In [124]:

    

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

from sklearn.neighbors.kde import KernelDensity

from astropy.time import Time
from astropy import units as u

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


    

In [4]:

    

# CDS X-Match between GCK and GALEX GR5

gck_gr5 = '1504810534436A.csv'
df = pd.read_csv(gck_gr5)
df.columns


    

    
Out[4]:





Index([u'angDist', u'_RAJ2000', u'_DEJ2000', u'Pl', u'GCK', u'RAJ2000',
       u'DEJ2000', u'NUVmag', u'e_NUVmag', u'NUVsn', u'dRad', u'KIC', u'ra',
       u'dec', u'fuv_mag', u'nuv_mag', u'fuv_magerr', u'nuv_magerr', u'objid',
       u'E_bv', u'objtype', u'fuv_flux', u'fuv_fluxerr', u'nuv_flux',
       u'nuv_fluxerr', u'fuv_artifact', u'nuv_artifact'],
      dtype='object')

In [11]:

    

plt.figure(figsize=(8,8))
plt.scatter(df[u'nuv_mag'], df[u'NUVmag'], s=4)
plt.xlim(20,11)
plt.ylim(20,11)
plt.xlabel('GALEX GR5 NUV (mag)')
plt.ylabel('GCK NUV (mag)')


    

    
Out[11]:





<matplotlib.text.Text at 0x112313210>






    

In [23]:

    

plt.figure(figsize=(8,8))
plt.scatter(df[u'nuv_mag'], df[u'NUVmag'], s=4)
plt.xlim(17,16)
plt.ylim(17,16)
plt.xlabel('GALEX GR5 NUV (mag)')
plt.ylabel('GCK NUV (mag)')


    

    
Out[23]:





<matplotlib.text.Text at 0x1123fd390>






    

In [29]:

    

plt.figure(figsize=(8,8))
plt.scatter(df[u'nuv_mag'], df[u'nuv_mag'] - df[u'NUVmag'], s=4)
plt.scatter(16.46, 16.46 - 16.499, s=80, c='r')
plt.xlim(17,16)
plt.ylim(-.2,.2)
plt.xlabel('GALEX GR5 NUV (mag)')
plt.ylabel('GR5 - GCK NUV (mag)')


    

    
Out[29]:





<matplotlib.text.Text at 0x11496ec50>






    

In [ ]:

    




    

In [122]:

    

GCK_near = 'GCK_seach.txt'
GR6_near = 'galex_1846338921.csv'

gck = pd.read_table(GCK_near, delimiter='|', names=('ra', 'dec', 'pl','gck','ra2000','de2000','nuvmag','e_nuvmag',
                                                    'nuvflux','e_nuvflux', 'nuvsn', 'drad','KIC'), comment='#')
gr6 = pd.read_csv(GR6_near)


    

In [53]:

    

plt.figure(figsize=(11,11))
plt.scatter(gck['ra'], gck['dec'], alpha=0.5)
plt.scatter(gr6['ra'], gr6['dec'], alpha=0.85,s=2)
plt.plot(301.5644, 44.45684, '+', markersize=50, c='k', alpha=0.5)


    

    
Out[53]:





[<matplotlib.lines.Line2D at 0x11a38b490>]






    

In [74]:

    

# match the datasets within 1arcsecond

mtch = np.zeros(len(gr6['ra'])) - 1

dlim = 1. / 3600.

for k in range(len(gr6['ra'])):
    dist = np.sqrt((gck['ra'].values - gr6['ra'].values[k])**2 + 
                   (gck['dec'].values - gr6['dec'].values[k])**2)
    x = np.where((dist <= dlim))[0]
    
    if len(x) > 0:
        mtch[k] = x[0]


ok = np.where((mtch > -1))[0]


    

In [84]:

    

plt.figure(figsize=(8,8))
plt.scatter(gr6['nuv_mag'].values[ok], gck['nuvmag'][mtch[ok]], alpha=0.75)
plt.plot([20,14],[20,14], c='r', alpha=0.5)
plt.xlim(20,14)
plt.ylim(20,14)
plt.xlabel('GALEX GR6 NUV (mag)')
plt.ylabel('GCK NUV (mag)')


    

    
Out[84]:





<matplotlib.text.Text at 0x11afc0090>






    

In [216]:

    

plt.figure(figsize=(8,8))
plt.errorbar(gr6['nuv_mag'].values[ok], gck['nuvmag'][mtch[ok]], yerr=gck['e_nuvmag'][mtch[ok]], 
             linestyle='none', marker='o')
plt.plot([20,14],[20,14], c='r', alpha=0.5)
plt.xlim(20,14)
plt.ylim(20,14)
plt.xlabel('GALEX GR6 NUV (mag)')
plt.ylabel('GCK NUV (mag)')
plt.savefig('GCK_GR6.png', dpi=150, bbox_inches='tight', pad_inches=0.25)


    

In [117]:

    

print(gck['nuvmag'][mtch[ok]].values[0], gr6['nuv_mag'][ok][0])
print(gr6['nuv_mag'][ok][0] - gck['nuvmag'][mtch[ok]].values[0])


    

    




(16.499000000000002, 16.460850000000001)
-0.03815

In [201]:

    

okc = np.where((mtch > -1) & (gr6['nuv_mag'] < 19))[0]

print(np.shape(okc))

_ = plt.hist(gr6['nuv_mag'].values[okc] - gck['nuvmag'][mtch[okc]])
plt.plot( (gr6['nuv_mag'][ok][0] - gck['nuvmag'][mtch[ok]].values[0]) * np.ones(2), [0,2])


    

    




(31,)






    
Out[201]:





[<matplotlib.lines.Line2D at 0x11fa0cad0>]






    

In [211]:

    

plt.figure(figsize=(8,8))
plt.errorbar(gr6['nuv_mag'].values[ok], gr6['nuv_mag'].values[ok] - gck['nuvmag'][mtch[ok]], 
             yerr=gck['e_nuvmag'][mtch[ok]], linestyle='none', marker='o')
plt.xlim(19,14.)
plt.ylim(-.2,.2)
plt.xlabel('GALEX GR6 NUV (mag)')
plt.ylabel('GR6 $-$ GCK NUV (mag)')


    

    
Out[211]:





<matplotlib.text.Text at 0x1205df790>






    

In [210]:

    

X = np.array(gr6['nuv_mag'].values[okc] - gck['nuvmag'][mtch[okc]].values)[:,None]

kde = KernelDensity(kernel='gaussian', bandwidth=np.mean(gck['e_nuvmag'][mtch[okc]]) * 2 ).fit(X)
kde


    

    
Out[210]:





KernelDensity(algorithm='auto', atol=0, bandwidth=0.0254838709677,
       breadth_first=True, kernel='gaussian', leaf_size=40,
       metric='euclidean', metric_params=None, rtol=0)

In [217]:

    

X_new = np.linspace(-.25,.25,1000)[:, None]
log_dens = np.exp(kde.score_samples(X_new))
plt.plot(X_new[:,0], log_dens)
plt.plot( (gr6['nuv_mag'][ok][0] - gck['nuvmag'][mtch[ok]].values[0]) * np.ones(2), [0,8])
_ = plt.hist(gr6['nuv_mag'].values[okc] - gck['nuvmag'][mtch[okc]], histtype='step')
plt.xlim(-0.14, 0.14)
plt.ylim(0,7.2)
plt.xlabel('GR6 $-$ GCK NUV (mag)')
plt.savefig('hist_diff.png', dpi=150, bbox_inches='tight', pad_inches=0.25)


    

In [220]:

    

kde.score_samples(X)


    

    
Out[220]:





array([ 1.6692204 ,  1.67315671,  1.49394832,  1.83279452,  1.80383286,
        1.67897793,  1.76001204,  1.73933927,  1.5510749 ,  1.82142066,
        0.90392906,  1.18908153,  1.33699593,  1.83191295,  1.21321777,
        1.79844385,  1.70220279,  0.93408817,  1.07601544,  1.68498259,
        1.09490883,  1.60151023,  1.01521768,  0.55254331,  1.64404685,
        1.09106778,  1.83138907,  1.17857938,  1.6227065 , -0.05473408,
        1.826314  ])

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