def sky_coords(cluster):
"""Get the sky coordinates of every star in the cluster
Parameters
----------
cluster : class
StarCluster
Returns
-------
ra,dec,d0,pmra,pmdec,vr0 : float
on-sky positions and velocities of cluster stars
History
-------
2018 - Written - Webb (UofT)
"""
origin0 = cluster.origin
if origin0 != "galaxy":
cluster.to_galaxy()
x0, y0, z0 = bovy_coords.galcenrect_to_XYZ(cluster.x,
cluster.y,
cluster.z,
Xsun=8.0,
Zsun=0.025).T
vx0, vy0, vz0 = bovy_coords.galcenrect_to_vxvyvz(
cluster.vx,
cluster.vy,
cluster.vz,
Xsun=8.0,
Zsun=0.025,
vsun=[-11.1, 244.0, 7.25],
).T
l0, b0, d0 = bovy_coords.XYZ_to_lbd(x0, y0, z0, degree=True).T
ra, dec = bovy_coords.lb_to_radec(l0, b0, degree=True).T
vr0, pmll0, pmbb0 = bovy_coords.vxvyvz_to_vrpmllpmbb(vx0,
vy0,
vz0,
l0,
b0,
d0,
degree=True).T
pmra, pmdec = bovy_coords.pmllpmbb_to_pmrapmdec(pmll0,
pmbb0,
l0,
b0,
degree=True).T
if origin0 == "centre":
cluster.to_centre()
elif origin0 == "cluster":
cluster.to_cluster()
return ra, dec, d0, pmra, pmdec, vr0
def sky_coords(cluster):
"""
NAME:
sky_coords
PURPOSE:
Get the sky coordinates of every star in the cluster
INPUT:
cluster - a StarCluster-class object
OUTPUT:
ra,dec,d0,pmra,pmdec,vr0
HISTORY:
2018 - Written - Webb (UofT)
"""
origin0 = cluster.origin
if origin0 != "galaxy":
cluster.to_galaxy()
x0, y0, z0 = bovy_coords.galcenrect_to_XYZ(
cluster.x, cluster.y, cluster.z, Xsun=8.0, Zsun=0.025
).T
vx0, vy0, vz0 = bovy_coords.galcenrect_to_vxvyvz(
cluster.vx,
cluster.vy,
cluster.vz,
Xsun=8.0,
Zsun=0.025,
vsun=[-11.1, 244.0, 7.25],
).T
l0, b0, d0 = bovy_coords.XYZ_to_lbd(x0, y0, z0, degree=True).T
ra, dec = bovy_coords.lb_to_radec(l0, b0, degree=True).T
vr0, pmll0, pmbb0 = bovy_coords.vxvyvz_to_vrpmllpmbb(
vx0, vy0, vz0, l0, b0, d0, degree=True
).T
pmra, pmdec = bovy_coords.pmllpmbb_to_pmrapmdec(pmll0, pmbb0, l0, b0, degree=True).T
if origin0 == "centre":
cluster.to_centre()
elif origin0 == "cluster":
cluster.to_cluster()
return ra, dec, d0, pmra, pmdec, vr0
def test_vxvyvz_to_vrpmllpmbb():
vx,vy,vz= -20.*4.74047,10.,-10.*4.74047
X,Y,Z= 0.,1.,0.
vrpmllpmbb= bovy_coords.vxvyvz_to_vrpmllpmbb(vx,vy,vz,X,Y,Z,
XYZ=True)
assert numpy.fabs(vrpmllpmbb[0]-10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[1]-20.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[2]+10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
# also try with degree=True (that shouldn't fail!)
vrpmllpmbb= bovy_coords.vxvyvz_to_vrpmllpmbb(vx,vy,vz,X,Y,Z,
XYZ=True,
degree=True)
assert numpy.fabs(vrpmllpmbb[0]-10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[1]-20.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[2]+10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
# also for lbd
vrpmllpmbb= bovy_coords.vxvyvz_to_vrpmllpmbb(vx,vy,vz,90.,0.,1.,
XYZ=False,degree=True)
assert numpy.fabs(vrpmllpmbb[0]-10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[1]-20.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[2]+10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
# also for lbd, not in degree
vrpmllpmbb= bovy_coords.vxvyvz_to_vrpmllpmbb(vx,vy,vz,numpy.pi/2.,0.,1.,
XYZ=False,degree=False)
assert numpy.fabs(vrpmllpmbb[0]-10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[1]-20.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.fabs(vrpmllpmbb[2]+10.) < 10.**-10., 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
# and for arrays
os= numpy.ones(2)
vrpmllpmbb= bovy_coords.vxvyvz_to_vrpmllpmbb(os*vx,os*vy,os*vz,
os*numpy.pi/2.,os*0.,os,
XYZ=False,degree=False)
assert numpy.all(numpy.fabs(vrpmllpmbb[:,0]-10.) < 10.**-10.), 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.all(numpy.fabs(vrpmllpmbb[:,1]-20.) < 10.**-10.), 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
assert numpy.all(numpy.fabs(vrpmllpmbb[:,2]+10.) < 10.**-10.), 'vxvyvz_to_vrpmllpmbb conversion did not work as expected'
return None
def xyzuvw_to_skycoord(xyzuvw_in, solarmotion='schoenrich', reverse_x_sign=True):
"""Converts XYZUVW with respect to the LSR or the sun
to RAdeg, DEdeg, plx, pmra, pmdec, RV
Parameters
----------
xyzuvw_in:
XYZUVW with respect to the LSR.
solarmotion: string
The reference of assumed solar motion. "schoenrich" or None if inputs are already
relative to the sun.
reverse_x_sign: bool
Do we reverse the sign of the X co-ordinate? This is needed for dealing with
galpy sign conventions.
TODO: not working at all... fix this
"""
if solarmotion==None:
xyzuvw_sun = np.zeros(6)
elif solarmotion=='schoenrich':
xyzuvw_sun = [0,0,25,11.1,12.24,7.25]
else:
raise UserWarning
#Make coordinates relative to sun
xyzuvw = xyzuvw_in - xyzuvw_sun
#Special for the sun itself...
#FIXME: This seems like a hack.
#if (np.sum(xyzuvw**2) < 1) and solarmotion != None:
# return [0,0,1e5, 0,0,0]
#Find l, b and distance.
#!!! WARNING: the X-coordinate may have to be reversed here, just like everywhere else,
#because of the convention in Orbit.x(), which doesn't seem to match X.
if reverse_x_sign:
lbd = bovy_coords.XYZ_to_lbd(-xyzuvw[0]/1e3, xyzuvw[1]/1e3, xyzuvw[2]/1e3, degree=True)
else:
lbd = bovy_coords.XYZ_to_lbd(xyzuvw[0]/1e3, xyzuvw[1]/1e3, xyzuvw[2]/1e3, degree=True)
radec = bovy_coords.lb_to_radec(lbd[0], lbd[1], degree=True)
vrpmllpmbb = bovy_coords.vxvyvz_to_vrpmllpmbb(xyzuvw[3],xyzuvw[4], xyzuvw[5],\
lbd[0],lbd[1],lbd[2], degree=True)
pmrapmdec = bovy_coords.pmllpmbb_to_pmrapmdec(vrpmllpmbb[1],vrpmllpmbb[2],lbd[0],lbd[1],degree=True)
return [radec[0], radec[1], 1.0/lbd[2], pmrapmdec[0], pmrapmdec[1], vrpmllpmbb[0]]
def convertHelioCentricToRADEC(xyzuvw_hc, kpc=False):
"""
Generate astrometry values from cartesian coordinates centred on sun
Parameters
----------
xyzuvw_hc : [6] array
[kpc, kpc, kpc, km/s, km/s, km/s]
Returns
-------
astrometry : [6] array
[RA, DEC, pi, pm_ra, pm_dec, vr]
"""
# if not kpc:
# xyzuvw_hc = xyzuvw_hc.copy()
# xyzuvw_hc[:3] /= 1e3
logging.debug("Positions is: {}".format(xyzuvw_hc[:3]))
logging.debug("Velocity is: {}".format(xyzuvw_hc[3:]))
lbdist = convertHelioCentricTolbdist(xyzuvw_hc)
radec = bovy_coords.lb_to_radec(lbdist[0], lbdist[1], degree=True)
vrpmllpmbb = bovy_coords.vxvyvz_to_vrpmllpmbb(xyzuvw_hc[3],
xyzuvw_hc[4],
xyzuvw_hc[5],
lbdist[0],
lbdist[1],
lbdist[2],
degree=True)
pmrapmdec = bovy_coords.pmllpmbb_to_pmrapmdec(vrpmllpmbb[1],
vrpmllpmbb[2],
lbdist[0],
lbdist[1],
degree=True)
return [
radec[0], radec[1], 1.0 / lbdist[2], pmrapmdec[0], pmrapmdec[1],
vrpmllpmbb[0]
]
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 xyzuvw_to_skycoord(xyzuvw_in,
solarmotion='schoenrich',
reverse_x_sign=True):
"""Converts XYZUVW with respect to the LSR or the sun
to RAdeg, DEdeg, plx, pmra, pmdec, RV
Parameters
----------
xyzuvw_in:
XYZUVW with respect to the LSR.
solarmotion: string
The reference of assumed solar motion. "schoenrich" or None if inputs are already
relative to the sun.
reverse_x_sign: bool
Do we reverse the sign of the X co-ordinate? This is needed for dealing with
galpy sign conventions.
TODO: not working at all... fix this
"""
if solarmotion == None:
xyzuvw_sun = np.zeros(6)
elif solarmotion == 'schoenrich':
xyzuvw_sun = [0, 0, 25, 11.1, 12.24, 7.25]
else:
raise UserWarning
#Make coordinates relative to sun
xyzuvw = xyzuvw_in - xyzuvw_sun
#Special for the sun itself...
#FIXME: This seems like a hack.
#if (np.sum(xyzuvw**2) < 1) and solarmotion != None:
# return [0,0,1e5, 0,0,0]
#Find l, b and distance.
#!!! WARNING: the X-coordinate may have to be reversed here, just like everywhere else,
#because of the convention in Orbit.x(), which doesn't seem to match X.
if reverse_x_sign:
lbd = bovy_coords.XYZ_to_lbd(-xyzuvw[0] / 1e3,
xyzuvw[1] / 1e3,
xyzuvw[2] / 1e3,
degree=True)
else:
lbd = bovy_coords.XYZ_to_lbd(xyzuvw[0] / 1e3,
xyzuvw[1] / 1e3,
xyzuvw[2] / 1e3,
degree=True)
radec = bovy_coords.lb_to_radec(lbd[0], lbd[1], degree=True)
vrpmllpmbb = bovy_coords.vxvyvz_to_vrpmllpmbb(xyzuvw[3],xyzuvw[4], xyzuvw[5],\
lbd[0],lbd[1],lbd[2], degree=True)
pmrapmdec = bovy_coords.pmllpmbb_to_pmrapmdec(vrpmllpmbb[1],
vrpmllpmbb[2],
lbd[0],
lbd[1],
degree=True)
return [
radec[0], radec[1], 1.0 / lbd[2], pmrapmdec[0], pmrapmdec[1],
vrpmllpmbb[0]
]
def fakeDFData(binned,qdf,ii,params,fehs,afes,options,
rmin,rmax,
platelb,
grmin,grmax,
fehrange,
colordist,
fehdist,feh,sf,
mapfehs,mapafes,
ro=None,vo=None,
ndata=None,#If set, supersedes binned, only to be used w/ returnlist=True
returnlist=False): #last one useful for pixelFitDF normintstuff
if ro is None:
ro= get_ro(params,options)
if vo is None:
vo= get_vo(params,options,len(fehs))
thishr= qdf.estimate_hr(1.,z=0.125)*_REFR0*ro #qdf._hr*_REFR0*ro
thishz= qdf.estimate_hz(1.,z=0.125)*_REFR0*ro
if thishr < 0.: thishr= 10. #Probably close to flat
if thishz < 0.1: thishz= 0.2
thissr= qdf._sr*_REFV0*vo
thissz= qdf._sz*_REFV0*vo
thishsr= qdf._hsr*_REFR0*ro
thishsz= qdf._hsz*_REFR0*ro
if True:
if options.aAmethod.lower() == 'staeckel':
#Make everything 10% larger
thishr*= 1.2
thishz*= 1.2
thishsr*= 1.2
thishsz*= 1.2
thissr*= 2.
thissz*= 2.
else:
#Make everything 20% larger
thishr*= 1.2
thishz*= 1.2
thishsr*= 1.2
thishsz*= 1.2
thissr*= 2.
thissz*= 2.
#Find nearest mono-abundance bin that has a measurement
abindx= numpy.argmin((fehs[ii]-mapfehs)**2./0.01 \
+(afes[ii]-mapafes)**2./0.0025)
#Calculate the r-distribution for each plate
nrs= 1001
ngr, nfeh= 11, 11 #BOVY: INCREASE?
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]
rs= numpy.linspace(rmin,rmax,nrs)
rdists= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
#outlier model that we want to sample (not the one to aid in the sampling)
srhalo= _SRHALO/vo/_REFV0
sphihalo= _SPHIHALO/vo/_REFV0
szhalo= _SZHALO/vo/_REFV0
logoutfrac= numpy.log(get_outfrac(params,ii,options))
loghalodens= numpy.log(ro*outDens(1.,0.,None))
#Calculate surface(R=1.) for relative outlier normalization
logoutfrac+= numpy.log(qdf.surfacemass_z(1.,ngl=options.ngl))
if options.mcout:
fidoutfrac= get_outfrac(params,ii,options)
rdistsout= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
lagoutfrac= 0.15 #.0000000000000000000000001 #seems good
#Setup density model
use_real_dens= True
if use_real_dens:
#nrs, nzs= 101, 101
nrs, nzs= 64, 64
thisRmin, thisRmax= 4./_REFR0, 15./_REFR0
thiszmin, thiszmax= 0., .8
Rgrid= numpy.linspace(thisRmin,thisRmax,nrs)
zgrid= numpy.linspace(thiszmin,thiszmax,nzs)
surfgrid= numpy.empty((nrs,nzs))
for ll in range(nrs):
for jj in range(nzs):
sys.stdout.write('\r'+"Working on grid-point %i/%i" % (jj+ll*nzs+1,nzs*nrs))
sys.stdout.flush()
surfgrid[ll,jj]= qdf.density(Rgrid[ll],zgrid[jj],
nmc=options.nmcv,
ngl=options.ngl)
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
if _SURFSUBTRACTEXPON:
Rs= numpy.tile(Rgrid,(nzs,1)).T
Zs= numpy.tile(zgrid,(nrs,1))
ehr= qdf.estimate_hr(1.,z=0.125)
# ehz= qdf.estimate_hz(1.,zmin=0.5,zmax=0.7)#Get large z behavior right
ehz= qdf.estimate_hz(1.,z=0.125)
surfInterp= interpolate.RectBivariateSpline(Rgrid,zgrid,
numpy.log(surfgrid)
+Rs/ehr+numpy.fabs(Zs)/ehz,
kx=3,ky=3,
s=0.)
# s=10.*float(nzs*nrs))
else:
surfInterp= interpolate.RectBivariateSpline(Rgrid,zgrid,
numpy.log(surfgrid),
kx=3,ky=3,
s=0.)
# s=10.*float(nzs*nrs))
if _SURFSUBTRACTEXPON:
compare_func= lambda x,y,du: numpy.exp(surfInterp.ev(x/ro/_REFR0,numpy.fabs(y)/ro/_REFR0)-x/ro/_REFR0/ehr-numpy.fabs(y)/ehz/ro/_REFR0)
else:
compare_func= lambda x,y,du: numpy.exp(surfInterp.ev(x/ro/_REFR0,numpy.fabs(y)/ro/_REFR0))
else:
compare_func= lambda x,y,z: fidDens(x,y,thishr,thishz,z)
for jj in range(len(sf.plates)):
p= sf.plates[jj]
sys.stdout.write('\r'+"Working on plate %i (%i/%i)" % (p,jj+1,len(sf.plates)))
sys.stdout.flush()
rdists[jj,:,:,:]= _predict_rdist_plate(rs,
compare_func,
None,rmin,rmax,
platelb[jj,0],platelb[jj,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[jj],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
if options.mcout:
rdistsout[jj,:,:,:]= _predict_rdist_plate(rs,
lambda x,y,z: outDens(x,y,z),
None,rmin,rmax,
platelb[jj,0],platelb[jj,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[jj],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
sys.stdout.write('\r'+_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)
if options.mcout:
totfid= numbers[-1]
numbers/= numbers[-1]
rdists= numpy.cumsum(rdists,axis=1)
for ll in range(len(sf.plates)):
for jj in range(ngr):
for kk in range(nfeh):
rdists[ll,:,jj,kk]/= rdists[ll,-1,jj,kk]
if options.mcout:
numbersout= numpy.sum(rdistsout,axis=3)
numbersout= numpy.sum(numbersout,axis=2)
numbersout= numpy.sum(numbersout,axis=1)
numbersout= numpy.cumsum(numbersout)
totout= fidoutfrac*numbersout[-1]
totnumbers= totfid+totout
totfid/= totnumbers
totout/= totnumbers
if _DEBUG:
print totfid, totout
numbersout/= numbersout[-1]
rdistsout= numpy.cumsum(rdistsout,axis=1)
for ll in range(len(sf.plates)):
for jj in range(ngr):
for kk in range(nfeh):
rdistsout[ll,:,jj,kk]/= rdistsout[ll,-1,jj,kk]
#Now sample
thisout= []
newrs= []
newls= []
newbs= []
newplate= []
newgr= []
newfeh= []
newds= []
newzs= []
newvas= []
newRs= []
newphi= []
newvr= []
newvt= []
newvz= []
newlogratio= []
newfideval= []
newqdfeval= []
newpropeval= []
if ndata is None:
thisdata= binned(fehs[ii],afes[ii])
thisdataIndx= binned.callIndx(fehs[ii],afes[ii])
ndata= len(thisdata)
#First sample from spatial density
for ll in range(ndata):
#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
if options.mcout and numpy.random.uniform() < totout: #outlier
while rdistsout[kk,jj,cc,ff] < ran: jj+= 1
thisoutlier= True
else:
while rdists[kk,jj,cc,ff] < ran: jj+= 1
thisoutlier= False
#r=jj
newrs.append(rs[jj])
newls.append(platelb[kk,0])
newbs.append(platelb[kk,1])
newplate.append(sf.plates[kk])
newgr.append(tgrs[cc])
newfeh.append(tfehs[ff])
dist= _ivezic_dist(tgrs[cc],rs[jj],tfehs[ff])
newds.append(dist)
#calculate R,z
XYZ= bovy_coords.lbd_to_XYZ(platelb[kk,0],platelb[kk,1],
dist,degree=True)
R= ((_REFR0-XYZ[0])**2.+XYZ[1]**2.)**(0.5)
newRs.append(R)
phi= numpy.arcsin(XYZ[1]/R)
if (_REFR0-XYZ[0]) < 0.:
phi= numpy.pi-phi
newphi.append(phi)
z= XYZ[2]+_ZSUN
newzs.append(z)
newrs= numpy.array(newrs)
newls= numpy.array(newls)
newbs= numpy.array(newbs)
newplate= numpy.array(newplate)
newgr= numpy.array(newgr)
newfeh= numpy.array(newfeh)
newds= numpy.array(newds)
newRs= numpy.array(newRs)
newzs= numpy.array(newzs)
newphi= numpy.array(newphi)
#Add mock velocities
newvr= numpy.empty_like(newrs)
newvt= numpy.empty_like(newrs)
newvz= numpy.empty_like(newrs)
use_sampleV= True
if use_sampleV:
for kk in range(ndata):
newv= qdf.sampleV(newRs[kk]/_REFR0,newzs[kk]/_REFR0,n=1)
newvr[kk]= newv[0,0]*_REFV0*vo
newvt[kk]= newv[0,1]*_REFV0*vo
newvz[kk]= newv[0,2]*_REFV0*vo
else:
accept_v= numpy.zeros(ndata,dtype='bool')
naccept= numpy.sum(accept_v)
sigz= thissz*numpy.exp(-(newRs-_REFR0)/thishsz)
sigr= thissr*numpy.exp(-(newRs-_REFR0)/thishsr)
va= numpy.empty_like(newrs)
sigphi= numpy.empty_like(newrs)
maxqdf= numpy.empty_like(newrs)
nvt= 101
tvt= numpy.linspace(0.1,1.2,nvt)
for kk in range(ndata):
#evaluate qdf for vt
pvt= qdf(newRs[kk]/ro/_REFR0+numpy.zeros(nvt),
numpy.zeros(nvt),
tvt,
newzs[kk]/ro/_REFR0+numpy.zeros(nvt),
numpy.zeros(nvt),log=True)
pvt_maxindx= numpy.argmax(pvt)
va[kk]= (1.-tvt[pvt_maxindx])*_REFV0*vo
if options.aAmethod.lower() == 'adiabaticgrid' and options.flatten >= 0.9:
maxqdf[kk]= pvt[pvt_maxindx]+numpy.log(250.)
elif options.aAmethod.lower() == 'adiabaticgrid' and options.flatten >= 0.8:
maxqdf[kk]= pvt[pvt_maxindx]+numpy.log(250.)
else:
maxqdf[kk]= pvt[pvt_maxindx]+numpy.log(40.)
sigphi[kk]= _REFV0*vo*4.*numpy.sqrt(numpy.sum(numpy.exp(pvt)*tvt**2.)/numpy.sum(numpy.exp(pvt))-(numpy.sum(numpy.exp(pvt)*tvt)/numpy.sum(numpy.exp(pvt)))**2.)
ntries= 0
ngtr1= 0
while naccept < ndata:
sys.stdout.write('\r %i %i %i \r' % (ntries,naccept,ndata))
sys.stdout.flush()
#print ntries, naccept, ndata
ntries+= 1
accept_v_comp= True-accept_v
prop_vr= numpy.random.normal(size=ndata)*sigr
prop_vt= numpy.random.normal(size=ndata)*sigphi+vo*_REFV0-va
prop_vz= numpy.random.normal(size=ndata)*sigz
qoverp= numpy.zeros(ndata)-numpy.finfo(numpy.dtype(numpy.float64)).max
qoverp[accept_v_comp]= (qdf(newRs[accept_v_comp]/ro/_REFR0,
prop_vr[accept_v_comp]/vo/_REFV0,
prop_vt[accept_v_comp]/vo/_REFV0,
newzs[accept_v_comp]/ro/_REFR0,
prop_vz[accept_v_comp]/vo/_REFV0,log=True)
-maxqdf[accept_v_comp] #normalize max to 1
-(-0.5*(prop_vr[accept_v_comp]**2./sigr[accept_v_comp]**2.+prop_vz[accept_v_comp]**2./sigz[accept_v_comp]**2.+(prop_vt[accept_v_comp]-_REFV0*vo+va[accept_v_comp])**2./sigphi[accept_v_comp]**2.)))
if numpy.any(qoverp > 0.):
ngtr1+= numpy.sum((qoverp > 0.))
if ngtr1 > 5:
qindx= (qoverp > 0.)
print naccept, ndata, newRs[qindx], newzs[qindx], prop_vr[qindx], va[qindx], sigphi[qindx], prop_vt[qindx], prop_vz[qindx], qoverp[qindx]
raise RuntimeError("max qoverp = %f > 1, but shouldn't be" % (numpy.exp(numpy.amax(qoverp))))
accept_these= numpy.log(numpy.random.uniform(size=ndata))
#print accept_these, (accept_these < qoverp)
accept_these= (accept_these < qoverp)
newvr[accept_these]= prop_vr[accept_these]
newvt[accept_these]= prop_vt[accept_these]
newvz[accept_these]= prop_vz[accept_these]
accept_v[accept_these]= True
naccept= numpy.sum(accept_v)
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
"""
ntot= 0
nsamples= 0
itt= 0
fracsuccess= 0.
fraccomplete= 0.
while fraccomplete < 1.:
if itt == 0:
nthis= numpy.amax([ndata,_NMIN])
else:
nthis= int(numpy.ceil((1-fraccomplete)/fracsuccess*ndata))
itt+= 1
count= 0
while count < nthis:
count+= 1
sigz= thissz*numpy.exp(-(R-_REFR0)/thishsz)
sigr= thissr*numpy.exp(-(R-_REFR0)/thishsr)
sigphi= sigr #/numpy.sqrt(2.) #BOVY: FOR NOW
#Estimate asymmetric drift
va= sigr**2./2./_REFV0/vo\
*(-.5+R*(1./thishr+2./thishsr))+10.*numpy.fabs(z)
newvas.append(va)
if options.mcout and thisoutlier:
#Sample from outlier gaussian
newvz.append(numpy.random.normal()*_SZHALOFAKE*2.)
newvr.append(numpy.random.normal()*_SRHALOFAKE*2.)
newvt.append(numpy.random.normal()*_SPHIHALOFAKE*2.)
elif numpy.random.uniform() < lagoutfrac:
#Sample from lagging gaussian
newvz.append(numpy.random.normal()*_SZHALOFAKE)
newvr.append(numpy.random.normal()*_SRHALOFAKE)
newvt.append(numpy.random.normal()*_SPHIHALOFAKE*2.+_REFV0*vo/4.)
else:
#Sample from disk gaussian
newvz.append(numpy.random.normal()*sigz)
newvr.append(numpy.random.normal()*sigr)
newvt.append(numpy.random.normal()*sigphi+_REFV0*vo-va)
newlogratio= list(newlogratio)
fidlogeval= numpy.log(1.-lagoutfrac)\
-numpy.log(sigr)-numpy.log(sigphi)-numpy.log(sigz)-0.5*(newvr[-1]**2./sigr**2.+newvz[-1]**2./sigz**2.+(newvt[-1]-_REFV0*vo+va)**2./sigphi**2.)
lagoutlogeval= numpy.log(lagoutfrac)\
-numpy.log(_SRHALOFAKE)\
-numpy.log(_SPHIHALOFAKE*2.)\
-numpy.log(_SZHALOFAKE)\
-0.5*(newvr[-1]**2./_SRHALOFAKE**2.+newvz[-1]**2./_SZHALOFAKE**2.+(newvt[-1]-_REFV0*vo/4.)**2./_SPHIHALOFAKE**2./4.)
if use_real_dens:
fidlogeval+= numpy.log(compare_func(R,z,None)[0])
lagoutlogeval+= numpy.log(compare_func(R,z,None)[0])
else:
fidlogeval+= numpy.log(fidDens(R,z,thishr,thishz,None))
lagoutlogeval+= numpy.log(fidDens(R,z,thishr,thishz,None))
newfideval.append(fidlogeval)
if options.mcout:
fidoutlogeval= numpy.log(fidoutfrac)\
+numpy.log(outDens(R,z,None))\
-numpy.log(_SRHALOFAKE*2.)\
-numpy.log(_SPHIHALOFAKE*2.)\
-numpy.log(_SZHALOFAKE*2.)\
-0.5*(newvr[-1]**2./_SRHALOFAKE**2./4.+newvz[-1]**2./_SZHALOFAKE**2./4.+newvt[-1]**2./_SPHIHALOFAKE**2./4.)
newpropeval.append(logsumexp([fidoutlogeval,fidlogeval,
lagoutlogeval]))
else:
newpropeval.append(logsumexp([lagoutlogeval,fidlogeval]))
qdflogeval= qdf(R/ro/_REFR0,newvr[-1]/vo/_REFV0,newvt[-1]/vo/_REFV0,z/ro/_REFR0,newvz[-1]/vo/_REFV0,log=True)
if isinstance(qdflogeval,(list,numpy.ndarray)):
qdflogeval= qdflogeval[0]
if options.mcout:
outlogeval= logoutfrac+loghalodens\
-numpy.log(srhalo)-numpy.log(sphihalo)-numpy.log(szhalo)\
-0.5*((newvr[-1]/vo/_REFV0)**2./srhalo**2.+(newvz[-1]/vo/_REFV0)**2./szhalo**2.+(newvt[-1]/vo/_REFV0)**2./sphihalo**2.)\
-1.5*numpy.log(2.*numpy.pi)
newqdfeval.append(logsumexp([qdflogeval,outlogeval]))
else:
newqdfeval.append(qdflogeval)
newlogratio.append(qdflogeval
-newpropeval[-1])#logsumexp([fidlogeval,fidoutlogeval]))
newlogratio= numpy.array(newlogratio)
thisnewlogratio= copy.copy(newlogratio)
maxnewlogratio= numpy.amax(thisnewlogratio)
if False:
argsort_thisnewlogratio= numpy.argsort(thisnewlogratio)[::-1]
thisnewlogratio-= thisnewlogratio[argsort_thisnewlogratio[2]] #3rd largest
else:
thisnewlogratio-= numpy.amax(thisnewlogratio)
thisnewratio= numpy.exp(thisnewlogratio)
if len(thisnewratio.shape) > 1 and thisnewratio.shape[1] == 1:
thisnewratio= numpy.reshape(thisnewratio,(thisnewratio.shape[0]))
#Rejection sample
accept= numpy.random.uniform(size=len(thisnewratio))
accept= (accept < thisnewratio)
fraccomplete= float(numpy.sum(accept))/ndata
fracsuccess= float(numpy.sum(accept))/len(thisnewratio)
if _DEBUG:
print fraccomplete, fracsuccess, ndata
print numpy.histogram(thisnewratio,bins=16)
indx= numpy.argmax(thisnewratio)
print numpy.array(newvr)[indx], \
numpy.array(newvt)[indx], \
numpy.array(newvz)[indx], \
numpy.array(newrs)[indx], \
numpy.array(newds)[indx], \
numpy.array(newls)[indx], \
numpy.array(newbs)[indx], \
numpy.array(newfideval)[indx]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(numpy.array(newvt),
numpy.exp(numpy.array(newqdfeval)),'b,',
xrange=[-300.,500.],yrange=[0.,1.])
bovy_plot.bovy_plot(newvt,
numpy.exp(numpy.array(newpropeval+maxnewlogratio)),
'g,',
overplot=True)
bovy_plot.bovy_plot(numpy.array(newvt),
numpy.exp(numpy.array(newlogratio-maxnewlogratio)),
'b,',
xrange=[-300.,500.],
# xrange=[0.,20.],
# xrange=[0.,3.],
# xrange=[6.,9.],
yrange=[0.001,1.],semilogy=True)
bovy_plot.bovy_end_print('/home/bovy/public_html/segue-local/test.png')
#Now collect the samples
newrs= numpy.array(newrs)[accept][0:ndata]
newls= numpy.array(newls)[accept][0:ndata]
newbs= numpy.array(newbs)[accept][0:ndata]
newplate= numpy.array(newplate)[accept][0:ndata]
newgr= numpy.array(newgr)[accept][0:ndata]
newfeh= numpy.array(newfeh)[accept][0:ndata]
newvr= numpy.array(newvr)[accept][0:ndata]
newvt= numpy.array(newvt)[accept][0:ndata]
newvz= numpy.array(newvz)[accept][0:ndata]
newphi= numpy.array(newphi)[accept][0:ndata]
newds= numpy.array(newds)[accept][0:ndata]
newqdfeval= numpy.array(newqdfeval)[accept][0:ndata]
"""
vx, vy, vz= bovy_coords.galcencyl_to_vxvyvz(newvr,newvt,newvz,newphi,
vsun=[_VRSUN,_VTSUN,_VZSUN])
vrpmllpmbb= bovy_coords.vxvyvz_to_vrpmllpmbb(vx,vy,vz,newls,newbs,newds,
XYZ=False,degree=True)
pmrapmdec= bovy_coords.pmllpmbb_to_pmrapmdec(vrpmllpmbb[:,1],
vrpmllpmbb[:,2],
newls,newbs,
degree=True)
#Dump everything for debugging the coordinate transformation
from galpy.util import save_pickles
save_pickles('dump.sav',
newds,newls,newbs,newphi,
newvr,newvt,newvz,
vx, vy, vz,
vrpmllpmbb,
pmrapmdec)
if returnlist:
out= []
for ii in range(ndata):
out.append([newrs[ii],
newgr[ii],
newfeh[ii],
newls[ii],
newbs[ii],
newplate[ii],
newds[ii],
False, #outlier?
vrpmllpmbb[ii,0],
vrpmllpmbb[ii,1],
vrpmllpmbb[ii,2]])#,
# newqdfeval[ii]])
return out
#Load into data
binned.data.feh[thisdataIndx]= newfeh
oldgr= thisdata.dered_g-thisdata.dered_r
oldr= thisdata.dered_r
if options.noerrs:
binned.data.dered_r[thisdataIndx]= newrs
else:
binned.data.dered_r[thisdataIndx]= newrs\
+numpy.random.normal(size=numpy.sum(thisdataIndx))\
*ivezic_dist_gr(oldgr,0., #g-r is all that counts
binned.data.feh[thisdataIndx],
dg=binned.data[thisdataIndx].g_err,
dr=binned.data[thisdataIndx].r_err,
dfeh=binned.data[thisdataIndx].feh_err,
return_error=True,
_returndmr=True)
binned.data.dered_r[(binned.data.dered_r >= rmax)]= rmax #tweak to make sure everything stays within the observed range
if False:
binned.data.dered_r[(binned.data.dered_r <= rmin)]= rmin
binned.data.dered_g[thisdataIndx]= oldgr+binned.data[thisdataIndx].dered_r
#Also change plate and l and b
binned.data.plate[thisdataIndx]= newplate
radec= bovy_coords.lb_to_radec(newls,newbs,degree=True)
binned.data.ra[thisdataIndx]= radec[:,0]
binned.data.dec[thisdataIndx]= radec[:,1]
binned.data.l[thisdataIndx]= newls
binned.data.b[thisdataIndx]= newbs
if options.noerrs:
binned.data.vr[thisdataIndx]= vrpmllpmbb[:,0]
binned.data.pmra[thisdataIndx]= pmrapmdec[:,0]
binned.data.pmdec[thisdataIndx]= pmrapmdec[:,1]
else:
binned.data.vr[thisdataIndx]= vrpmllpmbb[:,0]+numpy.random.normal(size=numpy.sum(thisdataIndx))*binned.data.vr_err[thisdataIndx]
binned.data.pmra[thisdataIndx]= pmrapmdec[:,0]+numpy.random.normal(size=numpy.sum(thisdataIndx))*binned.data.pmra_err[thisdataIndx]
binned.data.pmdec[thisdataIndx]= pmrapmdec[:,1]+numpy.random.normal(size=numpy.sum(thisdataIndx))*binned.data.pmdec_err[thisdataIndx]
return binned
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 to_radec(cluster, do_order=False, do_key_params=False, ro=8.0, vo=220.0):
"""Convert to 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)
ro : float
galpy radius scaling parameter
vo : float
galpy velocity scaling parameter
Returns
-------
None
History:
-------
2018 - Written - Webb (UofT)
"""
if len(cluster.ra) == len(cluster.x):
cluster.x = copy(cluster.ra)
cluster.y = copy(cluster.dec)
cluster.z = copy(cluster.dist)
cluster.vx = copy(cluster.pmra)
cluster.vy = copy(cluster.pmdec)
cluster.vz = copy(cluster.vlos)
cluster.units = "radec"
cluster.origin = "sky"
else:
units0, origin0 = cluster.units, cluster.origin
cluster.to_galaxy()
cluster.to_kpckms()
x0, y0, z0 = bovy_coords.galcenrect_to_XYZ(cluster.x,
cluster.y,
cluster.z,
Xsun=8.0,
Zsun=0.025).T
cluster.dist = np.sqrt(x0**2.0 + y0**2.0 + z0**2.0)
vx0, vy0, vz0 = bovy_coords.galcenrect_to_vxvyvz(
cluster.vx,
cluster.vy,
cluster.vz,
Xsun=8.0,
Zsun=0.025,
vsun=[-11.1, 244.0, 7.25],
).T
cluster.vlos = (vx0 * x0 + vy0 * y0 +
vz0 * z0) / np.sqrt(x0**2.0 + y0**2.0 + z0**2.0)
l0, b0, cluster.dist = bovy_coords.XYZ_to_lbd(x0, y0, z0,
degree=True).T
cluster.ra, cluster.dec = bovy_coords.lb_to_radec(l0, b0,
degree=True).T
vr0, pmll0, pmbb0 = bovy_coords.vxvyvz_to_vrpmllpmbb(vx0,
vy0,
vz0,
l0,
b0,
cluster.dist,
degree=True).T
cluster.pmra, cluster.pmdec = bovy_coords.pmllpmbb_to_pmrapmdec(
pmll0, pmbb0, l0, b0, degree=True).T
x0, y0, z0 = bovy_coords.galcenrect_to_XYZ(cluster.xgc,
cluster.ygc,
cluster.zgc,
Xsun=8.0,
Zsun=0.025)
vx0, vy0, vz0 = bovy_coords.galcenrect_to_vxvyvz(
cluster.vxgc,
cluster.vygc,
cluster.vzgc,
Xsun=8.0,
Zsun=0.025,
vsun=[-11.1, 244.0, 7.25],
)
cluster.vlos_gc = (vx0 * x0 + vy0 * y0 +
vz0 * z0) / np.sqrt(x0**2.0 + y0**2.0 + z0**2.0)
l0, b0, cluster.dist_gc = bovy_coords.XYZ_to_lbd(x0,
y0,
z0,
degree=True)
cluster.ra_gc, cluster.dec_gc = bovy_coords.lb_to_radec(l0,
b0,
degree=True)
vr0, pmll0, pmbb0 = bovy_coords.vxvyvz_to_vrpmllpmbb(vx0,
vy0,
vz0,
l0,
b0,
cluster.dist_gc,
degree=True)
cluster.pmra_gc, cluster.pmdec_gc = bovy_coords.pmllpmbb_to_pmrapmdec(
pmll0, pmbb0, l0, b0, degree=True)
cluster.x = copy(cluster.ra)
cluster.y = copy(cluster.dec)
cluster.z = copy(cluster.dist)
cluster.vx = copy(cluster.pmra)
cluster.vy = copy(cluster.pmdec)
cluster.vz = copy(cluster.vlos)
cluster.xgc = copy(cluster.ra_gc)
cluster.ygc = copy(cluster.dec_gc)
cluster.zgc = copy(cluster.dist_gc)
cluster.vxgc = copy(cluster.pmra_gc)
cluster.vygc = copy(cluster.pmdec_gc)
cluster.vzgc = copy(cluster.vlos_gc)
cluster.units = "radec"
cluster.origin = "sky"
cluster.rv3d()
if do_key_params:
cluster.key_params(do_order=do_order)
def plot_stream_lb(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
sindx= numpy.fabs(thetar-numpy.pi) > (1.5*numpy.median(numpy.fabs(thetar-numpy.median(thetar)))) #stars in the stream
#Transform to (l,b)
XYZ= bovy_coords.galcenrect_to_XYZ(data[:,1],data[:,3],data[:,2],Xsun=8.)
lbd= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=True)
vXYZ= bovy_coords.galcenrect_to_vxvyvz(data[:,4],data[:,6],data[:,5],
vsun=[0.,30.24*8.,0.])
vlbd= bovy_coords.vxvyvz_to_vrpmllpmbb(vXYZ[0],vXYZ[1],vXYZ[2],
lbd[:,0],lbd[:,1],lbd[:,2],
degree=True)
includeorbit= True
if includeorbit:
npts= 201
pot= potential.LogarithmicHaloPotential(normalize=1.,q=0.9)
pts= numpy.linspace(0.,4.,npts)
#Calculate progenitor orbit around this point
pox= numpy.median(data[:,1])
poy= numpy.median(data[:,3])
poz= numpy.median(data[:,2])
povx= numpy.median(data[:,4])
povy= numpy.median(data[:,6])
povz= numpy.median(data[:,5])
pR,pphi,pZ= bovy_coords.rect_to_cyl(pox,poy,poz)
pvR,pvT,pvZ= bovy_coords.rect_to_cyl_vec(povx,povy,povz,pR,
pphi,pZ,cyl=True)
ppo= Orbit([pR/8.,pvR/220.,pvT/220.,pZ/8.,pvZ/220.,pphi])
pno= Orbit([pR/8.,-pvR/220.,-pvT/220.,pZ/8.,-pvZ/220.,pphi])
ppo.integrate(pts,pot)
pno.integrate(pts,pot)
pvec= numpy.zeros((6,npts*2-1))
pvec[0,:npts-1]= pno.x(pts)[::-1][:-1]
pvec[1,:npts-1]= pno.z(pts)[::-1][:-1]
pvec[2,:npts-1]= pno.y(pts)[::-1][:-1]
pvec[0,npts-1:]= ppo.x(pts)
pvec[1,npts-1:]= ppo.z(pts)
pvec[2,npts-1:]= ppo.y(pts)
pvec[3,:npts-1]= -pno.vx(pts)[::-1][:-1]
pvec[4,:npts-1]= -pno.vz(pts)[::-1][:-1]
pvec[5,:npts-1]= -pno.vy(pts)[::-1][:-1]
pvec[3,npts-1:]= ppo.vx(pts)
pvec[4,npts-1:]= ppo.vz(pts)
pvec[5,npts-1:]= ppo.vy(pts)
pvec[:3,:]*= 8.
pvec[3:,:]*= 220.
pXYZ= bovy_coords.galcenrect_to_XYZ(pvec[0,:],pvec[2,:],pvec[1,:],
Xsun=8.)
plbd= bovy_coords.XYZ_to_lbd(pXYZ[0],pXYZ[1],pXYZ[2],degree=True)
pvXYZ= bovy_coords.galcenrect_to_vxvyvz(pvec[3,:],pvec[5,:],pvec[4,:],
vsun=[0.,30.24*8.,0.])
pvlbd= bovy_coords.vxvyvz_to_vrpmllpmbb(pvXYZ[0],pvXYZ[1],pvXYZ[2],
plbd[:,0],plbd[:,1],plbd[:,2],
degree=True)
includetrack= True
if includetrack:
#Setup stream model
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,
0.88719443,-0.47713334,0.12019596])
sdf= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=True,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
#Plot
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
if 'ld' in plotfilename:
lbindx= 2
ylabel=r'$\mathrm{Distance}\,(\mathrm{kpc})$'
yrange=[0.,30.]
elif 'lvlos' in plotfilename:
lbindx= 0
ylabel=r'$V_\mathrm{los}\,(\mathrm{km\,s}^{-1})$'
yrange=[-500.,500.]
elif 'lpmll' in plotfilename:
lbindx= 1
ylabel=r'$\mu_{l}\cos b\,(\mathrm{mas\,yr}^{-1})$'
yrange=[-2.,13.5]
elif 'lpmbb' in plotfilename:
lbindx= 2
ylabel=r'$\mu_{b}\,(\mathrm{mas\,yr}^{-1})$'
yrange=[-8.,7.]
else:
lbindx= 1
yrange=[-10.,60.]
ylabel=r'$\mathrm{Galactic\ latitude}\,(\mathrm{deg})$'
if 'vlos' in plotfilename or 'pm' in plotfilename:
#Stream
bovy_plot.bovy_plot(lbd[sindx,0],vlbd[sindx,lbindx],'k,',
xlabel=r'$\mathrm{Galactic\ longitude}\,(\mathrm{deg})$',
ylabel=ylabel,
xrange=[0.,290.],
yrange=yrange)
#Progenitor
pindx= copy.copy(True-sindx)
pindx[0:9900]= False
bovy_plot.bovy_plot(lbd[pindx,0],vlbd[pindx,lbindx],'k,',overplot=True)
else:
bovy_plot.bovy_plot(lbd[sindx,0],lbd[sindx,lbindx],'k,',
xlabel=r'$\mathrm{Galactic\ longitude}\,(\mathrm{deg})$',
ylabel=ylabel,
xrange=[0.,290.],
yrange=yrange)
#Progenitor
pindx= copy.copy(True-sindx)
pindx[0:9900]= False
bovy_plot.bovy_plot(lbd[pindx,0],lbd[pindx,lbindx],'k,',overplot=True)
if includeorbit:
if 'vlos' in plotfilename or 'pm' in plotfilename:
bovy_plot.bovy_plot(plbd[npts,0],pvlbd[npts,lbindx],
'o',color='0.5',mec='none',overplot=True,ms=8)
bovy_plot.bovy_plot(plbd[:,0],pvlbd[:,lbindx],'k--',overplot=True)
else:
bovy_plot.bovy_plot(plbd[npts,0],plbd[npts,lbindx],
'o',color='0.5',mec='none',overplot=True,ms=8)
bovy_plot.bovy_plot(plbd[:,0],plbd[:,lbindx],'k--',overplot=True)
if includetrack:
d1= 'll'
if 'vlos' in plotfilename:
d2= 'vlos'
elif 'pmll' in plotfilename:
d2= 'pmll'
elif 'pmbb' in plotfilename:
d2= 'pmbb'
elif 'ld' in plotfilename:
d2= 'dist'
else:
d2= 'bb'
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Insets
if 'vlos' in plotfilename:
xmin, xmax= 220., 250.
ymin, ymax= 230., 390.
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,152.],[ymin,-100.],'k:')
pyplot.plot([xmin,152.],[ymax,460.],'k:')
insetAxes= pyplot.axes([0.15,0.42,0.38,0.45])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],vlbd[:,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([240.,240.],[250.,275.],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(241.,255.,r'$25\,\mathrm{km\,s}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
elif 'pmll' in plotfilename:
xmin, xmax= 158.,205.
ymin, ymax= 10.5, 13.
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,113.],[ymin,6.1],'k:')
pyplot.plot([xmax,227.],[ymin,6.1],'k:')
insetAxes= pyplot.axes([0.43,0.12,0.3,0.4])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[sindx,0],vlbd[sindx,lbindx],'k,',
overplot=True)
bovy_plot.bovy_plot(lbd[pindx,0],vlbd[pindx,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([168.5,168.5],[10.75,11.25],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(169.8,10.875,r'$0.5\,\mathrm{mas\,yr}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
elif 'pmbb' in plotfilename:
xmin, xmax= 185., 230.
ymin, ymax= -7.4, -4.7
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,159.],[ymax,1.],'k:')
pyplot.plot([xmax,287.],[ymax,1.],'k:')
#2nd inset
xmin2, xmax2= 80., 125.
ymin2, ymax2= 4.2, 5.8
pyplot.plot([xmin2,xmin2],[ymin2,ymax2],'k-')
pyplot.plot([xmax2,xmax2],[ymin2,ymax2],'k-')
pyplot.plot([xmin2,xmax2],[ymin2,ymin2],'k-')
pyplot.plot([xmin2,xmax2],[ymax2,ymax2],'k-')
pyplot.plot([xmin2,8.],[ymin2,-1.],'k:')
pyplot.plot([xmax2,155.],[ymin2,-1.],'k:')
insetAxes= pyplot.axes([0.55,0.57,0.34,0.3])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],vlbd[:,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([200.,200.],[-5.75,-5.25],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(201.25,-5.675,r'$0.5\,\mathrm{mas\,yr}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
#Also make second inset
insetAxes= pyplot.axes([0.14,0.12,0.4,0.35])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],vlbd[:,lbindx],'k,',
overplot=True)
sdft.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([103.,103.],[4.35,4.85],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(104.,4.5,r'$0.5\,\mathrm{mas\,yr}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin2,xmax2)
insetAxes.set_ylim(ymin2,ymax2)
elif 'ld' in plotfilename:
xmin, xmax= 158., 227.
ymin, ymax= 7.7,9.5
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,70.],[ymax,18.5],'k:')
pyplot.plot([xmax,248.],[ymax,18.5],'k:')
#2nd inset
xmin2, xmax2= 72.,100.
ymin2, ymax2= 11.5, 16.1
pyplot.plot([xmin2,xmin2],[ymin2,ymax2],'k-')
pyplot.plot([xmax2,xmax2],[ymin2,ymax2],'k-')
pyplot.plot([xmin2,xmax2],[ymin2,ymin2],'k-')
pyplot.plot([xmin2,xmax2],[ymax2,ymax2],'k-')
pyplot.plot([xmin2,66.5],[ymax2,15.85],'k:')
pyplot.plot([xmin2,66.5],[ymin2,0.5],'k:')
insetAxes= pyplot.axes([0.31,0.6,0.48,0.27])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[sindx,0],lbd[sindx,lbindx],'k,',
overplot=True)
bovy_plot.bovy_plot(lbd[pindx,0],lbd[pindx,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([168.,168.],[8.7,9.2],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(169.7,8.8,r'$0.5\,\mathrm{kpc}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
#Also make second inset
insetAxes= pyplot.axes([0.13,0.12,0.17,0.4])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],lbd[:,lbindx],'k,',
overplot=True)
sdft.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([74.,74.],[11.95,12.45],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(76.,12.01,r'$0.5\,\mathrm{kpc}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin2,xmax2)
insetAxes.set_ylim(ymin2,ymax2)
else:
xmin, xmax= 90., 165.
ymin, ymax= 47., 59.
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,70.],[ymin,31.],'k:')
pyplot.plot([xmax,213.],[ymin,31.],'k:')
insetAxes= pyplot.axes([0.31,0.12,0.38,0.45])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[sindx,0],lbd[sindx,lbindx],'k,',
overplot=True)
bovy_plot.bovy_plot(lbd[pindx,0],lbd[pindx,lbindx],'k,',
overplot=True)
sdft.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([115.,115.],[48.5,49.5],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(117.2,48.5,r'$1^\circ$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
bovy_plot.bovy_end_print(plotfilename)