#check of 1 sphere MeO1 test simplified, k space, k_weight=0. Without restraint

#set variables, now without norms for simplifying
  unguess
  guess(N_MeO1=7)
  guess(R_MeO1=2.10)
  guess(Sig_MeO1=0.004)
  guess(E_MeO1=0)
S02=0.921
#set path Hf-O1
  path(index=1,
     feff='feff0001.dat',
     label='MeO1',
     s02=S02*N_MeO1/degen,
     delr=R_MeO1-reff,
     sigma2=Sig_MeO1,
     e0=E_MeO1)

noise_FT=3.92e-3
#reading the spectrum, interpolating, k range and num of data points
#spectrum is 1 sphere hfo1 from the same feff with e1=5.1523,n1=7.0612,r1=2.1107,s1=7.4408e-3, the same S02
#generated in viper so could be some deviation, the result of fitting here is normal
  read_data(file=hfo1.chi,
           group=Me_chi_nonaligned,type=chi,title_lines=1)
  Me_chi.k=range(0,ceil(Me_chi_nonaligned.k),0.05)
  Me_chi.chi=interp(Me_chi_nonaligned.k,Me_chi_nonaligned.chi,Me_chi.k)
  kmin_Me_chi=floor(Me_chi_nonaligned.k)
  kmax_Me_chi=ceil(Me_chi_nonaligned.k)

#fit
  feffit(1,k=Me_chi.k,chi=Me_chi.chi,group=fit,
    kmin=kmin_Me_chi,kmax=kmax_Me_chi, rmin=0,rmax=2.4,kwindow=hanning, dk=0,
    epsilon_k=noise_FT,kweight=0,fit_space=k)
  show @variables
#calculate chi_square by formula manually
  k_step=0.05
  small=k_step*1e-2
  datmodel.func=(Me_chi.chi-fit.chi)**2
  n_kmin=(kmin_Me_chi+small)/k_step+1
  n_kmax=(kmax_Me_chi+small)/k_step
              #array has k=0 point as first, something not sure with indices, but now right
  datmodel_sliced.func=slice(datmodel.func,n_kmin+1,n_kmax+1) 
  N_pts_Me_chi=npts(datmodel_sliced.func)
  chi_square_calc=N_idp/(2*N_pts_Me_chi)*vsum(datmodel_sliced.func)/epsilon_k**2
#compare manual calculation with program value
  show chi_square_calc, chi_square
  norm_chi=chi_square_calc/chi_square
  show norm_chi