Fussing with the Garnett data products

In [1]:

    

# imports
import numpy as np

from pkg_resources import resource_filename

from astropy.table import Table, MaskedColumn

from specdb.cat_utils import match_ids

from dla_cnn import io as dla_io


    

    




read_sets: Using set file -- 
  /home/xavier/local/Python/linetools/linetools/lists/sets/llist_v1.2.ascii
Loading abundances from Asplund2009
Abundances are relative by number on a logarithmic scale with H=12






    




/home/xavier/local/Python/linetools/linetools/lists/linelist.py:402: RuntimeWarning: divide by zero encountered in log10
  self._extra_table['log(w*f)'] = np.log10(qm_strength)

In [2]:

    

g16_abs = dla_io.load_garnett16(orig=True)


    

In [3]:

    

g16_abs.keys()


    

    
Out[3]:





['THINGID',
 'SDSS',
 'Plate',
 'MJD',
 'Fiber',
 'RAdeg',
 'DEdeg',
 'z_QSO',
 'SNRSpec',
 'b_z_DLA',
 'B_z_DLA',
 'log.pnDLA',
 'log.pDLA',
 'log.pDnDLA',
 'log.pDDLA',
 'pnDLAD',
 'pDLAD',
 'z_DLA',
 'log.NHI',
 'flg_BAL',
 'SNR']

In [4]:

    

tbl1_file = resource_filename('dla_cnn', 'catalogs/boss_dr12/MNRAS/table1.dat')


    

In [5]:

    

tbl1 = Table.read(tbl1_file, format='ascii')


    

In [6]:

    

tbl1[0:5]


    

    
Out[6]:




<Table length=5>

THINGID
SDSS
Plate
MJD
Fiber
RAdeg
DEdeg
z_QSO
SNR
flg_BAL
int64
str18
int64
int64
int64
float64
float64
float64
float64
int64
268514930
000000.45+174625.4
6173
56238
528
0.0018983
17.7737391
2.3091
0.7795
0
245101352
000000.66+145828.8
6177
56268
595
0.0027564
14.9746755
2.4979
3.5009
0
154279486
000000.97+044947.2
4415
55831
464
0.0040524
4.8297806
1.6188
7.1102
1000
62145359
000001.27-020159.7
4354
55810
678
0.005317
-2.0332733
1.3604
6.1981
1000
66692618
000001.37-011930.0
4354
55810
646
0.0057462
-1.3250088
2.328
8.2577
0

In [7]:

    

tbl2_file = resource_filename('dla_cnn', 'catalogs/boss_dr12/MNRAS/table2.dat')


    

In [8]:

    

tbl2 = Table.read(tbl2_file, format='ascii')


    

    




WARNING: OverflowError converting to FloatType in column pDLAD, possibly resulting in degraded precision. [astropy.io.ascii.fastbasic]

In [9]:

    

tbl2[0:5]


    

    
Out[9]:




<Table length=5>

THINGID
zmin
zmax
log.pnDLA
log.pDLA
log.pDnDLA
log.pDDLA
pnDLAD
pDLAD
z_DLA
log.NHI
int64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
268514930
1.9654
2.2989
-0.03081
-3.49537
-1043.59
-1042.56
0.919661
0.0803389
2.216
20.0077
245101352
1.9661
2.4877
-0.04589
-3.10426
-621.961
-609.072
5.37594e-05
0.999946
1.997
20.0544
66692618
1.9552
2.318
-0.03247
-3.44363
-345.637
-350.097
0.999618
0.000381529
1.997
20.0038
374107623
1.9328
2.3666
-0.03603
-3.34124
-1074.77
-1079.59
0.999703
0.000296901
1.9438
20.0021
237410936
2.544
3.7018
-0.10786
-2.28033
-579.993
-588.618
0.99998
2.04506e-05
2.8005
20.0045

In [10]:

    

len(tbl1), len(tbl2)


    

    
Out[10]:





(297301, 162858)

In [11]:

    

match_ids?


    

In [12]:

    

rows = match_ids(tbl2['THINGID'], tbl1['THINGID'])


    

In [13]:

    

for key in tbl2.keys():
    if key == 'THINGID':
        continue
    # 
    print('adding column {:s}'.format(key))
    new_clm = MaskedColumn(-999.*np.ones_like(tbl1['RAdeg']))
    new_clm[rows] = tbl2[key]
    # Mask it
    msk = new_clm == -999.
    new_clm.mask[msk] = True
    # Add it
    tbl1.add_column(new_clm, name=key)


    

    




adding column zmin
INFO: Upgrading Table to masked Table. Use Table.filled() to convert to unmasked table. [astropy.table.table]
adding column zmax
adding column log.pnDLA
adding column log.pDLA
adding column log.pDnDLA
adding column log.pDDLA
adding column pnDLAD
adding column pDLAD
adding column z_DLA
adding column log.NHI

In [14]:

    

tbl1[0:5]


    

    
Out[14]:




<Table masked=True length=5>

THINGID
SDSS
Plate
MJD
Fiber
RAdeg
DEdeg
z_QSO
SNR
flg_BAL
zmin
zmax
log.pnDLA
log.pDLA
log.pDnDLA
log.pDDLA
pnDLAD
pDLAD
z_DLA
log.NHI
int64
str18
int64
int64
int64
float64
float64
float64
float64
int64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
268514930
000000.45+174625.4
6173
56238
528
0.0018983
17.7737391
2.3091
0.7795
0
1.9654
2.2989
-0.03081
-3.49537
-1043.59
-1042.56
0.919661
0.0803389
2.216
20.0077
245101352
000000.66+145828.8
6177
56268
595
0.0027564
14.9746755
2.4979
3.5009
0
1.9661
2.4877
-0.04589
-3.10426
-621.961
-609.072
5.37594e-05
0.999946
1.997
20.0544
154279486
000000.97+044947.2
4415
55831
464
0.0040524
4.8297806
1.6188
7.1102
1000
--
--
--
--
--
--
--
--
--
--
62145359
000001.27-020159.7
4354
55810
678
0.005317
-2.0332733
1.3604
6.1981
1000
--
--
--
--
--
--
--
--
--
--
66692618
000001.37-011930.0
4354
55810
646
0.0057462
-1.3250088
2.328
8.2577
0
1.9552
2.318
-0.03247
-3.44363
-345.637
-350.097
0.999618
0.000381529
1.997
20.0038

In [15]:

    

merged_file = resource_filename('dla_cnn', 'catalogs/boss_dr12/MNRAS/merged_g16.fits')


    

In [16]:

    

tbl1.write(merged_file)


    

In [ ]: