fitlistreader.peak

Module Contents

Classes

BackgroundColl	Container class to order attributes more nicely.
Peak	Supply a sub-xml tree element <fit> and which peak in that fit you want to extract, ennumerated top-down via 0-indexing.

class fitlistreader.peak.BackgroundColl

Container class to order attributes more nicely.

__slots__ = ('regions', 'model', 'nparams', 'params', 'vol', 'dvol')

class fitlistreader.peak.Peak(fit, peak_index, spc, ID, use_val='cal', binwidth=1)

Supply a sub-xml tree element <fit> and which peak in that fit you want to extract, ennumerated top-down via 0-indexing. use_val is an internal devision between “cal” and “uncal” for all marker-values, which will be different if the spectrum was calibrated by HDTV.

Usually you are interested in the “cal” version, which is the default.

build_bg(bg_xml)

_poly(coeff)

returns polinomial function with coefficients as python function of signature f(x::float)=>poly(x)::float

_vol_from_poly(coeff, a, b)

computes area under polinomial with coefficients coeff between x=a and x=b analytically

_better_vol_poly(coeff, a, b)

more efficient implementation of _vol_from_poly. Does not require re-building of coefficients. Independant of _poly

_general_poly_vol_err_indep(coeff, dcoeff, a, da, b, db)

general formula for the uncertainty in the volume determination of a polynomial with coefficients coeff between x=a and x=b. The single uncertainties dcoeff, da and db are assumed to be independant. dcoeff must have the same dimension as coeff.

classmethod build_empty()

generates a valid Peak object with all filds containing None values or similar

dummy()