def test_Linefit():
hdu = fits.open(inimage)
# create the data arra
data = hdu[1].data
# create the header information
grating = hdu[1].header['GRATING'].strip()
grang = hdu[1].header['GR-ANGLE']
arang = hdu[1].header['AR-ANGLE']
slit = float(hdu[1].header['MASKID'])
xbin, ybin = hdu[1].header['CCDSUM'].strip().split()
# print instrume, grating, grang, arang, filter
# print xbin, ybin
# print len(data), len(data[0])
# create the RSS Model
rssmodel = RSSModel.RSSModel(grating_name=grating, gratang=grang,
camang=arang, slit=slit, xbin=int(xbin),
ybin=int(ybin))
alpha = rssmodel.rss.gratang
beta = rssmodel.rss.gratang - rssmodel.rss.camang
sigma = 1e7 * rssmodel.rss.calc_resolelement(alpha, beta)
# create the observed spectrum
midpoint = int(0.5 * len(data))
xarr = np.arange(len(data[0]), dtype='float')
farr = data[midpoint, :]
obs_spec = Spectrum(xarr, farr, stype='continuum')
# create artificial spectrum
stype = 'line'
w, s = np.loadtxt(inspectra, usecols=(0, 1), unpack=True)
cal_spec = Spectrum(w, s, wrange=[4000, 5000], dw=0.1, stype=stype, sigma=sigma)
cal_spec.flux = cal_spec.set_dispersion(sigma=sigma)
cal_spec.flux = cal_spec.flux * obs_spec.flux.max() / cal_spec.flux.max() + 1
lf = LF.LineFit(obs_spec, cal_spec, function='legendre', order=3)
lf.set_coef([4.23180070e+03, 2.45517852e-01, -4.46931562e-06, -2.22067766e-10])
print(lf(2000))
print(lf.obs_spec.get_flux(2000), lf.flux(2000))
print('chisq ', (lf.errfit(lf.coef, xarr, farr) ** 2).sum() / 1e7)
lf.set_coef([4.23280070e+03, 2.45517852e-01, -4.46931562e-06, -2.22067766e-10])
print(lf(2000))
print(lf.obs_spec.get_flux(2000), lf.flux(2000))
print('chisq ', (lf.errfit(lf.coef, xarr, farr) ** 2).sum() / 1e7)
# print lf.lfit(xarr)
# print lf.coef
# print lf(2000)
# print lf.results
pl.figure()
pl.plot(lf(lf.obs_spec.wavelength), lf.obs_spec.get_flux(xarr))
pl.plot(lf.cal_spec.wavelength, lf.cal_spec.flux)
pl.show()
def test_Linefit():
hdu = fits.open(inimage)
# create the header information
grating = hdu[1].header['GRATING'].strip()
grang = hdu[1].header['GR-ANGLE']
arang = hdu[1].header['AR-ANGLE']
slit = float(hdu[1].header['MASKID'])
xbin, ybin = hdu[1].header['CCDSUM'].strip().split()
# print instrume, grating, grang, arang, filter
# print xbin, ybin
# print len(data), len(data[0])
# create the RSS Model
rssmodel = RSSModel.RSSModel(grating_name=grating, gratang=grang,
camang=arang, slit=slit, xbin=int(xbin),
ybin=int(ybin))
alpha = rssmodel.rss.gratang
beta = rssmodel.rss.gratang - rssmodel.rss.camang
sigma = 1e7 * rssmodel.rss.calc_resolelement(alpha, beta)
# create artificial spectrum
# create the spectrum
stype = 'line'
w, s = np.loadtxt(inspectra, usecols=(0, 1), unpack=True)
spec = Spectrum(w, s, wrange=[4000, 5000], dw=0.1, stype=stype, sigma=sigma)
spec.flux = spec.set_dispersion(sigma=sigma)
def test_Linefit():
hdu = pyfits.open(inimage)
# create the data arra
data = hdu[1].data
# create the header information
instrume = hdu[1].header['INSTRUME'].strip()
grating = hdu[1].header['GRATING'].strip()
grang = hdu[1].header['GR-ANGLE']
arang = hdu[1].header['AR-ANGLE']
filter = hdu[1].header['FILTER'].strip()
slit = float(hdu[1].header['MASKID'])
xbin, ybin = hdu[1].header['CCDSUM'].strip().split()
# print instrume, grating, grang, arang, filter
# print xbin, ybin
# print len(data), len(data[0])
# create the RSS Model
rssmodel = RSSModel.RSSModel(grating_name=grating,
gratang=grang,
camang=arang,
slit=slit,
xbin=int(xbin),
ybin=int(ybin))
alpha = rssmodel.rss.gratang
beta = rssmodel.rss.gratang - rssmodel.rss.camang
sigma = 1e7 * rssmodel.rss.calc_resolelement(alpha, beta)
# create artificial spectrum
# create the spectrum
stype = 'line'
w, s = np.loadtxt(inspectra, usecols=(0, 1), unpack=True)
spec = Spectrum(w,
s,
wrange=[4000, 5000],
dw=0.1,
stype='line',
sigma=sigma)
spec.flux = spec.set_dispersion(sigma=sigma)
sw_arr, sf_arr = spec.wavelength, spec.flux
def test_Linefit():
hdu = fits.open(inimage)
# create the data arra
data = hdu[1].data
# create the header information
grating = hdu[1].header['GRATING'].strip()
grang = hdu[1].header['GR-ANGLE']
arang = hdu[1].header['AR-ANGLE']
slit = float(hdu[1].header['MASKID'])
xbin, ybin = hdu[1].header['CCDSUM'].strip().split()
# print instrume, grating, grang, arang, filter
# print xbin, ybin
# print len(data), len(data[0])
# create the RSS Model
rssmodel = RSSModel.RSSModel(grating_name=grating,
gratang=grang,
camang=arang,
slit=slit,
xbin=int(xbin),
ybin=int(ybin))
alpha = rssmodel.rss.gratang
beta = rssmodel.rss.gratang - rssmodel.rss.camang
sigma = 1e7 * rssmodel.rss.calc_resolelement(alpha, beta)
# create the observed spectrum
midpoint = int(0.5 * len(data))
xarr = np.arange(len(data[0]), dtype='float')
farr = data[midpoint, :]
obs_spec = Spectrum(xarr, farr, stype='continuum')
# create artificial spectrum
stype = 'line'
w, s = np.loadtxt(inspectra, usecols=(0, 1), unpack=True)
cal_spec = Spectrum(w,
s,
wrange=[4000, 5000],
dw=0.1,
stype=stype,
sigma=sigma)
cal_spec.flux = cal_spec.set_dispersion(sigma=sigma)
cal_spec.flux = cal_spec.flux * obs_spec.flux.max() / cal_spec.flux.max(
) + 1
lf = LF.LineFit(obs_spec, cal_spec, function='legendre', order=3)
lf.set_coef(
[4.23180070e+03, 2.45517852e-01, -4.46931562e-06, -2.22067766e-10])
print(lf(2000))
print(lf.obs_spec.get_flux(2000), lf.flux(2000))
print('chisq ', (lf.errfit(lf.coef, xarr, farr)**2).sum() / 1e7)
lf.set_coef(
[4.23280070e+03, 2.45517852e-01, -4.46931562e-06, -2.22067766e-10])
print(lf(2000))
print(lf.obs_spec.get_flux(2000), lf.flux(2000))
print('chisq ', (lf.errfit(lf.coef, xarr, farr)**2).sum() / 1e7)
# print lf.lfit(xarr)
# print lf.coef
# print lf(2000)
# print lf.results
pl.figure()
pl.plot(lf(lf.obs_spec.wavelength), lf.obs_spec.get_flux(xarr))
pl.plot(lf.cal_spec.wavelength, lf.cal_spec.flux)
pl.show()
