def __init__(self, coordinate_or_galaxy):
"""
This function ...
:param coordinate_or_galaxy:
:return:
"""
# Check whether the user/extinction directory exists
if not fs.is_directory(extinction_path): fs.create_directory(extinction_path)
# Get query
if isinstance(coordinate_or_galaxy, basestring): query = coordinate_or_galaxy
else: query = coordinate_or_galaxy.to_astropy()
# Determine the path to the local extinction table
path = fs.join(extinction_path, str(query))
# Check if the local file exists
if not fs.is_file(path):
# Get the extinction table from IRSA
self.table = IrsaDust.get_extinction_table(query)
# Save the table
tables.write(self.table, path)
# Load the table
else: self.table = tables.from_file(path)
def bsnip_edits():
bsnip = glob.glob ('../../data/bsnip/*.flm')
bsnip_sn = []
b = []
v = []
for i in range (len(bsnip)):
bsnip[i] = bsnip[i].split('-')[0]
bsnip[i] = bsnip[i].split('/')
bsnip[i] = bsnip[i][len(bsnip[i])-1]
if i > 0:#removes redundant sn names
if bsnip[i-1] != bsnip[i]:
bsnip_sn.append(bsnip[i])
cut_index = 0
flag = False
for i in range(len(bsnip_sn)):
if (not flag):
print "passing",bsnip_sn[i]
#certain cases for specfic sn names
if bsnip_sn[i] == 'sn2007s1':#want to start at first changed sn
print "start with",len(bsnip_sn),"sn"
print "START at index:",i
cut_index = i
flag = True #start here
if(flag):
print "looking at",bsnip_sn[i]
if bsnip_sn[i] == "sn2007s1":
bsnip_sn[i] = "SNF20071021-000"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s1":
bsnip_sn[i] = "SNF20080514-002"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008r3":
bsnip_sn[i] = "sn1989a"###doesn't work
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s3":
bsnip_sn[i] = "SNF20080825-006"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s4":
bsnip_sn[i] = "sn1989a"###can't find correct name
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s5":
bsnip_sn[i] = "SNF20080909-030"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s8":
bsnip_sn[i] = "sn1989a"###look for correct names
print "changed to",bsnip_sn[i]
ext = IrsaDust.get_extinction_table(bsnip_sn[i])
print ext
print ext[1][0],ext[1][3]
print ext[2][0],ext[2][3]
b.append(ext[1][3])
v.append(ext[2][3])
print "stopped successfullly?...cutting table"
del bsnip_sn[:cut_index]
param = Table([bsnip_sn,b,v])
print param
ascii.write(param,'bsnip_extinc_added.dat')
def query_bsnip():
#Creates extinction.dat from bsnip data
#list of files
bsnip = glob.glob ('../../data/bsnip/*.flm')
bsnip_sn = []
for i in range (len(bsnip)):
bsnip[i] = bsnip[i].split('-')[0]
bsnip[i] = bsnip[i].split('/')
bsnip[i] = bsnip[i][len(bsnip[i])-1]
if i > 0:#removes redundant sn names
if bsnip[i-1] != bsnip[i]:
bsnip_sn.append(bsnip[i])
b = []
v = []
for i in range(len(bsnip_sn)):
print "looking at",bsnip_sn[i]
#certain cases for specfic sn names
if bsnip_sn[i] == 'sn2007s1':
print "WARNING"
del bsnip_sn[i:]
break
ext = IrsaDust.get_extinction_table(bsnip_sn[i])
print ext
print ext[1][0],ext[1][3]
print ext[2][0],ext[2][3]
b.append(ext[1][3])
v.append(ext[2][3])
#makes table in format 'sn','B','V'
param = Table([bsnip_sn,b,v])
ascii.write(param,'bsnip_extinc.dat')
def atable(self):
""" Return the extinction in mag in starndard filters """
#query IRAS service
#http://irsa.ipac.caltech.edu/applications/DUST/
if (self.coord == -1):
print "Error: coordinates must be set"
exit()
from astroquery.irsa_dust import IrsaDust
tab = IrsaDust.get_extinction_table(self.coord)
return tab
def __init__(self, coordinate_or_galaxy):
"""
This function ...
:param coordinate_or_galaxy:
:return:
"""
# Check whether the user/attenuation directory exists
if not fs.is_directory(attenuation_path):
fs.create_directory(attenuation_path)
# Get query
if types.is_string_type(coordinate_or_galaxy):
query = coordinate_or_galaxy
else:
query = coordinate_or_galaxy.to_astropy()
# Determine the path to the local extinction table
path = fs.join(attenuation_path, str(query))
# Check if the local file exists
if not fs.is_file(path):
# Get the extinction table from IRSA
self.table = IrsaDust.get_extinction_table(query)
# Save the table
tables.write(self.table, path)
# Load the table
else:
self.table = tables.from_file(path)
# Check whether the RFILTER table is present, load
if fs.is_file(rfilter_table_path):
self.rfilter = RFilterTable.from_file(rfilter_table_path)
else: # or create new
self.rfilter = RFilterTable()
self.rfilter.path = rfilter_table_path
self.rfilter.save()
def query_cfa():
#gets cfa folder pathnames
cfa = glob.glob ('../../data/cfa/*')
#Truncates cfa pathname into needed format and removes cfa.dat files
del cfa[0]
del cfa[0]
for i in range(len(cfa)):
cfa[i]= cfa[i][15:]
print cfa[i]
b = []
v = []
for i in range(len(cfa)):
print "looking at",cfa[i]
ext = IrsaDust.get_extinction_table(cfa[i])
print ext
print ext[1][0],ext[1][3]
print ext[2][0],ext[2][3]
b.append(ext[1][3])
v.append(ext[2][3])
#makes table in format 'sn','B','V'
param = Table([cfa,b,v])
ascii.write(param,'extinctioncfa.dat')
def tableFill(dam, ra, dec, appender, nme):
t = Table(None)
Am = Column(name='Arcminute')
North = Column(name='North')
East = Column(name='East')
South = Column(name='South')
West = Column(name='West')
t.add_columns([Am, North, East, South, West])
tA_v = []
curVal = [None] * 4 #n = 0, e = 1, s = 2, w = 3
coord = [None] * 4 #n = 0, e = 1, s = 2, w = 3
#get values for each arcminute
for j in range(0, dam + 1):
fourCoord(j, ra, dec, coord)
t.add_row()
t[j][0] = j
for i in range(0, 4):
C = coordinates.SkyCoord(coord[i])
table = IrsaDust.get_extinction_table(C.fk5, show_progress=False)
curVal[i] = (table['A_SandF'][2])
t[j][i + 1] = curVal[i]
curVal = curVal[:]
tA_v.append(curVal)
t.add_row()
for i in range(
0, 5): #this adds a blank line to the table to separate queries
t[j + 1][i] = None
n = [nme]
namesTable = Table([n], names=('n'))
final_name = namesTable.to_pandas()
final_vals = t.to_pandas()
from pandas import ExcelWriter
with open('A_v Values.csv', appender) as f:
final_name.to_csv(f, header=False, index=False)
appender = 'a'
with open('A_v Values.csv', appender) as f:
final_vals.to_csv(f, header=True, index=False, sep=',')
return (tA_v) #gets the data from IRSA database and stores A_v in array
def sed(epic='211800191',ra='132.884782',dec='17.319834'):
os.chdir('outputs/')
urllib.urlretrieve('http://vizier.u-strasbg.fr/viz-bin/sed?-c='+ra+"%2C"+dec+'&-c.rs=1', epic+'_sed.vot')
print 'http://vizier.u-strasbg.fr/viz-bin/sed?-c='+ra+"%2C"+dec+'&-c.rs=0.005'
tb = votable.parse_single_table(epic+'_sed.vot')
data = tb.array
wav_all = 3e5 * 1e4 / data['_sed_freq'].data #angstrom
f_all = data['_sed_flux'].data
unc_all = data['_sed_eflux'].data
filters = data['_sed_filter'].data
filter_dict = {'2MASS:Ks':'2MASS Ks','2MASS:J':'2MASS J','2MASS:H':'2MASS H','WISE:W1':'WISE-1','WISE:W2':'WISE-2','SDSS:u':'SDSS u','SDSS:g':'SDSS g',\
'SDSS:r':'SDSS r','SDSS:i':'SDSS i','SDSS:z':'SDSS z'}
c = coord.SkyCoord(float(ra)*u.deg,float(dec)*u.deg,frame='icrs')
tb = IrsaDust.get_extinction_table(c)
filters2 = tb['Filter_name']
allA = tb['A_SandF']
A = []
f_ob_orig = []
wav_ob = []
unc = []
for f in filters:
if f in filter_dict.keys():
filtmatch = filter_dict[f]
ind = where(filters2==filtmatch)[0]
A.append(mean(allA[ind]))
ind = where(filters==f)[0]
f_ob_orig.append(mean(f_all[ind]))
wav_ob.append(mean(wav_all[ind]))
unc.append(mean(unc_all[ind]))
f_ob_orig = array(f_ob_orig)
A = array(A)
wav_ob = array(wav_ob)
unc = array(unc)
f_ob = (f_ob_orig*10**(A/2.5))
metallicity = ['ckp00']
m = [0.0]
t = arange(3500,13000,250)
t2 = arange(14000,50000,1000)
t = concatenate((t,t2),axis=1)
log_g = ['g20','g25','g30','g35','g40','g45','g50']
g = arange(2.,5.,0.5)
best_m =0
best_t =0
best_g =0
best_off =0.0
chi2_rec = 1e6
os.chdir('..')
for im, mval in enumerate(m):
for it, tval in enumerate(t):
for ig, gval in enumerate(g):
#load model
hdulist = pyfits.open('fits/'+metallicity[im]+'/'+metallicity[im]+'_'+str(tval)+'.fits')
data = hdulist[1].data
wmod = data['WAVELENGTH']
fmod = data[log_g[ig]]*3.34e4*wmod**2
#fit observations
f_int = exp( interp(log(wav_ob), log(wmod), log(fmod)) )
offsets = linspace(log(min(f_ob/f_int)), log(max(f_ob/f_int)), 51)
for i_off, offset in enumerate(offsets):
chi2 = sum((f_int*exp(offset)-f_ob)**2)
print 'chi2=', chi2, mval, tval, gval
if chi2 < chi2_rec:
chi2_rec = chi2
best_m = im
best_g = ig
best_t = it
best_off = offset
print 'best fit: m=', m[best_m], 'T=', t[best_t], 'log g=', g[best_g]
hdulist = pyfits.open('fits/'+metallicity[best_m]+'/'+metallicity[best_m]+'_'+str(t[best_t])+'.fits')
data = hdulist[1].data
wmod = data['WAVELENGTH']
fmod = data[log_g[best_g]]*3.34e4*wmod**2
fmod *= exp(best_off)
plt.close('all')
fig = plt.figure(figsize=(8,5))
plt.plot(wmod/1e4, fmod,label='Castelli & Kurucz model')
plt.xscale('log')
plt.plot(wav_ob/1e4, f_ob_orig, lw=0, marker='s', label='Uncorrected',ms=10)
plt.plot(wav_ob/1e4, f_ob, lw=0, marker='o', label='Corrected for extinction',ms=10)
plt.xlabel(r'${\rm Wavelength} \ (\mu m)}$',fontsize=18)
plt.xlim(0.1,max(wmod)/1e4)
plt.ylabel(r'$F_{\nu} \ {\rm (Jy)}$',fontsize=18)
plt.legend()
plt.savefig('outputs/'+epic+'_sed.pdf',dpi=150)
return t[best_t]
def sed(kic, ra, dec, ax):
"""
Retrieves star's magnitudes in different bands from ExoFOP and fits SED from Castelli & Kurucz library.
Note: you need an account to access Kepler ExoFOP data. I do not have one, so I'm using the format for K2 targets
instead.
:param kic: K2 EPIC ID of target.
:param ra: RA of target.
:param dec: Dec of target.
:param ax: plot handle of SED plot.
:return: ax: SED plot.
"""
urllib.urlretrieve(
'http://vizier.u-strasbg.fr/viz-bin/sed?-c=' + ra + "%2C" + dec +
'&-c.rs=1', kic + '_sed.vot')
print 'http://vizier.u-strasbg.fr/viz-bin/sed?-c=' + ra + "%2C" + dec + '&-c.rs=0.005'
tb = votable.parse_single_table(kic + '_sed.vot')
data = tb.array
wav_all = 3e5 * 1e4 / data['_sed_freq'].data # angstrom
f_all = data['_sed_flux'].data
unc_all = data['_sed_eflux'].data
filters = data['_sed_filter'].data
filter_dict = {'2MASS:Ks': '2MASS Ks', '2MASS:J': '2MASS J', '2MASS:H': '2MASS H', 'WISE:W1': 'WISE-1',
'WISE:W2': 'WISE-2', 'SDSS:u': 'SDSS u', 'SDSS:g': 'SDSS g', \
'SDSS:r': 'SDSS r', 'SDSS:i': 'SDSS i', 'SDSS:z': 'SDSS z'}
c = coord.SkyCoord(float(ra) * u.deg, float(dec) * u.deg, frame='icrs')
tb = IrsaDust.get_extinction_table(c)
filters2 = tb['Filter_name']
allA = tb['A_SandF']
A = []
f_ob_orig = []
wav_ob = []
unc = []
for f in filters:
if f in filter_dict.keys():
filtmatch = filter_dict[f]
ind = np.where(filters2 == filtmatch)[0]
A.append(np.mean(allA[ind]))
ind = np.where(filters == f)[0]
f_ob_orig.append(np.mean(f_all[ind]))
wav_ob.append(np.mean(wav_all[ind]))
unc.append(np.mean(unc_all[ind]))
f_ob_orig = np.array(f_ob_orig)
A = np.array(A)
wav_ob = np.array(wav_ob)
f_ob = (f_ob_orig * 10**(A / 2.5))
metallicity = ['ckp00']
m = [0.0]
t = np.arange(3500, 13000, 250)
t2 = np.arange(14000, 50000, 1000)
t = np.concatenate((t, t2))
log_g = ['g20', 'g25', 'g30', 'g35', 'g40', 'g45', 'g50']
g = np.arange(2., 5., 0.5)
best_m = 0
best_t = 0
best_g = 0
best_off = 0.0
chi2_rec = 1e6
# Do grid search to find best-fit SED
# This loop can probably be parallelized to save time
for im, mval in enumerate(m):
for it, tval in enumerate(t):
for ig, gval in enumerate(g):
# load model
hdulist = pyfits.open('fits/' + metallicity[im] + '/' +
metallicity[im] + '_' + str(tval) +
'.fits')
data = hdulist[1].data
wmod = data['WAVELENGTH']
fmod = data[log_g[ig]] * 3.34e4 * wmod**2
# fit observations
f_int = np.exp(
np.interp(np.log(wav_ob), np.log(wmod), np.log(fmod)))
offsets = np.linspace(np.log(min(f_ob / f_int)),
np.log(max(f_ob / f_int)), 51)
for i_off, offset in enumerate(offsets):
chi2 = sum((f_int * np.exp(offset) - f_ob)**2)
print 'chi2=', chi2, mval, tval, gval
if chi2 < chi2_rec:
chi2_rec = chi2
best_m = im
best_g = ig
best_t = it
best_off = offset
print 'best fit: m=', m[best_m], 'T=', t[best_t], 'log g=', g[best_g]
hdulist = pyfits.open('fits/' + metallicity[best_m] + '/' +
metallicity[best_m] + '_' + str(t[best_t]) + '.fits')
data = hdulist[1].data
wmod = data['WAVELENGTH']
fmod = data[log_g[best_g]] * 3.34e4 * wmod**2
fmod *= np.exp(best_off)
ax.plot(wmod / 1e4, fmod, label='Castelli & Kurucz model')
ax.set_xscale('log')
ax.plot(wav_ob / 1e4,
f_ob_orig,
lw=0,
marker='s',
label='Uncorrected',
ms=10)
ax.plot(wav_ob / 1e4,
f_ob,
lw=0,
marker='o',
label='Corrected for extinction',
ms=10)
ax.set_xlabel(r'${\rm Wavelength} \ (\mu m)}$', fontsize=18)
ax.set_xlim(0.1, max(wmod) / 1e4)
ax.set_ylabel(r'$F_{\nu} \ {\rm (Jy)}$', fontsize=18)
ax.legend()
return ax
cspmini= [] # a shorter list
for i in range(len(csp)):
csp[i] = 'sn20'+csp[i][20:24]
# print csp[i]
for sn in csp: # delete duplicate items
if sn not in cspmini:
cspmini.append(sn)
for i in range(len(cspmini)): # For single-letter SNs
if cspmini[i].endswith('_') :
cspmini[i] = cspmini[i][:-1]
print cspmini
b = []
v = []
for i in range(len(cspmini)):
print "looking at SN",cspmini[i]
ext = IrsaDust.get_extinction_table(cspmini[i])
print ext
print ext[1][0],ext[1][3]
print ext[2][0],ext[2][3]
b.append(ext[1][3])
v.append(ext[2][3])
param = Table([cspmini,b,v],names=('sn','B','V'))
ascii.write(param,'extinctioncsp.dat')
def add_extinction_old(catalogue,
getOptical=True,
getIR=True,
url_ned='http://ned.ipac.caltech.edu/cgi-bin/calc'):
nsrcs = len(catalogue)
A = np.full((nsrcs, 10), np.nan)
coords = SkyCoord(ra=catalogue['posRA'], dec=catalogue['posDec'])
start = 0
for i in trange(nsrcs):
# Get optical extinction from NED
if getOptical:
payload = {
'in_csys': 'Equatorial',
'in_equinox': 'J2000.0',
'obs_epoch': '2000.0',
'out_csys': 'Equatorial',
'out_equinox': 'J2000.0',
'lon': '{:.6f}d'.format(coords[i].ra.value),
'lat': '{:.6f}d'.format(coords[i].dec.value)
}
end = time.time()
if end - start < 1.0:
time.sleep(1.0)
else:
r = requests.get(url_ned, params=payload)
start = time.time()
soup = BeautifulSoup(r.text, 'html5lib')
ned_table = soup.find('div', id='moreBANDS')
ned_table = ned_table.find('table')
j = 0
for row in ned_table.findAll("tr"):
cells = row.findAll("td")
if len(cells):
if cells[0].contents[0] == 'PS1' and j < 5:
A[i, j] = cells[3].contents[0]
j += 1
if getIR:
t = IrsaDust.get_extinction_table(coords[i])
# NIR extinction
if catalogue['NIRobjID'][i] > 0:
if catalogue['NIR_SURVEY'][i] == '2MASS':
A[i, 5:8] = t['A_SandF'][16:19]
elif (catalogue['NIR_SURVEY'][i] == 'UKIDSS'
or catalogue['NIR_SURVEY'][i] == 'VISTA'):
A[i, 5:8] = t['A_SandF'][13:16]
# WISE extinction
if catalogue['WSID'][i] > 0:
A[i, 8:] = t['A_SandF'][23:25]
A = Table(
A,
names=['Ag', 'Ar', 'Ai', 'Az', 'Ay', 'AJ', 'AH', 'AK', 'AW1', 'AW2'])
catalogue = hstack([catalogue, A], join_type='exact')
return catalogue
