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core.py
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core.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
# WORKING COPY OF KAST REDUCTION CODE
# THINGS THAT NEED FIXING
# - fix flux calibration "wiggle" at red end of blue dispersion
# - documentation
# - function docstrings
# - if output dictionary exists, won't do anything! -> can't used saved files
# imports - internal
import copy
import os
import re
import shutil
import sys
import warnings
import warnings
# imports - external
import matplotlib
import matplotlib.pyplot as plt
import glob
import numpy
import pandas
import pickle
from astropy.io import ascii, fits # for reading in spreadsheet
from astropy.coordinates import SkyCoord # coordinate conversion
from astropy import units as u # standard units
from astropy import constants as const # physical constants in SI units
from scipy import stats, signal
from scipy.integrate import trapz # for numerical integration
from scipy.interpolate import interp1d
if sys.version_info.major != 2 and sys.version_info.major != 3:
raise NameError('\nKASTREDUX only works on Python 2.7 and 3.X\n')
if sys.version_info.major == 2: # switch for those using python 3
import string
warnings.simplefilter('ignore', numpy.RankWarning)
############################################################
# PROGRAM PACKAGE KEYWORDS
############################################################
NAME = 'kastredux'
VERSION = '2022.12.09'
__version__ = VERSION
CODE_PATH = os.path.dirname(os.path.abspath(__file__))
GITHUB_URL = 'https://github.com/aburgasser/kastredux/'
AUTHORS = [
'Adam Burgasser (PI)',
'Ryan Low',]
EMAIL = 'aburgasser@ucsd.edu'
CITATION = ''
BIBCODE = ''
############################################################
# WELCOME!
############################################################
print('\n\nWelcome to the KASTredux reduction package!')
print('This package was developed by Adam Burgasser (aburgasser@ucsd.edu)')
print('You are currently using version {}'.format(VERSION))
#print('If you make use of any features of this toolkit for your research, please remember to cite the Kastredux paper:')
#print('\n{}; Bibcode: {}\n'.format(CITATION,BIBCODE))
print('If you make use of any spectra or models in this package, please remember to cite the original source.')
print('Please report any errors are feature requests to our github page, {}\n\n'.format(GITHUB_URL))
############################################################
# RESOURCE DATA
############################################################
FLUXCALFOLDER = CODE_PATH+'/resources/flux_standards/'
FLUXCALS = {
'HILTNER600': {'FILE' : 'fhilt600.dat','DESIGNATION': 'J06451337+0208146'},
'FEIGE34': {'FILE' : 'ffeige34.dat','DESIGNATION': 'J10393674+4306092'},
'FEIGE66': {'FILE' : 'ffeige66.dat','DESIGNATION': 'J12372352+2503598'},
'FEIGE67': {'FILE' : 'ffeige67.dat','DESIGNATION': 'J12415179+1731197'},
'PG1708+602': {'FILE' : 'fpg1708602.dat','DESIGNATION': 'J17091588+6010108'},
'LTT7987': {'FILE' : 'fltt7987.dat','DESIGNATION': 'J20105686-3013064'},
'BD28+4211': {'FILE' : 'fbd28d4211.dat','DESIGNATION': 'J21511102+2851504'},
'FEIGE110': {'FILE' : 'ffeige110.dat','DESIGNATION': 'J23195840-0509561'},
}
SPTSTDFOLDER = CODE_PATH+'/resources/spectral_standards/'
SPTSTDS = {}
PLOT_DEFAULTS = {'figsize': [6,4], 'fontsize': 16,
'color': 'k', 'ls': '-', 'alpha': 1,
'background_color': 'grey', 'background_ls': '--', 'background_alpha': 1,
'comparison_color': 'm', 'comparison_ls': '--', 'comparison_alpha': 1,
'unc_color': 'grey', 'unc_ls': '--', 'unc_alpha': 1,
'zero_color': 'k', 'zero_ls': '--', 'zero_alpha': 1,
}
TELLSTDFOLDER = CODE_PATH+'/resources/telluric_standards/'
SAMPLEFOLDER = CODE_PATH+'/resources/sample_spectra/'
ERROR_CHECKING = False
############################################################
# KAST INSTRUMENT CONSTANTS
############################################################
DEFAULT_WAVE_UNIT = u.Angstrom
DEFAULT_FLUX_UNIT = u.erg/u.cm/u.cm/u.Angstrom/u.s
DEFAULT_NO_UNIT = u.m/u.m
INSTRUMENT_MODES = {
'RED': {'PREFIX': 'r', 'SUFFIX': '', 'ALTNAME': ['kast-red','r','rd','long','ir','nir'], 'NAME': 'KAST red', 'VERSION': 'kastr', 'ROTATE': True, 'TRIM': [],},
'BLUE': {'PREFIX': 'b', 'SUFFIX': '', 'ALTNAME': ['kast-blue','b','bl','short','uv','vis'], 'NAME': 'KAST blue', 'VERSION': 'kastb', 'ROTATE': False, 'TRIM': [],},
'LDSS3': {'PREFIX': 'ccd', 'SUFFIX': 'c1', 'ALTNAME': ['ldss-3','ldss-3c'], 'NAME': 'LDSS-3', 'VERSION': '', 'ROTATE': True, 'TRIM': [],},
}
DISPERSIONS = {
'452/3306': {'MODE': 'BLUE', 'RESOLUTION': 1.41, 'LAM0': 5460.74},
'600/3000': {'MODE': 'RED', 'RESOLUTION': 1.29, 'LAM0': 5460.74},
'600/4310': {'MODE': 'BLUE', 'RESOLUTION': 1.02, 'LAM0': 4358.33},
'830/3460': {'MODE': 'BLUE', 'RESOLUTION': 0.63, 'LAM0': 5460.74},
'1200/5000': {'MODE': 'RED', 'RESOLUTION': 0.65, 'LAM0': 5460.74},
'600/5000': {'MODE': 'RED', 'RESOLUTION': 1.3, 'LAM0': 7032.41},
'600/7500': {'MODE': 'RED', 'RESOLUTION': 1.3, 'LAM0': 7032.41},
'830/8460': {'MODE': 'RED', 'RESOLUTION': 0.94, 'LAM0': 7032.41},
'300/4230': {'MODE': 'RED', 'RESOLUTION': 2.53, 'LAM0': 7032.41},
'300/7500': {'MODE': 'RED', 'RESOLUTION': 2.53, 'LAM0': 7032.41},
'VPH-RED': {'MODE': 'LDSS3', 'RESOLUTION': 1.18, 'LAM0': 9122.95},
}
CCD_HEADER_KEYWORDS = {
'RED': {
'MODE': 'VERSION', # kastr = red, kastb = blue
'SLIT': 'SLIT_N',
'DISPERSER': 'GRATNG_N',
'DATE-OBS': 'DATE-OBS',
'OBSERVER': 'OBSERVER',
'OBJECT': 'OBJECT',
'RA': 'RA',
'DEC': 'DEC',
'HA': 'HA',
'AIRMASS': 'AIRMASS',
'EXPTIME': 'EXPTIME',
},
'BLUE': {
'MODE': 'VERSION', # kastr = red, kastb = blue
'SLIT': 'SLIT_N',
'DISPERSER': 'GRISM_N',
'DATE-OBS': 'DATE-OBS',
'OBSERVER': 'OBSERVER',
'OBJECT': 'OBJECT',
'RA': 'RA',
'DEC': 'DEC',
'HA': 'HA',
'AIRMASS': 'AIRMASS',
'EXPTIME': 'EXPTIME',
},
'LDSS3': {
'MODE': 'INSTRUME',
'SLIT': 'APERTURE',
'DISPERSER': 'GRISM',
'SPEED': 'SPEED',
'GAIN': 'EGAIN',
'RN': 'ENOISE',
'DATE-OBS': 'DATE-OBS',
'TIME-OBS': 'TIME-OBS',
'OBSERVER': 'OBSERVER',
'OBJECT': 'OBJECT',
'RA': 'RA',
'DEC': 'DEC',
'HA': 'HA',
'AIRMASS': 'AIRMASS',
'EXPTIME': 'EXPTIME',
},
}
# these will be replaced with the above merged dictionary
KAST_CCD_HEADER_KEYWORDS = {
'MODE': 'VERSION', # kastr = red, kastb = blue
'GRISM': 'GRISM_N',
'BLUE_DISPERSION': 'GRISM_N',
'GRATING': 'GRATNG_N',
'RED_DISPERSION': 'GRATNG_N',
'SLIT': 'SLIT_N',
'AIRMASS': 'AIRMASS',
'RA': 'RA',
'DEC': 'DEC',
'OBJECT': 'OBJECT',
'DATE-OBS': 'DATE-OBS',
'EXPTIME': 'EXPTIME',
}
LDSS3_CCD_HEADER_KEYWORDS = {
'MODE': 'INSTRUME',
'SLIT': 'APERTURE',
'DISPERSER': 'GRISM',
'SPEED': 'SPEED',
'GAIN': 'EGAIN',
'RN': 'ENOISE',
'DATE-OBS': 'DATE-OBS',
'TIME-OBS': 'TIME-OBS',
'OBSERVER': 'OBSERVER',
'OBJECT': 'OBJECT',
'RA': 'RA',
'DEC': 'DEC',
'HA': 'HA',
'AIRMASS': 'AIRMASS',
'EXPTIME': 'EXPTIME',
}
CCD_PARAMETERS = {
'RED-FAST': {'GAIN': 0.55, 'RN': 4.3},
'RED-SLOW': {'GAIN': 1.9, 'RN': 3.7},
'BLUE-FAST': {'GAIN': 1.3, 'RN': 6.5},
'BLUE-SLOW': {'GAIN': 1.2, 'RN': 3.8},
'LDSS3-SLOW': {'GAIN': 0.13, 'RN': 5.3},
'LDSS3-FAST': {'GAIN': 1.45, 'RN': 7.2},
}
############################################################
# OTHER PROGAM CONSTANTS
############################################################
MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
FEATURES = { \
'HI': {'altname': ['hi','h1'], 'label': r'H I', 'type': 'line', 'wavelengths': [[4861]*u.Angstrom,[6563]*u.Angstrom]},\
'LiI': {'altname': ['lii','li1'], 'label': r'Li I', 'type': 'line', 'wavelengths': [[6708]*u.Angstrom]}, \
'NaI': {'altname': ['nai','na1'], 'label': r'Na I', 'type': 'line', 'wavelengths': [[8183,8195]*u.Angstrom]}, \
'CsI': {'altname': ['csi','cs1'], 'label': r'Cs I', 'type': 'line', 'wavelengths': [[8521]*u.Angstrom,[8943]*u.Angstrom]}, \
'RbI': {'altname': ['rbi','rb1'], 'label': r'Rb I', 'type': 'line', 'wavelengths': [[7800]*u.Angstrom,[7948]*u.Angstrom]}, \
'CaI': {'altname': ['cai','ca1'], 'label': r'Ca I', 'type': 'line', 'wavelengths': [[6572]*u.Angstrom,[7209,7213]*u.Angstrom,[7326]*u.Angstrom]}, \
'MgI': {'altname': ['mgi','mg1'], 'label': r'Mg I', 'type': 'line', 'wavelengths': [[8806]*u.Angstrom]}, \
'CaII': {'altname': ['ca2'], 'label': r'Ca II', 'type': 'line', 'wavelengths': [[8498,8542,8662]*u.Angstrom]}, \
'TiI': {'altname': ['tii','ti1'], 'label': r'Ti I', 'type': 'line', 'wavelengths': [[6085,6126,6259]*u.Angstrom,[6743]*u.Angstrom,[7209,7248]*u.Angstrom,[8382,8412,8435]*u.Angstrom]}, \
'FeI': {'altname': ['fei','fe1'], 'label': r'Fe I', 'type': 'line', 'wavelengths': [[7583]*u.Angstrom,[8327,8388]*u.Angstrom,[8514]*u.Angstrom,[8824]*u.Angstrom,[9000]*u.Angstrom]}, \
'KI': {'altname': ['ki','k1'], 'label': r'K I', 'type': 'line', 'wavelengths': [[7665,7699]*u.Angstrom]}, \
'H2O': {'altname': [], 'label': r'H$_2$O', 'type': 'band', 'wavelengths': [[9250,9500]*u.Angstrom]}, \
'TiO': {'altname': [], 'label': r'TiO', 'type': 'band', 'wavelengths': [[6150,6280]*u.Angstrom,[6569,6852]*u.Angstrom,[7053,7270]*u.Angstrom,[7590,8000]*u.Angstrom,[8206,8400]*u.Angstrom,[8432,8600]*u.Angstrom]}, \
'VO': {'altname': [], 'label': r'VO', 'type': 'band', 'wavelengths': [[7334,7534]*u.Angstrom,[7851,7973]*u.Angstrom,[9540,9630]*u.Angstrom]}, \
'CaH': {'altname': [], 'label': r'CaH', 'type': 'band', 'wavelengths': [[6346,6390]*u.Angstrom,[6750,7050]*u.Angstrom]}, \
'CrH': {'altname': [], 'label': r'CrH', 'type': 'band', 'wavelengths': [[8611,8681]*u.Angstrom]}, \
'FeH': {'altname': [], 'label': r'FeH', 'type': 'band', 'wavelengths': [[8692,8750]*u.Angstrom,[9896,10300]*u.Angstrom]}, \
}
INDEX_SETS = {
'kirkpatrick1991': {'altname': ['kirkpatrick91','kir91'], 'bibcode': '1991ApJS...77..417K', 'indices': {\
'K91-A': {'ranges': ([7020,7050]*u.Angstrom,[6960,6990]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
'K91-B': {'ranges': ([7375,7385]*u.Angstrom,[7353,7363]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
'K91-C': {'ranges': ([8100,8130]*u.Angstrom,[8174,8204]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
'K91-D': {'ranges': ([8567,8577]*u.Angstrom,[8537,8547]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
}},\
'kirkpatrick1995': {'altname': ['kirkpatrick95','kir95'], 'bibcode': '1995AJ....109..797K', 'indices': {\
'VO7445': {'ranges': ([7350,7400]*u.Angstrom,[7510,7560]*u.Angstrom,[7420,7470]*u.Angstrom),'scaling':(0.5625,0.4375,1.0), 'method': 'line_scaling', 'sample': 'integrate'},\
}},\
'kirkpatrick1999': {'altname': ['kirkpatrick','kirkpatrick99','kir99'], 'bibcode': '1999ApJ...519..802K', 'indices': {\
'Rb-a': {'ranges': ([.77752,.77852]*u.micron,[.78152,.78252]*u.micron,[.77952,.78052]*u.micron), 'method': 'line', 'sample': 'integrate'},\
'Rb-b': {'ranges': ([.79226,.79326]*u.micron,[.79626,.79726]*u.micron,[.79426,.79526]*u.micron), 'method': 'line', 'sample': 'integrate'},\
'Na-a': {'ranges': ([.81533,.81633]*u.micron,[.81783,.81883]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'Na-b': {'ranges': ([.81533,.81633]*u.micron,[.81898,.81998]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'Cs-a': {'ranges': ([.84961,.85061]*u.micron,[.85361,.85461]*u.micron,[.85161,.85261]*u.micron), 'method': 'line', 'sample': 'integrate'},\
'Cs-b': {'ranges': ([.89185,.89285]*u.micron,[.89583,.89683]*u.micron,[.89385,.89485]*u.micron), 'method': 'line', 'sample': 'integrate'},\
'TiO-a': {'ranges': ([.7033,.7048]*u.micron,[.7058,.7073]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'TiO-b': {'ranges': ([.8400,.8415]*u.micron,[.8435,.8470]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'VO-a': {'ranges': ([.7350,.7370]*u.micron,[.7550,.7570]*u.micron,[.7430,.7470]*u.micron), 'method': 'sumnum', 'sample': 'integrate'},\
'VO-b': {'ranges': ([.7860,.7880]*u.micron,[.8080,.8100]*u.micron,[.7960,.8000]*u.micron), 'method': 'sumnum', 'sample': 'integrate'},\
'CrH-a': {'ranges': ([.8580,.8600]*u.micron,[.8621,.8641]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'CrH-b': {'ranges': ([.9940,.9960]*u.micron,[.9970,.9990]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'FeH-a': {'ranges': ([.8660,.8680]*u.micron,[.8700,.8720]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'FeH-b': {'ranges': ([.9863,.9883]*u.micron,[.9908,.9928]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'Color-a': {'ranges': ([.9800,.9850]*u.micron,[.7300,.7350]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'Color-b': {'ranges': ([.9800,.9850]*u.micron,[.7000,.7050]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'Color-c': {'ranges': ([.9800,.9850]*u.micron,[.8100,.8150]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'Color-d': {'ranges': ([.9675,.9850]*u.micron,[.7350,.7550]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
}},\
'martin1999': {'altname': ['martin','martin99','mar99'], 'bibcode': '1999AJ....118.2466M', 'indices': {\
'PC3': {'ranges': ([.823,.827]*u.micron,[.754,.758]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'PC6': {'ranges': ([.909,.913]*u.micron,[.650,.654]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'CrH1': {'ranges': ([.856,.860]*u.micron,[.861,.865]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'CrH2': {'ranges': ([.984,.988]*u.micron,[.997,1.001]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'FeH1': {'ranges': ([.856,.860]*u.micron,[.8685,.8725]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'FeH2': {'ranges': ([.984,.988]*u.micron,[.990,.994]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'H2O1': {'ranges': ([.919,.923]*u.micron,[.928,.932]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'TiO1': {'ranges': ([.700,.704]*u.micron,[.706,.710]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'TiO2': {'ranges': ([.838,.842]*u.micron,[.844,.848]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'VO1': {'ranges': ([.754,.758]*u.micron,[.742,.746]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
'VO2': {'ranges': ([.799,.803]*u.micron,[.790,.794]*u.micron), 'method': 'ratio', 'sample': 'integrate'},\
}},\
'gizis1997': {'altname': ['gizis','gizis97','giz97'], 'bibcode': '', 'indices': {\
'CaH1': {'ranges': [[6380,6390]*u.Angstrom,[6410,6420]*u.Angstrom,[6345,6355]*u.Angstrom],'method': 'avedenom','sample': 'average'},\
'CaH2': {'ranges': [[6814, 6846]*u.Angstrom,[7042, 7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'CaH3': {'ranges': [[6960,6990]*u.Angstrom,[7042,7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'TiO5': {'ranges': [[7126,7135]*u.Angstrom,[7042,7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
}},\
'hawley2002': {'altname': ['hawley02','hawley','haw02'], 'bibcode': '2002AJ....123.3409H', 'indices': {\
'VO-7434': {'ranges': ([7430,7470]*u.Angstrom,[7550,7570]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
'VO-7912': {'ranges': ([7900,7980]*u.Angstrom,[8400,8420]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
'Na-8190': {'ranges': ([8140,8165]*u.Angstrom,[8173,8210]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
'TiO-8440': {'ranges': ([8440,8470]*u.Angstrom,[8400,8420]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
'Color-1': {'ranges': ([8900,9100]*u.Angstrom,[7350,7550]*u.Angstrom), 'method': 'ratio', 'sample': 'average'},\
}},\
'lepine2003': {'altname': ['lepine','lepine03','lep03'], 'bibcode': '', 'indices': {\
'CaH1': {'ranges': [[6380,6390]*u.Angstrom,[6410,6420]*u.Angstrom,[6345,6355]*u.Angstrom],'method': 'avedenom','sample': 'average'},\
'CaH2': {'ranges': [[6814, 6846]*u.Angstrom,[7042, 7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'CaH3': {'ranges': [[6960,6990]*u.Angstrom,[7042,7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'TiO5': {'ranges': [[7126,7135]*u.Angstrom,[7042,7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'VO1': {'ranges': [[7430, 7470]*u.Angstrom,[7550,7570]*u.Angstrom],'method': 'ratio','sample': 'average'},\
# 'TiO6': {'ranges': [[7550,7570]*u.Angstrom,[7745,7765]*u.Angstrom],'method': 'ratio','sample': 'average'},\ CORRECTED BELOW
'TiO6': {'ranges': [[7745,7765]*u.Angstrom,[7550,7570]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'VO2': {'ranges': [[7920,7960]*u.Angstrom,[8130,8150]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'TiO7': {'ranges': [[8440, 8470]*u.Angstrom,[8400, 8420]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'Color-M': {'ranges': [[8105, 8155]*u.Angstrom,[6510, 6560]*u.Angstrom],'method': 'ratio','sample': 'average'},\
}},\
'reid1995': {'altname': ['reid','reid95','rei95'], 'bibcode': '', 'indices': {\
'TiO1': {'ranges': [[6718, 6723]*u.Angstrom,[6703, 6708]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'TiO2': {'ranges': [[7058, 7061]*u.Angstrom,[7043, 7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'TiO3': {'ranges': [[7092, 7097]*u.Angstrom,[7079, 7084]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'TiO4': {'ranges': [[7130, 7135]*u.Angstrom,[7115, 7120]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'TiO5': {'ranges': [[7126, 7135]*u.Angstrom,[7042, 7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'CaH1': {'ranges': [[6380,6390]*u.Angstrom,[6410,6420]*u.Angstrom,[6345,6355]*u.Angstrom],'method': 'avdenom','sample': 'average'},\
'CaH2': {'ranges': [[6814, 6846]*u.Angstrom,[7042, 7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'CaH3': {'ranges': [[6960,6990]*u.Angstrom,[7042,7046]*u.Angstrom],'method': 'ratio','sample': 'average'},\
'CaOH': {'ranges': [[6230, 6240]*u.Angstrom,[6345, 6354]*u.Angstrom],'method': 'ratio','sample': 'average'},\
}},\
'burgasser2003': {'altname': ['burgasser','burgasser03','bur03'], 'bibcode': '', 'indices': {\
'CsI-A': {'ranges': [[8496.1, 8506.1]*u.Angstrom, [8536.1, 8546.1]*u.Angstrom,[8516.1, 8626.1]*u.Angstrom],'method': 'sumnum_twicedenom','sample': 'average'},\
'CsI-B': {'ranges': [[8918.5, 8928.5]*u.Angstrom, [8958.3, 8968.3]*u.Angstrom,[8938.5, 8948.3]*u.Angstrom],'method': 'sumnum_twicedenom','sample': 'average'},\
'H2O': {'ranges': [[9220, 9240]*u.Angstrom,[9280, 9300]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'CrH-A': {'ranges': [[8560, 8600]*u.Angstrom,[8610, 8650]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'CrH-B': {'ranges': [[9855, 9885]*u.Angstrom,[9970, 10000]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'FeH-A': {'ranges': [[8560, 8600]*u.Angstrom,[8685, 8725]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'FeH-B': {'ranges': [[9855, 9885]*u.Angstrom,[9905, 9935]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'Color-e': {'ranges': [[9140, 9240]*u.Angstrom,[8400, 8500]*u.Angstrom],'method': 'ratio','sample': 'average'},\
}},\
'burgasser2003': {'altname': ['burgasser','burgasser03','bur03'], 'bibcode': '', 'indices': {\
'CsI-A': {'ranges': [[8496.1, 8506.1]*u.Angstrom, [8536.1, 8546.1]*u.Angstrom,[8516.1, 8626.1]*u.Angstrom],'method': 'sumnum_twicedenom','sample': 'average'},\
'CsI-B': {'ranges': [[8918.5, 8928.5]*u.Angstrom, [8958.3, 8968.3]*u.Angstrom,[8938.5, 8948.3]*u.Angstrom],'method': 'sumnum_twicedenom','sample': 'average'},\
'H2O': {'ranges': [[9220, 9240]*u.Angstrom,[9280, 9300]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'CrH-A': {'ranges': [[8560, 8600]*u.Angstrom,[8610, 8650]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'CrH-B': {'ranges': [[9855, 9885]*u.Angstrom,[9970, 10000]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'FeH-A': {'ranges': [[8560, 8600]*u.Angstrom,[8685, 8725]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'FeH-B': {'ranges': [[9855, 9885]*u.Angstrom,[9905, 9935]*u.Angstrom],'method': 'ratio','sample': 'integrate'},\
'Color-e': {'ranges': [[9140, 9240]*u.Angstrom,[8400, 8500]*u.Angstrom],'method': 'ratio','sample': 'average'},\
}},\
'riddick2006': {'altname': ['riddick','riddick06','rid06'], 'bibcode': '', 'indices': {\
'R1': {'ranges': [[8025,8130]*u.Angstrom, [8015,8025]*u.Angstrom],'method': 'ratio','sample': 'median'},\
'R2': {'ranges': [[8145,8460]*u.Angstrom, [8460,8470]*u.Angstrom],'method': 'ratio','sample': 'median'},\
'R3': {'ranges': [[8025,8130]*u.Angstrom, [8145,8460]*u.Angstrom,[8015,8025]*u.Angstrom, [8460,8470]*u.Angstrom],'method': 'doublesum','sample': 'median'},\
'R4': {'ranges': [[8854,8857]*u.Angstrom, [8454,8458]*u.Angstrom,[8873,8878]*u.Angstrom],'method': 'inverse_line','sample': 'median'},\
}},\
'stauffer1999': {'altname': ['stauffer','stauffer99','sta99'], 'bibcode': '', 'indices': {\
'c81': {'ranges': [[8115,8165]*u.Angstrom, [7865,7915]*u.Angstrom,[8490,8540]*u.Angstrom],'method': 'inverse_line','sample': 'median'},\
}},\
'slesnick2006': {'altname': ['slesnick','slesnick06','sle06'], 'bibcode': '', 'indices': {\
'TiO-7140': {'ranges': [[7010,7060]*u.Angstrom, [7115,7165]*u.Angstrom],'method': 'ratio','sample': 'median'},\
'TiO-8465': {'ranges': [[8405,8425]*u.Angstrom, [8455,8475]*u.Angstrom],'method': 'ratio','sample': 'median'},\
'Na-8189': {'ranges': [[8174,8204]*u.Angstrom, [8135,8165]*u.Angstrom],'method': 'ratio','sample': 'median'},\
}},\
}
EW_SETS = {
'mann2013': {'altname': ['mann','mann13','man13'], 'reference': 'Mann et al. (2013)', 'bibcode': '2013AJ....145...52M', 'continuum_fit_order': 1, 'features': {\
'f01': {'linecenter': 0.4648*u.micron,'width': 0.00115*u.micron, 'recenter': False,'continuum': [0.461,0.4625,0.468,0.47]*u.micron},\
'f02': {'linecenter': 0.5608*u.micron,'width': 0.0010*u.micron,'recenter': False,'continuum': [0.5269,0.5299,0.566,0.5675]*u.micron},\
'f03': {'linecenter': 0.6118*u.micron,'width': 0.0010*u.micron, 'recenter': False,'continuum': [0.566,0.5675,0.6586,0.6607]*u.micron},\
'f04': {'linecenter': 0.6232*u.micron,'width': 0.0010*u.micron, 'recenter': False,'continuum': [0.566,0.5675,0.6586,0.6607]*u.micron},\
'f05': {'linecenter': 0.6416*u.micron,'width': 0.00205*u.micron, 'recenter': False,'continuum': [0.566,0.5675,0.6586,0.6607]*u.micron},\
'f06': {'linecenter': 0.7540*u.micron,'width': 0.0010*u.micron, 'recenter': False,'continuum': [0.7390,0.75,0.81,0.816]*u.micron},\
'f07': {'linecenter': 0.8208*u.micron,'width': 0.00175*u.micron, 'recenter': False,'continuum': [0.81,0.816,0.823,0.83]*u.micron},\
'f08': {'linecenter': 0.8684*u.micron,'width': 0.0013*u.micron, 'recenter': False,'continuum': [0.859,0.892,0.91,0.912]*u.micron},\
}},
}
ZETA_RELATIONS = {
'lepine2007': {'altname': ['lepine','lepine07','lep07'],'bibcode':'2007ApJ...669.1235L', 'coeff': [-0.164,0.67,-0.118,-0.05],'range': [], 'classes': [0.825,0.5,0.2]},
'lepine2013': {'altname': ['lepine13','lep13'],'bibcode':'2013AJ....145..102L', 'coeff': [-0.588,2.211,-1.906,0.622],'range': [], 'classes': [0.825,0.5,0.2]},
'dhital2012': {'altname': ['dhital','dhital12','dhi12'], 'reference': 'Dhital et al. (2012)','bibcode':'2012AJ....143...67D', 'coeff': [-0.005,-0.183,0.694,-0.127,-0.047],'range': [], 'classes': [0.825,0.5,0.2]},
'zhang2019': {'altname': ['zhang','zhang19','zha19'], 'reference': 'Zhang et al. (2019)','bibcode':'2019ApJS..240...31Z', 'coeff': [-0.1069,0.3863,0.312,-0.2849],'range': [], 'classes': [0.75,0.5,0.2]},
}
METALLICITY_RELATIONS = {
'mann2013': {'altname': ['mann','mann13','man13'], 'reference': 'Mann et al. (2013)', 'bibcode': '2013AJ....145...52M', 'features':['mann2013','hawley2002'],'relations': {
'feh-early': {'features': ['f07','f01','f02','Color-1'],'coeff': [0.53,0.26,-0.16,-0.784,-0.34], 'unc': 0.13, 'range': ['K5.5','M2']},
'mh-early': {'features': ['f07','f01','f08','Color-1'],'coeff': [0.38,0.21,0.29,-0.504,-0.79], 'unc': 0.11, 'range': ['K5.5','M2']},
'feh-late': {'features': ['f05','f08','f07','f03','Color-1'],'coeff': [-0.20,0.48,0.24,0.14,-0.204,-0.32], 'unc': 0.14, 'range': ['M2','M6']},
'mh-late': {'features': ['f05','f04','f06','Color-1'],'coeff': [-0.065,-0.071,-0.30,0.719,-0.24], 'unc': 0.11, 'range': ['M2','M6']},
}},
}
ZETA_METALLICITY_RELATIONS = {
'lepine2013': {'altname': ['lepine','lepine13','lep13','lepine-n12','lepine13-n12','lep13-n12'], 'bibcode':'', 'type':'[Fe/H]','coeff': [0.750,-0.743],'unc': 0.383,'range': [0.9,1.2]},
'lepine2013-ra12': {'altname': ['lepine-ra12','lepine13-ra12','lep13-ra12'], 'bibcode':'', 'type':'[Fe/H]','coeff': [1.071,-1.096],'unc': 0.654,'range': [0.9,1.2]},
'woolf2009': {'altname': ['woolf','woolf09','woo09'], 'bibcode':'2009PASP..121..117W', 'type':'[Fe/H]','coeff': [1.632,-1.685],'unc':0.3,'range': [0.05,1.1]},
'mann2013': {'altname': ['mann','mann13','man13'], 'bibcode': '2013AJ....145...52M', 'type':'[Fe/H]','coeff': [1.26,-1.25],'unc': 0.20,'range': [-0.2,1.2]},
'mann2013-mh': {'altname': ['mann-mh','mann13-mh','man13-mh'], 'bibcode': '2013AJ....145...52M', 'type':'[M/H]','coeff': [0.88,-0.89],'unc': 0.20,'range': [-0.2,1.2]},
}
EW_LINES = {
'HI': {'altname': ['H','H1','H I'], 'lines': [4861*u.Angstrom,6563*u.Angstrom]},\
'LiI': {'altname': ['Li','Li1','Li I'], 'lines': [6708*u.Angstrom]},\
'KI': {'altname': ['K','K1','K I'], 'lines': [7665*u.Angstrom,7699*u.Angstrom]},\
'NaI': {'altname': ['Na','Na1','Na I'], 'lines': [8183*u.Angstrom,8195*u.Angstrom]},\
'CsI': {'altname': ['Cs','Cs1','Cs I'], 'lines': [8521*u.Angstrom,8943*u.Angstrom]},\
'RbI': {'altname': ['Rb','Rb1','Rb I'], 'lines': [7800*u.Angstrom,7948*u.Angstrom]},\
'MgI': {'altname': ['Mg','Mg1','Mg I'], 'lines': [8806*u.Angstrom]},\
'CaI': {'altname': ['Ca','Ca1','Ca I'], 'lines': [6572*u.Angstrom,7209*u.Angstrom,7213*u.Angstrom,7326*u.Angstrom]},\
'CaII': {'altname': ['Ca2','Ca II'], 'lines': [8498*u.Angstrom,8542*u.Angstrom,8662*u.Angstrom,]},\
'FeI': {'altname': ['Fe','Fe1','Fe I'], 'lines': [7583*u.Angstrom,8327*u.Angstrom,8388*u.Angstrom,8514*u.Angstrom,8824*u.Angstrom,9000*u.Angstrom]},\
'TiI': {'altname': ['Ti','Ti1','Ti I'], 'lines': [6085*u.Angstrom,6126*u.Angstrom,6259*u.Angstrom,6743*u.Angstrom,7209*u.Angstrom,7248*u.Angstrom,8382*u.Angstrom,8412*u.Angstrom,8435*u.Angstrom]},\
}
CHI_RELATIONS = {
'schmidt2014': {'altname': ['schmidt','schmidt14'], 'reference': 'Schmidt et al. (2014)','bibcode':'2014PASP..126..642S', 'sptoffset': 0, 'method': 'interpolate', 'scale': 1.e-6,
'spt': [17,18,19,20,21,22,23,24,25,26,27], \
'values': [10.28,4.26,2.52,1.98,2.25,2.11,1.67,1.16,1.46,1.23,0.73],\
'scatter': [3.13,1.18,0.58,0.27,0.11,0.36,0.22,0.3,0.28,0.3,0.3],\
},
'douglas2014': {'altname': ['douglas','douglas14'], 'reference': 'Douglas et al. (2014)','bibcode':'2014ApJ...795..161D', 'sptoffset': 0, 'method': 'interpolate', 'scale': 1.e-5,
'spt': [10,11,12,13,14,15,16,17,18,19], \
'values': [6.6453,6.0334,5.2658,4.4872,3.5926,2.4768,1.7363,1.2057,0.6122,0.3522],\
'scatter': [0.6207,0.5326,0.5963,0.4967,0.5297,0.4860,0.3475,0.3267,0.2053,0.1432],\
},
}
INDEX_CLASSIFICATION_RELATIONS = {
'reid1995': {'altname': ['reid','reid95','rei95'], 'bibcode': '1995AJ....110.1838R', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['lepine2003'], 'indices': {\
'TiO5': {'range': ['K7','M6'], 'coeff': [-10.775,8.2],'fitunc': 0.5,'offset':0,'log': False}, \
}},
'gizis1997': {'altname': ['gizis','gizis97','giz97'], 'bibcode': '1997AJ....113..806G', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['lepine2003'], 'indices': {\
'TiO5': {'range': ['K7','M6'], 'coeff': [-9.64,7.76],'fitunc': 0.5,'offset':0,'log': False}, \
'CaH2': {'range': ['K7','M6'], 'coeff': [7.91,-20.63,10.71],'fitunc': 0.5,'offset':0,'log': False}, \
'CaH3': {'range': ['K7','M6'], 'coeff': [-18.00,15.80],'fitunc': 0.5,'offset':0,'log': False}, \
}},
'martin1999': {'altname': ['martin','martin99','mar99','martin1999-m','martin-m','martin99-m','mar99-m'], 'bibcode': '', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['martin1999'], 'indices': {\
'PC3': {'range': ['M2.5','L1'], 'coeff': [-2.024,11.715,-6.685],'fitunc': 0.28,'offset':0,'log': False}, \
}},
'martin1999-l': {'altname': ['martin-l','martin99-l','mar99-l'], 'bibcode': '', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['martin1999'], 'indices': {\
'PC3': {'range': ['L1','L6'], 'coeff': [-0.047,1.181,8.557],'fitunc': 0.54,'offset':0,'log': False}, \
}},
'lepine2003': {'altname': ['lepine2003-dwarf','lepine','lepine03','lep03','lepine-d','lepine03-d','lep03-d'], 'bibcode': '2003AJ....125.1598L', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['lepine2003'], 'indices': {\
'CaH2': {'range': ['M0','M6'], 'coeff': [7.91,-20.63,10.71],'fitunc': 0.5,'offset':0,'log': False}, \
'CaH3': {'range': ['M0','M6'], 'coeff': [-18.00,15.80],'fitunc': 0.5,'offset':0,'log': False}, \
'TiO5': {'range': ['M0','M6'], 'coeff': [-9.64,7.76],'fitunc': 0.5,'offset':0,'log': False}, \
'VO1': {'range': ['M2','M8'], 'coeff': [-30.5,32.2],'fitunc': 0.5,'offset':0,'log': False}, \
'TiO6': {'range': ['M2','M8'], 'coeff': [-11.2,11.9],'fitunc': 0.5,'offset':0,'log': False}, \
'VO2': {'range': ['M3','M9'], 'coeff': [-10.5,12.4],'fitunc': 0.5,'offset':0,'log': False}, \
'TiO7': {'range': ['M3','M9'], 'coeff': [-11.0,13.7],'fitunc': 0.5,'offset':0,'log': False}, \
'Color-M': {'range': ['M2','M8'], 'coeff': [7.5,1.6],'fitunc': 0.5,'offset':0,'log': True}, \
}},
'lepine2003-sd': {'altname': ['lepine-sd','lepine03-sd','lep03-sd'], 'bibcode': '2003AJ....125.1598L', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['lepine2003'], 'indices': {\
'CaH2': {'range': ['K5','M7'], 'coeff': [7.91,-20.63,10.71],'fitunc': 0.5,'offset':0,'log': False}, \
'CaH3': {'range': ['K5','M7'], 'coeff': [-16.02,13.78],'fitunc': 0.5,'offset':0,'log': False}, \
'Color-M': {'range': ['M2','M8'], 'coeff': [7.3,-0.6],'fitunc': 0.5,'offset':0,'log': True}, \
}},
'lepine2003-esd': {'altname': ['lepine-esd','lepine03-esd','lep03-esd'], 'bibcode': '2003AJ....125.1598L', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['lepine2003'], 'indices': {\
'CaH2': {'range': ['K5','M7'], 'coeff': [7.91,-20.63,10.71],'fitunc': 0.5,'offset':0,'log': False}, \
'CaH3': {'range': ['K5','M7'], 'coeff': [-13.47,11.50],'fitunc': 0.5,'offset':0,'log': False}, \
'Color-M': {'range': ['M2','M8'], 'coeff': [22,-4.1],'fitunc': 0.5,'offset':0,'log': True}, \
}},
'lepine2013': {'altname': ['lepine2013-dwarf','lepine13','lep13','lepine13-d','lep13-d'], 'bibcode': '2013AJ....145..102L', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['lepine2003'], 'indices': {\
'CaH2': {'range': ['K7','M6'], 'coeff': [7.99,21.71,11.50],'fitunc': 0.5,'offset':0,'log': False}, \
'CaH3': {'range': ['K7','M6'], 'coeff': [-21.68,18.80],'fitunc': 0.5,'offset':0,'log': False}, \
'TiO5': {'range': ['K7','M6'], 'coeff': [-9.55,7.83],'fitunc': 0.5,'offset':0,'log': False}, \
# 'VO1': {'range': ['M2','M8'], 'coeff': [-71.4,69.8],'fitunc': 0.5,'offset':0,'log': False}, \
'TiO6': {'range': ['K7','M6'], 'coeff': [-16.65,21.23,-15.68,9.92],'fitunc': 0.5,'offset':0,'log': False}, \
# 'VO2': {'range': ['M3','M9'], 'coeff': [-19.59,22.33,-12.47,9.56],'fitunc': 0.5,'offset':0,'log': False}, \
}},
'riddick2007': {'altname': ['riddick','riddick07','rid07'], 'bibcode': '2007MNRAS.381.1067R', 'method': 'polynomial', 'sptoffset': 'M0', 'sets': ['kirkpatrick1995','kirkpatrick1999','reid1995','stauffer1999','riddick2006','slesnick2006','lepine2003'], 'indices': {\
'VO7445': {'range': ['M5','M8'], 'coeff': [13.078,17.121,5.0881],'fitunc': 0.5,'offset':-0.982,'log': False}, \
'VO-a': {'range': ['M5','M8'], 'coeff': [6.7099,11.226,5.0705],'fitunc': 0.5,'offset':-0.982,'log': False}, \
'VO-b': {'range': ['M3','M8'], 'coeff': [-325.44,394.28,-156.53,29.469,3.4875],'fitunc': 0.5,'offset':-1.017,'log': False}, \
'VO2': {'range': ['M3','M8'], 'coeff': [-14.66,-8.3231,-7.9389,2.6102],'fitunc': 0.5,'offset':-0.963,'log': False}, \
'R1': {'range': ['M2.5','M8'], 'coeff': [60.755,-53.025,21.085,2.8078],'fitunc': 0.5,'offset':-1.044,'log': False}, \
'R2': {'range': ['M3','M8'], 'coeff': [8.5121,-14.105,10.503,2.9091],'fitunc': 0.5,'offset':-1.035,'log': False}, \
'R3': {'range': ['M2.5','M8'], 'coeff': [52.531,-47.679,19.708,2.8379],'fitunc': 0.5,'offset':-1.035,'log': False}, \
'TiO-8465': {'range': ['M3','M8'], 'coeff': [5.6765,-10.142,8.7311,3.2147],'fitunc': 0.5,'offset':-1.085,'log': False}, \
'c81': {'range': ['M2.5','M8'], 'coeff': [3.0567,-6.817,8.0558,2.4331],'fitunc': 0.5,'offset':-1.036,'log': False}, \
}},
}
############################################################
# KAST IMAGE FUNCTIONS
############################################################
# extract image mode from image header
def kastRBMode(hdr,keyword='VERSION'):
if keyword not in list(hdr.keys()):
raise ValueError('Header does not contain red/blue mode keyword {}'.format(keyword))
md = hdr[keyword].strip()
mode = ''
for r in list(INSTRUMENT_MODES.keys()):
if INSTRUMENT_MODES[r]['VERSION'] == md: mode=r
if mode=='': raise ValueError('Could not identify spectral image mode from keyword {}'.format(keyword))
return mode
# extract read mode from image header
def kastReadMode(hdr,keyword_mode='VERSION',keyword_read='READ-SPD'):
# get mode
mode = kastRBMode(hdr,keyword=keyword_mode)
# get read mode
if keyword_read not in list(hdr.keys()):
raise ValueError('Header does not contain read speed keyword {}'.format(keyword_read))
rd = float(hdr[keyword_read])
rdspd = ''
if mode=='RED':
if rd==20.: rdspd='FAST'
elif rd==40.: rdspd='SLOW'
else: raise ValueError('Read speed {} does not conform to a standard read speed for mode {}'.format(rd,mode))
elif mode=='BLUE':
if rd==40.: rdspd='FAST'
elif rd==80.: rdspd='SLOW'
else: raise ValueError('Read speed {} does not conform to a standard read speed for mode {}'.format(rd,mode))
else:
raise ValueError('Do not recognize mode {}'.format(mode))
return rdspd
# extract gain from image header
def kastGain(hdr,keyword_mode='VERSION',keyword_read='READ-SPD'):
# get R/B mode
mode = kastRBMode(hdr,keyword=keyword_mode)
# get read mode
rdmode = kastReadMode(hdr,keyword_mode=keyword_mode,keyword_read=keyword_read)
index = '{}-{}'.format(mode,rdmode)
if index not in list(CCD_PARAMETERS.keys()):
raise ValueError('Do not know how to interpret mode {} and read mode {}'.format(mode,rdmode))
else:
return CCD_PARAMETERS[index]['GAIN']
# extract read noise from image header
def kastRN(hdr,keyword_mode='VERSION',keyword_read='READ-SPD'):
# get R/B mode
mode = kastRBMode(hdr,keyword=keyword_mode)
# get read mode
rdmode = kastReadMode(hdr,keyword_mode=keyword_mode,keyword_read=keyword_read)
index = '{}-{}'.format(mode,rdmode)
if index not in list(CCD_PARAMETERS.keys()):
raise ValueError('Do not know how to interpret mode {} and read mode {}'.format(mode,rdmode))
else:
return CCD_PARAMETERS[index]['RN']
# extract dispersion from image header
def kastDispersion(hdr,keyword_mode='VERSION',keyword_blue_dispersion='GRISM_N',keyword_red_dispersion='GRATNG_N'):
# get R/B mode
mode = kastRBMode(hdr,keyword=keyword_mode)
# get key
if mode=='RED': key = keyword_red_dispersion
elif mode=='BLUE': key = keyword_blue_dispersion
else:
raise ValueError('Do not know how to interpret dispersion mode {}'.format(mode))
# get header
return kastHeaderValue(hdr,key)
# extract header parameter using look-up table as backup
def kastHeaderValue(hdr,keyword):
if keyword in list(hdr.keys()): return hdr[keyword]
elif keyword.upper() in list(hdr.keys()): return hdr[keyword.upper()]
elif keyword in list(KAST_CCD_HEADER_KEYWORDS.keys()): return hdr[KAST_CCD_HEADER_KEYWORDS[keyword]]
elif keyword.upper() in list(KAST_CCD_HEADER_KEYWORDS.keys()): return hdr[KAST_CCD_HEADER_KEYWORDS[keyword.upper()]]
else:
raise ValueError('Cannot find keyword {} in header or header lookup table'.format(keyword))
############################################################
# SPECTRUM CLASS
############################################################
class Spectrum(object):
'''
Container for spectrum object
Includes wavelength, flux, variance, background, mask vectors; trace; and header
Includes methods for combining spectrum objects together, reading/writing, conversion
'''
def __init__(self,**kwargs):
core_attributes = {'instr': 'KAST red','name': 'Unknown source','wave': [],'flux': [],'unc': [],'variance':[],'background': [],'mask': [],'header': {},}
# default_units = {'wave': DEFAULT_WAVE_UNIT,'flux': DEFAULT_FLUX_UNIT,'unc': DEFAULT_FLUX_UNIT,'background': DEFAULT_FLUX_UNIT,'variance': DEFAULT_FLUX_UNIT**2}
# set inputs
for k in list(core_attributes.keys()): setattr(self,k,core_attributes[k])
for k in list(kwargs.keys()): setattr(self,k.lower(),kwargs[k])
for k in ['wave','flux','unc','variance','background','mask']:
if not isinstance(getattr(self,k),numpy.ndarray): setattr(self,k,numpy.array(getattr(self,k)))
if len(self.flux) == 0: raise ValueError('Spectrum object must be initiated with a flux array')
if isUnit(self.flux) == False: self.flux*=DEFAULT_FLUX_UNIT
if len(self.wave) == 0: self.wave = numpy.arange(len(self.flux))
if isUnit(self.wave) == False: self.wave*=DEFAULT_WAVE_UNIT
if len(self.unc) == 0: self.unc = numpy.array([numpy.nan]*len(self.flux))*self.flux.unit
# if len(self.unc) == 0: self.unc = numpy.zeros(len(self.flux))
if isUnit(self.unc) == False: self.unc*=self.flux.unit
if len(self.variance) == 0: self.variance = self.unc**2
if len(self.background) == 0: self.background = numpy.zeros(len(self.flux))*self.flux.unit
if isUnit(self.background) == False: self.background*=self.flux.unit
if len(self.mask) == 0: self.mask = numpy.array([False]*len(self.flux))
# set units
# for k in list(default_units.keys()):
# if isUnit(getattr(self,k))==False: setattr(self,k,getattr(self,k)*default_units[k])
# for k in list(default_units.keys()):
# try:
# setattr(self,k,getattr(self,k)).to(default_units[k])
# except:
# pass
# clean up
self.original = copy.deepcopy(self)
self.history = ['{} spectrum of {} successfully loaded'.format(self.instr,self.name)]
return
def setbase(self):
'''
:Purpose: Sets the current state of spectrum as default, eliminates prior original
'''
self.original = copy.deepcopy(self)
return
def reset(self):
'''
:Purpose: Reset a spectrum to its original read-in state
'''
for k in list(self.original.__dict__.keys()):
if k != 'history':
try:
setattr(self,k,getattr(self.original,k))
except:
pass
self.history.append('Returned to original state')
self.original = copy.deepcopy(self)
return
def clean(self):
'''
:Purpose: Cleans up spectrum elements to make sure they are properly configured
'''
# set up units
# try: funit = self.flux.unit
# except: funit = DEFAULT_FLUX_UNIT
# try: wunit = self.wave.unit
# except: wunit = DEFAULT_WAVE_UNIT
# clean wavelength vector
try: self.wave = self.wave.to(DEFAULT_WAVE_UNIT)
except: pass
# clean flux vector
for k in ['flux','unc','background']:
try: setattr(self,k,getattr(self,k).to(DEFAULT_FLUX_UNIT))
except: pass
# set variance
self.variance = self.unc**2
# clean out nans:
# for kk in ['flux','unc']:
# w = numpy.where(numpy.isfinite(getattr(self,kk).value)==False)
# if len(w[0])>0:
# w = numpy.where(numpy.isfinite(getattr(self,kk).value)==True)
# for k in ['wave','flux','unc','background']: setattr(self,k,getattr(self,k)[w])
self.history.append('Spectrum cleaned')
return
def __copy__(self):
'''
:Purpose: Make a copy of a Spectrum object
'''
s = type(self)()
s.__dict__.update(self.__dict__)
return s
def __repr__(self):
'''
:Purpose: A simple representation of the Spectrum object
'''
return '{} spectrum of {}'.format(self.instr,self.name)
def __add__(self,other):
'''
:Purpose: A representation of addition for Spectrum objects which correctly interpolates as a function of wavelength and combines variances
:Output: a new Spectrum object equal to the spectral sum of the inputs
'''
try:
other.wave = other.wave.to(self.wave.unit)
except:
raise ValueError('Cannot add spectra with wave units {} and {}'.format(self.wave.unit,other.wave.unit))
try:
other.flux = other.flux.to(self.flux.unit)
except:
raise ValueError('Cannot add spectra with flux units {} and {}'.format(self.flux.unit,other.flux.unit))
# establish the baseline wavelength grid
out = copy.deepcopy(self)
wave = numpy.array(copy.deepcopy(self.wave.value))
wself = numpy.where(numpy.logical_and(self.wave.value < numpy.nanmax(other.wave.value),self.wave.value > numpy.nanmin(other.wave.value)))
out.wave = wave[wself]
out.wave = out.wave*self.wave.unit
out.mask = self.mask[wself]
# wother = numpy.where(numpy.logical_and(other.wave.value <= numpy.nanmax(out.wave.value),other.wave.value >= numpy.nanmin(out.wave.value)))
# do the math
for k in ['flux','background']:
fself = interp1d(self.wave.value,getattr(self,k).value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,getattr(other,k).value,bounds_error=False,fill_value=0.)
setattr(out,k,(fself(out.wave.value)+fother(out.wave.value))*(getattr(self,k)).unit)
# special for variance
fself = interp1d(self.wave.value,self.variance.value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,other.variance.value,bounds_error=False,fill_value=0.)
if numpy.random.choice(numpy.isfinite(self.variance.value))==True and numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = (fself(out.wave.value)+fother(out.wave.value))*self.variance.unit
elif numpy.random.choice(numpy.isfinite(self.variance.value))==True:
out.variance = fself(out.wave.value)*self.variance.unit
elif numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = fother(out.wave.value)*self.variance.unit
else:
out.variance = numpy.array([numpy.nan]*len(out.wave))
out.unc = out.variance**0.5
# update other information
out.name = self.name+' + '+other.name
out.history.append('Sum of {} and {}'.format(self.name,other.name))
out.original = copy.deepcopy(out)
return out
def __sub__(self,other):
'''
:Purpose: A representation of addition for Spectrum objects which correctly interpolates as a function of wavelength and combines variances
:Output: a new Spectrum object equal to the spectral sum of the inputs
'''
try:
other.wave = other.wave.to(self.wave.unit)
except:
raise ValueError('Cannot subtract spectra with wave units {} and {}'.format(self.wave.unit,other.wave.unit))
try:
other.flux = other.flux.to(self.flux.unit)
except:
raise ValueError('Cannot subtract spectra with flux units {} and {}'.format(self.flux.unit,other.flux.unit))
# establish the baseline wavelength grid
out = copy.deepcopy(self)
wave = numpy.array(copy.deepcopy(self.wave.value))
wself = numpy.where(numpy.logical_and(self.wave.value < numpy.nanmax(other.wave.value),self.wave.value > numpy.nanmin(other.wave.value)))
out.wave = wave[wself]
out.wave = out.wave*self.wave.unit
out.mask = self.mask[wself]
# wother = numpy.where(numpy.logical_and(other.wave.value <= numpy.nanmax(out.wave.value),other.wave.value >= numpy.nanmin(out.wave.value)))
# do the math
for k in ['flux','background']:
fself = interp1d(self.wave.value,getattr(self,k).value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,getattr(other,k).value,bounds_error=False,fill_value=0.)
if k=='variance':
setattr(out,k,(fself(out.wave.value)+fother(out.wave.value))*(getattr(self,k).unit))
else:
setattr(out,k,(fself(out.wave.value)-fother(out.wave.value))*(getattr(self,k).unit))
# special for variance
fself = interp1d(self.wave.value,self.variance.value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,other.variance.value,bounds_error=False,fill_value=0.)
if numpy.random.choice(numpy.isfinite(self.variance.value))==True and numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = (fself(out.wave.value)+fother(out.wave.value))*self.variance.unit
elif numpy.random.choice(numpy.isfinite(self.variance.value))==True:
out.variance = fself(out.wave.value)*self.variance.unit
elif numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = fother(out.wave.value)*self.variance.unit
else:
out.variance = numpy.array([numpy.nan]*len(out.wave))
out.unc = out.variance**0.5
# update other information
out.name = self.name+' - '+other.name
out.history.append('Difference of {} and {}'.format(self.name,other.name))
out.original = copy.deepcopy(out)
return out
def __mul__(self,other):
'''
:Purpose: A representation of addition for Spectrum objects which correctly interpolates as a function of wavelength and combines variances
:Output: a new Spectrum object equal to the spectral sum of the inputs
'''
try:
other.wave = other.wave.to(self.wave.unit)
except:
raise ValueError('Cannot multiply spectra with wave units {} and {}'.format(self.wave.unit,other.wave.unit))
# establish the baseline wavelength grid
out = copy.deepcopy(self)
wave = numpy.array(copy.deepcopy(self.wave.value))
wself = numpy.where(numpy.logical_and(self.wave.value < numpy.nanmax(other.wave.value),self.wave.value > numpy.nanmin(other.wave.value)))
out.wave = wave[wself]
out.wave = out.wave*self.wave.unit
out.mask = self.mask[wself]
# wother = numpy.where(numpy.logical_and(other.wave.value <= numpy.nanmax(out.wave.value),other.wave.value >= numpy.nanmin(out.wave.value)))
# do the math
for k in ['flux','background']:
fself = interp1d(self.wave.value,getattr(self,k).value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,getattr(other,k).value,bounds_error=False,fill_value=0.)
setattr(out,k,numpy.multiply(fself(out.wave.value),fother(out.wave.value))*(getattr(self,k).unit)*(getattr(other,k).unit))
if k=='flux': flxs,flxo = fself,fother
# special for variance
fself = interp1d(self.wave.value,self.variance.value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,other.variance.value,bounds_error=False,fill_value=0.)
if numpy.random.choice(numpy.isfinite(self.variance.value))==True and numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = numpy.multiply(out.flux.value**2,((numpy.divide(flxs(out.wave.value),fself(out.wave.value),out=numpy.zeros_like(flxs(out.wave.value)), where=fself(out.wave.value)!=0)**2)+(numpy.divide(flxo(out.wave.value),fother(out.wave.value),out=numpy.zeros_like(flxo(out.wave.value)), where=fother(out.wave.value)!=0)**2)))*self.variance.unit*other.variance.unit
elif numpy.random.choice(numpy.isfinite(self.variance.value))==True:
out.variance = (numpy.multiply(flxo(out.wave.value),fself(out.wave.value))**2)*self.variance.unit*(other.flux.unit**2)
elif numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = (numpy.multiply(flxs(out.wave.value),fother(out.wave.value))**2)*other.variance.unit*(self.flux.unit**2)
else:
out.variance = numpy.array([numpy.nan]*len(out.wave))
out.unc = out.variance**0.5
# update other information
out.name = self.name+' - '+other.name
out.history.append('Product of {} and {}'.format(self.name,other.name))
out.original = copy.deepcopy(out)
return out
def __div__(self,other):
'''
:Purpose: A representation of addition for Spectrum objects which correctly interpolates as a function of wavelength and combines variances
:Output: a new Spectrum object equal to the spectral sum of the inputs
'''
try:
other.wave = other.wave.to(self.wave.unit)
except:
raise ValueError('Cannot divide spectra with wave units {} and {}'.format(self.wave.unit,other.wave.unit))
# establish the baseline wavelength grid
out = copy.deepcopy(self)
wave = numpy.array(copy.deepcopy(self.wave.value))
wave = wave[wave<=numpy.nanmax(other.wave.value)]
wave = wave[wave>=numpy.nanmin(other.wave.value)]
out.wave = wave*self.wave.unit
# out.mask = self.mask[wself]
# wother = numpy.where(numpy.logical_and(other.wave.value <= numpy.nanmax(out.wave.value),other.wave.value >= numpy.nanmin(out.wave.value)))
# do the math
for k in ['flux','background']:
fself = interp1d(self.wave.value,getattr(self,k).value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,getattr(other,k).value,bounds_error=False,fill_value=0.)
setattr(out,k,numpy.divide(fself(out.wave.value),fother(out.wave.value),out=numpy.zeros_like(fself(out.wave.value)), where=fother(out.wave.value)!=0)*(getattr(self,k).unit)/(getattr(other,k).unit))
if k=='flux': flxs,flxo = fself,fother
# special for variance
fself = interp1d(self.wave.value,self.variance.value,bounds_error=False,fill_value=0.)
fother = interp1d(other.wave.value,other.variance.value,bounds_error=False,fill_value=0.)
if numpy.random.choice(numpy.isfinite(self.variance.value))==True and numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = numpy.multiply(out.flux.value**2,((numpy.divide(flxs(out.wave.value),fself(out.wave.value),out=numpy.zeros_like(flxs(out.wave.value)), where=fself(out.wave.value)!=0)**2)+(numpy.divide(flxo(out.wave.value),fother(out.wave.value),out=numpy.zeros_like(flxo(out.wave.value)), where=fother(out.wave.value)!=0)**2)))*self.variance.unit/other.variance.unit
elif numpy.random.choice(numpy.isfinite(self.variance.value))==True:
out.variance = (numpy.divide(fself(out.wave.value),flxo(out.wave.value),out=numpy.zeros_like(fself(out.wave.value)), where=flxo(out.wave.value)!=0)**2)*self.variance.unit/(other.flux.unit**2)
elif numpy.random.choice(numpy.isfinite(other.variance.value))==True:
out.variance = (numpy.divide(fother(out.wave.value),flxs(out.wave.value),out=numpy.zeros_like(fother(out.wave.value)), where=flxs(out.wave.value)!=0)**2)*self.variance.unit/(other.flux.unit**2)
else:
out.variance = numpy.array([numpy.nan]*len(out.wave))
out.unc = out.variance**0.5
# update other information
out.name = self.name+' - '+other.name
out.history.append('Ratio of {} and {}'.format(self.name,other.name))
out.original = copy.deepcopy(out)
return out
def __truediv__(self,other):
return self.__div__(other)
def scale(self,fact):
'''
Scale spectrum by a float value
'''
for k in ['flux','background','unc']:
if k in list(self.__dict__.keys()): setattr(self,k,getattr(self,k)*fact)
self.clean()
self.history.append('Spectrum scaled by factor {}'.format(fact))
return
def normalize(self,rng=[]):
'''
Scale spectrum by a float value
'''
if len(rng)==0:
rng=[numpy.nanmin(self.wave.value),numpy.nanmax(self.wave.value)]
if isUnit(rng[0]): rng = [r.to(self.wave.unit).value for r in rng]
if isUnit(rng): rng = rng.to(self.wave.unit).value
if numpy.nanmin(rng) > numpy.nanmax(self.wave.value) or numpy.nanmax(rng) < numpy.nanmin(self.wave.value):
print('Warning: normalization range {} is outside range of spectrum wave array: {}'.format(rng,[numpy.nanmin(self.wave.value),numpy.nanmax(self.wave.value)]))
return
if numpy.nanmax(rng) > numpy.nanmax(self.wave.value): rng[1] = numpy.nanmax(self.wave.value)
if numpy.nanmin(rng) < numpy.nanmin(self.wave.value): rng[0] = numpy.nanmin(self.wave.value)
w = numpy.where(numpy.logical_and(self.wave.value>=numpy.nanmin(rng),self.wave.value<=numpy.nanmax(rng)))
factor = numpy.nanmax(self.flux.value[w])
if factor == 0.:
print('\nWarning: normalize is attempting to divide by zero; ignoring')
elif numpy.isnan(factor) == True:
print('\nWarning: normalize is attempting to divide by nan; ignoring')
else:
self.scale(1./factor)
self.history.append('Spectrum normalized')
return
def smooth(self,scale,method='hanning'):
'''
Apply a smoothing profile
currently this is hanning or median
'''
# smoothing function
if method=='hanning':
smwin = signal.windows.hann(scale)
for k in ['flux','unc','background']:
setattr(self,k,(signal.convolve(getattr(self,k).value,smwin,mode='same')/numpy.sum(smwin))*getattr(self,k).unit)
# NOTE: THIS IS PRODUCING INCORRECT VECTOR ARRAY
# REPLACE WITH SCIPY.SIGNAL.MEDFILT
elif method=='median':
wv = self.wave.value
xsamp = numpy.arange(0,len(self.wave)-scale+1,scale)
# self.wave = numpy.array([self.wave.value[x+int(0.5*scale)] for x in xsamp])*self.wave.unit
for k in ['wave','flux','unc','background']:
repl = []
for x in xsamp:
if len(repl)<len(wv):
repl.extend([numpy.nanmedian(getattr(self,k).value[x:x+scale])]*scale)
setattr(self,k,numpy.array(repl)*getattr(self,k).unit)
# setattr(self,k,numpy.array([numpy.nanmedian(getattr(self,k).value[x:x+scale]) for x in xsamp])*getattr(self,k).unit)
else:
print('Warning: cannot smooth using method {}, no change to spectrum'.format(method))
return
self.clean()
self.history.append('Smoothed by {} using a scale of {} pixels'.format(method,scale))
return
def cleanCR(self,smooth=3,scale=5,replace=True,replace_value='local',local_range=50):
'''
Cleans out bad pixels by subtracting a smoothed version and identifying outliers
This is superior to template fitting methods
'''
# smoothing function
s0 = copy.deepcopy(self)
sm = copy.deepcopy(self)
sm.smooth(smooth)
self.maskComp(sm,rescale=False,apply=False,scale=scale)
# unmask HI emission line regions
for wv in FEATURES['HI']['wavelengths']:
w = numpy.where(numpy.absolute(self.wave.value-wv.value)<20.)
if len(w[0])>0: self.mask[w]=False
nclean = len(numpy.where(self.mask==True)[0])
if nclean>0:
self.applyMask(replace=replace,replace_value=replace_value,local_range=local_range)
self.clean()
self.history.append('CR cleaned {} pixels by comparing to smoothed by {} pixel with scale factor {}'.format(nclean,smooth,scale))
return
def trim(self,rng):
'''
Trim spectrum to range of wavelengths
'''
if isinstance(rng,list) == False: raise ValueError('Trim range should be 2-element list; you passed {}'.format(rng))
if isUnit(rng[0]): rng = [r.to(self.wave.unit).value for r in rng]
w = numpy.where(numpy.logical_and(self.wave.value>=numpy.nanmin(rng),self.wave.value<=numpy.nanmax(rng)))
if len(w[0]) > 0:
for k in ['wave','flux','unc','background','mask']: setattr(self,k,getattr(self,k)[w])
self.clean()
self.history.append('Trimmed to {}--{} {}'.format(rng[0],rng[1],self.wave.unit))
return
def sample(self,rng,method='median',verbose=ERROR_CHECKING):
'''
:Purpose:
Obtains a sample of spectrum over specified wavelength range
:Required Inputs:
:param range: the range(s) over which the spectrum is sampled
a single 2-element array or array of 2-element arrays
:Optional Inputs:
None
:Example:
TBD
'''
# single number = turn into small range
if isinstance(rng,float) or isinstance(rng,int):
rng = [rng-0.01*(numpy.nanmax(self.wave.value)-numpy.nanmin(self.wave.value)),rng+0.01*(numpy.nanmax(self.wave.value)-numpy.nanmin(self.wave.value))]
if not isinstance(rng,list): rng = list(rng)
if isUnit(rng[0]):
try: rng = [r.to(self.wave.unit).value for r in rng]
except: raise ValueError('Could not convert trim range unit {} to spectrum wavelength unit {}'.format(rng.unit,self.wave.unit))