Jha et al. 2007

In [1]:

    

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import matplotlib.pyplot as plt


    

In [2]:

    

import pandas as pd


    

In [3]:

    

#! mkdir ../data/Jha2007


    

In [4]:

    

#! wget -P ../data/Jha_2007ApJ...659..122J/ -e html_extension=Off http://iopscience.iop.org/article/10.1086/512054/fulltext/63969.tb1.txt


    

In [5]:

    

! head -n 4 ../data/Jha2007/63969.tb1.txt


    

    




1972E	314.84	+30.08	404	190	678	Sd/Irr	-100.0	-38.0	+8.7	UBV	0.056	1
1980N	240.16	-56.63	1760	1633	1653	S0	-20.0	+220.0	-1.0	UBVRI	0.021	2
1981B	292.97	+64.74	1808	1662	2151	Sbc	+42.0	+39.0	-0.2	UBVR	0.018	3, 4

In [6]:

    

names = ['SN_Ia','galactic_longitude','galactic_latitude','cz_km_s_sun','cz_km_s_LG',
         'cz_km_s_CMB','morphological_type','SN_offset_N_as','SN_offset_E_as','t_1_days',
         'filters','E_B_V','Refs']


    

In [7]:

    

df_tbl1 = pd.read_csv('../data/Jha2007/63969.tb1.txt', names=names, 
                      delim_whitespace=True, na_values='\ldots')


    

In [8]:

    

df_tbl1.tail()


    

    
Out[8]:







  

    

      

      
SN_Ia
      
galactic_longitude
      
galactic_latitude
      
cz_km_s_sun
      
cz_km_s_LG
      
cz_km_s_CMB
      
morphological_type
      
SN_offset_N_as
      
SN_offset_E_as
      
t_1_days
      
filters
      
E_B_V
      
Refs
    
  
  

    

      
128
      
2002bf
      
156.46
      
50.08
      
7254
      
7327
      
7418
      
Sb
      
4.0
      
0.6
      
-9.9
      
BVRI
      
0.011
      
42
    
    

      
129
      
2002bo
      
213.04
      
54.85
      
1293
      
1184
      
1609
      
Sa
      
-14.2
      
11.6
      
-13.4
      
UBVRI
      
0.025
      
34,
    
    

      
130
      
2002cx
      
318.71
      
69.14
      
7184
      
7085
      
7494
      
NaN
      
-18.0
      
11.0
      
-5.3
      
BVRI
      
0.032
      
45
    
    

      
131
      
2002er
      
28.67
      
25.83
      
2568
      
2681
      
2563
      
Sc
      
4.7
      
-12.3
      
-7.5
      
UBVRI
      
0.157
      
46
    
    

      
132
      
2003du
      
101.18
      
53.21
      
1912
      
2081
      
1992
      
Sd/Irr
      
-13.5
      
-8.8
      
-4.7
      
BVRI
      
0.010
      
42
    
  

In [9]:

    

import matplotlib.ticker as plticker


    

In [10]:

    

fig,ax=plt.subplots(figsize=(8,8))

#Spacing between each line
intervals = 1.0

loc = plticker.MultipleLocator(base=intervals)
ax.xaxis.set_major_locator(loc)
ax.yaxis.set_major_locator(loc)

# Add the grid
ax.grid(which='major', axis='both', linestyle='-')

ax.plot(df_tbl1.SN_offset_E_as/4.0, df_tbl1.SN_offset_N_as/4.0, '.')
ax.plot([0], [0], 'ro')
ax.set_xlim(-25, 25)
ax.set_ylim(-25, 25)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.set_title('Host galaxy type Ia supernovae distances')
ax.set_xlabel('Kepler pixels')
ax.set_ylabel('Kepler pixels');


    

In [11]:

    

rad_dist = np.sqrt(df_tbl1.SN_offset_E_as**2 + df_tbl1.SN_offset_N_as**2)


    

In [12]:

    

import seaborn as sns


    

In [13]:

    

sns.distplot(rad_dist/4.0)
plt.xlabel('$N$ Kepler pixels');


    

In [14]:

    

np.percentile(rad_dist/4.0, 80)


    

    
Out[14]:





12.97318622993243