def readSpectrum(self, fileName, colWave=0, colFlux=1, skipRows=0):
"""
Reading spectrum in text or FITS format
and update regions and points
"""
if not ".fits" in fileName:
self.spectrum = sp.readSpectrum(fileName,\
colWave=colWave,\
colFlux=colFlux,\
skipRows=skipRows)
else:
self.spectrum = sp.Spectrum()
""" Check more at
http://archive.eso.org/cms/eso-data/help/1dspectra.html
https://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/pyaslDoc/aslDoc/readFitsSpec.html
"""
self.spectrum.wave, self.spectrum.flux = pyasl.read1dFitsSpec(
fileName)
# self.spectrum.wave = self.spectrum.wave.byteswap().newbyteorder()
self.spectrum.flux = self.spectrum.flux.byteswap().newbyteorder(
) #TODO PyAstronomy bug
self.spectrum.name = fileName
self.radialVelocity = 0.0
self.oryginalWavelength = copy.deepcopy(self.spectrum.wave)
self.spectrumNote.set_spectrum(fileName)
def mk_detection_plots(IFU, image_catalog, layoutmode=False, single_id=None):
global wldetect, lineset, dlineset, flinelist, ax_pos
foutmap = "map_{}.fits.gz".format(IFU)
fincube = "data/sf2outcube_median_{}.fits.gz".format(IFU)
frawincube = "data/outcube_median_{}.fits.gz".format(IFU)
fcalincube = "data/coutcube_median_{}.fits.gz".format(IFU)
fnoisecube = "data/sf2ncoutcube_median_{}.fits.gz".format(IFU)
ftcal = "mean_cal.txt"
# load flux calibration
tcal = ascii.read(ftcal, format="fixed_width")
cal_interp = interp1d(tcal["wl[A]"],
tcal["cal[erg/s/cm^2/A/cnt]"],
kind='cubic',
bounds_error=False)
fcatalog = "catalog_{}.txt".format(IFU)
flinelist = "linelist.txt"
#threshold = 0.15
threshold = 0.1 # line detection threshold
wlwin = 50. # width of window for peak fit
#win = 50.
width = 25. / 3600. # size for slice plots and aux images
# load data
outmap = fits.getdata(foutmap)
s = spectrum.readSpectrum(fincube)
sraw = spectrum.readSpectrum(frawincube)
ns = spectrum.readSpectrum(fnoisecube)
t = ascii.read(fcatalog, format="ecsv")
print("Found {} sources in IFU {} ...".format(len(t), IFU))
_t = t
for i, r in enumerate(_t):
#try:
id, ra, dec = r["id"], r["ra_com"], r["dec_com"]
if single_id != None and not id == single_id:
continue
print("Creating plot for source {}, number {} in {}".format(
id, i + 1, len(_t)))
mk_detection_plot(IFU, id, r, s, sraw, ns, outmap, image_catalog,
threshold, wlwin, width, cal_interp, layoutmode)
def readTheoreticalSpectrum(self,
fileName,
colWave=0,
colFlux=1,
skipRows=0):
self.theoreticalSpectrum = sp.readSpectrum(fileName,\
colWave=colWave,\
colFlux=colFlux,\
skipRows=skipRows)
def readSpectrum(self, fileName, colWave=0, colFlux=1, skipRows=0):
"""
Reading spectrum in text or FITS format
and update regions and points
"""
if not ".fits" in fileName:
self.spectrum = sp.readSpectrum(fileName,\
colWave=colWave,\
colFlux=colFlux,\
skipRows=skipRows)
else:
self.spectrum = sp.Spectrum()
""" Check more at
http://archive.eso.org/cms/eso-data/help/1dspectra.html
https://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/pyaslDoc/aslDoc/readFitsSpec.html
"""
self.spectrum.wave = None
self.spectrum.flux = None
# Search FITS data for Molecfit keywords
waveKey = "lambda" # use orignal wavelength not the the molecfit corrected
fluxKey = "cflux" # use telluric absorption corrected flux
dataKeys = [waveKey, fluxKey]
fitsFile = fits.open(fileName)
for hdu in fitsFile:
if hdu.data is None or not hasattr(hdu.data, 'names'):
continue
if all(name in hdu.data.names for name in dataKeys):
self.spectrum.wave = hdu.data[waveKey]
self.spectrum.flux = hdu.data[fluxKey]
self.spectrum.wave = self.spectrum.wave * 1000 # micrometre to nanometre
break
fitsFile.close()
# If no Molecfit formated data is found, fall back to pyastronomy solution used in main version of HANDY
if self.spectrum.wave is None or self.spectrum.flux is None:
self.spectrum.wave, self.spectrum.flux = pyasl.read1dFitsSpec(
fileName)
self.spectrum.flux = self.spectrum.flux.byteswap(
).newbyteorder() # TODO PyAstronomy bug
self.spectrum.name = fileName
self.radialVelocity = 0.0
self.oryginalWavelength = copy.deepcopy(self.spectrum.wave)
default='',
help='Output map.')
parser.add_argument('-b',
'--bad_regions',
type=str,
default='',
help='Output map.')
args = parser.parse_args(sys.argv[1:])
badwlregions = []
t = ascii.read(args.bad_regions)
for r in t:
badwlregions.append([r["start"], r["end"]])
fcube = args.infile
fmapout = args.outfile
detect_threshold = args.detect_threshold
grow_threshold = args.grow_threshold
minsize = args.minsize
s = spectrum.readSpectrum(fcube)
c = s.data
for r in badwlregions:
c[r[0]:r[1]] = 0. # take out bad region
outmap = build_map(c, detect_threshold)
outmap = filter_minsize(outmap, minsize)
save_map(outmap, fmapout)
"dz": "px",
"diam": "arcsec",
"x_ext": "px",
"y_ext": "px",
"z_ext": "px",
"wl_com": "A",
"dwl": "A",
"xmin": "px",
"xmax": "px",
"ymin": "px",
"ymax": "px",
"zmin": "px",
"zmax": "px"
}
s = spectrum.readSpectrum(args.incube)
c = s.data
zz, yy, xx = [np.arange(s, dtype=int) for s in c.shape]
YY, ZZ, XX = np.meshgrid(yy, zz, xx)
w = wcs.WCS(s.hdu)
platescale = s.hdu.header["CDELT2"]
outmap = fits.getdata(args.map)
for n in names:
t[n].dtype = dtypes[n]
t[n].format = formats[n]
t[n].unit = units[n]
rr = np.sort(np.unique(outmap.flatten()))
return ww, np.array(all_sout), mask
#plt.imshow(mask)
if False:
IFU = '022'
foutmap = "map_{}.fits.gz".format(IFU)
fincube = "data/sf2outcube_median_{}.fits.gz".format(IFU)
fnoisecube = "data/sf2ncoutcube_median_{}.fits.gz".format(IFU)
fcatalog = "catalog_{}.txt".format(IFU)
flinelist = "linelist.txt"
outmap = fits.getdata(foutmap)
s = spectrum.readSpectrum(fincube)
ns = spectrum.readSpectrum(fnoisecube)
t = ascii.read(fcatalog, format="ecsv")
r = t[0]
ww, sout, mask = extract(r, s, outmap)
ww, nsout, mask = nextract(r, ns, outmap)
mm = np.std(nsout, axis=0)
plt.plot(ww, sout)
plt.fill_between(ww,
2. * mm,
-2. * mm,
alpha=0.3,
edgecolor='black',
facecolor='grey')