def extract(filename, outname, owave=None):
d = nirspecOpen(filename)
w = d[1].data.copy()
s = d[2].data.copy()
d = d[0].data[:-20].copy()
if owave is None:
w0 = w[0]
w1 = w[-1]
owave = numpy.linspace(w0, w1, w.size)
trace = d.sum(1)
maxd = ndimage.maximum_filter(trace, 3)
peaks = numpy.where(maxd == trace)[0]
peak = peaks[maxd[peaks].argmax() - 1]
spec = d[peak - 3:peak + 4].sum(0)
specmod = interpolate.splrep(w, spec)
outspec = interpolate.splev(owave, specmod)
skymod = interpolate.splrep(w, s)
sky = interpolate.splev(owave, skymod)
specmean = outspec.mean()
outspec /= specmean
var = ((sky * 7 + outspec) * 5. + 2 * 25**2) / (5. * specmean)**2
st.make_spec(outspec, var, owave, outname, clobber=True)
def nir_extract(filename,
outname,
x1=0,
x2=0,
y1=0,
y2=0,
informat='new',
outformat='text',
apmin=-4.,
apmax=4.,
muorder=3,
sigorder=3,
fitrange=None,
weight='gauss',
owave=None,
do_plot=True,
do_subplot=True,
stop_if_nan=True):
"""
Given the calibrated and sky-subtracted 2d spectrum, extracts the 1d
spectrum, taking into account things specific to the form of the 2d
NIRSPEC spectra.
"""
""" Set up NIRSPEC detector characteristics """
gain = 4. # Value in e-/ADU
rdnoise = 25. # Value in e-
""" Read the 2d spectrum """
print ""
d, w, v = read_nirspec_spec(filename, x1, x2, y1, y2, informat=informat)
if informat == 'old':
s = v
else:
s = numpy.sqrt(v)
""" Show the 2d spectrum """
mp, sp = c.sigma_clip(d)
if do_plot:
plot.figure(1)
plot.clf()
plot.imshow(d,
origin='lower',
cmap=plot.cm.gray,
vmin=mp - sp,
vmax=mp + 4 * sp)
""" Find the trace """
if (do_plot):
# plot.figure(2)
plot.figure(2, figsize=(15, 12))
plot.clf()
mu0, sig0 = ss.find_trace(d,
apmin=apmin,
apmax=apmax,
do_plot=do_plot,
do_subplot=do_subplot)
""" Trace the object down the 2d spectrum """
mupoly, sigpoly = ss.trace_spectrum(d,
mu0,
sig0,
fitrange=fitrange,
muorder=muorder,
sigorder=sigorder,
do_plot=do_plot,
do_subplot=do_subplot)
""" Extract the 1d spectrum """
spec, varspec = ss.extract_spectrum(d,
mupoly,
sigpoly,
apmin=apmin,
apmax=apmax,
weight=weight,
sky=s,
gain=gain,
rdnoise=rdnoise,
do_plot=do_plot,
do_subplot=do_subplot)
if informat == 'new':
varspec = v
""" Check for NaN values in the returned spectra """
spec_nan = False
if ((numpy.isnan(spec)).sum() > 0 or (numpy.isnan(varspec)).sum() > 0):
spec_nan = True
if (stop_if_nan and spec_nan):
print ""
print "WARNING: There is at least one NaN in the extracted spectrum "
print " or variance spectrum. Stopping here. "
print "If you understand the cause of this/these NaN value(s) and want"
print " to continue, re-run nir_extract with stop_if_nan=False"
print ""
return
elif (spec_nan):
spec[numpy.isnan(spec)] = 0.0
varspec[numpy.isnan(varspec)] = 2.0 * numpy.nanmax(varspec)
"""
Linearize the wavelength scale if needed (taken from code by Matt Auger)
Old format: explicitly linearize here
New format: wavelength scale is alread linearized by niredux
"""
if owave is None:
if informat == 'old':
w0 = w[0]
w1 = w[-1]
owave = numpy.linspace(w0, w1, w.size)
else:
owave = w
tmpwave, outspec = ss.resample_spec(w, spec, owave)
tmpwave, outvar = ss.resample_spec(w, varspec, owave)
tmpwave, sky = ss.resample_spec(w, s, owave)
""" Plot the linearized spectrum, with rms """
rms = numpy.sqrt(outvar)
if do_plot:
if (do_subplot):
plot.figure(3)
else:
# Re-use fig 4, which showed an earlier version of the extracted
# spectrum
plot.figure(4)
plot.clf()
plot.axhline(color='k')
plot.plot(owave, outspec, linestyle='steps', label='spectrum')
plot.plot(owave, rms, 'r', linestyle='steps', label='RMS')
plot.xlabel('Wavelength')
plot.ylabel('Relative Flux Density')
plot.title('Output Spectrum')
plot.legend(loc='best')
plot.xlim([owave[0], owave[-1]])
""" Write the output spectrum in the requested format """
if (outformat == 'mwa'):
st.make_spec(outspec, outvar, owave, outname, clobber=True)
else:
ss.save_spectrum(outname, owave, outspec, outvar)
def extract(image,
varimage,
outname,
width=2.,
offset=0.,
pos=None,
minwave=None,
maxwave=None,
response_image=None,
response_model=None,
regions=[],
centroid=None):
if outname.lower().find('fits') < 1:
outname = "%s.fits" % outname
resp = None
respwave = None
if response_image is not None:
if response_model is None:
# print "No response model included; not performing response correction."
import cPickle
resp = cPickle.load(open(response_image))
else:
resp = get_response(response_image, response_model, regions)
elif response_model is not None:
print "No response image included; not performing response correction."
if minwave is None:
minwave = -10.
if maxwave is None:
maxwave = 1e99
data = pyfits.open(image)[0].data.copy()
if data.ndim == 3:
if data.shape[0] > 1:
print "Only working on first image plane of 3D image."
data = data[0].copy()
wave = st.wavelength(image)
indx = scipy.where((wave > minwave) & (wave < maxwave))[0]
minx, maxx = indx.min(), indx.max()
wave = wave[minx:maxx]
data = data[:, minx:maxx]
tmp = data.copy()
tmp[numpy.isnan(data)] = 0.
ospec = wave * 0.
ovar = wave * 0.
x = numpy.arange(data.shape[0])
if centroid is None:
n = tmp.shape[1] / 100
peaks = []
bounds = numpy.linspace(0, tmp.shape[1], n + 1)
for i in range(n):
a, b = bounds[i], bounds[i + 1]
p = tmp[:, a:b].sum(1).argmax()
peaks.append([(a + b) / 2., p])
peak = sf.lsqfit(numpy.asarray(peaks), 'polynomial', 3)
if pos is not None:
peak['coeff'][0] = pos
peaks = sf.genfunc(numpy.arange(tmp.shape[1]), 0., peak)
center = wave * 0.
for i in range(center.size):
d = data[:, i].copy()
peak = peaks[i]
if peak < 0:
peak = 0
if peak >= d.shape:
peak = d.shape - 1.
fit = numpy.array([0., d[peak], peak, 1.])
cond = ~numpy.isnan(d)
input = numpy.empty((d[cond].size, 2))
input[:, 0] = x[cond].copy()
input[:, 1] = d[cond].copy()
fit = sf.ngaussfit(input, fit)[0]
center[i] = fit[2]
fit = sf.lsqfit(ndimage.median_filter(center, 17), 'polynomial', 5)
centroid = sf.genfunc(scipy.arange(wave.size), 0., fit)
#import pylab
#pylab.plot(center-centroid)
#pylab.show()
xvals = scipy.arange(data.shape[0])
var = pyfits.open(varimage)[0].data.copy()
if var.ndim == 3:
var = var[0].copy()
var = var[:, minx:maxx]
cond = (numpy.isnan(var)) | (numpy.isnan(data))
for i in range(ovar.size):
c = centroid[i] + offset
wid = width
mask = xvals * 0.
mask[(xvals - c < wid) & (xvals - c > 0.)] = wid - (xvals[
(xvals - c < wid) & (xvals - c > 0.)] - c)
mask[(xvals - c < wid - 1) & (xvals - c > 0.)] = 1.
mask[(c - xvals < wid + 1) & (c - xvals > 0.)] = (wid + 1) - (
c - xvals[(c - xvals < wid + 1) & (c - xvals > 0.)])
mask[(c - xvals < wid) & (c - xvals > 0.)] = 1.
mask[cond[:, i]] = 0.
mask /= mask.sum()
ospec[i] = (mask[~cond[:, i]] * data[:, i][~cond[:, i]]).sum()
ovar[i] = ((mask[~cond[:, i]]**2) * var[:, i][~cond[:, i]]).sum()
badpix = numpy.where(numpy.isnan(ospec))[0]
ospec[badpix] = 0.
ovar[badpix] = ovar[~numpy.isnan(ovar)].max() * 1e6
med = numpy.median(ospec)
ospec /= med
ovar /= med**2
if resp is not None:
# if respwave is None:
# resp = sf.genfunc(wave,0,resp)
# else:
# resp = interpolate.splrep(respwave,resp)
resp = interpolate.splev(wave, resp)
ospec *= resp
ovar *= resp**2
st.make_spec(ospec, ovar, wave, outname, clobber=True)
return centroid
def lris_extract(filename, outname, weightfile=None,trimfile=False,x1=0, x2=0, y1=0, y2=0,
informat='new',outformat='text', apmin=-4., apmax=4.,
muorder=3, sigorder=3, fitrange=None, weight='gauss',
owave=None, do_plot=True, do_subplot=True,
stop_if_nan=True, weighted_var=True, output_plot = None, output_plot_dir = None,crval1=None,dispaxis='x',findmultiplepeaks=False,return_data=True,nan_to_zero=False,maxpeaks=2):
#clear_all()
"""
Given the calibrated and sky-subtracted 2d spectrum, extracts the 1d
spectrum, taking into account things specific to the form of the 2d
LRIS spectra.
"""
""" Set up NIRSPEC detector characteristics """
gain = 4. # Value in e-/ADU
rdnoise = 25. # Value in e-
""" Read the 2d spectrum """
print ""
d,w,v = read_lris_spec(filename,weightfilename=weightfile,x1=x1,x2=x2,y1=y1,y2=y2,trimfile=trimfile,informat=informat,weighted_var=weighted_var,crval1=crval1)
if informat=='old':
s = v
else:
s = numpy.sqrt(v)
bounds_arr = np.array([0,np.min(np.shape(d))])
fitmp,fixmu = False,False
apertures = 4*np.ones(1)
if findmultiplepeaks:
if np.min(np.shape(d)) > 3+(1+2*np.fabs(apmin)+2*apmax)*(maxpeaks-1):
fitmp,fixmu,mp_out,bounds_arr,apertures = ss.find_multiple_peaks(d,maxpeaks=maxpeaks,output_plot=output_plot,output_plot_dir=output_plot_dir,check_aps=True)
else:
mpflag,ipk = False,maxpeaks-1
while ((not mpflag) & (ipk > 1)):
if np.min(np.shape(d)) > 3+(1+2*np.fabs(apmin)+2*apmax)*(ipk-1):
fitmp,fixmu,mp_out,bounds_arr,apertures = ss.find_multiple_peaks(d,maxpeaks=ipk,output_plot=output_plot,output_plot_dir=output_plot_dir,check_aps=True)
mpflag = True
ipk -= 1
if fitmp:
numpeaks = np.shape(mp_out)[1]
mutmp = mp_out[1]
for imle in range(0,numpeaks):
output_plot_tmp = output_plot
if output_plot != None:
if imle != 0: output_plot_tmp = 'line%i.'%(imle+1) + output_plot
dlb,dub = bounds_arr[2*imle],bounds_arr[2*imle+1]
if dispaxis == 'x':
owavet,outspect,outvart = lris_extract_(d[dlb:dub+1,:],w,v[dlb:dub+1,:],s[dlb:dub+1,:],gain,rdnoise,outname,informat=informat,outformat=outformat, apmin=-1*apertures[imle], apmax=apertures[imle],muorder=muorder, sigorder=sigorder, fitrange=fitrange, weight=weight,owave=None, do_plot=do_plot, do_subplot=do_subplot, stop_if_nan=stop_if_nan, weighted_var=weighted_var, output_plot = output_plot_tmp, output_plot_dir = output_plot_dir,dispaxis=dispaxis,nan_to_zero=nan_to_zero,return_data=return_data)
else:
owavet,outspect,outvart = lris_extract_(d[:,dlb:dub+1],w,v[dlb:dub+1,:],s[:,dlb:dub+1],gain,rdnoise,outname,informat=informat,outformat=outformat, apmin=-1*apertures[imle], apmax=apertures[imle],muorder=muorder, sigorder=sigorder, fitrange=fitrange, weight=weight,owave=None, do_plot=do_plot, do_subplot=do_subplot, stop_if_nan=stop_if_nan, weighted_var=weighted_var, output_plot = output_plot_tmp, output_plot_dir = output_plot_dir,dispaxis=dispaxis,nan_to_zero=nan_to_zero,return_data=return_data)
if imle == 0:
owave,outspec,outvar = np.zeros((numpeaks,len(owavet))),np.zeros((numpeaks,len(outspect))),np.zeros((numpeaks,len(outvart)))
owave[imle],outspec[imle],outvar[imle] = owavet,outspect,outvart
else:
dlb,dub = bounds_arr[0],bounds_arr[1]
if dispaxis == 'x':
owave,outspec,outvar = lris_extract_(d[dlb:dub+1,:],w,v[dlb:dub+1,:],s[dlb:dub+1,:],gain,rdnoise,outname,informat=informat,outformat=outformat, apmin=-1*apertures[0], apmax=apertures[0],muorder=muorder, sigorder=sigorder, fitrange=fitrange, weight=weight,owave=None, do_plot=do_plot, do_subplot=do_subplot, stop_if_nan=stop_if_nan, weighted_var=weighted_var, output_plot = output_plot, output_plot_dir = output_plot_dir,dispaxis=dispaxis,nan_to_zero=nan_to_zero,return_data=return_data)
else:
owave,outspec,outvar = lris_extract_(d[:,dlb:dub+1],w,v[:,dlb:dub+1],s[:,dlb:dub+1],gain,rdnoise,outname,informat=informat,outformat=outformat, apmin=-1*apertures[0], apmax=apertures[0],muorder=muorder, sigorder=sigorder, fitrange=fitrange, weight=weight,owave=None, do_plot=do_plot, do_subplot=do_subplot, stop_if_nan=stop_if_nan, weighted_var=weighted_var, output_plot = output_plot, output_plot_dir = output_plot_dir,dispaxis=dispaxis,nan_to_zero=nan_to_zero,return_data=return_data)
""" Write the output spectrum in the requested format """
if(outformat == 'mwa'):
st.make_spec(outspec,outvar,owave,outname,clobber=True)
else:
ss.save_spectrum(outname,owave,outspec,outvar)