In [1]:

    

%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib

from astropy import table

matplotlib.rcParams.update({'font.size':18})
matplotlib.rcParams.update({'font.family':'serif'})


    

In [2]:

    

def B_to_lat(B):
    return np.degrees(np.arcsin(B))


    

In [3]:

    

file = 'data/stats_table.csv'

df = pd.read_csv(file)


    

In [4]:

    

for s in df.columns:
    print(s)


    

    




ttest:out_of_transit&before_midtransit-vs-out_of_transit&after_midtransit
ks:in_transit&before_midtransit-vs-in_transit&after_midtransit
anderson:in_transit&before_midtransit-vs-in_transit&after_midtransit
anderson:out_of_transit&before_midtransit-vs-out_of_transit&after_midtransit
ks:out_of_transit&before_midtransit-vs-out_of_transit&after_midtransit
rms_ratio
kepid
ks:in_transit-vs-out_of_transit
ttest:in_transit&before_midtransit-vs-in_transit&after_midtransit
anderson:in_transit-vs-out_of_transit
ttest:in_transit-vs-out_of_transit
A
AUPPER
ALOWER
UA
AREF
AURL
AR
ARUPPER
ARLOWER
UAR
ARREF
ARURL
ASTROMETRY
B
BUPPER
BLOWER
UB
BREF
BURL
BIGOM
BIGOMUPPER
BIGOMLOWER
UBIGOM
BIGOMREF
BIGOMURL
BINARY
BINARYREF
BINARYURL
BMV
CHI2
COMP
DATE
DEC
DEC_STRING
DENSITY
DENSITYUPPER
DENSITYLOWER
UDENSITY
DENSITYREF
DENSITYURL
DEPTH
DEPTHUPPER
DEPTHLOWER
UDEPTH
DEPTHREF
DEPTHURL
DIST
DISTUPPER
DISTLOWER
UDIST
DISTREF
DISTURL
DR
DRUPPER
DRLOWER
UDR
DRREF
DRURL
DVDT
DVDTUPPER
DVDTLOWER
UDVDT
DVDTREF
DVDTURL
EANAME
EAURL
ECC
ECCUPPER
ECCLOWER
UECC
ECCREF
ECCURL
EOD
ETDNAME
ETDURL
FE
FEUPPER
FELOWER
UFE
FEREF
FEURL
FIRSTREF
FIRSTURL
FREEZE_ECC
GAMMA
GAMMAUPPER
GAMMALOWER
UGAMMA
GAMMAREF
GAMMAURL
GL
GRAVITY
GRAVITYUPPER
GRAVITYLOWER
UGRAVITY
GRAVITYREF
GRAVITYURL
H
HD
HIPP
HR
I
IUPPER
ILOWER
UI
IREF
IURL
IMAGING
J
JSNAME
EPEURL
K
KUPPER
KLOWER
UK
KREF
KURL
KOI
KS
KP
LAMBDA
LAMBDAUPPER
LAMBDALOWER
ULAMBDA
LAMBDAREF
LAMBDAURL
LOGG
LOGGUPPER
LOGGLOWER
ULOGG
LOGGREF
LOGGURL
MASS
MASSUPPER
MASSLOWER
UMASS
MASSREF
MASSURL
MICROLENSING
MSINI
MSINIUPPER
MSINILOWER
UMSINI
MSINIREF
MSINIURL
MSTAR
MSTARUPPER
MSTARLOWER
UMSTAR
MSTARREF
MSTARURL
MULT
NAME
NCOMP
NOBS
OM
OMUPPER
OMLOWER
UOM
OMREF
OMURL
ORBREF
ORBURL
OTHERNAME
PAR
PARUPPER
PARLOWER
UPAR
PER
PERUPPER
PERLOWER
UPER
PERREF
PERURL
PLANETDISCMETH
R
RUPPER
RLOWER
UR
RREF
RURL
RA
RA_STRING
RHK
RHOSTAR
RHOSTARUPPER
RHOSTARLOWER
URHOSTAR
RHOSTARREF
RHOSTARURL
RMS
RR
RRUPPER
RRLOWER
URR
RRREF
RRURL
RSTAR
RSTARUPPER
RSTARLOWER
URSTAR
RSTARREF
RSTARURL
SAO
SE
SEREF
SEURL
SEDEPTHJ
SEDEPTHJUPPER
SEDEPTHJLOWER
USEDEPTHJ
SEDEPTHJREF
SEDEPTHJURL
SEDEPTHH
SEDEPTHHUPPER
SEDEPTHHLOWER
USEDEPTHH
SEDEPTHHREF
SEDEPTHHURL
SEDEPTHKS
SEDEPTHKSUPPER
SEDEPTHKSLOWER
USEDEPTHKS
SEDEPTHKSREF
SEDEPTHKSURL
SEDEPTHKP
SEDEPTHKPUPPER
SEDEPTHKPLOWER
USEDEPTHKP
SEDEPTHKPREF
SEDEPTHKPURL
SEDEPTH36
SEDEPTH36UPPER
SEDEPTH36LOWER
USEDEPTH36
SEDEPTH36REF
SEDEPTH36URL
SEDEPTH45
SEDEPTH45UPPER
SEDEPTH45LOWER
USEDEPTH45
SEDEPTH45REF
SEDEPTH45URL
SEDEPTH58
SEDEPTH58UPPER
SEDEPTH58LOWER
USEDEPTH58
SEDEPTH58REF
SEDEPTH58URL
SEDEPTH80
SEDEPTH80UPPER
SEDEPTH80LOWER
USEDEPTH80
SEDEPTH80REF
SEDEPTH80URL
SEP
SEPUPPER
SEPLOWER
USEP
SEPREF
SEPURL
SET
SETUPPER
SETLOWER
USET
SETREF
SETURL
SHK
SIMBADNAME
SIMBADURL
SPECREF
SPECURL
STAR
STARDISCMETH
T0
T0UPPER
T0LOWER
UT0
T0REF
T0URL
T14
T14UPPER
T14LOWER
UT14
T14REF
T14URL
TEFF
TEFFUPPER
TEFFLOWER
UTEFF
TEFFREF
TEFFURL
TIMING
TRANSIT
TRANSITREF
TRANSITURL
TREND
TT
TTUPPER
TTLOWER
UTT
TTREF
TTURL
V
VREF
VURL
VSINI
VSINIUPPER
VSINILOWER
UVSINI
VSINIREF
VSINIURL
KEPID
KDE
kepoi_name
kepler_name
koi_disposition
koi_pdisposition
koi_score
koi_fpflag_nt
koi_fpflag_ss
koi_fpflag_co
koi_fpflag_ec
koi_period
koi_period_err1
koi_period_err2
koi_time0bk
koi_time0bk_err1
koi_time0bk_err2
koi_impact
koi_impact_err1
koi_impact_err2
koi_duration
koi_duration_err1
koi_duration_err2
koi_depth
koi_depth_err1
koi_depth_err2
koi_prad
koi_prad_err1
koi_prad_err2
koi_teq
koi_teq_err1
koi_teq_err2
koi_insol
koi_insol_err1
koi_insol_err2
koi_model_snr
koi_tce_plnt_num
koi_tce_delivname
koi_steff
koi_steff_err1
koi_steff_err2
koi_slogg
koi_slogg_err1
koi_slogg_err2
koi_srad
koi_srad_err1
koi_srad_err2
ra_str
dec_str
koi_kepmag
koi_kepmag_err

In [5]:

    

plt.scatter(df['B'], np.log10(df['ks:in_transit-vs-out_of_transit']))
plt.xscale('log')
plt.xlim(1e-3,1)
plt.xlabel('Impact Parameter (B)')
plt.ylabel('log KS (in vs out)')


    

    




/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log10
  if __name__ == '__main__':






    
Out[5]:





<matplotlib.text.Text at 0x1155d8b50>






    

In [ ]:

    




    

In [6]:

    

plt.scatter(df['B'], np.log10(df['ks:out_of_transit&before_midtransit-vs-out_of_transit&after_midtransit']))
plt.xscale('log')
plt.xlim(1e-3,1)
plt.xlabel('Impact Parameter (B)')
plt.ylabel('log KS (before vs after)')


    

    
Out[6]:





<matplotlib.text.Text at 0x116261e50>






    

In [7]:

    

plt.scatter(df['B'], np.log10(df['ttest:out_of_transit&before_midtransit-vs-out_of_transit&after_midtransit']))
plt.xscale('log')
plt.xlim(1e-3,1)
plt.xlabel('Impact Parameter (B)')
plt.ylabel('log TTest (before vs after)')


    

    
Out[7]:





<matplotlib.text.Text at 0x1164a1990>






    

In [ ]:

    




    

In [8]:

    

# sum((np.log10(df.iloc[:,3]) > -1.5) & # KS before/after
#     (np.log10(df.iloc[:,2]) > -0.7)) # TTest before/after

# ok = (np.log10(df.iloc[:,3]) > -1.5) & (np.log10(df.iloc[:,2]) > -1)


    

In [9]:

    

plt.scatter(df['PER'], np.log10(df['ks:in_transit-vs-out_of_transit']), 
            s=(df['R']*20), c=df['R'], alpha=0.7)

plt.xscale('log')
plt.xlabel('Period (days)')
plt.ylabel('log KS (in vs out)')
cm = plt.colorbar()
cm.set_label('R (J)')


    

    




/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log10
  if __name__ == '__main__':






    

In [10]:

    

ok20 = ((np.log10(df['ttest:out_of_transit&before_midtransit-vs-out_of_transit&after_midtransit']) > -2) & 
        (np.log10(df['ks:out_of_transit&before_midtransit-vs-out_of_transit&after_midtransit']) > -1.7) & 
        (df['PER'] >= 10))
sum(ok20)


    

    
Out[10]:





77

In [11]:

    

plt.scatter(B_to_lat(df['B']), np.log10(df['ks:in_transit-vs-out_of_transit']), s=5, c='k',alpha=0.5, marker='+')
# plt.scatter(B_to_lat(df['B'])[ok10], np.log10(df.iloc[:,1])[ok10])
plt.scatter(B_to_lat(df['B'])[ok20], np.log10(df['ks:in_transit-vs-out_of_transit'])[ok20])

plt.xlabel('Latitude [deg]')
plt.ylabel('log KS (in vs out)')
# plt.ylim(5,-50)


    

    




/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in arcsin
  from ipykernel import kernelapp as app
/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log10
  if __name__ == '__main__':
/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: RuntimeWarning: divide by zero encountered in log10
  app.launch_new_instance()






    
Out[11]:





<matplotlib.text.Text at 0x1169f5f50>






    

In [12]:

    

for k in range(sum(ok20)):
    plt.plot([B_to_lat(df['B'] - df['RR']/2.)[ok20].values[k], B_to_lat(df['B'] + df['RR']/2.)[ok20].values[k]],
             [np.log10(df['ks:in_transit-vs-out_of_transit'])[ok20].values[k], 
              np.log10(df['ks:in_transit-vs-out_of_transit'])[ok20].values[k]], 
             c='DodgerBlue', lw=3, alpha=0.6)

plt.xlabel('Latitude [deg]')
plt.ylabel('log KS (in vs out)')
plt.ylim(5,-50);


    

    




/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in arcsin
  from ipykernel import kernelapp as app
/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: RuntimeWarning: divide by zero encountered in log10
  app.launch_new_instance()
/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: divide by zero encountered in log10






    

In [13]:

    

# '''
# does the span of latitudes sampled correlate with the scatter measured?
# -> doesnt seem so, no
# '''
# plt.scatter(np.abs(B_to_lat(df['B'] - df['RR']/2.)[ok20] - B_to_lat(df['B'] + df['RR']/2.)[ok20]),
#             np.log10(df.iloc[:,1])[ok20])
# plt.xlabel('Latitude Span [deg]')
# plt.ylabel('log KS (in vs out)')
# plt.ylim(5,-50);
# # plt.xlim(0,10)


    

In [ ]:

    




    

In [23]:

    

for k in range(sum(ok20)):
    plt.plot([B_to_lat(df['B'] - df['RR']/2.)[ok20].values[k], 
              B_to_lat(df['B'] + df['RR']/2.)[ok20].values[k]],
             [df['rms_ratio'][ok20].values[k], 
              df['rms_ratio'][ok20].values[k]], 
             c='k', lw=3, alpha=0.6)

plt.xlabel('Latitude [deg]')
plt.ylabel('RMS ratio (in vs out)')
plt.yscale('log')
# plt.xlim(-5,20)
plt.savefig('kepler_rms.pdf', dpi=150, bbox_inches='tight', pad_inches=0.25)


    

    




/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in arcsin
  from ipykernel import kernelapp as app






    

In [24]:

    

plt.scatter(B_to_lat(df['B'] + df['RR']/2.)[ok20] - B_to_lat(df['B'] - df['RR']/2.)[ok20], 
            df['rms_ratio'][ok20], c='k',alpha=0.5)

plt.xlabel('Latitude Range [deg]')
plt.ylabel('RMS ratio (in vs out)')
plt.yscale('log')
plt.xscale('log')
plt.savefig('kepler_size.pdf', dpi=150, bbox_inches='tight', pad_inches=0.25)


    

    




/Users/james/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in arcsin
  from ipykernel import kernelapp as app






    

In [ ]: