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PhotometricRelations_class.py
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PhotometricRelations_class.py
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from __future__ import print_function
from __future__ import division
from builtins import str
from builtins import zip
from builtins import range
from dataclasses import dataclass, field
from typing import List
import warnings
import numpy as np
from astroquery.vizier import Vizier
from astropy import units as u
from astropy.coordinates import SkyCoord
import shapely.geometry as geom
from scipy import interpolate
from uncertainties import ufloat, unumpy
import mwdust
warnings.simplefilter("ignore")
@dataclass
class Quantity:
name: str
value: float = np.nan
error: float = np.nan
source: str = 'unknown'
def setval(self, v, e, s, condition=True):
if hasattr(v, 'value'):
if ~np.isnan(float(v.value)) and ~np.isnan(float(e.value)) and condition:
self.value = v
self.error = e
self.source = s
else:
if ~np.isnan(float(v)) and ~np.isnan(float(e)) and condition:
self.value = v
self.error = e
self.source = s
@property
def isvalid(self):
return self.source != 'unknown'
def totuple(self):
return [self.value, self.error, self.source]
@dataclass
class Magnitude(Quantity):
pfilter: str = 'unknown'
Av: float = 0.0
def setmag(self, v, e, s, f, condition=True):
self.setval(v, max(e, 0.01), s, condition=condition)
if ~np.isnan(self.value):
self.pfilter = f
def totuplemag(self):
return self.totuple()+[self.pfilter, self.Av]
def setAv(self, v):
if ~np.isnan(self.value) and ~np.isnan(float(v)) and (v >= 0.0):
self.Av = v
_magnitudes = ['V', 'B', 'Bt', 'Vt', 'J', 'H', 'K', 'b', 'y', 'm1', 'c1',\
'G', 'W1', 'W2', 'W3', 'R', 'I', 'BP', 'RP', 'E_BPRP']
def _make_mag_list():
return [Magnitude(s) for s in _magnitudes]
def _make_coord_list():
return [Quantity('RA'), Quantity('DEC')]
def _make_proper_motion_list():
return [Quantity('pmRA'), Quantity('pmDEC')]
@dataclass
class Star:
name: str
magnitudes: List[Magnitude] = field(default_factory=_make_mag_list)
coordinates: List[Quantity] = field(default_factory=_make_coord_list)
propermotion: List[Quantity] = field(default_factory=_make_proper_motion_list)
parallax: Quantity = Quantity('Parallax')
galactic_coords: List[float] = field(default_factory=list)
def getmag(self, mag):
return self.magnitudes[_magnitudes.index(mag)]
def getmags(self, mags):
return [self.getmag(m) for m in mags]
@property
def validmags(self):
return [self.getmag(m).name for m in _magnitudes if self.getmag(m).isvalid]
def change_coords(self, ra, dec, s):
[RA, DEC] = self.coordinates
RA.setval(ra, 0.0, s)
DEC.setval(dec, 0.0, s)
def change_propermotion(self, pmra, pmdec, err_ra, err_dec, s):
if ~np.isnan(pmra) and ~np.isnan(pmdec) and ~np.isnan(err_ra) and ~np.isnan(err_dec):
pmRA, pmDEC = self.propermotion
pmRA.setval(pmra, err_ra, s)
pmDEC.setval(pmdec, err_dec, s)
def todict(self):
d = {'name': self.name}
if all([c.isvalid for c in self.coordinates]):
d['RA'] = [self.coordinates[0].value, self.coordinates[0].source]
d['DEC'] = [self.coordinates[1].value, self.coordinates[1].source]
if all([c.isvalid for c in self.propermotion]):
d['pmRA'] = self.propermotion[0].totuple()
d['pmDEC'] = self.propermotion[1].totuple()
if self.parallax.isvalid:
d['parallax'] = self.parallax.totuple()
valid = self.validmags
for v in valid:
d[v] = self.getmag(v).totuplemag()
if self.galactic_coords:
d['galactic_coords'] = self.galactic_coords
return d
def search_catalog_name(starname, vizier_object, catlist, catname):
def _look_r(starname, catname, catlist, rlist, restrict=False):
for r in rlist:
result = vizier_object.query_object(starname, catalog=[catname], radius=r*u.arcsec)
if str(result) != 'Empty TableList':
if restrict:
p = result[0]['Plx']
if any(p >= 2.0):
catlist.append(result)
break
else:
catlist.append(result)
break
return catlist
if catname == "I/345/gaia2":
catlist = _look_r(starname, catname, catlist, [15., 25., 50.], restrict=True)
elif catname == "I/284/out":
catlist = _look_r(starname, catname, catlist, [10., 20.])
elif catname == "I/311/hip2":
catlist = _look_r(starname, catname, catlist, [5., 10., 20., 30.])
else:
catlist = _look_r(starname, catname, catlist, [5., 10., 20., 30., 50.])
return catlist
def search_catalog_coords(RA, DEC, vizier_object, catlist, catname):
def _look_r(C, catname, catlist, rlist, restrict=False):
for r in rlist:
result = vizier_object.query_region(SkyCoord(ra=C[0], dec=C[1], unit=(u.deg, u.deg),
frame='icrs'),\
width=r, catalog=[catname])
if str(result) != 'Empty TableList':
if restrict:
p = result[0]['Plx']
if any(p >= 2.0):
catlist.append(result)
break
else:
catlist.append(result)
break
return catlist
if catname == "I/345/gaia2":
catlist = _look_r([RA, DEC], catname, catlist, ['15s', '25s', '50s'], restrict=True)
elif catname == "I/284/out":
catlist = _look_r([RA, DEC], catname, catlist, ['10s', '20s'])
elif catname == "I/311/hip2":
catlist = _look_r([RA, DEC], catname, catlist, ['5s', '10s', '20s', '30s'])
else:
catlist = _look_r([RA, DEC], catname, catlist, ['5s', '10s', '20s', '30s', '50s'])
return catlist
def retrieve_catalogs(starname, vizier_object, use_coords=False, RA=None, DEC=None):
result = []
catalogues = ["II/246/out", "I/311/hip2", "I/259/tyc2", "J/MNRAS/373/13/table1",
"J/ApJS/168/128/survey", "II/215/catalog", "V/130/gcs3",
"J/MNRAS/403/1949/ubvri", "I/337/tgas", "II/237/colors",
"I/345/gaia2", "II/336/apass9", "I/284/out", "II/328/allwise"]
if use_coords:
for c in catalogues:
result = search_catalog_coords(RA, DEC, vizier_object, result, c)
else:
starname = starname.replace('A', '').replace('-', ' ').replace('_', ' ')
for c in catalogues:
result = search_catalog_name(starname, vizier_object, result, c)
return result
def vizier_params(starname, use_coords=False, RA=None, DEC=None):
star = Star(starname)
V, B, Bt, Vt, J, H, K, b, y, m1, c1, G, W1, W2, W3, R, I, BP, RP, E_BPRP = star.getmags(_magnitudes)
if use_coords:
star.change_coords(RA, DEC, "from user or fits header")
try:
e_hpmag = 0.01
v = Vizier(columns=["**"])
result = retrieve_catalogs(starname, v, use_coords, RA, DEC)
if result:
if len(result[0][0].columns) < 5:
v = Vizier(columns=["*"])
result = retrieve_catalogs(starname, v, use_coords, RA, DEC)
name_cats = [list(r.keys())[0] for r in result]
# 2MASS
if "II/246/out" in name_cats:
r = result[name_cats.index("II/246/out")][0]
if len(r['Jmag']) > 1:
nA = np.zeros(len(r['Qflg']))
for k, q in enumerate(r['Qflg']):
nA[k] = q.count('A')
maxA = np.argmax(nA)
else:
maxA = 0
if r['Qflg'][maxA].count('U') == 0:
J.setmag(r['Jmag'][maxA], r['e_Jmag'][maxA], "II/246/out", '2MASS J',\
condition=r['Qflg'][maxA][0] != 'U')
H.setmag(r['Hmag'][maxA], r['e_Hmag'][maxA], "II/246/out", '2MASS H',\
condition=r['Qflg'][maxA][1] != 'U')
K.setmag(r['Kmag'][maxA], r['e_Kmag'][maxA], "II/246/out", '2MASS Ks',\
condition=r['Qflg'][maxA][2] != 'U')
star.change_coords(r['RAJ2000'][maxA], r['DEJ2000'][maxA], "II/246/out")
# GAIA DR2
if "I/345/gaia2" in name_cats:
r = result[name_cats.index("I/345/gaia2")][0]
i2 = np.where(r['Teff'] > 0.0)[0]
if len(r['Plx'][i2]) >= 1:
if len(r['Plx'][i2]) > 1:
i0 = np.where(r['Plx'][i2] > 2.0)[0]
if len(i0) > 1:
iav0 = np.where(np.abs(r['RV'][i2][i0]) > 0.0)[0]
if len(iav0) > 1:
itemp = np.argmax(r['Teff'][i2][i0[iav0]])
i0 = int(i0[iav0[itemp]])
elif len(iav0) == 1:
i0 = int(i0[iav0])
else:
i0 = int(i0[0])
elif len(i0) == 1:
i0 = int(i0[0])
else:
i0 = 0
else:
i0 = 0
if r['Plx'][i2][i0] > 2.0:
star.parallax.setval(r['Plx'][i2][i0]*u.mas, r['e_Plx'][i2][i0]*u.mas,
"I/345/gaia2")
star.change_coords(r['RA_ICRS'][i2][i0], r['DE_ICRS'][i2][i0],
"I/345/gaia2")
G.setmag(r['Gmag'][i2][i0], r['e_Gmag'][i2][i0], "I/345/gaia2", "GAIA G")
G.setAv(r['AG'][i2][i0])
star.change_propermotion(r['pmRA'][i2][i0], r['pmDE'][i2][i0],
r['e_pmRA'][i2][i0], r['e_pmDE'][i2][i0],
"I/345/gaia2")
BP.setmag(r['BPmag'][i2][i0], r['e_BPmag'][i2][i0], "I/345/gaia2", "GAIA BP")
RP.setmag(r['RPmag'][i2][i0], r['e_RPmag'][i2][i0], "I/345/gaia2", "GAIA RP")
E_BPRP.setmag(r['E_BP-RP_'][i2][i0], 0.1, "I/345/gaia2", "GAIA E(BP-RP)")
elif r['Plx'][0] > 2.0:
star.parallax.setval(r['Plx'][0]*u.mas, r['e_Plx'][0]*u.mas, "I/345/gaia2",
condition=r['Plx'][0] > 0.0)
star.change_coords(r['RA_ICRS'][0], r['DE_ICRS'][0], "I/345/gaia2")
G.setmag(r['Gmag'][0], r['e_Gmag'][0], "I/345/gaia2", "GAIA G")
G.setAv(r['AG'][0])
star.change_propermotion(r['pmRA'][0], r['pmDE'][0],
r['e_pmRA'][0], r['e_pmDE'][0], "I/345/gaia2")
BP.setmag(r['BPmag'][0], r['e_BPmag'][0], "I/345/gaia2", "GAIA BP")
RP.setmag(r['RPmag'][0], r['e_RPmag'][0], "I/345/gaia2", "GAIA RP")
E_BPRP.setmag(r['E_BP-RP_'][0], 0.1, "I/345/gaia2", "GAIA E(BP-RP)")
# Correct for the systematic from Stassun & Torres 2018
if star.parallax.isvalid:
plx_corr = ufloat(star.parallax.value.value,
star.parallax.error.value)\
+ ufloat(82, 33)*1e-3
star.parallax.setval(plx_corr.n*u.mas, plx_corr.s*u.mas, "I/345/gaia2")
# GAIA
elif "I/337/tgas" in name_cats:
r = result[name_cats.index("I/337/tgas")][0]
star.parallax.setval(r['Plx'][0]*u.mas, r['e_Plx'][0]*u.mas, "I/337/tgas",
condition=r['Plx'][0] > 0.0)
star.change_coords(r['RA_ICRS'][0], r['DE_ICRS'][0], "I/337/tgas")
# HIPPARCOS
if ("I/311/hip2" in name_cats) and (not star.parallax.isvalid):
r = result[name_cats.index("I/311/hip2")][0]
star.parallax.setval(r['Plx'][0]*u.mas, r['e_Plx'][0]*u.mas, "I/311/hip2",
condition=r['Plx'][0] > 0.0)
star.change_coords(r['RArad'][0], r['DErad'][0], "I/311/hip2")
if all([c.isvalid is False for c in star.propermotion]):
star.change_propermotion(r['pmRA'][0], r['pmDE'][0],
r['e_pmRA'][0], r['e_pmDE'][0], "I/311/hip2")
e_hpmag = r['e_Hpmag'][0] if r['e_Hpmag'][0] != 0.0 else 0.01
# USNO-B1.0 (Monet+ 2003) for proper motions
if "I/284/out" in name_cats:
r = result[name_cats.index("I/284/out")][0]
if all([c.isvalid is False for c in star.propermotion]):
star.change_propermotion(r['pmRA'][0], r['pmDE'][0],
r['e_pmRA'][0], r['e_pmDE'][0], "I/284/out")
# WISE
if "II/328/allwise" in name_cats:
r = result[name_cats.index("II/328/allwise")][0]
if len(r['qph']) > 1:
counts = np.zeros(len(r['qph']))
flag_vals = {'A':6, 'B':5, 'C':4, 'U':3, 'X':2, 'Z':1}
for q, q_val in enumerate(r['qph']):
counts[q] = sum([flag_vals[l]*q_val.count(l) for l in flag_vals])
i0 = np.argmax(counts)
del counts
else:
i0 = 0
if all([~np.isnan(float(r['e_%smag' % k][i0])) for k in ['W1', 'W2', 'W3']]):
W1.setmag(r['W1mag'][i0], r['e_W1mag'][i0], "II/328/allwise", 'WISE-1')
W2.setmag(r['W2mag'][i0], r['e_W2mag'][i0], "II/328/allwise", 'WISE-2')
W3.setmag(r['W3mag'][i0], r['e_W3mag'][i0], "II/328/allwise", 'WISE-3')
# The Tycho-2 Catalogue (Hog+ 2000)
if "I/259/tyc2" in name_cats:
r = result[name_cats.index("I/259/tyc2")][0]
Bt.setmag(r['BTmag'][0], r['e_BTmag'][0], "I/259/tyc2", 'HIPPARCOS BT')
Vt.setmag(r['VTmag'][0], r['e_VTmag'][0], "I/259/tyc2", 'HIPPARCOS VT')
star.change_coords(r['RA_ICRS_'][0], r['DE_ICRS_'][0], "I/259/tyc2")
# Koen et al. 2010
if "J/MNRAS/403/1949/ubvri" in name_cats:
r = result[name_cats.index("J/MNRAS/403/1949/ubvri")][0]
V.setmag(r['Vmag'][0], 0.01, "J/MNRAS/403/1949/ubvri", 'Landolt V')
B.setmag(r['B-V'][0] + r['Vmag'][0], 0.01, "J/MNRAS/403/1949/ubvri", 'Landolt B')
R.setmag(r['Vmag'][0] - r['V-Rc'][0], 0.01, "J/MNRAS/403/1949/ubvri", 'Landolt R')
I.setmag(r['Vmag'][0] - r['V-Ic'][0], 0.01, "J/MNRAS/403/1949/ubvri", 'Landolt I')
else:
# Casagrande et al. 2006
if "J/MNRAS/373/13/table1" in name_cats:
r = result[name_cats.index("J/MNRAS/373/13/table1")][0]
V.setmag(r['Vmag'][0], 0.01, "J/MNRAS/373/13/table1", 'Landolt V')
B.setmag(r['B-V'][0] + r['Vmag'][0], 0.01, "J/MNRAS/373/13/table1", 'Landolt B')
R.setmag(r['Vmag'][0] - r['V-Rc'][0], 0.01,
"J/MNRAS/373/13/table1", 'Landolt R')
I.setmag(r['Vmag'][0] - r['V-Rc'][0] - r['R-Ic'][0], 0.01,
"J/MNRAS/373/13/table1", 'Landolt I')
J.setmag(r['Jmag'][0], r['e_Jmag'][0], "J/MNRAS/373/13/table1", '2MASS J')
H.setmag(r['Hmag'][0], r['e_Hmag'][0], "J/MNRAS/373/13/table1", '2MASS H')
K.setmag(r['Ksmag'][0], r['e_Ksmag'][0], "J/MNRAS/373/13/table1", '2MASS Ks')
if not star.parallax.isvalid:
star.parallax.setval(r['Plx'][0]*u.mas, r['e_Plx'][0]*u.mas,
"J/MNRAS/373/13/table1", condition=r['Plx'][0] > 0.0)
# Beers et al. 2007
elif "J/ApJS/168/128/survey" in name_cats:
r = result[name_cats.index("J/ApJS/168/128/survey")][0]
V.setmag(r['Vmag'][0], r['e_Vmag'][0], "J/ApJS/168/128/survey", 'Landolt V')
B.setmag(r['B-V'][0] + r['Vmag'][0],
np.sqrt(r['e_Vmag'][0]**2 + r['e_B-V'][0]**2),
"J/ApJS/168/128/survey", 'Landolt B')
R.setmag(r['Vmag'][0] - r['V-Rc'][0],
np.sqrt(r['e_Vmag'][0]**2. + r['e_V-Rc'][0]**2.),
"J/ApJS/168/128/survey", 'Landolt R')
I.setmag(r['Vmag'][0] - r['V-Ic'][0],
np.sqrt(r['e_Vmag'][0]**2. + r['e_V-Ic'][0]**2.),
"J/ApJS/168/128/survey", 'Landolt I')
J.setmag(r['Jmag'][0], r['e_Jmag'][0], "J/ApJS/168/128/survey", '2MASS J')
H.setmag(r['Hmag'][0], r['e_Hmag'][0], "J/ApJS/168/128/survey", '2MASS H')
K.setmag(r['Ksmag'][0], r['e_Ksmag'][0], "J/ApJS/168/128/survey", '2MASS Ks')
# HAUCK
if "II/215/catalog" in name_cats:
r = result[name_cats.index("II/215/catalog")][0]
if not V.isvalid:
V.setmag(r['Vmag'][0], r['e_Vmag'][0], "II/215/catalog", 'Landolt V')
b.setmag(r['b-y'][0], r['e_b-y'][0], "II/215/catalog", 'Stromgren b')
y.setmag(0., r['e_b-y'][0], "II/215/catalog", 'Stromgren y')
m1.setmag(r['m1'][0], r['e_m1'][0], "II/215/catalog", 'f')
c1.setmag(r['c1'][0], r['e_c1'][0], "II/215/catalog", 'f')
else:
# GENEVA
if "V/130/gcs3" in name_cats:
r = result[name_cats.index("V/130/gcs3")][0]
if not V.isvalid:
V.setmag(r['Vmag'][0], 0.01, "V/130/gcs3", 'Landolt V')
b.setmag(r['b-y'][0], 0.01, "V/130/gcs3", 'Stromgren b')
y.setmag(0., e_hpmag, "V/130/gcs3", 'Stromgren y')
m1.setmag(r['m1'][0], 0.01, "V/130/gcs3", 'f')
c1.setmag(r['c1'][0], 0.01, "V/130/gcs3", 'f')
if all([not M.isvalid for M in [R, I, V, B]]) or\
any([M.error > 0.2 for M in [J, H, K]]) or\
all([not M.isvalid for M in [R, I, B]]):
# Ducati 2002
if "II/237/colors" in name_cats:
r = result[name_cats.index("II/237/colors")][0]
V.setmag(r['Vmag'][0], e_hpmag, "II/237/colors", 'Landolt V')
B.setmag(r['B-V'][0] + r['Vmag'][0], 0.01, "II/237/colors", 'Landolt B')
R.setmag(r['R-V'][0] + r['Vmag'][0], 0.01, "II/237/colors", 'Landolt R')
I.setmag(r['I-V'][0] + r['Vmag'][0], 0.01, "II/237/colors", 'Landolt I')
if any([M.error > 0.2 for M in [J, H, K]]):
J.setmag(r['J-V'][0] + r['Vmag'][0], r['Jsig'][0]*1e-2,
"II/237/colors", '2MASS J')
H.setmag(r['H-V'][0] + r['Vmag'][0], r['Hsig'][0]*1e-2,
"II/237/colors", '2MASS H')
K.setmag(r['K-V'][0] + r['Vmag'][0], r['Ksig'][0]*1e-2,
"II/237/colors", '2MASS Ks')
elif "II/336/apass9" in name_cats:
r = result[name_cats.index("II/336/apass9")][0]
if Vt.isvalid:
V.setmag(r['Vmag'][0], r['e_Vmag'][0], "II/336/apass9", 'Landolt V',
condition=np.abs(Vt.value - r['Vmag'][0]) < 0.25)
else:
V.setmag(r['Vmag'][0], r['e_Vmag'][0], "II/336/apass9", 'Landolt V')
if Bt.isvalid:
B.setmag(r['Bmag'][0], r['e_Bmag'][0], "II/336/apass9", 'Landolt B',
condition=np.abs(Bt.value - r['e_Bmag'][0]) < 0.25)
else:
B.setmag(r['Bmag'][0], r['e_Bmag'][0], "II/336/apass9", 'Landolt B')
del result
except Exception as e:
print('error', e)
# Get galactic coordinates if possible
if all([c.isvalid for c in star.coordinates]):
ra, dec = star.coordinates
l, b = get_galactic_coords(ra, dec)
star.galactic_coords = [l, b]
photometry = star.todict()
photometry = correct_extinction(photometry)
del star
return photometry
#******************************************************************************
#******************************************************************************
def get_galactic_coords(ra, dec):
c = SkyCoord(ra=ra.value*u.deg, dec=dec.value*u.deg, frame='icrs')
l = c.galactic.l.degree
b = c.galactic.b.degree
del c
return l, b
def get_Av(photometry):
A_V = 0.0
if 'galactic_coords' in photometry:
l, b = photometry['galactic_coords']
if 'parallax' in photometry:
p = photometry['parallax'][0]
d = p.to(u.pc, equivalencies=u.parallax()).value/1000. # in Kpc
ext_map = mwdust.Combined15()
else:
d = 1.
ext_map = mwdust.SFD()
A_V = 2.742*ext_map(l, b, d)
del ext_map
return A_V
def correct_extinction(photometry):
def _correct_wave(l, b, d, wave, ext_map):
if wave in ('HIPPARCOS BT', 'HIPPARCOS VT'):
ext_map._filter = 'Landolt B'
A_B = ext_map(l, b, d)
ext_map._filter = 'Landolt V'
A_V = ext_map(l, b, d)
# Transform A_wave to the Hipparcos system
if wave == 'HIPPARCOS BT':
A_wave = 1.090/0.850*(A_B-A_V) + A_V
else:
A_wave = A_V + 0.090/0.850*(A_B-A_V)
else:
ext_map._filter = wave
A_wave = ext_map(l, b, d)
return max(A_wave[0], 0.0)
mag = ['B', 'V', 'R', 'I', 'J', 'H', 'K', 'b', 'y', 'Bt', 'Vt', 'W1', 'W2']
if 'galactic_coords' in photometry:
l, b = photometry['galactic_coords']
d = 1.0
if 'parallax' in photometry:
p = photometry['parallax'][0]
d = p.to(u.pc, equivalencies=u.parallax()).value/1000. # in Kpc
ext_map = mwdust.Combined15()
else:
ext_map = mwdust.SFD()
for m in mag:
if m in photometry:
wave = photometry[m][3]
A_wave = _correct_wave(l, b, d, wave, ext_map)
if not np.isnan(A_wave) and\
(np.isnan(photometry[m][4]) or photometry[m][4] == 0.0):
photometry[m][4] = A_wave
if 'G' in photometry:
ext_map._filter=None
E_BV = ext_map(l, b, d)[0]
if not np.isnan(E_BV):
photometry['G'][4] = max(2.35*E_BV, 0.0)
if all([m in photometry for m in ['BP', 'RP', 'E_BPRP', 'G']]):
ext_map._filter=None
E_BV = ext_map(l, b, d)[0]
A0 = 3.1*E_BV
G_BVRPm = photometry['BP'][0]-photometry['RP'][0]
E_BPRP = photometry['E_BPRP'][0]
G_BVRP0 = G_BVRPm-E_BPRP
c_BP = [1.1517, -0.0871, -0.0333, 0.0173, -0.0230, 0.0006, 0.0043]
c_RP = [0.6104, -0.0170, -0.0026, -0.0017, -0.0078, 0.00005, 0.0006]
c_G = [0.9761, -0.1704, 0.0086, 0.0011, -0.0438, 0.0013, 0.0099]
def Ax(c1, c2, c3, c4, c5, c6, c7):
return c1 + c2*G_BVRP0 + c3*G_BVRP0**2 + c4*G_BVRP0**3 + c5*A0 + c6*A0**2 + c7*G_BVRP0*A0
A_BP = Ax(*c_BP)*A0
A_RP = Ax(*c_RP)*A0
A_G = Ax(*c_G)*A0
photometry['BP'][4] = A_BP
photometry['RP'][4] = A_RP
#print(photometry['G'][4], A_G, E_BV*2.35)
photometry['G'][4] = A_G
del ext_map
return photometry
#******************************************************************************
#******************************************************************************
def stellar_class(photometry):
# Intrinsic colors of dwarf and giant stars, for different spectral types.
# From Bessell and Brett 1988
JH_dwarfs = np.array([-0.05, 0.0, 0.02, 0.06, 0.09, 0.13, 0.165, 0.23, 0.285, 0.305, 0.32,\
0.33, 0.37, 0.45, 0.5, 0.58, 0.61, 0.66, 0.695, 0.68, 0.665, 0.62,\
0.60, 0.62, 0.66])
HK_dwarfs = np.array([-0.035, 0.00, 0.005, 0.015, 0.025, 0.03, 0.035, 0.04, 0.045, 0.05,\
0.052, 0.055, 0.06, 0.075, 0.09, 0.105, 0.11, 0.13, 0.165, 0.20, 0.21,\
0.25, 0.275, 0.32, 0.37])
JH_giants = np.array([0.37, 0.47, 0.50, 0.50, 0.54, 0.58, 0.63, 0.68, 0.73, 0.79, 0.83, 0.85,\
0.87, 0.90, 0.93, 0.95, 0.96, 0.96])
HK_giants = np.array([0.065, 0.08, 0.085, 0.085, 0.095, 0.10, 0.115, 0.14, 0.15, 0.165, 0.19,\
0.205, 0.215, 0.235, 0.245, 0.285, 0.30, 0.31])
# Cenvert the Bessell and Brett magnitudes to 2mass filters using the
# relationships from Carpenter 2001
i_dwarfs = np.where(HK_dwarfs > 0.14)[0]
i_giants = np.where(HK_giants > 0.14)[0]
JH_dwarfs_2mass = 0.990*JH_dwarfs[i_dwarfs] - 0.049
JH_giants_2mass = 0.990*JH_giants[i_giants] - 0.049
HK_dwarfs_2mass = 1.00*HK_dwarfs[i_dwarfs] + 0.034
HK_giants_2mass = 1.00*HK_giants[i_giants] + 0.034
line_dwarfs = geom.LineString(list(zip(HK_dwarfs_2mass, JH_dwarfs_2mass)))
line_giants = geom.LineString(list(zip(HK_giants_2mass, JH_giants_2mass)))
sp_class = 'dwarf'
# Detect if JHK are present
if ('J' in photometry) and ('H' in photometry) and ('K' in photometry):
J = photometry['J'][0] - photometry['J'][4]
H = photometry['H'][0] - photometry['H'][4]
K = photometry['K'][0] - photometry['K'][4]
JH = J-H
HK = H-K
# Compute distance from curves
if HK > 0.14:
point = geom.Point(HK, JH)
d_dwarf = point.distance(line_dwarfs)
d_giant = point.distance(line_giants)
del point
if d_giant < d_dwarf:
sp_class = 'giant'
del line_dwarfs, line_giants
return sp_class
def stellar_class_pm(photometry):
# Use the proper motion of the star for classification, following
# Collier Cameron et al. 2007
sp_class = 'dwarf'
if all([k in photometry for k in ['J', 'H', 'pmRA', 'pmDEC']]):
try:
pmRA = photometry['pmRA'][0]/1000.
pmDEC = photometry['pmDEC'][0]/1000.
mu = np.sqrt(pmRA**2. + pmDEC**2.)
J = photometry['J'][0] - photometry['J'][4]
H = photometry['H'][0] - photometry['H'][4]
RPM = J + 5.*np.log10(mu)
if (-15. <= RPM <= 10.) and (0.0 <= (J-H) <= 1.0):
ycurve = -141.25*(J-H)**4. + 473.18*(J-H)**3.\
-583.60*(J-H)**2. + 313.42*(J-H) - 58.
if RPM < ycurve:
sp_class = 'giant'
else:
return stellar_class(photometry)
except:
return stellar_class(photometry)
else:
return stellar_class(photometry)
return sp_class
def ini_logg(T, sp_type):
if sp_type == 'dwarf':
return 4.68*1e-8*T**2. - 8.33*1e-4*T + 7.547
return max(1.0, -5.82*1e-7*T**2. + 6.73*1e-3*T - 10.65)
def ini_met(photometry):
met = 0.0
# From Martell & Laughlin 2002
if 'b' in photometry and 'y' in photometry and 'm1' in photometry and 'c1' in photometry:
b = photometry['b'][0] - photometry['b'][4]
y = photometry['y'][0] - photometry['y'][4]
by = b-y
m1 = photometry['m1'][0]
c1 = photometry['c1'][0]
if (0.288 < by < 0.571) and (0.058 < m1 < 0.497) and (0.116 < c1 < 0.745):
met = -10.424602 + 31.059003*by + 42.184476*m1 + 15.351995*c1 \
-11.239435*by**2. - 29.218135*m1**2. - 11.457610*c1**2. \
-138.92376*by*m1 - 52.033290*by*c1 + 11.259341*m1*c1 \
-46.087731*by**3. + 26.065099*m1**3. - 1.1017830*c1**3. \
+138.48588*by**2.*m1 + 39.012001*by**2.*c1 \
+23.225562*m1**2.*by - 69.146876*m1**2.*c1 \
+20.456093*c1**2.*by - 3.3302478*c1**2.*m1 \
+70.168761*by*m1*c1
if (met > 0.5) or (met < -2.0):
met = 0.0
return met
#******************************************************************************
#******************************************************************************
def correct_mamajek(Tc, color, inst):
# harps, feros, hires, uves
corrections = {'B-V': (-3.67, 54.8, -15.79, -6.12),\
'V-K': (15.23, 11.03, 43.25, 42.25),\
'J-H': (15.47, 32.62, 78.61, 59.06),\
'Bt-Vt': (64.61, 84.23, 108.15, 71.98)}
x = 0
if inst in ['harps', 'feros', 'hires', 'uves']:
x = corrections[color][['harps', 'feros', 'hires', 'uves'].index(inst)]
return Tc + x
def mamajek(photometry, inst):
# Define the spline representations
tck_bv = (np.array([3000., 3000., 3000., 3000., 3100., 3200., 3250.,
3410., 3500., 3550., 3650., 3700., 3850., 3880.,
3970., 4050., 4230., 4410., 4620., 4840., 5040.,
5170., 5280., 5340., 5490., 5530., 5590., 5660.,
5680., 5720., 5770., 5880., 5920., 6040., 6170.,
6240., 6340., 6510., 6640., 6720., 6810., 7030.,
7200., 7440., 7500., 7800., 8000., 8080., 8270.,
8550., 8840., 9200., 9700., 10400., 10700., 12500.,
14500., 14500., 14500., 14500.]),\
np.array([1.91, 1.89889695, 1.64625059, 1.69918967, 1.53801881,
1.55319352, 1.53106075, 1.48801053, 1.48838346, 1.47779774,
1.44527783, 1.4129107, 1.3861779, 1.30383319, 1.24749974,
1.12702398, 1.12023921, 0.98822216, 0.90145692, 0.84143165,
0.83585252, 0.74482766, 0.76955031, 0.70259105, 0.7107385,
0.68402323, 0.67252669, 0.657925, 0.65103668, 0.60442773,
0.5946758, 0.53880897, 0.53974386, 0.5054044, 0.48018914,
0.43561453, 0.42026151, 0.38487402, 0.36939307, 0.34017362,
0.28593417, 0.25906698, 0.24421401, 0.22166016, 0.17370659,
0.15529447, 0.14294398, 0.07675079, 0.07944651, 0.03617357,
-0.00639459, -0.04057817, -0.10613727, -0.10490602, -0.1217432,
-0.14, 0., 0., 0., 0.]), 3)
tck_vks = (np.array([3000., 3000., 3000., 3000., 3100., 3200., 3250.,
3410., 3500., 3550., 3650., 3700., 3850., 3880.,
3970., 4050., 4230., 4410., 4620., 4840., 5040.,
5170., 5280., 5340., 5490., 5530., 5590., 5660.,
5680., 5720., 5770., 5880., 5920., 6040., 6170.,
6240., 6340., 6510., 6640., 6720., 6810., 7030.,
7200., 7440., 7500., 7800., 8000., 8080., 8270.,
8550., 8840., 9200., 9700., 10400., 10700., 12500.,
14500., 14500., 14500., 14500.]),\
np.array([6.50000000e+00, 5.62931496e+00, 5.75367026e+00,
5.34076308e+00, 4.79071352e+00, 4.61391909e+00,
4.51720164e+00, 4.13159547e+00, 4.14709596e+00,
4.03097681e+00, 3.85578132e+00, 3.70546709e+00,
3.54424972e+00, 3.31949749e+00, 3.16721742e+00,
2.81656851e+00, 2.79358299e+00, 2.39537651e+00,
2.17786419e+00, 2.02134802e+00, 1.99884413e+00,
1.78559326e+00, 1.84401087e+00, 1.68769762e+00,
1.70675113e+00, 1.64431540e+00, 1.61765528e+00,
1.58260890e+00, 1.56619519e+00, 1.45865442e+00,
1.43726664e+00, 1.30978554e+00, 1.31255319e+00,
1.23323500e+00, 1.17676105e+00, 1.07373032e+00,
1.03727999e+00, 9.50782384e-01, 9.14314901e-01,
8.42414011e-01, 7.12893077e-01, 6.47709841e-01,
6.12165798e-01, 5.57520113e-01, 4.37245386e-01,
3.91233825e-01, 3.60535900e-01, 1.93847001e-01,
2.05967449e-01, 6.93649668e-02, 3.93919827e-02,
-5.51783950e-03, -2.63670696e-01, -2.23184229e-01,
-3.14197676e-01, -3.58000000e-01, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00]), 3)
tck_jh = (np.array([3000., 3000., 3000., 3000., 3100., 3200., 3250.,
3410., 3500., 3550., 3650., 3700., 3850., 3880.,
3970., 4050., 4230., 4410., 4620., 4840., 5040.,
5170., 5280., 5340., 5490., 5530., 5590., 5660.,
5680., 5720., 5770., 5880., 5920., 6040., 6170.,
6240., 6340., 6510., 6640., 6720., 6810., 7030.,
7200., 7440., 7500., 7800., 8000., 8080., 8270.,
8550., 8840., 9200., 9700., 10400., 10700., 12500.,
14500., 14500., 14500., 14500.]),\
np.array([0.588, 0.57925679, 0.55974591, 0.55662775, 0.55739209,
0.58028578, 0.58376255, 0.60634672, 0.60546662, 0.6120368,
0.61989396, 0.62481195, 0.63594655, 0.61500676, 0.60674412,
0.55966082, 0.55230917, 0.49293409, 0.43609132, 0.40202698,
0.39806407, 0.34706382, 0.35960846, 0.32332711, 0.3280911,
0.31165243, 0.30654383, 0.29678003, 0.29401661, 0.26762904,
0.26081307, 0.22934639, 0.23111651, 0.20988225, 0.19754575,
0.17130757, 0.16340732, 0.1446157, 0.13839745, 0.12210648,
0.09356932, 0.08068036, 0.07127585, 0.06038954, 0.03801209,
0.02849096, 0.02386206, -0.00845365, -0.00723668, -0.02947868,
-0.0309659, -0.04121424, -0.06064657, -0.06648605, -0.07545834,
-0.081, 0., 0., 0., 0.]), 3)
tck_btvt = (np.array([3850., 3850., 3850., 3850., 3970., 4050., 4230.,
4410., 4620., 4840., 5040., 5170., 5280., 5340.,
5490., 5530., 5590., 5660., 5680., 5720., 5770.,
5880., 5920., 6040., 6170., 6240., 6340., 6510.,
6640., 6720., 6810., 7030., 7200., 7440., 7500.,
7800., 8000., 8080., 8270., 8550., 8840., 9200.,
9700., 10400., 10700., 12500., 14500., 14500., 14500.,
14500.]),\
np.array([1.65, 1.62529982, 1.61107882, 1.49837621, 1.4387566,
1.36306363, 1.32576584, 1.16207044, 1.06271882, 0.97207866,
0.97009969, 0.85213091, 0.88670237, 0.79478911, 0.8051525,
0.76802269, 0.75579062, 0.73434453, 0.72579215, 0.66145907,
0.64843825, 0.567861, 0.56983969, 0.52834313, 0.4990927,
0.4526401, 0.43614294, 0.40085542, 0.38573171, 0.3529186,
0.31238673, 0.28288031, 0.26840011, 0.24412267, 0.19710763,
0.18078778, 0.17080152, 0.09332344, 0.09761852, 0.04697616,
0.00787136, -0.02889995, -0.10493612, -0.10123972, -0.12268253,
-0.142, 0., 0., 0., 0.]), 3)
T = np.array([])
err_mag = np.array([])
mult_zeros = []
colors = ['B-V', 'V-K', 'J-H', 'Bt-Vt']
tcks = [tck_bv, tck_vks, tck_jh, tck_btvt]
for i in range(4):
c1, c2 = colors[i].split('-')
if (c1 in photometry) and (c2 in photometry):
C1 = photometry[c1][0] - photometry[c1][4]
C2 = photometry[c2][0] - photometry[c2][4]
C = C1-C2
e_C = np.sqrt(photometry[c1][1]**2. + photometry[c2][1]**2.)
tck_new = (tcks[i][0], tcks[i][1]-C, tcks[i][2])
zeros = interpolate.sproot(tck_new)
correction = correct_mamajek(0.0, colors[i], inst)
zeros = zeros + correction
if len(zeros) == 1:
T = np.append(T, zeros[0])
err_mag = np.append(err_mag, e_C)
if len(zeros) > 1:
mult_zeros.append((i, colors[i], zeros, e_C))
if T.size > 0:
T_average = np.average(T, weights=1./err_mag)
if mult_zeros:
for m in mult_zeros:
d = np.abs(m[2]-T_average)
T = np.append(T, m[2][np.argmin(d)])
err_mag = np.append(err_mag, m[3])
T_average = np.average(T, weights=1./err_mag)
err_T_average = np.sqrt(np.average((T - T_average)**2., weights=1./err_mag))
if err_T_average == 0.0:
err_T_average = 100.
else:
T_average = 0.0
err_T_average = 0.0
return T_average, err_T_average
def alonso1999(photometry, met):
colors = np.array(['V-K', 'V-K', 'J-H', 'J-K'])
coefficients = [[0.5558, 0.2105, 0.001981, -0.009965, 0.01325, -0.002726],\
[0.3770, 0.3660, -0.03170, -0.003074, -0.002765, -0.002973],\
[0.5977, 1.015, -0.1020, -0.01029, 0.03006, 0.01013],\
[0.5816, 0.9134, -0.1443, 0.0000, 0.0000, 0.0000]]
e_Teff = np.array([40., 25., 170., 125.])
color_ranges = {'V-K':[{'FeH': [-0.5, 0.2], 'color': [0.20, 2.50], 'r': 0},
{'FeH': [-1.5, -0.5], 'color': [1.00, 2.50], 'r': 0},
{'FeH': [-2.5, -1.5], 'color': [1.20, 2.50], 'r': 0},
{'FeH': [-3.0, -2.5], 'color': [1.70, 2.50], 'r': 0},
{'FeH': [-0.5, 0.2], 'color': [2.00, 4.90], 'r': 1},
{'FeH': [-1.5, -0.5], 'color': [2.00, 4.60], 'r': 1},
{'FeH': [-2.5, -1.5], 'color': [2.00, 3.40], 'r': 1},
{'FeH': [-3.0, -2.5], 'color': [2.00, 2.80], 'r': 1}],
'J-H':[{'FeH': [-0.5, 0.2], 'color': [0.00, 0.90]},
{'FeH': [-1.5, -0.5], 'color': [0.20, 0.80]},
{'FeH': [-2.5, -1.5], 'color': [0.30, 0.70]},
{'FeH': [-3.0, -2.5], 'color': [0.35, 0.65]}],
'J-K':[{'FeH': [-0.5, 0.2], 'color': [0.00, 1.10]},
{'FeH': [-1.5, -0.5], 'color': [0.20, 1.00]},
{'FeH': [-2.5, -1.5], 'color': [0.30, 0.90]},
{'FeH': [-3.0, -2.5], 'color': [0.40, 0.80]}]}
#corrections = np.array([0.0, 0.0, 0.0, 0.0])
a_corr = np.array([2.70107158e-01, 8.86192858e-02, 2.57890150e-01])
b_corr = np.array([3.58390280e+03, 4.48537163e+03, 3.72508078e+03])
list_colors = ['V-K', 'J-H', 'J-K']
T_array = np.ones(len(list_colors))*np.nan
err_T_array = np.ones(len(list_colors))*np.nan
T_final = 0.0
err_T_final = 0.0
for i, lc in enumerate(list_colors):
c1, c2 = lc.split('-')
if (c1 in photometry) and (c2 in photometry):
C1 = photometry[c1][0] - photometry[c1][4]
C2 = photometry[c2][0] - photometry[c2][4]
C = C1-C2
icolor = np.where(colors == lc)[0]
coefs = None
err_T_int = None
# If there is only one relation for the color
if len(icolor) == 1:
# Check metallicity and color ranges
in_ranges = 0
for k in color_ranges[lc]:
range_m = k['FeH']
range_c = k['color']
if (range_m[0] <= met <= range_m[1]) and (range_c[0] <= C <= range_c[1]):
in_ranges = 1
if in_ranges > 0:
coefs = coefficients[int(icolor)]
err_T_int = e_Teff[int(icolor)]
# There are two equations for the color, depending on the color value
else:
in_ranges = 0
r = -99
for k in color_ranges[lc]:
range_m = k['FeH']
range_c = k['color']
range_r = k['r']
if (range_m[0] <= met <= range_m[1]) and (range_c[0] <= C <= range_c[1]):
in_ranges += 1
r = range_r
if in_ranges == 1:
coefs = coefficients[icolor[r]]
err_T_int = e_Teff[icolor[r]]
elif in_ranges > 1:
imin = np.argmin(e_Teff[icolor])
coefs = coefficients[icolor[imin]]
err_T_int = e_Teff[icolor[imin]]
if coefs is not None and err_T_int is not None:
theta = coefs[0] + coefs[1]*C + coefs[2]*C**2 + coefs[3]*C*met\
+ coefs[4]*met + coefs[5]*met**2
if theta != 0.0:
Teff = 5040./theta + ufloat(0.0, err_T_int)
T_array[i] = Teff.n
err_T_array[i] = Teff.s
ii = np.where(~np.isnan(T_array))[0]
if ii.size > 0:
Tcorr = [np.polyval([a_corr[ii][i_], b_corr[ii][i_]], T_array[ii][i_])\
for i_ in range(len(ii))]
Tmean = unumpy.uarray(Tcorr, err_T_array[ii])
#T_final = np.average(T_array[ii], weights=1./err_T_array[ii])
T_final = np.average(Tcorr, weights=1./err_T_array[ii])
#T_final = np.median(T_array[ii])
err_T_final = np.median(Tmean).s
#Tmean = unumpy.uarray(T_array[ii]+corrections[ii], err_T_array[ii])
#T_final = np.median(Tmean).n
#err_T_final = np.median(Tmean).s
return T_final, err_T_final
#******************************************************************************
#******************************************************************************
def correct_mann(Tc, color, inst, met):
# harps, feros, hires, uves
corrections = {'V-J': ((41.3, 26.3, 89.7, 53.4), (-87.8, -73.3, -87.8, -48.2)),\
'V-I': ((0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0))}
m = 0 if met else 1
x = 0
if inst in ['harps', 'feros', 'hires', 'uves']:
x = corrections[m][color][['harps', 'feros', 'hires', 'uves'].index(inst)]
return Tc + x
def coefs_mann(type_color, met=True):
if met:
if type_color == 'V-J':
return [2.515, -1.054, 0.2965, -0.04150, 0.002245, 0.05262]
if type_color == 'V-I':
return [1.901, -0.6564, 0.1471, -0.01274, 0.0, 0.04697]
return [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
if type_color == 'V-J':
return [2.769, -1.421, 0.4284, -0.06133, 0.003310, 0.1333, 0.05416]
if type_color == 'V-I':
return [1.568, -0.4381, 0.07749, -0.005610, 0.0, 0.2441, -0.09257]
return [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
def mann(photometry, met):
if met is not None:
if ('V' in photometry) and ('J' in photometry):
i = coefs_mann('V-J', met=True)
C1 = photometry['V'][0] - photometry['V'][4]
C2 = photometry['J'][0] - photometry['J'][4]
c = C1-C2
err_c = np.sqrt(photometry['J'][1]**2. + photometry['V'][1]**2.)
color = ufloat(c, err_c)
elif ('V' in photometry) and ('I' in photometry):
i = coefs_mann('V-I', met=True)
C1 = photometry['V'][0] - photometry['V'][4]
C2 = photometry['I'][0] - photometry['I'][4]
c = C1-C2
err_c = np.sqrt(photometry['I'][1]**2. + photometry['V'][1]**2.)
color = ufloat(c, err_c)
else:
i = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
c = 0.0
color = ufloat(c, 999.0)
T = i[0] + i[1]*color + i[2]*color**2 + i[3]*color**3 + i[4]*color**4 + i[5]*met
else:
if ('V' in photometry) and ('J' in photometry):
i = coefs_mann('V-J', met=False)
C1 = photometry['V'][0] - photometry['V'][4]
C2 = photometry['J'][0] - photometry['J'][4]
c = C1-C2
err_c = np.sqrt(photometry['J'][1]**2. + photometry['V'][1]**2.)
color = ufloat(c, err_c)
elif ('V' in photometry) and ('I' in photometry):
i = coefs_mann('V-I', met=False)