def input_wrapper(*args,**kwargs):
if len(args) >= 2 and isinstance(args[0],(list,numpy.ndarray)) \
and isinstance(args[1],(list,numpy.ndarray)):
# wavelength, spectrum
return func(args[0],args[1],*args[2:],**kwargs)
elif len(args) >= 1 and isinstance(args[0],(list,numpy.ndarray)):
# spectrum on standard re-sampled wavelength grid
lam=apStarWavegrid()
apStarBlu_lo,apStarBlu_hi,apStarGre_lo,apStarGre_hi,apStarRed_lo,apStarRed_hi = _apStarPixelLimits(dr=None)
aspcapBlu_start,aspcapGre_start,aspcapRed_start,aspcapTotal = _aspcapPixelLimits(dr=None)
if len(args[0]) == aspcapTotal: # Input is on ASPCAP grid
spec= numpy.zeros(len(lam))
spec[apStarBlu_lo:apStarBlu_hi]= args[0][:aspcapGre_start]
spec[apStarGre_lo:apStarGre_hi]= args[0][aspcapGre_start:aspcapRed_start]
spec[apStarRed_lo:apStarRed_hi]= args[0][aspcapRed_start:]
else:
spec= args[0]
return func(lam,spec,*args[1:],**kwargs)
elif isinstance(args[0],(int,numpy.short,str)) \
and isinstance(args[1],str):
# location ID and APOGEE ID (loc ID can be string for 1m sample)
if kwargs.get('apStar',False):
spec, hdr= apread.apStar(args[0],args[1],header=True,
ext=kwargs.pop('ext',1))
spec= spec[numpy.amin([kwargs.pop('apStarIndx',1),
len(spec)-1])]
else: #aspcapStar
spec, hdr= apread.aspcapStar(args[0],args[1],header=True,
ext=kwargs.pop('ext',1))
lam= 10.**numpy.arange(hdr['CRVAL1'],
hdr['CRVAL1']+len(spec)*hdr['CDELT1'],
hdr['CDELT1'])
return func(lam,spec,*args[2:],**kwargs)
def input_wrapper(*args,**kwargs):
if len(args) >= 2 and isinstance(args[0],(list,numpy.ndarray)) \
and isinstance(args[1],(list,numpy.ndarray)):
# wavelength, spectrum
return func(args[0],args[1],*args[2:],**kwargs)
elif len(args) >= 1 and isinstance(args[0],(list,numpy.ndarray)):
# spectrum on standard re-sampled wavelength grid
lam=apStarWavegrid()
apStarBlu_lo,apStarBlu_hi,apStarGre_lo,apStarGre_hi,apStarRed_lo,apStarRed_hi = _apStarPixelLimits(dr=None)
aspcapBlu_start,aspcapGre_start,aspcapRed_start,aspcapTotal = _aspcapPixelLimits(dr=None)
if len(args[0]) == aspcapTotal: # Input is on ASPCAP grid
spec= numpy.zeros(len(lam))
spec[apStarBlu_lo:apStarBlu_hi]= args[0][:aspcapGre_start]
spec[apStarGre_lo:apStarGre_hi]= args[0][aspcapGre_start:aspcapRed_start]
spec[apStarRed_lo:apStarRed_hi]= args[0][aspcapRed_start:]
else:
spec= args[0]
return func(lam,spec,*args[1:],**kwargs)
elif isinstance(args[0],(int,numpy.short,str)) \
and isinstance(args[1],str):
# location ID and APOGEE ID (loc ID can be string for 1m sample)
if kwargs.get('apStar',False):
spec, hdr= apread.apStar(args[0],args[1],header=True,
ext=kwargs.pop('ext',1))
spec= spec[numpy.amin([kwargs.pop('apStarIndx',1),
len(spec)-1])]
else: #aspcapStar
spec, hdr= apread.aspcapStar(args[0],args[1],header=True,
ext=kwargs.pop('ext',1))
lam= 10.**numpy.arange(hdr['CRVAL1'],
hdr['CRVAL1']+len(spec)*hdr['CDELT1'],
hdr['CDELT1'])
return func(lam,spec,*args[2:],**kwargs)
def input_wrapper(*args,**kwargs):
if len(args) >= 2 and isinstance(args[0],(list,numpy.ndarray)) \
and isinstance(args[1],(list,numpy.ndarray)):
# wavelength, spectrum
return func(args[0],args[1],*args[2:],**kwargs)
elif len(args) >= 1 and isinstance(args[0],(list,numpy.ndarray)):
# spectrum on standard re-sampled wavelength grid
lam=apStarWavegrid()
if len(args[0]) == 7214: # Input is on ASPCAP grid
spec= numpy.zeros(len(lam))
spec[322:3242]= args[0][:2920]
spec[3648:6048]= args[0][2920:5320]
spec[6412:8306]= args[0][5320:]
else:
spec= args[0]
return func(lam,spec,*args[1:],**kwargs)
elif isinstance(args[0],(int,numpy.short,str)) \
and isinstance(args[1],str):
# location ID and APOGEE ID (loc ID can be string for 1m sample)
if kwargs.get('apStar',False):
spec, hdr= apread.apStar(args[0],args[1],header=True,
ext=kwargs.pop('ext',1))
spec= spec[numpy.amin([kwargs.pop('apStarIndx',1),
len(spec)-1])]
else: #aspcapStar
spec, hdr= apread.aspcapStar(args[0],args[1],header=True,
ext=kwargs.pop('ext',1))
lam= 10.**numpy.arange(hdr['CRVAL1'],
hdr['CRVAL1']+len(spec)*hdr['CDELT1'],
hdr['CDELT1'])
return func(lam,spec,*args[2:],**kwargs)
def runFinder(ranger):
interestingTargets = []
skippedTargets = []
locationID = 4590
apogeeID = "2M00050265+0116236"
interestingTarget = False
badheader, header = apread.apStar(locationID, apogeeID, ext=0, dr="13", header=True)
skippedTargets.append([locationID, apogeeID])
nvisits = header["NVISITS"]
"""sys.stdout.write("\r{0}\t\t\t".format('Target ' + str(i + 1) + '/' + str(targetCount)))
sys.stdout.flush()"""
positions = []
for visit in range(1, nvisits + 1):
data = apread.apStar(locationID, apogeeID, ext=9, header=False, dr="13")
if nvisits != 1:
ccf = data["CCF"][0][1 + visit]
else:
ccf = data["CCF"][0]
pos = getMaxPositions(ccf, ranger)
r = calcR(ccf)
if (str(pos[0]) != "none") and ((str(pos[1]) != "none")):
interestingTarget = True
# r = calcR(ccf, pos[0], pos[1])
"""elif r < 1.0:
interestingTarget = True"""
positions.append([pos[0], pos[1], r])
# reportPositions(locationID, apogeeID, ranger, positions)
if interestingTarget == True:
interestingTargets.append([locationID, apogeeID])
interestingTarget = False
reportTargets(interestingTargets, ranger, "interestingTargets")
reportTargets(skippedTargets, ranger, "skippedTargets")
del interestingTargets[:]
del skippedTargets[:]
def constructParams(self): ''' Constructs the parameter given the data table provided in HDU9 of the targets apStar file. ''' data = apread.apStar(self.locationID, self.apogeeID, ext=9, header=False) self.modelParamA.constructParams(data) self.modelParamB.constructParams(data) self.maxTeffA = self.modelParamA.teff + 100. self.minTeffA = self.modelParamA.teff - 100. self.teffStepA = 50. self.maxTeffB = self.maxTeffA + 100. self.minTeffB = self.minTeffA - 100. self.teffStepB = self.teffStepA
def grid(passCount, gridParams, minimizedVisitParams):
'''
The binary model fitting grid. This function will fit the targets of the following parameters:
1) Teff of component A
2) Teff of component B
3) Flux Ratio of component B
4) Relative Heliocentric Velocity of Component A
5) Relative Heliocentric Velocity of Component B
After chi2 minimization of the above parameters, the parameters used to get the minimized chi2 value is written into
lists/chi2.lis. The other parameters that were tested on the grid and their corresponding chi2 values can be found
in lists/chi2/FIELD_ID/2M_ID.lis.
:param passCount: [in] The amount of maximum amount of passes the grid will go through
:param gridParams: [in/out] The list of GridParams that contain the targets fitting data (built in runGrid)
:param minimizedVisitParams: [out] All the visits with the minimized chi2 parameters
'''
targetCount = len(gridParams)
tpass = Timer()
tpassSum = 0.0
for j in range(passCount):
tpass.start()
print('-------------PASS ' + str(j+1) + '/' + str(passCount) + '-------------')
ttarget = Timer()
ttargetSum = 0.0
for i in range(targetCount):
locationID = gridParams[i].locationID
apogeeID = gridParams[i].apogeeID
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True)
nvisits = header['NVISITS']
print('Fitting: ' + locationID + ', ' + apogeeID + ', nvisits: ' + str(nvisits))
print('On target: ' + str(i+1) + '/' + str(targetCount))
ttarget.start()
gridParams[i], minimizedVisitParams[i] = bg.targetGrid(gridParams[i], minimizedVisitParams[i], plot=False)
temp = ttarget.end()
ttargetSum+= temp
print('Target run time: ' + str(round(temp, 2)) + str('s'))
temp = tpass.end()
tpassSum+= temp
print('Pass run time: ' + str(round(temp, 2)) + str('s'))
print('Average target run time: ' + str(round(ttargetSum/targetCount, 2)) + str('s'))
print('Average pass run time: ' + str(round(tpassSum/passCount, 2)) + str('s'))
def get_spectra_ap(data,ext = 1,indx = None):
"""
Returns apStar spectra and header information for each object specified in data
data: labels for a subset of the APOGEE survey
"""
specs = np.zeros((len(data),7214),dtype=np.int16)
hdrs = {}
goodind = []
badind = []
for i in range(len(data)):
try:
specs[i] = apread.apStar(data['LOCATION_ID'][i],data['APOGEE_ID'][i],ext = ext, header = False, aspcapWavegrid=True)[indx]
goodind.append(i)
except IOError as e:
badind.append(i)
print i,data['CLUSTER'][i],' File missing'
continue
if badind == []:
return specs
if badind != []:
return (specs,(np.array(goodind),))
def getRVs(locationID, apogeeID, visit):
'''
Returns the velocities of the binar components.
:param locationID: The location ID of the binary.
:param apogeeID: The apogee ID of the binary.
:param visit: The visit we are using to test against.
:return: The velocities of the individual binary components in the system.
'''
# Contains the dir that holds martins data (deltaV's)
martin_data = '/Volumes/CoveyData-1/APOGEE_Spectra/Martin/Data/Highly_Likely/rv_tables/'
# Get the Julian Dates, velocity of components A and B (km/s), and residual velocities (km/s)
# TODO: just get the line we want... no need to load the whole file. line = visit
jDates, velA, velB, residual = np.loadtxt(martin_data + str(locationID) + '_' + apogeeID + '_rvs.tbl', skiprows=1, unpack=True)
# Get the master HDU of the binary
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True)
row = -1
# Check if there is only one visit
if (header['NVISITS'] == 1):
return [ velA - velB, velB - velA ]
# Find the correct row from the rvs table
else:
try:
for i in range(header['NVISITS']):
if (int(header['JD' + str(visit)] * 10) == int(jDates[i] * 10)):
row = i
except IndexError:
print('WARNING: rvs table for ' + str(locationID) + ', ' + apogeeID + ' may not have the same visit count.')
pass
if(row == -1):
raise Exception('ERROR: visit not found. Check rvs tables and master HDU of ' + str(locationID) + '_' + apogeeID)
return [ velA[row], velB[row] ]
def input_wrapper(*args, **kwargs):
if len(args) >= 2 and isinstance(args[0],(list,numpy.ndarray)) \
and isinstance(args[1],(list,numpy.ndarray)):
# wavelength, spectrum
return func(args[0], args[1], *args[2:], **kwargs)
elif len(args) >= 1 and isinstance(args[0], (list, numpy.ndarray)):
# spectrum on standard re-sampled wavelength grid
lam = apStarWavegrid()
if len(args[0]) == 7214: # Input is on ASPCAP grid
spec = numpy.zeros(len(lam))
spec[322:3242] = args[0][:2920]
spec[3648:6048] = args[0][2920:5320]
spec[6412:8306] = args[0][5320:]
else:
spec = args[0]
return func(lam, spec, *args[1:], **kwargs)
elif isinstance(args[0],(int,numpy.short,str)) \
and isinstance(args[1],str):
# location ID and APOGEE ID (loc ID can be string for 1m sample)
if kwargs.get('apStar', False):
spec, hdr = apread.apStar(args[0],
args[1],
header=True,
ext=kwargs.pop('ext', 1))
spec = spec[numpy.amin(
[kwargs.pop('apStarIndx', 1),
len(spec) - 1])]
else: #aspcapStar
spec, hdr = apread.aspcapStar(args[0],
args[1],
header=True,
ext=kwargs.pop('ext', 1))
lam = 10.**numpy.arange(hdr['CRVAL1'],
hdr['CRVAL1'] + len(spec) * hdr['CDELT1'],
hdr['CDELT1'])
return func(lam, spec, *args[2:], **kwargs)
def get_spectra(name, red_clump, location):
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'NGC 2682')
red_clump : str
If the red clump stars in rcsample are to be removed, set to 'True'. If all stars are to be used,
set to 'False'.
location : str
If running locally, set to 'personal'. If running on the server, set to 'server'.
Returns
-------
apogee_cluster_data (all stars) or apogee_cluster_data_final (red clumps removed) : structured array
All cluster data from APOGEE
spectra_50 (all stars) or spectra_final (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
spectra_err_50 (all stars) or spectra_err_final (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
good_T (all stars) or T_final (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
#Path, strip spaces in cluster name
if location == 'personal':
path = '/Users/chloecheng/Personal/' + str(name).replace(' ', '') + '.hdf5'
elif location == 'server':
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name).replace(' ', '') + '.hdf5'
#If the data file for this cluster exists, save the data to variables
if glob.glob(path):
if red_clump == 'False':
file = h5py.File(path, 'r')
apogee_cluster_data = file['apogee_cluster_data'][()]
spectra_50 = file['spectra'][()]
spectra_err_50 = file['spectra_errs'][()]
good_T = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name, ' complete.')
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True':
file = h5py.File(path, 'r')
apogee_cluster_data_final = file['apogee_cluster_data'][()]
spectra_final = file['spectra'][()]
spectra_err_final = file['spectra_errs'][()]
T_final = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name, ' complete.')
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
#If the file does not exist, get the data from APOGEE
else:
#Get red clump stars from rcsample
rc_data = rcsample(dr='14')
rc_stars = []
for i in range(len(rc_data)):
#rc_stars.append(rc_data[i][2]) - REMOVE IN FINAL VERSION
rc_stars.append(rc_data[i][2].decode('UTF-8'))
rc_stars = np.array(rc_stars)
#Read in APOGEE catalogue data, removing duplicated stars and replacing ASPCAP with astroNN abundances
apogee_cat = apread.allStar(use_astroNN_abundances=True)
unique_apoids,unique_inds = np.unique(apogee_cat['APOGEE_ID'],return_index=True)
apogee_cat = apogee_cat[unique_inds]
#Read in overall cluster information
cls = afits.open('occam_cluster-DR14.fits')
cls = cls[1].data
#Read in information about cluster members
members = afits.open('occam_member-DR14.fits')
members = members[1].data
#Select all members of a given cluster
cluster_members = (members['CLUSTER']==name) & (members['MEMBER_FLAG']=='GM') #second part of the mask indicates to only use giant stars
member_list = members[cluster_members]
#Find APOGEE entries for that cluster
#numpy.in1d finds the 1D intersection between two lists.
#In this case we're matching using the unique APOGEE ID assigned to each star
#The indices given by numpy.in1d are for the first argument, so in this case the apogee catalogue
cluster_inds = np.in1d((apogee_cat['APOGEE_ID']).astype('U100'),member_list['APOGEE_ID'])
apogee_cluster_data = apogee_cat[cluster_inds]
T = photometric_Teff(apogee_cluster_data)
#Mark red clump stars in the members of the cluster as NaNs
cluster_stars = member_list['APOGEE_ID']
cluster_marked = np.copy(cluster_stars)
for i in range(len(cluster_stars)):
for j in range(len(rc_stars)):
if cluster_stars[i] == rc_stars[j]:
cluster_marked[i] = np.nan
#Get spectra, spectral errors, and bitmask for each star - apStar
#We can use the APOGEE package to read each star's spectrum
#We'll read in the ASPCAP spectra, which have combined all of the visits for each star and removed the spaces between the spectra
number_of_members = len(member_list)
spectra = np.zeros((number_of_members, 7514))
spectra_errs = np.zeros((number_of_members, 7514))
bitmask = np.zeros((number_of_members, 7514))
for s,star in enumerate(apogee_cluster_data):
spectra[s] = apread.aspcapStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=1,header=False,dr='14',aspcapWavegrid=True)
spectra_errs[s] = apread.aspcapStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=2,header=False,dr='14',aspcapWavegrid=True)
bitmask[s] = apread.apStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=3,header=False,dr='14', aspcapWavegrid=True)[1]
#Set all entries in bitmask to integers
bitmask = bitmask.astype(int)
bitmask_flip = np.zeros_like(bitmask)
for i in range(len(spectra)):
for j in range(7514):
if bitmask[i][j] == 0:
bitmask_flip[i][j] = 1
else:
bitmask_flip[i][j] = 0
#Remove empty spectra
full_spectra = []
full_spectra_errs = []
full_bitmask = []
full_T = []
full_stars = []
for i in range(len(spectra)):
if any(spectra[i,:] != 0):
full_spectra.append(spectra[i])
full_spectra_errs.append(spectra_errs[i])
full_bitmask.append(bitmask_flip[i])
full_T.append(T[i])
full_stars.append(i)
full_spectra = np.array(full_spectra)
full_spectra_errs = np.array(full_spectra_errs)
full_bitmask = np.array(full_bitmask)
full_T = np.array(full_T)
full_stars = np.array(full_stars)
full_marked_stars = cluster_marked[full_stars]
#Create array of NaNs to replace flagged values in spectra
masked_spectra = np.empty_like(full_spectra)
masked_spectra_errs = np.empty_like(full_spectra_errs)
masked_spectra[:] = np.nan
masked_spectra_errs[:] = np.nan
#Mask the spectra
for i in range(len(full_spectra)):
for j in range(7514):
if full_bitmask[i][j] != 0:
masked_spectra[i][j] = full_spectra[i][j]
masked_spectra_errs[i][j] = full_spectra_errs[i][j]
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (full_T < 5000)
final_spectra = masked_spectra[good_T_inds]
final_spectra_errs = masked_spectra_errs[good_T_inds]
good_T = full_T[good_T_inds]
apogee_cluster_data = apogee_cluster_data[good_T_inds]
full_bitmask = full_bitmask[good_T_inds]
final_stars = full_marked_stars[good_T_inds]
rgs = (final_stars != 'nan') #Get indices for final red giant stars to be used
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(final_spectra_errs)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j]/final_spectra_errs[i][j] <= 200:
spectra_err_200[i][j] = final_spectra_errs[i][j]
else:
spectra_err_200[i][j] = final_spectra[i][j]/200
#Cut errors with SNR of less than 50
spectra_50 = np.copy(final_spectra)
spectra_err_50 = np.copy(spectra_err_200)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j]/spectra_err_200[i][j] <= 50:
spectra_50[i][j] = np.nan
spectra_err_50[i][j] = np.nan
#Cut red clumps
logg = apogee_cluster_data['LOGG']
apogee_cluster_data_final = apogee_cluster_data[rgs]
spectra_final = spectra_50[rgs]
spectra_err_final = spectra_err_50[rgs]
T_final = good_T[rgs]
bitmask_final = full_bitmask[rgs]
if red_clump == 'False':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data
file['spectra'] = spectra_50
file['spectra_errs'] = spectra_err_50
file['T'] = good_T
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete')
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data_final
file['spectra'] = spectra_final
file['spectra_errs'] = spectra_err_final
file['T'] = T_final
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete')
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
#filename = 'lists/binaries2.dat'
filename = '/Volumes/CoveyData-1/APOGEE_Spectra/APOGEE2_DR13/Bisector/BinaryFinder4/kevin_candidate_list.csv'
locationIDs, apogeeIDs = np.loadtxt(filename, unpack=True, delimiter=',', dtype=str)
targetCount = len(locationIDs)
print(targetCount, 'targets')
locationIDs, apogeeIDs = bf.removeSingle(locationIDs, apogeeIDs, 'kevin_candidate_list')
targetCount = len(locationIDs)
print(targetCount, 'targets')
plt.rcParams["figure.figsize"] = [20.0, 15.0]
for i in range(targetCount):
locationID = locationIDs[i]
apogeeID = apogeeIDs[i]
print(locationID, apogeeID)
badheader, header = apread.apStar(locationID, apogeeID, ext=0, dr='13', header=True)
data = apread.apStar(locationID, apogeeID, ext=9, header=False, dr='13')
nvisits = header['NVISITS']
for visit in range(0, nvisits):
if (nvisits != 1):
ccf = data['CCF'][0][2 + visit]
else:
ccf = data['CCF'][0]
plt.plot(ccf + visit,label= 'Visit: '+str(1+visit))
#axes = plt.gca()
#axes.set_xlim([100,300])
plt.xlabel('CCF Lag',fontsize=15)
def recordTargets(locationIDs, apogeeIDs): ''' With the given field and 2M IDs, record all the BFData :param locationIDs: Field IDs :param apogeeIDs: 2M IDs ''' interestingTargetsr = [] interestingTargetsDualPeak = [] skippedTargets = [] targetCount = len(locationIDs) for i in range(targetCount): locationID = locationIDs[i] apogeeID = apogeeIDs[i] # Get fits files try: badheader, header = apread.apStar(locationID, apogeeID, ext=0, dr='13', header=True) data = apread.apStar(locationID, apogeeID, ext=9, header=False, dr='13') except IOError: skippedTargets.append([locationID, apogeeID]) continue # Calculate r and test for second peak nvisits = header['NVISITS'] positions = [] rRecorded = False dpRecorded = False for visit in range(0, nvisits): if (nvisits != 1): ccf = data['CCF'][0][2 + visit] snr = header['SNRVIS' + str(1+visit)] else: ccf = data['CCF'][0] snr = header['SNRVIS1'] max1, max2, peakhDiff = getMaxPositions(ccf) # Calculate r values r = [] # Calculate r by reflecting about the highest peak ccfCount = len(ccf) if max2 != np.nan: peakLoc = max(max1, max2) else: peakLoc = max1 try: if (ccfCount > peakLoc*2): r.append(calcR(ccf, pos2=peakLoc*2, peakLoc=peakLoc)) else: r.append(calcR(ccf, pos1=2*peakLoc-ccfCount+1, pos2=ccfCount-1, peakLoc=peakLoc)) except: r.append(np.nan) print(locationID, apogeeID) # calculate r by reflecting about the center (201) for cut in range(20): r.append(calcR(ccf, pos1=cut*10+1, pos2=(401 - (cut * 10)), peakLoc=201)) if (r[0] < 7.0) and (rRecorded is False): rRecorded = True interestingTargetsr.append([locationID, apogeeID]) if (np.isnan(max2) == False) and (dpRecorded is False): dpRecorded = True interestingTargetsDualPeak.append([locationID, apogeeID]) positions.append([snr, max1, max2, peakhDiff, r]) recordBFData(locationID, apogeeID, positions) recordTargetsCSV(interestingTargetsr, 'interestingTargetsr') recordTargetsCSV(interestingTargetsDualPeak, 'interestingTargetsDualPeak') recordTargetsCSV(skippedTargets, 'skippedTargets')
'CCF_372', 'CCF_373', 'CCF_374', 'CCF_375', 'CCF_376', 'CCF_377',
'CCF_378', 'CCF_379', 'CCF_380', 'CCF_381', 'CCF_382', 'CCF_383',
'CCF_384', 'CCF_385', 'CCF_386', 'CCF_387', 'CCF_388', 'CCF_389',
'CCF_390', 'CCF_391', 'CCF_392', 'CCF_393', 'CCF_394', 'CCF_395',
'CCF_396', 'CCF_397', 'CCF_398', 'CCF_399', 'CCF_400', 'CCF_401'
]
writer = csv.DictWriter(output, delimiter=',', fieldnames=names)
writer.writeheader()
for i in range(len(locationIDs)):
locationID = locationIDs[i]
apogeeID = apogeeIDs[i]
if locationID != 1:
header = apread.apStar(locationID, apogeeID, ext=0, header=True)
Data = apread.apStar(locationID, apogeeID, ext=9, header=False)
nvisits = header[1]['NVISITS']
for visit in range(0, nvisits):
snr = header[1]['SNRVIS' + str(visit + 1)]
if (nvisits != 1):
CCF = Data['CCF'][0][2 + visit]
else:
CCF = Data['CCF'][0]
writer.writerow({
'Location_ID': locationID,
'Apogee_ID': apogeeID,
'SNR': snr,
'CCF_1': CCF[j],
'CCF_2': CCF[j + 1],
running = True
visitSum = 0.0
while running:
for i in range(3):
if procs[i].is_alive() == False:
badheader, header = apread.apStar(locationIDs[i], apogeeIDs[i], ext=0, header=True, dr='13')
nvisits = header['NVISITS']
visitSum+= timers[i].end() / nvisits
if procs[0].is_alive() == False and procs[1].is_alive() == False and procs[2].is_alive() == False:
running = False
time.sleep(2)'''
timer = Timer()
visitSum = 0.0
for i in range(targetCount):
badheader, header = apread.apStar(locationIDs[i], apogeeIDs[i], ext=0, header=True, dr='13')
nvisits = header['NVISITS']
timer.start()
runTarget(targets[i])
visitSum+= timer.end() / nvisits
print(visitSum)
print('avg visit time:', visitSum/targetCount)
'''
done=4
print('------------Target ' + str(done + 1) + '/' + str(targetCount) + ' ------------')
while targetQueue.empty() == False:
# runTarget(targetQueue.get_nowait())
for i in range(4):
if procs[i].is_alive() == False:
del(procs[i])
def runTarget(gridParam):
locationID = gridParam.locationID
apogeeID = gridParam.apogeeID
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True, dr='13')
specs = apread.apStar(locationID, apogeeID, ext=1, header=False, dr='13')
specerrs = apread.apStar(locationID, apogeeID, ext=2, header=False, dr='13')
nvisits = header['NVISITS']
gridParamVists = []
for visit in range(1, nvisits + 1):
print('Visit ' + str(visit) + '/' + str(nvisits))
if nvisits is 1:
spec = specs
specerr = specerrs
else:
spec = specs[1 + nvisits]
specerr = specerrs[ 1+ nvisits]
aspec= np.reshape(spec,(1, len(spec)))
aspecerr= np.reshape(specerr,(1, len(specerr)))
cont= spec / continuum.fit(aspec, aspecerr, type='aspcap')[0]
conterr = specerr / continuum.fit(aspec, aspecerr, type='aspcap')[0]
gridParam = GridParam(locationID, apogeeID)
gridParam.constructParams()
gridParam.spec = bm.shiftFlux(cont, header['VHELIO' + str(visit)])
gridParam.specErr = bm.shiftFlux(conterr, header['VHELIO' + str(visit)])
gridParam.getRVs(visit)
gridParam.visit = visit
nSteps = 200
sampler = MCMC(gridParam, nSteps=nSteps)
circular_samples = sampler.chain[:, :, :].reshape((-1, 5))
results = np.asarray(list(map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),
zip(*np.percentile(circular_samples, [16, 50, 84], axis=0)))))
fig, ax = plt.subplots(5, 1, sharex='col')
for i in range(5):
for j in range(len(sampler.chain[:, 0, i])):
ax[i].plot(np.linspace(0, nSteps, num=nSteps), sampler.chain[j, :, i], 'k', alpha=0.2)
ax[i].plot(np.linspace(0, nSteps, num=nSteps) , np.ones(nSteps)*results[i][0], 'b', lw=2)
fig.set_figheight(20)
fig.set_figwidth(15)
if not os.path.exists('plots/walker/' + str(locationID) + '/' + str(apogeeID) + '/'):
os.makedirs('plots/walker/' + str(locationID) + '/' + str(apogeeID) + '/')
plt.savefig('plots/walker/' + str(locationID) + '/' + str(apogeeID) + '/' + str(visit) + '.png')
plt.close('all')
gridParam.modelParamA.teff = results[0][0]
gridParam.modelParamB.teff = results[1][0]
gridParam.modelParamB.fluxRatio = results[2][0]
gridParam.modelParamA.rv = results[3][0]
gridParam.modelParamB.rv = results[4][0]
gridParam.chi2 = -1.0 * fitModel(None, gridParam, plot=True)
gridParamVists.append(gridParam)
if not os.path.exists('lists/chi2/' + str(locationID) + '/'):
os.makedirs('lists/chi2/' + str(locationID) + '/')
filename = 'lists/chi2/' + str(locationID) + '/' + str(apogeeID) + '.tbl'
writeGridToFile(gridParamVists, filename=filename)
binModel, peak[i][j], residuals[i][j] = mg.binaryModelGen(locationID, apogeeID, params, visit, plot=True);
# Get the max peak value
peakMax = np.argmax(peak)
# Create fit params array to return
fitParams = np.full((6, 2), 0.)
max1 = int(peakMax / len(rangeTeff))
max2 = int(peakMax % len(rangeTeff))
fitParams = [ [rangeTeff[max1], rangeTeff[max2]],
[logg, logg],
[metals, metals],
[am, am],
[nm, nm],
[cm, cm] ]
binPlot.plotTeffGrid(locationID, apogeeID, visit, rangeTeff, peak, 'CCF');
binPlot.plotTeffGrid(locationID, apogeeID, visit, rangeTeff, residuals, 'Residual');
return fitParams
locationIDs, apogeeIDs = np.loadtxt('binaries.dat', unpack=True, delimiter=',', dtype=str)
for i in range(len(locationIDs)):
print('Fitting: ' + locationIDs[i] + ', ' + apogeeIDs[i])
badheader, header = apread.apStar(int(locationIDs[i]), apogeeIDs[i], ext=0, header=True)
printParams(binaryGridFit(int(locationIDs[i]), apogeeIDs[i], params, visit + 1, rangeTeff))
for visit in range(header['NVISITS']):
print('---------------VISIT ' + str(visit + 1) + '---------------')
printParams(binaryGridFit(int(locationIDs[i]), apogeeIDs[i], params, visit + 1, rangeTeff))
import apogee.tools.read as apread import apogee.spec.plot as splot import matplotlib.pyplot as plt import numpy as np from scipy.stats.stats import pearsonr teff1 = 5000. teff2 = 5250. logg = 4.7 metals = am = nm = cm = 0. locationID = 4611 apogeeID = '2M05350392-0529033' spec, hdr= apread.apStar(locationID,apogeeID,ext=1) # mspec1= ferre.interpolate(teff1,logg,metals,am,nm,cm) # mspec2= ferre.interpolate(teff2,logg,metals,am,nm,cm) # print(mspec1.shape) # print(pearsonr(mspec1, mspec2)) spec[0][spec[0] <= 0.] = np.nan nan_vals = [i for i in range(len(spec[0])) if np.isnan(spec[0][i])] nan_ranges = [(nan_vals[i] + 1, nan_vals[i+1]) for i in range(len(nan_vals) - 1) if nan_vals[i+1]!=nan_vals[i]+1] print(nan_ranges) # print(spec[0][0:nan_ranges[0][0]]) '''ms1 = np.array(mspec1[np.isnan(mspec1) == False])
def get_spectra(name, red_clump, location):
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'PJ_26')
red_clump : bool
If the red clump stars in rcsample are to be removed, set to True. If all stars are to be used,
set to False.
Returns
-------
cluster_data_full (all stars) or cluster_data (red clumps removed) : structured array
All cluster data from APOGEE
cluster_spectra_full (all stars) or cluster_spectra (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
cluster_spectra_errs_full (all stars) or cluster_spectra_errs (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
cluster_T_full (all stars) or cluster_T (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
if location == 'personal':
path = '/Users/chloecheng/Personal/' + str(name) + '.hdf5'
elif location == 'server':
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name) + '.hdf5'
#If the data file for this cluster exists, save the data to variables
if glob.glob(path):
if red_clump == 'False':
file = h5py.File(path, 'r')
cluster_data_full = file['apogee_cluster_data'][()]
cluster_spectra_full = file['spectra'][()]
cluster_spectra_errs_full = file['spectra_errs'][()]
cluster_T_full = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name, ' complete.')
return cluster_data_full, cluster_spectra_full, cluster_spectra_errs_full, cluster_T_full, full_bitmask
elif red_clump == 'True':
file = h5py.File(path, 'r')
cluster_data = file['apogee_cluster_data'][()]
cluster_spectra = file['spectra'][()]
cluster_spectra_errs = file['spectra_errs'][()]
cluster_T = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name, ' complete.')
return cluster_data, cluster_spectra, cluster_spectra_errs, cluster_T, bitmask_final
#If the file does not exist
else:
#Get red clump stars from rcsample
rc_data = rcsample(dr='14')
rc_stars = []
for i in range(len(rc_data)):
if location == 'personal':
rc_stars.append(rc_data[i][2])
elif location == 'server':
rc_stars.append(rc_data[i][2].decode('UTF-8'))
rc_stars = np.array(rc_stars)
#Read in PJ catalogue data
if location == 'personal':
apogee_cluster_data = np.load(
'/Users/chloecheng/Personal/published_clusters.npy')
elif location == 'server':
apogee_cluster_data = np.load(
'/geir_data/scr/ccheng/AST425/Personal/published_clusters.npy')
#Get temperatures
#T = photometric_Teff(apogee_cluster_data)
T = apogee_cluster_data['TEFF']
#Get spectra for each star
number_of_members = 360
spectra = np.zeros((number_of_members, 7514))
spectra_errs = np.zeros((number_of_members, 7514))
bitmask = np.zeros((number_of_members, 7514))
missing_spectra = []
stars = []
for s, star in enumerate(apogee_cluster_data):
loc = star['FIELD'].decode('utf-8')
apo = star['APOGEE_ID'].decode('utf-8')
stars.append(apo)
try:
spectra[s] = apread.aspcapStar(
loc,
apo,
ext=1,
header=False,
dr='16',
aspcapWavegrid=True,
telescope=star['TELESCOPE'].decode('utf-8'))
spectra_errs[s] = apread.aspcapStar(
loc,
apo,
ext=2,
header=False,
dr='16',
aspcapWavegrid=True,
telescope=star['TELESCOPE'].decode('utf-8'))
bitmask[s] = apread.apStar(
loc,
apo,
ext=3,
header=False,
dr='16',
aspcapWavegrid=True,
telescope=star['TELESCOPE'].decode('utf-8'))[1]
#If the spectrum is missing, set bitmask to value that will be removed
except OSError:
bitmask[s] = -1.0
missing_spec.append(s)
print('missing ', star['APOGEE_ID'].decode("utf-8"))
#Mark red clump stars
PJ_stars = np.array(stars)
PJ_marked = np.copy(PJ_stars)
for i in range(len(PJ_stars)):
for j in range(len(rc_stars)):
if PJ_stars[i] == rc_stars[j]:
PJ_marked[i] = np.nan
#Set all entries in bitmask to integers
bitmask = bitmask.astype(int)
bitmask_flip = np.zeros_like(bitmask)
for i in range(len(spectra)):
for j in range(7514):
if bitmask[i][j] == 0:
bitmask_flip[i][j] = 1
else:
bitmask_flip[i][j] = 0
#Remove empty spectra
full_spectra = []
full_spectra_errs = []
full_bitmask = []
full_stars = []
full_T = []
for i in range(len(spectra)):
if any(spectra[i, :] != 0):
full_spectra.append(spectra[i])
full_spectra_errs.append(spectra_errs[i])
full_bitmask.append(bitmask_flip[i])
full_stars.append(i)
full_T.append(T[i])
full_spectra = np.array(full_spectra)
full_spectra_errs = np.array(full_spectra_errs)
full_bitmask = np.array(full_bitmask)
full_stars = np.array(full_stars)
full_T = np.array(full_T)
full_marked_stars = PJ_marked[full_stars]
#Create array of nans to replace flagged values in spectra
masked_spectra = np.empty_like(full_spectra)
masked_spectra_errs = np.empty_like(full_spectra_errs)
masked_spectra[:] = np.nan
masked_spectra_errs[:] = np.nan
#Mask the spectra
for i in range(len(full_spectra)):
for j in range(7514):
if full_bitmask[i][j] != 0:
masked_spectra[i][j] = full_spectra[i][j]
masked_spectra_errs[i][j] = full_spectra_errs[i][j]
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (full_T < 5000)
final_spectra = masked_spectra[good_T_inds]
final_spectra_errs = masked_spectra_errs[good_T_inds]
good_T = full_T[good_T_inds]
apogee_cluster_data = apogee_cluster_data[good_T_inds]
full_bitmask = full_bitmask[good_T_inds]
final_stars = full_marked_stars[good_T_inds] #ADDED
rgs = (final_stars != 'nan') #ADDED
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(final_spectra_errs)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j] / final_spectra_errs[i][j] <= 200:
spectra_err_200[i][j] = final_spectra_errs[i][j]
else:
spectra_err_200[i][j] = final_spectra[i][j] / 200
#Cut errors with SNR of less than 50
spectra_50 = np.copy(final_spectra)
spectra_err_50 = np.copy(spectra_err_200)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j] / spectra_err_200[i][j] <= 50:
spectra_50[i][j] = np.nan
spectra_err_50[i][j] = np.nan
#Separate out individual clusters
cluster_ids = apogee_cluster_data['CLUSTER_ID']
PJ_26 = []
PJ_95 = []
PJ_471 = []
PJ_162 = []
PJ_398 = []
PJ_151 = []
PJ_230 = []
PJ_939 = []
PJ_262 = []
PJ_289 = []
PJ_359 = []
PJ_396 = []
PJ_899 = []
PJ_189 = []
PJ_574 = []
PJ_641 = []
PJ_679 = []
PJ_1976 = []
PJ_88 = []
PJ_1349 = []
PJ_1811 = []
for i in range(len(apogee_cluster_data)):
if cluster_ids[i] == 26:
PJ_26.append(i)
elif cluster_ids[i] == 95:
PJ_95.append(i)
elif cluster_ids[i] == 471:
PJ_471.append(i)
elif cluster_ids[i] == 162:
PJ_162.append(i)
elif cluster_ids[i] == 398:
PJ_398.append(i)
elif cluster_ids[i] == 151:
PJ_151.append(i)
elif cluster_ids[i] == 230:
PJ_230.append(i)
elif cluster_ids[i] == 939:
PJ_939.append(i)
elif cluster_ids[i] == 262:
PJ_262.append(i)
elif cluster_ids[i] == 289:
PJ_289.append(i)
elif cluster_ids[i] == 359:
PJ_359.append(i)
elif cluster_ids[i] == 396:
PJ_396.append(i)
elif cluster_ids[i] == 899:
PJ_899.append(i)
elif cluster_ids[i] == 189:
PJ_189.append(i)
elif cluster_ids[i] == 574:
PJ_574.append(i)
elif cluster_ids[i] == 641:
PJ_641.append(i)
elif cluster_ids[i] == 679:
PJ_679.append(i)
elif cluster_ids[i] == 1976:
PJ_1976.append(i)
elif cluster_ids[i] == 88:
PJ_88.append(i)
elif cluster_ids[i] == 1349:
PJ_1349.append(i)
elif cluster_ids[i] == 1811:
PJ_1811.append(i)
cluster_dict = {
'PJ_26': PJ_26,
'PJ_95': PJ_95,
'PJ_471': PJ_471,
'PJ_162': PJ_162,
'PJ_398': PJ_398,
'PJ_151': PJ_151,
'PJ_230': PJ_230,
'PJ_939': PJ_939,
'PJ_262': PJ_262,
'PJ_289': PJ_289,
'PJ_359': PJ_359,
'PJ_396': PJ_396,
'PJ_899': PJ_899,
'PJ_189': PJ_189,
'PJ_574': PJ_574,
'PJ_641': PJ_641,
'PJ_679': PJ_679,
'PJ_1976': PJ_1976,
'PJ_88': PJ_88,
'PJ_1349': PJ_1349,
'PJ_1811': PJ_1811
}
cluster_data_full = apogee_cluster_data[cluster_dict[name]]
cluster_spectra_full = spectra_50[cluster_dict[name]]
cluster_spectra_errs_full = spectra_err_50[cluster_dict[name]]
cluster_T_full = good_T[cluster_dict[name]]
#Cut red clump stars
cluster_rgs = rgs[cluster_dict[name]]
cluster_data = cluster_data_full[cluster_rgs]
cluster_spectra = cluster_spectra_full[cluster_rgs]
cluster_spectra_errs = cluster_spectra_errs_full[cluster_rgs]
cluster_T = cluster_T_full[cluster_rgs]
bitmask_final = full_bitmask[rgs]
if red_clump == 'False':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = cluster_data_full
file['spectra'] = cluster_spectra_full
file['spectra_errs'] = cluster_spectra_errs_full
file['T'] = cluster_T_full
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete')
return cluster_data_full, cluster_spectra_full, cluster_spectra_errs_full, cluster_T_full, full_bitmask
elif red_clump == 'True':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = cluster_data
file['spectra'] = cluster_spectra
file['spectra_errs'] = cluster_spectra_errs
file['T'] = cluster_T
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete')
return cluster_data, cluster_spectra, cluster_spectra_errs, cluster_T, bitmask_final
import apogee.tools.read as apread
import apogee.spec.plot as splot
from apogee.modelspec import ferre
import matplotlib.pyplot as plt
import apogee.spec.plot as splot
import numpy as np
locationIDs, apogeeIDs = np.loadtxt("lists/binaries3.dat", unpack=True, delimiter=",", dtype=str)
targetCount = len(locationIDs)
visit = 1
f = open("chipRanges.txt", "w")
for i in range(len(locationIDs)):
locationID = locationIDs[i]
apogeeID = apogeeIDs[i]
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True)
nvisits = header["NVISITS"]
print("Getting chip ranges of: " + locationIDs[i] + ", " + apogeeIDs[i] + ", nvisits: " + str(nvisits))
print(str(i + 1) + "/" + str(targetCount) + " targets completed")
spec = apread.apStar(locationID, apogeeID, ext=1, header=False)[2]
for visit in spec:
for chip in visit:
print(chip)
nan_vals_spec = np.where(chip == 0)[0]
nan_ranges_spec = [
(nan_vals_spec[i] + 1, nan_vals_spec[i + 1])
for i in range(len(nan_vals_spec) - 1)
if nan_vals_spec[i + 1] != nan_vals_spec[i] + 1
]
print(nan_ranges_spec)
f.write(
from apogee.spec import continuum import apogee.spec.plot as splot import matplotlib.pyplot as plt import BinModelGen as bm import BinPlot from BinaryGrid import calcChi2 from GridParam import GridParam from Timer import Timer locationID = 4586 apogeeID = "2M03441568+3231282" restLambda = splot.apStarWavegrid() visit = 1 badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True) gridParam = GridParam(locationID, apogeeID) gridParam.constructParams() spec = apread.apStar(locationID, apogeeID, ext=1, header=False)[1 + visit] specerr = apread.apStar(locationID, apogeeID, ext=2, header=False)[1 + visit] # plt.plot(restLambda, spec) aspec = np.reshape(spec, (1, len(spec))) aspecerr = np.reshape(specerr, (1, len(specerr))) cont = spec / continuum.fit(aspec, aspecerr, type="aspcap")[0] conterr = specerr / continuum.fit(aspec, aspecerr, type="aspcap")[0] shiftedSpec = bm.shiftFlux(cont, header["VHELIO" + str(visit)]) conterr = bm.shiftFlux(cont, header["VHELIO" + str(visit)]) BinPlot.plotDeltaVCheck(
def get_combined_spectrum_single_object(apogee_id,
catalog=None,
save_local=False):
'''
apogee_id should be a byte-like object; i.e b'2M13012770+5754582'
This downloads a single combined spectrum and the associated error array,
and it normalizes both.
'''
# read in the allStar catalog if you haven't already
if catalog is None:
catalog, fibers = read_apogee_catalog()
# Set up bad pixel mask
badcombpixmask = bitmask.badpixmask() + 2**bitmask.apogee_pixmask_int(
"SIG_SKYLINE")
_COMBINED_INDEX = 1
msk = np.where(catalog['APOGEE_ID'] == apogee_id)[0]
if not len(msk):
raise ValueError(
'the desired Apogee ID was not found in the allStar catalog.')
field = catalog['FIELD'][msk[0]].decode()
ap_id = apogee_id.decode()
loc_id = catalog['LOCATION_ID'][msk[0]]
if loc_id == 1:
temp1 = apread.apStar(field,
ap_id,
ext=1,
header=False,
aspcapWavegrid=True)
temp2 = apread.apStar(field,
ap_id,
ext=2,
header=False,
aspcapWavegrid=True)
temp3 = apread.apStar(field,
ap_id,
ext=3,
header=False,
aspcapWavegrid=True)
else:
temp1 = apread.apStar(loc_id,
ap_id,
ext=1,
header=False,
aspcapWavegrid=True)
temp2 = apread.apStar(loc_id,
ap_id,
ext=2,
header=False,
aspcapWavegrid=True)
temp3 = apread.apStar(loc_id,
ap_id,
ext=3,
header=False,
aspcapWavegrid=True)
if temp1.shape[0] > 6000:
spec = temp1
specerr = temp2
mask = temp3
else:
spec = temp1[_COMBINED_INDEX]
specerr = temp2[_COMBINED_INDEX]
mask = temp3[_COMBINED_INDEX]
# Inflate uncertainties for bad pixels
specerr[(mask & (badcombpixmask)) != 0] += 100 * np.mean(
spec[np.isfinite(spec)])
# Inflate pixels with high SNR to 0.5
highsnr = spec / specerr > 200.
specerr[highsnr] = 0.005 * np.fabs(spec[highsnr])
# Continuum-normalize
cont = utils.get_apogee_continuum(wavelength=wavelength,
spec=spec,
spec_err=specerr,
cont_pixels=cont_pixels)
spec /= cont
specerr /= cont
specerr[highsnr] = 0.005
if save_local:
np.savez('spectra/combined/spectrum_ap_id_' + str(apogee_id.decode()) +
'_.npz',
spectrum=spec,
spec_err=specerr)
return spec, specerr
apogeeID = '2M05350392-0529033' '''spec, hdr = apread.apStar(locationID,apogeeID,ext=1) err, hdr = apread.apStar(locationID, apogeeID, ext=2) # Clean the zero's spec[0][spec[0] <= 0.] = np.nan nan_vals = [i for i in range(len(spec[0])) if np.isnan(spec[0][i])] nan_ranges = [(nan_vals[i] + 1, nan_vals[i+1]) for i in range(len(nan_vals) - 1) if nan_vals[i+1]!=nan_vals[i]+1] print(nan_ranges) specChunk = spec[0][nan_ranges[0][0]:nan_ranges[0][1]] errChunk = err[0][nan_ranges[0][0]:nan_ranges[0][1]]''' aspec= apread.apStar(locationID, apogeeID, ext=1, header=False)[1] aspecerr= apread.apStar(locationID, apogeeID, ext=2, header=False)[1] # Input needs to be (nspec,nwave) aspec= np.reshape(aspec,(1,len(aspec))) aspecerr= np.reshape(aspecerr,(1,len(aspecerr))) # Fit the continuum from apogee.spec import continuum cont= continuum.fit(aspec,aspecerr,type='aspcap') cspec= apread.aspcapStar(locationID, apogeeID,ext=1,header=False) import apogee.spec.plot as splot splot.waveregions(aspec[0]/cont[0]) splot.waveregions(cspec,overplot=True) '''params = ferre.fit(locationID, apogeeID, teff=teff1, fixteff=True,
# This is for VS code only. import vsEnvironSetup vsEnvironSetup.setVariables() import apogee.tools.read as apread import apogee.spec.plot as splot import matplotlib.pyplot as plt import numpy as np locationID = 4611 apogeeID = '2M05350392-0529033' rMin, rMax = 16740., 16820. restLambda = splot.apStarWavegrid() ind = np.argwhere(np.logical_and(restLambda > rMin, restLambda < rMax)) rMin, rMax = ind[0], ind[-1] # Get the continuum-normalized spectrum cspec = apread.aspcapStar(locationID, apogeeID, ext=1, header=False) # Get visit 1 spec = apread.apStar(locationID, apogeeID, ext=1, header=False)[3] spec[np.isnan(spec)] = 0. specNorm = spec[rMin:rMax] / spec[rMin:rMax].max(axis=0) # compare plt.plot(restLambda[rMin:rMax], cspec[rMin:rMax]) plt.plot(restLambda[rMin:rMax], specNorm) plt.draw() plt.show()
def targetGrid(gridParam, minimizedVisitParams, plot=True):
'''
The grid tests against ranging effective temperatures for both stars and the flux ratio of the
secondary component. This is done by target.
:param gridParam: [in/out] The GridParam of the target
:param gridRes: [out] The visits that have the same paramters as the minimized chi2 visit
:param plot: [in] If true makes plots to see intermediate steps (default=True)
'''
locationID = gridParam.locationID
apogeeID = gridParam.apogeeID
badheader, header = apread.apStar(locationID, apogeeID, ext=0, header=True)
specs = apread.apStar(locationID, apogeeID, ext=1, header=False)
specerrs = apread.apStar(locationID, apogeeID, ext=2, header=False)
nvisits = header['NVISITS']
# chi2 = np.full((nvisits, nrangeTeffA, nrangeTeffB, nrangeFluxRatio), -1.)
#chi2 = np.full((nvisits, nrangeTeffA, nrangeTeffB, nrangeFluxRatio, nrangeRVA, nrangeRVB), -1.)
ipg = ferre.Interpolator(lib='GK')
ipf = ferre.Interpolator(lib='F')
# Create file to store all the chi2 values
path = 'lists/all_chi2/' + str(locationID) + '/'
if not os.path.exists(path):
os.makedirs(path)
fn = open(path + apogeeID + '.lis', 'w')
fn.write(gridParam.toStringHeader())
timer = Timer()
timeSum = 0.0
allChi2 = []
visitGridParamsBuffer = []
for visit in range(1, nvisits + 1):
timer.start()
if (nvisits != 1):
spec = specs[1+visit]
specerr = specerrs[1+visit]
else:
spec = specs
specerr = specerrs
if (len(minimizedVisitParams) == 0):
gridParam = GridParam(locationID, apogeeID)
gridParam.constructParams()
gridParam.getRVs(visit)
else:
gridParam = minimizedVisitParams[visit - 1]
visitGridParamsBuffer.append(gridParam)
# Prepare grid ranges
rangeTeffA = np.arange(gridParam.minTeffA, gridParam.maxTeffA, gridParam.teffStepA)
rangeTeffB = np.arange(gridParam.minTeffB, gridParam.maxTeffB, gridParam.teffStepB)
rangeFluxRatio = np.arange(gridParam.minFluxRatio, gridParam.maxFluxRatio, gridParam.fluxStep)
rangeRVA = np.arange(gridParam.minRVA, gridParam.maxRVA, gridParam.rvAStep)
rangeRVB = np.arange(gridParam.minRVB, gridParam.maxRVB, gridParam.rvBStep)
nrangeTeffA = len(rangeTeffA)
nrangeTeffB = len(rangeTeffB)
nrangeFluxRatio = len(rangeFluxRatio)
nrangeRVA =len(rangeRVA)
nrangeRVB =len(rangeRVB)
chi2 = np.full((nrangeTeffA, nrangeTeffB, nrangeFluxRatio, nrangeRVA, nrangeRVB), -1.)
print('Visit: ' + str(visit) ,'Grid dimensions: ' + str(chi2.shape))
# Prep Spectra
aspec= np.reshape(spec,(1, len(spec)))
aspecerr= np.reshape(specerr,(1, len(specerr)))
cont= spec / continuum.fit(aspec, aspecerr, type='aspcap')[0]
conterr = specerr / continuum.fit(aspec, aspecerr, type='aspcap')[0]
shiftedSpec = bm.shiftFlux(cont, header['VHELIO' + str(visit)])
# Run grid
for i in range(nrangeTeffA):
gridParam.modelParamA.teff = rangeTeffA[i]
componentA = bm.genComponent(gridParam.modelParamA, ipf, ipg)
for j in range(nrangeTeffB):
gridParam.modelParamB.teff = rangeTeffB[j]
componentB = bm.genComponent(gridParam.modelParamB, ipf, ipg)
for k in range(nrangeFluxRatio):
gridParam.modelParamB.fluxRatio = rangeFluxRatio[k]
componentBR = componentB * rangeFluxRatio[k]
for l in range(nrangeRVA):
gridParam.modelParamA.rv = rangeRVA[l]
componentAS = bm.shiftFlux(componentA, rangeRVA[l])
for m in range(nrangeRVB):
gridParam.modelParamB.rv = rangeRVB[m]
componentBS = bm.shiftFlux(componentBR, rangeRVB[m])
binaryFlux = bm.combineFlux(componentAS, componentBS)
chi2[i][j][k][l][m] = calcChi2(binaryFlux, shiftedSpec, conterr) / (len(binaryFlux) - 5.0)
gridParam.chi2 = chi2[i][j][k][l][m]
fn.write(gridParam.toString())
if (plot is True):
restLambda = splot.apStarWavegrid()
BinPlot.plotDeltaVCheck(locationID, apogeeID, visit,
[ [ restLambda, binaryFlux, 'blue', 'model' ],
[ restLambda, cont, 'orange', 'unshifted' ],
[ restLambda, shiftedSpec, 'green', 'shifted' ]],
[gridParam.modelParamA.teff,gridParam.modelParamB.teff, gridParam.modelParamB.fluxRatio],
'Delta V Shift', folder='grid_deltaVCheck')
timeSum+=timer.end()
allChi2.append(chi2)
fn.close()
print('Average visit time: ' + str(round(timeSum/nvisits, 2)) + str('s'))
# Get minized values for each visit
indices = None
for i in range(nvisits):
inds = getMinIndicies(allChi2[i])
rangeTeffA = np.arange(visitGridParamsBuffer[i].minTeffA, visitGridParamsBuffer[i].maxTeffA, visitGridParamsBuffer[i].teffStepA)
rangeTeffB = np.arange(visitGridParamsBuffer[i].minTeffB, visitGridParamsBuffer[i].maxTeffB, visitGridParamsBuffer[i].teffStepB)
rangeFluxRatio = np.arange(visitGridParamsBuffer[i].minFluxRatio, visitGridParamsBuffer[i].maxFluxRatio, visitGridParamsBuffer[i].fluxStep)
rangeRVA = np.arange(visitGridParamsBuffer[i].minRVA, visitGridParamsBuffer[i].maxRVA, visitGridParamsBuffer[i].rvAStep)
rangeRVB = np.arange(visitGridParamsBuffer[i].minRVB, visitGridParamsBuffer[i].maxRVB, visitGridParamsBuffer[i].rvBStep)
nrangeTeffA = len(rangeTeffA)
nrangeTeffB = len(rangeTeffB)
nrangeFluxRatio = len(rangeFluxRatio)
nrangeRVA =len(rangeRVA)
nrangeRVB =len(rangeRVB)
visitGridParamsBuffer[i].setParams(i + 1, rangeTeffA[inds[0]], rangeTeffB[inds[1]], rangeFluxRatio[inds[2]],
rangeRVA[inds[3]], rangeRVB[inds[4]], allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]])
if (indices is None):
indices = [i + 1, inds, allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]]]
if (allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]] < indices[2]):
indices = [i + 1, inds, allChi2[i][inds[0]][inds[1]][inds[2]][inds[3]][inds[4]]]
inds = getMinIndicies(allChi2)
gridParam = visitGridParamsBuffer[inds[0]]
return gridParam, visitGridParamsBuffer
def read_batch_of_spectra(batch_count, batch_size=10000):
'''
Download a bunch of *combined* spectra in one go. Set the uncertainties to a large
value in bad pixels, normalize, and save the batch locally.
'''
# read in the catalog catalog
catalog, fibers = read_apogee_catalog()
catalog = catalog[batch_count * batch_size:(batch_count + 1) * batch_size]
fibers = fibers[batch_count * batch_size:(batch_count + 1) * batch_size]
_COMBINED_INDEX = 1
nspec = len(catalog)
spec = np.zeros((nspec, 7214))
specerr = np.zeros((nspec, 7214))
# Set up bad pixel mask
badcombpixmask = bitmask.badpixmask() + 2**bitmask.apogee_pixmask_int(
"SIG_SKYLINE")
# loop through the individual targets
for ii in range(nspec):
field = catalog['FIELD'][ii].decode()
ap_id = catalog['APOGEE_ID'][ii].decode()
loc_id = catalog['LOCATION_ID'][ii]
print('processing target %d with id %s' % (ii, ap_id))
try:
if loc_id == 1:
temp1 = apread.apStar(field,
ap_id,
ext=1,
header=False,
aspcapWavegrid=True)
temp2 = apread.apStar(field,
ap_id,
ext=2,
header=False,
aspcapWavegrid=True)
temp3 = apread.apStar(field,
ap_id,
ext=3,
header=False,
aspcapWavegrid=True)
else:
temp1 = apread.apStar(loc_id,
ap_id,
ext=1,
header=False,
aspcapWavegrid=True)
temp2 = apread.apStar(loc_id,
ap_id,
ext=2,
header=False,
aspcapWavegrid=True)
temp3 = apread.apStar(loc_id,
ap_id,
ext=3,
header=False,
aspcapWavegrid=True)
if temp1.shape[0] > 6000:
spec[ii] = temp1
specerr[ii] = temp2
mask = temp3
else:
spec[ii] = temp1[_COMBINED_INDEX]
specerr[ii] = temp2[_COMBINED_INDEX]
mask = temp3[_COMBINED_INDEX]
# Inflate uncertainties for bad pixels
specerr[ii, (mask & (badcombpixmask)) != 0] += \
100. * np.mean(spec[ii, np.isfinite(spec[ii])])
# Inflate pixels with high SNR to 0.5
highsnr = spec[ii] / specerr[ii] > 200.
specerr[ii, highsnr] = 0.005 * np.fabs(spec[ii, highsnr])
# Continuum-normalize
cont = utils.get_apogee_continuum(wavelength=wavelength,
spec=spec[ii],
spec_err=specerr[ii],
cont_pixels=cont_pixels)
spec[ii] /= cont
specerr[ii] /= cont
specerr[ii, highsnr] = 0.005
except OSError:
print('target could not be found!')
continue
# save spectra
np.savez('spectra/apogee_all_spectra_' + str(batch_count) + '.npz',
wavelength=wavelength,
spectra=spec,
spec_err=specerr,
apogee_id=np.array(catalog["APOGEE_ID"]),
apogee_fiber_id=fibers)
def read_spectra(cluster,teffmin=4000.,teffmax=5000.,cont_type='cannon',
cont_deg=4):
"""
NAME:
read_spectra
PURPOSE:
Read the APOGEE spectra and their errors for stars in a given cluster
INPUT:
cluster - Name of the cluster (name in one of the data files)
teffmin= (4000.) minimum temperature
teffmax= (5000.) maximum temperature
cont_type = ('cannon') type of continuum normalization to perform
cont_deg= (4) degree polynomial to fit for continuum normalization
OUTPUT:
(data, spec, specerr) - (full data structure, spectra [nspec,nlam], spectral uncertainties [nspec,nlam]) nlam=7214 on ASPCAP grid
HISTORY:
2015-08-13 - Written based on some older code - Bovy (UofT)
"""
if cluster.upper() in _GCS:
data= read_meszarosgcdata()
else:
data= read_caldata()
# Cut to just this cluster and temperature range
if 'rc' in cluster.lower():
# Only for NGC 6819
rc= True
cluster= cluster[:-2]
else:
rc= False
data= data[data['CLUSTER'] == cluster.upper()]
data= data[(data['TEFF'] < teffmax)\
*(data['TEFF'] > teffmin)]
if cluster.lower() == 'n6819':
g4CN= good4CN(cluster,data)
g4CN[10]= False # another one, by hand!
if rc:
data= data[True-g4CN] # Just those!
else:
data= data[g4CN] # Just those!
# Load all the spectra
nspec= len(data)
spec= numpy.zeros((nspec,7214))
specerr= numpy.zeros((nspec,7214))
# Setup bad pixel mask
badcombpixmask= bitmask.badpixmask()\
+2**bitmask.apogee_pixmask_int("SIG_SKYLINE")
for ii in range(nspec):
sys.stdout.write('\r'+"Loading spectrum %i / %i ...\r" % (ii+1,nspec))
sys.stdout.flush()
spec[ii]= apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=1,header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii]= apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=2,header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
# Inflate uncertainties for bad pixels
mask= apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=3,header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii,(mask & (badcombpixmask)) != 0]+=\
100.*numpy.mean(spec[ii,True-numpy.isnan(spec[ii])])
# Also inflate pixels with high SNR to 0.5%
highsnr= spec[ii]/specerr[ii] > 200.
specerr[ii,highsnr]= 0.005*numpy.fabs(spec[ii,highsnr])
# Continuum-normalize
cont= continuum.fit(spec[ii],specerr[ii],type=cont_type,deg=cont_deg)
spec[ii]/= cont
specerr[ii]/= cont
specerr[ii,highsnr]= 0.005 # like standard APOGEE reduction
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
return (data,spec,specerr)
def get_spectra(
name, red_clump, location
): ###Function to read the allStar file and get the spectra, correct spectra for
###small and large uncertainties, remove red clump stars
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'NGC 2682')
red_clump : str
If the red clump stars in rcsample are to be removed, set to 'True'. If all stars are to be used,
set to 'False'.
location : str
If running locally, set to 'personal'. If running on the server, set to 'server'.
Returns
-------
apogee_cluster_data (all stars) or apogee_cluster_data_final (red clumps removed) : structured array
All cluster data from APOGEE
spectra_50 (all stars) or spectra_final (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
spectra_err_50 (all stars) or spectra_err_final (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
good_T (all stars) or T_final (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
#Path, strip spaces in cluster name
if location == 'personal': ###If running on my Mac
path = '/Users/chloecheng/Personal/' + str(name).replace(
' ', '') + '.hdf5' ###Path to folder named after cluster
elif location == 'server': ###If running on the server
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name).replace(
' ', '') + '.hdf5' ###Path to cluster folder
#If the data file for this cluster exists, save the data to variables and return them
if glob.glob(path): ###If the file exists
if red_clump == 'False': ###If we're keeping all of the stars, read in the data
file = h5py.File(path, 'r')
apogee_cluster_data = file['apogee_cluster_data'][()]
spectra_50 = file['spectra'][()]
spectra_err_50 = file['spectra_errs'][()]
good_T = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name,
' complete.') ###Notification that this function is done
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True': ###If we're removing the red clumps, read in the data
file = h5py.File(path, 'r')
apogee_cluster_data_final = file['apogee_cluster_data'][()]
spectra_final = file['spectra'][()]
spectra_err_final = file['spectra_errs'][()]
T_final = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name,
' complete.') ###Notification that this function is done
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
#If the file does not exist, get the data from APOGEE
else: ###If the file does not exist
#Get red clump stars from rcsample
rc_data = rcsample(dr='14') ###Get the rcsample data for DR14
rc_stars = [] ###Empty list for the stars
for i in range(len(rc_data)): ###Iterate through the rcsample data
if location == 'personal': ###If running on Mac
rc_stars.append(
rc_data[i][2]) ###Append just the names of the stars
elif location == 'server': ###If running on server
rc_stars.append(
rc_data[i][2].decode('UTF-8')
) ###Append just the names of the stars (decode because on server the names are bitwise for some reason)
rc_stars = np.array(
rc_stars) ###Make list of red clump star names into array
#Read in APOGEE catalogue data, removing duplicated stars and replacing ASPCAP with astroNN abundances
apogee_cat = apread.allStar(
use_astroNN_abundances=True
) ###Read the allStar file, using the astroNN abundances
unique_apoids, unique_inds = np.unique(
apogee_cat['APOGEE_ID'], return_index=True) ###Get the APOGEE IDs
apogee_cat = apogee_cat[unique_inds] ###Get the APOGEE IDs
#Read in overall cluster information
cls = afits.open('occam_cluster-DR14.fits') ###Read in the OCCAM data
cls = cls[1].data ###Get the cluster information
#Read in information about cluster members
members = afits.open(
'occam_member-DR14.fits') ###Read in the OCCAM members data
members = members[1].data ###Get the member information
#Select all members of a given cluster
cluster_members = (members['CLUSTER'] == name) & (
members['MEMBER_FLAG'] == 'GM'
) #second part of the mask indicates to only use giant stars
member_list = members[
cluster_members] ###Make a list of all member stars in the cluster
#Find APOGEE entries for that cluster
#numpy.in1d finds the 1D intersection between two lists.
#In this case we're matching using the unique APOGEE ID assigned to each star
#The indices given by numpy.in1d are for the first argument, so in this case the apogee catalogue
cluster_inds = np.in1d((apogee_cat['APOGEE_ID']).astype('U100'),
member_list['APOGEE_ID']
) ###Get the indices of the cluster members
apogee_cluster_data = apogee_cat[
cluster_inds] ###Get the allStar data for these members
T = photometric_Teff(
apogee_cluster_data
) ###Compute the photometric effective temperature
#Mark red clump stars in the members of the cluster as NaNs
cluster_stars = member_list[
'APOGEE_ID'] ###Get a list of all the names of the member stars in the cluster
cluster_marked = np.copy(
cluster_stars
) ###Create a copy of this list to mark which stars are red clumps
for i in range(len(cluster_stars)
): ###Iterate through all of the stars in the cluster
for j in range(len(
rc_stars)): ###Iterate through all of the rcsample stars
if cluster_stars[i] in rc_stars[
j]: ###If a cluster member is also a member of the rcsample stars
cluster_marked[
i] = np.nan ###Replace the name of that star with a NaN to ignore it
#Get spectra, spectral errors, and bitmask for each star - apStar
#We can use the APOGEE package to read each star's spectrum
#We'll read in the ASPCAP spectra, which have combined all of the visits for each star and removed the spaces between the spectra
number_of_members = len(
member_list) ###Number of members in the cluster
spectra = np.zeros((number_of_members,
7514)) ###Create an empty array to add the spectra
spectra_errs = np.zeros(
(number_of_members,
7514)) ###Create an empty array to add the spectral errors
bitmask = np.zeros((number_of_members,
7514)) ###Create an empty array to add the bitmask
for s, star in enumerate(
apogee_cluster_data): ###Iterate through the allStar data
spectra[s] = apread.aspcapStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=1,
header=False,
dr='14',
aspcapWavegrid=True) ###Get the spectra
spectra_errs[s] = apread.aspcapStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=2,
header=False,
dr='14',
aspcapWavegrid=True) ###Get the spectral errors
bitmask[s] = apread.apStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=3,
header=False,
dr='14',
aspcapWavegrid=True)[1] ###Get the bitmask
#Set all entries in bitmask to integers
bitmask = bitmask.astype(
int) ###Set all entries in the bitmask to integers
bitmask_flip = np.zeros_like(
bitmask
) ###Create an empty array for the bitmask with flipped entries
for i in range(
len(spectra
)): ###Iterate through the number of stars in the cluster
for j in range(7514): ###Iterate through the wavelength range
if bitmask[i][j] == 0: ###If the bitmask entry is set to 0
bitmask_flip[i][j] = 1 ###Set it to 1
else: ###If the bitmask entry is not set to 0
bitmask_flip[i][j] = 0 ###Set it to 0
###I do this part because the unmasked entries are always 0 in the original bitmask but I think before I was maybe adding in other values to include in the mask that may not have necessarily been 1 so I just set all masked bits to 0 and all unmasked bits to 1 (or maybe this just made more sense in my head for masked to be 0 and unmasked to be 1)
#Remove empty spectra
full_spectra = [
] ###Empty list for the spectra sans empty ones, list not array because we don't know how many stars will be eliminated
full_spectra_errs = [
] ###Empty list for the spectral errors sans empty spectra
full_bitmask = [] ###Empty list for bitmask sans empty spectra
full_T = [] ###Empty list for temperatures sans empty spectra
full_stars = [] ###Empty list for indices of stars sans empty spectra
for i in range(len(spectra)): ###Iterate through the number of stars
if any(spectra[i, :] != 0
): ###For all of the rows whose entries are not all 0
full_spectra.append(spectra[i]) ###Append those spectra
full_spectra_errs.append(
spectra_errs[i]) ###Append those spectral errors
full_bitmask.append(
bitmask_flip[i]) ###Append those bitmask rows
full_T.append(T[i]) ###Append those temperatures
full_stars.append(i) ###Append the indices of those stars
full_spectra = np.array(full_spectra) ###Make list into array
full_spectra_errs = np.array(
full_spectra_errs) ###Make list into array
full_bitmask = np.array(full_bitmask) ###Make list into array
full_T = np.array(full_T) ###Make list into array
full_stars = np.array(full_stars) ###Make list into array
full_marked_stars = cluster_marked[
full_stars] ###Use array of stars left to index marked stars so we know which ones are red clump stars
#Create array of NaNs to replace flagged values in spectra
masked_spectra = np.empty_like(
full_spectra
) ###Create an empty array that is the same shape as full_spectra
masked_spectra_errs = np.empty_like(
full_spectra_errs
) ###Create an empty array that is the same shape as full_spectra_errs
masked_spectra[:] = np.nan ###Set all of the entries to NaNs
masked_spectra_errs[:] = np.nan ###Set all of the entries to NaNs
#Mask the spectra
for i in range(
len(full_spectra)): ###Iterate through the number of stars
for j in range(7514): ###Iterate through the wavelength range
if full_bitmask[i][
j] != 0: ###If the bitmask is not 0 (i.e. if the bit is unmasked)
masked_spectra[i][j] = full_spectra[i][
j] ###Retain the value of the unmasked spectra here
masked_spectra_errs[i][j] = full_spectra_errs[i][
j] ###Retain the value of the unmasked spectral errors here
###All of the masked bits that were not captured by this if statement will remain NaNs and will thus be ignored
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (
full_T < 5000
) ###Get the indices of the temperatures that are between 4000K and 5000K
final_spectra = masked_spectra[
good_T_inds] ###Index the spectra to only keep stars that are within the temperature limits
final_spectra_errs = masked_spectra_errs[
good_T_inds] ###Index the spectral errors to only keep stars within Teff limits
good_T = full_T[
good_T_inds] ###Index the temperatures to keep only stars within Teff limits
apogee_cluster_data = apogee_cluster_data[
good_T_inds] ###Index the allStar data to keep stars only within Teff limits
full_bitmask = full_bitmask[
good_T_inds] ###Index the bitmask to keep stars only within Teff limits
final_stars = full_marked_stars[
good_T_inds] ###Index the array of red-clump-marked stars to keep only those within Teff limits
rgs = (final_stars != 'nan'
) #Get indices for final red giant stars to be used
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(
final_spectra_errs
) ###Create an empty array to add corrected spectral errors to - shape will not change, just altering values
for i in range(len(final_spectra)): ###Iterate through the stars
for j in range(7514): ###Iterate through wavelength range
if final_spectra[i][j] / final_spectra_errs[i][
j] <= 200: ###If errors are of a reasonable size
spectra_err_200[i][j] = final_spectra_errs[i][
j] ###Leave them as they are
else: ###If errors are too small
spectra_err_200[i][j] = final_spectra[i][
j] / 200 ###Make them a bit bigger
#Cut errors with SNR of less than 50
spectra_50 = np.copy(
final_spectra
) ###Create a copy of the spectra to cut large error pixels
spectra_err_50 = np.copy(
spectra_err_200
) ###Create a copy of the spectral errors to cut large error pixels
for i in range(len(final_spectra)): ###Iterate through stars
for j in range(7514): ###Iterate through wavelength range
if final_spectra[i][j] / spectra_err_200[i][
j] <= 50: ###If an error is too big
spectra_50[i][
j] = np.nan ###Set the corresponding entry in the spectra to be a NaN, will be ignored
spectra_err_50[i][
j] = np.nan ###Set the corresponding entry in the spectral errors to be a NaN, will be ignored
#Cut red clumps
logg = apogee_cluster_data[
'LOGG'] ###Get the logg values for the cluster (all corrections have been applied)
apogee_cluster_data_final = apogee_cluster_data[
rgs] ###Get the allStar data for the RGB stars only (no red clumps)
spectra_final = spectra_50[
rgs] ###Get the spectra for the RGB stars only
spectra_err_final = spectra_err_50[
rgs] ###Get the spectral errors for the RGB stars only
T_final = good_T[rgs] ###Get the temperatures for the RGB stars only
bitmask_final = full_bitmask[
rgs] ###Get the bitmask for the RGB stars only
if red_clump == 'False': ###If we are looking at all of the stars, save all data before red clumps were cut to file
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data
file['spectra'] = spectra_50
file['spectra_errs'] = spectra_err_50
file['T'] = good_T
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete') ###Notification that this function is done
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True': ###If we are removing the red clump stars, save the data after red clumps cut to file
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data_final
file['spectra'] = spectra_final
file['spectra_errs'] = spectra_err_final
file['T'] = T_final
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete') ###Notification that this function is done
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
def read_spectra(cluster,
teffmin=4000.,
teffmax=5000.,
cont_type='cannon',
cont_deg=4):
"""
NAME:
read_spectra
PURPOSE:
Read the APOGEE spectra and their errors for stars in a given cluster
INPUT:
cluster - Name of the cluster (name in one of the data files)
teffmin= (4000.) minimum temperature
teffmax= (5000.) maximum temperature
cont_type = ('cannon') type of continuum normalization to perform
cont_deg= (4) degree polynomial to fit for continuum normalization
OUTPUT:
(data, spec, specerr) - (full data structure, spectra [nspec,nlam], spectral uncertainties [nspec,nlam]) nlam=7214 on ASPCAP grid
HISTORY:
2015-08-13 - Written based on some older code - Bovy (UofT)
"""
if cluster.upper() in _GCS:
data = read_meszarosgcdata()
else:
data = read_caldata()
# Cut to just this cluster and temperature range
if 'rc' in cluster.lower():
# Only for NGC 6819
rc = True
cluster = cluster[:-2]
else:
rc = False
data = data[data['CLUSTER'] == cluster.upper()]
data= data[(data['TEFF'] < teffmax)\
*(data['TEFF'] > teffmin)]
if cluster.lower() == 'n6819':
g4CN = good4CN(cluster, data)
g4CN[10] = False # another one, by hand!
if rc:
data = data[True - g4CN] # Just those!
else:
data = data[g4CN] # Just those!
# Load all the spectra
nspec = len(data)
spec = numpy.zeros((nspec, 7214))
specerr = numpy.zeros((nspec, 7214))
# Setup bad pixel mask
badcombpixmask= bitmask.badpixmask()\
+2**bitmask.apogee_pixmask_int("SIG_SKYLINE")
for ii in range(nspec):
sys.stdout.write('\r' + "Loading spectrum %i / %i ...\r" %
(ii + 1, nspec))
sys.stdout.flush()
spec[ii] = apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=1,
header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii] = apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=2,
header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
# Inflate uncertainties for bad pixels
mask = apread.apStar(data['LOCATION_ID'][ii],
data['ID'][ii],
ext=3,
header=False,
aspcapWavegrid=True)[_COMBINED_INDEX]
specerr[ii,(mask & (badcombpixmask)) != 0]+=\
100.*numpy.mean(spec[ii,True-numpy.isnan(spec[ii])])
# Also inflate pixels with high SNR to 0.5%
highsnr = spec[ii] / specerr[ii] > 200.
specerr[ii, highsnr] = 0.005 * numpy.fabs(spec[ii, highsnr])
# Continuum-normalize
cont = continuum.fit(spec[ii],
specerr[ii],
type=cont_type,
deg=cont_deg)
spec[ii] /= cont
specerr[ii] /= cont
specerr[ii, highsnr] = 0.005 # like standard APOGEE reduction
sys.stdout.write('\r' + _ERASESTR + '\r')
sys.stdout.flush()
return (data, spec, specerr)