def ReduceTwo(speclist,
flatlist='',
biaslist='',
HeNeAr_file='',
stdstar='',
trace_recenter=False,
ntracesteps=15,
airmass_file='apoextinct.dat',
flat_mode='spline',
flat_order=9,
flat_response=True,
apwidth=8,
skysep=3,
skywidth=7,
skydeg=0,
HeNeAr_prev=False,
HeNeAr_interac=True,
HeNeAr_tol=20,
HeNeAr_order=3,
display_HeNeAr=False,
std_mode='spline',
std_order=12,
display_std=False,
trim=True,
write_reduced=True,
display=True,
display_final=True):
if (len(biaslist) > 0):
bias = pydis.biascombine(biaslist, trim=trim)
else:
bias = 0.0
if (len(biaslist) > 0) and (len(flatlist) > 0):
flat, fmask_out = pydis.flatcombine(flatlist,
bias,
trim=trim,
mode=flat_mode,
display=False,
flat_poly=flat_order,
response=flat_response)
else:
flat = 1.0
fmask_out = (1, )
if HeNeAr_prev is False:
prev = ''
else:
prev = HeNeAr_file + '.lines'
# do the HeNeAr mapping first, must apply to all science frames
if (len(HeNeAr_file) > 0):
wfit = pydis.HeNeAr_fit(HeNeAr_file,
trim=trim,
fmask=fmask_out,
interac=HeNeAr_interac,
previous=prev,
mode='poly',
display=display_HeNeAr,
tol=HeNeAr_tol,
fit_order=HeNeAr_order)
# read in the list of target spectra
# assumes specfile is a list of file names of object
#-> wrap with array and flatten because Numpy sucks with one-element arrays...
specfile = np.array([np.loadtxt(speclist, dtype='string')]).flatten()
for i in range(len(specfile)):
spec = specfile[i]
print("> Processing file " + spec + " [" + str(i) + "/" +
str(len(specfile)) + "]")
# raw, exptime, airmass, wapprox = pydis.OpenImg(spec, trim=trim)
img = pydis.OpenImg(spec, trim=trim)
raw = img.data
exptime = img.exptime
airmass = img.airmass
wapprox = img.wavelength
# remove bias and flat, divide by exptime
data = ((raw - bias) / flat) / exptime
if display is True:
plt.figure()
plt.imshow(np.log10(data),
origin='lower',
aspect='auto',
cmap=cm.Greys_r)
plt.title(spec + ' (flat and bias corrected)')
plt.show()
# with reduced data, trace BOTH apertures
trace_1 = pydis.ap_trace(data,
fmask=fmask_out,
nsteps=ntracesteps,
recenter=trace_recenter,
interac=True)
trace_2 = pydis.ap_trace(data,
fmask=fmask_out,
nsteps=ntracesteps,
recenter=trace_recenter,
interac=True)
xbins = np.arange(data.shape[1])
if display is True:
plt.figure()
plt.imshow(np.log10(data),
origin='lower',
aspect='auto',
cmap=cm.Greys_r)
plt.plot(xbins, trace_1, 'b', lw=1)
plt.plot(xbins, trace_1 - apwidth, 'r', lw=1)
plt.plot(xbins, trace_1 + apwidth, 'r', lw=1)
plt.plot(xbins, trace_1 - apwidth - skysep, 'g', lw=1)
plt.plot(xbins, trace_1 - apwidth - skysep - skywidth, 'g', lw=1)
plt.plot(xbins, trace_1 + apwidth + skysep, 'g', lw=1)
plt.plot(xbins, trace_1 + apwidth + skysep + skywidth, 'g', lw=1)
plt.plot(xbins, trace_2, 'b', lw=1)
plt.plot(xbins, trace_2 - apwidth, 'r', lw=1)
plt.plot(xbins, trace_2 + apwidth, 'r', lw=1)
plt.plot(xbins, trace_2 - apwidth - skysep, 'g', lw=1)
plt.plot(xbins, trace_2 - apwidth - skysep - skywidth, 'g', lw=1)
plt.plot(xbins, trace_2 + apwidth + skysep, 'g', lw=1)
plt.plot(xbins, trace_2 + apwidth + skysep + skywidth, 'g', lw=1)
plt.title('(Both Traces, with aperture and sky regions)')
plt.show()
t_indx = 1
# now do the processing for both traces as if separate stars
for trace in [trace_1, trace_2]:
tnum = '_' + str(t_indx)
t_indx = t_indx + 1
# extract the spectrum, measure sky values along trace, get flux errors
ext_spec, sky, fluxerr = pydis.ap_extract(data,
trace,
apwidth=apwidth,
skysep=skysep,
skywidth=skywidth,
skydeg=skydeg,
coaddN=1)
if (len(HeNeAr_file) > 0):
wfinal = pydis.mapwavelength(trace, wfit, mode='poly')
else:
# if no line lamp given, use approx from the img header
wfinal = wapprox
# subtract local sky level, divide by exptime to get flux units
# (counts / sec)
flux_red = (ext_spec - sky)
# now correct the spectrum for airmass extinction
flux_red_x = pydis.AirmassCor(wfinal,
flux_red,
airmass,
airmass_file=airmass_file)
# now get flux std IF stdstar is defined
# !! assume first object in list is std star !!
if (len(stdstar) > 0) and (i == 0):
sens_flux = pydis.DefFluxCal(wfinal,
flux_red_x,
stdstar=stdstar,
mode=std_mode,
polydeg=std_order,
display=display_std)
sens_wave = wfinal
elif (len(stdstar) == 0) and (i == 0):
# if 1st obj not the std, then just make array of 1's to multiply thru
sens_flux = np.ones_like(flux_red_x)
sens_wave = wfinal
# final step in reduction, apply sensfunc
ffinal, efinal = pydis.ApplyFluxCal(wfinal, flux_red_x, fluxerr,
sens_wave, sens_flux)
if write_reduced is True:
pydis._WriteSpec(spec + tnum, wfinal, ffinal, efinal, trace)
now = datetime.datetime.now()
lout = open(spec + tnum + '.log', 'w')
lout.write(
'# This file contains the reduction parameters \n' +
'# used in autoreduce for ' + spec + '\n')
lout.write('DATE-REDUCED = ' + str(now) + '\n')
lout.write('HeNeAr_tol = ' + str(HeNeAr_tol) + '\n')
lout.write('HeNeAr_order = ' + str(HeNeAr_order) + '\n')
lout.write('trace1 = ' + str(False) + '\n')
lout.write('ntracesteps = ' + str(ntracesteps) + '\n')
lout.write('trim = ' + str(trim) + '\n')
lout.write('response = ' + str(flat_response) + '\n')
lout.write('apwidth = ' + str(apwidth) + '\n')
lout.write('skysep = ' + str(skysep) + '\n')
lout.write('skywidth = ' + str(skywidth) + '\n')
lout.write('skydeg = ' + str(skydeg) + '\n')
lout.write('stdstar = ' + str(stdstar) + '\n')
lout.write('airmass_file = ' + str(airmass_file) + '\n')
lout.close()
if display_final is True:
# the final figure to plot
plt.figure()
# plt.plot(wfinal, ffinal)
plt.errorbar(wfinal, ffinal, yerr=efinal)
plt.xlabel('Wavelength')
plt.ylabel('Flux')
plt.title(spec)
#plot within percentile limits
plt.ylim((np.percentile(ffinal, 2), np.percentile(ffinal, 98)))
plt.show()
return
# plt.figure()
# plt.plot(wfinal,'r')
# plt.show()
# subtract local sky level, divide by exptime to get flux units
# (counts / sec)
flux_red = (ext_spec - sky)
# now correct the spectrum for airmass extinction
flux_red_x = pydis.AirmassCor(wfinal, flux_red, airmass,
airmass_file=airmass_file)
# now get flux std IF stdstar is defined
# !! assume first object in list is std star !!
if (len(stdstar) > 0) and (i==0):
sens_flux = pydis.DefFluxCal(wfinal, flux_red_x, stdstar=stdstar,
mode=std_mode, polydeg=std_order, display=display_std)
sens_wave = wfinal
elif (len(stdstar) == 0) and (i==0):
# if 1st obj not the std, then just make array of 1's to multiply thru
sens_flux = np.ones_like(flux_red_x)
sens_wave = wfinal
# final step in reduction, apply sensfunc
ffinal,efinal = pydis.ApplyFluxCal(wfinal, flux_red_x, fluxerr,
def autoreduce(speclist,
flatlist='',
biaslist='',
HeNeAr_file='',
stdstar='',
trace_recenter=False,
trace_interac=True,
trace1=False,
ntracesteps=15,
airmass_file='apoextinct.dat',
flat_mode='spline',
flat_order=9,
flat_response=True,
apwidth=8,
skysep=3,
skywidth=7,
skydeg=0,
HeNeAr_prev=False,
HeNeAr_interac=True,
HeNeAr_tol=20,
HeNeAr_order=3,
display_HeNeAr=False,
std_mode='spline',
std_order=12,
display_std=False,
trim=True,
write_reduced=True,
display=True,
display_final=True,
silent=True):
"""
A wrapper routine to carry out the full steps of the spectral
reduction and calibration. Steps include:
1) combines bias and flat images
2) maps wavelength in the HeNeAr image
3) perform simple image reduction: Data = (Raw - Bias)/Flat
4) trace spectral aperture
5) extract spectrum
6) measure sky along extracted spectrum
7) apply flux calibration
8) write output files
Parameters
----------
speclist : str
Path to file containing list of science images.
flatlist : str
Path to file containing list of flat images.
biaslist : str
Path to file containing list of bias images.
HeNeAr_file : str
Path to the HeNeAr calibration image
stdstar : str
Name of the standard star to use for flux calibration. If
nothing is entered for "stdstar", no flux calibration will be
computed. (Default is '').
NOTE1: must include the subdir for the star, e.g. 'spec50cal/feige34'.
NOTE2: Assumes the first star in "speclist" is the standard star.
trace1 : bool, optional
use trace1=True if only perform aperture trace on first object in
speclist. Useful if e.g. science targets are faint, and first
object is a bright standard star. Note: assumes star placed at
same position in spatial direction. (Default is False)
trace_recenter : bool, optional
If trace1=True, set this to True to allow for small linear
adjustments to the trace (default is False)
trace_interac : bool, optional
Set to True if user should interactively select aperture center
for each object spectrum. (Default is True)
ntracesteps : int, optional
Number of bins in X direction to chop image into. Use
fewer bins if ap_trace is having difficulty, such as with faint
targets (default here is 25, minimum is 4)
apwidth : int, optional
The width along the Y axis of the trace to extract. Note: a fixed
width is used along the whole trace. (default here is 3 pixels)
skysep : int, optional
The separation in pixels from the aperture to the sky window.
(Default is 25)
skywidth : int, optional
The width in pixels of the sky windows on either side of the
aperture. (Default is 75)
HeNeAr_interac : bool, optional
Should the HeNeAr identification be done interactively (manually)?
(Default here is False)
HeNeAr_tol : int, optional
When in automatic mode, the tolerance in pixel units between
linelist entries and estimated wavelengths for the first few
lines matched... use carefully. (Default here is 20)
HeNeAr_order : int, optional
The polynomial order to use to interpolate between identified
peaks in the HeNeAr (Default is 2)
display_HeNeAr : bool, optional
std_mode : str, optional
Fit mode to use with the flux standard star. Options are 'spline'
and 'poly' (Default is 'spline')
std_order : int, optional
The order of polynomial to fit, if std_mode='poly'. (Default is 12)
display_std : bool, optional
If set, display plots of the flux standard being fit (Default is
False)
trim : bool, optional
Trim the image using the DATASEC keyword in the header, assuming
has format of [0:1024,0:512] (Default is True)
write_reduced : bool, optional
Set to True to write output files, including the .spec file with
columns (wavelength, flux); the .trace file with columns
(X pixel number, Y pixel of trace); .log file with record of
settings used in this routine for reduction. (Default is True)
display : bool, optional
Set to True to display intermediate steps along the way.
(Default is True)
display_final : bool, optional
Set to False to suppress plotting the final reduced spectrum to
the screen. Useful for running in quiet batch mode. (Default is
True)
"""
if (len(biaslist) > 0):
bias = pydis.biascombine(biaslist, trim=trim, silent=silent)
else:
bias = 0.0
if (len(biaslist) > 0) and (len(flatlist) > 0):
flat, fmask_out = pydis.flatcombine(flatlist,
bias,
trim=trim,
mode=flat_mode,
display=False,
flat_poly=flat_order,
response=flat_response)
else:
flat = 1.0
fmask_out = (1, )
if HeNeAr_prev is False:
prev = ''
else:
prev = HeNeAr_file + '.lines'
# do the HeNeAr mapping first, must apply to all science frames
if (len(HeNeAr_file) > 0):
wfit = pydis.HeNeAr_fit(HeNeAr_file,
trim=trim,
fmask=fmask_out,
interac=HeNeAr_interac,
previous=prev,
mode='poly',
display=display_HeNeAr,
tol=HeNeAr_tol,
fit_order=HeNeAr_order)
# read in the list of target spectra
# assumes specfile is a list of file names of object
specfile = np.array([np.loadtxt(speclist, dtype='string')]).flatten()
for i in range(len(specfile)):
spec = specfile[i]
if silent is False:
print("> Processing file " + spec + " [" + str(i) + "/" +
str(len(specfile)) + "]")
# raw, exptime, airmass, wapprox = pydis.OpenImg(spec, trim=trim)
img = pydis.OpenImg(spec, trim=trim)
raw = img.data
exptime = img.exptime
airmass = img.airmass
wapprox = img.wavelength
# remove bias and flat, divide by exptime
data = ((raw - bias) / flat) / exptime
if display is True:
plt.figure()
plt.imshow(np.log10(data),
origin='lower',
aspect='auto',
cmap=cm.Greys_r)
plt.title(spec + ' (flat and bias corrected)')
plt.show()
# with reduced data, trace the aperture
if (i == 0) or (trace1 is False):
trace = pydis.ap_trace(data,
fmask=fmask_out,
nsteps=ntracesteps,
recenter=trace_recenter,
interac=trace_interac)
# extract the spectrum, measure sky values along trace, get flux errors
ext_spec, sky, fluxerr = pydis.ap_extract(data,
trace,
apwidth=apwidth,
skysep=skysep,
skywidth=skywidth,
skydeg=skydeg,
coaddN=1)
xbins = np.arange(data.shape[1])
if display is True:
plt.figure()
plt.imshow(np.log10(data),
origin='lower',
aspect='auto',
cmap=cm.Greys_r)
plt.plot(xbins, trace, 'b', lw=1)
plt.plot(xbins, trace - apwidth, 'r', lw=1)
plt.plot(xbins, trace + apwidth, 'r', lw=1)
plt.plot(xbins, trace - apwidth - skysep, 'g', lw=1)
plt.plot(xbins, trace - apwidth - skysep - skywidth, 'g', lw=1)
plt.plot(xbins, trace + apwidth + skysep, 'g', lw=1)
plt.plot(xbins, trace + apwidth + skysep + skywidth, 'g', lw=1)
plt.title('(with trace, aperture, and sky regions)')
plt.show()
if (len(HeNeAr_file) > 0):
wfinal = pydis.mapwavelength(trace, wfit, mode='poly')
else:
# if no line lamp given, use approx from the img header
wfinal = wapprox
# plt.figure()
# plt.plot(wfinal,'r')
# plt.show()
# subtract local sky level, divide by exptime to get flux units
# (counts / sec)
flux_red = (ext_spec - sky)
# now correct the spectrum for airmass extinction
flux_red_x = pydis.AirmassCor(wfinal,
flux_red,
airmass,
airmass_file=airmass_file)
# now get flux std IF stdstar is defined
# !! assume first object in list is std star !!
if (len(stdstar) > 0) and (i == 0):
sens_flux = pydis.DefFluxCal(wfinal,
flux_red_x,
stdstar=stdstar,
mode=std_mode,
polydeg=std_order,
display=display_std)
sens_wave = wfinal
elif (len(stdstar) == 0) and (i == 0):
# if 1st obj not the std, then just make array of 1's to multiply thru
sens_flux = np.ones_like(flux_red_x)
sens_wave = wfinal
# final step in reduction, apply sensfunc
ffinal, efinal = pydis.ApplyFluxCal(wfinal, flux_red_x, fluxerr,
sens_wave, sens_flux)
if write_reduced is True:
pydis._WriteSpec(spec, wfinal, ffinal, efinal, trace)
now = datetime.datetime.now()
lout = open(spec + '.log', 'w')
lout.write('# This file contains the reduction parameters \n' +
'# used in autoreduce for ' + spec + '\n')
lout.write('DATE-REDUCED = ' + str(now) + '\n')
lout.write('HeNeAr_tol = ' + str(HeNeAr_tol) + '\n')
lout.write('HeNeAr_order = ' + str(HeNeAr_order) + '\n')
lout.write('trace1 = ' + str(trace1) + '\n')
lout.write('ntracesteps = ' + str(ntracesteps) + '\n')
lout.write('trim = ' + str(trim) + '\n')
lout.write('response = ' + str(flat_response) + '\n')
lout.write('apwidth = ' + str(apwidth) + '\n')
lout.write('skysep = ' + str(skysep) + '\n')
lout.write('skywidth = ' + str(skywidth) + '\n')
lout.write('skydeg = ' + str(skydeg) + '\n')
lout.write('stdstar = ' + str(stdstar) + '\n')
lout.write('airmass_file = ' + str(airmass_file) + '\n')
lout.close()
if display_final is True:
# the final figure to plot
plt.figure()
# plt.plot(wfinal, ffinal)
plt.errorbar(wfinal, ffinal, yerr=efinal)
plt.xlabel('Wavelength')
plt.ylabel('Flux')
plt.title(spec)
#plot within percentile limits
plt.ylim((np.percentile(ffinal, 2), np.percentile(ffinal, 98)))
plt.show()
return