def load_unknown_list(lines, unknwn_file=None, all=False):
"""
Parameters
----------
lines : list
Restricted lines; use all=True for all
unknwn_file : str, optional
all : bool, optional
Returns
-------
unknwn_lines : Table
"""
line_dict = defs.lines()
# Load
line_path = arclines.__path__[0] + '/data/lists/'
if unknwn_file is None:
unknwn_file = line_path + 'UNKNWNs.dat'
line_list = load_line_list(unknwn_file)
# Cut on input lamps?
if all:
return line_list
else:
msk = np.array([False] * len(line_list))
for line in lines:
line_flag = line_dict[line]
match = line_list['line_flag'] % (2 * line_flag) >= line_flag
msk[match] = True
# Finish
return line_list[msk]
def get_line_flag(ions):
"""
Parameters
----------
ions
Returns
-------
"""
line_dict = defs.lines()
line_flag = 0
for uion in ions:
line_flag += line_dict[uion]
# Return
return line_flag
def purge_unknowns(line_list, write=False, tol_llist=2., verbose=True):
line_dict = defs.lines()
# Load
unknwns = arcl_io.load_unknown_list([], all=True)
mask = np.array([True] * len(unknwns))
updated = False
# Loop
for ss, row in enumerate(unknwns):
dwv = np.abs(line_list['wave'] - row['wave'])
imin = np.argmin(np.abs(dwv))
# Match?
if dwv[imin] < tol_llist:
line_flag = line_dict[line_list['ion'][imin]]
if row['line_flag'] % (2 * line_flag) >= line_flag:
mask[ss] = False
updated = True
if verbose:
print("Will purge UNKNOWN line \n {}".format(row))
print("Matched to \n {}".format(line_list[imin]))
# Write?
if write and updated:
arcl_io.write_line_list(unknwns[mask], unk_file)
import json
import pdb
from astropy.table import Table
import arclines # For path
src_path = arclines.__path__[0] + '/data/sources/'
from arclines import plots as arcl_plots
from arclines import utils as arcl_utils
# Hard-coded string lengths, and more
from arclines import defs
str_len_dict = defs.str_len()
instr_dict = defs.instruments()
line_dict = defs.lines()
def load(source, **kwargs):
"""
Parameters
----------
src_file
format
Returns
-------
"""
# Parse
src_file = source['File']