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Todo List

Class Plot
Use enum for flag.

Class PlotLimits
Add 2 integers to properly set the number of digits to be printed (e.g. with labdig in DISLIN)

File apply_lfff.c
Make a full port to SNIFS standards.

File wcalib.c
Make a full port to SNIFS standards. Discard call to NAG. Complete the documentation.

File wrebin.c
Make a full port to SNIFS standards

Global get_lenses_col (E3D_file *frame, char *lab_col, void *zlens, int *il)
In the E3D case, one has to specify the science table extension name in which to look to for the specified column. For the moment, look in the table associated to the cube (the data table?)

Global plot_set_bkgnd (IMAGE2D *bkgndima, Plot *plot, float mincut, float maxcut, int *nx, int *ny, float ***zmat)
Set the limits so that the border spaxels are displayed completely.

Global nllsqfit_mask (long *mode, long *nlenses, long *npar, long *ldfj, double *par, double f[], double fjac[], long *nstate, long *iuser, double user[])
Add 0th/2nd order fit, with blaze function as weighting (no need to look for a line/max if there's nothing!).

Should we use another function than RMS(dx) in fit to max? And what about a completely diffrent way to fit the arc. E.g. one could go through a peak detection scheme, and use a more-intuitive distance criterion to the peaks, still taking into account the fact that one peak should correspond to a single arc line...

File snifs_structures.h
(Re)Implement the path element in TABLE structure

Implement a LocalModel structure to be used for dx(lambda) and sigma(lambda) -nlenses -ncoeff -**coeff -type (NAG, Fit_polynom) -domain of validity in lambda

Page create_mask
To compute the sky coordinates of the lenses, one needs to come back to the MLA level, study the distortion there, undistort the observed positions, and then compute the sky coordinates.

Implement the blaze option, in order to fit 0th/2nd orders weighted according to the blaze function.

Discard the need for the arc frame or for the max if they are not actually adjusted.

It is probably not a good idea to use a classical minimization scheme on such a noisy ill-conditionned problem. Maybe have a look at ``simulated annealing'' algorithm (cf. GSL), which could be however computation-time costly.

Compute decent value for arc normalisation factor glnormmax (arc frame mean? 1st step value?)

Use Fit_polynom (with automatic adjustment of polynomial degree) instead of fit_poly_rej_nag_tab in local adjustment. Furthermore, since the sigma=f(lambda) is noisy, the sigma-clipping is not rebost enough, and one should enforce a physical selection over sigma right after pup_get_maxdata

Page extract_spec
Expand the domain of validity of the Chebyshev expansion for sigma (and dx?) from the useful wavelength range [lbda_inf,lbda_sup] to the total wavelength range [lbda_inf_tot,lbda_sup_tot] (requires changes in create_mask and snifs_instrument::h) (see comment "get lbda = f(yd)")

CHECK CAREFULLY THE OPTIMAL EXTRACTION (signal and variance). In particular, use the variance extension during the optimal extraction. One could also have a look at Khmil & Surdej 2002 (optimal extraction with maximum entropy).

Test the option -linfit

Use a fancier interpolation scheme in the final spectrum writing

The spatial coordinates still have to be computed.

Page find_max
Read the variance of the input continuum, as the preprocess might flag the bad columns in tthe variance extension. Just a question: what is the variance of a median?

Page find_mpup
Test the existence and use the variance extension in the preproc images for X-disp. profile fits.

Page prepare_mask
Test the existence and use the variance extension in the preproc images.

Page center_gauss
Should unify with Fit_Xpeak using an intermediate 2D-array.

Page focus_spectro
Test (native SNIFS format) multiple-extension input frames

Test different zones in the arc frame (size and position, but beware of 0th and 2nd orders)

Fit multiple zones in the arc frame and derive a CCD-tilt

Check out why the Y-error bars are so different between the red and blue channels. The formal error estimates derived with nllsqfit_bnd seem theoritically correct.


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