def setUpClass(cls):
np.random.seed(1111)
num = 1000
level = np.array([134, 53, 261, 111])
noise = 0.01 * level
# create standard noise
wave = np.linspace(100, 200, num=num)
flux1 = np.random.normal(level[0], noise[0], num)
flux2 = np.random.normal(level[1], noise[1], num)
flux3 = np.random.normal(level[2], noise[2], num)
flux4 = np.random.normal(level[3], noise[3], num)
# create cosmics
flux1[467] *= 2.5
flux2[197] *= 2.6
flux3[816] *= 2.4
flux4[23] *= 2.8
# create spectral feature
flux1[365] *= 1.40
flux2[365] *= 1.43
flux3[365] *= 1.38
flux4[365] *= 1.39
waves = np.array([wave, wave, wave, wave])
fluxes = np.array([flux1, flux2, flux3, flux4])
vrads = [555., 565., 545., 560.]
wave1, flux1, accepted, rejected = tools.merge_cosmic_clipping(
waves,
fluxes,
sigma=2.0,
base='median',
offset='std',
window=51,
runs=2,
full_output=True)
wave2, flux2 = tools.merge_cosmic_clipping(waves,
fluxes,
vrads=vrads,
sigma=2.0,
base='median',
offset='std',
window=51,
runs=2)
cls.wave1 = wave1
cls.flux1 = flux1
cls.wave2 = wave2
cls.flux2 = flux2
cls.accepted = accepted
cls.rejected = rejected
cls.waves = waves
cls.fluxes = fluxes
cls.vrads = vrads
cls.num = num
cls.avg = np.sum(level) * 1.05
def setUpClass(cls):
np.random.seed(1111)
num = 1000
level = np.array([134, 53, 261, 111])
noise = 0.01 * level
# create standard noise
wave = np.linspace(100,200, num=num)
flux1 = np.random.normal(level[0], noise[0], num)
flux2 = np.random.normal(level[1], noise[1], num)
flux3 = np.random.normal(level[2], noise[2], num)
flux4 = np.random.normal(level[3], noise[3], num)
# create cosmics
flux1[467] *= 2.5
flux2[197] *= 2.6
flux3[816] *= 2.4
flux4[23] *= 2.8
# create spectral feature
flux1[365] *= 1.40
flux2[365] *= 1.43
flux3[365] *= 1.38
flux4[365] *= 1.39
waves = np.array([wave, wave, wave, wave])
fluxes = np.array([flux1, flux2, flux3, flux4])
vrads = [555., 565., 545., 560.]
wave1, flux1, accepted, rejected = tools.merge_cosmic_clipping(waves, fluxes,
sigma=2.0, base='median', offset='std', window=51,
runs=2, full_output=True)
wave2, flux2 = tools.merge_cosmic_clipping(waves, fluxes, vrads=vrads,
sigma=2.0, base='median', offset='std', window=51, runs=2)
cls.wave1 = wave1
cls.flux1 = flux1
cls.wave2 = wave2
cls.flux2 = flux2
cls.accepted = accepted
cls.rejected = rejected
cls.waves = waves
cls.fluxes = fluxes
cls.vrads = vrads
cls.num = num
cls.avg = np.sum(level) * 1.05
def setUpClass(cls):
temp = '/STER/mercator/hermes/%s/reduced/%s_HRF_OBJ_ext_CosmicsRemoved_log_merged_c.fits'
objlist = [('20090619', '237033'), ('20090701', '240226'),
('20090712', '241334'), ('20090712', '241335'),
('20100107', '00268012'), ('20100120', '00272619'),
('20100120', '00272620'), ('20100203', '00273577'),
('20100203', '00273578'), ('20100303', '00275671'),
('20100303', '00275672'), ('20100410', '00281505'),
('20100519', '00284636'), ('20110222', '00334558'),
('20110319', '00336547'), ('20110324', '00339848'),
('20110401', '00342273'), ('20110402', '00342363'),
('20110406', '00342699'), ('20110408', '00342896'),
('20120107', '00391289'), ('20120110', '00391633'),
('20120116', '00392217'), ('20120127', '00393151'),
('20120209', '00394175'), ('20120330', '00399697'),
('20120420', '00404769'), ('20120506', '00406531'),
('20130106', '00445346'), ('20130215', '00452556'),
('20130406', '00457718'), ('20130530', '00474128')]
mergeList = [temp % o for o in objlist]
waves, fluxes = [], []
for ifile in mergeList:
w, f = fits.read_spectrum(ifile)
f = f[(w > 5000) & (w < 7000)]
w = w[(w > 5000) & (w < 7000)]
waves.append(w)
fluxes.append(f)
wave, flux, accepted, rejected = tools.merge_cosmic_clipping(
waves,
fluxes,
sigma=5.0,
base='average',
offset='std',
window=51,
runs=2,
full_output=True)
cls.nspek = len(objlist)
cls.wave = wave
cls.flux = flux
cls.accepted = accepted
cls.rejected = rejected
def setUpClass(cls):
temp = '/STER/mercator/hermes/%s/reduced/%s_HRF_OBJ_ext_CosmicsRemoved_log_merged_c.fits'
objlist = [('20090619','237033'), ('20090701','240226'),
('20090712','241334'), ('20090712','241335'),
('20100107','00268012'), ('20100120','00272619'),
('20100120','00272620'), ('20100203','00273577'),
('20100203','00273578'), ('20100303','00275671'),
('20100303','00275672'), ('20100410','00281505'),
('20100519','00284636'), ('20110222','00334558'),
('20110319','00336547'), ('20110324','00339848'),
('20110401','00342273'), ('20110402','00342363'),
('20110406','00342699'), ('20110408','00342896'),
('20120107','00391289'), ('20120110','00391633'),
('20120116','00392217'), ('20120127','00393151'),
('20120209','00394175'), ('20120330','00399697'),
('20120420','00404769'), ('20120506','00406531'),
('20130106','00445346'), ('20130215','00452556'),
('20130406','00457718'), ('20130530','00474128')]
mergeList = [temp%o for o in objlist]
waves, fluxes = [], []
for ifile in mergeList:
w, f = fits.read_spectrum(ifile)
f = f[(w>5000) & (w<7000)]
w = w[(w>5000) & (w<7000)]
waves.append(w)
fluxes.append(f)
wave, flux, accepted, rejected = tools.merge_cosmic_clipping(waves, fluxes, sigma=5.0,
base='average', offset='std', window=51, runs=2, full_output=True)
cls.nspek = len(objlist)
cls.wave = wave
cls.flux = flux
cls.accepted = accepted
cls.rejected = rejected
def merge_hermes_spectra(objlist, wscalelist=None, **kwargs):
"""
Combines HERMES spectra with sigma clipping to remove cosmics. The input spectra
are given as a list of filenames for the objects and for the wavelength scales.
The output is the wavelength scale of the first object, the merged and sigma clipped
flux, and the adapted header of the object. In this adapted header, the exposure
time is the sum of all individual exposure times, and the observing time is averaged.
Uses the L{ivs.spectra.tools.merge_cosmic_clipping} method to merge the spectra.
>>> data = search('KIC9540226')
>>> objlist = data['filename']
>>> wave, flux, header = merge_hermes_spectra(objlist, wscalelist=None)
@param objlist: list of OBJ or order merged filenames
@param wscalelist: list of wavelengthscale filenames
@param vrads: list of radial velocities (optional)
@param vrad_units: units of the radial velocities
@param sigma: value used for sigma clipping
@param window: window size used in median filter
@param runs: number of iterations through the spectra
@return: The combined spectrum, cosmic clipped. (wave, flux, header)
@rtype: [array, array, dict]
"""
kwargs['full_output'] = False
if wscalelist == None:
#-- Order Merged spectra are straight forward
exptime, bjd = 0, np.zeros(len(objlist))
waves, fluxes = [],[]
for i, ofile in enumerate(objlist):
w, f, h = fits.read_spectrum(ofile, return_header=True)
waves.append(w)
fluxes.append(f)
exptime += h['exptime']
#bjd[i] = h['bjd']
waves, fluxes = np.array(waves), np.array(fluxes)
mwave, mflux = sptools.merge_cosmic_clipping(waves, fluxes, **kwargs)
else:
#-- The OBJ spectra need to be merged order per order
# read all spectra
exptime, bjd = 0, np.zeros(len(objlist))
waves, fluxes = np.zeros((55,4608, len(objlist))), np.zeros((55,4608, len(wscalelist)))
for i, (ofile, wfile) in enumerate(zip(objlist, wscalelist)):
odata = pf.getdata(ofile, 0).T # The flux has format (4608, 55) thus is transposed
oheader = pf.getheader(ofile, 0)
wdata = pf.getdata(wfile, 0)
fluxes[:,:,i] = odata
waves[:,:,i] = wdata
exptime += oheader['exptime']
#bjd[i] = oheader['bjd']
# merge the spectra
mwave, mflux = waves[:,:,0], np.zeros((55, 4608))
for i in range(55):
wave, flux = sptools.merge_cosmic_clipping(waves[i,:,:].T, fluxes[i,:,:].T, **kwargs)
mflux[i] = flux
header = pf.getheader(objlist[0], 0)
header['exptime'] = exptime
#header['bjd'] = np.average(bjd)
return mwave, mflux, header
def merge_hermes_spectra(objlist, wscalelist=None, **kwargs):
"""
Combines HERMES spectra with sigma clipping to remove cosmics. The input spectra
are given as a list of filenames for the objects and for the wavelength scales.
The output is the wavelength scale of the first object, the merged and sigma clipped
flux, and the adapted header of the object. In this adapted header, the exposure
time is the sum of all individual exposure times, and the observing time is averaged.
Uses the L{ivs.spectra.tools.merge_cosmic_clipping} method to merge the spectra.
>>> data = search('KIC9540226')
>>> objlist = data['filename']
>>> wave, flux, header = merge_hermes_spectra(objlist, wscalelist=None)
@param objlist: list of OBJ or order merged filenames
@param wscalelist: list of wavelengthscale filenames
@param vrads: list of radial velocities (optional)
@param vrad_units: units of the radial velocities
@param sigma: value used for sigma clipping
@param window: window size used in median filter
@param runs: number of iterations through the spectra
@return: The combined spectrum, cosmic clipped. (wave, flux, header)
@rtype: [array, array, dict]
"""
kwargs['full_output'] = False
if wscalelist == None:
#-- Order Merged spectra are straight forward
exptime, bjd = 0, np.zeros(len(objlist))
waves, fluxes = [],[]
for i, ofile in enumerate(objlist):
w, f, h = fits.read_spectrum(ofile, return_header=True)
waves.append(w)
fluxes.append(f)
exptime += h['exptime']
#bjd[i] = h['bjd']
waves, fluxes = np.array(waves), np.array(fluxes)
mwave, mflux = sptools.merge_cosmic_clipping(waves, fluxes, **kwargs)
else:
#-- The OBJ spectra need to be merged order per order
# read all spectra
exptime, bjd = 0, np.zeros(len(objlist))
waves, fluxes = np.zeros((55,4608, len(objlist))), np.zeros((55,4608, len(wscalelist)))
for i, (ofile, wfile) in enumerate(zip(objlist, wscalelist)):
odata = pf.getdata(ofile, 0).T # The flux has format (4608, 55) thus is transposed
oheader = pf.getheader(ofile, 0)
wdata = pf.getdata(wfile, 0)
fluxes[:,:,i] = odata
waves[:,:,i] = wdata
exptime += oheader['exptime']
#bjd[i] = oheader['bjd']
# merge the spectra
mwave, mflux = waves[:,:,0], np.zeros((55, 4608))
for i in range(55):
wave, flux = sptools.merge_cosmic_clipping(waves[i,:,:].T, fluxes[i,:,:].T, **kwargs)
mflux[i] = flux
header = pf.getheader(objlist[0], 0)
header['exptime'] = exptime
#header['bjd'] = np.average(bjd)
return mwave, mflux, header
