia.launch_job_async(sqltext , dump_to_file=False)
# r = job.get_results()
# if verbose == True: print('Number of records: ',len(r['ra']))
# # Construct coordinates array for all stars returned in cone search
# gaiacoord = SkyCoord( ra=r['ra'] , dec=r['dec'] , distance=(1000.0/r['parallax'])*u.parsec , \
# frame='icrs' , \
# pm_ra_cosdec=r['pmra'] , pm_dec=r['pmdec'] )
lit_sc = SkyCoord(
ra=lit_gaia.ra.to_numpy(dtype='float') * u.deg,
dec=lit_gaia.dec.to_numpy(dtype='float') * u.deg,
pm_ra_cosdec=lit_gaia.pmra.to_numpy(dtype='float') * u.mas / u.yr,
pm_dec=lit_gaia.pmdec.to_numpy(dtype='float') * u.mas / u.yr,
distance=u.pc * (1000. / lit_gaia.parallax.to_numpy(dtype='float')))
sep = lit_sc.separation(Pcoord) #in degrees
sep3d = lit_sc.separation_3d(Pcoord) #in parsec
Pllbb = bc.radec_to_lb(Pcoord.ra.value, Pcoord.dec.value, degree=True)
Ppmllpmbb = bc.pmrapmdec_to_pmllpmbb( Pcoord.pm_ra_cosdec.value , Pcoord.pm_dec.value , \
Pcoord.ra.value , Pcoord.dec.value , degree=True )
Pvxvyvz = bc.vrpmllpmbb_to_vxvyvz(Pcoord.radial_velocity.value , Ppmllpmbb[0] , Ppmllpmbb[1] , \
Pllbb[0] , Pllbb[1] , Pcoord.distance.value/1000.0 , XYZ=False , degree=True)
Gllbb = bc.radec_to_lb(lit_sc.ra.value, lit_sc.dec.value, degree=True)
Gxyz = bc.lbd_to_XYZ(Gllbb[:, 0],
Gllbb[:, 1],
lit_sc.distance / 1000.0,
degree=True)
Gvrpmllpmbb = bc.vxvyvz_to_vrpmllpmbb( \
Pvxvyvz[0]*np.ones(len(Gxyz[:,0])) , Pvxvyvz[1]*np.ones(len(Gxyz[:,1])) , Pvxvyvz[2]*np.ones(len(Gxyz[:,2])) , \
Gxyz[:,0] , Gxyz[:,1] , Gxyz[:,2] , XYZ=True)
Gpmrapmdec = bc.pmllpmbb_to_pmrapmdec(Gvrpmllpmbb[:, 1],
Gvrpmllpmbb[:, 2],
Gllbb[:, 0],
Gllbb[:, 1],
degree=True)
# Code in case I want to do chi^2 cuts someday
Gvtanerr = 1.0 * np.ones(len(Gxyz[:, 0]))
Gpmerr = Gvtanerr * 206265000.0 * 3.154e7 / (lit_sc.distance.value *
3.086e13)
Gchi2 = ((Gpmrapmdec[:, 0] - lit_sc.pm_ra_cosdec.value)**2 +
(Gpmrapmdec[:, 1] - lit_sc.pm_dec.value)**2)**0.5
vtanoff = Gchi2 / Gpmerr #this is reported Vtan,off(km/s)
##vr pred
vr_pred = Gvrpmllpmbb[:, 0]
#create results dataframe
res = pd.DataFrame(
data={
'tic': lit_gaia.tic.to_numpy(dtype='str'),
'designation': lit_gaia.designation.to_numpy(dtype='str'),
'ra': lit_sc.ra.value,
'dec': lit_sc.dec.value,
'sep2D(deg)': sep.value,
'sep3D(pc)': sep3d.value,
'Vtan,off(km/s)': vtanoff,
'Vr,pred(km/s)': vr_pred
})
return (res)
def make_rcsample(parser):
options,args= parser.parse_args()
savefilename= options.savefilename
if savefilename is None:
#Create savefilename if not given
savefilename= os.path.join(appath._APOGEE_DATA,
'rcsample_'+appath._APOGEE_REDUX+'.fits')
print "Saving to %s ..." % savefilename
#Read the base-sample
data= apread.allStar(adddist=_ADDHAYDENDIST,rmdups=options.rmdups)
#Remove a bunch of fields that we do not want to keep
data= esutil.numpy_util.remove_fields(data,
['TARGET_ID',
'FILE',
'AK_WISE',
'SFD_EBV',
'SYNTHVHELIO_AVG',
'SYNTHVSCATTER',
'SYNTHVERR',
'SYNTHVERR_MED',
'RV_TEFF',
'RV_LOGG',
'RV_FEH',
'RV_CCFWHM',
'RV_AUTOFWHM',
'SYNTHSCATTER',
'CHI2_THRESHOLD',
'APSTAR_VERSION',
'ASPCAP_VERSION',
'RESULTS_VERSION',
'REDUCTION_ID',
'SRC_H',
'PM_SRC'])
if not appath._APOGEE_REDUX.lower() == 'current' \
and int(appath._APOGEE_REDUX[1:]) < 500:
data= esutil.numpy_util.remove_fields(data,
['ELEM'])
#Select red-clump stars
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
if appath._APOGEE_REDUX.lower() == 'current' \
or int(appath._APOGEE_REDUX[1:]) > 600:
from apogee.tools import paramIndx
if False:
#Use my custom logg calibration that's correct for the RC
logg= (1.-0.042)*data['FPARAM'][:,paramIndx('logg')]-0.213
lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.255
hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.3726
else:
#Use my custom logg calibration that's correct on average
logg= (1.+0.03)*data['FPARAM'][:,paramIndx('logg')]-0.37
lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.34
hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.256
else:
logg= data['LOGG']
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= rcmodel.loggteffcut(data['TEFF'],z,upper=False))\
*(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
data= data[indx]
#Add more aggressive flag cut
data= esutil.numpy_util.add_fields(data,[('ADDL_LOGG_CUT',numpy.int32)])
data['ADDL_LOGG_CUT']= ((data['TEFF']-4800.)/1000.+2.75) > data['LOGG']
if options.loggcut:
data= data[data['ADDL_LOGG_CUT'] == 1]
print "Making catalog of %i objects ..." % len(data)
#Add distances
data= esutil.numpy_util.add_fields(data,[('RC_DIST', float),
('RC_DM', float),
('RC_GALR', float),
('RC_GALPHI', float),
('RC_GALZ', float)])
rcd= rcmodel.rcdist()
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
data['RC_DIST']= rcd(jk,z,appmag=data['K0'])*options.distfac
data['RC_DM']= 5.*numpy.log10(data['RC_DIST'])+10.
XYZ= bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST'],
degree=True)
R,phi,Z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],
XYZ[:,1],
XYZ[:,2],
Xsun=8.,Zsun=0.025)
data['RC_GALR']= R
data['RC_GALPHI']= phi
data['RC_GALZ']= Z
#Save
fitsio.write(savefilename,data,clobber=True)
if not options.nostat:
#Determine statistical sample and add flag
apo= apogee.select.apogeeSelect()
statIndx= apo.determine_statistical(data)
mainIndx= apread.mainIndx(data)
data= esutil.numpy_util.add_fields(data,[('STAT',numpy.int32),
('INVSF',float)])
data['STAT']= 0
data['STAT'][statIndx*mainIndx]= 1
for ii in range(len(data)):
if (statIndx*mainIndx)[ii]:
data['INVSF'][ii]= 1./apo(data['LOCATION_ID'][ii],
data['H'][ii])
else:
data['INVSF'][ii]= -1.
if options.nopm:
fitsio.write(savefilename,data,clobber=True)
return None
#Get proper motions
from astroquery.vizier import Vizier
import astroquery
from astropy import units as u
import astropy.coordinates as coord
pmfile= savefilename.split('.')[0]+'_pms.fits'
if os.path.exists(pmfile):
pmdata= fitsio.read(pmfile,1)
else:
pmdata= numpy.recarray(len(data),
formats=['f8','f8','f8','f8','f8','f8','i4'],
names=['RA','DEC','PMRA','PMDEC',
'PMRA_ERR','PMDEC_ERR','PMMATCH'])
rad= u.Quantity(4./3600.,u.degree)
v= Vizier(columns=['RAJ2000','DEJ2000','pmRA','pmDE','e_pmRA','e_pmDE'])
for ii in range(len(data)):
#if ii > 100: break
sys.stdout.write('\r'+"Getting pm data for point %i / %i" % (ii+1,len(data)))
sys.stdout.flush()
pmdata.RA[ii]= data['RA'][ii]
pmdata.DEC[ii]= data['DEC'][ii]
co= coord.ICRS(ra=data['RA'][ii],
dec=data['DEC'][ii],
unit=(u.degree, u.degree))
trying= True
while trying:
try:
tab= v.query_region(co,rad,catalog='I/322') #UCAC-4 catalog
except astroquery.exceptions.TimeoutError:
pass
else:
trying= False
if len(tab) == 0:
pmdata.PMMATCH[ii]= 0
print "Didn't find a match for %i ..." % ii
continue
else:
pmdata.PMMATCH[ii]= len(tab)
if len(tab[0]['pmRA']) > 1:
print "Found more than 1 match for %i ..." % ii
try:
pmdata.PMRA[ii]= float(tab[0]['pmRA'])
except TypeError:
jj= 1
while len(tab[0]['pmRA']) > 1 and jj < 4:
trad= u.Quantity((4.-jj)/3600.,u.degree)
trying= True
while trying:
try:
tab= v.query_region(co,trad,catalog='I/322') #UCAC-4 catalog
except astroquery.exceptions.TimeoutError:
pass
else:
trying= False
jj+= 1
if len(tab) == 0:
pmdata.PMMATCH[ii]= 0
print "Didn't find a unambiguous match for %i ..." % ii
continue
pmdata.PMRA[ii]= float(tab[0]['pmRA'])
pmdata.PMDEC[ii]= float(tab[0]['pmDE'])
pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'])
pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'])
if numpy.isnan(float(tab[0]['pmRA'])): pmdata.PMMATCH[ii]= 0
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
#Match proper motions
try: #These already exist currently, but may not always exist
data= esutil.numpy_util.remove_fields(data,['PMRA','PMDEC'])
except ValueError:
pass
data= esutil.numpy_util.add_fields(data,[('PMRA', numpy.float),
('PMDEC', numpy.float),
('PMRA_ERR', numpy.float),
('PMDEC_ERR', numpy.float),
('PMMATCH',numpy.int32)])
data['PMMATCH']= 0
h=esutil.htm.HTM()
m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data['PMRA'][m2]= pmdata['PMRA'][m1]
data['PMDEC'][m2]= pmdata['PMDEC'][m1]
data['PMRA_ERR'][m2]= pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR'][m2]= pmdata['PMDEC_ERR'][m1]
data['PMMATCH'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx= data['PMMATCH'] == 1
data['PMRA'][True-pmindx]= -9999.99
data['PMDEC'][True-pmindx]= -9999.99
data['PMRA_ERR'][True-pmindx]= -9999.99
data['PMDEC_ERR'][True-pmindx]= -9999.99
#Calculate Galactocentric velocities
data= esutil.numpy_util.add_fields(data,[('GALVR', numpy.float),
('GALVT', numpy.float),
('GALVZ', numpy.float)])
lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],data['PMDEC'],
data['RA'],data['DEC'],
degree=True)
vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:,0],
pmllpmbb[:,1],
lb[:,0],lb[:,1],data['RC_DIST'],
degree=True)
vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
vxvyvz[:,1],
vxvyvz[:,2],
8.-XYZ[:,0],
XYZ[:,1],
XYZ[:,2]+0.025,
vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR']= vR
data['GALVT']= vT
data['GALVZ']= vZ
data['GALVR'][True-pmindx]= -9999.99
data['GALVT'][True-pmindx]= -9999.99
data['GALVZ'][True-pmindx]= -9999.99
#Get proper motions
pmfile= savefilename.split('.')[0]+'_pms_ppmxl.fits'
if os.path.exists(pmfile):
pmdata= fitsio.read(pmfile,1)
else:
pmdata= numpy.recarray(len(data),
formats=['f8','f8','f8','f8','f8','f8','i4'],
names=['RA','DEC','PMRA','PMDEC',
'PMRA_ERR','PMDEC_ERR','PMMATCH'])
rad= u.Quantity(4./3600.,u.degree)
v= Vizier(columns=['RAJ2000','DEJ2000','pmRA','pmDE','e_pmRA','e_pmDE'])
for ii in range(len(data)):
#if ii > 100: break
sys.stdout.write('\r'+"Getting pm data for point %i / %i" % (ii+1,len(data)))
sys.stdout.flush()
pmdata.RA[ii]= data['RA'][ii]
pmdata.DEC[ii]= data['DEC'][ii]
co= coord.ICRS(ra=data['RA'][ii],
dec=data['DEC'][ii],
unit=(u.degree, u.degree))
trying= True
while trying:
try:
tab= v.query_region(co,rad,catalog='I/317') #PPMXL catalog
except astroquery.exceptions.TimeoutError:
pass
else:
trying= False
if len(tab) == 0:
pmdata.PMMATCH[ii]= 0
print "Didn't find a match for %i ..." % ii
continue
else:
pmdata.PMMATCH[ii]= len(tab)
if len(tab[0]['pmRA']) > 1:
pass
#print "Found more than 1 match for %i ..." % ii
try:
pmdata.PMRA[ii]= float(tab[0]['pmRA'])
except TypeError:
#Find nearest
cosdists= numpy.zeros(len(tab[0]['pmRA']))
for jj in range(len(tab[0]['pmRA'])):
cosdists[jj]= cos_sphere_dist(tab[0]['RAJ2000'][jj],
tab[0]['DEJ2000'][jj],
data['RA'][ii],
data['DEC'][ii])
closest= numpy.argmax(cosdists)
pmdata.PMRA[ii]= float(tab[0]['pmRA'][closest])
pmdata.PMDEC[ii]= float(tab[0]['pmDE'][closest])
pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'][closest])
pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'][closest])
if numpy.isnan(float(tab[0]['pmRA'][closest])): pmdata.PMMATCH[ii]= 0
else:
pmdata.PMDEC[ii]= float(tab[0]['pmDE'])
pmdata.PMRA_ERR[ii]= float(tab[0]['e_pmRA'])
pmdata.PMDEC_ERR[ii]= float(tab[0]['e_pmDE'])
if numpy.isnan(float(tab[0]['pmRA'])): pmdata.PMMATCH[ii]= 0
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
#Match proper motions to ppmxl
data= esutil.numpy_util.add_fields(data,[('PMRA_PPMXL', numpy.float),
('PMDEC_PPMXL', numpy.float),
('PMRA_ERR_PPMXL', numpy.float),
('PMDEC_ERR_PPMXL', numpy.float),
('PMMATCH_PPMXL',numpy.int32)])
data['PMMATCH_PPMXL']= 0
h=esutil.htm.HTM()
m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data['PMRA_PPMXL'][m2]= pmdata['PMRA'][m1]
data['PMDEC_PPMXL'][m2]= pmdata['PMDEC'][m1]
data['PMRA_ERR_PPMXL'][m2]= pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR_PPMXL'][m2]= pmdata['PMDEC_ERR'][m1]
data['PMMATCH_PPMXL'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx= data['PMMATCH_PPMXL'] == 1
data['PMRA_PPMXL'][True-pmindx]= -9999.99
data['PMDEC_PPMXL'][True-pmindx]= -9999.99
data['PMRA_ERR_PPMXL'][True-pmindx]= -9999.99
data['PMDEC_ERR_PPMXL'][True-pmindx]= -9999.99
#Calculate Galactocentric velocities
data= esutil.numpy_util.add_fields(data,[('GALVR_PPMXL', numpy.float),
('GALVT_PPMXL', numpy.float),
('GALVZ_PPMXL', numpy.float)])
lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA_PPMXL'],
data['PMDEC_PPMXL'],
data['RA'],data['DEC'],
degree=True)
vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:,0],
pmllpmbb[:,1],
lb[:,0],lb[:,1],data['RC_DIST'],
degree=True)
vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
vxvyvz[:,1],
vxvyvz[:,2],
8.-XYZ[:,0],
XYZ[:,1],
XYZ[:,2]+0.025,
vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR_PPMXL']= vR
data['GALVT_PPMXL']= vT
data['GALVZ_PPMXL']= vZ
data['GALVR_PPMXL'][True-pmindx]= -9999.99
data['GALVT_PPMXL'][True-pmindx]= -9999.99
data['GALVZ_PPMXL'][True-pmindx]= -9999.99
#Save
fitsio.write(savefilename,data,clobber=True)
return None
def from_radec(cluster, do_order=False, do_key_params=False):
"""Calculate galactocentric coordinates from on-sky position, proper motion, and radial velocity of cluster
Parameters
----------
cluster : class
StarCluster
do_order : bool
sort star by radius after coordinate change (default: False)
do_key_params : bool
call key_params to calculate key parameters after unit change (default: False)
Returns
-------
None
History:
-------
2018 - Written - Webb (UofT)
"""
if cluster.units == "radec" and cluster.origin == "sky":
origin0 = cluster.origin
l, b = bovy_coords.radec_to_lb(cluster.ra, cluster.dec, degree=True).T
x0, y0, z0 = bovy_coords.lbd_to_XYZ(l, b, cluster.dist, degree=True).T
cluster.x, cluster.y, cluster.z = bovy_coords.XYZ_to_galcenrect(
x0, y0, z0, Xsun=8.0, Zsun=0.025).T
pml, pmb = bovy_coords.pmrapmdec_to_pmllpmbb(cluster.pmra,
cluster.pmdec,
cluster.ra,
cluster.dec,
degree=True).T
vx0, vy0, vz0 = bovy_coords.vrpmllpmbb_to_vxvyvz(cluster.vlos,
pml,
pmb,
l,
b,
cluster.dist,
degree=True).T
cluster.vx, cluster.vy, cluster.vz = bovy_coords.vxvyvz_to_galcenrect(
vx0,
vy0,
vz0,
vsun=[0.0, 220.0, 0.0],
Xsun=8.0,
Zsun=0.025,
_extra_rot=True,
).T
l_gc, b_gc = bovy_coords.radec_to_lb(cluster.ra_gc,
cluster.dec_gc,
degree=True)
x0_gc, y0_gc, z0_gc = bovy_coords.lbd_to_XYZ(l_gc,
b_gc,
cluster.dist_gc,
degree=True)
cluster.xgc, cluster.ygc, cluster.zgc = bovy_coords.XYZ_to_galcenrect(
x0_gc, y0_gc, z0_gc, Xsun=8.0, Zsun=0.025)
pml_gc, pmb_gc = bovy_coords.pmrapmdec_to_pmllpmbb(cluster.pmra_gc,
cluster.pmdec_gc,
cluster.ra_gc,
cluster.dec_gc,
degree=True)
vx0_gc, vy0_gc, vz0_gc = bovy_coords.vrpmllpmbb_to_vxvyvz(
cluster.vlos_gc,
pml_gc,
pmb_gc,
l_gc,
b_gc,
cluster.dist_gc,
degree=True)
cluster.vx_gc, cluster.vy_gc, cluster.vz_gc = bovy_coords.vxvyvz_to_galcenrect(
vx0_gc,
vy0_gc,
vz0_gc,
vsun=[0.0, 220.0, 0.0],
Xsun=8.0,
Zsun=0.025,
_extra_rot=True,
)
cluster.origin = "galaxy"
cluster.units = "kpckms"
cluster.rv3d()
if do_key_params:
cluster.key_params(do_order=do_order)
def calcMass(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=True)
elif options.select.lower() == 'fakebimodal':
raw= read_gdwarfs(_FAKEBIMODALGDWARFFILE,
logg=True,ebv=True,sn=True)
options.select= 'all'
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=True)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=True)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Savefile
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
mass= pickle.load(savefile)
ii= pickle.load(savefile)
jj= pickle.load(savefile)
savefile.close()
else:
mass= []
ii, jj= 0, 0
#parameters
if os.path.exists(args[1]):#Load initial
savefile= open(args[1],'rb')
fits= pickle.load(savefile)
savefile.close()
else:
print "Error: must provide parameters of best fits"
print "Returning ..."
return None
#Sample?
if options.mcsample:
if ii < len(binned.fehedges)-1 and jj < len(binned.afeedges)-1:
print "First do all of the best-fit mass estimates ..."
print "Returning ..."
return None
if os.path.exists(args[2]): #Load savefile
savefile= open(args[2],'rb')
masssamples= pickle.load(savefile)
ii= pickle.load(savefile)
jj= pickle.load(savefile)
savefile.close()
else:
masssamples= []
ii, jj= 0, 0
if os.path.exists(args[3]): #Load savefile
savefile= open(args[3],'rb')
denssamples= pickle.load(savefile)
savefile.close()
else:
print "If mcsample you need to provide the file with the density samples ..."
print "Returning ..."
return None
#Set up model etc.
if options.model.lower() == 'hwr':
densfunc= _HWRDensity
elif options.model.lower() == 'twodblexp':
densfunc= _TwoDblExpDensity
like_func= _HWRLikeMinus
pdf_func= _HWRLike
if options.sample.lower() == 'g':
colorrange=[0.48,0.55]
elif options.sample.lower() == 'k':
colorrange=[0.55,0.75]
#Load selection function
plates= numpy.array(list(set(list(raw.plate))),dtype='int') #Only load plates that we use
print "Using %i plates, %i stars ..." %(len(plates),len(raw))
sf= segueSelect(plates=plates,type_faint='tanhrcut',
sample=options.sample,type_bright='tanhrcut',
sn=True,select=options.select)
platelb= bovy_coords.radec_to_lb(sf.platestr.ra,sf.platestr.dec,
degree=True)
indx= [not 'faint' in name for name in sf.platestr.programname]
platebright= numpy.array(indx,dtype='bool')
indx= ['faint' in name for name in sf.platestr.programname]
platefaint= numpy.array(indx,dtype='bool')
if options.sample.lower() == 'g':
grmin, grmax= 0.48, 0.55
rmin,rmax= 14.5, 20.2
#Run through the bins
while ii < len(binned.fehedges)-1:
while jj < len(binned.afeedges)-1:
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
if options.mcsample: masssamples.append(None)
else: mass.append(None)
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
break
continue
print binned.feh(ii), binned.afe(jj), len(data)
fehindx= binned.fehindx(binned.feh(ii))
afeindx= binned.afeindx(binned.afe(jj))
#set up feh and color
feh= binned.feh(ii)
fehrange= [binned.fehedges[ii],binned.fehedges[ii+1]]
#FeH
fehdist= DistSpline(*numpy.histogram(data.feh,bins=5,
range=fehrange),
xrange=fehrange,dontcuttorange=False)
#Color
colordist= DistSpline(*numpy.histogram(data.dered_g\
-data.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Age marginalization
afe= binned.afe(jj)
if options.simpleage:
agemin, agemax= 0.5, 10.
else:
if afe > 0.25: agemin, agemax= 7.,10.
else: agemin,agemax= 1.,8.
if options.mcsample:
#Loop over samples
thissamples= denssamples[afeindx+fehindx*binned.npixafe()]
if options.nsamples < len(thissamples):
print "Taking random ..."
#Random permutation
thissamples= numpy.random.permutation(thissamples)[0:options.nsamples]
thismasssamples= []
print "WARNING: DISK MASS IN CALCMASS ONLY FOR G COLORS"
for kk in range(len(thissamples)):
thisparams= thissamples[kk]
thismasssamples.append(predictDiskMass(densfunc,
thisparams,sf,
colordist,fehdist,
fehrange[0],
fehrange[1],feh,
data,0.45,
0.58,
agemin,agemax,
normalize=options.normalize,
imfmodel=options.imfmodel))
#Print some stuff
print numpy.mean(numpy.array(thismasssamples)), numpy.std(numpy.array(thismasssamples))
masssamples.append(thismasssamples)
else:
thisparams= fits[afeindx+fehindx*binned.npixafe()]
print "WARNING: DISK MASS IN CALCMASS ONLY FOR G COLORS"
mass.append(predictDiskMass(densfunc,
thisparams,sf,
colordist,fehdist,
fehrange[0],
fehrange[1],feh,
data,0.45,
0.58,
agemin,agemax,
normalize=options.normalize,
imfmodel=options.imfmodel))
print mass[-1]
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
if options.mcsample: save_pickles(args[2],masssamples,ii,jj)
else: save_pickles(args[0],mass,ii,jj)
if jj == 0: #this means we've reset the counter
break
if options.mcsample: save_pickles(args[2],masssamples,ii,jj)
else: save_pickles(args[0],mass,ii,jj)
return None
def action(ra_deg, dec_deg, d_kpc, pm_ra_masyr, pm_dec_masyr, v_los_kms,
verbose=False):
"""
parameters:
----------
ra_deg: (float)
RA in degrees.
dec_deg: (float)
Dec in degress.
d_kpc: (float)
Distance in kpc.
pm_ra_masyr: (float)
RA proper motion in mas/yr.
pm_decmasyr: (float)
Dec proper motion in mas/yr.
v_los_kms: (float)
RV in kms.
returns:
------
R_kpc, phi_rad, z_kpc, vR_kms, vT_kms, vz_kms
jR: (float)
Radial action.
lz: (float)
Vertical ang mom.
jz: (float)
Vertical action.
"""
ra_rad = ra_deg * (np.pi / 180.) # RA [rad]
dec_rad = dec_deg * (np.pi / 180.) # dec [rad]
# Galactocentric position of the Sun:
X_gc_sun_kpc = 8. # [kpc]
Z_gc_sun_kpc = 0.025 # [kpc]
# Galactocentric velocity of the Sun:
vX_gc_sun_kms = -9.58 # = -U [kms]
vY_gc_sun_kms = 10.52 + 220. # = V+v_circ(R_Sun) [kms]
vZ_gc_sun_kms = 7.01 # = W [kms]
# a. convert spatial coordinates (ra,dec,d) to (R,z,phi)
# (ra,dec) --> Galactic coordinates (l,b):
lb = bovy_coords.radec_to_lb(ra_rad, dec_rad, degree=False, epoch=2000.0)
# l_rad = lb[:, 0]
# b_rad = lb[:, 1]
l_rad = lb[0]
b_rad = lb[1]
# (l,b,d) --> Galactocentric cartesian coordinates (x,y,z):
xyz = bovy_coords.lbd_to_XYZ(l_rad, b_rad, d_kpc, degree=False)
# x_kpc = xyz[:, 0]
# y_kpc = xyz[:, 1]
# z_kpc = xyz[:, 2]
x_kpc = xyz[0]
y_kpc = xyz[1]
z_kpc = xyz[2]
# (x,y,z) --> Galactocentric cylindrical coordinates (R,z,phi):
Rzphi = bovy_coords.XYZ_to_galcencyl(x_kpc, y_kpc, z_kpc,
Xsun=X_gc_sun_kpc, Zsun=Z_gc_sun_kpc)
# R_kpc = Rzphi[:, 0]
# phi_rad = Rzphi[:, 1]
# z_kpc = Rzphi[:, 2]
R_kpc = Rzphi[0]
phi_rad = Rzphi[1]
z_kpc = Rzphi[2]
# b. convert velocities (pm_ra,pm_dec,vlos) to (vR,vz,vT)
# (pm_ra,pm_dec) --> (pm_l,pm_b):
pmlpmb = bovy_coords.pmrapmdec_to_pmllpmbb(pm_ra_masyr, pm_dec_masyr,
ra_rad, dec_rad, degree=False,
epoch=2000.0)
# pml_masyr = pmlpmb[:, 0]
# pmb_masyr = pmlpmb[:, 1]
pml_masyr = pmlpmb[0]
pmb_masyr = pmlpmb[1]
# (v_los,pm_l,pm_b) & (l,b,d) --> (vx,vy,vz):
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(v_los_kms, pml_masyr, pmb_masyr,
l_rad, b_rad, d_kpc, XYZ=False,
degree=False)
# vx_kms = vxvyvz[:, 0]
# vy_kms = vxvyvz[:, 1]
# vz_kms = vxvyvz[:, 2]
vx_kms = vxvyvz[0]
vy_kms = vxvyvz[1]
vz_kms = vxvyvz[2]
# (vx,vy,vz) & (x,y,z) --> (vR,vT,vz):
vRvTvZ = bovy_coords.vxvyvz_to_galcencyl(vx_kms, vy_kms, vz_kms, R_kpc,
phi_rad, z_kpc,
vsun=[vX_gc_sun_kms,
vY_gc_sun_kms,
vZ_gc_sun_kms],
galcen=True)
# vR_kms = vRvTvZ[:, 0]
# vT_kms = vRvTvZ[:, 1]
# vz_kms = vRvTvZ[:, 2]
vR_kms = vRvTvZ[0]
vT_kms = vRvTvZ[1]
vz_kms = vRvTvZ[2]
if verbose:
print("R = ", R_kpc, "\t kpc")
print("phi = ", phi_rad, "\t rad")
print("z = ", z_kpc, "\t kpc")
print("v_R = ", vR_kms, "\t km/s")
print("v_T = ", vT_kms, "\t km/s")
print("v_z = ", vz_kms, "\t km/s")
jR, lz, jz = calc_actions(R_kpc, phi_rad, z_kpc, vR_kms, vT_kms, vz_kms)
return R_kpc, phi_rad, z_kpc, vR_kms, vT_kms, vz_kms, jR, lz, jz
def calc_eccentricity(args, options):
table = os.path.join(args[0],'table2.dat')
readme = os.path.join(args[0],'ReadMe')
dierickx = ascii.read(table, readme=readme)
vxvv = np.dstack([dierickx['RAdeg'], dierickx['DEdeg'], dierickx['Dist']/1e3, dierickx['pmRA'], dierickx['pmDE'], dierickx['HRV']])[0]
ro, vo, zo = 8., 220., 0.025
ra, dec= vxvv[:,0], vxvv[:,1]
lb= bovy_coords.radec_to_lb(ra,dec,degree=True)
pmra, pmdec= vxvv[:,3], vxvv[:,4]
pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(pmra,pmdec,ra,dec,degree=True)
d, vlos= vxvv[:,2], vxvv[:,5]
rectgal= bovy_coords.sphergal_to_rectgal(lb[:,0],lb[:,1],d,vlos,pmllpmbb[:,0], pmllpmbb[:,1],degree=True)
vsolar= np.array([-10.1,4.0,6.7])
vsun= np.array([0.,1.,0.,])+vsolar/vo
X = rectgal[:,0]/ro
Y = rectgal[:,1]/ro
Z = rectgal[:,2]/ro
vx = rectgal[:,3]/vo
vy = rectgal[:,4]/vo
vz = rectgal[:,5]/vo
vsun= np.array([0.,1.,0.,])+vsolar/vo
Rphiz= bovy_coords.XYZ_to_galcencyl(X,Y,Z,Zsun=zo/ro)
vRvTvz= bovy_coords.vxvyvz_to_galcencyl(vx,vy,vz,Rphiz[:,0],Rphiz[:,1],Rphiz[:,2],vsun=vsun,Xsun=1.,Zsun=zo/ro,galcen=True)
#do the integration and individual analytic estimate for each object
ts= np.linspace(0.,20.,10000)
lp= LogarithmicHaloPotential(normalize=1.)
e_ana = numpy.zeros(len(vxvv))
e_int = numpy.zeros(len(vxvv))
print('Performing orbit integration and analytic parameter estimates for Dierickx et al. sample...')
for i in tqdm(range(len(vxvv))):
try:
orbit = Orbit(vxvv[i], radec=True, vo=220., ro=8.)
e_ana[i] = orbit.e(analytic=True, pot=lp, c=True)
except UnboundError:
e_ana[i] = np.nan
orbit.integrate(ts, lp)
e_int[i] = orbit.e(analytic=False)
fig = plt.figure()
fig.set_size_inches(1.5*columnwidth, 1.5*columnwidth)
plt.scatter(e_int, e_ana, s=1, color='Black', lw=0.)
plt.xlabel(r'$\mathrm{galpy\ integrated}\ e$')
plt.ylabel(r'$\mathrm{galpy\ analytic}\ e$')
plt.xlim(0.,1.)
plt.ylim(0.,1.)
fig.tight_layout()
plt.savefig(os.path.join(args[0],'dierickx-integratedeanalytice.png'), format='png', dpi=200)
fig = plt.figure()
fig.set_size_inches(1.5*columnwidth, 1.5*columnwidth)
plt.hist(e_int, bins=30)
plt.xlim(0.,1.)
plt.xlabel(r'$\mathrm{galpy}\ e$')
fig.tight_layout()
plt.savefig(os.path.join(args[0], 'dierickx-integratedehist.png'), format='png', dpi=200)
fig = plt.figure()
fig.set_size_inches(1.5*columnwidth, 1.5*columnwidth)
plt.scatter(dierickx['e'], e_int, s=1, color='Black', lw=0.)
plt.xlabel(r'$\mathrm{Dierickx\ et\ al.}\ e$')
plt.ylabel(r'$\mathrm{galpy\ integrated}\ e$')
plt.xlim(0.,1.)
plt.ylim(0.,1.)
fig.tight_layout()
plt.savefig(os.path.join(args[0],'dierickx-integratedee.png'), format='png', dpi=200)
fig = plt.figure()
fig.set_size_inches(1.5*columnwidth, 1.5*columnwidth)
plt.scatter(dierickx['e'], e_ana, s=1, color='Black', lw=0.)
plt.xlabel(r'$\mathrm{Dierickx\ et\ al.}\ e$')
plt.ylabel(r'$\mathrm{galpy\ estimated}\ e$')
plt.xlim(0.,1.)
plt.ylim(0.,1.)
fig.tight_layout()
plt.savefig(os.path.join(args[0],'dierickx-analyticee.png'), format='png', dpi=200)
arr = numpy.recarray(len(e_ana), dtype=[('analytic_e', float), ('integrated_e', float)])
arr['analytic_e'] = e_ana
arr['integrated_e'] = e_int
with open(os.path.join(args[0],'eccentricities.dat'), 'w') as file:
pickle.dump(arr, file)
file.close()
def shuffle(self,
GRVScut=16.,
vtotcut=450 * u.km / u.s,
dustmap=None,
N=100):
'''
Randomize the galactocentric right ascension and
computes how many stars satisfy the photometric/dynamical conditions GRVS < GRVScut, vtot<vtotcut. Do this N times.
Parameters
----------
GRVScut : float
Magnitude cut to impose
vtotcut : Quantity
Galactocentric velocity cut to impose
dustmap : DustMap
Dustmap to be used to compute the photometry
N : int
Number of random realizations
Returns
-------
NGaia : ndarray
Array of size N containing the number of selected stars per each realization.
'''
import astropy.coordinates as coord
from galpy.util.bovy_coords import radec_to_lb, pmrapmdec_to_pmllpmbb
vSun = [
-self.solarmotion[0], self.solarmotion[1], self.solarmotion[2]
] * u.km / u.s # (U, V, W)
vrot = [0., 220., 0.] * u.km / u.s
RSun = 8. * u.kpc
zSun = 0 * u.pc
v_sun = coord.CartesianDifferential(vSun + vrot)
GCCS = coord.Galactocentric(galcen_distance=RSun,
z_sun=zSun,
galcen_v_sun=v_sun)
GCS = coord.Galactic()
data = radec_to_lb(self.ra.to('deg').value,
self.dec.to('deg').value,
degree=True)
ll, bb = data[:, 0], data[:, 1]
data = pmrapmdec_to_pmllpmbb(self.pmra,
self.pmdec,
self.ra.to('deg').value,
self.dec.to('deg').value,
degree=True)
pmll, pmbb = data[:, 0], data[:, 1]
galactic_coords = coord.Galactic(l=ll * u.deg,
b=bb * u.deg,
distance=self.dist,
pm_l_cosb=pmll * u.mas / u.yr,
pm_b=pmbb * u.mas / u.yr,
radial_velocity=self.vlos)
galactocentric_coords = galactic_coords.transform_to(GCCS)
phi = np.arctan2(galactocentric_coords.y, galactocentric_coords.x)
r = (galactocentric_coords.y**2. + galactocentric_coords.x**2.)**0.5
self.vtot = np.sqrt(galactocentric_coords.v_x**2. +
galactocentric_coords.v_y**2. +
galactocentric_coords.v_z**2.).to(u.km / u.s)
NGaia = np.zeros(N)
for i in xrange(N):
phi2 = (2 * np.random.random(phi.size) - 1) * np.pi
galactocentric_coords2 = coord.Galactocentric(
x=r * np.cos(phi2),
y=r * np.sin(phi2),
z=galactocentric_coords.z,
galcen_distance=RSun,
z_sun=zSun,
galcen_v_sun=v_sun)
galactic2 = galactocentric_coords2.transform_to(GCS)
self.ll, self.bb = galactic2.l.to(u.deg).value, galactic2.b.to(
u.deg).value
self.dist = galactic2.distance
self.photometry(dustmap=dustmap, v=False)
NGaia[i] = ((self.GRVS < GRVScut) & (self.vtot > vtotcut)).sum()
self.cattype = 2
return NGaia
def plotMetallicityColor(options,args):
if options.png: ext= 'png'
else: ext= 'ps'
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal=options.metal,
sample=options.sample)
#Load plates
platestr= segueSelect._load_fits(os.path.join(segueSelect._SEGUESELECTDIR,
'segueplates.fits'))
#Set up ranges
if options.sample.lower() == 'g':
xrange=[0.46,0.57]
rmin, rmax= 14.5, 20.2
grmin, grmax= 0.48, 0.55
rx= 0.47
colorrange=[0.48,0.55]
elif options.sample.lower() == 'k':
xrange=[0.51,0.79]
rmin, rmax= 14.5, 19.
grmin, grmax= 0.55,0.75
rx= 0.01/.11*(xrange[1]-xrange[0])+xrange[0]
colorrange=[0.55,0.75]
if options.metal.lower() == 'rich':
yrange=[-0.55,0.5]
ry= 0.275
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'poor':
yrange=[-1.6,0.3]
ry= yrange[1]-(.5-0.275)*(yrange[1]-yrange[0])
fehrange= _ARICHFEHRANGE
#First plot all data, faint, and bright
bovy_plot.bovy_print()
_plotMC_single(data,options,args,all=True,overplot=False,xrange=xrange,
yrange=yrange,fehrange=fehrange,colorrange=colorrange)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_gr_'+options.sample+'_'+options.metal+'.'+ext))
#Then plot them pixel-by-pixel
platelb= bovy_coords.radec_to_lb(platestr.ra,platestr.dec,
degree=True)
nside= 1
for ii in range(12*nside**2):
#First find all of the plates in this pixel
indx= (platestr.healpix_level1 == ii)
if numpy.sum(indx) == 0: continue #No match
thisplatestr= platestr[indx]
platels= platelb[indx,0]
platebs= platelb[indx,1]
indx= []
for jj in range(len(data.ra)):
if data[jj].plate in list(thisplatestr.plate):
indx.append(True)
else:
indx.append(False)
indx= numpy.array(indx,dtype='bool')
if numpy.sum(indx) == 0: continue #No matches
bovy_plot.bovy_print()
_plotMC_single(data[indx],options,args,all=False,overplot=False,
xrange=xrange,yrange=yrange,platels=platels,
platebs=platebs,
ry=ry,rx=rx,
rmin=rmin,rmax=rmax,grmin=grmin,grmax=grmax)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_gr_'+options.sample+'_'+options.metal+'_%i.' % ii + ext))
return None
# other properties # June Y18 version # age = star['age_1'][sindx] # age_68l = star['age_68L_1'][sindx] # age_68u = star['age_68U_1'][sindx] # feh = star['feh_1'][sindx] # alpha = star['alpha'][sindx] # data13Jul18 age = star['age'][sindx] age_68l = star['age_68L'][sindx] age_68u = star['age_68U'][sindx] feh = star['FE_H_ADOP_COR'][sindx] alpha = star['ALP_FE_ADOP_COR'][sindx] # convert coordinates Tllbb = bovy_coords.radec_to_lb(ra, dec, degree=True, epoch=None) glon = Tllbb[:, 0] glat = Tllbb[:, 1] # assumed solar motion, from Adibekyan et al. (2012) usun = 11.1 vsun = 12.24 wsun = 7.25 zsun = 0.014 # set vxvv for galpy # vxvs=[] # for i in range(0,nstar-1): # vxvs.append([ra[i],dec[i],dist[i],pmra[i],pmdec[i],vhel[i]]) # vxvv=numpy.array(vxvs)
def resampleMags(raw,comps,options,args):
#For each data point's line-of-sight and color and feh
#calculate the "thin" and "thick" distributions
model_thick= _DblExpDensity
model_thin= _DblExpDensity
params_thick= numpy.array([numpy.log(options.hz_thick/1000.),
-numpy.log(options.hr_thick)])
params_thin= numpy.array([numpy.log(options.hz_thin/1000.),
-numpy.log(options.hr_thin)])
if options.allthin:
params_thick= params_thin
elif options.allthick:
params_thin= params_thick
#Load sf
sf= segueSelect(sample=options.sample,sn=True,
type_bright='tanhrcut',
type_faint='tanhrcut')
platelb= bovy_coords.radec_to_lb(sf.platestr.ra,sf.platestr.dec,
degree=True)
#Cut out bright stars on faint plates and vice versa
cutbright= False
if cutbright:
indx= []
for ii in range(len(raw.feh)):
if sf.platebright[str(raw[ii].plate)] and raw[ii].dered_r >= 17.8:
indx.append(False)
elif not sf.platebright[str(raw[ii].plate)] and raw[ii].dered_r < 17.8:
indx.append(False)
else:
indx.append(True)
indx= numpy.array(indx,dtype='bool')
raw= raw[indx]
comps= comps[indx]
#Loadthe data into the pixelAfeFeh structure
raw= _append_field_recarray(raw,'comps',comps)
binned= pixelAfeFeh(raw,dfeh=0.1,dafe=0.05,fehmin=-1.6,
fehmax=0.5,afemin=-0.05,afemax=0.55)
#Color
if options.sample.lower() == 'g':
colorrange=[0.48,0.55]
rmax= 20.2
elif options.sample.lower() == 'k':
colorrange=[0.55,0.75]
rmax= 19.
if options.sample.lower() == 'g':
grmin, grmax= 0.48, 0.55
rmin,rmax= 14.5, 20.2
ngr, nfeh, nrs= 2, 2, 201
grs= numpy.linspace(grmin,grmax,ngr)
rs= numpy.linspace(rmin,rmax,nrs)
#Run through the bins
ii, jj= 0, 0
while ii < len(binned.fehedges)-1:
while jj < len(binned.afeedges)-1:
data= binned(binned.feh(ii),binned.afe(jj))
rawIndx= binned.callIndx(binned.feh(ii),binned.afe(jj))
if len(data) < 1:
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
break
continue
#Set up feh and color
feh= binned.feh(ii)
fehrange= [binned.fehedges[ii],binned.fehedges[ii+1]]
#FeH
fehdist= DistSpline(*numpy.histogram(data.feh,bins=5,
range=fehrange),
xrange=fehrange,dontcuttorange=False)
#Color
colordist= DistSpline(*numpy.histogram(data.dered_g\
-data.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Predict the r-distribution for all plates
#Thick or thin?
thick_amp= numpy.mean(data.comps)
rdists_thin= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
rdists_thick= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
for pp in range(len(sf.plates)):
sys.stdout.write('\r'+"Working on bin %i / %i: plate %i / %i" \
% (ii*(len(binned.afeedges)-1)+jj+1,
(len(binned.afeedges)-1)*(len(binned.fehedges)-1),pp+1,len(sf.plates))+'\r')
sys.stdout.flush()
rdists_thin[pp,:,:,:]= _predict_rdist_plate(rs,model_thin,
params_thin,
rmin,rmax,
platelb[pp,0],platelb[pp,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[pp],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
rdists_thick[pp,:,:,:]= _predict_rdist_plate(rs,model_thick,
params_thick,
rmin,rmax,
platelb[pp,0],platelb[pp,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[pp],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
rdists= thick_amp*rdists_thick+(1.-thick_amp)*rdists_thin
rdists[numpy.isnan(rdists)]= 0.
numbers= numpy.sum(rdists,axis=3)
numbers= numpy.sum(numbers,axis=2)
numbers= numpy.sum(numbers,axis=1)
numbers= numpy.cumsum(numbers)
numbers/= numbers[-1]
rdists= numpy.cumsum(rdists,axis=1)
for ww in range(len(sf.plates)):
for ll in range(ngr):
for kk in range(nfeh):
if rdists[ww,-1,ll,kk] != 0.:
rdists[ww,:,ll,kk]/= rdists[ww,-1,ll,kk]
#Now sample
nout= 0
while nout < len(data):
#First sample a plate
ran= numpy.random.uniform()
kk= 0
while numbers[kk] < ran: kk+= 1
#plate==kk; now sample from the rdist of this plate
ran= numpy.random.uniform()
ll= 0
#Find cc and ff for this data point
cc= int(numpy.floor((data[nout].dered_g-data[nout].dered_r-colorrange[0])/(colorrange[1]-colorrange[0])*ngr))
ff= int(numpy.floor((data[nout].feh-fehrange[0])/(fehrange[1]-fehrange[0])*nfeh))
while rdists[kk,ll,cc,ff] < ran and ll < nrs-1: ll+= 1
#r=ll
oldgr= data.dered_g[nout]-data.dered_r[nout]
oldr= data.dered_r[nout]
data.dered_r[nout]= rs[ll]
data.dered_g[nout]= oldgr+data.dered_r[nout]
#Also change plate and l and b
data.plate[nout]= sf.plates[kk]
data.ra[nout]= sf.platestr.ra[kk]
data.dec[nout]= sf.platestr.dec[kk]
data.l[nout]= platelb[kk,0]
data.b[nout]= platelb[kk,1]
nout+= 1
raw.dered_r[rawIndx]= data.dered_r
raw.dered_g[rawIndx]= data.dered_g
raw.plate[rawIndx]= data.plate
raw.ra[rawIndx]= data.ra
raw.dec[rawIndx]= data.dec
raw.l[rawIndx]= data.l
raw.b[rawIndx]= data.b
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
#Dump raw
fitsio.write(args[0],raw,clobber=True)
def calc_actions(ra_deg, dec_deg, d_kpc, pm_ra_masyr, pm_dec_masyr, v_los_kms):
ra_rad = ra_deg * (np.pi / 180.) # RA [rad]
dec_rad = dec_deg * (np.pi / 180.) # dec [rad]
# Galactocentric position of the Sun:
X_gc_sun_kpc = 8. # [kpc]
Z_gc_sun_kpc = 0.025 # [kpc]
# Galactocentric velocity of the Sun:
vX_gc_sun_kms = -9.58 # = -U [kms]
vY_gc_sun_kms = 10.52 + 220. # = V+v_circ(R_Sun) [kms]
vZ_gc_sun_kms = 7.01 # = W [kms]
# a. convert spatial coordinates (ra,dec,d) to (R,z,phi)
# (ra,dec) --> Galactic coordinates (l,b):
lb = bovy_coords.radec_to_lb(ra_rad, dec_rad, degree=False, epoch=2000.0)
l_rad = lb[:, 0]
b_rad = lb[:, 1]
# (l,b,d) --> Galactocentric cartesian coordinates (x,y,z):
xyz = bovy_coords.lbd_to_XYZ(l_rad, b_rad, d_kpc, degree=False)
x_kpc = xyz[:, 0]
y_kpc = xyz[:, 1]
z_kpc = xyz[:, 2]
# (x,y,z) --> Galactocentric cylindrical coordinates (R,z,phi):
Rzphi = bovy_coords.XYZ_to_galcencyl(x_kpc,
y_kpc,
z_kpc,
Xsun=X_gc_sun_kpc,
Zsun=Z_gc_sun_kpc)
R_kpc = Rzphi[:, 0]
phi_rad = Rzphi[:, 1]
z_kpc = Rzphi[:, 2]
# b. convert velocities (pm_ra,pm_dec,vlos) to (vR,vz,vT)
# (pm_ra,pm_dec) --> (pm_l,pm_b):
pmlpmb = bovy_coords.pmrapmdec_to_pmllpmbb(pm_ra_masyr,
pm_dec_masyr,
ra_rad,
dec_rad,
degree=False,
epoch=2000.0)
pml_masyr = pmlpmb[:, 0]
pmb_masyr = pmlpmb[:, 1]
# (v_los,pm_l,pm_b) & (l,b,d) --> (vx,vy,vz):
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(v_los_kms,
pml_masyr,
pmb_masyr,
l_rad,
b_rad,
d_kpc,
XYZ=False,
degree=False)
vx_kms = vxvyvz[:, 0]
vy_kms = vxvyvz[:, 1]
vz_kms = vxvyvz[:, 2]
# (vx,vy,vz) & (x,y,z) --> (vR,vT,vz):
vRvTvZ = bovy_coords.vxvyvz_to_galcencyl(
vx_kms,
vy_kms,
vz_kms,
R_kpc,
phi_rad,
z_kpc,
vsun=[vX_gc_sun_kms, vY_gc_sun_kms, vZ_gc_sun_kms],
galcen=True)
vR_kms = vRvTvZ[:, 0]
vT_kms = vRvTvZ[:, 1]
vz_kms = vRvTvZ[:, 2]
print("R = ", R_kpc, "\t kpc")
print("phi = ", phi_rad, "\t rad")
print("z = ", z_kpc, "\t kpc")
print("v_R = ", vR_kms, "\t km/s")
print("v_T = ", vT_kms, "\t km/s")
print("v_z = ", vz_kms, "\t km/s")
return vz_kms
rdata[:,2]=rdata[:,2]*1000.0 rdata[:,8]=rdata[:,8]*1000.0 rdata[:,14]=rdata[:,14]*1000.0 # arcsec/yr -> mas/yr rdata[:,3]=rdata[:,3]*1000.0 rdata[:,4]=rdata[:,4]*1000.0 rdata[:,9]=rdata[:,9]*1000.0 rdata[:,10]=rdata[:,10]*1000.0 rdata[:,15]=rdata[:,15]*1000.0 rdata[:,16]=rdata[:,16]*1000.0 # Galactic coordinates # True l and b RA_true=rdata[:,0] DEC_true=rdata[:,1] Tllbb=bovy_coords.radec_to_lb(RA_true,DEC_true,degree=True,epoch=2000.0) GLON_true=Tllbb[:,0] GLAT_true=Tllbb[:,1] # True pmGLON, pmGLAT pmRA_true=rdata[:,3] pmDEC_true=rdata[:,4] Tpmllbb=bovy_coords.pmrapmdec_to_pmllpmbb(pmRA_true,pmDEC_true \ ,RA_true,DEC_true,degree=True,epoch=2000.0) pmGLON_true=Tpmllbb[:,0] pmGLAT_true=Tpmllbb[:,1] # observed RA_obs=rdata[:,6] DEC_obs=rdata[:,7] pmRA_obs=rdata[:,9] pmDEC_obs=rdata[:,10] Tpmllbb=bovy_coords.pmrapmdec_to_pmllpmbb(pmRA_obs,pmDEC_obs \
def generate_fakeDFData(options,args):
#Check whether the savefile already exists
if os.path.exists(args[0]):
savefile= open(args[0],'rb')
print "Savefile already exists, not re-sampling and overwriting ..."
return None
#Read the data
print "Reading the data ..."
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=options.snmin,nosolar=True,nocoords=True)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=options.snmin,nosolar=True,nocoords=True)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=options.snmin,nosolar=True,nocoords=True)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=options.snmin,nosolar=True,
nocoords=True)
if not options.bmin is None:
#Cut on |b|
raw= raw[(numpy.fabs(raw.b) > options.bmin)]
if not options.fehmin is None:
raw= raw[(raw.feh >= options.fehmin)]
if not options.fehmax is None:
raw= raw[(raw.feh < options.fehmax)]
if not options.afemin is None:
raw= raw[(raw.afe >= options.afemin)]
if not options.afemax is None:
raw= raw[(raw.afe < options.afemax)]
if not options.plate is None and not options.loo:
raw= raw[(raw.plate == options.plate)]
elif not options.plate is None:
raw= raw[(raw.plate != options.plate)]
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
if not options.singlefeh is None:
if options.loo:
pass
else:
#Set up single feh
indx= binned.callIndx(options.singlefeh,options.singleafe)
if numpy.sum(indx) == 0:
raise IOError("Bin corresponding to singlefeh and singleafe is empty ...")
data= copy.copy(binned.data[indx])
print "Using %i data points ..." % (len(data))
#Bin again
binned= pixelAfeFeh(data,dfeh=options.dfeh,dafe=options.dafe)
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
#Setup the selection function
#Load selection function
plates= numpy.array(list(set(list(raw.plate))),dtype='int') #Only load plates that we use
print "Using %i plates, %i stars ..." %(len(plates),len(raw))
sf= segueSelect(plates=plates,type_faint='tanhrcut',
sample=options.sample,type_bright='tanhrcut',
sn=options.snmin,select=options.select,
indiv_brightlims=options.indiv_brightlims)
platelb= bovy_coords.radec_to_lb(sf.platestr.ra,sf.platestr.dec,
degree=True)
if options.sample.lower() == 'g':
grmin, grmax= 0.48, 0.55
rmin,rmax= 14.50001, 20.199999 #so we don't go out of the range
if options.sample.lower() == 'k':
grmin, grmax= 0.55, 0.75
rmin,rmax= 14.50001, 18.999999
colorrange=[grmin,grmax]
mapfehs= monoAbundanceMW.fehs()
mapafes= monoAbundanceMW.afes()
#Setup params
if not options.init is None:
#Load initial parameters from file
savefile= open(options.init,'rb')
tparams= pickle.load(savefile)
savefile.close()
#Setup the correct form
params= initialize(options,fehs,afes)
params[0:6]= get_dfparams(tparams,options.index,options,log=True)
params[6:11]= tparams[-5:len(tparams)]
else:
params= initialize(options,fehs,afes)
#Setup potential
if (options.potential.lower() == 'flatlog' or options.potential.lower() == 'flatlogdisk') \
and not options.flatten is None:
#Set flattening
potparams= list(get_potparams(params,options,len(fehs)))
potparams[0]= options.flatten
params= set_potparams(potparams,params,options,len(fehs))
pot= setup_potential(params,options,len(fehs))
aA= setup_aA(pot,options)
if not options.multi is None:
binned= fakeDFData_abundance_singles(binned,options,args,fehs,afes)
else:
for ii in range(len(fehs)):
print "Working on population %i / %i ..." % (ii+1,len(fehs))
#Setup qdf
dfparams= get_dfparams(params,ii,options,log=False)
vo= get_vo(params,options,len(fehs))
ro= get_ro(params,options)
if options.dfmodel.lower() == 'qdf':
#Normalize
hr= dfparams[0]/ro
sr= dfparams[1]/vo
sz= dfparams[2]/vo
hsr= dfparams[3]/ro
hsz= dfparams[4]/ro
print hr, sr, sz, hsr, hsz
qdf= quasiisothermaldf(hr,sr,sz,hsr,hsz,pot=pot,aA=aA,cutcounter=True)
#Some more selection stuff
data= binned(fehs[ii],afes[ii])
#feh and color
feh= fehs[ii]
fehrange= [feh-options.dfeh/2.,feh+options.dfeh/2.]
#FeH
fehdist= DistSpline(*numpy.histogram(data.feh,bins=5,
range=fehrange),
xrange=fehrange,dontcuttorange=False)
#Color
colordist= DistSpline(*numpy.histogram(data.dered_g\
-data.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Re-sample
binned= fakeDFData(binned,qdf,ii,params,fehs,afes,options,
rmin,rmax,
platelb,
grmin,grmax,
fehrange,
colordist,
fehdist,feh,sf,
mapfehs,mapafes,
ro=None,vo=None)
#Save to new file
fitsio.write(args[0],binned.data)
return None
def findfriends(targname,radial_velocity,velocity_limit=5.0,search_radius=25.0,rvcut=5.0,radec=[None,None],output_directory = None,showplots=False,verbose=False,DoGALEX=True,DoWISE=True,DoROSAT=True):
radvel= radial_velocity * u.kilometer / u.second
if output_directory == None:
outdir = './' + targname.replace(" ", "") + '_friends/'
else:
outdir = output_directory
if os.path.isdir(outdir) == True:
print('Output directory ' + outdir +' Already Exists!!')
print('Either Move it, Delete it, or input a different [output_directory] Please!')
return
os.mkdir(outdir)
if velocity_limit < 0.00001 :
print('input velocity_limit is too small, try something else')
print('velocity_limit: ' + str(velocity_limit))
if search_radius < 0.0000001:
print('input search_radius is too small, try something else')
print('search_radius: ' + str(search_radius))
# Search parameters
vlim=velocity_limit * u.kilometer / u.second
searchradpc=search_radius * u.parsec
if (radec[0] != None) & (radec[1] != None):
usera,usedec = radec[0],radec[1]
else: ##use the target name to get simbad ra and dec.
print('Asking Simbad for RA and DEC')
result_table = Simbad.query_object(targname)
usera,usedec = result_table['RA'][0],result_table['DEC'][0]
if verbose == True:
print('Target name: ',targname)
print('Coordinates: ' + str(usera) +' '+str(usedec))
print()
c = SkyCoord( ra=usera , dec=usedec , unit=(u.hourangle, u.deg) , frame='icrs')
if verbose == True: print(c)
# Find precise coordinates and distance from Gaia, define search radius and parallax cutoff
print('Asking Gaia for precise coordinates')
sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE CONTAINS( \
POINT('ICRS',gaiaedr3.gaia_source.ra,gaiaedr3.gaia_source.dec), \
CIRCLE('ICRS'," + str(c.ra.value) +","+ str(c.dec.value) +","+ str(6.0/3600.0) +"))=1;"
job = Gaia.launch_job_async(sqltext , dump_to_file=False)
Pgaia = job.get_results()
if verbose == True:
print(sqltext)
print()
print(Pgaia['source_id','ra','dec','phot_g_mean_mag','parallax','ruwe'].pprint_all())
print()
minpos = Pgaia['phot_g_mean_mag'].tolist().index(min(Pgaia['phot_g_mean_mag']))
Pcoord = SkyCoord( ra=Pgaia['ra'][minpos]*u.deg , dec=Pgaia['dec'][minpos]*u.deg , \
distance=(1000.0/Pgaia['parallax'][minpos])*u.parsec , frame='icrs' , \
radial_velocity=radvel , \
pm_ra_cosdec=Pgaia['pmra'][minpos]*u.mas/u.year , pm_dec=Pgaia['pmdec'][minpos]*u.mas/u.year )
searchraddeg = np.arcsin(searchradpc/Pcoord.distance).to(u.deg)
minpar = (1000.0 * u.parsec) / (Pcoord.distance + searchradpc) * u.mas
if verbose == True:
print(Pcoord)
print()
print('Search radius in deg: ',searchraddeg)
print('Minimum parallax: ',minpar)
# Query Gaia with search radius and parallax cut
# Note, a cut on parallax_error was added because searches at low galactic latitude
# return an overwhelming number of noisy sources that scatter into the search volume - ALK 20210325
print('Querying Gaia for neighbors')
Pllbb = bc.radec_to_lb(Pcoord.ra.value , Pcoord.dec.value , degree=True)
if ( np.abs(Pllbb[1]) > 10.0): plxcut = max( 0.5 , (1000.0/Pcoord.distance.value/10.0) )
else: plxcut = 0.5
print('Parallax cut: ',plxcut)
if (searchradpc < Pcoord.distance):
sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE CONTAINS( \
POINT('ICRS',gaiaedr3.gaia_source.ra,gaiaedr3.gaia_source.dec), \
CIRCLE('ICRS'," + str(Pcoord.ra.value) +","+ str(Pcoord.dec.value) +","+ str(searchraddeg.value) +"))\
=1 AND parallax>" + str(minpar.value) + " AND parallax_error<" + str(plxcut) + ";"
if (searchradpc >= Pcoord.distance):
sqltext = "SELECT * FROM gaiaedr3.gaia_source WHERE parallax>" + str(minpar.value) + " AND parallax_error<" + str(plxcut) + ";"
print('Note, using all-sky search')
if verbose == True:
print(sqltext)
print()
job = Gaia.launch_job_async(sqltext , dump_to_file=False)
r = job.get_results()
if verbose == True: print('Number of records: ',len(r['ra']))
# Construct coordinates array for all stars returned in cone search
gaiacoord = SkyCoord( ra=r['ra'] , dec=r['dec'] , distance=(1000.0/r['parallax'])*u.parsec , \
frame='icrs' , \
pm_ra_cosdec=r['pmra'] , pm_dec=r['pmdec'] )
sep = gaiacoord.separation(Pcoord)
sep3d = gaiacoord.separation_3d(Pcoord)
if verbose == True:
print('Printing angular separations in degrees as sanity check')
print(sep.degree)
Pllbb = bc.radec_to_lb(Pcoord.ra.value , Pcoord.dec.value , degree=True)
Ppmllpmbb = bc.pmrapmdec_to_pmllpmbb( Pcoord.pm_ra_cosdec.value , Pcoord.pm_dec.value , \
Pcoord.ra.value , Pcoord.dec.value , degree=True )
Pvxvyvz = bc.vrpmllpmbb_to_vxvyvz(Pcoord.radial_velocity.value , Ppmllpmbb[0] , Ppmllpmbb[1] , \
Pllbb[0] , Pllbb[1] , Pcoord.distance.value/1000.0 , XYZ=False , degree=True)
if verbose == True:
print('Science Target Name: ',targname)
print('Science Target RA/DEC: ',Pcoord.ra.value,Pcoord.dec.value)
print('Science Target Galactic Coordinates: ',Pllbb)
print('Science Target UVW: ',Pvxvyvz)
print()
Gllbb = bc.radec_to_lb(gaiacoord.ra.value , gaiacoord.dec.value , degree=True)
Gxyz = bc.lbd_to_XYZ( Gllbb[:,0] , Gllbb[:,1] , gaiacoord.distance/1000.0 , degree=True)
Gvrpmllpmbb = bc.vxvyvz_to_vrpmllpmbb( \
Pvxvyvz[0]*np.ones(len(Gxyz[:,0])) , Pvxvyvz[1]*np.ones(len(Gxyz[:,1])) , Pvxvyvz[2]*np.ones(len(Gxyz[:,2])) , \
Gxyz[:,0] , Gxyz[:,1] , Gxyz[:,2] , XYZ=True)
Gpmrapmdec = bc.pmllpmbb_to_pmrapmdec( Gvrpmllpmbb[:,1] , Gvrpmllpmbb[:,2] , Gllbb[:,0] , Gllbb[:,1] , degree=True)
# Code in case I want to do chi^2 cuts someday
Gvtanerr = 1.0 * np.ones(len(Gxyz[:,0]))
Gpmerr = Gvtanerr * 206265000.0 * 3.154e7 / (gaiacoord.distance.value * 3.086e13)
Gchi2 = ( (Gpmrapmdec[:,0]-gaiacoord.pm_ra_cosdec.value)**2 + (Gpmrapmdec[:,1]-gaiacoord.pm_dec.value)**2 )**0.5
Gchi2 = Gchi2 / Gpmerr
if verbose == True:
print('Predicted PMs if comoving:')
print(Gpmrapmdec , "\n")
print('Actual PMRAs from Gaia:')
print(gaiacoord.pm_ra_cosdec.value , "\n")
print('Actual PMDECs from Gaia:')
print(gaiacoord.pm_dec.value , "\n")
print('Predicted PM errors:')
print(Gpmerr , "\n")
print('Chi^2 values:')
print(Gchi2)
# Query external list(s) of RVs
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) )
yy = zz[0][np.argsort(sep3d[zz])]
RV = np.empty(np.array(r['ra']).size)
RVerr = np.empty(np.array(r['ra']).size)
RVsrc = np.array([ ' None' for x in range(np.array(r['ra']).size) ])
RV[:] = np.nan
RVerr[:] = np.nan
print('Populating RV table')
for x in range(0 , np.array(yy).size):
if np.isnan(r['dr2_radial_velocity'][yy[x]]) == False: # First copy over DR2 RVs
RV[yy[x]] = r['dr2_radial_velocity'][yy[x]]
RVerr[yy[x]] = r['dr2_radial_velocity_error'][yy[x]]
RVsrc[yy[x]] = 'Gaia DR2'
if os.path.isfile('LocalRV.csv'):
with open('LocalRV.csv') as csvfile: # Now check for a local RV that would supercede
readCSV = csv.reader(csvfile, delimiter=',')
for row in readCSV:
ww = np.where(r['designation'] == row[0])[0]
if (np.array(ww).size == 1):
RV[ww] = row[2]
RVerr[ww] = row[3]
RVsrc[ww] = row[4]
if verbose == True:
print('Using stored RV: ',row)
print(r['ra','dec','phot_g_mean_mag'][ww])
print(RV[ww])
print(RVerr[ww])
print(RVsrc[ww])
# Create Gaia CMD plot
mamajek = np.loadtxt(datapath+'/sptGBpRp.txt')
pleiades = np.loadtxt(datapath+'/PleGBpRp.txt')
tuchor = np.loadtxt(datapath+'/TucGBpRp.txt')
usco = np.loadtxt(datapath+'/UScGBpRp.txt')
chai = np.loadtxt(datapath+'/ChaGBpRp.txt')
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (np.isnan(r['bp_rp']) == False) ) # Note, this causes an error because NaNs
yy = zz[0][np.argsort(sep3d[zz])]
zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) & \
(r['phot_bp_rp_excess_factor'] < (1.3 + 0.06*r['bp_rp']**2)) & \
(np.isnan(r['bp_rp']) == False) ) # Note, this causes an error because NaNs
yy2= zz2[0][np.argsort((-Gchi2)[zz2])]
figname=outdir + targname.replace(" ", "") + "cmd.png"
if verbose == True: print(figname)
fig,ax1 = plt.subplots(figsize=(12,8))
ax1.axis([ math.floor(min(r['bp_rp'][zz])) , \
math.ceil(max(r['bp_rp'][zz])), \
math.ceil(max((r['phot_g_mean_mag'][zz] - (5.0*np.log10(gaiacoord.distance[zz].value)-5.0))))+1, \
math.floor(min((r['phot_g_mean_mag'][zz] - (5.0*np.log10(gaiacoord.distance[zz].value)-5.0))))-1 ] )
ax1.set_xlabel(r'$B_p-R_p$ (mag)' , fontsize=16)
ax1.set_ylabel(r'$M_G$ (mag)' , fontsize=16)
ax1.tick_params(axis='both',which='major',labelsize=12)
ax2 = ax1.twiny()
ax2.set_xlim(ax1.get_xlim())
spttickvals = np.array([ -0.037 , 0.377 , 0.782 , 0.980 , 1.84 , 2.50 , 3.36 , 4.75 ])
sptticklabs = np.array([ 'A0' , 'F0' , 'G0' , 'K0' , 'M0' , 'M3' , 'M5' , 'M7' ])
xx = np.where( (spttickvals >= math.floor(min(r['bp_rp'][zz]))) & (spttickvals <= math.ceil(max(r['bp_rp'][zz]))) )[0]
ax2.set_xticks(spttickvals[xx])
ax2.set_xticklabels( sptticklabs[xx] )
ax2.set_xlabel('SpT' , fontsize=16, labelpad=15)
ax2.tick_params(axis='both',which='major',labelsize=12)
ax1.plot( chai[:,1] , chai[:,0] , zorder=1 , label='Cha-I (0-5 Myr)')
ax1.plot( usco[:,1] , usco[:,0] , zorder=2 , label='USco (11 Myr)')
ax1.plot( tuchor[:,1] , tuchor[:,0] , zorder=3 , label='Tuc-Hor (40 Myr)')
ax1.plot(pleiades[:,1] , pleiades[:,0] , zorder=4 , label='Pleiades (125 Myr)')
ax1.plot( mamajek[:,2] , mamajek[:,1] , zorder=5 , label='Mamajek MS')
for x in range(0 , np.array(yy2).size):
msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2
mcolor = Gchi2[yy2[x]]
medge = 'black'
mzorder= 7
if (r['ruwe'][yy2[x]] < 1.2):
mshape='o'
if (r['ruwe'][yy2[x]] >= 1.2):
mshape='s'
if (np.isnan(rvcut) == False):
if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) > rvcut):
mshape='+'
mcolor='black'
mzorder=6
if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) <= rvcut):
medge='blue'
ccc = ax1.scatter(r['bp_rp'][yy2[x]] , (r['phot_g_mean_mag'][yy2[x]] - (5.0*np.log10(gaiacoord.distance[yy2[x]].value)-5.0)) , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2')
temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2')
temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving')
temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier')
ax1.plot(r['bp_rp'][yy[0]] , (r['phot_g_mean_mag'][yy[0]] - (5.0*np.log10(gaiacoord.distance[yy[0]].value)-5.0)) , \
'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=10 , label=targname)
ax1.arrow( 1.3 , 2.5 , 0.374, 0.743 , length_includes_head=True , head_width=0.07 , head_length = 0.10 )
ax1.text( 1.4 , 2.3, r'$A_V=1$' , fontsize=12)
ax1.legend(fontsize=11)
cb = plt.colorbar(ccc , ax=ax1)
cb.set_label(label='Velocity Difference (km/s)',fontsize=14)
plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200)
if showplots == True: plt.show()
plt.close('all')
# Create PM plot
zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) )
yy2= zz2[0][np.argsort((-Gchi2)[zz2])]
zz3= np.where( (sep3d.value < searchradpc.value) & (sep.degree > 0.00001) )
figname=outdir + targname.replace(" ", "") + "pmd.png"
fig,ax1 = plt.subplots(figsize=(12,8))
ax1.axis([ (max(r['pmra'][zz2]) + 0.05*np.ptp(r['pmra'][zz2]) ) , \
(min(r['pmra'][zz2]) - 0.05*np.ptp(r['pmra'][zz2]) ) , \
(min(r['pmdec'][zz2])- 0.05*np.ptp(r['pmra'][zz2]) ) , \
(max(r['pmdec'][zz2])+ 0.05*np.ptp(r['pmra'][zz2]) ) ] )
ax1.tick_params(axis='both',which='major',labelsize=16)
if ((max(r['pmra'][zz2]) + 0.05*np.ptp(r['pmra'][zz2])) > 0.0) & \
((min(r['pmra'][zz2]) - 0.05*np.ptp(r['pmra'][zz2])) < 0.0) & \
((min(r['pmdec'][zz2])- 0.05*np.ptp(r['pmra'][zz2])) < 0.0) & \
((max(r['pmdec'][zz2])+ 0.05*np.ptp(r['pmra'][zz2])) > 0.0):
ax1.plot( [0.0,0.0] , [-1000.0,1000.0] , 'k--' , linewidth=1 )
ax1.plot( [-1000.0,1000.0] , [0.0,0.0] , 'k--' , linewidth=1 )
ax1.errorbar( (r['pmra'][yy2]) , (r['pmdec'][yy2]) , \
yerr=(r['pmdec_error'][yy2]) , xerr=(r['pmra_error'][yy2]) , fmt='none' , ecolor='k' )
ax1.scatter( (r['pmra'][zz3]) , (r['pmdec'][zz3]) , \
s=(0.5)**2 , marker='o' , c='black' , zorder=2 , label='Field' )
for x in range(0 , np.array(yy2).size):
msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2
mcolor = Gchi2[yy2[x]]
medge = 'black'
mzorder= 7
if (r['ruwe'][yy2[x]] < 1.2):
mshape='o'
if (r['ruwe'][yy2[x]] >= 1.2):
mshape='s'
if (np.isnan(rvcut) == False):
if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) > rvcut):
mshape='+'
mcolor='black'
mzorder=6
if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) <= rvcut):
medge='blue'
ccc = ax1.scatter(r['pmra'][yy2[x]] , r['pmdec'][yy2[x]] , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2')
temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2')
temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving')
temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier')
ax1.plot( Pgaia['pmra'][minpos] , Pgaia['pmdec'][minpos] , \
'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname)
ax1.set_xlabel(r'$\mu_{RA}$ (mas/yr)' , fontsize=22 , labelpad=10)
ax1.set_ylabel(r'$\mu_{DEC}$ (mas/yr)' , fontsize=22 , labelpad=10)
ax1.legend(fontsize=12)
cb = plt.colorbar(ccc , ax=ax1)
cb.set_label(label='Tangential Velocity Difference (km/s)',fontsize=18 , labelpad=10)
plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200)
if showplots == True: plt.show()
plt.close('all')
# Create RV plot
zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) & \
(np.isnan(RV) == False) )
yy2= zz2[0][np.argsort((-Gchi2)[zz2])]
zz3= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) & \
(np.isnan(RV) == False) & (np.isnan(r['phot_g_mean_mag']) == False) & \
(np.abs(RV-Gvrpmllpmbb[:,0]) < 20.0) ) # Just to set Y axis
fig,ax1 = plt.subplots(figsize=(12,8))
ax1.axis([ -20.0 , +20.0, \
max( np.append( np.array(r['phot_g_mean_mag'][zz3] - (5.0*np.log10(gaiacoord.distance[zz3].value)-5.0)) , 0.0 )) + 0.3 , \
min( np.append( np.array(r['phot_g_mean_mag'][zz3] - (5.0*np.log10(gaiacoord.distance[zz3].value)-5.0)) , 15.0 )) - 0.3 ])
ax1.tick_params(axis='both',which='major',labelsize=16)
ax1.plot( [0.0,0.0] , [-20.0,25.0] , 'k--' , linewidth=1 )
ax1.errorbar( (RV[yy2]-Gvrpmllpmbb[yy2,0]) , \
(r['phot_g_mean_mag'][yy2] - (5.0*np.log10(gaiacoord.distance[yy2].value)-5.0)) , \
yerr=None,xerr=(RVerr[yy2]) , fmt='none' , ecolor='k' )
for x in range(0 , np.array(yy2).size):
msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2
mcolor = Gchi2[yy2[x]]
medge = 'black'
mzorder= 2
if (r['ruwe'][yy2[x]] < 1.2):
mshape='o'
if (r['ruwe'][yy2[x]] >= 1.2):
mshape='s'
ccc = ax1.scatter( (RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) , \
(r['phot_g_mean_mag'][yy2[x]] - (5.0*np.log10(gaiacoord.distance[yy2[x]].value)-5.0)) , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2')
temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2')
temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving')
if ( (Pgaia['phot_g_mean_mag'][minpos] - (5.0*np.log10(Pcoord.distance.value)-5.0)) < \
(max( np.append( np.array(r['phot_g_mean_mag'][zz3] - (5.0*np.log10(gaiacoord.distance[zz3].value)-5.0)) , 0.0 )) + 0.3) ):
ax1.plot( [0.0] , (Pgaia['phot_g_mean_mag'][minpos] - (5.0*np.log10(Pcoord.distance.value)-5.0)) , \
'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname)
ax1.set_ylabel(r'$M_G$ (mag)' , fontsize=22 , labelpad=10)
ax1.set_xlabel(r'$v_{r,obs}-v_{r,pred}$ (km/s)' , fontsize=22 , labelpad=10)
ax1.legend(fontsize=12)
cb = plt.colorbar(ccc , ax=ax1)
cb.set_label(label='Tangential Velocity Difference (km/s)',fontsize=18 , labelpad=10)
figname=outdir + targname.replace(" ", "") + "drv.png"
plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200)
if showplots == True: plt.show()
plt.close('all')
# Create XYZ plot
Pxyz = bc.lbd_to_XYZ( Pllbb[0] , Pllbb[1] , Pcoord.distance.value/1000.0 , degree=True)
fig,axs = plt.subplots(2,2)
fig.set_figheight(16)
fig.set_figwidth(16)
fig.subplots_adjust(hspace=0.03,wspace=0.03)
zz2= np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) )
yy2= zz2[0][np.argsort((-Gchi2)[zz2])]
for x in range(0 , np.array(yy2).size):
msize = (17-12.0*(sep3d[yy2[x]].value/searchradpc.value))**2
mcolor = Gchi2[yy2[x]]
medge = 'black'
mzorder= 3
if (r['ruwe'][yy2[x]] < 1.2):
mshape='o'
if (r['ruwe'][yy2[x]] >= 1.2):
mshape='s'
if (np.isnan(rvcut) == False):
if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) > rvcut):
mshape='+'
mcolor='black'
mzorder=2
if (np.isnan(RV[yy2[x]])==False) & (np.abs(RV[yy2[x]]-Gvrpmllpmbb[yy2[x],0]) <= rvcut):
medge='blue'
ccc = axs[0,0].scatter( 1000.0*Gxyz[yy2[x],0] , 1000.0*Gxyz[yy2[x],1] , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
ccc = axs[0,1].scatter( 1000.0*Gxyz[yy2[x],2] , 1000.0*Gxyz[yy2[x],1] , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
ccc = axs[1,0].scatter( 1000.0*Gxyz[yy2[x],0] , 1000.0*Gxyz[yy2[x],2] , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
temp1 = axs[0,0].scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2')
temp2 = axs[0,0].scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2')
temp3 = axs[0,0].scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving')
temp4 = axs[0,0].scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier')
axs[0,0].plot( 1000.0*Pxyz[0] , 1000.0*Pxyz[1] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red')
axs[0,1].plot( 1000.0*Pxyz[2] , 1000.0*Pxyz[1] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red')
axs[1,0].plot( 1000.0*Pxyz[0] , 1000.0*Pxyz[2] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=1 , label = targname)
axs[0,0].set_xlim( [1000.0*Pxyz[0]-(search_radius+1.0) , 1000.0*Pxyz[0]+(search_radius+1.0)] )
axs[0,0].set_ylim( [1000.0*Pxyz[1]-(search_radius+1.0) , 1000.0*Pxyz[1]+(search_radius+1.0)] )
axs[0,1].set_xlim( [1000.0*Pxyz[2]-(search_radius+1.0) , 1000.0*Pxyz[2]+(search_radius+1.0)] )
axs[0,1].set_ylim( [1000.0*Pxyz[1]-(search_radius+1.0) , 1000.0*Pxyz[1]+(search_radius+1.0)] )
axs[1,0].set_xlim( [1000.0*Pxyz[0]-(search_radius+1.0) , 1000.0*Pxyz[0]+(search_radius+1.0)] )
axs[1,0].set_ylim( [1000.0*Pxyz[2]-(search_radius+1.0) , 1000.0*Pxyz[2]+(search_radius+1.0)] )
axs[0,0].set_xlabel(r'$X$ (pc)',fontsize=20,labelpad=10)
axs[0,0].set_ylabel(r'$Y$ (pc)',fontsize=20,labelpad=10)
axs[1,0].set_xlabel(r'$X$ (pc)',fontsize=20,labelpad=10)
axs[1,0].set_ylabel(r'$Z$ (pc)',fontsize=20,labelpad=10)
axs[0,1].set_xlabel(r'$Z$ (pc)',fontsize=20,labelpad=10)
axs[0,1].set_ylabel(r'$Y$ (pc)',fontsize=20,labelpad=10)
axs[0,0].xaxis.set_ticks_position('top')
axs[0,1].xaxis.set_ticks_position('top')
axs[0,1].yaxis.set_ticks_position('right')
axs[0,0].xaxis.set_label_position('top')
axs[0,1].xaxis.set_label_position('top')
axs[0,1].yaxis.set_label_position('right')
for aa in [0,1]:
for bb in [0,1]:
axs[aa,bb].tick_params(top=True,bottom=True,left=True,right=True,direction='in',labelsize=18)
fig.delaxes(axs[1][1])
strsize = 26
if (len(targname) > 12.0): strsize = np.floor(24 / (len(targname)/14.5))
fig.legend( bbox_to_anchor=(0.92,0.37) , prop={'size':strsize})
cbaxes = fig.add_axes([0.55,0.14,0.02,0.34])
cb = plt.colorbar( ccc , cax=cbaxes )
cb.set_label( label='Velocity Difference (km/s)' , fontsize=24 , labelpad=20 )
cb.ax.tick_params(labelsize=18)
figname=outdir + targname.replace(" ", "") + "xyz.png"
plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200)
if showplots == True: plt.show()
plt.close('all')
# Create sky map
# Hacked from cartopy.mpl.gridliner
_DEGREE_SYMBOL = u'\u00B0'
def _east_west_formatted(longitude, num_format='g'):
fmt_string = u'{longitude:{num_format}}{degree}'
return fmt_string.format(longitude=(longitude if (longitude >= 0) else (longitude + 360)) , \
num_format=num_format,degree=_DEGREE_SYMBOL)
def _north_south_formatted(latitude, num_format='g'):
fmt_string = u'{latitude:{num_format}}{degree}'
return fmt_string.format(latitude=latitude, num_format=num_format,degree=_DEGREE_SYMBOL)
LONGITUDE_FORMATTER = mticker.FuncFormatter(lambda v, pos:
_east_west_formatted(v))
LATITUDE_FORMATTER = mticker.FuncFormatter(lambda v, pos:
_north_south_formatted(v))
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) )
yy = zz[0][np.argsort((-Gchi2)[zz])]
searchcircle = Pcoord.directional_offset_by( (np.arange(0,360)*u.degree) , searchraddeg*np.ones(360))
circleRA = searchcircle.ra.value
circleDE = searchcircle.dec.value
ww = np.where(circleRA > 180.0)
circleRA[ww] = circleRA[ww] - 360.0
RAlist = gaiacoord.ra[yy].value
DElist = gaiacoord.dec[yy].value
ww = np.where( RAlist > 180.0 )
RAlist[ww] = RAlist[ww] - 360.0
polelat = ((Pcoord.dec.value+90) if (Pcoord.dec.value<0) else (90-Pcoord.dec.value))
polelong= (Pcoord.ra.value if (Pcoord.dec.value<0.0) else (Pcoord.ra.value+180.0))
polelong= (polelong if polelong < 180 else polelong - 360.0)
if verbose == True:
print('Alignment variables: ',polelat,polelong,Pcoord.ra.value)
print(Pcoord.dec.value+searchraddeg.value)
rotated_pole = ccrs.RotatedPole( \
pole_latitude=polelat , \
pole_longitude=polelong , \
central_rotated_longitude=90.0 )#\
# (Pcoord.ra.value if (Pcoord.dec.value > 0.0) else (Pcoord.ra.value+180.0)) )
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(1, 1, 1, projection=rotated_pole)
ax.gridlines(draw_labels=True,x_inline=True,y_inline=True, \
xformatter=LONGITUDE_FORMATTER,yformatter=LATITUDE_FORMATTER)
ax.plot( circleRA , circleDE , c="gray" , ls="--" , transform=ccrs.Geodetic())
figname=outdir + targname.replace(" ", "") + "sky.png"
base=plt.cm.get_cmap('cubehelix')
for x in range(0 , np.array(yy).size):
msize = (17-12.0*(sep3d[yy[x]].value/searchradpc.value))
mcolor = base(Gchi2[yy[x]]/vlim.value)
medge = 'black'
mzorder= 3
if (r['ruwe'][yy[x]] < 1.2):
mshape='o'
if (r['ruwe'][yy[x]] >= 1.2):
mshape='s'
if (np.isnan(rvcut) == False):
if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) > rvcut):
mshape='+'
mcolor='black'
mzorder=2
if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) <= rvcut):
medge='blue'
ccc = ax.plot( RAlist[x] , DElist[x] , marker=mshape , \
markeredgecolor=medge , ms = msize , mfc = mcolor , transform=ccrs.Geodetic() )
ax.plot( (Pcoord.ra.value-360.0) , Pcoord.dec.value , \
'rx' , markersize=18 , mew=3 , transform=ccrs.Geodetic())
plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200)
if showplots == True: plt.show()
plt.close('all')
## Query GALEX and 2MASS data
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) )
yy = zz[0][np.argsort((-Gchi2)[zz])]
NUVmag = np.empty(np.array(r['ra']).size)
NUVerr = np.empty(np.array(r['ra']).size)
NUVmag[:] = np.nan
NUVerr[:] = np.nan
print('Searching on neighbors in GALEX')
##suppress the stupid noresultswarning from the catalogs package
warnings.filterwarnings("ignore",category=NoResultsWarning)
for x in range(0 , np.array(yy).size):
querystring=((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) \
else str(gaiacoord.ra[yy[x]].value+360.0)) + " " + str(gaiacoord.dec[yy[x]].value))
print('GALEX query ',x,' of ',np.array(yy).size, end='\r')
if verbose == True: print('GALEX query ',x,' of ',np.array(yy).size)
if verbose == True: print(querystring)
if (DoGALEX == True):
galex = Catalogs.query_object(querystring , catalog="Galex" , radius=0.0028 , TIMEOUT=600)
if ((np.where(galex['nuv_magerr'] > 0.0)[0]).size > 0):
ww = np.where( (galex['nuv_magerr'] == min(galex['nuv_magerr'][np.where(galex['nuv_magerr'] > 0.0)])))
NUVmag[yy[x]] = galex['nuv_mag'][ww][0]
NUVerr[yy[x]] = galex['nuv_magerr'][ww][0]
if verbose == True: print(galex['distance_arcmin','ra','nuv_mag','nuv_magerr'][ww])
Jmag = np.empty(np.array(r['ra']).size)
Jerr = np.empty(np.array(r['ra']).size)
Jmag[:] = np.nan
Jerr[:] = np.nan
print('Searching on neighbors in 2MASS')
for x in range(0 , np.array(yy).size):
if ( np.isnan(NUVmag[yy[x]]) == False ):
querycoord = SkyCoord((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) else \
str(gaiacoord.ra[yy[x]].value+360.0)) , str(gaiacoord.dec[yy[x]].value) , \
unit=(u.deg,u.deg) , frame='icrs')
print('2MASS query ',x,' of ',np.array(yy).size, end='\r')
if verbose == True: print('2MASS query ',x,' of ',np.array(yy).size)
if verbose == True: print(querycoord)
tmass = []
if (DoGALEX == True):
tmass = Irsa.query_region(querycoord , catalog='fp_psc' , radius='0d0m10s' )
if ((np.where(tmass['j_m'] > -10.0)[0]).size > 0):
ww = np.where( (tmass['j_m'] == min(tmass['j_m'][np.where(tmass['j_m'] > 0.0)])))
Jmag[yy[x]] = tmass['j_m'][ww][0]
Jerr[yy[x]] = tmass['j_cmsig'][ww][0]
if verbose == True: print(tmass['j_m','j_cmsig'][ww])
# Create GALEX plots
mamajek = np.loadtxt(datapath+'/sptGBpRp.txt')
f = interp1d( mamajek[:,2] , mamajek[:,0] , kind='cubic')
zz2 = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) )
yy2 = zz[0][np.argsort(sep3d[zz])]
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) )
yy = zz[0][np.argsort((-Gchi2)[zz])]
fnuvj = (3631.0 * 10**6 * 10**(-0.4 * NUVmag)) / (1594.0 * 10**6 * 10**(-0.4 * Jmag))
spt = f(r['bp_rp'].filled(np.nan))
sptstring = ["nan" for x in range(np.array(r['bp_rp']).size)]
for x in range(0 , np.array(zz2).size):
if (round(spt[yy2[x]],1) >= 17.0) and (round(spt[yy2[x]],1) < 27.0):
sptstring[yy2[x]] = 'M' + ('% 3.1f' % (round(spt[yy2[x]],1)-17.0)).strip()
if (round(spt[yy2[x]],1) >= 16.0) and (round(spt[yy2[x]],1) < 17.0):
sptstring[yy2[x]] = 'K' + ('% 3.1f' % (round(spt[yy2[x]],1)-9.0)).strip()
if (round(spt[yy2[x]],1) >= 10.0) and (round(spt[yy2[x]],1) < 16.0):
sptstring[yy2[x]] = 'K' + ('% 3.1f' % (round(spt[yy2[x]],1)-10.0)).strip()
if (round(spt[yy2[x]],1) >= 0.0) and (round(spt[yy2[x]],1) < 10.0):
sptstring[yy2[x]] = 'G' + ('% 3.1f' % (round(spt[yy2[x]],1)-0.0)).strip()
if (round(spt[yy2[x]],1) >= -10.0) and (round(spt[yy2[x]],1) < 0.0):
sptstring[yy2[x]] = 'F' + ('% 3.1f' % (round(spt[yy2[x]],1)+10.0)).strip()
if (round(spt[yy2[x]],1) >= -20.0) and (round(spt[yy2[x]],1) < -10.0):
sptstring[yy2[x]] = 'A' + ('% 3.1f' % (round(spt[yy2[x]],1)+20.0)).strip()
if (round(spt[yy2[x]],1) >= -30.0) and (round(spt[yy2[x]],1) < -20.0):
sptstring[yy2[x]] = 'B' + ('% 3.1f' % (round(spt[yy2[x]],1)+30.0)).strip()
figname=outdir + targname.replace(" ", "") + "galex.png"
if verbose == True: print(figname)
##Muck with the axis to get two x axes
fig,ax1 = plt.subplots(figsize=(12,8))
ax1.set_yscale('log')
ax1.axis([5.0 , 24.0 , 0.000004 , 0.02])
ax2 = ax1.twiny()
ax2.set_xlim(ax1.get_xlim())
ax1.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0]))
ax1.set_xticklabels(['G5','K0','K5','M0','M5','M7'])
ax1.set_xlabel('SpT' , fontsize=20, labelpad=15)
ax1.tick_params(axis='both',which='major',labelsize=16)
ax2.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0]))
ax2.set_xticklabels(['0.85','0.98','1.45','1.84','3.36','4.75'])
ax2.set_xlabel(r'$B_p-R_p$ (mag)' , fontsize=20, labelpad=15)
ax2.tick_params(axis='both',which='major',labelsize=16)
ax1.set_ylabel(r'$F_{NUV}/F_{J}$' , fontsize=22, labelpad=0)
##Hyades
hyades = readsav(datapath +'/HYsaved.sav')
hyadesfnuvj = (3631.0 * 10**6 * 10**(-0.4 * hyades['clnuv'])) / (1594.0 * 10**6 * 10**(-0.4 * hyades['clJ']))
ax1.plot(hyades['clspt'] , hyadesfnuvj , 'x' , markersize=4 , mew=1 , markeredgecolor='black' , zorder=1 , label='Hyades' )
for x in range(0 , np.array(yy).size):
msize = (17-12.0*(sep3d[yy[x]].value/searchradpc.value))**2
mcolor = Gchi2[yy[x]]
medge = 'black'
mzorder= 3
if (r['ruwe'][yy[x]] < 1.2):
mshape='o'
if (r['ruwe'][yy[x]] >= 1.2):
mshape='s'
if (np.isnan(rvcut) == False):
if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) > rvcut):
mshape='+'
mcolor='black'
mzorder=2
if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) <= rvcut):
medge='blue'
ccc = ax1.scatter( spt[yy[x]] , fnuvj[yy[x]] , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2')
temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2')
temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving')
temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier')
# Plot science target
if (spt[yy[0]] > 5): ax1.plot(spt[yy[0]] , fnuvj[yy[0]] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname )
ax1.legend(fontsize=16 , loc='lower left')
cb = fig.colorbar(ccc , ax=ax1)
cb.set_label(label='Velocity Offset (km/s)',fontsize=13)
if (DoGALEX == True): plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200)
if showplots == True: plt.show()
plt.close('all')
# Query CatWISE for W1+W2 and AllWISE for W3+W4
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) )
yy = zz[0][np.argsort((-Gchi2)[zz])]
WISEmag = np.empty([np.array(r['ra']).size,4])
WISEerr = np.empty([np.array(r['ra']).size,4])
WISEmag[:] = np.nan
WISEerr[:] = np.nan
print('Searching on neighbors in WISE')
##there's an annoying nan warning here, hide it for now as it's not a problem
warnings.filterwarnings("ignore",category=UserWarning)
for x in range(0 , np.array(yy).size):
querycoord = SkyCoord((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) else \
str(gaiacoord.ra[yy[x]].value+360.0)) , str(gaiacoord.dec[yy[x]].value) , \
unit=(u.deg,u.deg) , frame='icrs')
print('WISE query ',x,' of ',np.array(yy).size, end='\r')
if verbose == True: print('WISE query ',x,' of ',np.array(yy).size)
if verbose == True: print(querycoord)
wisecat = []
if (DoWISE == True):
wisecat = Irsa.query_region(querycoord,catalog='catwise_2020' , radius='0d0m10s')
if ((np.where(wisecat['w1mpro'] > -10.0)[0]).size > 0):
ww = np.where( (wisecat['w1mpro'] == min( wisecat['w1mpro'][np.where(wisecat['w1mpro'] > -10.0)]) ))
WISEmag[yy[x],0] = wisecat['w1mpro'][ww][0]
WISEerr[yy[x],0] = wisecat['w1sigmpro'][ww][0]
if ((np.where(wisecat['w2mpro'] > -10.0)[0]).size > 0):
ww = np.where( (wisecat['w2mpro'] == min( wisecat['w2mpro'][np.where(wisecat['w2mpro'] > -10.0)]) ))
WISEmag[yy[x],1] = wisecat['w2mpro'][ww][0]
WISEerr[yy[x],1] = wisecat['w2sigmpro'][ww][0]
if (DoWISE == True):
wisecat = Irsa.query_region(querycoord,catalog='allwise_p3as_psd' , radius='0d0m10s')
if ((np.where(wisecat['w1mpro'] > -10.0)[0]).size > 0):
ww = np.where( (wisecat['w1mpro'] == min( wisecat['w1mpro'][np.where(wisecat['w1mpro'] > -10.0)]) ))
if (np.isnan(WISEmag[yy[x],0]) == True) | (wisecat['w1mpro'][ww][0] < 11.0): # Note, only if CatWISE absent/saturated
WISEmag[yy[x],0] = wisecat['w1mpro'][ww][0]
WISEerr[yy[x],0] = wisecat['w1sigmpro'][ww][0]
if ((np.where(wisecat['w2mpro'] > -10.0)[0]).size > 0):
ww = np.where( (wisecat['w2mpro'] == min( wisecat['w2mpro'][np.where(wisecat['w2mpro'] > -10.0)]) ))
if (np.isnan(WISEmag[yy[x],1]) == True) | (wisecat['w2mpro'][ww][0] < 11.0): # Note, only if CatWISE absent/saturated
WISEmag[yy[x],1] = wisecat['w2mpro'][ww][0]
WISEerr[yy[x],1] = wisecat['w2sigmpro'][ww][0]
if ((np.where(wisecat['w3mpro'] > -10.0)[0]).size > 0):
ww = np.where( (wisecat['w3mpro'] == min( wisecat['w3mpro'][np.where(wisecat['w3mpro'] > -10.0)]) ))
WISEmag[yy[x],2] = wisecat['w3mpro'][ww][0]
WISEerr[yy[x],2] = wisecat['w3sigmpro'][ww][0]
if ((np.where(wisecat['w4mpro'] > -10.0)[0]).size > 0):
ww = np.where( (wisecat['w4mpro'] == min( wisecat['w4mpro'][np.where(wisecat['w4mpro'] > -10.0)]) ))
WISEmag[yy[x],3] = wisecat['w4mpro'][ww][0]
WISEerr[yy[x],3] = wisecat['w4sigmpro'][ww][0]
if verbose == True: print(yy[x],WISEmag[yy[x],:],WISEerr[yy[x],:])
# Create WISE plots
W13 = WISEmag[:,0]-WISEmag[:,2]
W13err = ( WISEerr[:,0]**2 + WISEerr[:,2]**2 )**0.5
zz = np.argwhere( np.isnan(W13err) )
W13[zz] = np.nan
W13err[zz] = np.nan
zz = np.where( (W13err > 0.15) )
W13[zz] = np.nan
W13err[zz] = np.nan
warnings.filterwarnings("default",category=UserWarning)
zz2 = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value))
yy2 = zz[0][np.argsort(sep3d[zz])]
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) & (sep.degree > 0.00001) )
yy = zz[0][np.argsort((-Gchi2)[zz])]
figname=outdir + targname.replace(" ", "") + "wise.png"
if verbose == True: print(figname)
plt.figure(figsize=(12,8))
if (verbose == True) & ((np.where(np.isfinite(W13+W13err))[0]).size > 0): print('Max y value: ' , (max((W13+W13err)[np.isfinite(W13+W13err)])+0.1) )
plt.axis([ 5.0 , 24.0 , \
max( [(min(np.append((W13-W13err)[ np.isfinite(W13-W13err) ],-0.1))-0.1) , -0.3]) , \
max( [(max(np.append((W13+W13err)[ np.isfinite(W13+W13err) ],+0.0))+0.2) , +0.6]) ])
ax1 = plt.gca()
ax2 = ax1.twiny()
ax2.set_xlim(5.0,24.0)
ax1.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0]))
ax1.set_xticklabels(['G5','K0','K5','M0','M5','M7'])
ax1.set_xlabel('SpT' , fontsize=20, labelpad=15)
ax1.tick_params(axis='both',which='major',labelsize=16)
ax2.set_xticks(np.array([5.0 , 10.0 , 15.0 , 17.0 , 22.0 , 24.0]))
ax2.set_xticklabels(['0.85','0.98','1.45','1.84','3.36','4.75'])
ax2.set_xlabel(r'$B_p-R_p$ (mag)' , fontsize=20, labelpad=15)
ax2.tick_params(axis='both',which='major',labelsize=16)
ax1.set_ylabel(r'$W1-W3$ (mag)' , fontsize=22, labelpad=0)
# Plot field sequence from Tuc-Hor (Kraus et al. 2014)
fldspt = [ 5 , 7 , 10 , 12 , 15 , 17 , 20 , 22 , 24 ]
fldW13 = [ 0 , 0 , 0 , .02, .06, .12, .27, .40, .60]
plt.plot(fldspt , fldW13 , zorder=0 , label='Photosphere')
# Plot neighbors
ax1.errorbar( spt[yy] , W13[yy] , yerr=W13err[yy] , fmt='none' , ecolor='k')
for x in range(0 , np.array(yy).size):
msize = (17-12.0*(sep3d[yy[x]].value/searchradpc.value))**2
mcolor = Gchi2[yy[x]]
medge = 'black'
mzorder= 3
if (r['ruwe'][yy[x]] < 1.2):
mshape='o'
if (r['ruwe'][yy[x]] >= 1.2):
mshape='s'
if (np.isnan(rvcut) == False):
if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) > rvcut):
mshape='+'
mcolor='black'
mzorder=2
if (np.isnan(RV[yy[x]])==False) & (np.abs(RV[yy[x]]-Gvrpmllpmbb[yy[x],0]) <= rvcut):
medge='blue'
ccc = ax1.scatter( spt[yy[x]] , W13[yy[x]] , \
s=msize , c=mcolor , marker=mshape , edgecolors=medge , zorder=mzorder , \
vmin=0.0 , vmax=vlim.value , cmap='cubehelix' , label='_nolabel' )
temp1 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='o' , s=12**2 , label = 'RUWE < 1.2')
temp2 = ax1.scatter([] , [] , c='white' , edgecolors='black', marker='s' , s=12**2 , label = 'RUWE >= 1.2')
temp3 = ax1.scatter([] , [] , c='white' , edgecolors='blue' , marker='o' , s=12**2 , label = 'RV Comoving')
temp4 = ax1.scatter([] , [] , c='black' , marker='+' , s=12**2 , label = 'RV Outlier')
# Plot science target
if (spt[yy2[0]] > 5):
plt.plot(spt[yy2[0]] , W13[yy2[0]] , 'rx' , markersize=18 , mew=3 , markeredgecolor='red' , zorder=3 , label=targname )
plt.legend(fontsize=16 , loc='upper left')
cb = plt.colorbar(ccc , ax=ax1)
cb.set_label(label='Velocity Offset (km/s)',fontsize=14)
if (DoWISE == True): plt.savefig(figname , bbox_inches='tight', pad_inches=0.2 , dpi=200)
if showplots == True: plt.show()
plt.close('all')
# Cross-reference with ROSAT
v = Vizier(columns=["**", "+_R"] , catalog='J/A+A/588/A103/cat2rxs' )
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) )
yy = zz[0][np.argsort(sep3d[zz])]
ROSATflux = np.empty([np.array(r['ra']).size])
ROSATflux[:] = np.nan
print('Searching on neighbors in ROSAT')
for x in range(0 , np.array(yy).size):
querycoord = SkyCoord((str(gaiacoord.ra[yy[x]].value) if (gaiacoord.ra[yy[x]].value > 0) else \
str(gaiacoord.ra[yy[x]].value+360.0)) , str(gaiacoord.dec[yy[x]].value) , \
unit=(u.deg,u.deg) , frame='icrs')
print('ROSAT query ',x,' of ',np.array(yy).size, end='\r')
if verbose == True: print('ROSAT query ',x,' of ',np.array(yy).size)
if verbose == True: print(querycoord)
if (DoROSAT == True):
rosatcat = v.query_region(querycoord , radius='0d1m0s' )
if (len(rosatcat) > 0):
rosatcat = rosatcat['J/A+A/588/A103/cat2rxs']
if verbose == True: print(rosatcat)
if ((np.where(rosatcat['CRate'] > -999)[0]).size > 0):
ww = np.where( (rosatcat['CRate'] == max(rosatcat['CRate'][np.where(rosatcat['CRate'] > -999)])))
ROSATflux[yy[x]] = rosatcat['CRate'][ww][0]
if verbose == True: print(x,yy[x],ROSATflux[yy[x]])
# Create output table with results
print('Creating Output Tables with Results')
if verbose == True:
print('Reminder, there were this many input entries: ',len(Gxyz[:,0]))
print('The search radius in velocity space is: ',vlim)
print()
zz = np.where( (sep3d.value < searchradpc.value) & (Gchi2 < vlim.value) )
sortlist = np.argsort(sep3d[zz])
yy = zz[0][sortlist]
fmt1 = "%11.7f %11.7f %6.3f %6.3f %11.3f %8.4f %8.4f %8.2f %8.2f %8.2f %8.3f %4s %8.6f %6.2f %7.3f %7.3f %35s"
fmt2 = "%11.7f %11.7f %6.3f %6.3f %11.3f %8.4f %8.4f %8.2f %8.2f %8.2f %8.3f %4s %8.6f %6.2f %7.3f %7.3f %35s"
filename=outdir + targname.replace(" ", "") + ".txt"
warnings.filterwarnings("ignore",category=UserWarning)
if verbose == True:
print('Also creating SIMBAD query table')
print(filename)
print('RA DEC Gmag Bp-Rp Voff(km/s) Sep(deg) 3D(pc) Vr(pred) Vr(obs) Vrerr Plx(mas) SpT FnuvJ W1-W3 RUWE XCrate RVsrc')
with open(filename,'w') as file1:
file1.write('RA DEC Gmag Bp-Rp Voff(km/s) Sep(deg) 3D(pc) Vr(pred) Vr(obs) Vrerr Plx(mas) SpT FnuvJ W1-W3 RUWE XCrate RVsrc \n')
for x in range(0 , np.array(zz).size):
if verbose == True:
print(fmt1 % (gaiacoord.ra[yy[x]].value,gaiacoord.dec[yy[x]].value, \
r['phot_g_mean_mag'][yy[x]], r['bp_rp'][yy[x]] , \
Gchi2[yy[x]] , sep[yy[x]].value , sep3d[yy[x]].value , \
Gvrpmllpmbb[yy[x],0] , RV[yy[x]] , RVerr[yy[x]] , \
r['parallax'][yy[x]], \
sptstring[yy[x]] , fnuvj[yy[x]] , W13[yy[x]] , r['ruwe'][yy[x]] , ROSATflux[yy[x]] , RVsrc[yy[x]]) )
with open(filename,'a') as file1:
file1.write(fmt2 % (gaiacoord.ra[yy[x]].value,gaiacoord.dec[yy[x]].value, \
r['phot_g_mean_mag'][yy[x]], r['bp_rp'][yy[x]] , \
Gchi2[yy[x]],sep[yy[x]].value,sep3d[yy[x]].value , \
Gvrpmllpmbb[yy[x],0] , RV[yy[x]] , RVerr[yy[x]] , \
r['parallax'][yy[x]], \
sptstring[yy[x]] , fnuvj[yy[x]] , W13[yy[x]] , r['ruwe'][yy[x]] , ROSATflux[yy[x]] , RVsrc[yy[x]]) )
file1.write("\n")
filename=outdir + targname.replace(" ", "") + ".csv"
with open(filename,mode='w') as result_file:
wr = csv.writer(result_file)
wr.writerow(['RA','DEC','Gmag','Bp-Rp','Voff(km/s)','Sep(deg)','3D(pc)','Vr(pred)','Vr(obs)','Vrerr','Plx(mas)','SpT','FnuvJ','W1-W3','RUWE','XCrate','RVsrc'])
for x in range(0 , np.array(zz).size):
wr.writerow(( "{0:.7f}".format(gaiacoord.ra[yy[x]].value) , "{0:.7f}".format(gaiacoord.dec[yy[x]].value) , \
"{0:.3f}".format(r['phot_g_mean_mag'][yy[x]]), "{0:.3f}".format(r['bp_rp'][yy[x]]) , \
"{0:.3f}".format(Gchi2[yy[x]]) , "{0:.4f}".format(sep[yy[x]].value) , "{0:.4f}".format(sep3d[yy[x]].value) , \
"{0:.2f}".format(Gvrpmllpmbb[yy[x],0]) , "{0:.2f}".format(RV[yy[x]]) , "{0:.2f}".format(RVerr[yy[x]]) , \
"{0:.3f}".format(r['parallax'][yy[x]]), \
sptstring[yy[x]] , "{0:.6f}".format(fnuvj[yy[x]]) , "{0:.2f}".format(W13[yy[x]]) , \
"{0:.3f}".format(r['ruwe'][yy[x]]) , "{0:.3f}".format(ROSATflux[yy[x]]) , RVsrc[yy[x]].strip()) )
if verbose == True: print('All output can be found in ' + outdir)
return outdir
def make_rcsample(parser):
options, args = parser.parse_args()
savefilename = options.savefilename
if savefilename is None:
#Create savefilename if not given
savefilename = os.path.join(
appath._APOGEE_DATA, 'rcsample_' + appath._APOGEE_REDUX + '.fits')
print("Saving to %s ..." % savefilename)
#Read the base-sample
data = apread.allStar(adddist=_ADDHAYDENDIST, rmdups=options.rmdups)
#Remove a bunch of fields that we do not want to keep
data = esutil.numpy_util.remove_fields(data, [
'TARGET_ID', 'FILE', 'AK_WISE', 'SFD_EBV', 'SYNTHVHELIO_AVG',
'SYNTHVSCATTER', 'SYNTHVERR', 'SYNTHVERR_MED', 'RV_TEFF', 'RV_LOGG',
'RV_FEH', 'RV_ALPHA', 'RV_CARB', 'RV_CCFWHM', 'RV_AUTOFWHM',
'SYNTHSCATTER', 'STABLERV_CHI2', 'STABLERV_RCHI2',
'STABLERV_CHI2_PROB', 'CHI2_THRESHOLD', 'APSTAR_VERSION',
'ASPCAP_VERSION', 'RESULTS_VERSION', 'WASH_M', 'WASH_M_ERR', 'WASH_T2',
'WASH_T2_ERR', 'DDO51', 'DDO51_ERR', 'IRAC_3_6', 'IRAC_3_6_ERR',
'IRAC_4_5', 'IRAC_4_5_ERR', 'IRAC_5_8', 'IRAC_5_8_ERR', 'IRAC_8_0',
'IRAC_8_0_ERR', 'WISE_4_5', 'WISE_4_5_ERR', 'TARG_4_5', 'TARG_4_5_ERR',
'WASH_DDO51_GIANT_FLAG', 'WASH_DDO51_STAR_FLAG', 'REDUCTION_ID',
'SRC_H', 'PM_SRC'
])
if not appath._APOGEE_REDUX.lower() == 'current' \
and not 'l30' in appath._APOGEE_REDUX \
and int(appath._APOGEE_REDUX[1:]) < 500:
data = esutil.numpy_util.remove_fields(data, ['ELEM'])
#Select red-clump stars
jk = data['J0'] - data['K0']
z = isodist.FEH2Z(data['METALS'], zsolar=0.017)
if 'l30' in appath._APOGEE_REDUX:
logg = data['LOGG']
elif appath._APOGEE_REDUX.lower() == 'current' \
or int(appath._APOGEE_REDUX[1:]) > 600:
from apogee.tools import paramIndx
if False:
#Use my custom logg calibration that's correct for the RC
logg = (1. - 0.042) * data['FPARAM'][:, paramIndx('logg')] - 0.213
lowloggindx = data['FPARAM'][:, paramIndx('logg')] < 1.
logg[lowloggindx] = data['FPARAM'][lowloggindx,
paramIndx('logg')] - 0.255
hiloggindx = data['FPARAM'][:, paramIndx('logg')] > 3.8
logg[hiloggindx] = data['FPARAM'][hiloggindx,
paramIndx('logg')] - 0.3726
else:
#Use my custom logg calibration that's correct on average
logg = (1. + 0.03) * data['FPARAM'][:, paramIndx('logg')] - 0.37
lowloggindx = data['FPARAM'][:, paramIndx('logg')] < 1.
logg[lowloggindx] = data['FPARAM'][lowloggindx,
paramIndx('logg')] - 0.34
hiloggindx = data['FPARAM'][:, paramIndx('logg')] > 3.8
logg[hiloggindx] = data['FPARAM'][hiloggindx,
paramIndx('logg')] - 0.256
else:
logg = data['LOGG']
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= rcmodel.loggteffcut(data['TEFF'],z,upper=False))\
*(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
data = data[indx]
#Add more aggressive flag cut
data = esutil.numpy_util.add_fields(data, [('ADDL_LOGG_CUT', numpy.int32)])
data['ADDL_LOGG_CUT'] = (
(data['TEFF'] - 4800.) / 1000. + 2.75) > data['LOGG']
if options.loggcut:
data = data[data['ADDL_LOGG_CUT'] == 1]
print("Making catalog of %i objects ..." % len(data))
#Add distances
data = esutil.numpy_util.add_fields(data, [('RC_DIST', float),
('RC_DM', float),
('RC_GALR', float),
('RC_GALPHI', float),
('RC_GALZ', float)])
rcd = rcmodel.rcdist()
jk = data['J0'] - data['K0']
z = isodist.FEH2Z(data['METALS'], zsolar=0.017)
data['RC_DIST'] = rcd(jk, z, appmag=data['K0']) * options.distfac
data['RC_DM'] = 5. * numpy.log10(data['RC_DIST']) + 10.
XYZ = bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST'],
degree=True)
R, phi, Z = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=8.,
Zsun=0.025)
data['RC_GALR'] = R
data['RC_GALPHI'] = phi
data['RC_GALZ'] = Z
#Save
fitsio.write(savefilename, data, clobber=True)
# Add Tycho-2 matches
if options.tyc2:
data = esutil.numpy_util.add_fields(data, [('TYC2MATCH', numpy.int32),
('TYC1', numpy.int32),
('TYC2', numpy.int32),
('TYC3', numpy.int32)])
data['TYC2MATCH'] = 0
data['TYC1'] = -1
data['TYC2'] = -1
data['TYC3'] = -1
# Write positions
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=2', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:Tycho2',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Directly match on input RA
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
usecols=(1, 2, 7, 8, 9))
iis = numpy.arange(len(data))
mai = [iis[data['RA'] == ma[ii, 0]][0] for ii in range(len(ma))]
data['TYC2MATCH'][mai] = 1
data['TYC1'][mai] = ma[:, 2]
data['TYC2'][mai] = ma[:, 3]
data['TYC3'][mai] = ma[:, 4]
os.remove(posfilename)
os.remove(resultfilename)
if not options.nostat:
#Determine statistical sample and add flag
apo = apogee.select.apogeeSelect()
statIndx = apo.determine_statistical(data)
mainIndx = apread.mainIndx(data)
data = esutil.numpy_util.add_fields(data, [('STAT', numpy.int32),
('INVSF', float)])
data['STAT'] = 0
data['STAT'][statIndx * mainIndx] = 1
for ii in range(len(data)):
if (statIndx * mainIndx)[ii]:
data['INVSF'][ii] = 1. / apo(data['LOCATION_ID'][ii],
data['H'][ii])
else:
data['INVSF'][ii] = -1.
if options.nopm:
fitsio.write(savefilename, data, clobber=True)
return None
#Get proper motions, in a somewhat roundabout way
pmfile = savefilename.split('.')[0] + '_pms.fits'
if os.path.exists(pmfile):
pmdata = fitsio.read(pmfile, 1)
else:
pmdata = numpy.recarray(
len(data),
formats=['f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4'],
names=[
'RA', 'DEC', 'PMRA', 'PMDEC', 'PMRA_ERR', 'PMDEC_ERR',
'PMMATCH'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:UCAC4',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)
},
usecols=(4, 5, 15, 16, 17, 18))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsio.read(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions
try: #These already exist currently, but may not always exist
data = esutil.numpy_util.remove_fields(data, ['PMRA', 'PMDEC'])
except ValueError:
pass
data = esutil.numpy_util.add_fields(data, [('PMRA', numpy.float),
('PMDEC', numpy.float),
('PMRA_ERR', numpy.float),
('PMDEC_ERR', numpy.float),
('PMMATCH', numpy.int32)])
data['PMMATCH'] = 0
h = esutil.htm.HTM()
m1, m2, d12 = h.match(pmdata['RA'],
pmdata['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data['PMRA'][m2] = pmdata['PMRA'][m1]
data['PMDEC'][m2] = pmdata['PMDEC'][m1]
data['PMRA_ERR'][m2] = pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR'][m2] = pmdata['PMDEC_ERR'][m1]
data['PMMATCH'][m2] = pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx = data['PMMATCH'] == 1
data['PMRA'][True - pmindx] = -9999.99
data['PMDEC'][True - pmindx] = -9999.99
data['PMRA_ERR'][True - pmindx] = -9999.99
data['PMDEC_ERR'][True - pmindx] = -9999.99
#Calculate Galactocentric velocities
data = esutil.numpy_util.add_fields(data, [('GALVR', numpy.float),
('GALVT', numpy.float),
('GALVZ', numpy.float)])
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
XYZ = bovy_coords.lbd_to_XYZ(lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],
data['PMDEC'],
data['RA'],
data['DEC'],
degree=True)
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:, 0],
pmllpmbb[:, 1],
lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
vR, vT, vZ = bovy_coords.vxvyvz_to_galcencyl(
vxvyvz[:, 0],
vxvyvz[:, 1],
vxvyvz[:, 2],
8. - XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2] + 0.025,
vsun=[-11.1, 30.24 * 8.,
7.25]) #Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR'] = vR
data['GALVT'] = vT
data['GALVZ'] = vZ
data['GALVR'][True - pmindx] = -9999.99
data['GALVT'][True - pmindx] = -9999.99
data['GALVZ'][True - pmindx] = -9999.99
#Get PPMXL proper motions, in a somewhat roundabout way
pmfile = savefilename.split('.')[0] + '_pms_ppmxl.fits'
if os.path.exists(pmfile):
pmdata = fitsio.read(pmfile, 1)
else:
pmdata = numpy.recarray(
len(data),
formats=['f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4'],
names=[
'RA', 'DEC', 'PMRA', 'PMDEC', 'PMRA_ERR', 'PMDEC_ERR',
'PMMATCH'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:PPMXL',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)
},
usecols=(4, 5, 15, 16, 19, 20))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsio.read(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions to ppmxl
data = esutil.numpy_util.add_fields(data,
[('PMRA_PPMXL', numpy.float),
('PMDEC_PPMXL', numpy.float),
('PMRA_ERR_PPMXL', numpy.float),
('PMDEC_ERR_PPMXL', numpy.float),
('PMMATCH_PPMXL', numpy.int32)])
data['PMMATCH_PPMXL'] = 0
h = esutil.htm.HTM()
m1, m2, d12 = h.match(pmdata['RA'],
pmdata['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data['PMRA_PPMXL'][m2] = pmdata['PMRA'][m1]
data['PMDEC_PPMXL'][m2] = pmdata['PMDEC'][m1]
data['PMRA_ERR_PPMXL'][m2] = pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR_PPMXL'][m2] = pmdata['PMDEC_ERR'][m1]
data['PMMATCH_PPMXL'][m2] = pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx = data['PMMATCH_PPMXL'] == 1
data['PMRA_PPMXL'][True - pmindx] = -9999.99
data['PMDEC_PPMXL'][True - pmindx] = -9999.99
data['PMRA_ERR_PPMXL'][True - pmindx] = -9999.99
data['PMDEC_ERR_PPMXL'][True - pmindx] = -9999.99
#Calculate Galactocentric velocities
data = esutil.numpy_util.add_fields(data, [('GALVR_PPMXL', numpy.float),
('GALVT_PPMXL', numpy.float),
('GALVZ_PPMXL', numpy.float)])
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
XYZ = bovy_coords.lbd_to_XYZ(lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA_PPMXL'],
data['PMDEC_PPMXL'],
data['RA'],
data['DEC'],
degree=True)
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:, 0],
pmllpmbb[:, 1],
lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
vR, vT, vZ = bovy_coords.vxvyvz_to_galcencyl(
vxvyvz[:, 0],
vxvyvz[:, 1],
vxvyvz[:, 2],
8. - XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2] + 0.025,
vsun=[-11.1, 30.24 * 8.,
7.25]) #Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR_PPMXL'] = vR
data['GALVT_PPMXL'] = vT
data['GALVZ_PPMXL'] = vZ
data['GALVR_PPMXL'][True - pmindx] = -9999.99
data['GALVT_PPMXL'][True - pmindx] = -9999.99
data['GALVZ_PPMXL'][True - pmindx] = -9999.99
#Save
fitsio.write(savefilename, data, clobber=True)
return None
def dust_correction(self, r_ks=0.306, r_j=0.72, r_h=0.46):
"""
For 2MASS bandpasses: ks, j and h magnitudes, this function will return the corrected magnitudes based of the
SFD E(B-V) values at a given for a line of sight in Galactic coordinates (l,b).
Exctinction coefficients r_ are bandpassses taken by Yuan et al. 2013.
Input
------
data(astropy.table): Astropy table contaning (contains: ks_mag, j_mag, h_mag)
Output
-------
data(astropy.table): Initial data table in addition to new columns: (ks0, j0, h0, aks, aj, ah)
"""
# Transform RA, DEC to l,b
lb = bovy_coords.radec_to_lb(self.data['ra'],
self.data['dec'],
degree=True)
l, b = lb[:, 0], lb[:, 1]
N = len(l) # Total number of stars in data file
query_max = 1000000 # maximum number of sources we can query
N_int = N / query_max # Split the data to query
L = np.arange(0, (int(N_int) + 1) * query_max, step=query_max)
L = np.append(L, N) # add last value of total N points
sfd = SFDWebQuery() # call SFD query function
ks0 = np.ndarray(shape=(N, 1)) # empty array for corrected magnitudes
j0 = np.ndarray(shape=(N, 1)) # empty array for corrected magnitudes
h0 = np.ndarray(shape=(N, 1)) # empty array for corrected magnitudes
aks = np.ndarray(shape=(N, 1)) # empty array for corrected magnitudes
aj = np.ndarray(shape=(N, 1)) # empty array for corrected magnitudes
ah = np.ndarray(shape=(N, 1)) # empty array for corrected magnitudes
for i in range(0, len(L) - 1):
# Select the Galactic coordinates we want to estimate E(B-V)
l_ad = l[L[i]:L[i + 1]]
b_ad = b[L[i]:L[i + 1]]
coords = SkyCoord(l_ad * u.deg, b_ad * u.deg, frame='galactic')
# Query coords and return E(B-V) SFD for each source
ebv_sfd = sfd(coords)
# Apply corrections to each magnitude band based on E(B-V) estimates
corr_ks = ((self.data['ks_m'])[L[i]:L[i + 1]]) - (r_ks * ebv_sfd)
corr_j = ((self.data['j_m'])[L[i]:L[i + 1]]) - (r_j * ebv_sfd)
corr_h = ((self.data['h_m'])[L[i]:L[i + 1]]) - (r_h * ebv_sfd)
# corrected magnitudes
ks0[L[i]:L[i + 1], 0] = corr_ks
j0[L[i]:L[i + 1], 0] = corr_j
h0[L[i]:L[i + 1], 0] = corr_h
# exctinction coefficient appends
aks[L[i]:L[i + 1], 0] = r_ks * ebv_sfd
aj[L[i]:L[i + 1], 0] = r_j * ebv_sfd
ah[L[i]:L[i + 1], 0] = r_h * ebv_sfd
dat = Table([ks0, j0, h0, aks, aj, ah],
names=('ks0', 'j0', 'h0', 'A_ks', 'A_j', 'A_h'))
self.data.add_columns([
dat['ks0'], dat['j0'], dat['h0'], dat['A_ks'], dat['A_j'],
dat['A_h']
])
return (self.data)
def photometry(self, dustmap=None, v=True):
'''
Computes the Grvs-magnitudes and total velocity in Galactocentric restframe.
Parameters
----------
dustmap : DustMap
Dustmap object to be used
'''
from hvs.utils import DustMap
from hvs.utils.gaia import get_GRVS
from galpy.util.bovy_coords import radec_to_lb
if (self.cattype < 1):
raise RuntimeError('The catalog needs to be propagated!')
if (not isinstance(dustmap, DustMap)):
raise ValueError(
'You must provide an instance of the class DustMap.')
if (hasattr(self, 'll')):
self.GRVS, self.V, self.G, self.e_par, self.e_pmra, self.pmdec = get_GRVS(
self.dist.to('kpc').value, self.ll, self.bb,
self.m.to('Msun').value,
self.tage.to('Myr').value, dustmap)
else:
data = radec_to_lb(self.ra.to('deg').value,
self.dec.to('deg').value,
degree=True)
l, b = data[:, 0], data[:, 1]
self.GRVS, self.V, self.G, self.e_par, self.e_pmra, self.e_pmdec = get_GRVS(
self.dist.to('kpc').value, l, b,
self.m.to('Msun').value,
self.tage.to('Myr').value, dustmap)
self.e_par = self.e_par * u.uas
self.e_pmra = self.e_pmra * u.uas / u.yr
self.e_pmdec = self.e_pmdec * u.uas / u.yr
if (v):
import astropy.coordinates as coord
from galpy.util.bovy_coords import radec_to_lb, pmrapmdec_to_pmllpmbb
vSun = [
-self.solarmotion[0], self.solarmotion[1], self.solarmotion[2]
] * u.km / u.s # (U, V, W)
vrot = [0., 220., 0.] * u.km / u.s
RSun = 8. * u.kpc
zSun = 0 * u.pc
v_sun = coord.CartesianDifferential(vSun + vrot)
GCCS = coord.Galactocentric(galcen_distance=RSun,
z_sun=zSun,
galcen_v_sun=v_sun)
data = radec_to_lb(self.ra.to('deg').value,
self.dec.to('deg').value,
degree=True)
ll, bb = data[:, 0], data[:, 1]
data = pmrapmdec_to_pmllpmbb(self.pmra,
self.pmdec,
self.ra.to('deg').value,
self.dec.to('deg').value,
degree=True)
pmll, pmbb = data[:, 0], data[:, 1]
galactic_coords = coord.Galactic(l=ll * u.deg,
b=bb * u.deg,
distance=self.dist,
pm_l_cosb=pmll * u.mas / u.yr,
pm_b=pmbb * u.mas / u.yr,
radial_velocity=self.vlos)
galactocentric_coords = galactic_coords.transform_to(GCCS)
self.GCv = np.sqrt(galactocentric_coords.v_x**2. +
galactocentric_coords.v_y**2. +
galactocentric_coords.v_z**2.).to(u.km / u.s)
self.GCdist = np.sqrt(galactocentric_coords.x**2. +
galactocentric_coords.y**2. +
galactocentric_coords.z**2.).to(u.kpc)
self.cattype = 2
def volume(self,
vol_func,
xyz=False,
MJ=None,
JK=None,
ndists=101,
linearDist=False,
relative=False,
ncpu=None):
"""
NAME:
volume
PURPOSE:
Compute the effective volume of a spatial volume under this effective selection function
INPUT:
vol_func - function of
(a) (ra/deg,dec/deg,dist/kpc)
(b) heliocentric Galactic X,Y,Z if xyz
that returns 1. inside the spatial volume under consideration and 0. outside of it, should be able to take array input of a certain shape and return an array with the same shape
xyz= (False) if True, vol_func is a function of X,Y,Z (see above)
MJ= (object-wide default) absolute magnitude in J or an array of samples of absolute magnitudes in J for the tracer population
JK= (object-wide default) J-Ks color or an array of samples of the J-Ks color
relative= (False) if True, compute the effective volume completeness = effective volume / true volume; computed using the same integration grid, so will be more robust against integration errors (especially due to the finite HEALPix grid for the angular integration). For simple volumes, a more precise effective volume can be computed by using relative=True and multiplying in the correct true volume
ndists= (101) number of distances to use in the distance integration
linearDist= (False) if True, integrate in distance rather than distance modulus
ncpu= (None) if set to an integer, use this many CPUs to compute the effective selection function (only for non-zero extinction)
OUTPUT
effective volume
HISTORY:
2017-01-18 - Written - Bovy (UofT/CCA)
"""
# Pre-compute coordinates for integrand evaluation
if not hasattr(self,'_ra_cen_4vol') or \
(hasattr(self,'_ndists_4vol') and
(ndists != self._ndists_4vol or
linearDist != self._linearDist_4vol)):
theta,phi= healpy.pix2ang(\
_BASE_NSIDE,numpy.arange(_BASE_NPIX)\
[True^self._tgasSel._exclude_mask_skyonly],nest=True)
self._ra_cen_4vol = 180. / numpy.pi * phi
self._dec_cen_4vol = 90. - 180. / numpy.pi * theta
if linearDist:
dists = numpy.linspace(0.001, 10., ndists)
dms = 5. * numpy.log10(dists) + 10.
self._deltadm_4vol = dists[1] - dists[0]
else:
dms = numpy.linspace(0., 18., ndists)
self._deltadm_4vol = (dms[1] - dms[0]) * numpy.log(10.) / 5.
self._dists_4vol = 10.**(0.2 * dms - 2.)
self._tiled_dists3_4vol= numpy.tile(\
self._dists_4vol**(3.-linearDist),(len(self._ra_cen_4vol),1))
self._tiled_ra_cen_4vol = numpy.tile(self._ra_cen_4vol,
(len(self._dists_4vol), 1)).T
self._tiled_dec_cen_4vol = numpy.tile(self._dec_cen_4vol,
(len(self._dists_4vol), 1)).T
lb = bovy_coords.radec_to_lb(phi, numpy.pi / 2. - theta)
l = numpy.tile(lb[:, 0], (len(self._dists_4vol), 1)).T.flatten()
b = numpy.tile(lb[:, 1], (len(self._dists_4vol), 1)).T.flatten()
XYZ_4vol= \
bovy_coords.lbd_to_XYZ(l,b,
numpy.tile(self._dists_4vol,
(len(self._ra_cen_4vol),1)).flatten())
self._X_4vol = numpy.reshape(
XYZ_4vol[:,
0], (len(self._ra_cen_4vol), len(self._dists_4vol)))
self._Y_4vol = numpy.reshape(
XYZ_4vol[:,
1], (len(self._ra_cen_4vol), len(self._dists_4vol)))
self._Z_4vol = numpy.reshape(
XYZ_4vol[:,
2], (len(self._ra_cen_4vol), len(self._dists_4vol)))
# Cache effective-selection function
MJ, JK = self._parse_mj_jk(MJ, JK)
new_hash = hashlib.md5(numpy.array([MJ, JK])).hexdigest()
if not hasattr(self,'_vol_MJ_hash') or new_hash != self._vol_MJ_hash \
or (hasattr(self,'_ndists_4vol') and
(ndists != self._ndists_4vol or
linearDist != self._linearDist_4vol)):
# Need to update the effective-selection function
if isinstance(self._dmap3d, mwdust.Zero): #easy bc same everywhere
effsel_4vol = self(self._dists_4vol,
self._ra_cen_4vol[0],
self._dec_cen_4vol[0],
MJ=MJ,
JK=JK)
self._effsel_4vol = numpy.tile(effsel_4vol,
(len(self._ra_cen_4vol), 1))
else: # Need to treat each los separately
if ncpu is None:
self._effsel_4vol = numpy.empty(
(len(self._ra_cen_4vol), len(self._dists_4vol)))
for ii,(ra_cen, dec_cen) \
in enumerate(tqdm.tqdm(zip(self._ra_cen_4vol,
self._dec_cen_4vol))):
self._effsel_4vol[ii] = self(self._dists_4vol,
ra_cen,
dec_cen,
MJ=MJ,
JK=JK)
else:
multiOut= multi.parallel_map(\
lambda x: self(self._dists_4vol,
self._ra_cen_4vol[x],
self._dec_cen_4vol[x],MJ=MJ,JK=JK),
range(len(self._ra_cen_4vol)),
numcores=ncpu)
self._effsel_4vol = numpy.array(multiOut)
self._vol_MJ_hash = new_hash
self._ndists_4vol = ndists
self._linearDist_4vol = linearDist
out = 0.
if xyz:
out= numpy.sum(\
self._effsel_4vol\
*vol_func(self._X_4vol,self._Y_4vol,self._Z_4vol)\
*self._tiled_dists3_4vol)
else:
out= numpy.sum(\
self._effsel_4vol\
*vol_func(self._ra_cen_4vol,self._dec_cen_4vol,
self._dists_4vol)\
*self._tiled_dists3_4vol)
if relative:
if not hasattr(self, '_tgasEffSelUniform'):
tgasSelUniform = tgasSelectUniform(comp=1.)
self._tgasEffSelUniform = tgasEffectiveSelect(tgasSelUniform)
true_volume = self._tgasEffSelUniform.volume(vol_func,
xyz=xyz,
ndists=ndists,
linearDist=linearDist,
relative=False)
else:
true_volume = 1.
return out*healpy.nside2pixarea(_BASE_NSIDE)*self._deltadm_4vol\
/true_volume
# not use SDSS photometry
# input data
infile = '/Users/dkawata/work/obs/LAMOST/DR3/LAMOSTDR3_AstarxAPASSDR9.fits'
star_hdus = pyfits.open(infile)
star = star_hdus[1].data
star_hdus.close()
# read the data
# number of data points
print 'number of stars read =', len(star['obsid'])
# select stas with teff and logg
# Galactic coordinates
Tllbb = bovy_coords.radec_to_lb(star['ra'],
star['dec'],
degree=True,
epoch=2000.0)
glon = Tllbb[:, 0]
glat = Tllbb[:, 1]
sindx=np.where((star['teff']>7330.0) & (star['teff']<8040.0) \
& (star['logg']>3.2) \
& (star['Vmag']>0.0) & (star['Bmag']>0.0) \
& (star['rv_err']>0.0) & (star['rv_err']<10.0))
# & (glon>140.0) & (glon<220.0))
# & (glon>175.0) & (glon<185.0))
nstars = len(star['ra'][sindx])
print ' N selected=', nstars
# extract the necessary particle info
ra_s = star['ra'][sindx]
def mockDataSim(
x, v, selectionFunction=None, kDwarfs=False, ro=8.0, phio=0.0, fehrange=None, fehdist=None, colordist=None
):
"""
NAME:
mockDataSim
PURPOSE:
generate mock SEGUE data from an N-body simulation
INPUT:
x - position of all simulation particles [x,y,z], [:,3] (kpc)
v - velocity of all simulation particles [vx,vy,vz] [:,3] (km/s)
selectionFunction - instance of a selection function
kDwarfs= if True, generate K dwarfs
ro= mock solar radius [kpc]
phio= mock solar azimuth [deg]
OUTPUT:
HISTORY:
2013-01-31 - Written - Bovy (IAS)
"""
# Calculate l and b to associate data with los
Xsun = ro * numpy.cos(phio * _DEGTORAD)
Ysun = ro * numpy.sin(phio * _DEGTORAD)
Zsun = 0.0
X = -(x[:, 0] - Xsun)
Y = x[:, 1] - Ysun
Z = x[:, 2] - Zsun
lbd = bovy_coords.XYZ_to_lbd(X, Y, Z, degree=True)
# Now find all simulation particles for each los
platelb = bovy_coords.radec_to_lb(selectionFunction.platestr.ra, selectionFunction.platestr.dec, degree=True)
simIndices = []
inLos = numpy.zeros(len(x[:, 0]), dtype="bool")
for ii in range(len(selectionFunction.plates)):
thisIndx = findSimInLos(platelb[ii, 0], platelb[ii, 1], lbd[:, 0], lbd[:, 1])
simIndices.append(thisIndx)
inLos[thisIndx] = True
print platelb[ii, 0], platelb[ii, 1], numpy.sum(simIndices[-1])
return lbd
# Draw colors and metallicities for all stars that belong to a los
grs = numpy.empty(len(x[:, 0]))
fehs = numpy.empty(len(x[:, 0]))
# Calculate color and metallicity distributions
if kDwarfs:
grmin, grmax = 0.55, 0.77
else:
grmin, grmax = 0.48, 0.55
ngr, nfeh = 21, 21
tgrs = numpy.linspace(grmin, grmax, ngr)
tfehs = numpy.linspace(fehrange[0] + 0.00001, fehrange[1] - 0.00001, nfeh)
# Calcuate FeH and gr distriutions
fehdists = numpy.zeros(nfeh)
for jj in range(nfeh):
fehdists[jj] = fehdist(tfehs[jj])
fehdists = numpy.cumsum(fehdists)
fehdists /= fehdists[-1]
colordists = numpy.zeros(ngr)
for jj in range(ngr):
colordists[jj] = colordist(tgrs[jj])
colordists = numpy.cumsum(colordists)
colordists /= colordists[-1]
for ii in range(len(x[:, 0])):
if not inLos[ii]:
continue
def _add_proper_motions_pregaia(data, savefilename):
#Get proper motions, in a somewhat roundabout way
pmfile = savefilename.split('.')[0] + '_pms.fits'
if os.path.exists(pmfile):
pmdata = fitsread(pmfile, 1)
else:
pmdata = numpy.recarray(
len(data),
formats=['f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4'],
names=[
'RA', 'DEC', 'PMRA', 'PMDEC', 'PMRA_ERR', 'PMDEC_ERR',
'PMMATCH'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:UCAC4',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)
},
usecols=(4, 5, 15, 16, 17, 18))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitswrite(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsread(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions
try: #These already exist currently, but may not always exist
data = esutil.numpy_util.remove_fields(data, ['PMRA', 'PMDEC'])
except ValueError:
pass
data = esutil.numpy_util.add_fields(data, [('PMRA', numpy.float),
('PMDEC', numpy.float),
('PMRA_ERR', numpy.float),
('PMDEC_ERR', numpy.float),
('PMMATCH', numpy.int32)])
data['PMMATCH'] = 0
h = esutil.htm.HTM()
m1, m2, d12 = h.match(pmdata['RA'],
pmdata['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data['PMRA'][m2] = pmdata['PMRA'][m1]
data['PMDEC'][m2] = pmdata['PMDEC'][m1]
data['PMRA_ERR'][m2] = pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR'][m2] = pmdata['PMDEC_ERR'][m1]
data['PMMATCH'][m2] = pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx = data['PMMATCH'] == 1
data['PMRA'][True ^ pmindx] = -9999.99
data['PMDEC'][True ^ pmindx] = -9999.99
data['PMRA_ERR'][True ^ pmindx] = -9999.99
data['PMDEC_ERR'][True ^ pmindx] = -9999.99
#Calculate Galactocentric velocities
data = esutil.numpy_util.add_fields(data, [('GALVR', numpy.float),
('GALVT', numpy.float),
('GALVZ', numpy.float)])
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
XYZ = bovy_coords.lbd_to_XYZ(lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],
data['PMDEC'],
data['RA'],
data['DEC'],
degree=True)
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:, 0],
pmllpmbb[:, 1],
lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
vRvTvZ = bovy_coords.vxvyvz_to_galcencyl(
vxvyvz[:, 0],
vxvyvz[:, 1],
vxvyvz[:, 2],
8. - XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2] + 0.025,
vsun=[-11.1, 30.24 * 8.,
7.25]) #Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR'] = vRvTvZ[:, 0]
data['GALVT'] = vRvTvZ[:, 1]
data['GALVZ'] = vRvTvZ[:, 2]
data['GALVR'][True ^ pmindx] = -9999.99
data['GALVT'][True ^ pmindx] = -9999.99
data['GALVZ'][True ^ pmindx] = -9999.99
#Get HSOY proper motions, in a somewhat roundabout way
pmfile = savefilename.split('.')[0] + '_pms_ppmxl.fits'
if os.path.exists(pmfile):
pmdata = fitsread(pmfile, 1)
else:
pmdata = numpy.recarray(
len(data),
formats=['f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4'],
names=[
'RA', 'DEC', 'PMRA', 'PMDEC', 'PMRA_ERR', 'PMDEC_ERR',
'PMMATCH'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:I/339/hsoy',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
12: lambda s: float(s.strip() or -9999),
13: lambda s: float(s.strip() or -9999),
14: lambda s: float(s.strip() or -9999),
15: lambda s: float(s.strip() or -9999)
},
usecols=(3, 4, 12, 13, 14, 15))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitswrite(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsread(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions to ppmxl/HSOY
data = esutil.numpy_util.add_fields(data, [('PMRA_HSOY', numpy.float),
('PMDEC_HSOY', numpy.float),
('PMRA_ERR_HSOY', numpy.float),
('PMDEC_ERR_HSOY', numpy.float),
('PMMATCH_HSOY', numpy.int32)])
data['PMMATCH_HSOY'] = 0
h = esutil.htm.HTM()
m1, m2, d12 = h.match(pmdata['RA'],
pmdata['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data['PMRA_HSOY'][m2] = pmdata['PMRA'][m1]
data['PMDEC_HSOY'][m2] = pmdata['PMDEC'][m1]
data['PMRA_ERR_HSOY'][m2] = pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR_HSOY'][m2] = pmdata['PMDEC_ERR'][m1]
data['PMMATCH_HSOY'][m2] = pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx = data['PMMATCH_HSOY'] == 1
data['PMRA_HSOY'][True ^ pmindx] = -9999.99
data['PMDEC_HSOY'][True ^ pmindx] = -9999.99
data['PMRA_ERR_HSOY'][True ^ pmindx] = -9999.99
data['PMDEC_ERR_HSOY'][True ^ pmindx] = -9999.99
#Calculate Galactocentric velocities
data = esutil.numpy_util.add_fields(data, [('GALVR_HSOY', numpy.float),
('GALVT_HSOY', numpy.float),
('GALVZ_HSOY', numpy.float)])
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
XYZ = bovy_coords.lbd_to_XYZ(lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA_HSOY'],
data['PMDEC_HSOY'],
data['RA'],
data['DEC'],
degree=True)
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:, 0],
pmllpmbb[:, 1],
lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
vRvTvZ = bovy_coords.vxvyvz_to_galcencyl(
vxvyvz[:, 0],
vxvyvz[:, 1],
vxvyvz[:, 2],
8. - XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2] + 0.025,
vsun=[-11.1, 30.24 * 8.,
7.25]) #Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR_HSOY'] = vRvTvZ[:, 0]
data['GALVT_HSOY'] = vRvTvZ[:, 1]
data['GALVZ_HSOY'] = vRvTvZ[:, 2]
data['GALVR_HSOY'][True ^ pmindx] = -9999.99
data['GALVT_HSOY'][True ^ pmindx] = -9999.99
data['GALVZ_HSOY'][True ^ pmindx] = -9999.99
#Return
return data
return None
def make_rcsample(parser):
options,args= parser.parse_args()
savefilename= options.savefilename
if savefilename is None:
#Create savefilename if not given
savefilename= os.path.join(appath._APOGEE_DATA,
'rcsample_'+appath._APOGEE_REDUX+'.fits')
print("Saving to %s ..." % savefilename)
#Read the base-sample
data= apread.allStar(adddist=_ADDHAYDENDIST,rmdups=options.rmdups)
#Remove a bunch of fields that we do not want to keep
data= esutil.numpy_util.remove_fields(data,
['TARGET_ID',
'FILE',
'AK_WISE',
'SFD_EBV',
'SYNTHVHELIO_AVG',
'SYNTHVSCATTER',
'SYNTHVERR',
'SYNTHVERR_MED',
'RV_TEFF',
'RV_LOGG',
'RV_FEH',
'RV_ALPHA',
'RV_CARB',
'RV_CCFWHM',
'RV_AUTOFWHM',
'SYNTHSCATTER',
'STABLERV_CHI2',
'STABLERV_RCHI2',
'STABLERV_CHI2_PROB',
'CHI2_THRESHOLD',
'APSTAR_VERSION',
'ASPCAP_VERSION',
'RESULTS_VERSION',
'WASH_M',
'WASH_M_ERR',
'WASH_T2',
'WASH_T2_ERR',
'DDO51',
'DDO51_ERR',
'IRAC_3_6',
'IRAC_3_6_ERR',
'IRAC_4_5',
'IRAC_4_5_ERR',
'IRAC_5_8',
'IRAC_5_8_ERR',
'IRAC_8_0',
'IRAC_8_0_ERR',
'WISE_4_5',
'WISE_4_5_ERR',
'TARG_4_5',
'TARG_4_5_ERR',
'WASH_DDO51_GIANT_FLAG',
'WASH_DDO51_STAR_FLAG',
'REDUCTION_ID',
'SRC_H',
'PM_SRC'])
if not appath._APOGEE_REDUX.lower() == 'current' \
and not 'l30' in appath._APOGEE_REDUX \
and int(appath._APOGEE_REDUX[1:]) < 500:
data= esutil.numpy_util.remove_fields(data,
['ELEM'])
#Select red-clump stars
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
if 'l30' in appath._APOGEE_REDUX:
logg= data['LOGG']
elif appath._APOGEE_REDUX.lower() == 'current' \
or int(appath._APOGEE_REDUX[1:]) > 600:
from apogee.tools import paramIndx
if False:
#Use my custom logg calibration that's correct for the RC
logg= (1.-0.042)*data['FPARAM'][:,paramIndx('logg')]-0.213
lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.255
hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.3726
else:
#Use my custom logg calibration that's correct on average
logg= (1.+0.03)*data['FPARAM'][:,paramIndx('logg')]-0.37
lowloggindx= data['FPARAM'][:,paramIndx('logg')] < 1.
logg[lowloggindx]= data['FPARAM'][lowloggindx,paramIndx('logg')]-0.34
hiloggindx= data['FPARAM'][:,paramIndx('logg')] > 3.8
logg[hiloggindx]= data['FPARAM'][hiloggindx,paramIndx('logg')]-0.256
else:
logg= data['LOGG']
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= rcmodel.loggteffcut(data['TEFF'],z,upper=False))\
*(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
data= data[indx]
#Add more aggressive flag cut
data= esutil.numpy_util.add_fields(data,[('ADDL_LOGG_CUT',numpy.int32)])
data['ADDL_LOGG_CUT']= ((data['TEFF']-4800.)/1000.+2.75) > data['LOGG']
if options.loggcut:
data= data[data['ADDL_LOGG_CUT'] == 1]
print("Making catalog of %i objects ..." % len(data))
#Add distances
data= esutil.numpy_util.add_fields(data,[('RC_DIST', float),
('RC_DM', float),
('RC_GALR', float),
('RC_GALPHI', float),
('RC_GALZ', float)])
rcd= rcmodel.rcdist()
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
data['RC_DIST']= rcd(jk,z,appmag=data['K0'])*options.distfac
data['RC_DM']= 5.*numpy.log10(data['RC_DIST'])+10.
XYZ= bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST'],
degree=True)
R,phi,Z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],
XYZ[:,1],
XYZ[:,2],
Xsun=8.,Zsun=0.025)
data['RC_GALR']= R
data['RC_GALPHI']= phi
data['RC_GALZ']= Z
#Save
fitsio.write(savefilename,data,clobber=True)
# Add Tycho-2 matches
if options.tyc2:
data= esutil.numpy_util.add_fields(data,[('TYC2MATCH',numpy.int32),
('TYC1',numpy.int32),
('TYC2',numpy.int32),
('TYC3',numpy.int32)])
data['TYC2MATCH']= 0
data['TYC1']= -1
data['TYC2']= -1
data['TYC3']= -1
# Write positions
posfilename= tempfile.mktemp('.csv',dir=os.getcwd())
resultfilename= tempfile.mktemp('.csv',dir=os.getcwd())
with open(posfilename,'w') as csvfile:
wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA','DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'],data[ii]['DEC']])
# Send to CDS for matching
result= open(resultfilename,'w')
try:
subprocess.check_call(['curl',
'-X','POST',
'-F','request=xmatch',
'-F','distMaxArcsec=2',
'-F','RESPONSEFORMAT=csv',
'-F','cat1=@%s' % os.path.basename(posfilename),
'-F','colRA1=RA',
'-F','colDec1=DEC',
'-F','cat2=vizier:Tycho2',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Directly match on input RA
ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1,
usecols=(1,2,7,8,9))
iis= numpy.arange(len(data))
mai= [iis[data['RA'] == ma[ii,0]][0] for ii in range(len(ma))]
data['TYC2MATCH'][mai]= 1
data['TYC1'][mai]= ma[:,2]
data['TYC2'][mai]= ma[:,3]
data['TYC3'][mai]= ma[:,4]
os.remove(posfilename)
os.remove(resultfilename)
if not options.nostat:
#Determine statistical sample and add flag
apo= apogee.select.apogeeSelect()
statIndx= apo.determine_statistical(data)
mainIndx= apread.mainIndx(data)
data= esutil.numpy_util.add_fields(data,[('STAT',numpy.int32),
('INVSF',float)])
data['STAT']= 0
data['STAT'][statIndx*mainIndx]= 1
for ii in range(len(data)):
if (statIndx*mainIndx)[ii]:
data['INVSF'][ii]= 1./apo(data['LOCATION_ID'][ii],
data['H'][ii])
else:
data['INVSF'][ii]= -1.
if options.nopm:
fitsio.write(savefilename,data,clobber=True)
return None
#Get proper motions, in a somewhat roundabout way
pmfile= savefilename.split('.')[0]+'_pms.fits'
if os.path.exists(pmfile):
pmdata= fitsio.read(pmfile,1)
else:
pmdata= numpy.recarray(len(data),
formats=['f8','f8','f8','f8','f8','f8','i4'],
names=['RA','DEC','PMRA','PMDEC',
'PMRA_ERR','PMDEC_ERR','PMMATCH'])
# Write positions, again ...
posfilename= tempfile.mktemp('.csv',dir=os.getcwd())
resultfilename= tempfile.mktemp('.csv',dir=os.getcwd())
with open(posfilename,'w') as csvfile:
wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA','DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'],data[ii]['DEC']])
# Send to CDS for matching
result= open(resultfilename,'w')
try:
subprocess.check_call(['curl',
'-X','POST',
'-F','request=xmatch',
'-F','distMaxArcsec=4',
'-F','RESPONSEFORMAT=csv',
'-F','cat1=@%s' % os.path.basename(posfilename),
'-F','colRA1=RA',
'-F','colDec1=DEC',
'-F','cat2=vizier:UCAC4',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1,
converters={15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)},
usecols=(4,5,15,16,17,18))
h=esutil.htm.HTM()
m1,m2,d12 = h.match(data['RA'],data['DEC'],
ma[:,0],ma[:,1],4./3600.,maxmatch=1)
pmdata['PMMATCH']= 0
pmdata['RA']= data['RA']
pmdata['DEC']= data['DEC']
pmdata['PMMATCH'][m1]= 1
pmdata['PMRA'][m1]= ma[m2,2]
pmdata['PMDEC'][m1]= ma[m2,3]
pmdata['PMRA_ERR'][m1]= ma[m2,4]
pmdata['PMDEC_ERR'][m1]= ma[m2,5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions
try: #These already exist currently, but may not always exist
data= esutil.numpy_util.remove_fields(data,['PMRA','PMDEC'])
except ValueError:
pass
data= esutil.numpy_util.add_fields(data,[('PMRA', numpy.float),
('PMDEC', numpy.float),
('PMRA_ERR', numpy.float),
('PMDEC_ERR', numpy.float),
('PMMATCH',numpy.int32)])
data['PMMATCH']= 0
h=esutil.htm.HTM()
m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data['PMRA'][m2]= pmdata['PMRA'][m1]
data['PMDEC'][m2]= pmdata['PMDEC'][m1]
data['PMRA_ERR'][m2]= pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR'][m2]= pmdata['PMDEC_ERR'][m1]
data['PMMATCH'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx= data['PMMATCH'] == 1
data['PMRA'][True-pmindx]= -9999.99
data['PMDEC'][True-pmindx]= -9999.99
data['PMRA_ERR'][True-pmindx]= -9999.99
data['PMDEC_ERR'][True-pmindx]= -9999.99
#Calculate Galactocentric velocities
data= esutil.numpy_util.add_fields(data,[('GALVR', numpy.float),
('GALVT', numpy.float),
('GALVZ', numpy.float)])
lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],data['PMDEC'],
data['RA'],data['DEC'],
degree=True)
vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:,0],
pmllpmbb[:,1],
lb[:,0],lb[:,1],data['RC_DIST'],
degree=True)
vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
vxvyvz[:,1],
vxvyvz[:,2],
8.-XYZ[:,0],
XYZ[:,1],
XYZ[:,2]+0.025,
vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR']= vR
data['GALVT']= vT
data['GALVZ']= vZ
data['GALVR'][True-pmindx]= -9999.99
data['GALVT'][True-pmindx]= -9999.99
data['GALVZ'][True-pmindx]= -9999.99
#Get PPMXL proper motions, in a somewhat roundabout way
pmfile= savefilename.split('.')[0]+'_pms_ppmxl.fits'
if os.path.exists(pmfile):
pmdata= fitsio.read(pmfile,1)
else:
pmdata= numpy.recarray(len(data),
formats=['f8','f8','f8','f8','f8','f8','i4'],
names=['RA','DEC','PMRA','PMDEC',
'PMRA_ERR','PMDEC_ERR','PMMATCH'])
# Write positions, again ...
posfilename= tempfile.mktemp('.csv',dir=os.getcwd())
resultfilename= tempfile.mktemp('.csv',dir=os.getcwd())
with open(posfilename,'w') as csvfile:
wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA','DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'],data[ii]['DEC']])
# Send to CDS for matching
result= open(resultfilename,'w')
try:
subprocess.check_call(['curl',
'-X','POST',
'-F','request=xmatch',
'-F','distMaxArcsec=4',
'-F','RESPONSEFORMAT=csv',
'-F','cat1=@%s' % os.path.basename(posfilename),
'-F','colRA1=RA',
'-F','colDec1=DEC',
'-F','cat2=vizier:PPMXL',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma= numpy.loadtxt(resultfilename,delimiter=',',skiprows=1,
converters={15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)},
usecols=(4,5,15,16,19,20))
h=esutil.htm.HTM()
m1,m2,d12 = h.match(data['RA'],data['DEC'],
ma[:,0],ma[:,1],4./3600.,maxmatch=1)
pmdata['PMMATCH']= 0
pmdata['RA']= data['RA']
pmdata['DEC']= data['DEC']
pmdata['PMMATCH'][m1]= 1
pmdata['PMRA'][m1]= ma[m2,2]
pmdata['PMDEC'][m1]= ma[m2,3]
pmdata['PMRA_ERR'][m1]= ma[m2,4]
pmdata['PMDEC_ERR'][m1]= ma[m2,5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile,pmdata,clobber=True)
#To make sure we're using the same format below
pmdata= fitsio.read(pmfile,1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions to ppmxl
data= esutil.numpy_util.add_fields(data,[('PMRA_PPMXL', numpy.float),
('PMDEC_PPMXL', numpy.float),
('PMRA_ERR_PPMXL', numpy.float),
('PMDEC_ERR_PPMXL', numpy.float),
('PMMATCH_PPMXL',numpy.int32)])
data['PMMATCH_PPMXL']= 0
h=esutil.htm.HTM()
m1,m2,d12 = h.match(pmdata['RA'],pmdata['DEC'],
data['RA'],data['DEC'],
2./3600.,maxmatch=1)
data['PMRA_PPMXL'][m2]= pmdata['PMRA'][m1]
data['PMDEC_PPMXL'][m2]= pmdata['PMDEC'][m1]
data['PMRA_ERR_PPMXL'][m2]= pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR_PPMXL'][m2]= pmdata['PMDEC_ERR'][m1]
data['PMMATCH_PPMXL'][m2]= pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx= data['PMMATCH_PPMXL'] == 1
data['PMRA_PPMXL'][True-pmindx]= -9999.99
data['PMDEC_PPMXL'][True-pmindx]= -9999.99
data['PMRA_ERR_PPMXL'][True-pmindx]= -9999.99
data['PMDEC_ERR_PPMXL'][True-pmindx]= -9999.99
#Calculate Galactocentric velocities
data= esutil.numpy_util.add_fields(data,[('GALVR_PPMXL', numpy.float),
('GALVT_PPMXL', numpy.float),
('GALVZ_PPMXL', numpy.float)])
lb= bovy_coords.radec_to_lb(data['RA'],data['DEC'],degree=True)
XYZ= bovy_coords.lbd_to_XYZ(lb[:,0],lb[:,1],data['RC_DIST'],degree=True)
pmllpmbb= bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA_PPMXL'],
data['PMDEC_PPMXL'],
data['RA'],data['DEC'],
degree=True)
vxvyvz= bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:,0],
pmllpmbb[:,1],
lb[:,0],lb[:,1],data['RC_DIST'],
degree=True)
vR, vT, vZ= bovy_coords.vxvyvz_to_galcencyl(vxvyvz[:,0],
vxvyvz[:,1],
vxvyvz[:,2],
8.-XYZ[:,0],
XYZ[:,1],
XYZ[:,2]+0.025,
vsun=[-11.1,30.24*8.,7.25])#Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR_PPMXL']= vR
data['GALVT_PPMXL']= vT
data['GALVZ_PPMXL']= vZ
data['GALVR_PPMXL'][True-pmindx]= -9999.99
data['GALVT_PPMXL'][True-pmindx]= -9999.99
data['GALVZ_PPMXL'][True-pmindx]= -9999.99
#Save
fitsio.write(savefilename,data,clobber=True)
return None
def __init__(self,
vxvv=None,
uvw=False,
lb=False,
radec=False,
vo=235.,
ro=8.5,
zo=0.025,
solarmotion='hogg'):
"""
NAME:
__init__
PURPOSE:
Initialize an Orbit instance
INPUT:
vxvv - initial conditions
3D can be either
1) in Galactocentric cylindrical coordinates [R,vR,vT(,z,vz,phi)]
2) [ra,dec,d,mu_ra, mu_dec,vlos] in [deg,deg,kpc,mas/yr,mas/yr,km/s] (all J2000.0; mu_ra = mu_ra * cos dec)
3) [ra,dec,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms]
4) (l,b,d,mu_l, mu_b, vlos) in [deg,deg,kpc,mas/yr,mas/yr,km/s) (all J2000.0; mu_l = mu_l * cos b)
5) [l,b,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms]
4) and 5) also work when leaving out b and mu_b/W
OPTIONAL INPUTS:
radec - if True, input is 2) (or 3) above
uvw - if True, velocities are UVW
lb - if True, input is 4) or 5) above
vo - circular velocity at ro
ro - distance from vantage point to GC (kpc)
zo - offset toward the NGP of the Sun wrt the plane (kpc)
solarmotion - 'hogg' or 'dehnen', or 'schoenrich', or value in
[-U,V,W]
OUTPUT:
instance
HISTORY:
2010-07-20 - Written - Bovy (NYU)
"""
if isinstance(solarmotion, str) and solarmotion.lower() == 'hogg':
vsolar = nu.array([-10.1, 4.0, 6.7]) / vo
elif isinstance(solarmotion, str) and solarmotion.lower() == 'dehnen':
vsolar = nu.array([-10., 5.25, 7.17]) / vo
elif isinstance(solarmotion,str) \
and solarmotion.lower() == 'schoenrich':
vsolar = nu.array([-11.1, 12.24, 7.25]) / vo
else:
vsolar = nu.array(solarmotion) / vo
if radec or lb:
if radec:
l, b = coords.radec_to_lb(vxvv[0], vxvv[1], degree=True)
elif len(vxvv) == 4:
l, b = vxvv[0], 0.
else:
l, b = vxvv[0], vxvv[1]
if uvw:
X, Y, Z = coords.lbd_to_XYZ(l, b, vxvv[2], degree=True)
vx = vxvv[3]
vy = vxvv[4]
vz = vxvv[5]
else:
if radec:
pmll, pmbb = coords.pmrapmdec_to_pmllpmbb(vxvv[3],
vxvv[4],
vxvv[0],
vxvv[1],
degree=True)
d, vlos = vxvv[2], vxvv[5]
elif len(vxvv) == 4:
pmll, pmbb = vxvv[2], 0.
d, vlos = vxvv[1], vxvv[3]
else:
pmll, pmbb = vxvv[3], vxvv[4]
d, vlos = vxvv[2], vxvv[5]
X, Y, Z, vx, vy, vz = coords.sphergal_to_rectgal(l,
b,
d,
vlos,
pmll,
pmbb,
degree=True)
X /= ro
Y /= ro
Z /= ro
vx /= vo
vy /= vo
vz /= vo
vsun = nu.array([
0.,
1.,
0.,
]) + vsolar
R, phi, z = coords.XYZ_to_galcencyl(X, Y, Z, Zsun=zo / ro)
vR, vT, vz = coords.vxvyvz_to_galcencyl(vx,
vy,
vz,
R,
phi,
z,
vsun=vsun,
galcen=True)
if lb and len(vxvv) == 4: vxvv = [R, vR, vT, phi]
else: vxvv = [R, vR, vT, z, vz, phi]
self.vxvv = vxvv
if len(vxvv) == 2:
self._orb = linearOrbit(vxvv=vxvv)
elif len(vxvv) == 3:
self._orb = planarROrbit(vxvv=vxvv)
elif len(vxvv) == 4:
self._orb = planarOrbit(vxvv=vxvv)
elif len(vxvv) == 5:
self._orb = RZOrbit(vxvv=vxvv)
elif len(vxvv) == 6:
self._orb = FullOrbit(vxvv=vxvv)
def obs_to_galcen(ra,
dec,
dist,
pmra,
pmdec,
rv,
pmra_err,
pmdec_err,
pmra_pmdec_corr,
dist_err,
rv_err,
return_cov=True,
verbose=True,
return_rphiz=True,
ro=8.,
vo=220.,
zo=0.025,
parallax=False):
vxvv = np.dstack([ra, dec, dist, pmra, pmdec, rv])[0]
ra, dec = vxvv[:, 0], vxvv[:, 1]
lb = bovy_coords.radec_to_lb(ra, dec, degree=True)
pmra, pmdec = vxvv[:, 3], vxvv[:, 4]
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra,
pmdec,
ra,
dec,
degree=True)
d, vlos = vxvv[:, 2], vxvv[:, 5]
if parallax:
d = 1. / d
rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0],
lb[:, 1],
d,
vlos,
pmllpmbb[:, 0],
pmllpmbb[:, 1],
degree=True)
vsolar = np.array([-10.1, 4.0, 6.7])
vsun = np.array([
0.,
1.,
0.,
]) + vsolar / vo
X = rectgal[:, 0] / ro
Y = rectgal[:, 1] / ro
Z = rectgal[:, 2] / ro
vx = rectgal[:, 3] / vo
vy = rectgal[:, 4] / vo
vz = rectgal[:, 5] / vo
XYZ = np.dstack([X, Y, Z])[0]
vxyz = np.dstack([vx, vy, vz])[0]
if return_rphiz:
Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Zsun=zo / ro)
vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0],
vxyz[:, 1],
vxyz[:, 2],
Rpz[:, 0],
Rpz[:, 1],
Rpz[:, 2],
vsun=vsun,
Xsun=1.,
Zsun=zo / ro,
galcen=True)
if return_cov == True:
cov_pmradec = np.empty([len(pmra_err), 2, 2])
cov_pmradec[:, 0, 0] = pmra_err**2
cov_pmradec[:, 1, 1] = pmdec_err**2
cov_pmradec[:, 0, 1] = pmra_pmdec_corr * pmra_err * pmdec_err
cov_pmradec[:, 1, 0] = pmra_pmdec_corr * pmra_err * pmdec_err
if verbose:
print('propagating covariance in pmra pmdec -> pmll pmbb')
cov_pmllbb = bovy_coords.cov_pmrapmdec_to_pmllpmbb(cov_pmradec,
vxvv[:, 0],
vxvv[:, 1],
degree=True,
epoch='J2015')
if verbose:
print('propagating covariance in pmll pmbb -> vx vy vz')
cov_vxyz = bovy_coords.cov_dvrpmllbb_to_vxyz(vxvv[:, 2], dist_err,
rv_err, pmllpmbb[:, 0],
pmllpmbb[:,
1], cov_pmllbb,
lb[:, 0], lb[:, 1])
if not return_rphiz:
return XYZ, vxyz, cov_vxyz
if verbose:
print('propagating covariance in vx vy vz -> vR vT vz')
cov_galcencyl = bovy_coords.cov_vxyz_to_galcencyl(cov_vxyz,
Rpz[:, 1],
Xsun=1.,
Zsun=zo / ro)
return XYZ, vxyz, cov_vxyz, Rpz, vRvTvz, cov_galcencyl
if not return_rphiz:
return XYZ, vxyz
return XYZ, vxyz, Rpz, vRvTvz
def _add_proper_motions_gaia(data):
from gaia_tools import xmatch
gaia2_matches, matches_indx = xmatch.cds(data,
colRA='RA',
colDec='DEC',
xcat='vizier:I/345/gaia2')
# Add matches
try: #These already exist currently, but may not always exist
data = esutil.numpy_util.remove_fields(data, ['PMRA', 'PMDEC'])
except ValueError:
pass
data = esutil.numpy_util.add_fields(data, [('PLX', numpy.float),
('PMRA', numpy.float),
('PMDEC', numpy.float),
('PLX_ERR', numpy.float),
('PMRA_ERR', numpy.float),
('PMDEC_ERR', numpy.float),
('PMMATCH', numpy.int32)])
data['PMMATCH'] = 0
data['PMMATCH'][matches_indx] = 1
data['PLX'][matches_indx] = gaia2_matches['parallax']
data['PMRA'][matches_indx] = gaia2_matches['pmra']
data['PMDEC'][matches_indx] = gaia2_matches['pmdec']
data['PLX_ERR'][matches_indx] = gaia2_matches['parallax_error']
data['PMRA_ERR'][matches_indx] = gaia2_matches['pmra_error']
data['PMDEC_ERR'][matches_indx] = gaia2_matches['pmdec_error']
# Set values for those without match to -999
pmindx = data['PMMATCH'] == 1
data['PLX'][True ^ pmindx] = -9999.99
data['PMRA'][True ^ pmindx] = -9999.99
data['PMDEC'][True ^ pmindx] = -9999.99
data['PLX_ERR'][True ^ pmindx] = -9999.99
data['PMRA_ERR'][True ^ pmindx] = -9999.99
data['PMDEC_ERR'][True ^ pmindx] = -9999.99
#Calculate Galactocentric velocities
data = esutil.numpy_util.add_fields(data, [('GALVR', numpy.float),
('GALVT', numpy.float),
('GALVZ', numpy.float)])
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
XYZ = bovy_coords.lbd_to_XYZ(lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],
data['PMDEC'],
data['RA'],
data['DEC'],
degree=True)
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:, 0],
pmllpmbb[:, 1],
lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
vRvTvZ = bovy_coords.vxvyvz_to_galcencyl(
vxvyvz[:, 0],
vxvyvz[:, 1],
vxvyvz[:, 2],
8. - XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2] + 0.0208,
vsun=[-11.1, 30.24 * 8.15, 7.25]
) #Assumes proper motion of Sgr A* and R0=8.15 kpc, zo= 20.8 pc (Bennett & Bovy 2019)
data['GALVR'] = vRvTvZ[:, 0]
data['GALVT'] = vRvTvZ[:, 1]
data['GALVZ'] = vRvTvZ[:, 2]
data['GALVR'][True ^ pmindx] = -9999.99
data['GALVT'][True ^ pmindx] = -9999.99
data['GALVZ'][True ^ pmindx] = -9999.99
return data
def dat_to_galcen(
dat,
return_cov=True,
return_rphiz=True,
verbose=False,
ro=8.,
vo=220.,
zo=0.025,
keys=['ra', 'dec', 'BPG_meandist', 'pmra', 'pmdec', 'VHELIO_AVG'],
cov_keys=[
'pmra_error', 'pmdec_error', 'pmra_pmdec_corr', 'BPG_diststd',
'VERR'
],
parallax=False):
vxvv = np.dstack([dat[keys[i]] for i in range(len(keys))])[0]
ra, dec = vxvv[:, 0], vxvv[:, 1]
lb = bovy_coords.radec_to_lb(ra, dec, degree=True)
pmra, pmdec = vxvv[:, 3], vxvv[:, 4]
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra,
pmdec,
ra,
dec,
degree=True)
d, vlos = vxvv[:, 2], vxvv[:, 5]
if parallax:
d = 1. / d
rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0],
lb[:, 1],
d,
vlos,
pmllpmbb[:, 0],
pmllpmbb[:, 1],
degree=True)
vsolar = np.array([-11.1, 245.6,
7.25]) #use SBD10 vR and vZ and SGR proper motion vT
vsun = np.array([
0.,
0.,
0.,
]) + vsolar / vo
X = rectgal[:, 0] / ro
Y = rectgal[:, 1] / ro
Z = rectgal[:, 2] / ro
vx = rectgal[:, 3] / vo
vy = rectgal[:, 4] / vo
vz = rectgal[:, 5] / vo
XYZ = np.dstack([X, Y, Z])[0]
vxyz = np.dstack([vx, vy, vz])[0]
if return_rphiz:
Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Zsun=zo / ro)
vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0],
vxyz[:, 1],
vxyz[:, 2],
Rpz[:, 0],
Rpz[:, 1],
Rpz[:, 2],
vsun=vsun,
Xsun=1.,
Zsun=zo / ro,
galcen=True)
if return_cov == True:
cov_pmradec = np.array([[[
dat[cov_keys[0]][i]**2,
dat[cov_keys[2]][i] * dat[cov_keys[0]][i] * dat[cov_keys[1]][i]
],
[
dat[cov_keys[2]][i] *
dat[cov_keys[0]][i] * dat[cov_keys[1]][i],
dat[cov_keys[1]][i]**2
]] for i in range(len(dat))])
if verbose:
print('propagating covariance in pmra pmdec -> pmll pmbb')
cov_pmllbb = bovy_coords.cov_pmrapmdec_to_pmllpmbb(cov_pmradec,
vxvv[:, 0],
vxvv[:, 1],
degree=True,
epoch='J2015')
if verbose:
print('propagating covariance in pmll pmbb -> vx vy vz')
cov_vxyz = bovy_coords.cov_dvrpmllbb_to_vxyz(vxvv[:,
2], dat[cov_keys[3]],
dat[cov_keys[4]],
pmllpmbb[:,
0], pmllpmbb[:,
1],
cov_pmllbb, lb[:,
0], lb[:,
1])
if not return_rphiz:
return XYZ, vxyz, cov_vxyz
if verbose:
print('propagating covariance in vx vy vz -> vR vT vz')
cov_galcencyl = bovy_coords.cov_vxyz_to_galcencyl(cov_vxyz,
Rpz[:, 1],
Xsun=1.,
Zsun=zo / ro)
return XYZ, vxyz, cov_vxyz, Rpz, vRvTvz, cov_galcencyl
if not return_rphiz:
return XYZ, vxyz
return XYZ, vxyz, Rpz, vRvTvz
def radec_to_lb(ra,dec):
val = bovy_coords.radec_to_lb(ra,dec)
return val
def plot_distsystematic(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=options.snmin)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=options.snmin)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=options.snmin)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=options.snmin)
if not options.bmin is None:
#Cut on |b|
raw= raw[(numpy.fabs(raw.b) > options.bmin)]
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
else:
tightbinned= binned
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))+numpy.nan
#Run through the bins
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
if len(data) < options.minndata:
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
break
continue
#Create XYZ and R, vxvyvz, cov_vxvyvz
R= ((8.-data.xc)**2.+data.yc**2.)**0.5
#Confine to R-range?
if not options.rmin is None and not options.rmax is None:
dataindx= (R >= options.rmin)*\
(R < options.rmax)
data= data[dataindx]
R= R[dataindx]
XYZ= numpy.zeros((len(data),3))
XYZ[:,0]= data.xc
XYZ[:,1]= data.yc
XYZ[:,2]= data.zc+_ZSUN
vxvyvz= numpy.zeros((len(data),3))
vxvyvz[:,0]= data.vxc
vxvyvz[:,1]= data.vyc
vxvyvz[:,2]= data.vzc
"""
cov_vxvyvz= numpy.zeros((len(data),3,3))
cov_vxvyvz[:,0,0]= data.vxc_err**2.
cov_vxvyvz[:,1,1]= data.vyc_err**2.
cov_vxvyvz[:,2,2]= data.vzc_err**2.
cov_vxvyvz[:,0,1]= data.vxvyc_rho*data.vxc_err*data.vyc_err
cov_vxvyvz[:,0,2]= data.vxvzc_rho*data.vxc_err*data.vzc_err
cov_vxvyvz[:,1,2]= data.vyvzc_rho*data.vyc_err*data.vzc_err
"""
cosphi= (8.-XYZ[:,0])/R
sinphi= XYZ[:,1]/R
sinbeta= XYZ[:,2]/numpy.sqrt(R*R+XYZ[:,2]*XYZ[:,2])
cosbeta= R/numpy.sqrt(R*R+XYZ[:,2]*XYZ[:,2])
ndata= len(data.ra)
cov_pmradec= numpy.zeros((ndata,2,2))
cov_pmradec[:,0,0]= data.pmra_err**2.
cov_pmradec[:,1,1]= data.pmdec_err**2.
cov_pmllbb= bovy_coords.cov_pmrapmdec_to_pmllpmbb(cov_pmradec,data.ra,
data.dec,degree=True)
"""
vR= -vxvyvz[:,0]*cosphi+vxvyvz[:,1]*sinphi
vT= vxvyvz[:,0]*sinphi+vxvyvz[:,1]*cosphi
vz= vxvyvz[:,2]
vxvyvz[:,0]= vR
vxvyvz[:,1]= vT
for rr in range(len(XYZ[:,0])):
rot= numpy.array([[cosphi[rr],sinphi[rr]],
[-sinphi[rr],cosphi[rr]]])
sxy= cov_vxvyvz[rr,0:2,0:2]
sRT= numpy.dot(rot,numpy.dot(sxy,rot.T))
cov_vxvyvz[rr,0:2,0:2]= sRT
"""
#calculate x and y
lb= bovy_coords.radec_to_lb(data.ra,data.dec,degree=True)
lb*= _DEGTORAD
tuu= 1.-numpy.cos(lb[:,1])**2.*numpy.cos(lb[:,0])**2.
tuv= -0.5*numpy.cos(lb[:,1])**2.*numpy.sin(2.*lb[:,0])
tuw= -0.5*numpy.cos(lb[:,0])*numpy.sin(2.*lb[:,1])
tvv= 1.-numpy.cos(lb[:,1])**2.*numpy.sin(lb[:,0])**2.
tvw= -0.5*numpy.sin(2.*lb[:,1])*numpy.sin(lb[:,0])
tww= numpy.cos(lb[:,1])**2.
#x= tuu*_VRSUN+tuv*vxvyvz[:,1]+tuw*vxvyvz[:,2]
#y= -tww*_VZSUN+tuw*vxvyvz[:,0]+tvw*vxvyvz[:,1]
x= -tuu*numpy.mean(vxvyvz[:,0])+tuv*vxvyvz[:,1]+tuw*vxvyvz[:,2]
y= -tww*numpy.mean(vxvyvz[:,2])+tuw*vxvyvz[:,0]+tvw*vxvyvz[:,1]
if options.type.lower() == 'u':
corcorr=0.
plotthis[ii,jj]= (numpy.mean(vxvyvz[:,0]*x)-numpy.mean(vxvyvz[:,0])*numpy.mean(x))/(numpy.var(x)+numpy.mean(tuv**2.+tuw**2.)*numpy.var(vxvyvz[:,0]))
elif options.type.lower() == 'meanu':
plotthis[ii,jj]= numpy.mean(vxvyvz[:,0])
elif options.type.lower() == 'meanw':
plotthis[ii,jj]= numpy.mean(vxvyvz[:,2])
else:
corcorr= 0.25*numpy.mean(2.*sinbeta*cosbeta*numpy.sin(2.*lb[:,1])*numpy.cos(lb[:,0])*cosphi)*(numpy.var(vxvyvz[:,0])-numpy.var(vxvyvz[:,2]))\
-0.25*numpy.mean(2.*sinbeta*cosbeta*numpy.sin(2.*lb[:,1])*numpy.sin(lb[:,0])*sinphi)*(numpy.var(vxvyvz[:,0])-numpy.var(vxvyvz[:,2]))\
+0.25*numpy.mean(numpy.sin(lb[:,1])**2.*(cov_pmllbb[:,1,1]*data.dist**2.*4.74**2.-data.vr_err**2.))
plotthis[ii,jj]= (numpy.mean(vxvyvz[:,2]*y)-numpy.mean(vxvyvz[:,2])*numpy.mean(y)-corcorr)/(numpy.var(y)+numpy.mean(tvw**2.+tuw**2.)*numpy.var(vxvyvz[:,2]))
#print ii, jj, plotthis[ii,jj], corcorr, numpy.mean(vxvyvz[:,2]*y)-numpy.mean(vxvyvz[:,2])*numpy.mean(y)-corcorr
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
if jj == 0: #this means we've reset the counter
break
#print plotthis
#Set up plot
if options.type.lower() == 'meanu':
vmin, vmax= -20.,20.
zlabel=r'$\mathrm{mean}\ U$'
elif options.type.lower() == 'meanw':
vmin, vmax= -20.,20.
zlabel=r'$\mathrm{mean}\ W$'
else:
vmin, vmax= -0.2,0.2
zlabel=r'$\mathrm{fractional\ distance\ overestimate}$'
if options.tighten:
xrange=[-1.6,0.5]
yrange=[-0.05,0.55]
else:
xrange=[-2.,0.5]
yrange=[-0.2,0.6]
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
zlabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
contours=False,
colorbar=True,shrink=0.78)
bovy_plot.bovy_text(r'$\mathrm{median} = %.2f$' % (numpy.median(plotthis[numpy.isfinite(plotthis)])),
bottom_left=True,size=14.)
bovy_plot.bovy_end_print(options.outfilename)
return None
def __init__(self,vxvv=None,uvw=False,lb=False,
radec=False,vo=235.,ro=8.5,zo=0.025,
solarmotion='hogg'):
"""
NAME:
__init__
PURPOSE:
Initialize an Orbit instance
INPUT:
vxvv - initial conditions
3D can be either
1) in Galactocentric cylindrical coordinates [R,vR,vT(,z,vz,phi)]
2) [ra,dec,d,mu_ra, mu_dec,vlos] in [deg,deg,kpc,mas/yr,mas/yr,km/s] (all J2000.0; mu_ra = mu_ra * cos dec)
3) [ra,dec,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms]
4) (l,b,d,mu_l, mu_b, vlos) in [deg,deg,kpc,mas/yr,mas/yr,km/s) (all J2000.0; mu_l = mu_l * cos b)
5) [l,b,d,U,V,W] in [deg,deg,kpc,km/s,km/s,kms]
4) and 5) also work when leaving out b and mu_b/W
OPTIONAL INPUTS:
radec - if True, input is 2) (or 3) above
uvw - if True, velocities are UVW
lb - if True, input is 4) or 5) above
vo - circular velocity at ro
ro - distance from vantage point to GC (kpc)
zo - offset toward the NGP of the Sun wrt the plane (kpc)
solarmotion - 'hogg' or 'dehnen', or 'schoenrich', or value in
[-U,V,W]
OUTPUT:
instance
HISTORY:
2010-07-20 - Written - Bovy (NYU)
"""
if isinstance(solarmotion,str) and solarmotion.lower() == 'hogg':
vsolar= nu.array([-10.1,4.0,6.7])/vo
elif isinstance(solarmotion,str) and solarmotion.lower() == 'dehnen':
vsolar= nu.array([-10.,5.25,7.17])/vo
elif isinstance(solarmotion,str) \
and solarmotion.lower() == 'schoenrich':
vsolar= nu.array([-11.1,12.24,7.25])/vo
else:
vsolar= nu.array(solarmotion)/vo
if radec or lb:
if radec:
l,b= coords.radec_to_lb(vxvv[0],vxvv[1],degree=True)
elif len(vxvv) == 4:
l, b= vxvv[0], 0.
else:
l,b= vxvv[0],vxvv[1]
if uvw:
X,Y,Z= coords.lbd_to_XYZ(l,b,vxvv[2],degree=True)
vx= vxvv[3]
vy= vxvv[4]
vz= vxvv[5]
else:
if radec:
pmll, pmbb= coords.pmrapmdec_to_pmllpmbb(vxvv[3],vxvv[4],
vxvv[0],vxvv[1],
degree=True)
d, vlos= vxvv[2], vxvv[5]
elif len(vxvv) == 4:
pmll, pmbb= vxvv[2], 0.
d, vlos= vxvv[1], vxvv[3]
else:
pmll, pmbb= vxvv[3], vxvv[4]
d, vlos= vxvv[2], vxvv[5]
X,Y,Z,vx,vy,vz= coords.sphergal_to_rectgal(l,b,d,
vlos,pmll, pmbb,
degree=True)
X/= ro
Y/= ro
Z/= ro
vx/= vo
vy/= vo
vz/= vo
vsun= nu.array([0.,1.,0.,])+vsolar
R, phi, z= coords.XYZ_to_galcencyl(X,Y,Z,Zsun=zo/ro)
vR, vT,vz= coords.vxvyvz_to_galcencyl(vx,vy,vz,
R,phi,z,
vsun=vsun,galcen=True)
if lb and len(vxvv) == 4: vxvv= [R,vR,vT,phi]
else: vxvv= [R,vR,vT,z,vz,phi]
self.vxvv= vxvv
if len(vxvv) == 2:
self._orb= linearOrbit(vxvv=vxvv)
elif len(vxvv) == 3:
self._orb= planarROrbit(vxvv=vxvv)
elif len(vxvv) == 4:
self._orb= planarOrbit(vxvv=vxvv)
elif len(vxvv) == 5:
self._orb= RZOrbit(vxvv=vxvv)
elif len(vxvv) == 6:
self._orb= FullOrbit(vxvv=vxvv)
def calc_eccentricity(args, options):
table = os.path.join(args[0], 'table2.dat')
readme = os.path.join(args[0], 'ReadMe')
dierickx = ascii.read(table, readme=readme)
vxvv = np.dstack([
dierickx['RAdeg'], dierickx['DEdeg'], dierickx['Dist'] / 1e3,
dierickx['pmRA'], dierickx['pmDE'], dierickx['HRV']
])[0]
ro, vo, zo = 8., 220., 0.025
ra, dec = vxvv[:, 0], vxvv[:, 1]
lb = bovy_coords.radec_to_lb(ra, dec, degree=True)
pmra, pmdec = vxvv[:, 3], vxvv[:, 4]
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra,
pmdec,
ra,
dec,
degree=True)
d, vlos = vxvv[:, 2], vxvv[:, 5]
rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0],
lb[:, 1],
d,
vlos,
pmllpmbb[:, 0],
pmllpmbb[:, 1],
degree=True)
vsolar = np.array([-10.1, 4.0, 6.7])
vsun = np.array([
0.,
1.,
0.,
]) + vsolar / vo
X = rectgal[:, 0] / ro
Y = rectgal[:, 1] / ro
Z = rectgal[:, 2] / ro
vx = rectgal[:, 3] / vo
vy = rectgal[:, 4] / vo
vz = rectgal[:, 5] / vo
vsun = np.array([
0.,
1.,
0.,
]) + vsolar / vo
Rphiz = bovy_coords.XYZ_to_galcencyl(X, Y, Z, Zsun=zo / ro)
vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vx,
vy,
vz,
Rphiz[:, 0],
Rphiz[:, 1],
Rphiz[:, 2],
vsun=vsun,
Xsun=1.,
Zsun=zo / ro,
galcen=True)
#do the integration and individual analytic estimate for each object
ts = np.linspace(0., 20., 10000)
lp = LogarithmicHaloPotential(normalize=1.)
e_ana = numpy.zeros(len(vxvv))
e_int = numpy.zeros(len(vxvv))
print(
'Performing orbit integration and analytic parameter estimates for Dierickx et al. sample...'
)
for i in tqdm(range(len(vxvv))):
try:
orbit = Orbit(vxvv[i], radec=True, vo=220., ro=8.)
e_ana[i] = orbit.e(analytic=True, pot=lp, c=True)
except UnboundError:
e_ana[i] = np.nan
orbit.integrate(ts, lp)
e_int[i] = orbit.e(analytic=False)
fig = plt.figure()
fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth)
plt.scatter(e_int, e_ana, s=1, color='Black', lw=0.)
plt.xlabel(r'$\mathrm{galpy\ integrated}\ e$')
plt.ylabel(r'$\mathrm{galpy\ analytic}\ e$')
plt.xlim(0., 1.)
plt.ylim(0., 1.)
fig.tight_layout()
plt.savefig(os.path.join(args[0], 'dierickx-integratedeanalytice.png'),
format='png',
dpi=200)
fig = plt.figure()
fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth)
plt.hist(e_int, bins=30)
plt.xlim(0., 1.)
plt.xlabel(r'$\mathrm{galpy}\ e$')
fig.tight_layout()
plt.savefig(os.path.join(args[0], 'dierickx-integratedehist.png'),
format='png',
dpi=200)
fig = plt.figure()
fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth)
plt.scatter(dierickx['e'], e_int, s=1, color='Black', lw=0.)
plt.xlabel(r'$\mathrm{Dierickx\ et\ al.}\ e$')
plt.ylabel(r'$\mathrm{galpy\ integrated}\ e$')
plt.xlim(0., 1.)
plt.ylim(0., 1.)
fig.tight_layout()
plt.savefig(os.path.join(args[0], 'dierickx-integratedee.png'),
format='png',
dpi=200)
fig = plt.figure()
fig.set_size_inches(1.5 * columnwidth, 1.5 * columnwidth)
plt.scatter(dierickx['e'], e_ana, s=1, color='Black', lw=0.)
plt.xlabel(r'$\mathrm{Dierickx\ et\ al.}\ e$')
plt.ylabel(r'$\mathrm{galpy\ estimated}\ e$')
plt.xlim(0., 1.)
plt.ylim(0., 1.)
fig.tight_layout()
plt.savefig(os.path.join(args[0], 'dierickx-analyticee.png'),
format='png',
dpi=200)
arr = numpy.recarray(len(e_ana),
dtype=[('analytic_e', float),
('integrated_e', float)])
arr['analytic_e'] = e_ana
arr['integrated_e'] = e_int
with open(os.path.join(args[0], 'eccentricities.dat'), 'w') as file:
pickle.dump(arr, file)
file.close()
rdata[:, 2] = rdata[:, 2] * 1000.0 rdata[:, 8] = rdata[:, 8] * 1000.0 rdata[:, 14] = rdata[:, 14] * 1000.0 # arcsec/yr -> mas/yr rdata[:, 3] = rdata[:, 3] * 1000.0 rdata[:, 4] = rdata[:, 4] * 1000.0 rdata[:, 9] = rdata[:, 9] * 1000.0 rdata[:, 10] = rdata[:, 10] * 1000.0 rdata[:, 15] = rdata[:, 15] * 1000.0 rdata[:, 16] = rdata[:, 16] * 1000.0 # Galactic coordinates # True l and b RA_true = rdata[:, 0] DEC_true = rdata[:, 1] Tllbb = bovy_coords.radec_to_lb(RA_true, DEC_true, degree=True, epoch=2000.0) GLON_true = Tllbb[:, 0] GLAT_true = Tllbb[:, 1] # True pmGLON, pmGLAT pmRA_true = rdata[:, 3] pmDEC_true = rdata[:, 4] Tpmllbb=bovy_coords.pmrapmdec_to_pmllpmbb(pmRA_true,pmDEC_true \ ,RA_true,DEC_true,degree=True,epoch=2000.0) pmGLON_true = Tpmllbb[:, 0] pmGLAT_true = Tpmllbb[:, 1] # observed RA_obs = rdata[:, 6] DEC_obs = rdata[:, 7] pmRA_obs = rdata[:, 9] pmDEC_obs = rdata[:, 10] Tpmllbb=bovy_coords.pmrapmdec_to_pmllpmbb(pmRA_obs,pmDEC_obs \
def fakeDensData(parser):
numpy.random.seed(1)
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
#Set up density model
if options.model.lower() == 'hwr':
densfunc= _HWRDensity
if options.metal.lower() == 'rich':
params= numpy.array([-1.34316986e+00,1.75402412e+00,5.14667706e-04])
else:
params= numpy.array([-0.3508148171668,0.65752,0.00206572947631])
elif options.model.lower() == 'flare':
densfunc= _FlareDensity
if options.metal.lower() == 'rich':
params= numpy.log(numpy.array([0.3,2.5,2.5]))
else:
params= numpy.log(numpy.array([0.3,2.5,2.5]))
elif options.model.lower() == 'tiedflare':
densfunc= _TiedFlareDensity
if options.metal.lower() == 'rich':
params= numpy.log(numpy.array([0.3,2.5]))
else:
params= numpy.log(numpy.array([0.3,2.5]))
elif options.model.lower() == 'twovertical':
densfunc= _TwoVerticalDensity
if options.metal.lower() == 'rich':
params= numpy.log(numpy.array([0.3,0.8,2.5,1.05]))
else:
params= numpy.log(numpy.array([0.3,0.8,2.5,1.05]))
#Data
XYZ,vxvyvz,cov_vxvyvz,rawdata= readData(metal=options.metal,
sample=options.sample)
if options.metal.lower() == 'rich':
feh= -0.15
fehrange= [-0.4,0.5]
elif options.metal.lower() == 'poor':
feh= -0.65
fehrange= [-1.5,-0.5]
else:
feh= -0.5
#Load model distributions
if options.sample.lower() == 'g':
colorrange=[0.48,0.55]
elif options.sample.lower() == 'k':
colorrange=[0.55,0.75]
#FeH
fehdist= DistSpline(*numpy.histogram(rawdata.feh,bins=11,range=fehrange),
xrange=fehrange)
#Color
colordist= DistSpline(*numpy.histogram(rawdata.dered_g-rawdata.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Load selection function
sf= segueSelect.segueSelect(type_bright=options.sel_bright,
type_faint=options.sel_faint,
sample=options.sample)
platelb= bovy_coords.radec_to_lb(sf.platestr.ra,sf.platestr.dec,
degree=True)
if options.sample.lower() == 'g':
rmin, rmax= 14.5, 20.2
grmin, grmax= 0.48, 0.55
elif options.sample.lower() == 'k':
rmin, rmax= 14.5, 19.
grmin, grmax= 0.55, 0.75
#Calculate the r-distribution for each plate
nrs= 1001
ngr, nfeh= 21, 21
grs= numpy.linspace(grmin,grmax,ngr)
fehs= numpy.linspace(fehrange[0],fehrange[1],nfeh)
#Calcuate FeH and gr distriutions
fehdists= numpy.zeros(nfeh)
for ii in range(nfeh): fehdists[ii]= fehdist(fehs[ii])
fehdists= numpy.cumsum(fehdists)
fehdists/= fehdists[-1]
colordists= numpy.zeros(ngr)
for ii in range(ngr): colordists[ii]= colordist(grs[ii])
colordists= numpy.cumsum(colordists)
colordists/= colordists[-1]
rs= numpy.linspace(rmin,rmax,nrs)
rdists= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
for ii in range(len(sf.plates)):
p= sf.plates[ii]
sys.stdout.write('\r'+"Working on plate %i (%i/%i)" % (p,ii+1,len(sf.plates)))
sys.stdout.flush()
rdists[ii,:,:,:]= _predict_rdist_plate(rs,densfunc,params,rmin,rmax,
platelb[ii,0],platelb[ii,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[ii],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
sys.stdout.write('\r'+segueSelect._ERASESTR+'\r')
sys.stdout.flush()
numbers= numpy.sum(rdists,axis=3)
numbers= numpy.sum(numbers,axis=2)
numbers= numpy.sum(numbers,axis=1)
numbers= numpy.cumsum(numbers)
numbers/= numbers[-1]
rdists= numpy.cumsum(rdists,axis=1)
for ii in range(len(sf.plates)):
for jj in range(ngr):
for kk in range(nfeh):
rdists[ii,:,jj,kk]/= rdists[ii,-1,jj,kk]
#Now sample until we're done
out= []
while len(out) < options.nsamples:
#First sample a plate
ran= numpy.random.uniform()
kk= 0
while numbers[kk] < ran: kk+= 1
#Also sample a Feh and a color
ran= numpy.random.uniform()
ff= 0
while fehdists[ff] < ran: ff+= 1
ran= numpy.random.uniform()
cc= 0
while colordists[cc] < ran: cc+= 1
#plate==kk, feh=ff,color=cc; now sample from the rdist of this plate
ran= numpy.random.uniform()
jj= 0
while rdists[kk,jj,cc,ff] < ran: jj+= 1
#r=jj
out.append([rs[jj],grs[cc],fehs[ff],platelb[kk,0],platelb[kk,1],
sf.plates[kk]])
#Save as pickle
savefile= open(args[0],'wb')
pickle.dump(out,savefile)
savefile.close()
