Characterizing Flare Rate Evolution w/ Kepler Wide Binaries



In [1]:

    
%matplotlib inline
import numpy as np
from scipy import stats
import matplotlib 
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import matplotlib.cm as cm
import pandas as pd

#MAKE SURE ALL MASS VALUES ARE POSITIVE!!!



In [2]:

    
# Making figures look nice
matplotlib.rcParams.update({'font.size':18}) 
matplotlib.rcParams.update({'font.family':'serif'})

Reading in files:



In [3]:

    
Kep_ID, sep, prob, gkclr, prot = np.loadtxt ('Janes2017_data.txt', usecols=(1,6,7,9,10), unpack=True)

"""
Kep_ID = KIC number
sep = projected physical separation in arcseconds
prob = relative probability of chance alignment
gkclr = g-k color
prot = rotation period in days

"""









    Out[3]:





'\nKep_ID = KIC number\nsep = projected physical separation in arcseconds\nprob = relative probability of chance alignment\ngkclr = g-k color\nprot = rotation period in days\n\n'



In [4]:

    
row, Lfl_Lbol, jim_dist, giclr, kicnum, mass, tau = np.genfromtxt('kic_lflare_mass_dist.csv', 
                    usecols = (0,1,8,9,10,11,12), delimiter = ',', unpack = True, skip_header=True)

"""
Lfl_Lbol = Flare luminosity ratio (FLR): Fraction of total stellar luminosity from flares 
(see https://github.com/jradavenport/appaloosa for details)
jim_dist = parallaxes from Davenport (2016)
giclr = g-i color
kicnum = KIC number
mass = masses in units of M_sun
tau = Rosby number parameter
"""

#Normalizing very low-signal data
w=np.where((Lfl_Lbol < 1e-15))
Lfl_Lbol[w]=1e-15



In [5]:

    
#Check for negative mass values

print(len(mass))

for i in range(len(mass)):
    if mass[i] <= 0:
        np.delete(mass,[i])
        
print(len(mass))

No negative masses!

Crossmatching data sets:



In [6]:

    
m = np.array(np.zeros(len(Kep_ID)), dtype='int') 
## m is an array of indices that is the intersection of "Janes2017_data.txt" and "kic_lflare_mass_dist.csv"

## Removing zeros from Kep_ID
for k in range (len(Kep_ID)):
    
    if k==72:
        pass
    
    elif k==114:
        pass
    
    else:
        x = np.where(Kep_ID[k] == kicnum)
        #print(x,k)
    
        m[k] = x[0]
    
    
print(m,len(m)) 
# in database speak, this a Join (Inner Join)









    



[129756 129757 141257 141258 139652 139654 150200 153960 129850 129851
 105999 106001 129881 129882   7499   7500 146653 146654  28638  28642
  25352  25354 182991 182992  10631  10632  13457  13459 178808 180295
 178808 178809 128357 128358  24209  24210 160630 160632  36334  36335
 187743 187744  77349  77351  26608  26609  85433  85434  36391  36396
  41212  42005  70807  72227 121909 121910 189518 189520 158737 158738
 136808 136809 195354 195355 140185 140186 116783 116785 200940 200942
  38466  38467      0  10912  49778  49780  13801  13803  34427  34428
 158854 158857  38560  38561 141965 141967  50202  50204 147144 147149
  29077  29079  41384  41385  82186  82188 143796 143797  54595  54596
  69721  69723 130656 130660 152836 152837  58177  58178 135482 135483
   8188   8189  13985  13986      0 173725 186531 186536  84362  84364
 166314 166315 199794 199796 157152 157153  47336  47337  45053  45054
  34865  34866  90381  90382  41730  42578  15706  15708  75908  77145
  47540  48696  43473  43474 157500 157503  44390  44391 159623 159625
  39116  39117  84947  84949 112556 112561  41835  41836  15858  15859
 155578 155579  10174  10176  90546  90547  26281  26284 119456 119457
 196773 196774 109233 109235  83334  83335   7284   8858  14695  16082
 174144 174145   5665   5668 172873 172877] 186



In [7]:

    
m = np.delete(m,[72,114])#Deletes zeros in m - ONLY RUN ONCE!!!
print(len(m),m)









    



184 [129756 129757 141257 141258 139652 139654 150200 153960 129850 129851
 105999 106001 129881 129882   7499   7500 146653 146654  28638  28642
  25352  25354 182991 182992  10631  10632  13457  13459 178808 180295
 178808 178809 128357 128358  24209  24210 160630 160632  36334  36335
 187743 187744  77349  77351  26608  26609  85433  85434  36391  36396
  41212  42005  70807  72227 121909 121910 189518 189520 158737 158738
 136808 136809 195354 195355 140185 140186 116783 116785 200940 200942
  38466  38467  10912  49778  49780  13801  13803  34427  34428 158854
 158857  38560  38561 141965 141967  50202  50204 147144 147149  29077
  29079  41384  41385  82186  82188 143796 143797  54595  54596  69721
  69723 130656 130660 152836 152837  58177  58178 135482 135483   8188
   8189  13985  13986 173725 186531 186536  84362  84364 166314 166315
 199794 199796 157152 157153  47336  47337  45053  45054  34865  34866
  90381  90382  41730  42578  15706  15708  75908  77145  47540  48696
  43473  43474 157500 157503  44390  44391 159623 159625  39116  39117
  84947  84949 112556 112561  41835  41836  15858  15859 155578 155579
  10174  10176  90546  90547  26281  26284 119456 119457 196773 196774
 109233 109235  83334  83335   7284   8858  14695  16082 174144 174145
   5665   5668 172873 172877]

Sorting primary & secondary components:



In [8]:

    
a = np.arange(0,184,2) #primary components 
b = np.arange(1,184,2) #secondary components

for j in range(0,len(a)):
    if mass[m][a[j]] < mass[m][b[j]]:
        tmp = a[j]
        a[j] = b[j]
        b[j] = tmp



In [9]:

    
### Good_either index contains FLRs above minimum detection threshold: (1e-7)###

Good_either = np.where(((Lfl_Lbol[m][a] > 1e-7) | (Lfl_Lbol[m][b] > 1e-7)) & 
                        ((Lfl_Lbol[m][a] > 1e-15) & (Lfl_Lbol[m][b] > 1e-15)))
                        
print(np.size(Good_either))

Sample size = 58 wide binaries

Data Visualization:

Activity Comparison -



In [10]:

    
### FLR of primary vs. FLR of secondary ###

plt.figure(figsize=(8,8)) 
# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_a2]), np.log10(Lfl_Lbol[m][b][Good_both_a2]), 'ro', alpha = 0.6)
# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_b2]), np.log10(Lfl_Lbol[m][b][Good_both_b2]), 'bo', alpha = 0.6)
plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either]), np.log10(Lfl_Lbol[m][b][Good_either]), 
            alpha = 0.6, s=80, c='r', edgecolor='k', label='Active pairs')

#plt.scatter(np.log10(Lfl_Lbol[m][a]+1e-10), np.log10(Lfl_Lbol[m][b]+1e-10), s=5, c='k', alpha=0.2, 
            #label='Kepler wide binary pairs')


plt.plot([-11,-2], [-11,-2],'b', lw=3, alpha=0.5,label='Idealized')
#plt.plot([-11,-2], [-12,-3],'y', lw=3, alpha=0.5,label='Within solar variability')
#plt.plot([-11,-2], [-10,-1],'y', lw=3, alpha=0.5)

plt.scatter(-3.93,-4.00, marker='*', color='orange', edgecolor = 'black', s=300, alpha = 1)

#box1 = plt.Polygon([[-5,-4],[-11,-4],[-11,-2],[-3,-2]],fc='g',alpha=0.2,label='Active Secondary')
#box2 = plt.Polygon([[-4,-5],[-4,-11],[-2,-11],[-2,-3]],fc='b',alpha=0.2,label='Active Primary')
#circle = plt.Circle((-11, -11), radius=5, fc='r',alpha=0.2,label='Below noise threshold')
#plt.gca().add_patch(circle)
#plt.gca().add_patch(box1)
#plt.gca().add_patch(box2)

plt.xlim(-11, -2)
plt.ylim(-11, -2)
plt.xlabel(r'log $L_{fl}/L_{kp}$ (A component)')
plt.ylabel(r'log $L_{fl}/L_{kp}$ (B component)')
#plt.legend(fontsize=10, loc=2)
plt.savefig('AB_v1.pdf',dpi=500)



In [11]:

    
### Adding zone of expected variability for a solar-like cycle ###

plt.figure(figsize=(8,8)) 

# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_a2]), np.log10(Lfl_Lbol[m][b][Good_both_a2]), 'ro', alpha = 0.6)
# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_b2]), np.log10(Lfl_Lbol[m][b][Good_both_b2]), 'bo', alpha = 0.6)
plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either]), np.log10(Lfl_Lbol[m][b][Good_either]), 
            alpha = 0.6, s=80, c='r', edgecolor='k', label='Active pairs')

#plt.scatter(np.log10(Lfl_Lbol[m][a]+1e-10), np.log10(Lfl_Lbol[m][b]+1e-10), s=5, c='k', alpha=0.2, 
            #label='Kepler wide binary pairs')


plt.plot([-11,-2], [-11,-2],'b', lw=3, alpha=0.5,label='Idealized')
plt.plot([-11,-2], [-12,-3],'y', lw=3, alpha=0.5,label='Within solar variability')
plt.plot([-11,-2], [-10,-1],'y', lw=3, alpha=0.5)

#box1 = plt.Polygon([[-5,-4],[-11,-4],[-11,-2],[-3,-2]],fc='g',alpha=0.2,label='Active Secondary')
#box2 = plt.Polygon([[-4,-5],[-4,-11],[-2,-11],[-2,-3]],fc='b',alpha=0.2,label='Active Primary')
#circle = plt.Circle((-11, -11), radius=5, fc='r',alpha=0.2,label='Below noise threshold')
#plt.gca().add_patch(circle)
#plt.gca().add_patch(box1)
#plt.gca().add_patch(box2)

plt.xlim(-11, -2)
plt.ylim(-11, -2)
plt.xlabel(r'log $L_{fl}/L_{kp}$ (A component)')
plt.ylabel(r'log $L_{fl}/L_{kp}$ (B component)')
plt.legend(fontsize=10, loc=2)
plt.savefig('AB_v2.pdf',dpi=500)



In [13]:

    
### Adding zones for "weird" pairs ###

plt.figure(figsize=(8,8)) 

# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_a2]), np.log10(Lfl_Lbol[m][b][Good_both_a2]), 'ro', alpha = 0.6)
# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_b2]), np.log10(Lfl_Lbol[m][b][Good_both_b2]), 'bo', alpha = 0.6)
plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either]), np.log10(Lfl_Lbol[m][b][Good_either]), 
            alpha = 0.6, s=80, c='r', edgecolor = 'k', label='Active pairs')

#plt.scatter(np.log10(Lfl_Lbol[m][a]+1e-10), np.log10(Lfl_Lbol[m][b]+1e-10), s=5, c='k', alpha=0.2, 
            #label='Kepler wide binary pairs')


plt.plot([-11,-2], [-11,-2],'b', lw=3, alpha=0.5,label='Idealized')
plt.plot([-11,-2], [-12,-3],'y', lw=3, alpha=0.5,label='Within solar variability')
plt.plot([-11,-2], [-10,-1],'y', lw=3, alpha=0.5)

box1 = plt.Polygon([[-5,-4],[-11,-4],[-11,-2],[-3,-2]],fc='g',alpha=0.1,label='Active Secondary')
box2 = plt.Polygon([[-4,-5],[-4,-11],[-2,-11],[-2,-3]],fc='b',alpha=0.1,label='Active Primary')
circle = plt.Circle((-11, -11), radius=5, fc='r',alpha=0.1,label='Below noise threshold')
plt.gca().add_patch(circle)
plt.gca().add_patch(box1)
plt.gca().add_patch(box2)

plt.xlim(-11, -2)
plt.ylim(-11, -2)
plt.xlabel(r'log $L_{fl}/L_{kp}$ (A component)')
plt.ylabel(r'log $L_{fl}/L_{kp}$ (B component)')
plt.legend(fontsize=10, loc=2)
plt.savefig('AB_v3.png',dpi=500)



In [17]:

    
### BOX FILTERS ###

gbox = np.where((Lfl_Lbol[m][b][Good_either] > 1e-4) & (Lfl_Lbol[m][a][Good_either] < 1e-4))
bbox = np.where((Lfl_Lbol[m][b][Good_either] < 1e-4) & (Lfl_Lbol[m][a][Good_either] > 1e-4))
ybox = np.where(((np.log10(Lfl_Lbol[m][b][Good_either]) - np.log10(Lfl_Lbol[m][a][Good_either])) < 1) & 
                ((np.log10(Lfl_Lbol[m][b][Good_either]) - np.log10(Lfl_Lbol[m][a][Good_either])) > -1))



In [13]:

    
### Final form ###

plt.figure(figsize=(8,8.1)) 

# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_a2]), np.log10(Lfl_Lbol[m][b][Good_both_a2]), 'ro', alpha = 0.6)
# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_b2]), np.log10(Lfl_Lbol[m][b][Good_both_b2]), 'bo', alpha = 0.6)
plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][gbox]), np.log10(Lfl_Lbol[m][b][Good_either][gbox]), 
            alpha = 0.6, s=80, c='g', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][bbox]), np.log10(Lfl_Lbol[m][b][Good_either][bbox]), 
            alpha = 0.6, s=80, c='b', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][ybox]), np.log10(Lfl_Lbol[m][b][Good_either][ybox]), 
            alpha = 0.6, s=80, c='y', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either]), np.log10(Lfl_Lbol[m][b][Good_either]), s=10, c='k', alpha=0.4)


plt.plot([-11,-2], [-11,-2],'b', lw=3, alpha=0.5,label='Idealized')
plt.plot([-11,-2], [-12,-3],'y', lw=3, alpha=0.5,label='Within solar variability')
plt.plot([-11,-2], [-10,-1],'y', lw=3, alpha=0.5)

box1 = plt.Polygon([[-5,-4],[-11,-4],[-11,-2],[-3,-2]],fc='g',alpha=0.1,label='Active Secondary')
box2 = plt.Polygon([[-4,-5],[-4,-11],[-2,-11],[-2,-3]],fc='b',alpha=0.1,label='Active Primary')
circle = plt.Circle((-11, -11), radius=5, fc='r',alpha=0.1,label='Below noise threshold')
plt.gca().add_patch(circle)
plt.gca().add_patch(box1)
plt.gca().add_patch(box2)

plt.xlim(-11, -2)
plt.ylim(-11, -2)
plt.xlabel(r'log $L_{fl}/L_{kp}$ (A component)')
plt.ylabel(r'log $L_{fl}/L_{kp}$ (B component)')
#plt.legend(fontsize=10, loc=2)
plt.savefig('AB_v4.pdf',dpi=600)

Adding theoretical mass ratio tracks:



In [16]:

    
ABfile = 'ABtracks.npz'
npz = np.load(ABfile)



In [17]:

    
Lfl_Lkp_wb = npz['ABarray']
mass_wb = npz['mass']
age_wb = npz['age']



In [18]:

    
mass_wb









    Out[18]:





array([ 0.25,  0.3 ,  0.35,  0.4 ,  0.45,  0.5 ,  0.55,  0.6 ,  0.65,
        0.7 ,  0.75,  0.8 ,  0.85])



In [19]:

    
for k in range (mass_wb.size):
    plt.plot(Lfl_Lkp_wb[-1,:],Lfl_Lkp_wb[k,:],color='b')



In [20]:

    
### Final w/ mass ratio tracks ###

plt.figure(figsize=(8,8.1)) 

# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_a2]), np.log10(Lfl_Lbol[m][b][Good_both_a2]), 'ro', alpha = 0.6)
# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_b2]), np.log10(Lfl_Lbol[m][b][Good_both_b2]), 'bo', alpha = 0.6)
plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][gbox]), np.log10(Lfl_Lbol[m][b][Good_either][gbox]), 
            alpha = 0.6, s=80, c='g', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][bbox]), np.log10(Lfl_Lbol[m][b][Good_either][bbox]), 
            alpha = 0.6, s=80, c='b', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][ybox]), np.log10(Lfl_Lbol[m][b][Good_either][ybox]), 
            alpha = 0.6, s=80, c='y', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either]), np.log10(Lfl_Lbol[m][b][Good_either]), s=10, c='k', alpha=0.4)


plt.plot([-11,-2], [-11,-2],'b', lw=3, alpha=0.5,label='Idealized')
plt.plot([-11,-2], [-12,-3],'y', lw=3, alpha=0.5,label='Within solar variability')
plt.plot([-11,-2], [-10,-1],'y', lw=3, alpha=0.5)

box1 = plt.Polygon([[-5,-4],[-11,-4],[-11,-2],[-3,-2]],fc='g',alpha=0.1,label='Active Secondary')
box2 = plt.Polygon([[-4,-5],[-4,-11],[-2,-11],[-2,-3]],fc='b',alpha=0.1,label='Active Primary')
circle = plt.Circle((-11, -11), radius=5, fc='r',alpha=0.1,label='Below noise threshold')
plt.gca().add_patch(circle)
plt.gca().add_patch(box1)
plt.gca().add_patch(box2)


for k in range (mass_wb.size):
    plt.plot(Lfl_Lkp_wb[-1,:],Lfl_Lkp_wb[k,:],color='k')

plt.xlim(-11, -2)
plt.ylim(-11, -2)
plt.xlabel(r'log $L_{fl}/L_{kp}$ (A)')
plt.ylabel(r'log $L_{fl}/L_{kp}$ (B)')
plt.legend(fontsize=10, loc=2)
plt.savefig('AB_v4.png',dpi=600)

Mass analysis -



In [14]:

    
### Mass vs. Activity ###

plt.figure(figsize=(8,8))


# Highlighting systems of interest:
plt.scatter(mass[m][a][Good_either][gbox][0],np.log10(Lfl_Lbol[m][a][Good_either][gbox][0]),s=260,c='white',edgecolor='k',lw=3)
plt.scatter(mass[m][b][Good_either][gbox][0],np.log10(Lfl_Lbol[m][b][Good_either][gbox][0]),s=260,c='white',edgecolor='k',lw=3,marker='s')
plt.scatter(mass[m][a][Good_either][bbox][2],np.log10(Lfl_Lbol[m][a][Good_either][bbox][2]),s=260,c='white',edgecolor='k',lw=3)
plt.scatter(mass[m][b][Good_either][bbox][2],np.log10(Lfl_Lbol[m][b][Good_either][bbox][2]),s=260,c='white',edgecolor='k',lw=3,marker='s')

plt.scatter(mass[m][a][Good_either][bbox],np.log10(Lfl_Lbol[m][a][Good_either][bbox]),alpha = 0.6,s=80,c='b',edgecolor='k')
plt.scatter(mass[m][a][Good_either][gbox],np.log10(Lfl_Lbol[m][a][Good_either][gbox]),alpha = 0.6,s=80,c='g',edgecolor='k')
plt.scatter(mass[m][a][Good_either][ybox],np.log10(Lfl_Lbol[m][a][Good_either][ybox]),alpha = 0.6,s=80,c='y',edgecolor='k')
plt.scatter(mass[m][b][Good_either][bbox],np.log10(Lfl_Lbol[m][b][Good_either][bbox]),alpha = 0.6,s=80,c='b',edgecolor='k',marker='s')
plt.scatter(mass[m][b][Good_either][gbox],np.log10(Lfl_Lbol[m][b][Good_either][gbox]),alpha = 0.6,s=80,c='g',edgecolor='k',marker='s')
plt.scatter(mass[m][b][Good_either][ybox],np.log10(Lfl_Lbol[m][b][Good_either][ybox]),alpha = 0.6,s=80,c='y',edgecolor='k',marker='s')

#Drawing lines between paired stars:
for k in range (len(bbox[0])):
    plt.plot([mass[m][a][Good_either][bbox],mass[m][b][Good_either][bbox]],[np.log10(Lfl_Lbol[m][a][Good_either][bbox]),np.log10(Lfl_Lbol[m][b][Good_either][bbox])],'b',lw=0.2,alpha=0.5,
             label='Active Secondary')

for k in range (len(gbox[0])):
    plt.plot([mass[m][a][Good_either][gbox],mass[m][b][Good_either][gbox]],[np.log10(Lfl_Lbol[m][a][Good_either][gbox]),np.log10(Lfl_Lbol[m][b][Good_either][gbox])],'g',lw=0.2,alpha=0.5,
            label='Active Primary')

for k in range (len(ybox[0])):
    plt.plot([mass[m][a][Good_either][ybox],mass[m][b][Good_either][ybox]],[np.log10(Lfl_Lbol[m][a][Good_either][ybox]),np.log10(Lfl_Lbol[m][b][Good_either][ybox])],'y',lw=0.2,alpha=0.1,
            label='Within solar variability')
    
# ok = np.where(np.isfinite(giclr))
# plt.hist2d(giclr[ok],np.log10(Lfl_Lbol[ok]),bins=100, range=[[-1,4],[-10,-2]], cmap=cm.Greys,norm=LogNorm())
# plt.colorbar()
    
plt.ylabel(r'log $L_{fl}/L_{kp}$')
plt.xlabel(r'Mass ($M_{\odot}$)')
plt.xlim(1.5,.2)

plt.savefig('MassPlot.pdf',dpi=500)



In [15]:

    
### Mass ratio distribution ###

plt.figure(figsize=(10,5))

_ = plt.hist((mass[m][b][Good_either][ybox])/(mass[m][a][Good_either][ybox]), bins = np.arange(.4,1.1,0.1), histtype = 'stepfilled', color = 'y', edgecolor = 'k')
_ = plt.hist((mass[m][b][Good_either][gbox])/(mass[m][a][Good_either][gbox]), bins = np.arange(.4,1.1,0.1), histtype = 'stepfilled', color = 'g', edgecolor = 'k')
_ = plt.hist((mass[m][b][Good_either][bbox])/(mass[m][a][Good_either][bbox]), bins = np.arange(.4,1.1,0.1), histtype = 'stepfilled', color = 'b', edgecolor = 'k')

plt.ylabel('Number of Pairs')
plt.xlabel('Mass Ratio (B/A)')
plt.xlim(0,1)
plt.savefig('MassHist.pdf',dpi=500)

KS TEST



In [16]:

    
ydist = (mass[m][b][Good_either][ybox])/(mass[m][a][Good_either][ybox])
gdist = (mass[m][b][Good_either][gbox])/(mass[m][a][Good_either][gbox])
bdist = (mass[m][b][Good_either][bbox])/(mass[m][a][Good_either][bbox])

gbdist = np.concatenate((gdist,bdist))



In [17]:

    
## KS TEST

stats.ks_2samp(gbdist,ydist)









    Out[17]:





Ks_2sampResult(statistic=0.33888888888888885, pvalue=0.26782087011926448)

High p-value indicates we cannot reject the hypothesis that the samples are drawn from the same population.

IDing KICs of same-mass green/blue pairs



In [18]:

    
print(np.where((mass[m][b][Good_either][gbox])/(mass[m][a][Good_either][gbox]) > 0.8))
print(np.where((mass[m][b][Good_either][bbox])/(mass[m][a][Good_either][bbox]) > 0.8))









    



(array([0, 1]),)
(array([0, 2]),)



In [19]:

    
# Mass ratios of green pairs:
print((mass[m][b][Good_either][gbox])/(mass[m][a][Good_either][gbox]))









    



[ 0.95097019  0.98775179  0.5577816   0.76108634  0.6592446   0.75463712
  0.56029975]



In [20]:

    
# Grabbing indices of same-mass pairs:
print(m[a][Good_either][gbox][0])
print(m[b][Good_either][gbox][0])
print(m[a][Good_either][gbox][1])
print(m[b][Good_either][gbox][1])

print(m[a][Good_either][bbox][2])
print(m[b][Good_either][bbox][2])



In [21]:

    
## System 1 KICs
print(kicnum[146654])
print(kicnum[146653])

## System 2 KICs
print(kicnum[141965])
print(kicnum[38561])

print(kicnum[13986])
print(kicnum[173725])



In [22]:

    
## Rotation Periods

np.where((Kep_ID == 7871442) | (Kep_ID == 7871438))
print(prot[16])
print(prot[17])



In [23]:

    
## FLRs

print(Lfl_Lbol[146654])
print(Lfl_Lbol[146653])









    



1.061494557e-07
0.000285830055594



In [24]:

    
gmass = np.where((mass[m][b][Good_either][gbox])/(mass[m][a][Good_either][gbox]) > 0.8)
bmass = np.where((mass[m][b][Good_either][bbox])/(mass[m][a][Good_either][bbox]) > 0.8)
ymass = np.where((mass[m][b][Good_either][ybox])/(mass[m][a][Good_either][ybox]) > 0.8)
allmass = np.where((mass[m][b][Good_either])/(mass[m][a][Good_either]) > 0.8)



In [25]:

    
### Activity Comparison for pairs w/ mass ratios  > 0.8 ###

plt.figure(figsize=(8,8.1)) 

# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_a2]), np.log10(Lfl_Lbol[m][b][Good_both_a2]), 'ro', alpha = 0.6)
# plt.plot(np.log10(Lfl_Lbol[m][a][Good_both_b2]), np.log10(Lfl_Lbol[m][b][Good_both_b2]), 'bo', alpha = 0.6)
plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][gbox][gmass]), np.log10(Lfl_Lbol[m][b][Good_either][gbox][gmass]), 
            alpha = 0.6, s=80, c='g', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][bbox][bmass]), np.log10(Lfl_Lbol[m][b][Good_either][bbox][bmass]), 
            alpha = 0.6, s=80, c='b', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][ybox][ymass]), np.log10(Lfl_Lbol[m][b][Good_either][ybox][ymass]), 
            alpha = 0.6, s=80, c='y', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either][allmass]), np.log10(Lfl_Lbol[m][b][Good_either][allmass]), 
            alpha = 0.6, s=80, c='none', edgecolor='k')

plt.scatter(np.log10(Lfl_Lbol[m][a][Good_either]), np.log10(Lfl_Lbol[m][b][Good_either]), s=10, c='k', alpha=0.4)


plt.plot([-11,-2], [-11,-2],'b', lw=3, alpha=0.5,label='Idealized')
plt.plot([-11,-2], [-12,-3],'y', lw=3, alpha=0.5,label='Within solar variability')
plt.plot([-11,-2], [-10,-1],'y', lw=3, alpha=0.5)

box1 = plt.Polygon([[-5,-4],[-11,-4],[-11,-2],[-3,-2]],fc='g',alpha=0.1,label='Active Secondary')
box2 = plt.Polygon([[-4,-5],[-4,-11],[-2,-11],[-2,-3]],fc='b',alpha=0.1,label='Active Primary')
circle = plt.Circle((-11, -11), radius=5, fc='r',alpha=0.1,label='Below noise threshold')
plt.gca().add_patch(circle)
plt.gca().add_patch(box1)
plt.gca().add_patch(box2)

plt.xlim(-11, -2)
plt.ylim(-11, -2)
plt.xlabel(r'log $L_{fl}/L_{kp}$ (A component)')
plt.ylabel(r'log $L_{fl}/L_{kp}$ (B component)')
#plt.legend(fontsize=10, loc=2)
plt.savefig('AB_v5.pdf',dpi=600)

Rotation Analysis -



In [26]:

    
## Since we deleted problem indices in m, we need to delete them in prot ##
prot = np.delete(prot,[72,114])
print(prot)









    



[  8.27   1.98  19.66  16.31  14.07  24.54  22.84  41.97  41.7   13.21
  25.72  34.12  24.78  36.72  31.82  21.58   2.85  17.45   8.11   7.06
   3.51  30.96  34.12   0.    48.28  39.01  20.84  28.69  12.22  38.27
  12.22   0.61  12.46   4.99  14.64  13.52  14.45  46.4   15.9   20.38
  25.43  19.3   13.77   7.33  22.58   0.    15.44  41.45  20.74  12.57
   2.2   28.25  38.84  37.84  17.41  11.42  14.66   5.5   21.78  18.18
  19.66  15.62  16.08   7.73  19.31  10.38  16.45  20.22  12.11  15.59
  38.99  48.39  24.45  27.    10.29  22.32   7.23  36.17  39.21   5.2
  18.84   4.52   0.72  43.32   6.77  19.25  24.82  29.12   1.72  39.13
  53.99  15.88  19.38  26.08   0.     3.63  27.08  10.98  25.16  35.21
  39.07  42.87  18.89   0.98   0.99  22.14  24.51  45.32  48.65   9.89
   7.2    0.79  11.22  55.17  16.33  31.37  12.27   7.62   0.    11.32
  13.82  34.04   3.86  12.77  10.38  16.28  17.97  16.43  17.25  20.53
  23.5   22.85  21.08  14.19  36.54  20.38  24.77   9.62   0.     0.     5.45
   6.54  13.3   12.75  22.3    8.05  31.12  32.55  18.32  43.85  16.34
  30.85   7.32   2.17   1.47   1.46   0.74   2.82  13.51  13.18   1.52
   4.49  39.5   41.64   5.38   5.27   5.47   5.48  34.82  38.66  18.2
  22.16  19.82  26.84  16.09  44.01  37.92  17.23  53.37  38.86   6.02
   3.48  49.1   45.16]



In [27]:

    
# Calculating rosby numbers:
rosby = prot/tau[m]
print(rosby)









    



[ 0.50888803  0.17246794  0.68579472  0.52681341  0.90082304  0.94306092
  0.96303807  1.25872968  2.06617793  0.6503618   1.72771118  1.73774614
  1.80864069  1.80364966  0.84331398  0.72364618  0.09675563  0.63108277
  0.76056742  0.55796176  0.25122024  2.32277248  1.46537875  0.
  1.22445343  1.06147649  1.71577923  1.39479438  1.05338181  1.74586918
  1.05338181  0.06054803  0.70190318  0.41566952  1.15294115  1.17032159
  0.91560499  3.61183265  0.51746275  0.91445153  1.37485664  1.40162574
  0.70111696  0.59252231  0.73624706  0.          0.97598882  1.18691034
  1.12878019  1.06439258  0.08770137  1.83833337  1.18431202  0.73258123
  1.00538582  0.88759375  1.0007572   0.51164784  0.64805725  0.57616146
  0.67749199  0.64409728  0.7787246   0.48980115  0.9675763   0.73492583
  0.64790737  0.87836996  1.02760315  1.20739383  1.89211869  1.59518255
  1.06176865  1.88460964  0.75065323  1.42050622  0.6467497   0.82474456
  1.10378093  0.53919294  0.99435179  0.31317421  0.02818393  1.72314691
  0.15852494  1.50301081  0.93533801  1.98323355  0.11343555  1.54661942
  1.56977437  0.74793744  1.10539134  1.32974342  0.          0.2841436
  1.70971784  0.84432017  0.96278243  1.64748355  1.45294762  2.08094565
  1.35073394  0.03161413  0.046311    1.52832641  1.60000192  2.34744891
  1.4914013   0.70385496  0.44900564  0.07016405  0.32650032  1.27191827
  1.12673934  1.37553381  0.46289227  0.74690916  0.          0.76843992
  0.76630818  1.79934222  0.37660154  1.26837448  0.57281002  1.07494558
  0.83180849  1.40079278  0.7258636   0.72603288  0.90937303  0.96215718
  1.36904625  0.96596694  1.17022024  0.73509451  1.71384134  0.72733959
  0.          0.          0.82475431  0.32355128  0.78528598  1.01988093
  0.94408808  0.99543855  2.15319853  1.38762026  1.1209547   2.73073648
  0.96616817  0.86983355  0.58143001  0.09478749  0.04608464  0.03311674
  0.12360561  0.40975105  0.7578904   1.00266089  0.0640901   0.44127647
  1.52614833  1.74112336  0.44317733  0.48151793  0.29047171  0.1904359
  1.18247954  1.30683919  0.49704547  1.27068535  0.73347388  1.4731524
  0.88046919  1.25807005  0.95701554  0.56781197  1.69302033  1.17176527
  0.55872901  0.32724277  1.555585    1.44994683]



In [28]:

    
rosby[gbox]









    Out[28]:





array([ 1.25872968,  0.25122024,  1.71577923,  1.39479438,  0.        ,
        0.97598882,  1.06439258])



In [150]:

    
### Rosby number vs. Activity w/ points colored by primary/secondary ###

fig = plt.figure(figsize=(8,8))

plt.scatter(rosby[a][Good_either],np.log10(Lfl_Lbol[m][a][Good_either]),alpha = 0.6,s=80,c='r')
plt.scatter(rosby[b][Good_either],np.log10(Lfl_Lbol[m][b][Good_either]),alpha = 0.6,s=80,c='b')

for j in range (np.size(Good_either[0])):
    plt.plot([rosby[a][Good_either],rosby[b][Good_either]],[np.log10(Lfl_Lbol[m][a][Good_either]),np.log10(Lfl_Lbol[m][b][Good_either])],'k',lw=0.2,alpha=0.1)

plt.xscale('log')
plt.ylim(-8,-2)
plt.xlabel(r'Ro ($P_{rot}/\tau$)')
plt.ylabel(r'log $L_{fl}/L_{kp}$')
plt.savefig('ABrot.png',dpi=500)









    



/Users/Riley/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):



In [36]:

    
### Rosby number vs. Activity w/ points colored by y/g/b activity populations ###

fig = plt.figure(figsize=(8,8))#connect these by lines

for j in range (len(bbox[0])):
    plt.plot([rosby[a][Good_either][bbox],rosby[b][Good_either][bbox]],
             [np.log10(Lfl_Lbol[m][a][Good_either][bbox]),np.log10(Lfl_Lbol[m][b][Good_either][bbox])],'b',lw=0.2,alpha=0.5)

for j in range (len(gbox[0])):
    plt.plot([rosby[a][Good_either][gbox],rosby[b][Good_either][gbox]],
             [np.log10(Lfl_Lbol[m][a][Good_either][gbox]),np.log10(Lfl_Lbol[m][b][Good_either][gbox])],'g',lw=0.2,alpha=0.5)    
  
for j in range (len(ybox[0])):
    plt.plot([rosby[a][Good_either][ybox],rosby[b][Good_either][ybox]],
             [np.log10(Lfl_Lbol[m][a][Good_either][ybox]),np.log10(Lfl_Lbol[m][b][Good_either][ybox])],'y',lw=0.2,alpha=0.1)

# Highlighting pair of interest:
plt.scatter(rosby[a][Good_either][gbox][0],np.log10(Lfl_Lbol[m][a][Good_either][gbox][0]),s=260,c='white', edgecolor = 'k', linewidth=3)
plt.scatter(rosby[b][Good_either][gbox][0],np.log10(Lfl_Lbol[m][b][Good_either][gbox][0]),s=260,c='white', marker = 's', edgecolor = 'k', linewidth=3)
plt.scatter(rosby[a][Good_either][bbox][2],np.log10(Lfl_Lbol[m][a][Good_either][bbox][2]),s=260,c='white', edgecolor = 'k', linewidth=3)
plt.scatter(rosby[b][Good_either][bbox][2],np.log10(Lfl_Lbol[m][b][Good_either][bbox][2]),s=260,c='white', marker = 's', edgecolor = 'k', linewidth=3)

#plt.scatter(rosby[a][Good_either],np.log10(Lfl_Lbol[m][a][Good_either]),alpha = 0.3,s=80,c='k',marker = '.')
#plt.scatter(rosby[b][Good_either],np.log10(Lfl_Lbol[m][b][Good_either]),alpha = 0.3,s=80,c='k',marker = '.')

plt.scatter(rosby[a][Good_either][bbox],np.log10(Lfl_Lbol[m][a][Good_either][bbox]),alpha = 0.6,s=80,c='b',edgecolor = 'k')
plt.scatter(rosby[a][Good_either][gbox],np.log10(Lfl_Lbol[m][a][Good_either][gbox]),alpha = 0.6,s=80,c='g',edgecolor = 'k')
plt.scatter(rosby[a][Good_either][ybox],np.log10(Lfl_Lbol[m][a][Good_either][ybox]),alpha = 0.6,s=80,c='y',edgecolor = 'k')
plt.scatter(rosby[b][Good_either][bbox],np.log10(Lfl_Lbol[m][b][Good_either][bbox]),alpha = 0.6,s=80,c='b',marker='s',edgecolor = 'k')
plt.scatter(rosby[b][Good_either][gbox],np.log10(Lfl_Lbol[m][b][Good_either][gbox]),alpha = 0.6,s=80,c='g',marker='s',edgecolor = 'k')
plt.scatter(rosby[b][Good_either][ybox],np.log10(Lfl_Lbol[m][b][Good_either][ybox]),alpha = 0.6,s=80,c='y',marker='s',edgecolor = 'k')


plt.xscale('log')
plt.xlim(1e-2,1e1)
plt.xlabel(r'Ro ($P_{rot}/\tau$)')
plt.ylabel(r'log $L_{fl}/L_{kp}$')
plt.savefig('GBYrot.pdf',dpi=500)

Separation Distributions



In [37]:

    
# cross matched using CDS XMatch Service of:
# Gaia DR1 TGAS and KIC
file = '1497471274965A.csv'



In [38]:

    
gaia = pd.read_csv(file)
gaia.columns









    Out[38]:





Index(['angDist', 'ra_ep2000', 'dec_ep2000', 'errHalfMaj', 'errHalfMin',
       'errPosAng', 'ra', 'dec', 'hip', 'tycho2_id', 'solution_id',
       'source_id', 'random_index', 'ref_epoch', 'ra_error', 'dec_error',
       'parallax', 'parallax_error', 'pmra', 'pmra_error', 'pmdec',
       'pmdec_error', 'ra_dec_corr', 'ra_parallax_corr', 'ra_pmra_corr',
       'ra_pmdec_corr', 'dec_parallax_corr', 'dec_pmra_corr', 'dec_pmdec_corr',
       'parallax_pmra_corr', 'parallax_pmdec_corr', 'pmra_pmdec_corr',
       'astrometric_n_obs_al', 'astrometric_n_obs_ac',
       'astrometric_n_good_obs_al', 'astrometric_n_good_obs_ac',
       'astrometric_n_bad_obs_al', 'astrometric_n_bad_obs_ac',
       'astrometric_delta_q', 'astrometric_excess_noise',
       'astrometric_excess_noise_sig', 'astrometric_primary_flag',
       'astrometric_relegation_factor', 'astrometric_weight_al',
       'astrometric_weight_ac', 'astrometric_priors_used',
       'matched_observations', 'duplicated_source',
       'scan_direction_strength_k1', 'scan_direction_strength_k2',
       'scan_direction_strength_k3', 'scan_direction_strength_k4',
       'scan_direction_mean_k1', 'scan_direction_mean_k2',
       'scan_direction_mean_k3', 'scan_direction_mean_k4', 'phot_g_n_obs',
       'phot_g_mean_flux', 'phot_g_mean_flux_error', 'phot_g_mean_mag',
       'phot_variable_flag', 'l', 'b', 'ecl_lon', 'ecl_lat', 'KIC', 'RA',
       'Dec', 'umag', 'gmag', 'rmag', 'imag', 'zmag', 'Jmag', 'Hmag', 'Kmag',
       'kepmag', 'pmRA', 'pmDec', 'sg', 'v', 'cq', 'aq', 'fc'],
      dtype='object')



In [39]:

    
jfile = 'Janes2017_data.txt'

janes = pd.read_table(jfile, delim_whitespace=True, comment='#',
                      names=('Bid', 'KIC', 'pm_ra', 'pme_ra', 'pm_de', 'pme_de', 
                             'Sep','Irp', 'gmag', 'g_Ks', 'per','e_per','V','e_V', 'NQtrs', 'flag'))



In [40]:

    
janes.shape









    Out[40]:





(186, 16)



In [41]:

    
big_table = pd.merge(janes, gaia, how='inner', left_on='KIC', right_on=u'KIC')
big_table.shape









    Out[41]:





(55, 99)



In [42]:

    
gaia_dist = np.zeros(len(janes)) - 1

for i in range(len(janes)):
    x = np.where((gaia[u'KIC'] == janes['KIC'].values[i]))[0]
    if len(x) > 0:
         gaia_dist[i] = 1000./ gaia[u'parallax'].values[x]



In [43]:

    
psep1 = gaia_dist*sep # Distances from GAIA



In [44]:

    
sep = np.delete(sep,[72,114]) # Deleting problem indices



In [45]:

    
psep2 = jim_dist[m]*sep # Distances from Davenport (2016)



In [46]:

    
### Physical separation [AU] distribution ###

fig = plt.figure(figsize = (10,5))

plt.hist(psep2[a][Good_either],bins=20,range=(0,30000),histtype='stepfilled',color='grey',edgecolor='k')

plt.hist(psep2[a][Good_either][ybox],bins=20,range=(0,30000),histtype='stepfilled',color='y',edgecolor='k')
plt.hist(psep2[a][Good_either][gbox],bins=20,range=(0,30000),histtype='stepfilled',color='g',edgecolor='k')
plt.hist(psep2[a][Good_either][bbox],bins=20,range=(0,30000),histtype='stepfilled',color='b',edgecolor='k')

plt.xlabel('Projected Physical Separation [AU]')
plt.ylabel('Number of binaries')

plt.savefig('sephist.pdf', dpi=300)



In [47]:

    
prob = np.delete(prob,[72,114])



In [48]:

    
prob = prob*0.00001



In [49]:

    
fig = plt.figure(figsize = (10,5))

plt.hist(prob[a][Good_either][ybox],bins=20,histtype='stepfilled',color='y',edgecolor='k')
plt.hist(prob[a][Good_either][gbox],bins=20,histtype='stepfilled',color='g',edgecolor='k')
plt.hist(prob[a][Good_either][bbox],bins=20,histtype='stepfilled',color='b',edgecolor='k')

plt.xlabel('Probability of chance alignment')
plt.ylabel('Number of binaries')

plt.savefig('probhist.pdf',dpi=300)

Appendix A: Light Curves for Targets of Interest -



In [50]:

    
from astropy.io import fits



In [51]:

    
target_1b = fits.open('kplr007871438-2009166043257_llc.fits')
target_1a = fits.open('kplr007871442-2009166043257_llc.fits')
target_2a = fits.open('kplr010536761-2009166043257_llc.fits')
target_2b = fits.open('kplr008888573-2009350155506_llc.fits')



In [52]:

    
target_1a.info()









    



Filename: kplr007871442-2009166043257_llc.fits
No.    Name         Type      Cards   Dimensions   Format
  0  PRIMARY     PrimaryHDU      58   ()      
  1  LIGHTCURVE  BinTableHDU    155   1639R x 20C   [D, E, J, E, E, E, E, E, E, J, D, E, D, E, D, E, D, E, E, E]   
  2  APERTURE    ImageHDU        48   (6, 5)   int32



In [53]:

    
target_1b.info()









    



Filename: kplr007871438-2009166043257_llc.fits
No.    Name         Type      Cards   Dimensions   Format
  0  PRIMARY     PrimaryHDU      58   ()      
  1  LIGHTCURVE  BinTableHDU    155   1639R x 20C   [D, E, J, E, E, E, E, E, E, J, D, E, D, E, D, E, D, E, E, E]   
  2  APERTURE    ImageHDU        48   (6, 6)   int32



In [54]:

    
target_1a[0].header









    Out[54]:





SIMPLE  =                    T / conforms to FITS standards                     
BITPIX  =                    8 / array data type                                
NAXIS   =                    0 / number of array dimensions                     
EXTEND  =                    T / file contains extensions                       
NEXTEND =                    2 / number of standard extensions                  
EXTNAME = 'PRIMARY '           / name of extension                              
EXTVER  =                    1 / extension version number (not format version)  
ORIGIN  = 'NASA/Ames'          / institution responsible for creating this file 
DATE    = '2015-09-01'         / file creation date.                            
CREATOR = '796113 FluxExporter2PipelineModule' / pipeline job and program used t
PROCVER = 'svn+ssh://murzim/repo/soc/tags/release/9.3.22 r60269' / SW version   
FILEVER = '6.1     '           / file format version                            
TIMVERSN= 'OGIP/93-003'        / OGIP memo number for file format               
TELESCOP= 'Kepler  '           / telescope                                      
INSTRUME= 'Kepler Photometer'  / detector type                                  
OBJECT  = 'KIC 7871442'        / string version of target id                    
KEPLERID=              7871442 / unique Kepler target identifier                
CHANNEL =                    2 / CCD channel                                    
SKYGROUP=                   30 / roll-independent location of channel           
MODULE  =                    2 / CCD module                                     
OUTPUT  =                    2 / CCD output                                     
QUARTER =                    1 / Observing quarter                              
SEASON  =                    3 / mission season during which data was collected 
DATA_REL=                   25 / data release version number                    
OBSMODE = 'long cadence'       / observing mode                                 
MISSION = 'Kepler  '           / Mission name                                   
TTABLEID=                   20 / target table id                                
RADESYS = 'ICRS    '           / reference frame of celestial coordinates       
RA_OBJ  =           282.867320 / [deg] right ascension                          
DEC_OBJ =            43.670630 / [deg] declination                              
EQUINOX =               2000.0 / equinox of celestial coordinate system         
PMRA    =               0.0000 / [arcsec/yr] RA proper motion                   
PMDEC   =               0.0000 / [arcsec/yr] Dec proper motion                  
PMTOTAL =               0.0000 / [arcsec/yr] total proper motion                
PARALLAX=                      / [arcsec] parallax                              
GLON    =            73.285844 / [deg] galactic longitude                       
GLAT    =            18.347176 / [deg] galactic latitude                        
GMAG    =               15.893 / [mag] SDSS g band magnitude                    
RMAG    =               14.516 / [mag] SDSS r band magnitude                    
IMAG    =               13.852 / [mag] SDSS i band magnitude                    
ZMAG    =               13.503 / [mag] SDSS z band magnitude                    
D51MAG  =               15.659 / [mag] D51 magnitude,                           
JMAG    =               12.256 / [mag] J band magnitude from 2MASS              
HMAG    =               11.599 / [mag] H band magnitude from 2MASS              
KMAG    =               11.435 / [mag] K band magnitude from 2MASS              
KEPMAG  =               14.464 / [mag] Kepler magnitude (Kp)                    
GRCOLOR =                1.377 / [mag] (g-r) color, SDSS bands                  
JKCOLOR =                0.821 / [mag] (J-K) color, 2MASS bands                 
GKCOLOR =                4.458 / [mag] (g-K) color, SDSS g - 2MASS K            
TEFF    =                 3780 / [K] Effective temperature                      
LOGG    =                4.781 / [cm/s2] log10 surface gravity                  
FEH     =               -0.300 / [log10([Fe/H])]  metallicity                   
EBMINUSV=                0.033 / [mag] E(B-V) reddening                         
AV      =                0.104 / [mag] A_v extinction                           
RADIUS  =                0.464 / [solar radii] stellar radius                   
TMINDEX =            280909235 / unique 2MASS catalog ID                        
SCPID   =                      / unique SCP processing ID                       
CHECKSUM= 'EJRiE9PgEGPgE9Pg'   / HDU checksum updated 2015-09-01T15:21:03Z



In [55]:

    
target_1b[0].header









    Out[55]:





SIMPLE  =                    T / conforms to FITS standards                     
BITPIX  =                    8 / array data type                                
NAXIS   =                    0 / number of array dimensions                     
EXTEND  =                    T / file contains extensions                       
NEXTEND =                    2 / number of standard extensions                  
EXTNAME = 'PRIMARY '           / name of extension                              
EXTVER  =                    1 / extension version number (not format version)  
ORIGIN  = 'NASA/Ames'          / institution responsible for creating this file 
DATE    = '2015-09-01'         / file creation date.                            
CREATOR = '796113 FluxExporter2PipelineModule' / pipeline job and program used t
PROCVER = 'svn+ssh://murzim/repo/soc/tags/release/9.3.22 r60269' / SW version   
FILEVER = '6.1     '           / file format version                            
TIMVERSN= 'OGIP/93-003'        / OGIP memo number for file format               
TELESCOP= 'Kepler  '           / telescope                                      
INSTRUME= 'Kepler Photometer'  / detector type                                  
OBJECT  = 'KIC 7871438'        / string version of target id                    
KEPLERID=              7871438 / unique Kepler target identifier                
CHANNEL =                    2 / CCD channel                                    
SKYGROUP=                   30 / roll-independent location of channel           
MODULE  =                    2 / CCD module                                     
OUTPUT  =                    2 / CCD output                                     
QUARTER =                    1 / Observing quarter                              
SEASON  =                    3 / mission season during which data was collected 
DATA_REL=                   25 / data release version number                    
OBSMODE = 'long cadence'       / observing mode                                 
MISSION = 'Kepler  '           / Mission name                                   
TTABLEID=                   20 / target table id                                
RADESYS = 'ICRS    '           / reference frame of celestial coordinates       
RA_OBJ  =           282.863800 / [deg] right ascension                          
DEC_OBJ =            43.674520 / [deg] declination                              
EQUINOX =               2000.0 / equinox of celestial coordinate system         
PMRA    =               0.0000 / [arcsec/yr] RA proper motion                   
PMDEC   =               0.0000 / [arcsec/yr] Dec proper motion                  
PMTOTAL =               0.0000 / [arcsec/yr] total proper motion                
PARALLAX=                      / [arcsec] parallax                              
GLON    =            73.288755 / [deg] galactic longitude                       
GLAT    =            18.350915 / [deg] galactic latitude                        
GMAG    =               15.502 / [mag] SDSS g band magnitude                    
RMAG    =               14.152 / [mag] SDSS r band magnitude                    
IMAG    =               13.423 / [mag] SDSS i band magnitude                    
ZMAG    =               13.014 / [mag] SDSS z band magnitude                    
D51MAG  =               15.295 / [mag] D51 magnitude,                           
JMAG    =               11.659 / [mag] J band magnitude from 2MASS              
HMAG    =               11.006 / [mag] H band magnitude from 2MASS              
KMAG    =               10.814 / [mag] K band magnitude from 2MASS              
KEPMAG  =               14.047 / [mag] Kepler magnitude (Kp)                    
GRCOLOR =                1.350 / [mag] (g-r) color, SDSS bands                  
JKCOLOR =                0.845 / [mag] (J-K) color, 2MASS bands                 
GKCOLOR =                4.688 / [mag] (g-K) color, SDSS g - 2MASS K            
TEFF    =                 3757 / [K] Effective temperature                      
LOGG    =                4.753 / [cm/s2] log10 surface gravity                  
FEH     =               -0.100 / [log10([Fe/H])]  metallicity                   
EBMINUSV=                0.029 / [mag] E(B-V) reddening                         
AV      =                0.090 / [mag] A_v extinction                           
RADIUS  =                0.494 / [solar radii] stellar radius                   
TMINDEX =            280909224 / unique 2MASS catalog ID                        
SCPID   =                      / unique SCP processing ID                       
CHECKSUM= 'GGNjJEMhGEMhGEMh'   / HDU checksum updated 2015-09-01T15:21:03Z



In [56]:

    
cols = target_1a[1].columns



In [57]:

    
cols.info()









    



name:
    ['TIME', 'TIMECORR', 'CADENCENO', 'SAP_FLUX', 'SAP_FLUX_ERR', 'SAP_BKG', 'SAP_BKG_ERR', 'PDCSAP_FLUX', 'PDCSAP_FLUX_ERR', 'SAP_QUALITY', 'PSF_CENTR1', 'PSF_CENTR1_ERR', 'PSF_CENTR2', 'PSF_CENTR2_ERR', 'MOM_CENTR1', 'MOM_CENTR1_ERR', 'MOM_CENTR2', 'MOM_CENTR2_ERR', 'POS_CORR1', 'POS_CORR2']
format:
    ['D', 'E', 'J', 'E', 'E', 'E', 'E', 'E', 'E', 'J', 'D', 'E', 'D', 'E', 'D', 'E', 'D', 'E', 'E', 'E']
unit:
    ['BJD - 2454833', 'd', '', 'e-/s', 'e-/s', 'e-/s', 'e-/s', 'e-/s', 'e-/s', '', 'pixel', 'pixel', 'pixel', 'pixel', 'pixel', 'pixel', 'pixel', 'pixel', 'pixels', 'pixels']
null:
    ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']
bscale:
    ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']
bzero:
    ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']
disp:
    ['D14.7', 'E13.6', 'I10', 'E14.7', 'E14.7', 'E14.7', 'E14.7', 'E14.7', 'E14.7', 'B16.16', 'F10.5', 'E14.7', 'F10.5', 'E14.7', 'F10.5', 'E14.7', 'F10.5', 'E14.7', 'E14.7', 'E14.7']
start:
    ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']
dim:
    ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']



In [58]:

    
k1a_data = target_1a[1].data
k1b_data = target_1b[1].data
k2a_data = target_2a[1].data
k2b_data = target_2b[1].data



In [59]:

    
time_1a = k1a_data['TIME'] 
flux_1a = k1a_data['PDCSAP_FLUX'] 
rel_flux_1a = (flux_1a-np.nanmedian(flux_1a))/np.nanmedian(flux_1a)
err_1a = k1a_data['PDCSAP_FLUX_ERR']

time_1b = k1b_data['TIME'] 
flux_1b = k1b_data['PDCSAP_FLUX']
rel_flux_1b = (flux_1b-np.nanmedian(flux_1b))/np.nanmedian(flux_1b)
err_1b = k1b_data['PDCSAP_FLUX_ERR']

time_2a = k2a_data['TIME'] 
flux_2a = k2a_data['PDCSAP_FLUX']
rel_flux_2a = (flux_2a-np.nanmedian(flux_2a))/np.nanmedian(flux_2a)
err_2a = k2a_data['PDCSAP_FLUX_ERR']

time_2b = k2b_data['TIME'] 
flux_2b = k2b_data['PDCSAP_FLUX'] 
rel_flux_2b = (flux_2b-np.nanmedian(flux_2b))/np.nanmedian(flux_2b)
err_2b = k2b_data['PDCSAP_FLUX_ERR']

Light curves:



In [60]:

    
fig = plt.figure(figsize = (11,5))

plt.plot(time_1a,rel_flux_1a,color='g')
#plt.errorbar(time,flux,yerr=err,color='g',linestyle="None")
plt.xlim(150,160)
plt.ylim(-0.01,0.01)
plt.xlabel('Time [BJD-2454833]')
plt.ylabel('Relative Flux')
plt.title('KIC 7871442')

plt.savefig('lc1.pdf', dpi=300)



In [61]:

    
fig = plt.figure(figsize = (11,5))

plt.plot(time_1b,rel_flux_1b,color='g')
#plt.errorbar(time,flux,yerr=err,color='g',linestyle="None")
plt.xlim(150,160)
plt.ylim(-0.02,0.04)
plt.xlabel('Time [BJD-2454833]')
plt.ylabel('Relative Flux')
plt.title('KIC 7871438')

plt.savefig('lc2.pdf', dpi=300)



In [62]:

    
fig = plt.figure(figsize = (11,5))

plt.plot(time_2a,rel_flux_2a,color='b')
#plt.errorbar(time,flux,yerr=err,color='g',linestyle="None")
plt.xlim(150,160)
plt.ylim(-0.02,0.05)
plt.xlabel('Time [BJD-2454833]')
plt.ylabel('Relative Flux')
plt.title('KIC 10536761')

plt.savefig('lc3.pdf', dpi=300)



In [63]:

    
fig = plt.figure(figsize = (11,5))

plt.plot(time_2b,rel_flux_2b,color='b')
#plt.errorbar(time,flux,yerr=err,color='g',linestyle="None")
plt.ylim(-0.01,0.01)
plt.xlim(260,300)
plt.xlabel('Time [BJD-2454833]')
plt.ylabel('Relative Flux')
plt.title('KIC 8888573')

plt.savefig('lc4.pdf', dpi=300)

Appendix B: Table



In [10]:

    
from astropy.table import Table,Column



In [53]:

    
## Need to combine lists of A- and B-components such that binary pairs are listed together in each column.

kicall = np.zeros(116)
x=0
y=0

for i in range(116):
    if i%2 == 0:
        kicall[i] = kicnum[m][a][Good_either][x]
        x = x+1
    else:
        kicall[i] = kicnum[m][b][Good_either][y]
        y = y+1



In [54]:

    
giall = np.zeros(116)
x=0
y=0

for i in range(116):
    if i%2 == 0:
        giall[i] = giclr[m][a][Good_either][x]
        x = x+1
    else:
        giall[i] = giclr[m][b][Good_either][y]
        y = y+1



In [55]:

    
protall = np.zeros(116)
x=0
y=0

for i in range(116):
    if i%2 == 0:
        protall[i] = prot[a][Good_either][x]
        x = x+1
    else:
        protall[i] = prot[b][Good_either][y]
        y = y+1



In [56]:

    
Lfl_Lbol_all = np.zeros(116)
x=0
y=0

for i in range(116):
    if i%2 == 0:
        Lfl_Lbol_all[i] = Lfl_Lbol[m][a][Good_either][x]
        x = x+1
    else:
        Lfl_Lbol_all[i] = Lfl_Lbol[m][b][Good_either][y]
        y = y+1



In [57]:

    
mass_all = np.zeros(116)
x=0
y=0

for i in range(116):
    if i%2 == 0:
        mass_all[i] = mass[m][a][Good_either][x]
        x = x+1
    else:
        mass_all[i] = mass[m][b][Good_either][y]
        y = y+1



In [58]:

    
jim_dist_all = np.zeros(116)
x=0
y=0

for i in range(116):
    if i%2 == 0:
        jim_dist_all[i] = jim_dist[m][a][Good_either][x]
        x = x+1
    else:
        jim_dist_all[i] = jim_dist[m][b][Good_either][y]
        y = y+1



In [59]:

    
## Making an array for pair ID numbers, i.e. [1,1,2,2,3,3,...]

bnum = np.zeros(116)
cnt = 0
for j in range(116):
    if j%2 == 0:
        bnum[j] = cnt+1
       
    else:
        bnum[j] = cnt+1
        cnt = cnt+1
    

bnum = bnum.astype(int)



In [60]:

    
## Building table

t = Table(masked= True)

t['KIC'] = kicall.astype(int)
t['Binary Number'] = bnum
t['g-i'] = giall
t[r'$P_{rot}$ [days]'] = protall
t['Parallax [mas]'] = jim_dist_all
t[r'$L_{fl}/L_{kp}$'] = Lfl_Lbol_all
t[r'Mass $[M_{\odot}]$'] = mass_all

#Column formatting
t['g-i'].format = '7.2f'
t['Parallax [mas]'].format = '7.1f'
t[r'Mass $[M_{\odot}]$'].format = '7.3f'
t[r'$L_{fl}/L_{kp}$'].format = '7.3'

#Masking nans in giclr 
t['g-i'].mask = np.isnan(giall)



In [61]:

    
t.show_in_notebook()









    Out[61]:




<Table masked=True length=116>

idx KIC Binary Number g-i $P_{rot}$ [days] Parallax [mas] $L_{fl}/L_{kp}$ Mass $[M_{\odot}]$
0 7090654 1 0.43 1.98 140.7 3.4e-05 1.163
1 7090649 1 0.78 8.27 151.8 1.15e-06 0.923
2 7659402 2 2.12 19.66 247.8 3.04e-06 0.607
3 7659417 2 2.27 16.31 292.4 1.38e-05 0.571
4 7582687 3 0.72 14.07 274.3 3.25e-07 0.949
5 7582691 3 1.89 24.54 304.9 1.16e-07 0.655
6 8006740 4 -- 22.84 68.2 1.63e-07 0.703
7 8143903 4 2.40 41.97 92.1 8.1e-07 0.537
8 5936797 5 0.67 25.72 609.3 5.5e-06 0.980
9 5936811 5 1.09 34.12 667.7 1.19e-05 0.808
10 7093953 6 0.58 24.78 567.0 7.22e-07 1.036
11 7093968 6 1.16 36.72 624.4 7.96e-07 0.788
12 10255692 7 2.18 21.58 170.1 4.75e-07 0.588
13 10255689 7 2.55 31.82 212.0 1.12e-06 0.483
14 7871442 8 2.04 17.45 190.7 1.06e-07 0.625
15 7871438 8 2.08 2.85 131.0 0.000286 0.594
16 11069662 9 0.57 30.96 415.9 4.75e-07 1.055
17 11069655 9 0.59 3.51 254.3 3.63e-07 1.023
18 10388283 10 2.50 39.01 140.1 1.46e-05 0.494
19 10388259 10 2.61 48.28 147.1 6.58e-06 0.465
20 10518551 11 0.45 20.84 149.7 3.19e-08 1.122
21 10518563 11 1.19 28.69 350.2 1.29e-06 0.782
22 9139163 12 0.34 0.61 81.4 3.29e-07 1.263
23 9139151 12 0.45 12.22 97.6 1.58e-07 1.155
24 4995565 13 0.72 15.44 148.0 1.29e-08 0.941
25 4995581 13 2.45 41.45 195.7 2.7e-06 0.516
26 4043389 14 2.39 38.84 31.2 9.64e-07 0.544
27 4142913 14 -- 37.84 11.8 3.31e-07 0.361
28 6678383 15 0.51 11.42 389.3 7.3e-07 1.080
29 6678367 15 0.86 17.41 450.4 1.09e-06 0.884
30 9579208 16 0.38 5.5 187.1 7.04e-08 1.213
31 9579191 16 0.65 14.66 246.3 2.71e-07 0.991
32 7432575 17 1.69 15.62 252.1 1.6e-06 0.692
33 7432573 17 4.06 19.66 280.7 1.87e-06 0.601
34 9762519 18 0.72 7.73 455.1 0.000103 0.942
35 9762514 18 1.17 16.08 619.2 6.03e-06 0.779
36 7596937 19 0.62 10.38 345.3 3.97e-07 1.016
37 7596922 19 1.11 19.31 459.1 1.3e-06 0.799
38 12456757 20 0.61 27.0 354.3 2.51e-07 1.036
39 10529126 20 0.73 10.29 228.1 2.87e-06 0.945
40 7676737 21 1.74 0.72 137.7 3.98e-07 0.673
41 11709022 21 -- 4.52 59.5 0.000511 0.665
42 12507868 22 0.49 6.77 184.4 5.37e-08 1.083
43 7676799 22 2.65 43.32 176.6 4.04e-06 0.433
44 7885518 23 0.62 24.82 383.8 6.01e-07 0.990
45 12507882 23 1.96 19.25 282.4 6.48e-07 0.645
46 11861593 24 1.26 53.99 494.3 8.27e-07 0.764
47 11241109 24 2.51 39.13 96.4 1.81e-06 0.523
48 2442687 25 -- 27.08 196.0 1.67e-07 1.073
49 7750144 25 0.77 3.63 438.9 3.17e-06 0.940
50 7118431 26 1.19 39.07 197.2 1.14e-07 0.781
51 3955963 26 2.01 35.21 204.4 9.09e-07 0.639
52 7118479 27 0.36 42.87 334.4 9e-07 1.022
53 8098178 27 2.24 18.89 292.6 0.000148 0.570
54 2992956 28 0.68 0.99 113.9 9.53e-08 0.999
55 8098181 28 1.18 0.98 165.4 0.0012 0.760
56 2992960 29 0.74 22.14 118.3 3.85e-10 0.962
57 7364380 29 1.06 24.51 156.0 1.48e-07 0.818
58 10275409 30 0.60 48.65 257.7 2.11e-07 1.019
59 7364389 30 2.40 45.32 193.1 1.94e-06 0.547
60 10536753 31 -- 7.2 122.3 0.000156 1.178
61 10275420 31 0.76 9.89 335.6 1.57e-06 0.932
62 10536761 32 2.41 0.79 124.2 0.000129 0.524
63 8888573 32 2.69 11.22 66.5 1.41e-07 0.427
64 4931390 33 0.30 31.37 121.5 3.98e-07 1.253
65 4931385 33 1.96 16.33 148.5 5.83e-07 0.646
66 8565874 34 0.57 12.27 619.8 2.93e-05 1.042
67 8565877 34 0.62 7.62 464.2 3.08e-07 0.987
68 9897318 35 0.96 0.0 461.4 1.3e-06 0.859
69 9897328 35 1.03 11.32 433.0 1.97e-06 0.830
70 12214504 36 0.70 12.77 506.8 1.74e-06 0.969
71 12214492 36 0.94 3.86 505.4 3.77e-06 0.856
72 12068975 37 0.42 16.28 317.0 6.33e-07 1.147
73 12068971 37 1.27 10.38 361.1 9.06e-07 0.754
74 11515925 38 1.12 17.97 176.8 3.65e-07 0.702
75 11515931 38 2.09 16.43 191.8 1.3e-06 0.614
76 5202445 39 1.40 20.53 201.1 3.51e-06 0.703
77 5202421 39 1.90 17.25 235.3 2.06e-06 0.659
78 10612448 40 2.07 14.19 77.0 6.18e-07 0.624
79 10612424 40 2.33 21.08 88.5 5.1e-07 0.567
80 4484238 41 -- 20.38 62.4 3.9e-07 1.061
81 4386086 41 0.69 36.54 90.2 5.97e-07 1.000
82 12317678 42 0.29 9.62 98.5 1.54e-07 1.267
83 12218888 42 0.48 24.77 250.8 2.24e-09 1.121
84 8248671 43 0.52 6.54 207.7 3.78e-07 1.101
85 8248626 43 0.83 5.45 275.1 3.78e-07 0.897
86 12024098 44 -- 12.75 605.7 1.48e-09 1.445
87 12024088 44 1.46 13.3 454.5 1.6e-06 0.706
88 11724888 45 0.83 32.55 435.3 4.22e-06 0.931
89 11724885 45 0.85 31.12 628.5 8.68e-07 0.920
90 6225718 46 -- 16.34 41.3 1.24e-07 1.096
91 6225816 46 -- 30.85 43.7 0.000438 0.722
92 11876220 47 2.36 7.32 193.9 4.54e-05 0.557
93 11876227 47 2.76 2.17 133.2 0.000657 0.420
94 10616124 48 -0.06 1.47 329.7 1.25e-05 1.724
95 10616138 48 0.01 1.46 258.2 8.19e-06 1.591
96 8184081 49 0.54 2.82 228.2 1.69e-06 1.065
97 8184075 49 0.91 0.74 170.3 7.75e-07 0.866
98 10355856 50 0.31 13.18 117.8 3.05e-07 1.255
99 10355809 50 0.70 13.51 133.9 0.000272 0.703
100 5211089 51 1.69 4.49 363.4 2.41e-06 0.699
101 5211083 51 1.89 1.52 338.4 1.08e-06 0.658
102 11098013 52 0.38 41.64 260.3 1.22e-07 1.199
103 11098004 52 0.47 39.5 310.4 8.54e-07 1.122
104 6545403 53 -- 5.38 116.8 5.61e-07 0.833
105 6545415 53 2.12 5.27 176.1 5.86e-07 0.605
106 4864392 54 0.94 22.16 271.0 3.54e-07 0.853
107 4864391 54 2.02 18.2 316.5 8.01e-06 0.636
108 10230145 55 0.97 19.82 93.1 1.73e-08 0.851
109 10296031 55 2.37 26.84 139.0 3.45e-06 0.516
110 10622511 56 2.27 44.01 87.4 2.43e-06 0.580
111 10557342 56 2.64 16.09 139.7 4.53e-07 0.463
112 8909853 57 2.30 37.92 155.4 8.34e-06 0.562
113 8909876 57 2.39 17.23 175.8 3.11e-06 0.539
114 10164867 58 0.32 38.86 228.4 1.35e-07 1.221
115 10164839 58 0.33 53.37 258.4 1.9e-07 1.211



In [62]:

    
## Write table to deluxetable .tex file

t.write('wbtable', format='ascii.aastex', delimiter = '&', overwrite = True)



In [ ]:

idx	KIC	Binary Number	g-i	$P_{rot}$ [days]	Parallax [mas]	$L_{fl}/L_{kp}$	Mass $[M_{\odot}]$
0	7090654	1	0.43	1.98	140.7	3.4e-05	1.163
1	7090649	1	0.78	8.27	151.8	1.15e-06	0.923
2	7659402	2	2.12	19.66	247.8	3.04e-06	0.607
3	7659417	2	2.27	16.31	292.4	1.38e-05	0.571
4	7582687	3	0.72	14.07	274.3	3.25e-07	0.949
5	7582691	3	1.89	24.54	304.9	1.16e-07	0.655
6	8006740	4	--	22.84	68.2	1.63e-07	0.703
7	8143903	4	2.40	41.97	92.1	8.1e-07	0.537
8	5936797	5	0.67	25.72	609.3	5.5e-06	0.980
9	5936811	5	1.09	34.12	667.7	1.19e-05	0.808
10	7093953	6	0.58	24.78	567.0	7.22e-07	1.036
11	7093968	6	1.16	36.72	624.4	7.96e-07	0.788
12	10255692	7	2.18	21.58	170.1	4.75e-07	0.588
13	10255689	7	2.55	31.82	212.0	1.12e-06	0.483
14	7871442	8	2.04	17.45	190.7	1.06e-07	0.625
15	7871438	8	2.08	2.85	131.0	0.000286	0.594
16	11069662	9	0.57	30.96	415.9	4.75e-07	1.055
17	11069655	9	0.59	3.51	254.3	3.63e-07	1.023
18	10388283	10	2.50	39.01	140.1	1.46e-05	0.494
19	10388259	10	2.61	48.28	147.1	6.58e-06	0.465
20	10518551	11	0.45	20.84	149.7	3.19e-08	1.122
21	10518563	11	1.19	28.69	350.2	1.29e-06	0.782
22	9139163	12	0.34	0.61	81.4	3.29e-07	1.263
23	9139151	12	0.45	12.22	97.6	1.58e-07	1.155
24	4995565	13	0.72	15.44	148.0	1.29e-08	0.941
25	4995581	13	2.45	41.45	195.7	2.7e-06	0.516
26	4043389	14	2.39	38.84	31.2	9.64e-07	0.544
27	4142913	14	--	37.84	11.8	3.31e-07	0.361
28	6678383	15	0.51	11.42	389.3	7.3e-07	1.080
29	6678367	15	0.86	17.41	450.4	1.09e-06	0.884
30	9579208	16	0.38	5.5	187.1	7.04e-08	1.213
31	9579191	16	0.65	14.66	246.3	2.71e-07	0.991
32	7432575	17	1.69	15.62	252.1	1.6e-06	0.692
33	7432573	17	4.06	19.66	280.7	1.87e-06	0.601
34	9762519	18	0.72	7.73	455.1	0.000103	0.942
35	9762514	18	1.17	16.08	619.2	6.03e-06	0.779
36	7596937	19	0.62	10.38	345.3	3.97e-07	1.016
37	7596922	19	1.11	19.31	459.1	1.3e-06	0.799
38	12456757	20	0.61	27.0	354.3	2.51e-07	1.036
39	10529126	20	0.73	10.29	228.1	2.87e-06	0.945
40	7676737	21	1.74	0.72	137.7	3.98e-07	0.673
41	11709022	21	--	4.52	59.5	0.000511	0.665
42	12507868	22	0.49	6.77	184.4	5.37e-08	1.083
43	7676799	22	2.65	43.32	176.6	4.04e-06	0.433
44	7885518	23	0.62	24.82	383.8	6.01e-07	0.990
45	12507882	23	1.96	19.25	282.4	6.48e-07	0.645
46	11861593	24	1.26	53.99	494.3	8.27e-07	0.764
47	11241109	24	2.51	39.13	96.4	1.81e-06	0.523
48	2442687	25	--	27.08	196.0	1.67e-07	1.073
49	7750144	25	0.77	3.63	438.9	3.17e-06	0.940
50	7118431	26	1.19	39.07	197.2	1.14e-07	0.781
51	3955963	26	2.01	35.21	204.4	9.09e-07	0.639
52	7118479	27	0.36	42.87	334.4	9e-07	1.022
53	8098178	27	2.24	18.89	292.6	0.000148	0.570
54	2992956	28	0.68	0.99	113.9	9.53e-08	0.999
55	8098181	28	1.18	0.98	165.4	0.0012	0.760
56	2992960	29	0.74	22.14	118.3	3.85e-10	0.962
57	7364380	29	1.06	24.51	156.0	1.48e-07	0.818
58	10275409	30	0.60	48.65	257.7	2.11e-07	1.019
59	7364389	30	2.40	45.32	193.1	1.94e-06	0.547
60	10536753	31	--	7.2	122.3	0.000156	1.178
61	10275420	31	0.76	9.89	335.6	1.57e-06	0.932
62	10536761	32	2.41	0.79	124.2	0.000129	0.524
63	8888573	32	2.69	11.22	66.5	1.41e-07	0.427
64	4931390	33	0.30	31.37	121.5	3.98e-07	1.253
65	4931385	33	1.96	16.33	148.5	5.83e-07	0.646
66	8565874	34	0.57	12.27	619.8	2.93e-05	1.042
67	8565877	34	0.62	7.62	464.2	3.08e-07	0.987
68	9897318	35	0.96	0.0	461.4	1.3e-06	0.859
69	9897328	35	1.03	11.32	433.0	1.97e-06	0.830
70	12214504	36	0.70	12.77	506.8	1.74e-06	0.969
71	12214492	36	0.94	3.86	505.4	3.77e-06	0.856
72	12068975	37	0.42	16.28	317.0	6.33e-07	1.147
73	12068971	37	1.27	10.38	361.1	9.06e-07	0.754
74	11515925	38	1.12	17.97	176.8	3.65e-07	0.702
75	11515931	38	2.09	16.43	191.8	1.3e-06	0.614
76	5202445	39	1.40	20.53	201.1	3.51e-06	0.703
77	5202421	39	1.90	17.25	235.3	2.06e-06	0.659
78	10612448	40	2.07	14.19	77.0	6.18e-07	0.624
79	10612424	40	2.33	21.08	88.5	5.1e-07	0.567
80	4484238	41	--	20.38	62.4	3.9e-07	1.061
81	4386086	41	0.69	36.54	90.2	5.97e-07	1.000
82	12317678	42	0.29	9.62	98.5	1.54e-07	1.267
83	12218888	42	0.48	24.77	250.8	2.24e-09	1.121
84	8248671	43	0.52	6.54	207.7	3.78e-07	1.101
85	8248626	43	0.83	5.45	275.1	3.78e-07	0.897
86	12024098	44	--	12.75	605.7	1.48e-09	1.445
87	12024088	44	1.46	13.3	454.5	1.6e-06	0.706
88	11724888	45	0.83	32.55	435.3	4.22e-06	0.931
89	11724885	45	0.85	31.12	628.5	8.68e-07	0.920
90	6225718	46	--	16.34	41.3	1.24e-07	1.096
91	6225816	46	--	30.85	43.7	0.000438	0.722
92	11876220	47	2.36	7.32	193.9	4.54e-05	0.557
93	11876227	47	2.76	2.17	133.2	0.000657	0.420
94	10616124	48	-0.06	1.47	329.7	1.25e-05	1.724
95	10616138	48	0.01	1.46	258.2	8.19e-06	1.591
96	8184081	49	0.54	2.82	228.2	1.69e-06	1.065
97	8184075	49	0.91	0.74	170.3	7.75e-07	0.866
98	10355856	50	0.31	13.18	117.8	3.05e-07	1.255
99	10355809	50	0.70	13.51	133.9	0.000272	0.703
100	5211089	51	1.69	4.49	363.4	2.41e-06	0.699
101	5211083	51	1.89	1.52	338.4	1.08e-06	0.658
102	11098013	52	0.38	41.64	260.3	1.22e-07	1.199
103	11098004	52	0.47	39.5	310.4	8.54e-07	1.122
104	6545403	53	--	5.38	116.8	5.61e-07	0.833
105	6545415	53	2.12	5.27	176.1	5.86e-07	0.605
106	4864392	54	0.94	22.16	271.0	3.54e-07	0.853
107	4864391	54	2.02	18.2	316.5	8.01e-06	0.636
108	10230145	55	0.97	19.82	93.1	1.73e-08	0.851
109	10296031	55	2.37	26.84	139.0	3.45e-06	0.516
110	10622511	56	2.27	44.01	87.4	2.43e-06	0.580
111	10557342	56	2.64	16.09	139.7	4.53e-07	0.463
112	8909853	57	2.30	37.92	155.4	8.34e-06	0.562
113	8909876	57	2.39	17.23	175.8	3.11e-06	0.539
114	10164867	58	0.32	38.86	228.4	1.35e-07	1.221
115	10164839	58	0.33	53.37	258.4	1.9e-07	1.211