
resPath="./" # directory where generated plots are saved
HSCContext= "HSCv3"

%matplotlib inline
import astropy, time, sys, os, requests
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

from PIL import Image
from io import BytesIO

# check that version of mastcasjobs is new enough
# we are using some features not in version 0.0.1
from pkg_resources import get_distribution
from distutils.version import StrictVersion as V
assert V(get_distribution("mastcasjobs").version) >= V('0.0.2'), """
A newer version of mastcasjobs is required.
Update mastcasjobs to current version using this command:
pip install --upgrade git+git://github.com/rlwastro/mastcasjobs@master
"""

import mastcasjobs

## For handling ordinary astropy Tables
from astropy.table import Table
from astropy.io import fits, ascii

from fastkde import fastKDE
from scipy.interpolate import RectBivariateSpline
from astropy.modeling import models, fitting

# There are a number of relatively unimportant warnings that 
# show up, so for now, suppress them:
import warnings
warnings.filterwarnings("ignore")

# Set page width to fill browser for longer output lines
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# set width for pprint
astropy.conf.max_width = 150


import getpass
if not os.environ.get('CASJOBS_USERID'):
    os.environ['CASJOBS_USERID'] = input('Enter Casjobs UserID:')
if not os.environ.get('CASJOBS_PW'):
    os.environ['CASJOBS_PW'] = getpass.getpass('Enter Casjobs password:')


DBtable = "SWEEPS"
jobs = mastcasjobs.MastCasJobs(context="MyDB")

try:
    print("Retrieving table MyDB.{} (if it exists)".format(DBtable))
    tab = jobs.fast_table(DBtable, verbose=True)
except ValueError:
    print("Table MyDB.{} not found, running query to create it".format(DBtable))

    # drop table if it already exists
    jobs.drop_table_if_exists(DBtable)

    #get main information
    query = """
        select a.ObjID,  RA=a.raMean, Dec=a.decMean, RAerr=a.raMeanErr, Decerr=a.decMeanErr,
            c.NumFilters, c.NumVisits,
            a_f606w=i1.MagMed,  a_f606w_n=i1.n, a_f606w_mad=i1.MagMAD,
            a_f814w=i2.MagMed, a_f814w_n=i2.n, a_f814w_mad=i2.MagMAD,
            bpm=a.pmLat, lpm=a.pmLon, bpmerr=a.pmLatErr, lpmerr=a.pmLonErr,
            pmdev=sqrt(pmLonDev*pmLonDev+pmLatDev*pmLatDev),
            yr=(a.epochMean - 47892)/365.25+1990, 
            dT=(a.epochEnd-a.epochStart)/365.25,
            yrStart=(a.epochStart - 47892)/365.25+1990,
            yrEnd=(a.epochEnd - 47892)/365.25+1990
        into mydb.{}
        from AstromProperMotions a join AstromSumMagAper2 i1 on 
             i1.ObjID=a.ObjID and i1.n >=10 and i1.filter ='F606W' and i1.detector='ACS/WFC'
         join AstromSumMagAper2 i2 on 
             i2.ObjID=a.ObjID and i2.n >=10 and i2.filter ='F814W' and i2.detector='ACS/WFC'
         join AstromSumPropMagAper2Cat c on a.ObjID=c.ObjID
    """.format(DBtable)

    t0 = time.time()
    jobid = jobs.submit(query, task_name="SWEEPS", context=HSCContext)
    print("jobid=",jobid)
    results = jobs.monitor(jobid)
    print("Completed in {:.1f} sec".format(time.time()-t0))
    print(results)

    # slower version using CasJobs output queue
    # tab = jobs.get_table(DBtable, verbose=True)
    
    # fast version using special MAST Casjobs service
    tab = jobs.fast_table(DBtable, verbose=True)

tab

Retrieving table MyDB.SWEEPS (if it exists)
26.2 s: Retrieved 157.86MB table MyDB.SWEEPS
35.6 s: Converted to 443932 row table


x = tab['RA']
y = tab['Dec']

plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.scatter(x, y, s=1)
plt.autoscale(tight=True)
plt.xlabel('RA')
plt.ylabel('Dec')
dc=0.01
plt.xlim(min(x)-dc, max(x)+dc)
plt.ylim(min(y)-dc, max(y)+dc)
plt.gca().invert_xaxis()
plt.text(0.5,0.93,'{:,} stars in SWEEPS'.format(len(x)),
       horizontalalignment='left',
       transform=plt.gca().transAxes)

Text(0.5, 0.93, '443,932 stars in SWEEPS')


bin = 0.2
hrange = (-20,20)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['lpm'], range=hrange, bins=bincount, label='Longitude', 
           histtype='step', linewidth=2)
plt.hist(tab['bpm'], range=hrange, bins=bincount, label='Latitude', 
           histtype='step', linewidth=2)
plt.xlabel('Proper motion [mas/yr]')
plt.ylabel('Number [in {:.2} mas bins]'.format(bin))
plt.legend(loc='upper right')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,13500)
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_pmerr_hist.png'.format(resPath))


bin = 0.01
hrange = (0,2)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['lpmerr'], range=hrange, bins=bincount, label='Longitude Error', 
           histtype='step', cumulative=1, linewidth=2)
plt.hist(tab['bpmerr'], range=hrange, bins=bincount, label='Latitude Error', 
           histtype='step', cumulative=1, linewidth=2)
plt.xlabel('Proper motion error [mas/yr]')
plt.ylabel('Cumulative number [in {:0.2} mas bins]'.format(bin))
plt.legend(loc='upper right')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,500000)
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_pmerr_cumhist.png'.format(resPath))


bin = 0.01
hrange = (0,6)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['lpmerr'], range=hrange, bins=bincount, label='Longitude Error', 
           histtype='step', linewidth=2)
plt.hist(tab['bpmerr'], range=hrange, bins=bincount, label='Latitude Error', 
           histtype='step', linewidth=2)
plt.xlabel('Proper motion error [mas/yr]')
plt.ylabel('Number [in {:0.2} mas bins]'.format(bin))
plt.legend(loc='upper right')
plt.yscale('log')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,15000)
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_pmerr_loghist.png'.format(resPath))


# restrict to sources with dT > 1 year
dtmin = 1.0
w = np.where(tab['dT']>dtmin)[0]
if ('rw' not in locals()) or len(rw) != len(w):
    rw = np.random.random(len(w))
x = np.array(tab['dT'][w]) + 0.5*(rw-0.5)
y = np.log(np.array(tab['lpmerr'][w]))

# Calculate the point density
t0 = time.time()
myPDF,axes = fastKDE.pdf(x,y,numPoints=2**9+1)
print("kde took {:.1f} sec".format(time.time()-t0))

# interpolate to get z values at points
finterp = RectBivariateSpline(axes[1],axes[0],myPDF)
z = finterp(y,x,grid=False)

# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
xs, ys, zs = x[idx], y[idx], z[idx]

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs))*zs<0.05)[0]
print("Plotting {} of {} points".format(len(wran),len(zs)))
xs = xs[wran]
ys = ys[wran]
zs = zs[wran]

plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.yscale('log')
plt.scatter(xs, np.exp(ys), c=zs, s=2, edgecolor='none', cmap='plasma', 
              label='Longitude PM error')
plt.autoscale(tight=True, axis='y')
plt.xlim(0.0, max(x)*1.05)
plt.xlabel('Date range [yr]')
plt.ylabel('Proper motion error [mas/yr]')
plt.legend(loc='best')
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.colorbar()
plt.tight_layout()
# plt.savefig('{}sweeps_pmerr_vs_dt.png'.format(resPath))

kde took 3.6 sec
Plotting 170401 of 442682 points


bin = 0.01
hrange = (0,6)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1

tsplit = 6
dmaglim = 0.05

plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,12))
plt.subplot(211)
wmag = np.where((tab['a_f606w_mad']<dmaglim) & (tab['a_f814w_mad']<dmaglim))[0]
w = wmag[tab['dT'][wmag]<=tsplit]
plt.hist(tab['lpmerr'][w], range=hrange, bins=bincount, label='Longitude Error', 
           histtype='step', linewidth=2)
plt.hist(tab['bpmerr'][w], range=hrange, bins=bincount, label='Latitude Error', 
           histtype='step', linewidth=2)
plt.xlabel('Proper motion error [mas/yr]')
plt.ylabel('Number [in {:0.2} mas bins]'.format(bin))
plt.legend(loc='upper right')
plt.yscale('log')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,15000)
plt.title('{:,} stars in SWEEPS with dT < {} yrs, dmag < {}'.format(len(w),tsplit,dmaglim))
plt.tight_layout()

plt.subplot(212)
w = wmag[tab['dT'][wmag]>tsplit]
plt.hist(tab['lpmerr'][w], range=hrange, bins=bincount, label='Longitude Error', 
           histtype='step', linewidth=2)
plt.hist(tab['bpmerr'][w], range=hrange, bins=bincount, label='Latitude Error', 
           histtype='step', linewidth=2)
plt.xlabel('Proper motion error [mas/yr]')
plt.ylabel('Number [in {:0.2} mas bins]'.format(bin))
plt.legend(loc='upper right')
plt.yscale('log')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,15000)
plt.title('{:,} stars in SWEEPS with dT > {} yrs, dmag < {}'.format(len(w),tsplit,dmaglim))
plt.tight_layout()

plt.savefig('{}sweeps_pmerr_loghist2.png'.format(resPath))


bin = 1
hrange = (0,130)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['NumVisits'], range=hrange, bins=bincount, label='Number of visits ', 
           histtype='step', linewidth=2)
plt.xlabel('Number of visits')
plt.ylabel('Number of objects')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,200000)
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_numvisits_hist.png'.format(resPath))


bin = 1
hrange = (2000, 2020)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['yr'], range=hrange, bins=bincount, label='year ', histtype='step', linewidth=2)
plt.xlabel('mean detection epoch (year)')
plt.ylabel('Number of objects')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,300000)
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_year_hist.png'.format(resPath))


bin = 0.25
hrange = (0, 15)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['dT'], range=hrange, bins=bincount, label='year ', histtype='step', linewidth=2)
plt.xlabel('time span (years)')
plt.ylabel('Number of objects')
plt.autoscale(enable=True, axis='x', tight=True)
plt.yscale('log')
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_year_hist.png'.format(resPath))


bin = 0.025
hrange = (16,28)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['a_f606w'], range=hrange, bins=bincount, label='F606W', 
           histtype='step',  linewidth=2)
plt.hist(tab['a_f814w'], range=hrange, bins=bincount, label='F814W', 
           histtype='step', linewidth=2)
plt.xlabel('magnitude')
plt.ylabel('Number of stars[in {:0.2} magnitude bins]'.format(bin))
plt.legend(loc='upper right')
plt.autoscale(enable=True, axis='x', tight=True)
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_mag_hist.png'.format(resPath))


bin = 0.001
hrange = (0,0.2)
bincount = int((hrange[1]-hrange[0])/bin + 0.5) + 1
plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.hist(tab['a_f606w_mad'], range=hrange, bins=bincount, label='F606W', 
           histtype='step',  linewidth=2)
plt.hist(tab['a_f814w_mad'], range=hrange, bins=bincount, label='F814W', 
           histtype='step', linewidth=2)
plt.xlabel('magnitude error (median absolute deviation)')
plt.ylabel('Number of stars[in {:0.2} magnitude bins]'.format(bin))
plt.legend(loc='upper right')
plt.autoscale(enable=True, axis='x', tight=True)
plt.ylim(0,15000)
plt.title('{:,} stars in SWEEPS'.format(len(tab)))
plt.tight_layout()
plt.savefig('{}sweeps_magerr_hist.png'.format(resPath))


f606w = tab['a_f606w']
f814w = tab['a_f814w']
RminusI = f606w-f814w

# Calculate the point density
w = np.where((RminusI > -1) & (RminusI < 4))[0]
x = np.array(RminusI[w])
y = np.array(f606w[w])
t0 = time.time()
myPDF,axes = fastKDE.pdf(x,y,numPoints=2**10+1)
print("kde took {:.1f} sec".format(time.time()-t0))

# interpolate to get z values at points
finterp = RectBivariateSpline(axes[1],axes[0],myPDF)
z = finterp(y,x,grid=False)

# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
xs, ys, zs = x[idx], y[idx], z[idx]

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs))*zs<0.05)[0]
print("Plotting {} of {} points".format(len(wran),len(zs)))
xs = xs[wran]
ys = ys[wran]
zs = zs[wran]

plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.autoscale(tight=True)
plt.xlabel('A_F606W - A_F814W')
plt.ylabel('A_F606W')
plt.gca().invert_yaxis()
plt.xlim(-1,4)
plt.ylim(27.5,17.5)
plt.colorbar()
plt.text(.93,.93,'{:,} stars in SWEEPS'.format(len(x)),
       horizontalalignment='right',
       transform=plt.gca().transAxes)
plt.savefig("{}sweeps_colormag1.png".format(resPath))

kde took 4.1 sec
Plotting 147230 of 443502 points


# define function
def positions(Obj, jobs=None):
    """
    input parameter Obj is the value of the ObjID 
    optional jobs parameter re-uses casjobs jobs variable
    output plots change in (lon, lat) as a function of time
    overplots proper motion fit
    provides number of objects and magnitude/color information
    """
    if not jobs:
        jobs = mastcasjobs.MastCasJobs(context=HSCContext)

    # execute these as "system" queries so they don't fill up your Casjobs history

    # get the measured positions as a function of time
    query = """SELECT dT, dLon, dLat 
        from AstromSourcePositions where ObjID={}
        order by dT
        """.format(Obj)
    pos = jobs.quick(query, context=HSCContext, task_name="SWEEPS/Microlensing",
                     astropy=True, system=True)
    
    # get the PM fit parameters
    query = """SELECT pmlon, pmlonerr, pmlat, pmlaterr
        from AstromProperMotions where ObjID={}
        """.format(Obj)
    pm = jobs.quick(query, context=HSCContext, task_name="SWEEPS/Microlensing",
                    astropy=True, system=True)
    
    lpm = pm['pmlon'][0]
    bpm = pm['pmlat'][0]
    
    # get the intercept for the proper motion fit referenced to the start time
    # time between mean epoch and zero (ref) epoch (years)

    # get median magnitudes and colors for labeling
    query = """SELECT a_f606w=i1.MagMed, a_f606_m_f814w=i1.MagMed-i2.MagMed
        from AstromSumMagAper2 i1 
        join AstromSumMagAper2 i2 on i1.ObjID=i2.ObjID 
        where i1.ObjID={} and i1.filter='f606w' and i2.filter='f814w' 
        """.format(Obj)
    phot = jobs.quick(query, context=HSCContext, task_name="SWEEPS/Microlensing",
                      astropy=True, system=True)
    f606w = phot['a_f606w'][0]
    f606wmf814w = phot['a_f606_m_f814w'][0]

    x = pos['dT']
    y = pos['dLon']
    plt.rcParams.update({'font.size':10})
    plt.figure(1,(6,3))
    plt.subplot(121)
    plt.scatter(x, y, s=10)
    # xpm = np.linspace(0, max(x), 10)
    xpm = np.array([x.min(),x.max()])
    ypm = lpm*xpm
    plt.plot(xpm, ypm, '-r')
    plt.xlabel('dT (yrs)')
    plt.ylabel('dLon (mas)')
    y = pos['dLat']
    plt.subplot(122)
    plt.scatter(x, y, s=10)
    ypm = bpm*xpm
    plt.plot(xpm, ypm, '-r')
    plt.xlabel('dT (yrs)')
    plt.ylabel('dLat (mas)')
    plt.suptitle("""ObjID {0}
{1} detections,  (lpm, bpm) = ({2:.1f}, {3:.1f}) mas/yr
(f606w, f606w-f814w) = ({4:.1f}, {5:.1f})""".format(Obj, len(x), lpm, bpm, f606w, f606wmf814w),
                  size=10)
    plt.tight_layout(rect=[0, 0.0, 1, 0.88])
    plt.show()
    plt.close()


n = tab['NumVisits']
dev = tab['pmdev']
objid = tab['ObjID']
lpmerr0 = np.array(tab['lpmerr'])
bpmerr0 = np.array(tab['bpmerr'])
wi = np.where( (dev < 1.5) & (n > 90) & (np.sqrt(bpmerr0**2+lpmerr0**2) < 1.0))[0]
print("Plotting {} objects".format(len(wi)))
for o in objid[wi]:
    positions(o, jobs=jobs)

Plotting 21 objects


f606w = tab['a_f606w']
f814w = tab['a_f814w']
RminusI = f606w-f814w

w = np.where((RminusI > -1) & (RminusI < 4))[0]
f606w_mad = tab['a_f606w_mad']
f814w_mad = tab['a_f814w_mad']

t0=time.time()
# Calculate the point density
x1 = np.array(f606w[w])
y1 = np.array(f606w_mad[w])
y1log = np.log(y1)
myPDF1,axes1 = fastKDE.pdf(x1,y1log,numPoints=2**10+1)
finterp = RectBivariateSpline(axes1[1],axes1[0],myPDF1)
z1 = finterp(y1log,x1,grid=False)
# Sort the points by density, so that the densest points are plotted last
idx = z1.argsort()
xs1, ys1, zs1 = x1[idx], y1[idx], z1[idx]

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs1))*zs1<0.05)[0]
print("Plotting {} of {} points".format(len(wran),len(zs1)))
xs1 = xs1[wran]
ys1 = ys1[wran]
zs1 = zs1[wran]

x2 = np.array(f814w[w])
y2 = np.array(f814w_mad[w])
y2log = np.log(y2)
myPDF2,axes2 = fastKDE.pdf(x2,y2log,numPoints=2**10+1)
finterp = RectBivariateSpline(axes2[1],axes2[0],myPDF2)
z2 = finterp(y2log,x2,grid=False)
idx = z2.argsort()
xs2, ys2, zs2 = x2[idx], y2[idx], z2[idx]
print("{:.1f} s: completed kde".format(time.time()-t0))

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs2))*zs2<0.05)[0]
print("Plotting {} of {} points".format(len(wran),len(zs2)))
xs2 = xs2[wran]
ys2 = ys2[wran]
zs2 = zs2[wran]

xr = (18,27)
xx = np.arange(501)*(xr[1]-xr[0])/500.0 + xr[0]
xcut1 = 24.2
xnorm1 = 0.03
xcut2 = 23.0
xnorm2 = 0.03

# only plot a subset of the points to speed things up
qsel = 3
xs1 = xs1[::qsel]
ys1 = ys1[::qsel]
zs1 = zs1[::qsel]
xs2 = xs2[::qsel]
ys2 = ys2[::qsel]
zs2 = zs2[::qsel]

plt.figure(1,(15,8))
plt.subplot(121)
plt.yscale('log')
plt.scatter(xs1,ys1,c=zs1,s=2,edgecolor='none',cmap='plasma')
plt.autoscale(tight=True)
# overplot an error limit that varies with magnitude of the form listed below
plt.plot(xx,xnorm1 * (1. + 10.**(0.4*(xx-xcut1))),linewidth=2.0,
           label='$%.2f (1+10^{0.4(M-%.1f)})$' % (xnorm1,xcut1))
plt.legend(loc='upper left')
plt.xlabel('F606W')
plt.ylabel('F606W_MAD')

plt.subplot(122)
plt.yscale('log')
plt.scatter(xs2,ys2,c=zs2,s=2,edgecolor='none',cmap='plasma')
plt.autoscale(tight=True)
# overplot an error limit that varies with magnitude of the form listed below
plt.plot(xx,xnorm2 * (1. + 10.**(0.4*(xx-xcut2))),linewidth=2.0,
           label='$%.2f (1+10^{0.4(M-%.1f)})$' % (xnorm2,xcut2))
plt.legend(loc='upper left')
plt.xlabel('F814W')
plt.ylabel('F814W_MAD')
plt.tight_layout()

Plotting 274457 of 443502 points
8.5 s: completed kde
Plotting 250807 of 443502 points


def noisecut(tab, factor=1.0):
    """Return boolean array with noise cut in f606w and f814w using model
    factor is normalization factor to use (>1 means allow more noise)
    """
    f606w = tab['a_f606w']
    f814w = tab['a_f814w']
    f606w_mad = tab['a_f606w_mad']
    f814w_mad = tab['a_f814w_mad']
    
    # noise model computed above
    xcut_f606w = 24.2
    xnorm_f606w = 0.03 * factor
    xcut_f814w = 23.0
    xnorm_f814w = 0.03 * factor
    return ((f606w_mad < xnorm_f606w*(1+10.0**(0.4*(f606w-xcut_f606w))))
          & (f814w_mad < xnorm_f814w*(1+10.0**(0.4*(f814w-xcut_f814w))))
           )

# low-noise objects
good = noisecut(tab,factor=1.0)

# Calculate the point density
w = np.where((RminusI > 0) & (RminusI < 3) & good)[0]
x = np.array(RminusI[w])
y = np.array(f606w[w])
t0 = time.time()
myPDF,axes = fastKDE.pdf(x,y,numPoints=2**10+1)
print("kde took {:.1f} sec".format(time.time()-t0))

# interpolate to get z values at points
finterp = RectBivariateSpline(axes[1],axes[0],myPDF)
z = finterp(y,x,grid=False)

# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
xs, ys, zs = x[idx], y[idx], z[idx]

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs))*zs<0.075)[0]
print("Plotting {} of {} points".format(len(wran),len(zs)))
xs = xs[wran]
ys = ys[wran]
zs = zs[wran]

plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.xlabel('A_F606W - A_F814W')
plt.ylabel('A_F606W')
plt.gca().invert_yaxis()
plt.xlim(-1,4)
plt.ylim(27.5,17.5)
plt.colorbar()
plt.text(.93,.93,'{:,} stars in SWEEPS'.format(len(x)),
       horizontalalignment='right',
       transform=plt.gca().transAxes)
plt.savefig("{}sweeps_colormag2.png".format(resPath))

kde took 3.1 sec
Plotting 129892 of 333525 points


# good defined above
f606w = tab['a_f606w']
f814w = tab['a_f814w']
RminusI = f606w-f814w
w = np.where((RminusI > 0) & (RminusI < 3) & good)[0]
lpm = np.array(tab['lpm'][w])
bpm = np.array(tab['bpm'][w])
x = np.array(RminusI[w])
y = np.array(f606w[w])

nbins = 50
count2d, yedge, xedge = np.histogram2d(y, x, bins=nbins)
lpm_sum = np.histogram2d(y, x, bins=nbins, weights=lpm-lpm.mean())[0]
bpm_sum = np.histogram2d(y, x, bins=nbins, weights=bpm-bpm.mean())[0]
lpm_sumsq = np.histogram2d(y, x, bins=nbins, weights=(lpm-lpm.mean())**2)[0]
bpm_sumsq = np.histogram2d(y, x, bins=nbins, weights=(bpm-bpm.mean())**2)[0]

ccount = count2d.clip(1) 
lpm_mean = lpm_sum/ccount
bpm_mean = bpm_sum/ccount
lpm_rms = np.sqrt(lpm_sumsq/ccount-lpm_mean**2)
bpm_rms = np.sqrt(bpm_sumsq/ccount-bpm_mean**2)
lpm_msigma = lpm_rms/np.sqrt(ccount)
bpm_msigma = bpm_rms/np.sqrt(ccount)

plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
ww = np.where(count2d > 100)
yy, xx = np.mgrid[:nbins,:nbins]
xx = (0.5*(xedge[1:]+xedge[:-1]))[xx]
yy = (0.5*(yedge[1:]+yedge[:-1]))[yy]
# plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
Q = plt.quiver(xx[ww],yy[ww],lpm_mean[ww],bpm_mean[ww],color='red',width=0.0015)
qlength = 5
plt.quiverkey(Q,0.8,0.97,qlength,'{} mas/yr'.format(qlength),coordinates='axes',labelpos='W')
plt.gca().invert_yaxis()
plt.autoscale(tight=True)
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[-1],yedge[0]))
plt.xlabel('A_F606W - A_F814W')
plt.ylabel('A_F606W')
plt.savefig('{}sweeps_vecmean.png'.format(resPath))


plt.rcParams.update({'font.size': 16})
plt.figure(1,(20,6))

sub1 = plt.subplot(131)
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
plt.gca().invert_yaxis()
plt.xlabel('A_F606W - A_F814W')
plt.ylabel('A_F606W')
plt.text(0.97, 0.97,
       'Color-magnitude\n{:,} stars\nin SWEEPS'.format(len(x)),
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
plt.colorbar()

sub2 = plt.subplot(132)
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
mask = (lpm_msigma <= 1.0) & (count2d > 10)
im = (lpm_mean+lpm.mean())*mask
im[~mask] = np.nan
vmax = np.nanmax(np.abs(im))
plt.imshow(im,cmap='RdYlGn',aspect="auto",origin="lower",
            extent=(xedge[0],xedge[-1],yedge[0],yedge[-1]))
plt.gca().invert_yaxis()
plt.xlabel('A_F606W - A_F814W')
plt.text(0.97, 0.97,'Mean Longitude PM\n$\sigma(\mathrm{PM}) \leq 1$ and $N > 10$',
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
cbar = plt.colorbar()
cbar.ax.set_ylabel('mas/yr', rotation=270)

sub2 = plt.subplot(133)
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
im = lpm_rms*(count2d>10)
plt.imshow(im,cmap='magma',aspect="auto",origin="lower",
            extent=(xedge[0],xedge[-1],yedge[0],yedge[-1]),
            norm=LogNorm(vmin=im[im>0].min(), vmax=im.max()))
plt.gca().invert_yaxis()
plt.xlabel('A_F606W - A_F814W')
plt.text(0.97, 0.97,'RMS in Longitude PM\nN > 10',
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
cbar = plt.colorbar()
cbar.ax.set_ylabel('mas/yr', rotation=270)

plt.tight_layout()
plt.savefig("{}sweeps_PMlon.png".format(resPath))


plt.rcParams.update({'font.size': 16})
plt.figure(1,(20,6))

sub1 = plt.subplot(131)
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
plt.gca().invert_yaxis()
plt.xlabel('A_F606W - A_F814W')
plt.ylabel('A_F606W')
plt.text(0.97,0.97,
       'Color-magnitude\n{:,} stars\nin SWEEPS'.format(len(x)),
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
plt.colorbar()

sub2 = plt.subplot(132)
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
mask = (bpm_msigma <= 1.0) & (count2d > 10)
im = (bpm_mean+bpm.mean())*mask
im[~mask] = np.nan
vmax = np.nanmax(np.abs(im))
plt.imshow(im,cmap='RdYlGn',aspect="auto",origin="lower",
            extent=(xedge[0],xedge[-1],yedge[0],yedge[-1]))
plt.gca().invert_yaxis()
plt.xlabel('A_F606W - A_F814W')
plt.text(.97,.97,'Mean Latitude PM\n$\sigma(\mathrm{PM}) \leq 1$ and $N > 10$',
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
cbar = plt.colorbar()
cbar.ax.set_ylabel('mas/yr', rotation=270)

sub2 = plt.subplot(133)
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
im = bpm_rms*(count2d>10)
plt.imshow(im,cmap='magma',aspect="auto",origin="lower",
            extent=(xedge[0],xedge[-1],yedge[0],yedge[-1]),
            norm=LogNorm(vmin=im[im>0].min(), vmax=im.max()))
plt.gca().invert_yaxis()
plt.xlabel('A_F606W - A_F814W')
plt.text(.97,.97,'RMS in Latitude PM\nN > 10',
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
cbar = plt.colorbar()
cbar.ax.set_ylabel('mas/yr', rotation=270)

plt.tight_layout()
plt.savefig("{}sweeps_PMlat.png".format(resPath))


# locate ridge line
iridgex = np.argmax(myPDF,axis=1)
pdfx = myPDF[np.arange(len(iridgex),dtype=int),iridgex]
# pdfx = myPDF.max(axis=1)
wx = np.where(pdfx > 0.1)[0]
iridgex = iridgex[wx]
# use weighted sum of 2*hw+1 points around peak
hw = 10
pridgex = 0.0
pdenom = 0.0
for k in range(-hw,hw+1):
    wt = myPDF[wx,iridgex+k]
    pridgex = pridgex + k*wt
    pdenom = pdenom + wt
pridgex = iridgex + pridgex/pdenom
ridgex = np.interp(pridgex, np.arange(len(axes[0])), axes[0])

# Fit the data using a polynomial model
x0 = axes[1][wx].min()
x1 = axes[1][wx].max()
p_init = models.Polynomial1D(9)
fit_p = fitting.LinearLSQFitter()
xx = (axes[1][wx]-x0)/(x1-x0)
yoff = 0.65
yy = np.log(ridgex - yoff)
p = fit_p(p_init, xx, yy)

# define useful functions for the ridge line

def ridge_color(f606w, function=p, yoff=yoff, x0=x0, x1=x1):
    """Return R-I position of ridge line as a function of f606w magnitude
    
    function, yoff, x0, x1 are from polynomial fit above
    """
    return yoff + np.exp(p((f606w-x0)/(x1-x0)))

# calculate grid of function values for approximate inversion
rxgrid = axes[1][wx]
rygrid = ridge_color(rxgrid)
color_domain = [rygrid[0],rygrid[-1]]
mag_domain = [axes[1][wx[0]], axes[1][wx[-1]]]
print("color_domain {}".format(color_domain))
print("mag_domain   {}".format(mag_domain))

def ridge_mag(RminusI, xgrid=rxgrid, ygrid=rygrid):
    """Return f606w position of ridge line as a function of R-I color
    
    Uses simple linear interpolation to get approximate value
    """
    f606w = np.interp(RminusI,ygrid,xgrid)
    f606w[(RminusI < ygrid[0]) | (RminusI > ygrid[-1])] = np.nan
    return f606w

color_domain [0.6883374859498668, 2.156051675823446]
mag_domain   [19.356585323810577, 26.480549454689026]


ridgexf = yoff + np.exp(p(xx))

plt.figure(1,(12,6))
plt.subplot(121)
plt.plot(axes[1][wx], ridgex, 'bo')
plt.plot(axes[1][wx], ridgexf, color='red')
plt.ylabel('R-I')
plt.xlabel('R')

# check the derivative plot to see if it stays positive
deriv = np.exp(p(xx))*models.Polynomial1D.horner(xx, 
                            (p.parameters * np.arange(len(p.parameters)))[1:])
plt.subplot(122)
plt.semilogy(axes[1][wx], np.exp(p(xx))*models.Polynomial1D.horner(xx, 
                            (p.parameters * np.arange(len(p.parameters)))[1:]),color='red')
plt.xlabel('R')
plt.ylabel('Fit derivative')
plt.title('Min deriv {:.6f}'.format(deriv.min()))
plt.tight_layout()


plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
plt.gca().invert_yaxis()

# overplot ridge line
plt.plot(ridge_color(axes[1][wx]), axes[1][wx], color='red')
plt.plot(axes[0], ridge_mag(axes[0]), color='green')

plt.xlabel('A_F606W - A_F814W')
plt.ylabel('A_F606W')
plt.text(.97,.97,
       'Color-magnitude\n{:,} stars\nin SWEEPS'.format(len(x)),
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
plt.colorbar()
# plt.savefig('{}sweeps_ridgeline.png'.format(resPath))

<matplotlib.colorbar.Colorbar at 0x7f8ef8541e20>


yloc = ridge_mag(x)
x1 = x[np.isfinite(yloc)]
wy = np.where((axes[0] >= x1.min()) & (axes[0] <= x1.max()))[0]
ridgey = ridge_mag(axes[0][wy])

# Weighted histogram
dmagmin = -2.0
dmagmax = 1.0
xmax = axes[0][wy[-1]]
# xmin = axes[0][wy[0]]
xmin = 1.0
wsel = np.where((y-yloc >= dmagmin) & (y-yloc <= dmagmax) & (x >= xmin) & (x <= xmax))[0]

x2 = y[wsel]-yloc[wsel]
y2 = lpm[wsel]-lpm.mean()
hrange = (dmagmin, dmagmax)
hbins = 50
count1d, xedge1d = np.histogram(x2,range=hrange,bins=hbins)
lpm_sum1d = np.histogram(x2,range=hrange,bins=hbins,weights=y2)[0]
lpm_sumsq1d = np.histogram(x2,range=hrange,bins=hbins,weights=y2**2)[0]

ccount1d = count1d.clip(1)
lpm_mean1d = lpm_sum1d/ccount1d
lpm_rms1d = np.sqrt(lpm_sumsq1d/ccount1d-lpm_mean1d**2)
lpm_msigma1d = lpm_rms1d/np.sqrt(ccount1d)
lpm_mean1d += lpm.mean()

x1d = 0.5*(xedge1d[1:]+xedge1d[:-1])

plt.rcParams.update({'font.size': 16})
plt.figure(1,(14,6))

sub1 = plt.subplot(121)
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
plt.gca().invert_yaxis()
plt.xlabel('R - I (A_F606W - A_F814W)')
plt.ylabel('R (A_F606W)')
plt.text(.97,.97,
       'Color-magnitude\n{:,} stars\nin SWEEPS'.format(len(x)),
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
xboundary = np.hstack((axes[0][wy],axes[0][wy[::-1]]))
yboundary = np.hstack((ridgey+dmagmax,ridgey[::-1]+dmagmin))
wb = np.where((xboundary >= xmin) & (xboundary <= xmax))
xboundary = xboundary[wb]
yboundary = yboundary[wb]
print(xboundary[0],yboundary[0],xboundary[-1],yboundary[-1])
print(xboundary.shape,yboundary.shape)
xboundary = np.append(xboundary,xboundary[0])
yboundary = np.append(yboundary,yboundary[0])
plt.plot(xboundary, yboundary, color='red')

sub1 = plt.subplot(122)
# don't plot huge error points
wp = np.where(lpm_msigma1d < 1)
plt.errorbar(x1d[wp], lpm_mean1d[wp], xerr=(xedge1d[1]-xedge1d[0])/2.0, 
               yerr=lpm_msigma1d[wp], linestyle='')
plt.autoscale(tight=True)
plt.xlabel('Distance from ridge line [R mag]')
plt.ylabel('Mean Longitude PM [mas/yr]')
plt.gca().invert_xaxis()
plt.text(.03,.97,'{:,} stars in SWEEPS\n1-sigma error bars on mean PM'.format(len(x2)),
       horizontalalignment='left', verticalalignment='top',
       transform=plt.gca().transAxes)
plt.tight_layout()

1.0020693112164736 23.784969024110666 1.0020693112164736 20.784969024110666
(394,) (394,)


w = np.where((RminusI > 0) & (RminusI < 3) & good)[0]

# Calculate the point density
x = np.array(RminusI[w])
y = np.array(f606w[w])
myPDF,axes = fastKDE.pdf(x,y,numPoints=2**10+1)
finterp = RectBivariateSpline(axes[1],axes[0],myPDF)
z = finterp(y,x,grid=False)
idx = z.argsort()
xs, ys, zs = x[idx], y[idx], z[idx]

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs))*zs<0.1)[0]
print("Plotting {} of {} points".format(len(wran),len(zs)))
xs = xs[wran]
ys = ys[wran]
zs = zs[wran]

# locate ridge line in magnitude as a function of color
xloc = ridge_color(y)
ridgex = ridge_color(axes[1][wx])

# locate ridge line in color as function of magnitude
yloc = ridge_mag(x)
x1 = x[np.isfinite(yloc)]
wy = np.where((axes[0] >= x1.min()) & (axes[0] <= x1.max()))[0]
ridgey = ridge_mag(axes[0][wy])

# low-noise objects
print("Selected {:,} low-noise objects".format(len(w)))

# red objects
ylim = yloc - 1.5 - (yloc - 25.0).clip(0)/(1+10.0**(-0.4*(yloc-26.0)))
wred = np.where((y<25) & (y > 19.5) & (y < ylim) & (x-xloc > 0.35))[0]
#wred = np.where((y<25) & (y > 19.5) & ((y-yloc) < -1.5)
#                   & (x-xloc > 0.3))[0]
#                   & (x > 1.1) & (x < 2.5) & (x-xloc > 0.2))[0]
print("Selected {:,} red objects".format(len(wred)))
# main sequence objects
wmain = np.where((y>21) & (y<22.4) & (np.abs(x-xloc) < 0.1))[0]
print("Initially selected {:,} MS objects".format(len(wmain)))
# sort by distance from ridge and select the closest
wmain = wmain[np.argsort(np.abs(x[wmain]-xloc[wmain]))]
wmain = wmain[:len(wred)]
print("Selected {:,} MS objects closest to ridge".format(len(wmain)))

plt.rcParams.update({'font.size': 14})

plt.figure(1,(12,8))
plt.subplot2grid((2,3), (0,0), rowspan=2, colspan=2)
plt.scatter(xs,ys,c=zs,s=2,cmap='plasma',edgecolor='none')
plt.scatter(x[wred],y[wred],c='red',s=2,edgecolor='none')
plt.scatter(x[wmain],y[wmain],c='blue',s=2,edgecolor='none')
plt.xlim(0,3)
plt.ylim(18,27.5)
plt.xlabel('F606W-F814W [mag]')
plt.ylabel('F606W [mag]')
plt.gca().invert_yaxis()
plt.title('{:,} stars in SWEEPS'.format(len(x)))

lrange = (-20,5)
brange = (-12.5,12.5)

# plot MS and red points in random order
wsel = w[np.append(wmain,wred)]
colors = np.array(['blue']*len(wsel))
colors[len(wmain):] = 'red'
irs = np.argsort(np.random.random(len(wsel)))
wsel = wsel[irs]
colors = colors[irs]

plt.subplot2grid((2,3), (0,2), rowspan=1, colspan=1)
plt.scatter(tab['lpm'][w],tab['bpm'][w],c='darkgray',s=0.1)
plt.scatter(tab['lpm'][wsel],tab['bpm'][wsel],c=colors,s=2)
plt.xlim(*lrange)
plt.ylim(*brange)
plt.ylabel('Latitude PM [mas/yr]')

plt.subplot2grid((2,3), (1,2), rowspan=1, colspan=1)
bin = 0.5
bincount = int((lrange[1]-lrange[0])/bin + 0.5) + 1
plt.hist(tab['lpm'][w[wmain]], range=lrange, bins=bincount, label='MS', color='blue', 
           histtype='step')
plt.hist(tab['lpm'][w[wred]], range=lrange, bins=bincount, label='Red', color='red', 
           histtype='step')
plt.xlim(*lrange)
plt.xlabel('Longitude PM [mas/yr]')
plt.ylabel('Number [in {:.2} mas bins]'.format(bin))
plt.legend(loc='upper left')
plt.tight_layout()
plt.savefig('{}sweeps_calamida.png'.format(resPath))

Plotting 156351 of 333525 points
Selected 333,525 low-noise objects
Selected 2,797 red objects
Initially selected 59,605 MS objects
Selected 2,797 MS objects closest to ridge


lpmmain=np.mean(tab['lpm'][w[wmain]])
bpmmain=np.mean(tab['bpm'][w[wmain]])

norm=1.0/np.sqrt(len(tab['lpm'][w[wmain]]))
print("Bulge stars mean PM   longitude {:.2f} +- {:.3f} latitude {:.2f} +- {:.3f}".format(
    np.mean(tab['lpm'][w[wmain]]), norm*np.std(tab['lpm'][w[wmain]]),
    np.mean(tab['bpm'][w[wmain]]), norm*np.std(tab['bpm'][w[wmain]])
    ))

norm=1.2533/np.sqrt(len(tab['lpm'][w[wmain]]))
print("Bulge stars median PM longitude {:.2f} +- {:.3f} latitude {:.2f} +- {:.3f}".format(
    np.median(tab['lpm'][w[wmain]]), norm*np.std(tab['lpm'][w[wmain]]),
    np.median(tab['bpm'][w[wmain]]), norm*np.std(tab['bpm'][w[wmain]])
    ))

Bulge stars mean PM   longitude -6.31 +- 0.059 latitude -0.13 +- 0.053
Bulge stars median PM longitude -6.45 +- 0.074 latitude -0.13 +- 0.067


w = np.where((RminusI > 0) & (RminusI < 3) & good)[0]

wwd = np.where((RminusI < 0.7) 
               & (RminusI > 0) 
               & (f606w > 22.5) 
               & good 
               & (tab['lpm'] < 5) 
               & (tab['lpm'] > -20))[0]
xwd = np.array(RminusI[wwd])
ywd = np.array(f606w[wwd])

# Calculate the point density
x = np.array(RminusI[w])
y = np.array(f606w[w])

myPDF,axes = fastKDE.pdf(x,y,numPoints=2**10+1)
finterp = RectBivariateSpline(axes[1],axes[0],myPDF)
z = finterp(y,x,grid=False)
idx = z.argsort()
xs, ys, zs = x[idx], y[idx], z[idx]

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs))*zs<0.1)[0]
print("Plotting {} of {} points".format(len(wran),len(zs)))
xs = xs[wran]
ys = ys[wran]
zs = zs[wran]

# locate ridge line in magnitude as a function of color
xloc = ridge_color(y)
ridgex = ridge_color(axes[1][wx])

# locate ridge line in color as function of magnitude
yloc = ridge_mag(x)
x1 = x[np.isfinite(yloc)]
wy = np.where((axes[0] >= x1.min()) & (axes[0] <= x1.max()))[0]
ridgey = ridge_mag(axes[0][wy])

# low-noise objects
print("Selected {:,} low-noise objects".format(len(w)))

# red objects
ylim = yloc - 1.5 - (yloc - 25.0).clip(0)/(1+10.0**(-0.4*(yloc-26.0)))
wred = np.where((y<25) & (y > 19.5) & (y < ylim) & (x-xloc > 0.35))[0]
#wred = np.where((y<25) & (y > 19.5) & ((y-yloc) < -1.5)
#                   & (x-xloc > 0.3))[0]
#                   & (x > 1.1) & (x < 2.5) & (x-xloc > 0.2))[0]
print("Selected {:,} red objects".format(len(wred)))
# main sequence objects
wmain = np.where((y>21) & (y<22.4) & (np.abs(x-xloc) < 0.1))[0]
print("Initially selected {:,} MS objects".format(len(wmain)))
# sort by distance from ridge and select the closest
wmain = wmain[np.argsort(np.abs(x[wmain]-xloc[wmain]))]
wmain = wmain[:len(wred)]
print("Selected {:,} MS objects closest to ridge".format(len(wmain)))

plt.rcParams.update({'font.size': 14})

plt.figure(1,(12,8))
plt.subplot2grid((2,3), (0,0), rowspan=2, colspan=2)
plt.scatter(xs,ys,c=zs,s=2,cmap='plasma',edgecolor='none')
plt.scatter(x[wred],y[wred],c='red',s=2,edgecolor='none')
plt.scatter(x[wmain],y[wmain],c='blue',s=2,edgecolor='none')
plt.scatter(xwd,ywd,c='green',s=10,edgecolor='none')
plt.xlim(0,3)
plt.ylim(18,27.5)
plt.xlabel('F606W-F814W [mag]')
plt.ylabel('F606W [mag]')
plt.gca().invert_yaxis()
plt.title('{:,} stars in SWEEPS'.format(len(x)))

lrange = (-20,5)
brange = (-12.5,12.5)

# plot MS and red points in random order
wsel = w[np.append(wmain,wred)]
colors = np.array(['blue']*len(wsel))
colors[len(wmain):] = 'red'
irs = np.argsort(np.random.random(len(wsel)))
wsel = wsel[irs]
colors = colors[irs]

plt.subplot2grid((2,3), (0,2), rowspan=1, colspan=1)
plt.scatter(tab['lpm'][w],tab['bpm'][w],c='darkgray',s=0.1)
plt.scatter(tab['lpm'][wsel],tab['bpm'][wsel],c=colors,s=2)
plt.scatter(tab['lpm'][wwd],tab['bpm'][wwd],c='green',s=2)
plt.xlim(*lrange)
plt.ylim(*brange)
plt.ylabel('Latitude PM [mas/yr]')

plt.subplot2grid((2,3), (1,2), rowspan=1, colspan=1)
bin = 0.5
bincount = int((lrange[1]-lrange[0])/bin + 0.5) + 1
plt.hist(tab['lpm'][wwd], range=lrange, bins=bincount, label='WD', color='green', 
           histtype='step')
plt.hist(tab['lpm'][w[wmain]], range=lrange, bins=bincount, label='MS', color='blue', 
           histtype='step')
plt.hist(tab['lpm'][w[wred]], range=lrange, bins=bincount, label='Red', color='red', 
           histtype='step')
plt.xlim(*lrange)
plt.xlabel('Longitude PM [mas/yr]')
plt.ylabel('Number [in {:.2} mas bins]'.format(bin))
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('{}sweeps_calamida_wd.png'.format(resPath))

Plotting 156346 of 333525 points
Selected 333,525 low-noise objects
Selected 2,797 red objects
Initially selected 59,605 MS objects
Selected 2,797 MS objects closest to ridge


norm=1.0/np.sqrt(len(tab['lpm'][wwd]))
print("WD PM mean +- stdev longitude {:.2f} +- {:.3f} latitude {:.2f} +- {:.3f}".format(
    np.mean(tab['lpm'][wwd]), norm*np.std(tab['lpm'][wwd]),
    np.mean(tab['bpm'][wwd]), norm*np.std(tab['bpm'][wwd])
    ))

WD PM mean +- stdev longitude -5.60 +- 0.152 latitude -0.03 +- 0.245


bin = 0.5
bincount = int((lrange[1]-lrange[0])/bin + 0.5) + 1
plt.hist(tab['lpm'][wwd], range=lrange, bins=bincount, label='WD', color='green', 
           histtype='step', density=True, cumulative=1)
plt.hist(tab['lpm'][w[wmain]], range=lrange, bins=bincount, label='MS', color='blue', 
           histtype='step', density=True, cumulative=1)
plt.hist(tab['lpm'][w[wred]], range=lrange, bins=bincount, label='Red', color='red', 
           histtype='step', density=True, cumulative=1)
plt.xlabel('Longitude PM [mas/yr]')
plt.ylabel('Cumulative fraction'.format(bin))
plt.legend(loc='upper left')
plt.show()


wqso1 = np.where((RminusI < 0.5) 
                & (np.sqrt(tab['bpm']**2+tab['lpm']**2) < 1.0) 
                & (tab['NumFilters'] > 2))[0]
wqso1 = wqso1[np.argsort(f606w[wqso1])]

plt.rcParams.update({'font.size': 16})
plt.figure(1,(10,10))
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.plot(RminusI[wqso1],f606w[wqso1],'rs',markersize=10,fillstyle='none')
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
plt.gca().invert_yaxis()
plt.xlabel('R - I (A_F606W - A_F814W)')
plt.ylabel('R (A_F606W)')
plt.text(0.97,0.97, """{:,} stars in SWEEPS
{} low PM blue stars
with NumFilters$\\geq3$""".format(len(x),len(wqso1)),
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
tab[wqso1]


objid = tab['ObjID']
print("Plotting {} objects".format(len(wqso1)))
for o in objid[wqso1]:
    positions(o,jobs=jobs)

Plotting 14 objects


wqso2 = np.where((RminusI < 0.5) 
                & (np.sqrt(tab['bpm']**2+tab['lpm']**2) < 2.0)
                & (np.sqrt(tab['bpmerr']**2+tab['lpmerr']**2) < 2.0)
                & (tab['pmdev'] < 4.0)
                )[0]
wqso2 = wqso2[np.argsort(f606w[wqso2])]

plt.rcParams.update({'font.size': 16})
plt.figure(1,(10,10))
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.plot(RminusI[wqso2],f606w[wqso2],'rs',markersize=10,fillstyle='none')
plt.xlim((xedge[0],xedge[-1]))
plt.ylim((yedge[0],yedge[-1]))
plt.gca().invert_yaxis()
plt.xlabel('R - I (A_F606W - A_F814W)')
plt.ylabel('R (A_F606W)')
plt.text(0.97,0.97, """{:,} stars in SWEEPS
{} low PM blue stars
with NumFilters$\\geq3$""".format(len(x),len(wqso2)),
       horizontalalignment='right', verticalalignment='top',
       transform=plt.gca().transAxes)
tab[wqso2]


objid = tab['ObjID']
print("Plotting {} objects".format(len(wqso2)))
for o in objid[wqso2]:
    positions(o,jobs=jobs)

Plotting 10 objects


f606w = tab['a_f606w']
f814w = tab['a_f814w']
RminusI = f606w-f814w
lpm0 = np.array(tab['lpm'])
bpm0 = np.array(tab['bpm'])
lpmerr0 = np.array(tab['lpmerr'])
bpmerr0 = np.array(tab['bpmerr'])
pmtot0 = np.sqrt((bpm0-bpmmain)**2+(lpm0-lpmmain)**2)
pmerr0 = np.sqrt(bpmerr0**2+lpmerr0**2)

# sort samples by decreasing PM
wpml = np.where((pmtot0 > 12) & (pmtot0 < 15) & (pmerr0 < 1.0) & good)[0]
wpml = wpml[np.argsort(-pmtot0[wpml])]
xpml = np.array(RminusI[wpml])
ypml = np.array(f606w[wpml])

wpmh = np.where((pmtot0 > 15) & (pmerr0 < 1.0) & good)[0]
wpmh = wpmh[np.argsort(-pmtot0[wpmh])]
xpmh = np.array(RminusI[wpmh])
ypmh = np.array(f606w[wpmh])

# Calculate the point density
w = np.where((RminusI > -1) & (RminusI < 4) & good)[0]
x = np.array(RminusI[w])
y = np.array(f606w[w])
t0 = time.time()
myPDF,axes = fastKDE.pdf(x,y,numPoints=2**10+1)
print("kde took {:.1f} sec".format(time.time()-t0))

# interpolate to get z values at points
finterp = RectBivariateSpline(axes[1],axes[0],myPDF)
z = finterp(y,x,grid=False)

# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
xs, ys, zs = x[idx], y[idx], z[idx]

# select a random subset of points in the most crowded regions to speed up plotting
wran = np.where(np.random.random(len(zs))*zs<0.05)[0]
print("Plotting {} of {} points".format(len(wran),len(zs)))
xs = xs[wran]
ys = ys[wran]
zs = zs[wran]

plt.rcParams.update({'font.size': 16})
plt.figure(1,(12,10))
plt.scatter(xs, ys, c=zs, s=2, edgecolor='none', cmap='plasma')
plt.autoscale(tight=True)
plt.xlabel('A_F606W - A_F814W')
plt.ylabel('A_F606W')
plt.gca().invert_yaxis()
plt.xlim(-1,4)
plt.ylim(27.5,17.0)
plt.colorbar()
plt.scatter(xpml, ypml, s=40, c="green", marker ="^",  label='10 mas/yr < PM < 15 mas/yr')
plt.scatter(xpmh, ypmh, s=80, c="red", marker ="^",  label='15 mas/yr < PM')
plt.text(0.,0.95,'High proper motions relative to mean of main sequence sample (bulge) \n \n {:,} stars in SWEEPS'.format(len(x)),
       horizontalalignment='left',
       transform=plt.gca().transAxes)
plt.legend(loc='upper right')
# plt.savefig("{}sweeps_colormag_highpm.png".format(resPath))

kde took 3.3 sec
Plotting 98829 of 333743 points

<matplotlib.legend.Legend at 0x7f8ef00905b0>


print("Plotting {} objects".format(len(wpmh)))
for o in tab["ObjID"][wpmh]:
    positions(o,jobs=jobs)

Plotting 34 objects


wpmred =  np.where((pmtot0 > 12) & (pmerr0 < 1.0) & (RminusI > 2.6) & good)[0]
print("Plotting {} objects".format(len(wpmred)))
for o in tab["ObjID"][wpmred]:
    positions(o,jobs=jobs)

Plotting 4 objects


wpmblue =  np.where((pmtot0 > 8) & (pmerr0 < 1.0) & (RminusI < 0.5) & good)[0]
print("Plotting {} objects".format(len(wpmblue)))
for o in tab["ObjID"][wpmblue]:
    positions(o,jobs=jobs)

Plotting 12 objects


def query_hla(ra,dec,size=0.0,imagetype="color",inst="ACS",format="image/jpeg",
              spectral_elt=("f814w","f606w"),autoscale=95.0,asinh=1,
              naxis=33):
    # convert a list of filters to a comma-separated string
    if not isinstance(spectral_elt,str):
        spectral_elt = ",".join(spectral_elt)
    siapurl = ("https://hla.stsci.edu/cgi-bin/hlaSIAP.cgi?"
               "pos={ra},{dec}&size={size}&imagetype={imagetype}&inst={inst}"
               "&format={format}&spectral_elt={spectral_elt}"
               "&autoscale={autoscale}&asinh={asinh}"
               "&naxis={naxis}").format(**locals())
    votable = Table.read(siapurl,format="votable")
    return votable

def get_image(url):
    
    """Get image from a URL"""
    
    r = requests.get(url)
    im = Image.open(BytesIO(r.content))
    return im

# display earliest and latest images side-by-side
# wsel = wpmred
# wsel = wpmblue
# top 10 highest PM objects
wsel = wpmh[:10]
nim = len(wsel)
icols = 1        # objects per row
ncols = 2*icols  # two images for each object
nrows = (nim+icols-1)//icols

imsize = 33
xcross = np.array([-1,1,0,0,0])*2 + imsize/2
ycross = np.array([0,0,0,-1,1])*2 + imsize/2

plt.rcParams.update({"font.size":14})
plt.figure(1,(12, (12/ncols)*nrows))
for jim, i in enumerate(wsel):
    hlatab = query_hla(tab["RA"][i],tab["Dec"][i],naxis=imsize)
    # sort by observation date
    hlatab = hlatab[np.argsort(hlatab['StartTime'])]
    k = 0
    im1 = get_image(hlatab['URL'][k])
    plt.subplot(nrows,ncols,2*jim+1)
    plt.imshow(im1,origin="upper")
    plt.plot(xcross,ycross,'g')
    plt.title(hlatab['StartTime'][k])
    plt.ylabel("ObjID {}".format(tab["ObjID"][i]))

    k = -1
    im2 = get_image(hlatab['URL'][k])
    plt.subplot(nrows,ncols,2*jim+2)
    plt.imshow(im2,origin="upper")
    plt.plot(xcross,ycross,'g')
    plt.title(hlatab['StartTime'][k])


i = wpmh[0]
print(tab['ObjID','RA','Dec','a_f606w','a_f814w','bpm','lpm','yr','dT'][i])
imsize = 33
hlatab = query_hla(tab["RA"][i],tab["Dec"][i],naxis=imsize)
# sort by observation date
hlatab = hlatab[np.argsort(hlatab['StartTime'])]

nim = len(hlatab)
ncols = 8
nrows = (nim+ncols-1)//ncols

xcross = np.array([-1,1,0,0,0])*2 + imsize/2
ycross = np.array([0,0,0,-1,1])*2 + imsize/2

plt.rcParams.update({"font.size":11})
plt.figure(1,(20, (20/ncols)*nrows))
t0 = time.time()
for k in range(nim):
    im1 = get_image(hlatab['URL'][k])
    plt.subplot(nrows,ncols,k+1)
    plt.imshow(im1,origin="upper")
    plt.plot(xcross,ycross,'g')
    plt.title(hlatab['StartTime'][k])
    if ((k+1) % 10)==0:
        print("{:.1f} s: finished {} of {}".format(time.time()-t0,k+1,nim))
plt.tight_layout()
print("{:.1f} s: finished {}".format(time.time()-t0,nim))

    ObjID             RA                Dec              a_f606w           a_f814w              bpm                lpm                 yr                dT       
------------- ----------------- ------------------- ----------------- ------------------ ------------------ ------------------ ----------------- -----------------
4000711121911 269.7367256594695 -29.209699919117618 20.67275047302246 18.963199615478516 -18.43788346257518 -36.80145933087569 2004.194238143401 2.749260607770275
11.8 s: finished 10 of 77
24.1 s: finished 20 of 77
35.9 s: finished 30 of 77
47.6 s: finished 40 of 77
59.6 s: finished 50 of 77
71.1 s: finished 60 of 77
83.3 s: finished 70 of 77
92.1 s: finished 77

ObjID	RA	Dec	RAerr	Decerr	NumFilters	NumVisits	a_f606w	a_f606w_n	a_f606w_mad	a_f814w	a_f814w_n	a_f814w_mad	bpm	lpm	bpmerr	lpmerr	pmdev	yr	dT	yrStart	yrEnd
int64	float64	float64	float64	float64	int32	int32	float64	int32	float64	float64	int32	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
4000709002286	269.7911379669984	-29.206156187411423	0.6964818624528099	0.2730062330800141	2	47	22.127399444580078	47	0.021600723266601562	21.13010025024414	47	0.0167999267578125	2.087558644949346	-7.738272329430371	0.38854582276386007	0.22115673368981237	2.887154518133692	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002287	269.7955922590832	-29.206151631494986	0.24020216760386343	0.18524811391217816	2	47	21.508499145507812	47	0.029998779296875	20.69930076599121	47	0.023900985717773438	-2.8930568503344967	-0.7898583846555245	0.1316584790053578	0.12462185695877996	1.474676632663785	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002288	269.81608933789283	-29.206155196641195	0.3040684131020671	0.2850407586200256	2	47	21.654399871826172	47	0.03650093078613281	20.85770034790039	47	0.017101287841796875	4.65866649193795	-3.2098804580343785	0.13931172183651183	0.20648097604781626	1.9570357322713463	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002289	269.8259694163096	-29.20615668840751	0.3564325426522067	0.39542200297333663	2	47	19.79170036315918	47	0.028200149536132812	19.06909942626953	47	0.019300460815429688	-0.45662407290928664	-2.0909050045433832	0.15758175952333653	0.2763881282194908	2.2415238499377685	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002290	269.83486415728754	-29.206155266983643	0.16299639839198538	0.14062839407811836	2	46	20.566649436950684	46	0.015949249267578125	19.847750663757324	46	0.014801025390625	4.459275526783969	-2.0433632344343886	0.17899727943855331	0.18503594468835396	1.0091970907052557	2013.5152382701992	3.0067822490178155	2011.8013119543625	2014.8080942033803
4000709002291	269.83512411344606	-29.2061635244798	0.18282583105108072	0.2093503650681541	2	46	20.17770004272461	46	0.028949737548828125	19.489749908447266	46	0.03639984130859375	4.090870144734149	-8.059473158394072	0.20446351152189052	0.26206212529696193	1.293050027027329	2013.5152382701992	3.0067822490178155	2011.8013119543625	2014.8080942033803
4000709002292	269.7964913295107	-29.20618734483311	0.30491102397226527	0.26784496777851086	2	47	20.83639907836914	47	0.02239990234375	20.088300704956055	47	0.02230072021484375	-1.7001866534338244	-5.963967462759159	0.14814161799844547	0.1920517681653374	1.9671761489287654	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002293	269.7872745304419	-29.206257828852337	1.351885504360048	1.0460614189471378	2	46	25.99174976348877	46	0.0852499008178711	24.204350471496582	46	0.05684947967529297	-2.290436263458843	-9.596838559623627	0.779373480816473	0.6485354032580087	8.518045574208871	2013.3206150152016	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002294	269.80888716219647	-29.206189189626002	1.4134596118752103	1.2518047802862953	2	47	25.465349197387695	46	0.08104991912841797	23.27630043029785	47	0.032100677490234375	4.381302303073221	-4.701855876394848	0.4784428793001857	1.0219363775325352	6.469654565147953	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002295	269.8234425365187	-29.206188473661626	0.35597857562160923	1.5877104410672558	2	36	24.13195037841797	36	0.04940032958984375	23.0174503326416	36	0.038100242614746094	4.48015296877619	-8.000985108228505	0.5070876244065738	0.7286553380951986	4.141157872026995	2013.174250263767	11.298715372835678	2003.4361796620085	2014.7348950348442
4000709002296	269.8288659917366	-29.206188331342627	0.3957945604056192	0.5687208923740672	2	47	24.883499145507812	47	0.05340003967285156	23.122600555419922	47	0.02719879150390625	-4.005807625507199	-6.558826318246557	0.23540629057177406	0.34067420575610496	3.4689663362344842	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000709002297	269.83003885466655	-29.206190419891996	0.31307078829575036	0.25454955949348446	2	46	21.571399688720703	46	0.02180004119873047	20.795000076293945	46	0.027649879455566406	-0.8725751200872276	-7.508546040516253	0.43513589539477077	0.20855507876805024	1.8755318392170417	2013.5152382701992	3.0067822490178155	2011.8013119543625	2014.8080942033803
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4000937456444	269.76701171186426	-29.230781486914456	1.580152992881023	1.6658550649438768	2	11	24.251300811767578	11	0.023698806762695312	23.03260040283203	11	0.027099609375	-3.5849521715767922	-9.355442553537305	2.03043488693805	1.7417694248809494	5.358128308073762	2013.8647109613648	2.234081043501644	2012.5741947321342	2014.8082757756358
4000941127104	269.7541169187084	-29.192552431738424	15.943917890817005	5.288230465807725	2	13	25.18440055847168	13	0.06609916687011719	24.42060089111328	13	0.07610130310058594	-1769.4297962839264	2184.8990523434904	1928.3236885219533	1914.9658721714695	41.439734192938715	2004.1529414597892	0.017509189482158703	2004.14378291216	2004.1612921016422
4000941133220	269.7226622209822	-29.189773993145547	1.781363174670936	1.797325809542357	2	10	23.586899757385254	10	0.013850212097167969	22.529250144958496	10	0.03670024871826172	-262.66535678489396	-109.42569661510447	381.1594930877206	165.39805763629067	5.920443736143895	2004.153629846252	0.01716819423531972	2004.1448605605756	2004.1620287548108
4000946339500	269.6899686412092	-29.27697845186784	1.7581744127758168	0.9354091074080774	2	10	23.770350456237793	10	0.06089973449707031	22.6496000289917	10	0.037400245666503906	-4.433870501660679	-1.6701167031929542	2.2930714454340286	1.3766459346536568	4.298430412355294	2013.9000436508227	2.1913492111623043	2012.4990161054518	2014.6903653166141
4000946380565	269.71004704275265	-29.258318619418723	1.4337273570795164	1.471655230308599	2	11	23.99720001220703	11	0.03730010986328125	22.51849937438965	11	0.020000457763671875	1.6111158133637464	-6.745663286831152	1.9631691651841359	1.720280363564143	4.409524084435339	2013.9002540765803	2.3094412747548714	2012.4990161054518	2014.8084573802066
4000946404892	269.67530078168915	-29.247162734242675	1.3763906949106726	1.6133894601395353	2	12	24.929399490356445	12	0.02920055389404297	23.21535015106201	12	0.06190013885498047	-4.240018447223827	-5.301395921474695	2.3240489286645727	2.4646517810785467	5.034040969645841	2013.3725317320211	2.0458630494273624	2012.389031376682	2014.4348944261094
4000946417296	269.7016328152704	-29.246070466593743	3.130830982636958	3.185144112877607	2	11	25.600500106811523	11	0.23419952392578125	24.028099060058594	11	0.2503986358642578	0.3265864932598421	-6.491394647992156	6.396715459697641	2.5782652825923047	8.829447098663028	2013.7731122440448	2.01948620036275	2012.6708791162512	2014.6903653166141
4000949430259	269.722986295747	-29.21197112017265	1.361963963773738	1.158905612896655	3	15	24.502249717712402	14	0.06614971160888672	23.928850173950195	14	0.056850433349609375	4.745787093281551	-3.4149224348359475	0.9875243981622149	0.6006686739150457	5.081087460494774	2004.567449081762	6.212572361916957	2004.14378291216	2010.356355274077
4000949692413	269.847657491686	-29.213464437142868	2.3678034671958668	2.0224597819540673	2	10	24.422550201416016	10	0.11170005798339844	22.581549644470215	10	0.0209503173828125	1.6834992526734194	-14.490467506156065	3.4740642144508795	4.492656398011582	5.946113188318635	2013.955340339603	1.5098539308761485	2013.2982402725042	2014.8080942033803
4000949719295	269.8230388680129	-29.20072120181885	6.9256239011552925	2.49884610751816	2	10	25.977850914001465	10	0.10779953002929688	24.156999588012695	10	0.03194999694824219	-0.009307948981069264	-9.255580848582802	1.8581178329802082	1.5241924426465314	15.473545552827103	2012.4700600740625	10.768784408135662	2003.4361796620085	2014.2049640701443
4000979902333	269.804403240861	-29.192813265455396	2.3859956776447127	1.392848515948042	2	10	25.71024990081787	10	0.06535053253173828	24.112099647521973	10	0.05470085144042969	0.7550276872362829	-4.7745907025067345	3.6257686130953477	1.461781567688497	5.065034698466592	2013.6589915119716	2.5308665509739208	2012.2040284838704	2014.7348950348442
4000979908546	269.8156665822647	-29.189854516331526	0.9143748593616481	2.1238192007085037	2	12	25.4466495513916	12	0.15910053253173828	23.336549758911133	12	0.06319999694824219	-7.2055855532918205	-9.683292833608878	2.618264686941967	3.0724808097663883	5.246267309545055	2013.8340336110703	1.6017431136587867	2013.2063510897215	2014.8080942033803
4000980227788	269.7998513044728	-29.19707771926519	1.8494859814888565	1.5991491655370784	2	12	24.400450706481934	12	0.10389995574951172	22.93809986114502	12	0.02064990997314453	0.15278595605010709	-7.172152518535961	0.691934557090257	0.49373163797911374	5.7619744099400085	2012.6568721424214	11.371914541371764	2003.4361796620085	2014.8080942033803

ObjID	RA	Dec	RAerr	Decerr	NumFilters	NumVisits	a_f606w	a_f606w_n	a_f606w_mad	a_f814w	a_f814w_n	a_f814w_mad	bpm	lpm	bpmerr	lpmerr	pmdev	yr	dT	yrStart	yrEnd
int64	float64	float64	float64	float64	int32	int32	float64	int32	float64	float64	int32	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
4000710313439	269.74005171773325	-29.196032473369662	0.6265964047671119	0.8430363739939153	13	94	18.632399559020996	74	0.04414939880371094	18.75860023498535	77	0.22760009765625	-0.2729591957006581	0.0933685295161405	0.12007496779671313	0.19354443197999766	6.664561102885139	2010.4104580987043	12.138936292937231	2003.436727359001	2015.5756636519384
4000710257520	269.76634477578085	-29.223310166797457	0.7010607226863969	0.9635875312674559	14	96	18.71660041809082	75	0.03790092468261719	18.785300254821777	74	0.45225048065185547	-0.579616612423514	-0.004195674864531432	0.1286260805301132	0.22613515613212812	7.079734373347017	2010.4505865929348	12.138936292937231	2003.436727359001	2015.5756636519384
4000710307965	269.7417955221381	-29.198593859432016	0.9205935728204635	0.8518786138892231	14	94	18.73979949951172	73	0.06389999389648438	18.865349769592285	76	0.20005035400390625	0.3782536664095988	-0.3686895590223385	0.2125072995299893	0.17345047912667094	7.6732306607203995	2010.485877546229	12.138936292937231	2003.436727359001	2015.5756636519384
4000710352240	269.7337926320315	-29.18139487545298	0.6946356814006966	0.9061342261773224	4	79	18.78339958190918	74	0.023049354553222656	18.512550354003906	74	0.2772502899169922	0.6337179460443223	0.29715583737809054	0.15063272942700434	0.20070198442295878	6.738108122324751	2009.7737370001337	11.371185208171667	2003.436727359001	2014.8079125671727
4000710347699	269.7783405856686	-29.17911931116647	0.41155778832388173	0.4841933620053226	4	81	18.839149475097656	76	0.0339508056640625	18.898500442504883	73	0.23670005798339844	-0.11678100312847553	0.6499252564776745	0.084877817774843	0.11036052576232969	3.7905072891090166	2009.7600421422271	11.371185208171667	2003.436727359001	2014.8079125671727
4000710305789	269.7500895465239	-29.199706962876274	1.2377985340299074	0.9596173093379398	13	95	18.883800506591797	76	0.016399383544921875	18.647899627685547	75	0.10449981689453125	0.7613608897396442	0.06072801567912481	0.14458519679206283	0.307891087664392	8.669608674616846	2010.4513188181218	12.138936292937231	2003.436727359001	2015.5756636519384
4000710287023	269.73049137354224	-29.208786514685837	0.5886781414210673	0.8187862653505924	13	93	19.005799293518066	70	0.17949867248535156	19.03809928894043	77	0.4453010559082031	-0.027042326411294833	-0.10656181801795192	0.11368759414535684	0.18547384893472832	6.060019340850642	2010.4099808141818	12.138936292937231	2003.436727359001	2015.5756636519384
4000711198572	269.7837898799627	-29.171058346187387	0.24289061146320284	0.3787994581356928	4	56	20.384400367736816	52	0.030750274658203125	20.409199714660645	54	0.018199920654296875	-0.39276687371980523	-0.2614991280329616	0.07157257977281026	0.06954852374618407	2.6397832644001498	2008.0522535045122	10.997621905233284	2003.436727359001	2014.4343492642342
4000711114267	269.77699533206413	-29.213020339932903	4.24137055513596	3.9397646738783108	4	35	23.104300498962402	32	0.038299560546875	23.02050018310547	33	0.07789993286132812	-0.8306962177406612	-0.06858498728152404	0.9221622324872899	1.3496664163664274	18.925994277649572	2005.5542194355585	12.136400136689947	2003.436727359001	2015.573127495691
4000710335454	269.7613531455845	-29.187206361070864	7.474297329875963	1.4831362310495328	3	77	23.993050575256348	76	0.06319999694824219	24.481599807739258	79	0.20109939575195312	-0.5423185685515735	0.7060415611802732	1.4646930044718638	0.8183749010089348	44.542864374423985	2009.9793326372383	11.371185208171667	2003.436727359001	2014.8079125671727
4000710273567	269.7541240409338	-29.21578796024474	4.748214164796449	1.6940945228632924	3	78	24.544499397277832	76	0.07555007934570312	24.190000534057617	78	0.12094974517822266	-0.06045787883823814	-0.29699839716096693	0.9328189717625861	0.5780921890931368	28.400690195232727	2009.6896657994198	11.341399291029212	2003.436727359001	2014.7781266500303
4000710273213	269.762844233717	-29.215993073596458	2.8491412759641146	2.1589511176356435	3	58	25.248699188232422	57	0.072601318359375	25.19580078125	57	0.11439895629882812	0.5874532592404308	0.3961988878194328	0.5745702330625071	0.4892686278049602	17.035022816134603	2008.6575729055041	11.371185208171667	2003.436727359001	2014.8079125671727
4000710327380	269.7667196275462	-29.189803905653022	1.4975661577032107	4.6584789458436155	3	53	25.343299865722656	52	0.09395027160644531	25.171499252319336	35	0.27519989013671875	-0.5925764852209691	0.3510984622430878	0.40036043056387555	0.963110172251642	23.394971123121124	2008.1998409952648	11.341399291029212	2003.436727359001	2014.7781266500303
4000710296308	269.7744892710409	-29.20439243403908	3.8554370972569254	2.2643903195117683	3	78	25.49880027770996	75	0.09670066833496094	25.095499992370605	78	0.11275005340576172	0.18975302378694214	0.17171660653658127	0.7173616133509608	0.6443087550732374	27.184284641221293	2009.575015347384	11.371732905164137	2003.4361796620085	2014.8079125671727

ObjID	RA	Dec	RAerr	Decerr	NumFilters	NumVisits	a_f606w	a_f606w_n	a_f606w_mad	a_f814w	a_f814w_n	a_f814w_mad	bpm	lpm	bpmerr	lpmerr	pmdev	yr	dT	yrStart	yrEnd
int64	float64	float64	float64	float64	int32	int32	float64	int32	float64	float64	int32	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
4000710347699	269.7783405856686	-29.17911931116647	0.41155778832388173	0.4841933620053226	4	81	18.839149475097656	76	0.0339508056640625	18.898500442504883	73	0.23670005798339844	-0.11678100312847553	0.6499252564776745	0.084877817774843	0.11036052576232969	3.7905072891090166	2009.7600421422271	11.371185208171667	2003.436727359001	2014.8079125671727
4000711253077	269.6915378419676	-29.24020145019322	0.4704881733739859	0.5270162975364242	2	46	18.8927001953125	45	0.010900497436523438	18.636449813842773	42	0.1782999038696289	-1.7408991354864214	-0.5947077102632641	0.3613162305972112	0.7482345212707299	2.9613403618906435	2013.5034009429392	3.00678120252487	2011.8016761776817	2014.8084573802066
4000711347522	269.70065127433986	-29.232034952664744	0.45813739460650355	0.972550620957754	2	43	18.917999267578125	43	0.025800704956054688	18.648849487304688	38	0.2850008010864258	0.5245222694829835	-0.9025321571402278	0.5331879231467277	1.1329901624612693	3.7307809424771032	2013.4648969161435	3.00678120252487	2011.8016761776817	2014.8084573802066
4000709025473	269.82873131529516	-29.19512257484812	0.5411363937542479	0.6920450307671305	2	46	18.92685031890869	44	0.0126495361328125	18.75755023956299	42	0.3887491226196289	0.8548976473145768	-1.1578337695544032	0.2876906708357735	0.43355270853508593	3.456340262841035	2013.315425703565	11.371914541371764	2003.4361796620085	2014.8080942033803
4000710279756	269.7207706977463	-29.212308555948887	0.41374484392259747	0.4883003181612267	12	70	18.977749824523926	54	0.028249740600585938	18.614700317382812	55	0.3589000701904297	0.033714894109117335	-1.600702228805833	0.09052420732956741	0.09073750139459606	3.619701382694334	2009.679762289423	12.138936292937231	2003.436727359001	2015.5756636519384
4000711345953	269.71277619272627	-29.23130608012401	0.4732191836487131	1.0046856684238223	2	46	19.01865005493164	46	0.012249946594238281	18.701799392700195	39	0.1501007080078125	0.34968560305135327	1.9139934804091387	0.7254248849571668	1.0861879082833583	3.755315552645739	2013.5034009429392	3.00678120252487	2011.8016761776817	2014.8084573802066
4000709014231	269.8069522092499	-29.20021994406481	0.3834521659779482	0.549067011791737	2	47	19.06329917907715	46	0.037700653076171875	18.564849853515625	46	0.046050071716308594	1.0342947783087193	-1.4279240765589876	0.15526546888889428	0.36889847542727655	2.0017413819595076	2013.3007902147058	11.371914541371764	2003.4361796620085	2014.8080942033803
4000710326758	269.75587447129374	-29.189995188046638	0.39923238020201557	0.37137690294128917	13	95	19.077000617980957	76	0.021599769592285156	19.024099349975586	77	0.26650047302246094	-1.1141957269870415	-0.8674983559009859	0.09948022058874997	0.06424053625731577	3.097878914716658	2010.4513188181215	12.138936292937231	2003.436727359001	2015.5756636519384
4000711198572	269.7837898799627	-29.171058346187387	0.24289061146320284	0.3787994581356928	4	56	20.384400367736816	52	0.030750274658203125	20.409199714660645	54	0.018199920654296875	-0.39276687371980523	-0.2614991280329616	0.07157257977281026	0.06954852374618407	2.6397832644001498	2008.0522535045122	10.997621905233284	2003.436727359001	2014.4343492642342
4000724763944	269.7747901164537	-29.26523403383687	0.43377673171406406	0.4474216647110866	2	46	22.25380039215088	46	0.05949974060058594	21.910449981689453	46	0.033699989318847656	1.182122334720074	0.14084928991004503	0.5960881587431285	0.4264522488795796	3.3639208538613516	2013.503145357016	3.00678170959782	2011.801494066038	2014.8082757756358

Hubble Source Catalog SWEEPS Proper Motion Notebook¶

June 2019, Steve Lubow and Rick White¶

Instructions:¶

Table of Contents¶

Initialization ¶

Install Python modules¶

Set up your CasJobs account information¶

Other optional configuration¶

Create table in MyDB with selected SWEEPS objects¶

Properties of Full Catalog ¶

Sky Coverage ¶

Proper Motion Histograms ¶

Proper motion histograms for lon and lat¶

Proper motion error cumulative histogram for lon and lat¶

Proper motion error log histogram for lon and lat¶

Proper motion error as a function of dT¶

Proper motion error log histogram for lon and lat¶

Number of Visits Histogram ¶

Time Histograms ¶

Magnitude Histograms ¶

Aper2 magnitude histograms for F606W and F814W¶

Aper2 magnitude error histograms for F606W and F814W¶

Color-Magnitude Diagram ¶

Color-magnitude diagram¶

Detection Positions ¶

Plot positions of objects that are detected in more than 90 visits with a median absolute deviation from the fit of less than 1.5 mas and proper motion error less than 1.0 mas/yr.¶

Good Photometric Objects ¶

Look at photometric error distribution to pick out good photometry objects as a function of magnitude ¶

Define function to apply noise cut and plot color-magnitude diagram with cut¶

Science Applications ¶

Proper Motions of Good Objects ¶

Average proper motion in color-magnitude bins¶

RMS in longitude PM as a function of color/magnitude¶

Mean longitude PM as image¶

Mean latitude PM as image¶

Proper Motions in Bulge and Disk ¶

Fit a smooth function to the main ridgeline of color-magnitude diagram¶

Plot the ridgeline on the CMD¶

Binned distribution of PM(Long) vs magnitude offset from ridge line¶

Reproduce Figure 1 from Calamida et al. 2014¶

Mean and median proper motions of bulge stars compared with SgrA* (-6.379 $\pm$ 0.026, -0.202 $\pm$ 0.019) mas/yr Reid and Brunthaler (2004)¶

White Dwarfs ¶

White dwarf mean PM and uncertainity¶

WDs generally closer to bulge (MS) PM distribution¶

Look for quasar candidates (low PM blue stars) ¶

Try again with different criteria on proper motion fit quality¶

High Proper Motion Objects ¶

Very red high proper motion objects¶

Very blue high proper motion objects¶

Get HLA cutout images for selected objects ¶

Look at the entire collection of images for the highest PM object¶

Reproduce Figure 1 from Calamida et al. 2014 ¶