def makemasterflat(flatfiles, rawpath, plot=True):
# normalize the flat fields
for f in flatfiles:
binning = get_binning(f, rawpath)
# Use IRAF to get put the data in the right format and subtract the
# bias
# This will currently break if multiple flats are used for a single setting
iraf.unlearn(iraf.gsreduce)
if dobias:
biasfile = "bias{binning}".format(binning=binning)
else:
biasfile = ''
iraf.gsreduce('@' + f, outimages = f[:-4]+'.mef.fits',rawpath=rawpath, fl_bias=dobias,
bias=biasfile, fl_over=dooverscan, fl_flat=False, fl_gmosaic=False,
fl_fixpix=False, fl_gsappwave=False, fl_cut=False, fl_title=False,
fl_oversize=False, fl_vardq=dodq)
if do_qecorr:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True,
refimages=f[:-4].replace('flat', 'arc.arc.fits'),
corrimages=f[:-9] +'.qe.fits', verbose=True, fl_vardq=dodq)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4]+'.mos.fits', fl_vardq=dodq, fl_clean=False)
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4]+'.mos.fits', fl_vardq=dodq, fl_clean=False)
flat_hdu = fits.open(f[:-4] + '.mos.fits')
data = np.median(flat_hdu['SCI'].data, axis=0)
chip_edges = get_chipedges(data)
x = np.arange(len(data), dtype=np.float)
x /= x.max()
y = data / np.median(data)
fitme_x = x[chip_edges[0][0]:chip_edges[0][1]]
fitme_x = np.append(fitme_x, x[chip_edges[1][0]:chip_edges[1][1]])
fitme_x = np.append(fitme_x, x[chip_edges[2][0]:chip_edges[2][1]])
fitme_y = y[chip_edges[0][0]:chip_edges[0][1]]
fitme_y = np.append(fitme_y, y[chip_edges[1][0]:chip_edges[1][1]])
fitme_y = np.append(fitme_y, y[chip_edges[2][0]:chip_edges[2][1]])
fit = pfm.pffit(fitme_x, fitme_y, 21, 7, robust=True,
M=sm.robust.norms.AndrewWave())
if plot:
pyplot.ion()
pyplot.clf()
pyplot.plot(x, y)
pyplot.plot(x, pfm.pfcalc(fit, x))
_junk = raw_input('Press enter to continue')
flat_hdu['SCI'].data /= pfm.pfcalc(fit, x) * np.median(data)
flat_hdu.writeto(f[:-4] + '.fits')
def makemasterflat(flatfiles, rawpath, plot=True):
# normalize the flat fields
for f in flatfiles:
# Use IRAF to get put the data in the right format and subtract the
# bias
# This will currently break if multiple flats are used for a single setting
iraf.unlearn(iraf.gsreduce)
iraf.gsreduce('@' + f, outimages = f[:-4]+'.mef.fits',rawpath=rawpath,
bias="bias", fl_over=dooverscan, fl_flat=False, fl_gmosaic=False,
fl_fixpix=False, fl_gsappwave=False, fl_cut=False, fl_title=False,
fl_oversize=False)
if is_GS:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True,
refimages=f[:-4].replace('flat', 'arc.arc.fits'),
corrimages=f[:-9] +'.qe.fits', verbose=True)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4]+'.mos.fits')
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4]+'.mos.fits')
flat_hdu = pyfits.open(f[:-4] + '.mos.fits')
data = np.median(flat_hdu['SCI'].data, axis=0)
chip_edges = get_chipedges(data)
x = np.arange(len(data), dtype=np.float)
x /= x.max()
y = data / np.median(data)
fitme_x = x[chip_edges[0][0]:chip_edges[0][1]]
fitme_x = np.append(fitme_x, x[chip_edges[1][0]:chip_edges[1][1]])
fitme_x = np.append(fitme_x, x[chip_edges[2][0]:chip_edges[2][1]])
fitme_y = y[chip_edges[0][0]:chip_edges[0][1]]
fitme_y = np.append(fitme_y, y[chip_edges[1][0]:chip_edges[1][1]])
fitme_y = np.append(fitme_y, y[chip_edges[2][0]:chip_edges[2][1]])
fit = pfm.pffit(fitme_x, fitme_y, 15, 7, robust=True,
M=sm.robust.norms.AndrewWave())
if plot:
from matplotlib import pyplot
pyplot.ion()
pyplot.clf()
pyplot.plot(x, y)
pyplot.plot(x, pfm.pfcalc(fit, x))
_junk = raw_input('Press enter to continue')
flat_hdu['SCI'].data /= pfm.pfcalc(fit, x) * np.median(data)
flat_hdu.writeto(f[:-4] + '.fits')
def specsens(specfile, outfile, stdfile, extfile, airmass=None, exptime=None,
stdzp=3.68e-20, thresh=8, clobber=True):
# read in the specfile and create a spectrum object
obs_hdu = pyfits.open(specfile)
try:
obs_flux = obs_hdu[2].data.copy()[0]
obs_hdr = obs_hdu[2].header.copy()
except:
obs_flux = obs_hdu[0].data.copy()
obs_hdr = obs_hdu[0].header.copy()
obs_hdu.close()
obs_wave = fitshdr_to_wave(obs_hdr)
# Mask out everything below 3450 where there is no signal
obs_flux = obs_flux[obs_wave >= bluecut]
obs_wave = obs_wave[obs_wave >= bluecut]
# Figure out where the chip gaps are
chip_edges = get_chipedges(obs_flux)
try:
chip_gaps = np.ones(obs_flux.size, dtype=np.bool)
for edge in chip_edges:
chip_gaps[edge[0]: edge[1]] = False
except:
chip_gaps = np.zeros(obs_flux.size, dtype=np.bool)
template_spectrum = signal.savgol_filter(obs_flux, 21, 3)
noise = np.abs(obs_flux - template_spectrum)
noise = ndimage.filters.gaussian_filter1d(noise, 100.0)
if chip_gaps.sum() != len(chip_gaps):
# Smooth the chip gaps
intpr = interpolate.splrep(obs_wave[np.logical_not(chip_gaps)],
obs_flux[np.logical_not(chip_gaps)],
w=1 / noise[np.logical_not(chip_gaps)], k=2,
s=20 * np.logical_not(chip_gaps).sum())
obs_flux[chip_gaps] = interpolate.splev(obs_wave[chip_gaps], intpr)
# smooth the observed spectrum
# read in the std file and convert from magnitudes to fnu
# then convert it to fwave (ergs/s/cm2/A)
std_wave, std_mag, _stdbnd = np.genfromtxt(stdfile).transpose()
std_flux = magtoflux(std_wave, std_mag, stdzp)
# Get the typical bandpass of the standard star,
std_bandpass = np.diff(std_wave).mean()
# Smooth the observed spectrum to that bandpass
obs_flux = boxcar_smooth(obs_wave, obs_flux, std_bandpass)
# read in the extinction file (leave in magnitudes)
ext_wave, ext_mag = np.genfromtxt(extfile).transpose()
# calculate the calibrated spectra
cal_flux = cal_std(obs_wave, obs_flux, std_wave, std_flux, ext_wave,
ext_mag, airmass, exptime)
# Normalize the fit variables so the fit is well behaved
fitme_x = (obs_wave - obs_wave.min()) / (obs_wave.max() - obs_wave.min())
fitme_y = cal_flux / np.median(cal_flux)
coeffs = pfm.pffit(fitme_x, fitme_y, 5 , 7, robust=True,
M=sm.robust.norms.AndrewWave())
fitted_flux = pfm.pfcalc(coeffs, fitme_x) * np.median(cal_flux)
cal_mag = -1.0 * fluxtomag(fitted_flux)
# write the spectra out
cal_hdr = sanitizeheader(obs_hdr.copy())
cal_hdr['OBJECT'] = 'Sensitivity function for all apertures'
cal_hdr['CRVAL1'] = obs_wave.min()
cal_hdr['CRPIX1'] = 1
if is_GS:
cal_hdr['QESTATE'] = True
else:
cal_hdr['QESTATE'] = False
tofits(outfile, cal_mag, hdr=cal_hdr, clobber=True)
def specsens(specfile, outfile, stdfile, extfile, airmass=None, exptime=None,
stdzp=3.68e-20, thresh=8, clobber=True):
# read in the specfile and create a spectrum object
obs_hdu = fits.open(specfile)
try:
obs_flux = obs_hdu[2].data.copy()[0]
obs_hdr = obs_hdu[2].header.copy()
except:
obs_flux = obs_hdu[0].data.copy()
obs_hdr = obs_hdu[0].header.copy()
obs_hdu.close()
obs_wave = fitshdr_to_wave(obs_hdr)
# Mask out everything below 3450 where there is no signal
obs_flux = obs_flux[obs_wave >= bluecut]
obs_wave = obs_wave[obs_wave >= bluecut]
# Figure out where the chip gaps are
chip_edges = get_chipedges(obs_flux)
try:
chip_gaps = np.ones(obs_flux.size, dtype=np.bool)
for edge in chip_edges:
chip_gaps[edge[0]: edge[1]] = False
except:
chip_gaps = np.zeros(obs_flux.size, dtype=np.bool)
template_spectrum = signal.savgol_filter(obs_flux, 21, 3)
noise = np.abs(obs_flux - template_spectrum)
noise = ndimage.filters.gaussian_filter1d(noise, 100.0)
if chip_gaps.sum() != len(chip_gaps):
# Smooth the chip gaps
intpr = interpolate.splrep(obs_wave[np.logical_not(chip_gaps)],
obs_flux[np.logical_not(chip_gaps)],
w=1 / noise[np.logical_not(chip_gaps)], k=2,
s=20 * np.logical_not(chip_gaps).sum())
obs_flux[chip_gaps] = interpolate.splev(obs_wave[chip_gaps], intpr)
# smooth the observed spectrum
# read in the std file and convert from magnitudes to fnu
# then convert it to fwave (ergs/s/cm2/A)
std_wave, std_mag, _stdbnd = np.genfromtxt(stdfile).transpose()
std_flux = magtoflux(std_wave, std_mag, stdzp)
# Get the typical bandpass of the standard star,
std_bandpass = np.max([50.0, np.diff(std_wave).mean()])
# Smooth the observed spectrum to that bandpass
obs_flux = boxcar_smooth(obs_wave, obs_flux, std_bandpass)
# read in the extinction file (leave in magnitudes)
ext_wave, ext_mag = np.genfromtxt(extfile).transpose()
# calculate the calibrated spectra
cal_flux = cal_std(obs_wave, obs_flux, std_wave, std_flux, ext_wave,
ext_mag, airmass, exptime)
# Normalize the fit variables so the fit is well behaved
fitme_x = (obs_wave - obs_wave.min()) / (obs_wave.max() - obs_wave.min())
fitme_y = cal_flux / np.median(cal_flux)
coeffs = pfm.pffit(fitme_x, fitme_y, 5 , 7, robust=True,
M=sm.robust.norms.AndrewWave())
fitted_flux = pfm.pfcalc(coeffs, fitme_x) * np.median(cal_flux)
cal_mag = -1.0 * fluxtomag(fitted_flux)
# write the spectra out
cal_hdr = sanitizeheader(obs_hdr.copy())
cal_hdr['OBJECT'] = 'Sensitivity function for all apertures'
cal_hdr['CRVAL1'] = obs_wave.min()
cal_hdr['CRPIX1'] = 1
if do_qecorr:
cal_hdr['QESTATE'] = True
else:
cal_hdr['QESTATE'] = False
tofits(outfile, cal_mag, hdr=cal_hdr, clobber=True)
