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In [1]:



    


import numpy as np
import pandas as pd
import pystan
import matplotlib.pyplot as plt
import statsmodels.api as sm
import seaborn as sns



    











In [2]:



    


my_data = np.loadtxt('../Catalogue/Match07_smalldoubleclean_emlines.csv', delimiter=',', dtype=str)



    











In [3]:



    


my_dictionary = {}
for i in range(len(my_data[0, :])):                                         # Converting numpy array into dictionary
    my_dictionary[my_data[0, i]] = np.array(my_data[0 + 1:, i], dtype=str)



    











In [4]:



    


cataid       = my_dictionary['CATAID'].astype(str)
redshift     = my_dictionary['Z'].astype(float)
stellar_mass = my_dictionary['logmstar'].astype(float)   # stellar mass from sed fitting - log scale
survey       = my_dictionary['SURVEY'].astype(str)
uv_class     = my_dictionary['UV_CLASS_YI2011'].astype(str)
bpt_class    = my_dictionary['BPT_CLASS'].astype(str)
whan_class   = my_dictionary['WHAN_CLASS'].astype(str)



    











In [5]:



    


logit_class = []
indexes_of_interest = []
for i in range(cataid.size):
    if uv_class[i]=='UV_UPTURN':
        logit_class.append(1)
        indexes_of_interest.append(i)
    elif uv_class[i] == 'UV_WEAK':
        logit_class.append(0)
        indexes_of_interest.append(i)
    else:
        continue
logit_class = np.array(logit_class)



    











In [6]:



    


logit_dataframe                                  = {}
logit_dataframe['CATAID']                        = cataid[indexes_of_interest]
logit_dataframe['LOGIT_CLASS(1-UVUP;0-UVWEAK)']  = logit_class
logit_dataframe['Z']                             = redshift[indexes_of_interest]
logit_dataframe['STELLAR_MASS']                  = stellar_mass[indexes_of_interest]
logit_dataframe['UV_CLASS']                      = uv_class[indexes_of_interest]
logit_dataframe['BPT_CLASS']                     = bpt_class[indexes_of_interest]
logit_dataframe['WHAN_CLASS']                    = whan_class[indexes_of_interest]



    











In [7]:



    


logit_df = pd.DataFrame(logit_dataframe)



    











In [8]:



    


logit_df = logit_df.set_index('CATAID')



    











In [9]:



    


print logit_df



    


















    






        BPT_CLASS  LOGIT_CLASS(1-UVUP;0-UVWEAK)  STELLAR_MASS   UV_CLASS  \
CATAID                                                                     
593695         SF                             0     10.174240    UV_WEAK   
594913         NA                             0     10.792920    UV_WEAK   
594994         NA                             1     10.864960  UV_UPTURN   
598961         NA                             1     10.762900  UV_UPTURN   
600232      LINER                             1     10.471420  UV_UPTURN   
601015         NA                             0     10.738460    UV_WEAK   
609264         NA                             1     11.092890  UV_UPTURN   
609352         NA                             1     11.106870  UV_UPTURN   
609836      LINER                             1     10.666410  UV_UPTURN   
609958  Composite                             0     10.242340    UV_WEAK   
610293      LINER                             1     10.274660  UV_UPTURN   
610387         SF                             0     10.793110    UV_WEAK   
610441         NA                             1     11.272540  UV_UPTURN   
610691         SF                             0     10.761520    UV_WEAK   
611106      LINER                             0     10.690800    UV_WEAK   
611124         SF                             0     10.474800    UV_WEAK   
611468         NA                             1     11.273190  UV_UPTURN   
611561      LINER                             0     10.660730    UV_WEAK   
617460         SF                             1     11.202080  UV_UPTURN   
617899         NA                             1     10.461440  UV_UPTURN   
619166  Composite                             0     10.161170    UV_WEAK   
619222         NA                             1     11.016520  UV_UPTURN   
619575         NA                             1     10.537000  UV_UPTURN   
620066  Composite                             0     10.673620    UV_WEAK   
621993         NA                             0     10.460750    UV_WEAK   
622552         SF                             1     10.599880  UV_UPTURN   
623746         NA                             0     10.807780    UV_WEAK   
6840    Composite                             1     10.839210  UV_UPTURN   
7371           SF                             0     10.695780    UV_WEAK   
7980    Composite                             1      9.968707  UV_UPTURN   
...           ...                           ...           ...        ...   
227884         NA                             0     10.765650    UV_WEAK   
228436         NA                             0     10.740290    UV_WEAK   
228929         NA                             0     11.132460    UV_WEAK   
229130         NA                             0     10.750340    UV_WEAK   
229173         SF                             0     10.945760    UV_WEAK   
229442         NA                             0     10.546850    UV_WEAK   
230307         SF                             1      9.968827  UV_UPTURN   
230578         NA                             0     10.524610    UV_WEAK   
231032         SF                             1     10.358110  UV_UPTURN   
237743         NA                             1     10.785060  UV_UPTURN   
237736    Seyfert                             0     10.617110    UV_WEAK   
238173         NA                             0     10.621930    UV_WEAK   
238225         SF                             1     11.138960  UV_UPTURN   
238341         NA                             1     11.156290  UV_UPTURN   
238680         NA                             1     11.093400  UV_UPTURN   
238896  Composite                             1     10.582550  UV_UPTURN   
238934         SF                             1     10.105600  UV_UPTURN   
239220         SF                             0     10.179180    UV_WEAK   
239670         NA                             0     10.753060    UV_WEAK   
240001         NA                             0     11.191030    UV_WEAK   
240134         NA                             0     10.709510    UV_WEAK   
240455         NA                             1     10.454320  UV_UPTURN   
536599  Composite                             0     10.663930    UV_WEAK   
547811      LINER                             0     10.655470    UV_WEAK   
547814         NA                             0     10.946540    UV_WEAK   
574429         NA                             1     11.051220  UV_UPTURN   
575512         NA                             0     11.563260    UV_WEAK   
584520         NA                             0     11.191200    UV_WEAK   
584929         SF                             0     10.946660    UV_WEAK   
585358  Composite                             0     10.860180    UV_WEAK   

             WHAN_CLASS        Z  
CATAID                            
593695               SF  0.13198  
594913  Retired/Passive  0.13870  
594994  Retired/Passive  0.17168  
598961               NA  0.19304  
600232  Retired/Passive  0.15761  
601015  Retired/Passive  0.16470  
609264             wAGN  0.21197  
609352               SF  0.30252  
609836  Retired/Passive  0.13537  
609958  Retired/Passive  0.09512  
610293  Retired/Passive  0.13949  
610387               SF  0.24746  
610441  Retired/Passive  0.25032  
610691               SF  0.15410  
611106  Retired/Passive  0.12607  
611124             wAGN  0.18159  
611468               NA  0.26784  
611561  Retired/Passive  0.17122  
617460               SF  0.25459  
617899  Retired/Passive  0.12310  
619166             sAGN  0.12493  
619222  Retired/Passive  0.21629  
619575  Retired/Passive  0.17986  
620066  Retired/Passive  0.18917  
621993               NA  0.14224  
622552               SF  0.15922  
623746  Retired/Passive  0.15789  
6840    Retired/Passive  0.19309  
7371                 SF  0.12634  
7980               wAGN  0.08766  
...                 ...      ...  
227884               SF  0.16458  
228436               NA  0.40553  
228929  Retired/Passive  0.20178  
229130               SF  0.15036  
229173  Retired/Passive  0.19254  
229442               NA  0.19765  
230307               SF  0.10566  
230578               SF  0.16248  
231032               SF  0.12675  
237743               SF  0.17742  
237736             sAGN  0.17899  
238173               SF  0.13980  
238225               SF  0.18655  
238341               SF  0.25263  
238680               SF  0.25299  
238896  Retired/Passive  0.12752  
238934             wAGN  0.16899  
239220             sAGN  0.07818  
239670  Retired/Passive  0.13769  
240001  Retired/Passive  0.29361  
240134             sAGN  0.18052  
240455  Retired/Passive  0.12006  
536599             sAGN  0.16528  
547811  Retired/Passive  0.18281  
547814               NA  0.15667  
574429               NA  0.22940  
575512               NA  0.34830  
584520               NA  0.20231  
584929             wAGN  0.20539  
585358  Retired/Passive  0.18752  

[506 rows x 6 columns]

















In [10]:



    


logit_df.to_csv('./../Catalogue/binom_reg_dataset.csv', index=True)



    











In [ ]:

Content source: mdastro/UV_ETGs

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