antaress.ANTARESS_conversions.ANTARESS_binning module

antaress.ANTARESS_conversions.ANTARESS_binning module#

process_bin_prof(mode, data_type_gen, gen_dic, inst, vis_in, data_dic, coord_dic, data_prop, system_param, theo_dic, plot_dic, ar_dic={}, masterDIweigh=False)[source]#

Binning routine

Bins series of input spectral profile into a new series along the chosen temporal/spatial dimension.

  • for a given visit or between several visits

  • binned profiles are calculated as weighted means with weights specific to the type of profiles

  • binned profiles are used for analysis purposes

  • master profiles used to extract differential profiles from each exposure are calculated in extract_diff_profiles()

Parameters:

TBD

Returns:

TBD

init_bin_prof(data_type, ref_pl, idx_in_bin, dim_bin, coord_dic, inst, vis_to_bin, data_dic, gen_dic, bin_prop)[source]#

Binning routine: initialization

Initializes process_bin_prof().

Parameters:

TBD

Returns:

TBD

weights_bin_prof_calc(data_type_gen, data_type, gen_dic, data_dic, inst, check_var=True)[source]#

Binning routine: calculations

Identifies which components needs to be calculated

Parameters:

TBD

Returns:

TBD

weights_bin_prof(iord_orig_list, scaled_data_paths, inst, vis, gen_corr_Fbal, gen_corr_FbalOrd, save_data_dir, nord, iexp_glob, data_type, data_format, dim_exp, tell_exp, gcal_exp, cen_bins, dt, flux_ref_exp, cov_ref_exp, calc_cond, flux_est_loc_exp=None, cov_est_loc_exp=None, SpSstar_spec=None, glob_flux_sc=None, sdet_exp2=None, EFsc2_all_in=None, EFdiff2_in=None, EFintr2_in=None, EFem2_in=None, EAbs2_in=None)[source]#

Binning routine: weights

Defines weights to be used when binning profiles. Weights should only be defined using the inverse squared error if the weighted values are comparable, so that all profiles should have been scaled to comparable flux levels prior to binning. Profiles must be defined on the same spectral table to be binned together.

Parameters:

TBD

Returns:

TBD

calc_bin_prof(idx_to_bin, nord, dim_exp, nspec, data_to_bin_in, inst, n_in_bin, cen_bins_exp, edge_bins_exp, dx_ov_in=None)[source]#

Spectral profile binning

Main routine to bin input spectral profiles, defined over a common spectral table, into a single spectral profile

  • propagates covariance matrixes

  • uses spectral weight profiles

  • assumes profiles are independent along the binning dimension

Parameters:

TBD

Returns:

TBD

pre_calc_bin_prof(n_in_bin, dim_sec, idx_to_bin, resamp_mode, dx_ov_in, data_to_bin, edge_bins_resamp, nocov=False, tab_delete=None)[source]#

Spectral binning: pre-processing

Cleans and normalizes profiles and their weights before binning.

Parameters:

TBD

Returns:

TBD

resample_func(x_bd_low_in, x_bd_high_in, x_low_in_all, x_high_in_all, flux_in_all, err_in_all, remove_empty=True, dim_bin=0, cond_def_in_all=None, multi=False, adj_xtab=True)[source]#

General profiles binning

Bins input profiles into a new series of profiles, along the chosen dimension

  • Propagates variance only

  • Natural weighing by the size of the original bins along the binning dimension

  • Original and final bins can be discontinuous and of different sizes. Original bins can overlap and be given as a single input table. Input tables must have the same stucture and dimensions, except along the binned dimension .

  • This resampling assumes that the flux density is constant over each pixel, ie that the same number of photons is received by every portion of the pixel. In practice this is not the case, as the flux may vary sharply from one pixel to the next, and thus the flux density varies over a pixel (as we would measure at a higher spectral resolution). The advantage of the present resampling is that it conserves the flux, which is not necessarily the case for interpolation.

  • nan values can be left in the input tables, so that new pixels that overlap with them will be set conservatively to nan. They can also be removed before input to speed up calculation, in which case the new pixel will be defined based on defined, overlapping pixels.

  • If a new pixel is only partially covered by input pixels, its boundaries can be re-adjusted to the maximum overlapping range.

  • The x tables must not contain nan so that they can be sorted. The x tables must correspond to the dimension set by dim_bin.

  • Multiprocessing takes longer than the standard loop, even for tens of exposures and the full ESPRESSO spectra.

Parameters:

TBD

Returns:

TBD

sub_calc_bins(low_bin, high_bin, raw_loc_dic, nfilled_bins, calc_Fr=False, calc_gcal=False, adjust_bins=True)[source]#

Simplified binning routine

Used instead of the resampling function when only the flux is binned and/or the bins are large enough that we can neglect the covariance between them. This is also why we can bin the master and exposure over defined pixels (ie, ignoring some of the pixels that might be undefined within a bin). Otherwise the covariance matrix would need to be resampled over all consecutive pixels, included undefined ones, and the binned pixels would be set to undefined by the resampling function.

Parameters:

TBD

Returns:

TBD

sub_def_bins(bin_siz, idx_kept_ord, low_pix, high_pix, dpix_loc, pix_loc, sp1D_loc, Mstar_loc=None, var1D_loc=None, gcal_blaze=None)[source]#

Bins definition

Defines new bins from input bins

Parameters:

TBD

Returns:

TBD

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