def test():
tempname = 'data/templates/F0_+0.5_Dwarf.fits'
objname1 = '/home/zzx/workspace/data/xiamen/P200-Hale_spec/blue/reduce_second/specdir/fawftbblue0073.fits'
objname2 = '/home/zzx/workspace/data/xiamen/P200-Hale_spec/red/reduced_second/specdir/fawftbred0073.fits'
templat = Model(tempname)
spec1 = specio.Spectrum(objname1)
spec2 = specio.Spectrum(objname2)
maskwindow = [[6270.0, 6320.0], [6860.0, 6970.0], [7150.0, 7340.0]]
fit2(templat, spec1, spec2, mask=maskwindow)
def main():
lstname = '/home/zzx/workspace/data/xiamen/P200-Hale_spec/spec2/F0/name.lst'
dirname = os.path.dirname(lstname)
lst = open(lstname).readlines()
bluelst = [i.split()[0] for i in lst]
redlst = [i.split()[1] for i in lst]
tempname = 'data/templates/F0_+0.5_Dwarf.fits'
templat = template.Model(tempname)
# spec1 = specio.Spectrum(objname1)
# spec2 = specio.Spectrum(objname2)
maskwindow = [[6270.0, 6320.0], [6860.0, 6970.0], [7150.0, 7340.0]]
for i in range(len(bluelst)):
specb = specio.Spectrum(dirname + os.sep + bluelst[i])
specr = specio.Spectrum(dirname + os.sep + redlst[i])
# plt.plot(specb.wave, specb.flux)
# plt.plot(specr.wave, specr.flux)
# plt.show()
out = template.fit2(templat, specb, specr, mask=maskwindow)
def main():
fname = sys.argv[1]
dirname = os.path.dirname(fname)
if dirname is '':
dirname = '.'
spec = specio.Spectrum(fname)
outname = fname.replace('.fits', '') + '.txt'
fulloutname = dirname + os.sep + outname
text = 'write to file ' + fulloutname
print(text)
fil = open(fulloutname, 'w')
for ind in range(len(spec.wave)):
text = '%.4f %.6e %.6e' % (spec.wave[ind], spec.flux[ind],
spec.err[ind])
fil.write(text + '\n')
fil.close()
def main():
spec = specio.Spectrum('data/spec-4961-55719-0378.fits')
# spec = specio.Spectrum('/home/zzx/workspace/data/xiamen/P200-Hale_spec/spec2/fawftbred0027.fits')
shift = np.arange(-300, 300, 1.0)
print(len(shift))
result = libccf.iccf_spec(spec.wave, spec.flux, spec.wave, spec.flux,
shift)
# print(type(result))
# print(result)
plt.plot(shift, result)
plt.show()
# print(spec.err)
peak, cent = libccf.iccf_mc(spec.wave, spec.flux_unit, spec.err_unit,
spec.wave, spec.flux_unit, spec.err_unit,
shift, 1000)
print(len(peak))
print(len(cent))
# plt.hist(peak, bins=50)
plt.hist(cent, bins=50)
plt.show()
def fit_lamost():
bluelst, redlst = [], []
namelst = glob.glob('/home/zzx/workspace/data/stellar_X/*.fits')
namelst = [
'/home/zzx/workspace/data/stellar_X/med-58409-TD045606N223435B01_sp16-102.fits'
]
for name in namelst:
# fig1 = plt.figure()
# fig2 = plt.figure()
# ax1 = fig1.add_subplot(111)
# ax2 = fig2.add_subplot(111)
print(name)
size = len(fits.open(name))
for ind in range(3, size):
spec = specio.Spectrum(name, ind)
spec.clean_cosmic_ray()
if 'B' in spec.header['EXTNAME']:
bluelst.append(spec)
else:
redlst.append(spec)
name = namelst[0]
model_blue = Model(name, 3)
model_blue.clean_cosmic_ray()
model_red = Model(name, 11)
model_red.clean_cosmic_ray()
params = Parameters()
shiftparname = set_pars(params, 'shift', [1], valuelst=[0.0])
scalevalst = [
0.99608100, -0.00931768, 0.00319284, 5.5658e-04, -4.4060e-04, 0.0
]
bscaleparname = set_pars(params, 'b_scale', 5, valuelst=scalevalst)
rscaleparname = set_pars(params, 'r_scale', 5, valuelst=scalevalst)
bsimgapar = set_pars(params, 'b_sigma', [1], valuelst=[0.0004])
rsigmapar = set_pars(params, 'r_sigma', [1], valuelst=[0.0004])
model_blue.set_lmpar_name(bscaleparname, bsimgapar, shiftparname)
model_red.set_lmpar_name(rscaleparname, rsigmapar, shiftparname)
shiftlst, shifterrlst = [], []
def residual(pars, x1, data1, eps1, x2, data2, eps2):
res1 = model_blue.residual(pars, x1, data1, eps1)
res2 = model_red.residual(pars, x2, data2, eps2)
return np.append(res2, res1)
return res2
for ind in range(len(redlst)):
bspec = bluelst[ind]
# bspec = redlst[ind]
rspec = redlst[ind]
arg1 = func.select(bspec.wave, [[4920, 5300]])
bnw = bspec.wave[arg1]
bnf = bspec.flux[arg1]
bne = bspec.err[arg1]
arg2 = func.select(rspec.wave, [[6320, 6860]])
rnw = rspec.wave[arg2]
rnf = rspec.flux[arg2]
rne = rspec.err[arg2]
bfakeerr = np.ones(len(bnw), dtype=np.float64) * 0.01
rfakeerr = np.ones(len(rnw), dtype=np.float64) * 0.01
bne = bfakeerr
rne = rfakeerr
# out = minimize(model_blue.residual, params, args=(nw, nf))
# out = minimize(model_red.residual, params, args=(nw, nf))
out = minimize(residual, params, args=(bnw, bnf, bne, rnw, rnf, rne))
report_fit(out)
shiftlst.append(out.params['shift1'].value * c)
shifterrlst.append(out.params['shift1'].stderr * c)
plt.figure()
spec_fit_blue = model_blue.get_spectrum(out.params, model_blue.wave)
spec_fit_red = model_red.get_spectrum(out.params, model_red.wave)
plt.plot(bnw, bnf)
plt.plot(model_blue.wave, spec_fit_blue)
plt.figure()
plt.plot(rnw, rnf)
plt.plot(model_red.wave, spec_fit_red)
plt.show()
for ind, value in enumerate(shiftlst):
# print(value.to('km/s'))
print(value.to('km/s'), shifterrlst[ind].to('km/s'))