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 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 example_integrate_orbit():
# Use 'MWPotential2014' as an example
my_potential = MWPotential2014
# integration time in units of (_R0/_V0)
time_start = 0.
time_end = 100.
my_time_step = numpy.linspace(time_start, time_end, 1001)
# read 6D astrometric data
# (0) [mas] parallax
# (1) [deg] ell
# (2) [deg] b
# (3) [km/s] heliocentric line-of-sight velocity
# (4) [mas/yr] proper motion along ell direction (mu_ellstar = mu_ell * cos(b))
# (5) [mas/yr] proper motion along b direction (mu_b)
my_filename = 'parallax_ell_b_heliocentricLineOfSightVelocity_properMotionEllStar_properMotionB.txt'
my_file = open(my_filename, 'r')
my_data = numpy.loadtxt(my_file, comments='#')
my_parallax_mas = my_data[:, 0]
my_ell_deg = my_data[:, 1]
my_b_deg = my_data[:, 2]
my_hlosv_kms = my_data[:, 3]
my_muellstar_masyr = my_data[:, 4]
my_mub_masyr = my_data[:, 5]
# count sample size
my_sample_size = len(my_ell_deg)
for i in range(my_sample_size):
print('star ID=%d' % (i))
# convert parallax to distance
distance_kpc = 1. / my_parallax_mas[i]
# convert (ell, b) to (RA, DEC)
RA_deg, DEC_deg = bovy_coords.lb_to_radec(my_ell_deg[i],
my_b_deg[i],
degree=True,
epoch=_my_epoch)
# heliocentric line-of-sight velocity
hlosv_kms = my_hlosv_kms[i]
# convert (mu_ellstar, mu_b) to (mu_RAstar, mu_DEC)
muRAstar_masyr, muDEC_masyr = bovy_coords.pmllpmbb_to_pmrapmdec(
my_muellstar_masyr[i],
my_mub_masyr[i],
my_ell_deg[i],
my_b_deg[i],
degree=True,
epoch=_my_epoch)
# create orbit instance
obs_6D = Orbit(vxvv=[
RA_deg, DEC_deg, distance_kpc, muRAstar_masyr, muDEC_masyr,
hlosv_kms
],
radec=True,
ro=_R0,
vo=_V0,
zo=0.,
solarmotion=[_Usun, _Vsun, _Wsun])
# integrate orbit in my_potential
obs_6D.integrate(my_time_step, my_potential)
# file on which we write 6D data at each time step
outfile_i = open("t_x_y_z_vx_vy_vz_R__orbitID%03d.txt" % (i), 'w')
# For illustrative purpose, I use an explicit expression to access 6D data at each time.
for j in range(len(my_time_step)):
# Here I assume that
# (a) Galactic Center is located at (x,y)=(0,0) kpc
# (b) Sun is located at (x,y)=(-8,0) kpc
# (c) Nearby disc stars with circular orbits move towards (vx,vy)=(0,220) km/s
# This is why I add a minus sign (-) to x and vx.
x = -obs_6D.x(my_time_step[j])
y = obs_6D.y(my_time_step[j])
z = obs_6D.z(my_time_step[j])
vx = -obs_6D.vx(my_time_step[j])
vy = obs_6D.vy(my_time_step[j])
vz = obs_6D.vz(my_time_step[j])
R = obs_6D.R(my_time_step[j])
printline = '%lf %lf %lf %lf %lf %lf %lf %lf\n' % (
my_time_step[j], x, y, z, vx, vy, vz, R)
outfile_i.write(printline)
# close file
outfile_i.close()
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 propagate_initial(self,
potential,
index,
dt=0.01 * u.Myr,
size=100,
deltav=5. * u.km / u.s,
deltat=0.5 * u.Myr):
'''
Propagates the index-th star and _size_ more stars with random spread in
initial velocity and flight time dictated by deltav, deltat.
This overwrites the original catalogue!
Parameters
----------
index : int
Index of the star to propagate this in.
size : int
dv, dt : Quantity
Spread in initial velocity and
See self.propagate() for the other parameters.
'''
from galpy.orbit import Orbit
from galpy.util.bovy_coords import pmllpmbb_to_pmrapmdec, lb_to_radec, vrpmllpmbb_to_vxvyvz, lbd_to_XYZ
self.r0, self.theta0, self.phi0, self.v0, self.phiv0, self.thetav0, self.tflight, self.tage, self.m = np.ones(size)*self.r0[index], np.ones(size)*self.theta0[index], \
np.ones(size)*self.phi0[index], np.ones(size)*self.v0[index], np.ones(size)*self.phiv0[index], np.ones(size)*self.thetav0[index], \
np.ones(size)*self.tflight[index], np.ones(size)*self.tage[index], np.ones(size)*self.m[index]
self.v0 = self.v0 + np.random.normal(size=size) * deltav
self.tflight = self.tflight + np.random.normal(size=size) * deltat
self.size = size
# Integration time step
self.dt = dt
nsteps = np.ceil((self.tflight / self.dt).to('1').value)
nsteps[nsteps < 100] = 100
# Initialize position in cylindrical coords
rho = self.r0 * np.sin(self.theta0)
z = self.r0 * np.cos(self.theta0)
phi = self.phi0
#... and velocity
vR = self.v0 * np.sin(self.thetav0) * np.cos(self.phiv0)
vT = self.v0 * np.sin(self.thetav0) * np.sin(self.phiv0)
vz = self.v0 * np.cos(self.thetav0)
# Initialize empty arrays to save orbit data and integration steps
self.pmll, self.pmbb, self.ll, self.bb, self.vlos, self.dist, self.energy_var = \
(np.zeros(self.size) for i in xrange(7))
self.orbits = [None] * self.size
#Integration loop for the self.size orbits
for i in xrange(self.size):
ts = np.linspace(0, 1, nsteps[i]) * self.tflight[i]
self.orbits[i] = Orbit(
vxvv=[rho[i], vR[i], vT[i], z[i], vz[i], phi[i]],
solarmotion=self.solarmotion)
self.orbits[i].integrate(ts, potential, method='dopr54_c')
# Export the final position
self.dist[i], self.ll[i], self.bb[i], self.pmll[i], self.pmbb[i], self.vlos[i] = \
self.orbits[i].dist(self.tflight[i], use_physical=True), \
self.orbits[i].ll(self.tflight[i], use_physical=True), \
self.orbits[i].bb(self.tflight[i], use_physical=True), \
self.orbits[i].pmll(self.tflight[i], use_physical=True) , \
self.orbits[i].pmbb(self.tflight[i], use_physical=True) , \
self.orbits[i].vlos(self.tflight[i], use_physical=True)
# Radial velocity and distance + distance modulus
self.vlos, self.dist = self.vlos * u.km / u.s, self.dist * u.kpc
# Sky coordinates and proper motion
data = pmllpmbb_to_pmrapmdec(
self.pmll, self.pmbb, self.ll, self.bb,
degree=True) * u.mas / u.year
self.pmra, self.pmdec = data[:, 0], data[:, 1]
data = lb_to_radec(self.ll, self.bb, degree=True) * u.deg
self.ra, self.dec = data[:, 0], data[:, 1]
# Done propagating
self.cattype = 1
def propagate(self, potential, dt=0.01 * u.Myr, threshold=None):
'''
Propagates the sample in the Galaxy, changes cattype from 0 to 1.
Parameters
----------
potential : galpy potential instance
Potential instance of the galpy library used to integrate the orbits
dt : Quantity
Integration timestep. Defaults to 0.01 Myr
threshold : float
Maximum relative energy difference between the initial energy and the energy at any point needed
to consider an integration step an energy outliar. E.g. for threshold=0.01, any excess or
deficit of 1% (or more) of the initial energy is enough to be registered as outliar.
A table E_data.fits is created in the working directory containing for every orbit the percentage
of outliar points (pol)
'''
from galpy.orbit import Orbit
from galpy.util.bovy_coords import pmllpmbb_to_pmrapmdec, lb_to_radec, vrpmllpmbb_to_vxvyvz, lbd_to_XYZ
if (self.cattype > 0):
raise RuntimeError('This sample is already propagated!')
if (threshold is None):
check = False
else:
check = True
# Integration time step
self.dt = dt
nsteps = np.ceil((self.tflight / self.dt).to('1').value)
nsteps[nsteps < 100] = 100
# Initialize position in cylindrical coords
rho = self.r0 * np.sin(self.theta0)
z = self.r0 * np.cos(self.theta0)
phi = self.phi0
#... and velocity
vR = self.v0 * np.sin(self.thetav0) * np.cos(self.phiv0)
vT = self.v0 * np.sin(self.thetav0) * np.sin(self.phiv0)
vz = self.v0 * np.cos(self.thetav0)
# Initialize empty arrays to save orbit data and integration steps
self.pmll, self.pmbb, self.ll, self.bb, self.vlos, self.dist, self.energy_var = \
(np.zeros(self.size) for i in xrange(7))
self.orbits = [None] * self.size
#Integration loop for the self.size orbits
for i in xrange(self.size):
ts = np.linspace(0, 1, nsteps[i]) * self.tflight[i]
self.orbits[i] = Orbit(
vxvv=[rho[i], vR[i], vT[i], z[i], vz[i], phi[i]],
solarmotion=self.solarmotion)
self.orbits[i].integrate(ts, potential, method='dopr54_c')
# Export the final position
self.dist[i], self.ll[i], self.bb[i], self.pmll[i], self.pmbb[i], self.vlos[i] = \
self.orbits[i].dist(self.tflight[i], use_physical=True), \
self.orbits[i].ll(self.tflight[i], use_physical=True), \
self.orbits[i].bb(self.tflight[i], use_physical=True), \
self.orbits[i].pmll(self.tflight[i], use_physical=True) , \
self.orbits[i].pmbb(self.tflight[i], use_physical=True) , \
self.orbits[i].vlos(self.tflight[i], use_physical=True)
# Energy check
if (check):
energy_array = self.orbits[i].E(ts)
idx_energy = np.absolute(energy_array / energy_array[0] -
1) > threshold
self.energy_var[i] = float(
idx_energy.sum()) / nsteps[i] # percentage of outliars
# Radial velocity and distance + distance modulus
self.vlos, self.dist = self.vlos * u.km / u.s, self.dist * u.kpc
# Sky coordinates and proper motion
data = pmllpmbb_to_pmrapmdec(
self.pmll, self.pmbb, self.ll, self.bb,
degree=True) * u.mas / u.year
self.pmra, self.pmdec = data[:, 0], data[:, 1]
data = lb_to_radec(self.ll, self.bb, degree=True) * u.deg
self.ra, self.dec = data[:, 0], data[:, 1]
# Done propagating
self.cattype = 1
# Save the energy check information
if (check):
from astropy.table import Table
e_data = Table([self.m, self.tflight, self.energy_var],
names=['m', 'tflight', 'pol'])
e_data.write('E_data.fits', overwrite=True)
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 main(args: Optional[list] = None, opts: Optional[Namespace] = None):
"""Fit MWPotential2014 Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
opts : Namespace, optional
pre-constructed results of parsed args
if not None, used ONLY if args is None
"""
if opts is not None and args is None:
pass
else:
if opts is not None:
warnings.warn("Not using `opts` because `args` are given")
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# plot chains
print("Plotting Chains")
fig = mcmc_util.plot_chains(opath)
fig.savefig(fpath + "chains.pdf")
plt.close(fig)
print("Need to continue chains:", end=" ")
for i in range(32):
ngood = mcmc_util.determine_nburn(
filename=opath + f"mwpot14-fitsigma-{i:02}.dat", return_nsamples=True,
)
if ngood < 4000:
print(f"{i:02} (N={ngood})", end=", ")
###############################################################
# RESULTING PDFS
data, _, weights, _ = read_mcmc(nburn=None, skip=1, evi_func=lambda x: 1.0)
plot_corner(data, weights=weights)
plt.savefig("figures/PDFs/corner.pdf")
plt.close()
# --------------
savefilename = "output/pal5_forces_mcmc.pkl"
if not os.path.exists(savefilename):
data_wf, index_wf, weights_wf, evi_wf = read_mcmc(
nburn=None, addforces=True, skip=1, evi_func=lambda x: 1.0
)
save_pickles(savefilename, data_wf, index_wf, weights_wf, evi_wf)
else:
with open(savefilename, "rb") as savefile:
data_wf = pickle.load(savefile)
index_wf = pickle.load(savefile)
weights_wf = pickle.load(savefile)
evi_wf = pickle.load(savefile)
# --------------
plot_corner(data_wf, weights=weights_wf, addvcprior=False)
plt.savefig("figures/PDFs/corner_wf.pdf")
plt.close()
# --------------
# Which potential is preferred?
data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_harmonic)
fig = plt.figure(figsize=(6, 4))
bovy_plot.bovy_plot(
potindx,
np.log(evidences),
"o",
xrange=[-1, 34],
yrange=[-35, -22],
xlabel=r"$\mathrm{Potential\ index}$",
ylabel=r"$\ln\ \mathrm{evidence}$",
)
data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_laplace)
bovy_plot.bovy_plot(potindx, np.log(evidences) - 30.0, "d", overplot=True)
data_noforce, potindx, weights, evidences = read_mcmc(
evi_func=lambda x: np.exp(np.amax(x[:, -1]))
)
bovy_plot.bovy_plot(potindx, np.log(evidences) - 8.0, "s", overplot=True)
data_noforce, potindx, weights, evidences = read_mcmc(
evi_func=lambda x: np.exp(-25.0)
if (np.log(evi_harmonic(x)) > -25.0)
else np.exp(-50.0)
)
bovy_plot.bovy_plot(potindx, np.log(evidences), "o", overplot=True)
plt.savefig("figures/PDFs/preferred_pot.pdf")
plt.close()
###############################################################
# Look at the results for individual potentials
# --------------
# The flattening $c$
npot = 32
nwalkers = 12
plt.figure(figsize=(16, 6))
cmap = cm.plasma
maxl = np.zeros((npot, 2))
for en, ii in enumerate(range(npot)):
data_ip, _, weights_ip, evi_ip = read_mcmc(singlepot=ii, evi_func=evi_harmonic)
try:
maxl[en, 0] = np.amax(data_ip[:, -1])
maxl[en, 1] = np.log(evi_ip[0])
except ValueError:
maxl[en] = -10000000.0
plt.subplot(2, 4, en // 4 + 1)
bovy_plot.bovy_hist(
data_ip[:, 0],
range=[0.5, 2.0],
bins=26,
histtype="step",
color=cmap((en % 4) / 3.0),
normed=True,
xlabel=r"$c$",
lw=1.5,
overplot=True,
)
if en % 4 == 0:
bovy_plot.bovy_text(
r"$\mathrm{Potential\ %i\ to\ % i}$" % (en, en + 3),
size=17.0,
top_left=True,
)
plt.tight_layout()
plt.savefig("figures/flattening_c.pdf")
plt.close()
###############################################################
# What is the effective prior in $(F_R,F_Z)$?
frfzprior_savefilename = "frfzprior.pkl"
if not os.path.exists(frfzprior_savefilename):
# Compute for each potential separately
nvoc = 10000
ro = 8.0
npot = 32
fs = np.zeros((2, nvoc, npot))
for en, ii in tqdm(enumerate(range(npot))):
fn = f"output/fitsigma/mwpot14-fitsigma-{i:02}.dat"
# Read the potential parameters
with open(fn, "r") as savefile:
line1 = savefile.readline()
potparams = [float(s) for s in (line1.split(":")[1].split(","))]
for jj in range(nvoc):
c = np.random.uniform() * 1.5 + 0.5
tvo = np.random.uniform() * 50.0 + 200.0
pot = mw_pot.setup_potential(potparams, c, False, False, REFR0, tvo)
fs[:, jj, ii] = np.array(pal5_util.force_pal5(pot, 23.46, REFR0, tvo))[
:2
]
save_pickles(frfzprior_savefilename, fs)
else:
with open(frfzprior_savefilename, "rb") as savefile:
fs = pickle.load(savefile)
# --------------
plt.figure(figsize=(6, 6))
bovy_plot.scatterplot(
fs[0].flatten(),
fs[1].flatten(),
"k,",
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.2],
xlabel=r"$F_R(\mathrm{Pal\ 5})$",
ylabel=r"$F_Z(\mathrm{Pal\ 5})$",
onedhists=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.2],
justcontours=True,
cntrcolors="w",
overplot=True,
onedhists=True,
)
plt.axvline(-0.81, color=sns.color_palette()[0])
plt.axhline(-1.85, color=sns.color_palette()[0])
plt.savefig("figures/effective_force_prior.pdf")
plt.close()
# --------------
# The ratio of the posterior and the prior
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
plt.figure(figsize=(12.5, 4))
def axes_white():
for k, spine in plt.gca().spines.items(): # ax.spines is a dictionary
spine.set_color("w")
plt.gca().tick_params(axis="x", which="both", colors="w")
plt.gca().tick_params(axis="y", which="both", colors="w")
[t.set_color("k") for t in plt.gca().xaxis.get_ticklabels()]
[t.set_color("k") for t in plt.gca().yaxis.get_ticklabels()]
return None
bins = 32
trange = [[-1.75, -0.25], [-2.5, -1.2]]
tw = copy.deepcopy(weights_wf)
tw[index_wf == 14] = 0.0 # Didn't converge properly
H_prior, xedges, yedges = np.histogram2d(
fs[0].flatten(), fs[1].flatten(), bins=bins, range=trange, normed=True
)
H_post, xedges, yedges = np.histogram2d(
data_wf[:, 7], data_wf[:, 8], weights=tw, bins=bins, range=trange, normed=True,
)
H_like = H_post / H_prior
H_like[H_prior == 0.0] = 0.0
plt.subplot(1, 3, 1)
bovy_plot.bovy_dens2d(
H_prior.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
ylabel=r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Prior}$", top_left=True, size=19.0, color="w")
axes_white()
plt.subplot(1, 3, 2)
bovy_plot.bovy_dens2d(
H_post.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Posterior}$", top_left=True, size=19.0, color="w")
axes_white()
plt.subplot(1, 3, 3)
bovy_plot.bovy_dens2d(
H_like.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
vmin=0.1,
vmax=4.0,
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Likelihood}$", top_left=True, size=19.0, color="w")
axes_white()
def qline(FR, q=0.95):
return 2.0 * FR / q ** 2.0
q = 0.94
plt.plot([-1.25, -0.2], [qline(-1.25, q=q), qline(-0.2, q=q)], "w--")
bovy_plot.bovy_text(-1.7, -2.2, r"$q_\Phi = 0.94$", size=16.0, color="w")
plt.plot((-1.25, -1.02), (-2.19, qline(-1.02, q=q)), "w-", lw=0.8)
plt.tight_layout()
plt.savefig(
"figures/pal5post.pdf", bbox_inches="tight",
)
plt.close()
# --------------
# Projection onto the direction perpendicular to constant $q = 0.94$:
frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T
fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1))
plt.figure(figsize=(6, 4))
txlabel = r"$F_\perp$"
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_prior.flatten(),
bins=21,
histtype="step",
lw=2.0,
xrange=[-1.5, 1.5],
xlabel=txlabel,
normed=True,
)
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_post.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-1.5, 1.5],
normed=True,
)
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_like.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-1.5, 1.5],
normed=True,
)
plt.savefig("figures/PDFs/projection_perp_to_q0p94.pdf")
plt.close()
mq = (
np.sum(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten()
* H_like.flatten()
)
) / np.sum(H_like.flatten())
print(
mq,
np.sqrt(
(
np.sum(
((-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten() - mq)
** 2.0
* H_like.flatten()
)
)
/ np.sum(H_like.flatten())
),
)
# --------------
# Projection onto the direction parallel to constant $q = 0.94$:
frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T
fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1))
plt.figure(figsize=(6, 4))
txlabel = r"$F_\parallel$"
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_prior.flatten(),
bins=21,
histtype="step",
lw=2.0,
xrange=[-2.5, 2.5],
xlabel=txlabel,
normed=True,
)
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_post.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-2.5, 2.5],
normed=True,
)
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_like.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-2.5, 2.5],
normed=True,
)
plt.savefig("figures/PDFs/projection_prll_to_q0p94.pdf")
plt.close()
mq = (
np.sum(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten()
* H_like.flatten()
)
) / np.sum(H_like.flatten())
print(
mq,
np.sqrt(
(
np.sum(
((0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten() - mq)
** 2.0
* H_like.flatten()
)
)
/ np.sum(H_like.flatten())
),
)
# --------------
# Thus, there is only a weak constraint on $F_\parallel$.
nrow = int(np.ceil(npot / 4.0))
plt.figure(figsize=(16, nrow * 4))
for en, ii in enumerate(range(npot)):
plt.subplot(nrow, 4, en + 1)
if ii % 4 == 0:
tylabel = r"$F_Z(\mathrm{Pal\ 5})$"
else:
tylabel = None
if ii // 4 == nrow - 1:
txlabel = r"$F_R(\mathrm{Pal\ 5})$"
else:
txlabel = None
bovy_plot.scatterplot(
fs[0][:, en],
fs[1][:, en],
"k,",
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
xlabel=txlabel,
ylabel=tylabel,
gcf=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors="w",
overplot=True,
)
bovy_plot.scatterplot(
data_wf[index_wf == ii, 7],
data_wf[index_wf == ii, 8],
weights=weights_wf[index_wf == ii],
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
# cntrcolors=sns.color_palette()[2],
overplot=True,
)
plt.axvline(-0.80, color=sns.color_palette()[0])
plt.axhline(-1.83, color=sns.color_palette()[0])
bovy_plot.bovy_text(r"$\mathrm{Potential}\ %i$" % ii, size=17.0, top_left=True)
plt.savefig("figures/PDFs/constain_F_prll.pdf")
plt.close()
# --------------
# Let's plot four representative ones for the paper:
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
nullfmt = NullFormatter()
nrow = 1
plt.figure(figsize=(15, nrow * 4))
for en, ii in enumerate([0, 15, 24, 25]):
plt.subplot(nrow, 4, en + 1)
if en % 4 == 0:
tylabel = (
r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$"
)
else:
tylabel = None
if en // 4 == nrow - 1:
txlabel = (
r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$"
)
else:
txlabel = None
bovy_plot.scatterplot(
fs[0][:, ii],
fs[1][:, ii],
"k,",
bins=31,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
xlabel=txlabel,
ylabel=tylabel,
gcf=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=21,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors=sns.color_palette("colorblind")[2],
cntrls="--",
cntrlw=2.0,
overplot=True,
)
bovy_plot.scatterplot(
data_wf[index_wf == ii, 7],
data_wf[index_wf == ii, 8],
weights=weights_wf[index_wf == ii],
bins=21,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors=sns.color_palette("colorblind")[0],
cntrlw=2.5,
overplot=True,
)
if en > 0:
plt.gca().yaxis.set_major_formatter(nullfmt)
plt.tight_layout()
plt.savefig(
"figures/pal5post_examples.pdf", bbox_inches="tight",
)
plt.close()
###############################################################
# What about $q_\Phi$?
bins = 47
plt.figure(figsize=(6, 4))
dum = bovy_plot.bovy_hist(
np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()),
histtype="step",
lw=2.0,
bins=bins,
xlabel=r"$q_\mathrm{\Phi}$",
xrange=[0.7, 1.25],
normed=True,
)
dum = bovy_plot.bovy_hist(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]),
weights=weights_wf,
histtype="step",
lw=2.0,
bins=bins,
overplot=True,
xrange=[0.7, 1.25],
normed=True,
)
mq = np.sum(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) * weights_wf
) / np.sum(weights_wf)
sq = np.sqrt(
np.sum(
(np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) - mq) ** 2.0
* weights_wf
)
/ np.sum(weights_wf)
)
print("From posterior samples: q = %.3f +/- %.3f" % (mq, sq))
Hq_post, xedges = np.histogram(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]),
weights=weights_wf,
bins=bins,
range=[0.7, 1.25],
normed=True,
)
Hq_prior, xedges = np.histogram(
np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()),
bins=bins,
range=[0.7, 1.25],
normed=True,
)
qs = 0.5 * (xedges[:-1] + xedges[1:])
Hq_like = Hq_post / Hq_prior
Hq_like[Hq_post == 0.0] = 0.0
mq = np.sum(qs * Hq_like) / np.sum(Hq_like)
sq = np.sqrt(np.sum((qs - mq) ** 2.0 * Hq_like) / np.sum(Hq_like))
print("From likelihood of samples: q = %.3f +/- %.3f" % (mq, sq))
plt.savefig("figures/q_phi.pdf")
plt.close()
# It appears that $q_\Phi$ is the quantity that is the most strongly constrained by the Pal 5 data.
###############################################################
# A sampling of tracks from the MCMC
savefilename = "mwpot14-pal5-mcmcTracks.pkl"
pmdecpar = 2.257 / 2.296
pmdecperp = -2.296 / 2.257
if os.path.exists(savefilename):
with open(savefilename, "rb") as savefile:
pal5_track_samples = pickle.load(savefile)
forces = pickle.load(savefile)
all_potparams = pickle.load(savefile)
all_params = pickle.load(savefile)
else:
np.random.seed(1)
ntracks = 21
multi = 8
pal5_track_samples = np.zeros((ntracks, 2, 6, pal5varyc[0].shape[1]))
forces = np.zeros((ntracks, 2))
all_potparams = np.zeros((ntracks, 5))
all_params = np.zeros((ntracks, 7))
for ii in range(ntracks):
# Pick a random potential from among the set, but leave 14 out
pindx = 14
while pindx == 14:
pindx = np.random.permutation(32)[0]
# Load this potential
fn = f"output/fitsigma/mwpot14-fitsigma-{pindx:02}.dat"
with open(fn, "r") as savefile:
line1 = savefile.readline()
potparams = [float(s) for s in (line1.split(":")[1].split(","))]
all_potparams[ii] = potparams
# Now pick a random sample from this MCMC chain
tnburn = mcmc_util.determine_nburn(fn)
tdata = np.loadtxt(fn, comments="#", delimiter=",")
tdata = tdata[tnburn::]
tdata = tdata[np.random.permutation(len(tdata))[0]]
all_params[ii] = tdata
tvo = tdata[1] * REFV0
pot = mw_pot.setup_potential(potparams, tdata[0], False, False, REFR0, tvo)
forces[ii, :] = pal5_util.force_pal5(pot, 23.46, REFR0, tvo)[:2]
# Now compute the stream model for this setup
dist = tdata[2] * 22.0
pmra = -2.296 + tdata[3] + tdata[4]
pmdecpar = 2.257 / 2.296
pmdecperp = -2.296 / 2.257
pmdec = -2.257 + tdata[3] * pmdecpar + tdata[4] * pmdecperp
vlos = -58.7
sigv = 0.4 * np.exp(tdata[5])
prog = Orbit(
[229.018, -0.124, dist, pmra, pmdec, vlos],
radec=True,
ro=REFR0,
vo=tvo,
solarmotion=[-11.1, 24.0, 7.25],
)
tsdf_trailing, tsdf_leading = pal5_util.setup_sdf(
pot,
prog,
sigv,
10.0,
REFR0,
tvo,
multi=multi,
nTrackChunks=8,
trailing_only=False,
verbose=True,
useTM=False,
)
# Compute the track
for jj, sdf in enumerate([tsdf_trailing, tsdf_leading]):
trackRADec = bovy_coords.lb_to_radec(
sdf._interpolatedObsTrackLB[:, 0],
sdf._interpolatedObsTrackLB[:, 1],
degree=True,
)
trackpmRADec = bovy_coords.pmllpmbb_to_pmrapmdec(
sdf._interpolatedObsTrackLB[:, 4],
sdf._interpolatedObsTrackLB[:, 5],
sdf._interpolatedObsTrackLB[:, 0],
sdf._interpolatedObsTrackLB[:, 1],
degree=True,
)
# Store the track
pal5_track_samples[ii, jj, 0] = trackRADec[:, 0]
pal5_track_samples[ii, jj, 1] = trackRADec[:, 1]
pal5_track_samples[ii, jj, 2] = sdf._interpolatedObsTrackLB[:, 2]
pal5_track_samples[ii, jj, 3] = sdf._interpolatedObsTrackLB[:, 3]
pal5_track_samples[ii, jj, 4] = trackpmRADec[:, 0]
pal5_track_samples[ii, jj, 5] = trackpmRADec[:, 1]
save_pickles(
savefilename, pal5_track_samples, forces, all_potparams, all_params
)
# --------------
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
plt.figure(figsize=(12, 8))
gs = gridspec.GridSpec(2, 2, wspace=0.225, hspace=0.1, right=0.94)
ntracks = pal5_track_samples.shape[0]
cmap = cm.plasma
alpha = 0.7
for ii in range(ntracks):
tc = cmap((forces[ii, 1] + 2.5) / 1.0)
for jj in range(2):
# RA, Dec
plt.subplot(gs[0])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 1],
"-",
color=tc,
alpha=alpha,
)
# RA, Vlos
plt.subplot(gs[1])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 3],
"-",
color=tc,
alpha=alpha,
)
# RA, Dist
plt.subplot(gs[2])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 2] - all_params[ii, 2] * 22.0,
"-",
color=tc,
alpha=alpha,
)
# RA, pm_parallel
plt.subplot(gs[3])
plt.plot(
pal5_track_samples[ii, jj, 0, : 500 + 500 * (1 - jj)],
np.sqrt(1.0 + (2.257 / 2.296) ** 2.0)
* (
(pal5_track_samples[ii, jj, 4, : 500 + 500 * (1 - jj)] + 2.296)
* pmdecperp
- (pal5_track_samples[ii, jj, 5, : 500 + 500 * (1 - jj)] + 2.257)
)
/ (pmdecpar - pmdecperp),
"-",
color=tc,
alpha=alpha,
)
plot_data_add_labels(
p1=(gs[0],), p2=(gs[1],), noxlabel_dec=True, noxlabel_vlos=True
)
plt.subplot(gs[2])
plt.xlim(250.0, 221.0)
plt.ylim(-3.0, 1.5)
bovy_plot._add_ticks()
plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$")
plt.ylabel(r"$\Delta\mathrm{Distance}\,(\mathrm{kpc})$")
plt.subplot(gs[3])
plt.xlim(250.0, 221.0)
plt.ylim(-0.5, 0.5)
bovy_plot._add_ticks()
plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$")
plt.ylabel(r"$\Delta\mu_\parallel\,(\mathrm{mas\,yr}^{-1})$")
# Colorbar
gs2 = gridspec.GridSpec(2, 1, wspace=0.0, left=0.95, right=0.975)
plt.subplot(gs2[:, -1])
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=-2.5, vmax=-1.5))
sm._A = []
CB1 = plt.colorbar(sm, orientation="vertical", cax=plt.gca())
CB1.set_label(
r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
fontsize=18.0,
)
plt.savefig(
"figures/pal5tracksamples.pdf", bbox_inches="tight",
)
plt.close()
###############################################################
# What about Kuepper et al. (2015)?
# Can we recover the Kuepper et al. (2015) result as prior + $q_\Phi =
# 0.94 \pm 0.05$ + $V_c(R_0)$ between 200 and 280 km/s? For simplicity
# we will not vary $R_0$, which should not have a big impact.
s = sample_kuepper_flattening_post(50000, 0.94, 0.05)
plot_kuepper_samples(s)
plt.savefig("figures/kuepper_samples.pdf")
plt.close()
# The constraint on the potential flattening gets you far, but there
# is more going on (the constraint on the halo mass and scale
# parameter appear to come completely from the $V_c(R_0)$ constraint).
# Let's add the weak constraint on the sum of the forces, scaled to
# Kuepper et al.'s best-fit acceleration (which is higher than ours):
s = sample_kuepper_flatteningforce_post(50000, 0.94, 0.05, -0.83, 0.36)
plot_kuepper_samples(s)
plt.savefig("figures/kuepper_samples_flatF.pdf")
plt.close()
# This gets a tight relation between $M_\mathrm{halo}$ and the scale
# parameter of the halo, but does not lead to a constraint on either
# independently; the halo potential flattening constraint is that of
# Kuepper et al. Based on this, it appears that the information that
# ties down $M_\mathrm{halo}$ comes from the overdensities, which may
# be spurious (Thomas et al. 2016) and whose use in dynamical modeling
# is dubious anyway.
# What is Kuepper et al.'s prior on $a_{\mathrm{Pal\ 5}}$?
apal5s = []
ns = 100000
for ii in range(ns):
Mh = np.random.uniform() * (10.0 - 0.001) + 0.001
a = np.random.uniform() * (100.0 - 0.1) + 0.1
q = np.random.uniform() * (1.8 - 0.2) + 0.2
pot = setup_potential_kuepper(Mh, a)
FR, FZ = force_pal5_kuepper(pot, q)
apal5s.append(np.sqrt(FR ** 2.0 + FZ ** 2.0))
apal5s = np.array(apal5s)
plt.figure(figsize=(6, 4))
dum = bovy_plot.bovy_hist(
apal5s, range=[0.0, 10.0], lw=2.0, bins=51, histtype="step", normed=True,
)
plt.savefig("figures/kuepper_samples_prior.pdf")
plt.close()
