Internals

RvSpectML Internals

As a heads up, these functions and types are more likely to change going forward than functions and types that are exported.

Functions

General purpose

RvSpectML.bin_times — Method.

bintimes() Bins times from a SpectralTimeSeriesCommonWavelengths object using the groupings from timeidx in the metadata.

RvSpectML.calc_chunk_rvs_from_taylor_expansion — Method.

calc_chunk_rvs_from_taylor_expansion( spectra ) Inputs:

  • spectra: SpectralTimeSeriesCommonWavelengths

Optional Arguments:

  • mean: mean spectrum
  • deriv: dmeanflux/dlnλ
  • equal_weight: For now, spectra are equal weighted. (true)

Output named pair:

  • rv: Vector of vector of estimated radial velocities for each chunk
  • σ_rv: Vector of vector of uncertainties in rv estimates for each chunk
RvSpectML.calc_d2fluxdlnlambda2! — Method.

Estimate numerical second derivative of fluxes given wavelengths.

RvSpectML.calc_d2fluxdlnlambda2 — Method.

Estimate numerical second derivative of fluxes given wavelengths.

RvSpectML.calc_dfluxdlnlambda! — Method.

Estimate numerical derivative of fluxes given wavelengths.

RvSpectML.calc_dfluxdlnlambda — Method.

Estimate numerical derivative of fluxes given wavelengths.

RvSpectML.calc_mean_d2fluxdlnlambda2 — Method.

Return mean numerical derivative (d²flux/dlnλ²) based on a common set of wavelengths. Inputs:

  • flux (2d)
  • var (2d)
  • λ (1d)
  • chunk_map: Array of ranges specifying how each chunk maps into indexes of output derivative

Output:

  • d²flux/dlnλ²: (1d)
RvSpectML.calc_mean_dfluxdlnlambda — Method.

Return mean numerical derivative (dflux/dlnλ) based on a common set of wavelengths. Inputs:

  • flux (2d)
  • var (2d)
  • λ (1d)
  • chunk_map: Array of ranges specifying how each chunk maps into indexes of output derivative

Output:

  • dflux/dlnλ: (1d)
RvSpectML.calc_mean_spectrum — Method.

Return mean flux (averaging over observations at different times, variance weighted) based on a common set of wavelengths. Inputs: flux & var (2d: pixel, time)

RvSpectML.calc_rvs_from_taylor_expansion — Method.

calc_rvs_from_taylor_expansion( spectra ) Inputs:

  • spectra: SpectralTimeSeriesCommonWavelengths

Optional Arguments:

  • mean: mean spectrum
  • deriv: dmeanflux/dlnλ
  • idx: range of pixel indcies to use for calculation
  • equal_weight: For now, spectra are equal weighted. (true)

Output named pair:

  • rv: Vector of estimated radial velocities
  • σ_rv: Vector of uncertainties in rv estimates
RvSpectML.calc_rvs_from_taylor_expansion_alt — Method.

calc_rvs_from_taylor_expansion_alt( spectra ) Experimental version of calcrvsfromtaylorexpansion.

RvSpectML.repack_flux_vector_to_chunk_matrix — Method.

repackfluxvectortochunkmatrix(λ, flux, var, chunkmap, λc; alg ) Warning: This doesn't work yet

Radial Velocity Related

Modules = [RvSpectML.DCPCA, RvSpectML.LineFinder ] #, RvSpectML.PPCA ]
Public = false
Order = [ :function ]

Interpolation

RvSpectML.SincInterpolation.create_filter_curve_orig — Method.

Original author: Joe Ninan Converted to Julia and optimized by Christian Gilbertson & Eric Ford Returns a cubit interpolator for windowed sinc Filter curve. noofpoints: number of intepolation points to use in cubic inteprolator

RvSpectML.TemporalGPInterpolation.predict_gp_mean_var — Method.

predictgpmeanvar(gp, xpred ; uselogx, use_logy) Inputs:

  • gp:
  • xpred: Locations to predict GP at

Optional inputs:

  • use_logx: If true, apply log transform to xpred before evaluating GP
  • use_logy: If true, apply exp transform after evaluating GP

Returns tuple with vector of means and variance of GP posterior at locations in xpred.

Other

Types

General purpose

Radial Velocity Related

Modules = [ RvSpectML.DCPCA, RvSpectML.LineFinder ] #, RvSpectML.PPCA ]
Public = false
Order = [:type ]

Interpolation

Other