def plot_spectra(plan, date):
'''take plan[std name] -> (path, ver) and plot'''
figure(1)
clf()
figure(2)
clf()
xlim(360, 980)
for stdname, todo in plan.iteritems():
print stdname, todo
path, ver = todo
sp = path.split("/")
name, datetime, ver = sp[-3:]
FF = pf.open(os.path.join(path, "%s.fits" % name))
dat = FF[0].data
itime = FF[0].header['exptime']
airmass = FF[0].header['airmass']
l, s = dat[0, :], dat[2, :] / itime
magperairmass = Atm.ext(l * 10)
mag_ext = magperairmass * airmass
ext = 10**(-magperairmass / 2.5)
s /= ext
sourcefun = interp1d(l, s)
stdfun = get_std_spec(name)
figure(1)
plot(l, s)
plot(l, stdfun(l))
figure(2)
corr = stdfun(l) / s
semilogy(l, corr)
try:
all_corr += stdfun(all_l) / sourcefun(all_l)
except:
all_l = l
all_corr = stdfun(l) / s
all_corr /= len(plan)
figure(2)
legend(plan.keys())
figure(3)
ok = np.isfinite(all_corr)
all_l = all_l[ok]
all_corr = all_corr[ok]
ok = (all_l > 370) & (all_l < 920) & (all_corr > 0)
ff = interp1d(all_l[ok], all_corr[ok], bounds_error=False)
semilogy(all_l, all_corr, '.')
semilogy(all_l, ff(all_l))
np.save('correction_%s' % date, [all_l, all_corr])
show()
def plot_spectra(plan, date):
'''take plan[std name] -> (path, ver) and plot'''
figure(1) ; clf()
figure(2) ; clf()
xlim(360,980)
for stdname, todo in plan.iteritems():
print stdname,todo
path,ver = todo
sp = path.split("/")
name, datetime, ver = sp[-3:]
FF = pf.open(os.path.join(path, "%s.fits" % name))
dat = FF[0].data
itime = FF[0].header['exptime']
airmass = FF[0].header['airmass']
l,s = dat[0,:], dat[2,:]/itime
magperairmass = Atm.ext(l*10)
mag_ext = magperairmass * airmass
ext = 10**(-magperairmass/2.5)
s /= ext
sourcefun = interp1d(l, s)
stdfun = get_std_spec(name)
figure(1)
plot(l,s)
plot(l, stdfun(l))
figure(2)
corr = stdfun(l)/s
semilogy(l, corr)
try:
all_corr += stdfun(all_l)/sourcefun(all_l)
except:
all_l = l
all_corr = stdfun(l)/s
all_corr /= len(plan)
figure(2)
legend(plan.keys())
figure(3)
ok = np.isfinite(all_corr)
all_l = all_l[ok]
all_corr = all_corr[ok]
ok = (all_l > 370) & (all_l < 920) & (all_corr > 0)
ff = interp1d(all_l[ok], all_corr[ok],bounds_error = False)
semilogy(all_l,all_corr,'.')
semilogy(all_l, ff(all_l))
np.save('correction_%s' % date, [all_l, all_corr])
show()
def draw_res(self):
''' Draw the resulting spectrum'''
pl.figure(3)
pl.clf()
pl.xlim([350, 950])
pl.ylim(-1000, 4000)
allwav = allsky = allobj = None
headers = []
for i in xrange(len(self.RESULTS)):
res = self.RESULTS[i]
if res is None: continue
header = self.headers[i]
header = clean_header(header)
headers.append(header)
airmass = header['airmass']
wave, sky, obj = res.copy()
skyf = interp1d(wave, sky, fill_value=np.nan, bounds_error=False)
objf = interp1d(wave, obj, fill_value=np.nan, bounds_error=False)
if self.qecurve is None: correction = 1.0
else:
print "Applying correction"
ext = 10**(-Atm.ext(wave * 10) * airmass / 2.5)
correction = 1 / self.qecurve(wave * 10) * ext
correction = 1.
pl.step(wave, obj * correction)
if allwav is None:
allwav = wave[:]
allsky = sky[:]
allobj = obj[:]
else:
allsky += skyf(allwav)
allobj += objf(allwav)
if self.qecurve is None: correction = 1.0
else: correction = 1 / self.qecurve(allwav * 10)
pl.step(allwav, allobj * correction, linewidth=3)
#pl.step(allwav, allobj, linewidth=3)
# Raw data
result = np.array([allwav, allsky, allobj])
pf = pyfits.PrimaryHDU(result, header=header)
name = self.plan[self.index]['name']
pf.header["REDNAME"] = name
outpath = os.path.join(self.outdir, "%s.fits" % (name))
try:
os.remove(outpath)
except:
pass
pf.writeto(outpath)
# Corrected
if self.qecurve is not None:
result = np.array(
[allwav, allsky * correction, allobj * correction])
pf = pyfits.PrimaryHDU(result, header=header)
name = self.plan[self.index]['name']
pf.header["REDNAME"] = name
outpath = os.path.join(self.outdir, "%s_corr.fits" % (name))
try:
os.remove(outpath)
except:
pass
pf.writeto(outpath)
def handle_AB(A, B, fine, outname=None, corrfile=None,
Aoffset=None, Boffset=None, radius=2, flat_corrections=None,
nosky=False, lmin=650, lmax=700):
'''Loads 2k x 2k IFU frame "A" and "B" and extracts A-B and A+B spectra
from the "fine" location.
Args:
A (string): filename of ifu FITS file to extract from.
B (string): filename of ifu FITS file to extract from.
fine (string): filename of NumPy file with locations + wavelength
soln
outname (string): filename to write results to
Aoffset (2tuple): X (nm)/Y (pix) shift to apply for flexure correction
Boffset (2tuple): X (nm)/Y (pix) shift to apply for flexure correction
radius (float): Extraction radius in arcsecond
flat_corrections (list): A list of FlatCorrection objects for
correcting the extraction
nosky (Boolean): if True don't subtract sky, merely sum in aperture
Returns:
The extracted spectrum, a dictionary:
{'ph_10m_nm': Flux in photon / 10 m / nanometer integrated
'var'
'nm': Wavelength solution in nm
'N_spaxA': Total number of "A" spaxels
'N_spaxB': Total number of "B" spaxels
'skyph': Sky flux in photon / 10 m / nanometer / spaxel
'radius_as': Extraction radius in arcsec
'pos': X/Y extraction location of spectrum in arcsec}
Raises:
None
'''
fine = np.load(fine)
if outname is None:
outname = "%sm%s" % (A,B)
if Aoffset is not None:
ff = np.load(Aoffset)
flexure_x_corr_nm = ff[0]['dXnm']
flexure_y_corr_pix = -ff[0]['dYpix']
print "Dx %2.1f | Dy %2.1f" % (ff[0]['dXnm'], ff[0]['dYpix'])
else:
flexure_x_corr_nm = 0
flexure_y_corr_pix = 0
read_var = 5*5
if os.path.isfile(outname + ".fits.npy"):
print "USING extractions in %s!" % outname
E, meta = np.load(outname + ".fits.npy")
E_var, meta_var = np.load("var_" + outname + ".fits.npy")
else:
if not outname.endswith(".fits"):
outname = outname + ".fits"
diff = subtract(A,B, outname)
add(A,B, "tmpvar_" + outname)
adcspeed = diff[0].header["ADCSPEED"]
if adcspeed == 2: read_var = 22*22
else: read_var = 5*5
var = add("tmpvar_" + outname, str(read_var), "var_" + outname)
os.remove("tmpvar_" + outname + ".gz")
E, meta = Wavelength.wavelength_extract(diff, fine,
filename=outname,
flexure_x_corr_nm = flexure_x_corr_nm,
flexure_y_corr_pix = flexure_y_corr_pix,
flat_corrections=flat_corrections)
meta['airmass1'] = diff[0].header['airmass1']
meta['airmass2'] = diff[0].header['airmass2']
meta['airmass'] = diff[0].header['airmass']
header = {}
for k,v in diff[0].header.iteritems():
try: header[k] = v
except: pass
meta['HA'] = diff[0].header['HA']
meta['Dec'] = diff[0].header['Dec']
meta['RA'] = diff[0].header['RA']
meta['PRLLTC'] = diff[0].header['PRLLTC']
meta['equinox'] = diff[0].header['Equinox']
meta['utc'] = diff[0].header['utc']
meta['header'] = header
meta['exptime'] = diff[0].header['exptime']
np.save(outname, [E, meta])
exfile = "extracted_var_%s.npy" % outname
E_var, meta_var = Wavelength.wavelength_extract(var, fine,
filename=outname,
flexure_x_corr_nm = flexure_x_corr_nm,
flexure_y_corr_pix = flexure_y_corr_pix,
flat_corrections=flat_corrections)
np.save("var_" + outname, [E_var, meta_var])
sixA, posA, all_A = identify_spectra_gui(E, radius=radius,
PRLLTC=Angle(meta['PRLLTC'], unit='deg'),
lmin=lmin, lmax=lmax, airmass=meta['airmass'])
sixB, posB, all_B = identify_spectra_gui(E, radius=radius,
PRLLTC=Angle(meta['PRLLTC'], unit='deg'),
lmin=lmin, lmax=lmax, airmass=meta['airmass'])
to_image(E, meta, outname, posA=posA, posB=posB, adcpos=all_A)
skyA = identify_bgd_spectra(E, posA)
skyB = identify_bgd_spectra(E, posB)
allix = np.concatenate([sixA, sixB])
resA = interp_spectra(E, sixA, sign=1, outname=outname+"_A.pdf", corrfile=corrfile)
resB = interp_spectra(E, sixB, sign=-1, outname=outname+"_B.pdf", corrfile=corrfile)
skyA = interp_spectra(E, skyA, sign=1, outname=outname+"_skyA.pdf", corrfile=corrfile)
skyB = interp_spectra(E, skyB, sign=-1, outname=outname+"_skYB.pdf", corrfile=corrfile)
varA = interp_spectra(E_var, sixA, sign=1, outname=outname+"_A_var.pdf", corrfile=corrfile)
varB = interp_spectra(E_var, sixB, sign=1, outname=outname+"_B_var.pdf", corrfile=corrfile)
## Plot out the X/Y selected spectra
XSA = []
YSA = []
XSB = []
YSB = []
for ix in sixA:
XSA.append(E[ix].X_as)
YSA.append(E[ix].Y_as)
for ix in sixB:
XSB.append(E[ix].X_as)
YSB.append(E[ix].Y_as)
pl.figure()
pl.clf()
pl.ylim(-30,30)
pl.xlim(-30,30)
pl.scatter(XSA,YSA, color='blue', marker='H', linewidth=.1)
pl.scatter(XSB,YSB, color='red', marker='H', linewidth=.1)
pl.savefig("XYs_%s.pdf" % outname)
pl.close()
# / End Plot
np.save("sp_A_" + outname, resA)
np.save("sp_B_" + outname, resB)
np.save("var_A_" + outname, varA)
np.save("var_B_" + outname, varB)
ll = Wavelength.fiducial_spectrum()
sky_A = interp1d(skyA[0]['nm'], skyA[0]['ph_10m_nm'], bounds_error=False)
sky_B = interp1d(skyB[0]['nm'], skyB[0]['ph_10m_nm'], bounds_error=False)
sky = np.nanmean([sky_A(ll), sky_B(ll)], axis=0)
var_A = interp1d(varA[0]['nm'], varA[0]['ph_10m_nm'], bounds_error=False)
var_B = interp1d(varB[0]['nm'], varB[0]['ph_10m_nm'], bounds_error=False)
varspec = np.nanmean([var_A(ll), var_B(ll)], axis=0) * (len(sixA) + len(sixB))
res = np.copy(resA)
res = [{"doc": resA[0]["doc"], "ph_10m_nm": np.copy(resA[0]["ph_10m_nm"]),
"nm": np.copy(resA[0]["ph_10m_nm"])}]
res[0]['nm'] = np.copy(ll)
f1 = interp1d(resA[0]['nm'], resA[0]['ph_10m_nm'], bounds_error=False)
f2 = interp1d(resB[0]['nm'], resB[0]['ph_10m_nm'], bounds_error=False)
airmassA = meta['airmass1']
airmassB = meta['airmass2']
extCorrA = 10**(Atm.ext(ll*10)*airmassA/2.5)
extCorrB = 10**(Atm.ext(ll*10)*airmassB/2.5)
print "Median airmass corr: ", np.median(extCorrA), np.median(extCorrB)
# If requested merely sum in aperture, otherwise subtract sky
if nosky:
res[0]['ph_10m_nm'] = \
np.nansum([
f1(ll) * extCorrA,
f2(ll) * extCorrB], axis=0) * \
(len(sixA) + len(sixB))
else:
res[0]['ph_10m_nm'] = \
np.nansum([
(f1(ll)-sky_A(ll)) * extCorrA,
(f2(ll)-sky_B(ll)) * extCorrB], axis=0) * \
(len(sixA) + len(sixB))
res[0]['exptime'] = meta['exptime']
res[0]['Extinction Correction'] = 'Applied using Hayes & Latham'
res[0]['extinction_corr_A'] = extCorrA
res[0]['extinction_corr_B'] = extCorrB
res[0]['skyph'] = sky
res[0]['var'] = varspec
res[0]['radius_as'] = radius
res[0]['positionA'] = posA
res[0]['positionB'] = posA
res[0]['N_spaxA'] = len(sixA)
res[0]['N_spaxB'] = len(sixB)
res[0]['meta'] = meta
res[0]['object_spaxel_ids_A'] = sixA
res[0]['sky_spaxel_ids_A'] = skyA
res[0]['object_spaxel_ids_B'] = sixB
res[0]['sky_spaxel_ids_B'] = skyB
coef = chebfit(np.arange(len(ll)), ll, 4)
xs = np.arange(len(ll)+1)
newll = chebval(xs, coef)
res[0]['dlam'] = np.diff(newll)
np.save("sp_" + outname, res)
def handle_A(A, fine, outname=None, standard=None, corrfile=None,
Aoffset=None, radius=2, flat_corrections=None, nosky=False):
'''Loads 2k x 2k IFU frame "A" and extracts spectra from the locations
in "fine".
Args:
A (string): filename of ifu FITS file to extract from.
fine (string): filename of NumPy file with locations + wavelength
soln
outname (string): filename to write results to
Aoffset (2tuple): X (nm)/Y (pix) shift to apply for flexure correction
radius (float): Extraction radius in arcsecond
flat_corrections (list): A list of FlatCorrection objects for
correcting the extraction
nosky (Boolean): if True don't subtract sky, merely sum in aperture
Returns:
The extracted spectrum, a dictionary:
{'ph_10m_nm': Flux in photon / 10 m / nanometer integrated
'nm': Wavelength solution in nm
'N_spax': Total number of spaxels that created ph_10m_nm
'skyph': Sky flux in photon / 10 m / nanometer / spaxel
'radius_as': Extraction radius in arcsec
'pos': X/Y extraction location of spectrum in arcsec}
Raises:
None
'''
fine = np.load(fine)
if outname is None:
outname = "%s" % (A)
spec = pf.open(A)
if Aoffset is not None:
ff = np.load(Aoffset)
flexure_x_corr_nm = ff[0]['dXnm']
flexure_y_corr_pix = ff[0]['dYpix']
print "Dx %2.1f | Dy %2.1f" % (ff[0]['dXnm'], ff[0]['dYpix'])
else:
flexure_x_corr_nm = 0
flexure_y_corr_pix = 0
if os.path.isfile(outname+".npy"):
print "USING extractions in %s!" % outname
print "rm %s.npy # if you want to recreate extractions" % outname
E, meta = np.load(outname+".npy")
else:
print "CREATING extractions ..."
E, meta = Wavelength.wavelength_extract(spec, fine, filename=outname,
flexure_x_corr_nm=flexure_x_corr_nm,
flexure_y_corr_pix=flexure_y_corr_pix,
flat_corrections = flat_corrections)
meta['airmass'] = spec[0].header['airmass']
header = {}
for k,v in spec[0].header.iteritems():
try: header[k] = v
except: pass
meta['HA'] = spec[0].header['HA']
meta['Dec'] = spec[0].header['Dec']
meta['RA'] = spec[0].header['RA']
meta['PRLLTC'] = spec[0].header['PRLLTC']
meta['equinox'] = spec[0].header['Equinox']
meta['utc'] = spec[0].header['utc']
meta['header'] = header
np.save(outname, [E, meta])
'''six, pos, adcpos = identify_spectra_gui(E, radius=radius,
PRLLTC=Angle(meta['PRLLTC'], unit='deg'),
lmin=650, lmax=700, airmass=meta['airmass'])'''
six, pos, adcpos = identify_spectra_Gauss_fit(E, outname=outname, radius=radius,
PRLLTC=Angle(meta['PRLLTC'], unit='deg'),
lmin=650, lmax=700, airmass=meta['airmass'])
skyix = identify_bgd_spectra(E, pos, inner=radius*1.1)
res = interp_spectra(E, six, outname=outname+".pdf", corrfile=corrfile)
sky = interp_spectra(E, skyix, onto=res[0]['nm'], outname=outname+"_sky.pdf", corrfile=corrfile)
to_image(E, meta, outname, posA=pos, adcpos=adcpos)
if standard is not None:
print "STANDARD"
wav = standard[:,0]/10.0
flux = standard[:,1]
fun = interp1d(wav, flux, bounds_error=False, fill_value = np.nan)
correction = fun(res[0]['nm'])/res[0]['ph_10m_nm']
res[0]['std-correction'] = correction
airmass = meta['airmass']
extCorr = 10**(Atm.ext(res[0]['nm']*10) * airmass/2.5)
print "Median airmass corr: ", np.nanmedian(extCorr)
ff = interp1d(sky[0]['nm'], sky[0]['ph_10m_nm'], bounds_error=False)
skybgd = ff(res[0]['nm'])
res[0]['exptime'] = spec[0].header['exptime']
res[0]['Extinction Correction'] = 'Applied using Hayes & Latham'
res[0]['extinction_corr'] = extCorr
res[0]['skynm'] = sky[0]['nm']
res[0]['skyph'] = sky[0]['ph_10m_nm']
if not nosky:
res[0]['ph_10m_nm'] -= skybgd
res[0]['ph_10m_nm'] *= extCorr * len(six)
res[0]['radius_as'] = radius
res[0]['position'] = pos
res[0]['N_spax'] = len(six)
res[0]['meta'] = meta
res[0]['object_spaxel_ids'] = six
res[0]['sky_spaxel_ids'] = skyix
res[0]['sky_spectra'] = sky[0]['spectra']
np.save("sp_" + outname, res)
def draw_res(self):
""" Draw the resulting spectrum"""
pl.figure(3)
pl.clf()
pl.xlim([350, 950])
pl.ylim(-1000, 4000)
allwav = allsky = allobj = None
headers = []
for i in xrange(len(self.RESULTS)):
res = self.RESULTS[i]
if res is None:
continue
header = self.headers[i]
header = clean_header(header)
headers.append(header)
airmass = header["airmass"]
wave, sky, obj = res.copy()
skyf = interp1d(wave, sky, fill_value=np.nan, bounds_error=False)
objf = interp1d(wave, obj, fill_value=np.nan, bounds_error=False)
if self.qecurve is None:
correction = 1.0
else:
print "Applying correction"
ext = 10 ** (-Atm.ext(wave * 10) * airmass / 2.5)
correction = 1 / self.qecurve(wave * 10) * ext
correction = 1.0
pl.step(wave, obj * correction)
if allwav is None:
allwav = wave[:]
allsky = sky[:]
allobj = obj[:]
else:
allsky += skyf(allwav)
allobj += objf(allwav)
if self.qecurve is None:
correction = 1.0
else:
correction = 1 / self.qecurve(allwav * 10)
pl.step(allwav, allobj * correction, linewidth=3)
# pl.step(allwav, allobj, linewidth=3)
# Raw data
result = np.array([allwav, allsky, allobj])
pf = pyfits.PrimaryHDU(result, header=header)
name = self.plan[self.index]["name"]
pf.header["REDNAME"] = name
outpath = os.path.join(self.outdir, "%s.fits" % (name))
try:
os.remove(outpath)
except:
pass
pf.writeto(outpath)
# Corrected
if self.qecurve is not None:
result = np.array([allwav, allsky * correction, allobj * correction])
pf = pyfits.PrimaryHDU(result, header=header)
name = self.plan[self.index]["name"]
pf.header["REDNAME"] = name
outpath = os.path.join(self.outdir, "%s_corr.fits" % (name))
try:
os.remove(outpath)
except:
pass
pf.writeto(outpath)
def handle_AB(A,
B,
fine,
outname=None,
corrfile=None,
Aoffset=None,
Boffset=None,
radius=2,
flat_corrections=None,
lmin=650,
lmax=700):
'''Loads 2k x 2k IFU frame "A" and "B" and extracts A-B and A+B spectra
from the "fine" location.
Args:
A (string): filename of ifu FITS file to extract from.
B (string): filename of ifu FITS file to extract from.
fine (string): filename of NumPy file with locations + wavelength
soln
outname (string): filename to write results to
Aoffset (2tuple): X (nm)/Y (pix) shift to apply for flexure correction
Boffset (2tuple): X (nm)/Y (pix) shift to apply for flexure correction
radius (float): Extraction radius in arcsecond
flat_corrections (list): A list of FlatCorrection objects for
correcting the extraction
Returns:
The extracted spectrum, a dictionary:
{'ph_10m_nm': Flux in photon / 10 m / nanometer integrated
'var'
'nm': Wavelength solution in nm
'N_spaxA': Total number of "A" spaxels
'N_spaxB': Total number of "B" spaxels
'skyph': Sky flux in photon / 10 m / nanometer / spaxel
'radius_as': Extraction radius in arcsec
'pos': X/Y extraction location of spectrum in arcsec}
Raises:
None
'''
fine = np.load(fine)
if outname is None:
outname = "%sm%s" % (A, B)
if Aoffset is not None:
ff = np.load(Aoffset)
f2 = np.load(Aoffset)
flexure_x_corr_nm = ff[0]['dXnm']
flexure_y_corr_pix = -ff[0]['dYpix']
print "Dx %2.1f, %2.1f | Dy %2.1f %2.1f" % (
ff[0]['dXnm'], f2[0]['dXnm'], ff[0]['dYpix'], f2[0]['dYpix'])
else:
flexure_x_corr_nm = 0
flexure_y_corr_pix = 0
read_var = 5 * 5
if os.path.isfile(outname + ".fits.npy"):
print "USING extractions in %s!" % outname
E, meta = np.load(outname + ".fits.npy")
E_var, meta_var = np.load("var_" + outname + ".fits.npy")
else:
if not outname.endswith(".fits"):
outname = outname + ".fits"
diff = subtract(A, B, outname)
add(A, B, "tmpvar_" + outname)
adcspeed = diff[0].header["ADCSPEED"]
if adcspeed == 2: read_var = 22 * 22
else: read_var = 5 * 5
var = add("tmpvar_" + outname, str(read_var), "var_" + outname)
os.remove("tmpvar_" + outname + ".gz")
E, meta = Wavelength.wavelength_extract(
diff,
fine,
filename=outname,
flexure_x_corr_nm=flexure_x_corr_nm,
flexure_y_corr_pix=flexure_y_corr_pix,
flat_corrections=flat_corrections)
meta['airmass1'] = diff[0].header['airmass1']
meta['airmass2'] = diff[0].header['airmass2']
meta['airmass'] = diff[0].header['airmass']
header = {}
for k, v in diff[0].header.iteritems():
try:
header[k] = v
except:
pass
meta['HA'] = diff[0].header['HA']
meta['Dec'] = diff[0].header['Dec']
meta['RA'] = diff[0].header['RA']
meta['PRLLTC'] = diff[0].header['PRLLTC']
meta['equinox'] = diff[0].header['Equinox']
meta['utc'] = diff[0].header['utc']
meta['header'] = header
meta['exptime'] = diff[0].header['exptime']
np.save(outname, [E, meta])
exfile = "extracted_var_%s.npy" % outname
E_var, meta_var = Wavelength.wavelength_extract(
var,
fine,
filename=outname,
flexure_x_corr_nm=flexure_x_corr_nm,
flexure_y_corr_pix=flexure_y_corr_pix,
flat_corrections=flat_corrections)
np.save("var_" + outname, [E_var, meta_var])
sixA, posA, all_A = identify_spectra_gui(E,
radius=radius,
PRLLTC=Angle(meta['PRLLTC'],
unit='deg'),
lmin=lmin,
lmax=lmax,
airmass=meta['airmass'])
sixB, posB, all_B = identify_spectra_gui(E,
radius=radius,
PRLLTC=Angle(meta['PRLLTC'],
unit='deg'),
lmin=lmin,
lmax=lmax,
airmass=meta['airmass'])
to_image(E, meta, outname, posA=posA, posB=posB, adcpos=all_A)
skyA = identify_bgd_spectra(E, posA)
skyB = identify_bgd_spectra(E, posB)
allix = np.concatenate([sixA, sixB])
resA = interp_spectra(E,
sixA,
sign=1,
outname=outname + "_A.pdf",
corrfile=corrfile)
resB = interp_spectra(E,
sixB,
sign=-1,
outname=outname + "_B.pdf",
corrfile=corrfile)
skyA = interp_spectra(E,
skyA,
sign=1,
outname=outname + "_skyA.pdf",
corrfile=corrfile)
skyB = interp_spectra(E,
skyB,
sign=-1,
outname=outname + "_skYB.pdf",
corrfile=corrfile)
varA = interp_spectra(E_var,
sixA,
sign=1,
outname=outname + "_A_var.pdf",
corrfile=corrfile)
varB = interp_spectra(E_var,
sixB,
sign=1,
outname=outname + "_B_var.pdf",
corrfile=corrfile)
## Plot out the X/Y selected spectra
XSA = []
YSA = []
XSB = []
YSB = []
for ix in sixA:
XSA.append(E[ix].X_as)
YSA.append(E[ix].Y_as)
for ix in sixB:
XSB.append(E[ix].X_as)
YSB.append(E[ix].Y_as)
pl.figure()
pl.clf()
pl.ylim(-30, 30)
pl.xlim(-30, 30)
pl.scatter(XSA, YSA, color='blue', marker='H', linewidth=.1)
pl.scatter(XSB, YSB, color='red', marker='H', linewidth=.1)
pl.savefig("XYs_%s.pdf" % outname)
pl.close()
# / End Plot
np.save("sp_A_" + outname, resA)
np.save("sp_B_" + outname, resB)
np.save("var_A_" + outname, varA)
np.save("var_B_" + outname, varB)
ll = Wavelength.fiducial_spectrum()
sky_A = interp1d(skyA[0]['nm'], skyA[0]['ph_10m_nm'], bounds_error=False)
sky_B = interp1d(skyB[0]['nm'], skyB[0]['ph_10m_nm'], bounds_error=False)
sky = np.nanmean([sky_A(ll), sky_B(ll)], axis=0)
var_A = interp1d(varA[0]['nm'], varA[0]['ph_10m_nm'], bounds_error=False)
var_B = interp1d(varB[0]['nm'], varB[0]['ph_10m_nm'], bounds_error=False)
varspec = np.nanmean([var_A(ll), var_B(ll)],
axis=0) * (len(sixA) + len(sixB))
res = np.copy(resA)
res = [{
"doc": resA[0]["doc"],
"ph_10m_nm": np.copy(resA[0]["ph_10m_nm"]),
"nm": np.copy(resA[0]["ph_10m_nm"])
}]
res[0]['nm'] = np.copy(ll)
f1 = interp1d(resA[0]['nm'], resA[0]['ph_10m_nm'], bounds_error=False)
f2 = interp1d(resB[0]['nm'], resB[0]['ph_10m_nm'], bounds_error=False)
airmassA = meta['airmass1']
airmassB = meta['airmass2']
extCorrA = 10**(Atm.ext(ll * 10) * airmassA / 2.5)
extCorrB = 10**(Atm.ext(ll * 10) * airmassB / 2.5)
print "Median airmass corr: ", np.median(extCorrA), np.median(extCorrB)
res[0]['ph_10m_nm'] = \
np.nansum([
(f1(ll)-sky_A(ll)) * extCorrA,
(f2(ll)-sky_B(ll)) * extCorrB], axis=0) * \
(len(sixA) + len(sixB))
res[0]['exptime'] = meta['exptime']
res[0]['Extinction Correction'] = 'Applied using Hayes & Latham'
res[0]['extinction_corr_A'] = extCorrA
res[0]['extinction_corr_B'] = extCorrB
res[0]['skyph'] = sky
res[0]['var'] = varspec
res[0]['radius_as'] = radius
res[0]['positionA'] = posA
res[0]['positionB'] = posA
res[0]['N_spaxA'] = len(sixA)
res[0]['N_spaxB'] = len(sixB)
res[0]['meta'] = meta
res[0]['object_spaxel_ids_A'] = sixA
res[0]['sky_spaxel_ids_A'] = skyA
res[0]['object_spaxel_ids_B'] = sixB
res[0]['sky_spaxel_ids_B'] = skyB
coef = chebfit(np.arange(len(ll)), ll, 4)
xs = np.arange(len(ll) + 1)
newll = chebval(xs, coef)
res[0]['dlam'] = np.diff(newll)
np.save("sp_" + outname, res)
def handle_A(A,
fine,
outname=None,
standard=None,
corrfile=None,
Aoffset=None,
radius=2,
flat_corrections=None):
'''Loads 2k x 2k IFU frame "A" and extracts spectra from the locations
in "fine".
Args:
A (string): filename of ifu FITS file to extract from.
fine (string): filename of NumPy file with locations + wavelength
soln
outname (string): filename to write results to
Aoffset (2tuple): X (nm)/Y (pix) shift to apply for flexure correction
radius (float): Extraction radius in arcsecond
flat_corrections (list): A list of FlatCorrection objects for
correcting the extraction
Returns:
The extracted spectrum, a dictionary:
{'ph_10m_nm': Flux in photon / 10 m / nanometer integrated
'nm': Wavelength solution in nm
'N_spax': Total number of spaxels that created ph_10m_nm
'skyph': Sky flux in photon / 10 m / nanometer / spaxel
'radius_as': Extraction radius in arcsec
'pos': X/Y extraction location of spectrum in arcsec}
Raises:
None
'''
fine = np.load(fine)
if outname is None:
outname = "%s" % (A)
spec = pf.open(A)
if Aoffset is not None:
ff = np.load(Aoffset)
flexure_x_corr_nm = ff[0]['dXnm']
flexure_y_corr_pix = ff[0]['dYpix']
else:
flexure_x_corr_nm = 0
flexure_y_corr_pix = 0
if os.path.isfile(outname + ".npy"):
print "USING extractions in %s!" % outname
print "rm %s.npy # if you want to recreate extractions" % outname
E, meta = np.load(outname + ".npy")
else:
print "CREATING extractions ..."
E, meta = Wavelength.wavelength_extract(
spec,
fine,
filename=outname,
flexure_x_corr_nm=flexure_x_corr_nm,
flexure_y_corr_pix=flexure_y_corr_pix,
flat_corrections=flat_corrections)
meta['airmass'] = spec[0].header['airmass']
header = {}
for k, v in spec[0].header.iteritems():
try:
header[k] = v
except:
pass
meta['HA'] = spec[0].header['HA']
meta['Dec'] = spec[0].header['Dec']
meta['RA'] = spec[0].header['RA']
meta['PRLLTC'] = spec[0].header['PRLLTC']
meta['equinox'] = spec[0].header['Equinox']
meta['utc'] = spec[0].header['utc']
meta['header'] = header
np.save(outname, [E, meta])
six, pos, adcpos = identify_spectra_gui(E,
radius=radius,
PRLLTC=Angle(meta['PRLLTC'],
unit='deg'),
lmin=650,
lmax=700,
airmass=meta['airmass'])
skyix = identify_bgd_spectra(E, pos, inner=radius * 1.1)
res = interp_spectra(E, six, outname=outname + ".pdf", corrfile=corrfile)
sky = interp_spectra(E,
skyix,
onto=res[0]['nm'],
outname=outname + "_sky.pdf",
corrfile=corrfile)
to_image(E, meta, outname, posA=pos, adcpos=adcpos)
if standard is not None:
print "STANDARD"
wav = standard[:, 0] / 10.0
flux = standard[:, 1]
fun = interp1d(wav, flux, bounds_error=False, fill_value=np.nan)
correction = fun(res[0]['nm']) / res[0]['ph_10m_nm']
res[0]['std-correction'] = correction
airmass = meta['airmass']
extCorr = 10**(Atm.ext(res[0]['nm'] * 10) * airmass / 2.5)
print "Median airmass corr: ", np.median(extCorr)
ff = interp1d(sky[0]['nm'], sky[0]['ph_10m_nm'], bounds_error=False)
skybgd = ff(res[0]['nm'])
res[0]['exptime'] = spec[0].header['exptime']
res[0]['Extinction Correction'] = 'Applied using Hayes & Latham'
res[0]['extinction_corr'] = extCorr
res[0]['skynm'] = sky[0]['nm']
res[0]['skyph'] = sky[0]['ph_10m_nm']
res[0]['ph_10m_nm'] -= skybgd
res[0]['ph_10m_nm'] *= extCorr * len(six)
res[0]['radius_as'] = radius
res[0]['position'] = pos
res[0]['N_spax'] = len(six)
res[0]['meta'] = meta
res[0]['object_spaxel_ids'] = six
res[0]['sky_spaxel_ids'] = skyix
res[0]['sky_spectra'] = sky[0]['spectra']
np.save("sp_" + outname, res)
def handle_A(A, fine, outname=None, standard=None, corrfile=None,
Aoffset=None, radius=2, flat_corrections=None, nosky=False,
lmin=650, lmax=700):
'''Loads 2k x 2k IFU frame "A" and extracts spectra from the locations
in "fine".
Args:
A (string): filename of ifu FITS file to extract from.
fine (string): filename of NumPy file with locations + wavelength
soln
outname (string): filename to write results to
Aoffset (2tuple): X (nm)/Y (pix) shift to apply for flexure correction
radius (float): Extraction radius in arcsecond
flat_corrections (list): A list of FlatCorrection objects for
correcting the extraction
nosky (Boolean): if True don't subtract sky, merely sum in aperture
Returns:
The extracted spectrum, a dictionary:
{'ph_10m_nm': Flux in photon / 10 m / nanometer integrated
'nm': Wavelength solution in nm
'N_spax': Total number of spaxels that created ph_10m_nm
'skyph': Sky flux in photon / 10 m / nanometer / spaxel
'radius_as': Extraction radius in arcsec
'pos': X/Y extraction location of spectrum in arcsec}
Raises:
None
'''
fine = np.load(fine)
if outname is None:
outname = "%s" % (A)
if Aoffset is not None:
ff = np.load(Aoffset)
flexure_x_corr_nm = ff[0]['dXnm']
flexure_y_corr_pix = ff[0]['dYpix']
print "Dx %2.1f nm | Dy %2.1f px" % (ff[0]['dXnm'], ff[0]['dYpix'])
else:
flexure_x_corr_nm = 0
flexure_y_corr_pix = 0
if os.path.isfile(outname+".npy"):
print "USING extractions in %s.npy!" % outname
print "rm %s.npy # if you want to recreate extractions" % outname
E, meta = np.load(outname+".npy")
E_var, meta_var = np.load("var_" + outname + ".npy")
else:
print "\nCREATING extractions ..."
spec = pf.open(A)
adcspeed = spec[0].header["ADCSPEED"]
if adcspeed == 2: read_var = 22*22
else: read_var = 5*5
var = addcon(A, str(read_var), "var_" + outname + ".fits")
print "\nExtracting object spectra"
E, meta = Wavelength.wavelength_extract(spec, fine, filename=outname,
flexure_x_corr_nm=flexure_x_corr_nm,
flexure_y_corr_pix=flexure_y_corr_pix,
flat_corrections = flat_corrections)
meta['airmass'] = spec[0].header['airmass']
header = {}
for k,v in spec[0].header.iteritems():
try: header[k] = v
except: pass
meta['HA'] = spec[0].header['HA']
meta['Dec'] = spec[0].header['Dec']
meta['RA'] = spec[0].header['RA']
meta['PRLLTC'] = spec[0].header['PRLLTC']
meta['equinox'] = spec[0].header['Equinox']
meta['utc'] = spec[0].header['utc']
meta['header'] = header
meta['exptime'] = spec[0].header['exptime']
np.save(outname, [E, meta])
print "\nExtracting variance spectra"
E_var, meta_var = Wavelength.wavelength_extract(var, fine,
filename=outname,
flexure_x_corr_nm = flexure_x_corr_nm,
flexure_y_corr_pix = flexure_y_corr_pix,
flat_corrections=flat_corrections)
np.save("var_" + outname, [E_var, meta_var])
object = meta['header']['OBJECT'].split()[0]
sixA, posA, adcpos, radius_used = identify_spectra_gui(E, radius=radius,
PRLLTC=Angle(meta['PRLLTC'], unit='deg'),
lmin=lmin, lmax=lmax, object=object, airmass=meta['airmass'])
to_image(E, meta, outname, posA=posA, adcpos=adcpos)
if standard is None:
kixA = identify_bgd_spectra(E, posA, inner=radius_used*1.1)
else:
kixA = identify_sky_spectra(E, posA, inner=radius_used*1.1)
# get the mean spectrum over the selected spaxels
resA = interp_spectra(E, sixA, outname=outname+".pdf", corrfile=corrfile)
skyA = interp_spectra(E, kixA, outname=outname+"_sky.pdf", corrfile=corrfile)
varA = interp_spectra(E_var, sixA, outname=outname+"_var.pdf", corrfile=corrfile)
## Plot out the X/Y positions of the selected spaxels
XSA = []
YSA = []
XSK = []
YSK = []
for ix in sixA:
XSA.append(E[ix].X_as)
YSA.append(E[ix].Y_as)
for ix in kixA:
XSK.append(E[ix].X_as)
YSK.append(E[ix].Y_as)
pl.figure()
pl.clf()
pl.ylim(-30,30)
pl.xlim(-30,30)
pl.scatter(XSA,YSA, color='red', marker='H', linewidth=.1)
pl.scatter(XSK,YSK, color='green', marker='H', linewidth=.1)
pl.savefig("XYs_%s.pdf" % outname)
pl.close()
# / End Plot
# Define our standard wavelength grid
ll = Wavelength.fiducial_spectrum()
# Resample sky onto standard wavelength grid
sky_A = interp1d(skyA[0]['nm'], skyA[0]['ph_10m_nm'], bounds_error=False)
sky = sky_A(ll)
# Resample variance onto standard wavelength grid
var_A = interp1d(varA[0]['nm'], varA[0]['ph_10m_nm'], bounds_error=False)
varspec = var_A(ll)
# Copy and resample object spectrum onto standard wavelength grid
res = np.copy(resA)
res = [{"doc": resA[0]["doc"], "ph_10m_nm": np.copy(resA[0]["ph_10m_nm"]),
"spectra": np.copy(resA[0]["spectra"]),
"coefficients": np.copy(resA[0]["coefficients"]),
"nm": np.copy(resA[0]["ph_10m_nm"])}]
res[0]['nm'] = np.copy(ll)
f1 = interp1d(resA[0]['nm'], resA[0]['ph_10m_nm'], bounds_error=False)
# Calculate airmass correction
airmass = meta['airmass']
extCorr = 10**(Atm.ext(ll*10) * airmass/2.5)
print "Median airmass corr: %.4f" % np.median(extCorr)
# Calculate output corrected spectrum
if nosky:
# Account for airmass and aperture
res[0]['ph_10m_nm'] = f1(ll) * extCorr * len(sixA)
else:
# Account for sky, airmass and aperture
res[0]['ph_10m_nm'] = (f1(ll)-sky_A(ll)) * extCorr * len(sixA)
# Process standard star objects
if standard is not None:
print "STANDARD"
# Extract reference data
wav = standard[:,0]/10.0
flux = standard[:,1]
# Calculate/Interpolate correction onto object wavelengths
fun = interp1d(wav, flux, bounds_error=False, fill_value = np.nan)
correction0 = fun(res[0]['nm'])/res[0]['ph_10m_nm']
# Filter for resolution
flxf = filters.gaussian_filter(flux,19.)
# Calculate/Interpolate filtered correction
fun = interp1d(wav, flxf, bounds_error=False, fill_value = np.nan)
correction = fun(res[0]['nm'])/res[0]['ph_10m_nm']
# Use unfiltered for H-beta region
ROI = (res[0]['nm'] > 470.) & (res[0]['nm'] < 600.)
correction[ROI] = correction0[ROI]
res[0]['std-correction'] = correction
res[0]['std-maxnm'] = np.max(wav)
res[0]['exptime'] = meta['exptime']
res[0]['Extinction Correction'] = 'Applied using Hayes & Latham'
res[0]['extinction_corr'] = extCorr
res[0]['skyph'] = sky * len(sixA)
res[0]['skynm'] = ll
res[0]['var'] = varspec
res[0]['radius_as'] = radius_used
res[0]['position'] = posA
res[0]['N_spax'] = len(sixA)
res[0]['meta'] = meta
res[0]['object_spaxel_ids'] = sixA
res[0]['sky_spaxel_ids'] = kixA
res[0]['sky_spectra'] = skyA[0]['spectra']
coef = chebfit(np.arange(len(ll)), ll, 4)
xs = np.arange(len(ll)+1)
newll = chebval(xs, coef)
res[0]['dlam'] = np.diff(newll)
np.save("sp_" + outname, res)
print "Wrote sp_"+outname+".npy"