def cnfg_KOI1003():
lygos.init( \
ticitarg=122374527, \
abltarg='KOI 1003', \
#strgmast='KOI-1003', \
epocpmot=2019.3, \
)
def cnfg_TOI2406():
lygos.init( \
toiitarg=2406, \
boolplotrflx=True, \
#ticitarg=212957629, \
#labltarg='TOI-2406', \
)
def cnfg_GJ299():
lygos.init( \
#boolfittoffs=True, \
labltarg='GJ 299', \
ticitarg=334415465, \
epocpmot=2019.3, \
)
def IRAS090263817():
labltarg = 'IRAS09026-3817'
rasctarg = 136.1388872
decltarg = -38.4895431
for k in range(1, 4):
lygos.init(rasctarg=rasctarg, decltarg=decltarg, labltarg=labltarg, maxmnumbstar=k)
def cnfg_WASP121():
lygos.init( \
strgmast='WASP-121', \
boolplotquat=True, \
boolcalcconr=True, \
boolanim=True, \
boolanimframtotl=False, \
)
def cnfg_TOI1233():
lygos.init( \
toiitarg=1233, \
numbside=21, \
boolplotquat=True, \
boolplotrflx=True, \
boolanim=True, \
boolanimframtotl=False, \
)
def cnfg_GJ299():
lygos.init( \
#boolfittoffs=True, \
labltarg='GJ 299', \
#typepsfn='ontf', \
boolplotquat=True, \
boolanim=True, \
ticitarg=334415465, \
epocpmot=2019.3, \
)
def cnfg_WD1856():
ticitarg = 267574918
strgmast = 'TIC 267574918'
labltarg = 'WD 1856'
lygos.init( \
labltarg=labltarg, \
#strgmast=strgmast, \
ticitarg=ticitarg, \
#datatype='sapp', \
)
def cnfg_TIC284856863():
'''
12 July 2021, young star with accretion disk from Max (TYC 2597-735-1)
'''
lygos.init( \
boolregrforc=True, \
maxmnumbstar=999, \
maxmdmag=6, \
ticitarg=284856863, \
labltarg='TYC 2597-735-1', \
)
def ASASNN():
# a string that will be used to query the target on MAST, which should be resolvable on MAST
liststrgmast = [ \
'ASASSN-19bt', \
#'ASASNN-19fp', \
]
for strgmast in liststrgmast:
lygos.init( \
strgmast=strgmast, \
strgclus='ASASSN', \
)
def cnfg_test347543557():
ticitarg = 347543557
labltarg = 'TIC 347543557'
strgtarg = 'test347543557'
listlimttimeplot = [[2458428, 2458430]]
lygos.init( \
ticitarg=ticitarg, \
labltarg=labltarg, \
strgtarg=strgtarg, \
listlimttimeplot=listlimttimeplot, \
)
def cnfg_spec():
path = os.environ['LYGOS_DATA_PATH'] + '/data/List_for_MIT_pilot.txt'
data = np.loadtxt(path, delimiter='\t', skiprows=1)
numbtarg = data.shape[0]
indxtarg = np.arange(numbtarg)
for k in indxtarg:
ticitarg = int(data[k, 2])
lygos.init( \
ticitarg=ticitarg, \
labltarg='TIC %s' % ticitarg, \
strgtarg='speculus_%s' % ticitarg, \
)
def cnfg_arry(strgclus):
pathdata = os.environ['LYGOS_DATA_PATH'] + '/data/'
if strgclus == 'KeplerEBs':
path = pathdata + 'KeplerEBs/Kepler_binaries_priority.csv'
else:
path = pathdata + 'SPECULOOS/final_catalog.csv'
cntr = 0
indx
listtici = []
print('Reading from %s...' % path)
for line in open(path, 'r'):
if cntr == 0:
cntr += 1
continue
linesplt = line.split(',')
if strgclus == 'KeplerEBs':
listtici.append(int(linesplt[1]))
else:
listtici.append(int(linesplt[23]))
numbtarg = len(listtici)
indxtarg = np.arange(numbtarg)
listintgresu = np.empty(numbtarg)
for k in indxtarg:
temp = lygos.init( \
ticitarg=listtici[k], \
strgclus=strgclus, \
)
def cnfg_requests():
lygos.init( \
#strgmast='V563 Lyr', \
# from Ozgur Basturk
strgmast='HAT-P-19', \
# from Ben Rackham
#strgmast='Ross 619', \
# from Andrew Vanderburg, white dwarf
ticitarg=902906874, \
boolfasttpxf=True, \
numbside=7, \
#boolfittoffs=True, \
)
def cnfg_GRB191016A():
rasctarg = 30.2695
decltarg = 24.5099
labltarg = 'GRB191016A'
listlimttimeplot = []
for timedelt in [1.]:
listlimttimeplot.append(2458772.67 + np.array([-timedelt, timedelt]))
listtimeplotline = [2458772.67291666667]
boolfittoffs = False#True
boolcuttqual = False
lygos.init( \
rasctarg=rasctarg, \
decltarg=decltarg, \
labltarg=labltarg, \
boolcuttqual=boolcuttqual, \
boolfittoffs=boolfittoffs, \
numbside=9, \
listtimeplotline=listtimeplotline, \
listlimttimeplot=listlimttimeplot, \
)
def cnfg_movingobject():
labltarg = 'Synthetic Interstellar Object'
numbside = 11.
dicttrue = dict()
dicttrue['xpostarg'] = 5. + np.random.rand()
dicttrue['ypostarg'] = 5. + np.random.rand()
dicttrue['tmagtarg'] = 10.
dicttrue['pmxatarg'] = 1.
dicttrue['pmyatarg'] = 1.
numbtargneig = 10
dicttrue['xposneig'] = 5. + np.random.rand(numbtargneig)
dicttrue['yposneig'] = 5. + np.random.rand(numbtargneig)
dicttrue['tmagneig'] = 5. + np.random.rand(numbtargneig)
lygos.init( \
labltarg=labltarg, \
typedata='simugene', \
dicttrue=dicttrue, \
)
def cnfg_Luhman16():
pmratarg = -2762.16 # [mas/yr]
pmdetarg = 357.79 # [mas/yr]
# exofop (J2015.5)
#rasctarg = 162.328259
#decltarg = -53.319372°
# TICv8 (J2000)
rasctarg = 162.328814
decltarg = -53.319466
lygos.init( \
rasctarg=rasctarg, \
decltarg=decltarg, \
labltarg='Luhman 16', \
booladdddiscdetr=True, \
maxmnumbstar=5, \
epocpmot=2019., \
pmratarg=pmratarg, \
pmdetarg=pmdetarg, \
psfntype='lion', \
)
def cnfg_lindsey():
from astropy import units as u
from astropy.coordinates import SkyCoord
pathbase = os.environ['PERGAMON_DATA_PATH'] + '/featsupntess/'
pathdata = pathbase + 'data/'
pathimag = pathbase + 'imag/'
os.system('mkdir -p %s' % pathdata)
os.system('mkdir -p %s' % pathimag)
for strgclus in [ \
'Cycle1-matched', \
'Cycle2-matched', \
'Cycle3-matched', \
]:
pathcsvv = pathdata + '%s.csv' % strgclus
print('Reading from %s...' % pathcsvv)
objtfile = open(pathcsvv, 'r')
k = 0
for line in objtfile:
if k == 0:
k += 1
continue
linesplt = line.split(',')
labltarg = linesplt[2]
c = SkyCoord('%s %s' % (linesplt[3], linesplt[4]), unit=(u.hourangle, u.deg))
rasctarg = c.ra.degree
decltarg = c.dec.degree
print('labltarg')
print(labltarg)
print('linesplt[1]')
print(linesplt[1])
print('linesplt[2]')
print(linesplt[2])
print('rasctarg')
print(rasctarg)
print('decltarg')
print(decltarg)
dictoutp = lygos.init( \
rasctarg=rasctarg, \
decltarg=decltarg, \
labltarg=labltarg, \
#numbside=5, \
booldetrcbvs=False, \
strgclus=strgclus, \
booltpxflygo=False, \
)
listtsec = dictoutp['listtsec']
numbtsec = len(listtsec)
indxtsec = np.arange(numbtsec)
for o in indxtsec:
cmnd = 'cp %s%s/%s/imag/* %s%s/imag/' % (pathbase, strgclus, dictoutp['strgtarg'], pathbase, strgclus)
print(cmnd)
os.system(cmnd)
pathsaverflxtarg = dictoutp['pathsaverflxtargsc%02d' % listtsec[o]]
cmnd = 'cp %s %s%s/data/' % (pathsaverflxtarg, pathbase, strgclus)
print(cmnd)
os.system(cmnd)
k += 1
def cnfg_TOI2406():
lygos.init( \
toiitarg=2406, \
)
def tutorial():
lygos.init(strgmast='WASP-121')
lygos.init(toiitarg=1233)
lygos.init(rasctarg=124.343, decltarg=decltarg)
def cnfg_contamination():
'''
typdata:
'toyy': simulated images based on imaginary temporal footprint as well as imaginary RA, DEC, and Tmag for all sources
'mock': simulated images based on real temporal footprint as well as real RA, DEC, Tmag for all sources
'obsd': real images and real RA, DEC, and Tmag for all sources
'''
listtypedata = ['toyy', 'mock', 'obsd']
# get the features of highly-contaminated sources in the TIC
dictpopl = miletos.retr_dictcatltic8('ffimhcon')
numbsour = 10
indxsour = np.arange(numbsour)
for typedata in listtypedata:
for k in indxsour:
if typedata == 'toyy':
ticitarg = None
labltarg = 'Mock Target %d' % k
xpostarg = 5. + np.random.rand()
ypostarg = 5. + np.random.rand()
tmagtarg = 10.
numbneig = 10
xposneig = np.random.rand(numbneig) * 11
yposneig = np.random.rand(numbneig) * 11
tmagneig = np.random.rand(numbneig) * 4 + 10
else:
tmagtarg = None
ticitarg = dictpopl['tici'][k], \
xpostarg = None
ypostarg = None
xposneig = None
yposneig = None
labltarg = None
lygos.init( \
strgclus='contamination', \
typepsfn='ontf', \
boolplotrflx=True, \
boolplotcntp=True, \
boolplotquat=True, \
ticitarg=ticitarg, \
labltarg=labltarg, \
xpostarg=xpostarg, \
ypostarg=ypostarg, \
tmagtarg=tmagtarg, \
boolmile=True, \
xposneig=xposneig, \
yposneig=yposneig, \
tmagneig=tmagneig, \
boolfittoffs=True, \
#boolanim=True, \
typedata=typedata, \
)
def cnfg_V563Lyr():
lygos.init( \
strgmast='V563 Lyr', \
)
def cnfg_ASASSN20qc():
'''
13 July 2021, AGN from DJ
'''
rasctarg = 63.260208
decltarg = -53.0727
labltarg = 'ASASSN-20qc'
refrlistlabltser = [['Michael']]
path = os.environ['LYGOS_DATA_PATH'] + '/data/lc_2020adgm_cleaned_ASASSN20qc'
print('Reading from %s...' % path)
objtfile = open(path, 'r')
k = 0
linevalu = []
for line in objtfile:
if k == 0:
k += 1
continue
linesplt = line.split(' ')
linevalu.append([])
for linesplttemp in linesplt:
if linesplttemp != '':
linevalu[k-1].append(float(linesplttemp))
linevalu[k-1] = np.array(linevalu[k-1])
k += 1
linevalu = np.vstack(linevalu)
refrarrytser = np.empty((linevalu.shape[0], 3))
refrarrytser[:, 0] = linevalu[:, 0]
refrarrytser[:, 1] = linevalu[:, 2]
refrarrytser[:, 2] = linevalu[:, 3]
dictmileinpt = dict()
dictmileinpt['listtypemodl'] = ['supn']
listnumbside = [7, 11, 15]
#dictmileinpt['listlimttimemask'] = [[[[-np.inf, 2457000 + 2175], [2457000 + 2186.5, 2457000 + 2187.5]]]]
dictmileinpt['listlimttimemask'] = [[[[2457000 + 2186.5, 2457000 + 2187.5]]]]
for numbside in listnumbside:
if numbside == 11:
dictmileinpt['listtimescalbdtrspln'] = [0., 0.1, 0.5]
boolfittoffs = True
else:
dictmileinpt['listtimescalbdtrspln'] = [0.]
boolfittoffs = False
dictoutp = lygos.init( \
boolplotrflx=True, \
boolplotcntp=True, \
boolfittoffs=boolfittoffs, \
refrlistlabltser=refrlistlabltser, \
refrarrytser=refrarrytser, \
labltarg=labltarg, \
listtsecsele=[32], \
dictmileinpt=dictmileinpt, \
timeoffs=2459000, \
numbside=numbside, \
rasctarg=rasctarg, \
decltarg=decltarg, \
boolregrforc=True, \
)
def cnfg_TOI1233():
lygos.init( \
toiitarg=1233, \
numbside=21, \
)
def cnfg_syst(typeanls):
'''
Investigate systematics
typedata:
'simugene': simulated images based on imaginary temporal footprint as well as imaginary RA, DEC, and Tmag for all sources
'mock': simulated images based on real temporal footprint as well as real RA, DEC, Tmag for all sources
'obsd': real images and real RA, DEC, and Tmag for all sources
typemult:
'sing': single source
'doub': two sources
'''
# get the features of highly-contaminated sources in the TIC
dictpopl = ephesus.retr_dictpopltic8('ticihcon')
typedata, typemult = typeanls.split('_')
pathbase = os.environ['LYGOS_DATA_PATH'] + '/syst/'
pathimag = pathbase + 'imag/'
numbside = 11
if typemult == 'sing':
numbsour = 2
boolanim = False
boolfittoffs = True
else:
numbsour = 100
boolanim = False
boolfittoffs = False
indxsour = np.arange(numbsour)
listtmag = np.empty(numbsour)
listnois = np.empty(numbsour)
listsepa = np.empty(numbsour)
dictfitt = dict()
dicttrue = dict()
for k in indxsour:
if typemult == 'sing':
boolplot = True
else:
boolplot = False
if typemult == 'sing':
typepsfninfe = 'locl'
else:
typepsfninfe = 'fixd'
if typemult == 'sing' and k == 0:
dicttrue['typepsfnshap'] = 'gauselli'
dicttrue['sigmpsfnxpos'] = 0.9
dicttrue['sigmpsfnypos'] = 1.2
dicttrue['fracskewpsfnxpos'] = 0.4
dicttrue['fracskewpsfnypos'] = 0.7
if typemult == 'sing' and k == 1:
dicttrue['typepsfnshap'] = 'gauscirc'
if typedata == 'simugene' or typedata == 'inje':
ticitarg = None
if typemult == 'sing' or typemult == 'doub':
dicttrue['tmagtarg'] = 10.
else:
dicttrue['tmagtarg'] = tdpy.icdf_self(np.random.rand(), 7., 20.)
listtmag[k] = dicttrue['tmagtarg']
if typedata == 'simugene':
if typemult == 'sing':
strgtarg = 'simugene%s%starg' % (typemult, dicttrue['typepsfnshap'])
else:
strgtarg = 'simugene%starg%04d' % (typemult, k)
labltarg = 'Sim. Image, Sim. T=%.1f Source' % listtmag[k]
print('labltarg')
print(labltarg)
if typemult == 'isol':
dicttrue['cntpbackscal'] = 100. # [e-/s]
dictfitt['cntpbackscal'] = 90. # [e-/s]
if typemult == 'bkgd':
dicttrue['cntpbackscal'] = 100. # [e-/s]
dictfitt['cntpbackscal'] = 90. # [e-/s]
if typemult == 'psfn':
dicttrue['sigmpsfn'] = 1. # [px]
dictfitt['sigmpsfn'] = 0.9 # [px]
rasctarg = None
decltarg = None
if typemult == 'doub':
cent = (numbside - 1.) / 2.
offs = tdpy.icdf_self(np.random.rand(), 0., 1.)
listsepa[k] = 2. * offs
dicttrue['numbneig'] = 1
dicttrue['xpostarg'] = cent - offs
dicttrue['xposneig'] = np.array([cent + offs])
dicttrue['yposneig'] = np.array([cent])
dicttrue['tmagneig'] = np.array([10.])
if typemult == 'blen':
dicttrue['cntpbackscal'] = tdpy.icdf_self(np.random.rand(), 50., 300.) # [e-/s]
dicttrue['numbneig'] = 10
dicttrue['xposneig'] = tdpy.icdf_self(np.random.rand(dicttrue['numbneig']), 0., numbside - 1.)
dicttrue['yposneig'] = tdpy.icdf_self(np.random.rand(dicttrue['numbneig']), 0., numbside - 1.)
dicttrue['tmagneig'] = tdpy.icdf_self(np.random.rand(dicttrue['numbneig']), 10., 20.)
if typedata == 'inje':
strgtarg = 'injetarg%04d' % k
labltarg = 'Real Image, Injected T=%.3g Source' % listtmag[k]
rasctarg = np.random.rand() * 360.
decltarg = -90. + np.random.rand() * 180.
if typedata == 'obsd':
strgtarg = None
ticitarg = dictpopl['tici'][k]
listtmag[k] = dictpopl['tmag'][k]
dicttrue = None
rasctarg = None
decltarg = None
labltarg = None
dictoutp = lygos.init( \
strgclus='syst', \
ticitarg=ticitarg, \
rasctarg=rasctarg, \
decltarg=decltarg, \
strgtarg=strgtarg, \
labltarg=labltarg, \
boolanim=boolanim, \
boolplot=boolplot, \
boolfittoffs=boolfittoffs, \
typepsfninfe=typepsfninfe, \
dictfitt=dictfitt, \
dicttrue=dicttrue, \
boolmerg=False, \
seedrand=k, \
typedata=typedata, \
)
if len(dictoutp['listnois']) > 0:
listnois[k] = dictoutp['listnois'][0][1, 1]
if typemult == 'doub':
figr, axis = plt.subplots()
axis.scatter(listsepa, listnois, s=2)
axis.set_yscale('log')
axis.set_ylabel('1-hour CDPP [ppm]')
axis.set_xlabel('Separation [px]')
#plt.tight_layout()
path = pathimag + 'noissepa_%s.pdf' % typemult
plt.savefig(path)
plt.close()
if typemult != 'sing' and typemult != 'doub':
figr, axis = plt.subplots()
axis.scatter(listtmag, listnois, s=2)
axis.set_yscale('log')
axis.set_ylabel('1-hour CDPP [ppm]')
axis.set_xlabel('TESS Mag')
#plt.tight_layout()
path = pathimag + 'noistmag_%s.pdf' % typemult
plt.savefig(path)
plt.close()
def cnfg_Pleides():
lygos.init( \
strgmast='Electra', \
)
def cnfg_HATP19():
strgmast = 'HAT-P-19'
lygos.init( \
strgmast=strgmast, \
)
