fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1547.6, wl1) ] + [13.5, 20.3, auto.Wave2V(1551, wl2)] + [ 13, 20.3, auto.Wave2V(1547.8, wl1) ] + [13.2, 20.3, auto.Wave2V(1552.5, wl2)] + [ 13.3, 20.3, auto.Wave2V(1551.5, wl2) ] + [13.2, 20.3, auto.Wave2V(1550.6, wl2) ] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [1, 0, 2, 1, 2, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553]))
fn = fl / new_ctn fne = fe / new_ctn # In[108]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[119]: x0 = [13.5, 20.3, auto.Wave2V(1548.5, wl1)] + [ 13, 20.3, auto.Wave2V(1549.2, wl1) ] + [13.3, 20.3, auto.Wave2V(1550, wl2)] + [ 13.2, 20.3, auto.Wave2V(1549.5, wl1) ] #+ [13, 20.3, auto.Wave2V(1552.4, wl2)] #+ [13, 20.3, auto.Wave2V(1551.67, wl2)] feat = [0, 1, 2, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0)
fn = fl / new_ctn fne = fe / new_ctn # In[183]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[194]: x0 = [13, 20.3, auto.Wave2V(1547.8, wl1)] + [ 13.3, 20.3, auto.Wave2V(1548, wl1) ] + [13.3, 20.3, auto.Wave2V(1548.3, wl1)] + [ 13, 20.3, auto.Wave2V(1548.5, wl1) ] + [ 13, 20.3, auto.Wave2V(1547.2, wl1) ] #+ [13, 20.3, auto.Wave2V(1551.2, wl2)] + [13, 20.3, auto.Wave2V(1548.7, wl1)] feat = [0, 0, 1, 0, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1547.5, wl1)] + [ 13.3, 20.3, auto.Wave2V(1548.2, wl1) ] + [13.5, 20.3, auto.Wave2V(1548.6, wl1)] + [ 13.6, 20.3, auto.Wave2V(1552.5, wl2) ] + [ 13.5, 20.3, auto.Wave2V(1548.6, wl1) ] #+ [13.3, 20.3, auto.Wave2V(1550.8, wl2)] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] + [13.4, 20.3, auto.Wave2V(1549.4, wl2)] #+ [13.2, 20.3, auto.Wave2V(1552.5, wl2)] + [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [1, 0, 0, 2, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1549, wl1) ] + [13.5, 20.3, auto.Wave2V(1547.5, wl1)] + [ 13, 20.3, auto.Wave2V(1548.3, wl1) ] + [13.5, 20.3, auto.Wave2V(1550, wl2)] + [ 13.3, 20.3, auto.Wave2V(1548.2, wl1) ] + [13.2, 20.3, auto.Wave2V(1552, wl2) ] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 1, 1, 2, 1, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553]))
# In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1547.9, wl1)] + [13.5, 20.3, auto.Wave2V(1548.2, wl1)] + [13.3, 20.3, auto.Wave2V(1548.4, wl1)] + [13.3, 20.3, auto.Wave2V(1549.3, wl1)] + [13.3, 20.3, auto.Wave2V(1547.6, wl1)] + [13.3, 20.3, auto.Wave2V(1549, wl1)] #+ [13.3, 20.3, auto.Wave2V(1551, wl2)] + [13.3, 20.3, auto.Wave2V(1552.8, wl2)] + [13.3, 20.3, auto.Wave2V(1550.4, wl2)] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 0, 1, 1, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0) # In[11]: p0 = auto.make_ten(p0) N1, N2 = auto.make_features(p0, feat, wl, wl1, wl2, lsf, gamma1, gamma2) Ne1 = auto.nfle2Nev(fn, fne, f1, wl1) Ne2 = auto.nfle2Nev(fn, fne, f2, wl2) auto.plot_features(wl, wl1, wl2, N1, N2, Ne1, Ne2, [-500, 500])
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.2, wl1) ] + [13.5, 20.3, auto.Wave2V(1549.8, wl2)] + [ 13.5, 20.3, auto.Wave2V(1546.4, wl1) ] + [ 13.3, 20.3, auto.Wave2V(1548.5, wl1) ] #+ [13.3, 20.3, auto.Wave2V(1547.6, wl1)] #+ [13.3, 20.3, auto.Wave2V(1550.5, wl2)] + [13.2, 20.3, auto.Wave2V(1551.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 2, 0, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1546, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
fn = fl / new_ctn fne = fe / new_ctn # In[19]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[25]: x0 = [13.5, 20.3, auto.Wave2V(1547.9, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548, wl2) ] + [13.2, 20.3, auto.Wave2V(1548.8, wl1)] + [ 13, 20.3, auto.Wave2V(1547.5, wl1) ] #+ [12.5, 20.3, auto.Wave2V(1551, wl2)] + [13, 20.3, auto.Wave2V(1550.4, wl2)] #+ [13, 20.3, auto.Wave2V(1550.5, wl2)] feat = [0, 1, 1, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0)
fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [ 14, 20.3, auto.Wave2V(1547, wl1) ] + [14, 20.3, auto.Wave2V(1547.2, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548, wl1) ] + [13, 20.3, auto.Wave2V(1546.4, wl1)] + [ 13.5, 20.3, auto.Wave2V(1550.8, wl2) ] + [13, 20.3, auto.Wave2V(1552.5, wl2)] + [ 13, 20.3, auto.Wave2V(1551.2, wl2) ] #+ [13, 20.3, auto.Wave2V(1547.2, wl1)] + [13, 20.3, auto.Wave2V(1548.5, wl1)] feat = [0, 0, 0, 0, 2, 2, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1546, 1553]))
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.4, wl1) ] + [13.5, 20.3, auto.Wave2V(1549, wl1)] + [ 13.5, 20.3, auto.Wave2V(1550.7, wl2) ] + [13.5, 20.3, auto.Wave2V(1551, wl2)] + [ 13.5, 20.3, auto.Wave2V(1551.5, wl2) ] + [13.5, 20.3, auto.Wave2V(1552.8, wl2)] + [ 13.5, 20.3, auto.Wave2V(1552.2, wl2) ] + [13.5, 20.3, auto.Wave2V(1550, wl2) ] + [13.5, 20.3, auto.Wave2V(1551.8, wl2)] feat = [0, 0, 0, 2, 2, 2, 2, 2, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.6, wl1) ] + [13.5, 20.3, auto.Wave2V(1551.5, wl2)] + [ 13.5, 20.3, auto.Wave2V(1552, wl2) ] + [13.5, 20.3, auto.Wave2V(1552.4, wl2)] + [ 13.2, 20.3, auto.Wave2V(1549.6, wl2) ] #+ [13.3, 20.3, auto.Wave2V(1546.4, wl1)] #+ [13.4, 20.3, auto.Wave2V(1552, wl2)] + [13.2, 20.3, auto.Wave2V(1552.5, wl2)] #+ [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 2, 2, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548.3, wl1)] + [ 13.3, 20.3, auto.Wave2V(1549, wl1) ] + [ 13.3, 20.3, auto.Wave2V(1550.3, wl2) ] #+ [13.5, 20.3, auto.Wave2V(1548.3, wl1)] #+ [13, 20.3, auto.Wave2V(1551.5, wl2)] + [13, 20.3, auto.Wave2V(1547.8, wl1)] + [13.3, 20.3, auto.Wave2V(1552.5, wl2)] feat = [0, 0, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0)
# In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1547.5, wl1)] + [13.5, 20.3, auto.Wave2V(1548, wl1)] + [13.5, 20.3, auto.Wave2V(1548.2, wl1)] + [13.5, 20.3, auto.Wave2V(1551, wl2)] + [13, 20.3, auto.Wave2V(1551.6, wl2)] + [13.2, 20.3, auto.Wave2V(1552.5, wl2)] + [13.2, 20.3, auto.Wave2V(1547.2, wl1)] #+ [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 0, 2, 2, 2, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0) # In[11]: p0 = auto.make_ten(p0) N1, N2 = auto.make_features(p0, feat, wl, wl1, wl2, lsf, gamma1, gamma2) Ne1 = auto.nfle2Nev(fn, fne, f1, wl1) Ne2 = auto.nfle2Nev(fn, fne, f2, wl2) auto.plot_features(wl, wl1, wl2, N1, N2, Ne1, Ne2, [-500, 500])
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548.2, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.3, wl1) ] + [13.3, 20.3, auto.Wave2V(1548.5, wl1)] + [ 13.3, 20.3, auto.Wave2V(1551.2, wl2) ] #+ [13.3, 20.3, auto.Wave2V(1552.5, wl2)] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] #+ [13.4, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.2, 20.3, auto.Wave2V(1552.5, wl2)] + [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 1, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0)
# In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1547, wl1)] + [13.5, 20.3, auto.Wave2V(1547.2, wl1)] + [13.5, 20.3, auto.Wave2V(1547.8, wl1)] + [13.3, 20.3, auto.Wave2V(1548, wl1)] + [13.2, 20.3, auto.Wave2V(1548.6, wl1)] #+ [13, 20.3, auto.Wave2V(1549.2, wl1)] #+ [13.3, 20.3, auto.Wave2V(1552.8, wl2)] + [13.3, 20.3, auto.Wave2V(1548.2, wl1)] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 0, 0, 0] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1546, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1546, 1553]) #normally [1547, 1553] print(p0) # In[11]: p0 = auto.make_ten(p0) N1, N2 = auto.make_features(p0, feat, wl, wl1, wl2, lsf, gamma1, gamma2) Ne1 = auto.nfle2Nev(fn, fne, f1, wl1) Ne2 = auto.nfle2Nev(fn, fne, f2, wl2) auto.plot_features(wl, wl1, wl2, N1, N2, Ne1, Ne2, [-500, 500])
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548.2, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.3, wl1) ] + [13.5, 20.3, auto.Wave2V(1548.6, wl1)] + [ 13.4, 20.3, auto.Wave2V(1549, wl1) ] + [13.4, 20.3, auto.Wave2V(1549.3, wl1)] + [ 13.5, 20.3, auto.Wave2V(1549.5, wl1) ] + [13.5, 20.3, auto.Wave2V(1550, wl2)] + [ 13.5, 20.3, auto.Wave2V(1551.4, wl2) ] + [13.5, 20.3, auto.Wave2V(1549.2, wl1) ] #+ [13.5, 20.3, auto.Wave2V(1551.8, wl2)] feat = [1, 0, 1, 1, 1, 1, 2, 2, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1,
# In[56]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[63]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [13.5, 20.3, auto.Wave2V(1548.5, wl1)] + [13.5, 20.3, auto.Wave2V(1549.5, wl2)] + [13.5, 20.3, auto.Wave2V(1551.5, wl2)] #+ [13.4, 20.3, auto.Wave2V(1549.8, wl2)] #+ [13.5, 20.3, auto.Wave2V(1551.8, wl2)] + [13.5, 20.3, auto.Wave2V(1552.4, wl2)] + [13.5, 20.3, auto.Wave2V(1551.6, wl2)] + [13.5, 20.3, auto.Wave2V(1552.5, wl2)] #+ [13.5, 20.3, auto.Wave2V(1551.8, wl2)] feat = [0, 1, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0) # In[64]: p0 = auto.make_ten(p0) N1, N2 = auto.make_features(p0, feat, wl, wl1, wl2, lsf, gamma1, gamma2) Ne1 = auto.nfle2Nev(fn, fne, f1, wl1) Ne2 = auto.nfle2Nev(fn, fne, f2, wl2) auto.plot_features(wl, wl1, wl2, N1, N2, Ne1, Ne2, [-500, 500])
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1546.5, wl1)] + [ 13.5, 20.3, auto.Wave2V(1547, wl1) ] + [13.5, 20.3, auto.Wave2V(1547.5, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548, wl1) ] + [13.3, 20.3, auto.Wave2V(1550.4, wl2)] + [ 13.3, 20.3, auto.Wave2V(1550.8, wl2) ] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] + [13.4, 20.3, auto.Wave2V(1549.4, wl2)] #+ [13.2, 20.3, auto.Wave2V(1552.5, wl2)] + [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 0, 0, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1546, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1549.8, wl1) ] + [13.5, 20.3, auto.Wave2V(1549.5, wl1)] + [ 13.3, 20.3, auto.Wave2V(1550.4, wl2) ] + [ 13, 20.3, auto.Wave2V(1552.5, wl2) ] #+ [13, 20.3, auto.Wave2V(1549.2, wl1)] #+ [13.3, 20.3, auto.Wave2V(1552.8, wl2)] + [13.3, 20.3, auto.Wave2V(1548.2, wl1)] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 1, 1, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
# In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [13.5, 20.3, auto.Wave2V(1548.5, wl1)] + [13.4, 20.3, auto.Wave2V(1550.3, wl2)] #+ [13.5, 20.3, auto.Wave2V(1548.2, wl1)] #+ [13.3, 20.3, auto.Wave2V(1547.8, wl1)] #+ [13.3, 20.3, auto.Wave2V(1546.4, wl1)] #+ [13.4, 20.3, auto.Wave2V(1552, wl2)] + [13.2, 20.3, auto.Wave2V(1552.5, wl2)] #+ [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 1, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0) # In[11]: p0 = auto.make_ten(p0) N1, N2 = auto.make_features(p0, feat, wl, wl1, wl2, lsf, gamma1, gamma2) Ne1 = auto.nfle2Nev(fn, fne, f1, wl1) Ne2 = auto.nfle2Nev(fn, fne, f2, wl2) auto.plot_features(wl, wl1, wl2, N1, N2, Ne1, Ne2, [-500, 500])
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1547.2, wl1)] + [ 13.5, 20.3, auto.Wave2V(1547.7, wl1) ] + [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.3, 20.3, auto.Wave2V(1548.3, wl1) ] + [13.4, 20.3, auto.Wave2V(1551, wl2)] + [ 13.5, 20.3, auto.Wave2V(1551.8, wl2) ] + [13.5, 20.3, auto.Wave2V(1552.4, wl2)] + [ 13.5, 20.3, auto.Wave2V(1551.6, wl2) ] + [13.5, 20.3, auto.Wave2V(1552.5, wl2) ] #+ [13.5, 20.3, auto.Wave2V(1551.8, wl2)] feat = [0, 0, 0, 0, 2, 2, 2, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.2, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548, wl1) ] + [13.5, 20.3, auto.Wave2V(1549.5, wl2)] + [ 13.3, 20.3, auto.Wave2V(1551.4, wl2) ] #+ [13.5, 20.3, auto.Wave2V(1549, wl1)] + [13.5, 20.3, auto.Wave2V(1552.3, wl2)] + [13.3, 20.3, auto.Wave2V(1552.8, wl2)] + [13.3, 20.3, auto.Wave2V(1548.2, wl1)] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0)
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.2, wl1) ] + [ 13.5, 20.3, auto.Wave2V(1550.6, wl2) ] #+ [13.5, 20.3, auto.Wave2V(1549, wl1)] + [13.5, 20.3, auto.Wave2V(1549.4, wl1)] + [13.5, 20.3, auto.Wave2V(1552.6, wl2)] + [13.5, 20.3, auto.Wave2V(1552.9, wl2)] #+ [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0)
fn = fl / new_ctn fne = fe / new_ctn # In[156]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[165]: x0 = [13.5, 20.3, auto.Wave2V(1547.8, wl1)] + [ 13, 20.3, auto.Wave2V(1548, wl1) ] + [13.3, 20.3, auto.Wave2V(1548.8, wl1)] + [ 13, 20.3, auto.Wave2V(1549.5, wl2) ] + [13, 20.3, auto.Wave2V(1550.2, wl2)] + [ 13, 20.3, auto.Wave2V(1551.2, wl2) ] + [13, 20.3, auto.Wave2V(1548.7, wl1)] feat = [0, 0, 0, 2, 2, 2, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1547.4, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.4, wl1) ] + [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.3, 20.3, auto.Wave2V(1549.5, wl2) ] + [ 13.3, 20.3, auto.Wave2V(1547.8, wl1) ] #+ [13.3, 20.3, auto.Wave2V(1546.4, wl1)] #+ [13.4, 20.3, auto.Wave2V(1552, wl2)] + [13.2, 20.3, auto.Wave2V(1552.5, wl2)] #+ [13.3, 20.3, auto.Wave2V(1551.5, wl2)] + [13.2, 20.3, auto.Wave2V(1550.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 0, 2, 0] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf,
fn = fl / new_ctn fne = fe / new_ctn # In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1547.6, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.2, wl1) ] + [13.5, 20.3, auto.Wave2V(1548.7, wl1)] + [ 13.2, 20.3, auto.Wave2V(1549.5, wl2) ] + [12.5, 20.3, auto.Wave2V(1552.5, wl2)] + [ 13, 20.3, auto.Wave2V(1552.8, wl2) ] + [13, 20.3, auto.Wave2V(1550, wl2) ] + [13, 20.3, auto.Wave2V(1550.2, wl2)] + [ 13, 20.3, auto.Wave2V(1547.2, wl1) ] + [13, 20.3, auto.Wave2V(1548.5, wl1)] feat = [1, 0, 0, 2, 2, 2, 2, 2, 1, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1,
# In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [13.5, 20.3, auto.Wave2V(1548.2, wl1)] + [13.5, 20.3, auto.Wave2V(1547, wl1)] + [13.5, 20.3, auto.Wave2V(1547.5, wl1)] + [13.5, 20.3, auto.Wave2V(1548.3, wl1)] + [13.2, 20.3, auto.Wave2V(1549.4, wl2)] #+ [13.2, 20.3, auto.Wave2V(1549.8, wl2)] #+ [13.3, 20.3, auto.Wave2V(1547.6, wl1)] #+ [13.3, 20.3, auto.Wave2V(1550.5, wl2)] + [13.2, 20.3, auto.Wave2V(1551.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 0, 1, 1, 2, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1546.8, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1546.8, 1553]) #normally [1547, 1553] print(p0) # In[11]: p0 = auto.make_ten(p0) N1, N2 = auto.make_features(p0, feat, wl, wl1, wl2, lsf, gamma1, gamma2) Ne1 = auto.nfle2Nev(fn, fne, f1, wl1) Ne2 = auto.nfle2Nev(fn, fne, f2, wl2) auto.plot_features(wl, wl1, wl2, N1, N2, Ne1, Ne2, [-500, 500])
fn = fl / new_ctn fne = fe / new_ctn # In[59]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[61]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.5, 20.3, auto.Wave2V(1548.2, wl1) ] + [13.5, 20.3, auto.Wave2V(1548.4, wl1)] + [ 13, 20.3, auto.Wave2V(1548.7, wl1) ] #+ [12.8, 20.3, auto.Wave2V(1547, wl1)] + [13, 20.3, auto.Wave2V(1548.1, wl1)] #+ [13, 20.3, auto.Wave2V(1550, wl2)] + [13, 20.3, auto.Wave2V(1550.2, wl2)] + [13, 20.3, auto.Wave2V(1547.2, wl1)] + [13, 20.3, auto.Wave2V(1548.5, wl1)] feat = [0, 0, 1, 0] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0)
fn = fl / new_ctn fne = fe / new_ctn # In[464]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[467]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [ 13.3, 20.3, auto.Wave2V(1547.8, wl1) ] + [13.3, 20.3, auto.Wave2V(1552, wl2)] + [ 13.5, 20.3, auto.Wave2V(1550.5, wl2) ] + [13, 20.3, auto.Wave2V(1548.6, wl1) ] + [13, 20.3, auto.Wave2V(1552.5, wl2)] feat = [0, 0, 2, 2, 1, 2] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553]
# In[9]: #chi, chi_mean = auto.find_chi(wl, fl, fe, new_ctn) #auto.plot_chi_histogram(chi, chi_mean) #flag_chi = auto.mask_wl(wl, [1546, 1546.7, 1548, 1551.2]) # mask out regions for better calculation of chi histogram #new_chi, new_chi_mean = auto.find_chi(wl[flag_chi], fl[flag_chi], fe[flag_chi], new_ctn[flag_chi]) #auto.plot_chi_histogram(new_chi, new_chi_mean) # In[10]: x0 = [13.5, 20.3, auto.Wave2V(1548, wl1)] + [13.5, 20.3, auto.Wave2V(1548.1, wl1)] + [13.3, 20.3, auto.Wave2V(1548.4, wl1)] #+ [13.3, 20.3, auto.Wave2V(1550.2, wl2)] #+ [13.3, 20.3, auto.Wave2V(1547.8, wl1)] #+ [13.5, 20.3, auto.Wave2V(1551.6, wl2)] + [13.4, 20.3, auto.Wave2V(1550.4, wl2)] #+ [13.3, 20.3, auto.Wave2V(1552.4, wl2)] #+ [13.3, 20.3, auto.Wave2V(1548.2, wl1)] #+ [13.2, 20.3, auto.Wave2V(1552.6, wl2)] #+ [13.5, 20.3, auto.Wave2V(1549.6, wl2)] feat = [0, 0, 1] #which features used to model 0 - both, 1 - strong, 2 - weak p0, cov, a, b, c = auto.leastsq(auto.fitting, x0, full_output=1, args=(feat, wl, wl1, wl2, f1, f2, gamma1, gamma2, lsf, fn, fne, [1547, 1553])) auto.plot_model(p0, feat, wl, fn, wl1, wl2, f1, f2, gamma1, gamma2, lsf, [1547, 1553]) #normally [1547, 1553] print(p0) # In[11]: p0 = auto.make_ten(p0) N1, N2 = auto.make_features(p0, feat, wl, wl1, wl2, lsf, gamma1, gamma2) Ne1 = auto.nfle2Nev(fn, fne, f1, wl1) Ne2 = auto.nfle2Nev(fn, fne, f2, wl2) auto.plot_features(wl, wl1, wl2, N1, N2, Ne1, Ne2, [-500, 500])