def from_sfits(self):
'''Generate the Survey from a summary FITS file
'''
# Read
systems = QTable.read(self.summ_fits)
self.nsys = len(systems)
# Dict
kdict = dict(NHI=['NHI', 'logNHI'],
sigNHI=['sig(logNHI)'],
name=['Name'],
vlim=['vlim'],
zabs=['Z_LLS'],
zem=['Z_QSO'],
RA=['RA'],
Dec=['DEC', 'Dec'])
# Parse the Table
inputs = {}
for key in kdict.keys():
vals, tag = lsio.get_table_column(kdict[key], [systems], idx=0)
if vals is not None:
inputs[key] = vals
# Generate
for kk, row in enumerate(systems):
# Generate keywords
kwargs = {}
for key in inputs.keys():
kwargs[key] = inputs[key][kk]
# Instantiate
self._abs_sys.append(set_absclass(self.abs_type)(**kwargs))
def from_sfits(self):
'''Generate the Survey from a summary FITS file
'''
# Read
systems = QTable.read(self.summ_fits)
self.nsys = len(systems)
# Dict
kdict = dict(NHI=['NHI','logNHI'],
sigNHI=['sig(logNHI)'],
name=['Name'],
vlim=['vlim'],
zabs=['Z_LLS'],
zem=['Z_QSO'],
RA=['RA'],
Dec=['DEC','Dec'])
# Parse the Table
inputs = {}
for key in kdict.keys():
vals, tag = lsio.get_table_column(kdict[key],[systems],idx=0)
if vals is not None:
inputs[key] = vals
# Generate
for kk,row in enumerate(systems):
# Generate keywords
kwargs = {}
for key in inputs.keys():
kwargs[key] = inputs[key][kk]
# Instantiate
self._abs_sys.append(set_absclass(self.abs_type)(**kwargs))
def from_sfits(cls, summ_fits, **kwargs):
"""Generate the Survey from a summary FITS file
Handles SPEC_FILES too.
Parameters
----------
summ_fits : str or Table or QTable
Summary FITS file
**kwargs : dict
passed to __init__
"""
# Init
slf = cls(**kwargs)
# Read
if isinstance(summ_fits, Table):
systems = summ_fits
else:
systems = QTable.read(summ_fits)
nsys = len(systems)
# Dict
kdict = dict(NHI=['NHI', 'logNHI'],
sig_NHI=['sig(logNHI)', 'SIGNHI'],
name=['Name'], vlim=['vlim'],
zabs=['Z_LLS', 'ZABS', 'zabs'],
zem=['Z_QSO', 'QSO_ZEM'],
RA=['RA'], Dec=['DEC', 'Dec'])
# Parse the Table
inputs = {}
for key in kdict.keys():
vals, tag = lsio.get_table_column(kdict[key], [systems],idx=0)
if vals is not None:
inputs[key] = vals
# vlim
if 'vlim' not in inputs.keys():
default_vlim = [-1000, 1000.]* u.km / u.s
inputs['vlim'] = [default_vlim]*nsys
# Generate
for kk in range(nsys):
# Generate keywords
kwargs = {}
args = {}
for key in inputs.keys():
if key in ['vlim', 'zabs', 'RA', 'Dec']:
args[key] = inputs[key][kk]
else:
kwargs[key] = inputs[key][kk]
# Instantiate
abssys = class_by_type(slf.abs_type)((args['RA'], args['Dec']), args['zabs'], args['vlim'], **kwargs)
# spec_files
try:
abssys.spec_files += systems[kk]['SPEC_FILES'].tolist()
except (KeyError, AttributeError):
pass
slf._abs_sys.append(abssys)
# Mask
slf.init_mask()
# Return
return slf
def check_dup_table(table,
tol=1 * u.arcsec,
ra_tags=['TARG_RA', 'ra', 'RA'],
dec_tags=['TARG_DEC', 'dec', 'DEC']):
"""
Checks for duplicates in a Table based on their
sky coordinates given by RA and DEC.
Parameters
----------
table : Table
Table to check duplicates for
tol : Angle, optional
Tolerance for the duplicates search
ra_tags : str, list; optional
Name(s) of the column corresponding to RA.
If not given, it guesses it.
dec_tags : str, list; optional
Name(s) of the column corresponding to DEC.
If not given, it guesses it.
Returns
-------
isdup : boolean array, shape(len(table),)
Whether the row in `table` is duplicate
idx : array, shape(len(table),)
Indices of the first nearest neighbour as
given by astropy.coord.match_coordinates_sky()
coords : SkyCoord array for the table
"""
ra, _ = get_table_column(ra_tags, [table], idx=0)
dec, _ = get_table_column(dec_tags, [table], idx=0)
coords = SkyCoord(ra, dec, unit='deg')
isdup, idx = check_dup_coord(coords, tol=tol)
return isdup, idx, coords
def check_dup_table(table, tol=1*u.arcsec, ra_tags=['TARG_RA','ra','RA'], dec_tags=['TARG_DEC','dec','DEC']):
"""
Checks for duplicates in a Table based on their
sky coordinates given by RA and DEC.
Parameters
----------
table : Table
Table to check duplicates for
tol : Angle, optional
Tolerance for the duplicates search
ra_tags : str, list; optional
Name(s) of the column corresponding to RA.
If not given, it guesses it.
dec_tags : str, list; optional
Name(s) of the column corresponding to DEC.
If not given, it guesses it.
Returns
-------
isdup : boolean array, shape(len(table),)
Whether the row in `table` is duplicate
idx : array, shape(len(table),)
Indices of the first nearest neighbour as
given by astropy.coord.match_coordinates_sky()
coords : SkyCoord array for the table
"""
ra, _ = get_table_column(ra_tags, [table], idx=0)
dec, _ = get_table_column(dec_tags, [table], idx=0)
coords = SkyCoord(ra, dec, unit='deg')
isdup, idx = check_dup_coord(coords, tol=tol)
return isdup, idx, coords
def from_sfits(cls, summ_fits, **kwargs):
"""Generate the Survey from a summary FITS file or Table
Handles SPEC_FILES too.
Parameters
----------
summ_fits : str or Table or QTable
Summary FITS file
**kwargs : dict
passed to __init__
"""
# Init
slf = cls(**kwargs)
# Read
if isinstance(summ_fits, Table):
systems = summ_fits
else:
systems = QTable.read(summ_fits)
nsys = len(systems)
# Dict
kdict = dict(NHI=['NHI', 'logNHI'],
sig_NHI=['sig(logNHI)', 'SIGNHI', 'NHI_ERR'],
name=['Name'],
vlim=['vlim'],
zabs=['Z_LLS', 'ZABS', 'zabs'],
zem=['Z_QSO', 'QSO_ZEM', 'ZEM'],
RA=['RA'],
Dec=['DEC', 'Dec'])
# Parse the Table
inputs = {}
for key in kdict.keys():
vals, tag = lsio.get_table_column(kdict[key], [systems], idx=0)
if vals is not None:
inputs[key] = vals
# vlim
if 'vlim' not in inputs.keys():
default_vlim = [-1000, 1000.] * u.km / u.s
inputs['vlim'] = [default_vlim] * nsys
# Generate
for kk in range(nsys):
# Generate keywords
kwargs = {}
args = {}
for key in inputs.keys():
if key in ['vlim', 'zabs', 'RA', 'Dec']:
args[key] = inputs[key][kk]
else:
kwargs[key] = inputs[key][kk]
# Instantiate
abssys = class_by_type(slf.abs_type)((args['RA'], args['Dec']),
args['zabs'], args['vlim'],
**kwargs)
# spec_files
try:
abssys.spec_files += systems[kk]['SPEC_FILES'].tolist()
except (KeyError, AttributeError):
pass
slf._abs_sys.append(abssys)
# Mask
slf.init_mask()
# Return
return slf
