Models.EGH

Models.EGH#

Models.EGH.py

Copyright (c) 2017-2025, SAXS Team, KEK-PF

class EGH(**kwargs)#

Bases: Model

get_name()#
get_params_string(params)#
get_peaktop_xy(x, params)#
guess(y, x=None, negative=False, **kwargs)#
guess_a_peak_with_prop(x, y, prop)#

This function is used to guess a modeled peak with a given area ratio.

x_from_height_ratio(ecurve, ratio, params)#
class EGHA(**kwargs)#

Bases: EGH

get_name()#
get_param_hints(pkey)#
get_params_string(params)#
guess(y, x=None, negative=False, **kwargs)#
guess_a_peak_with_prop(x, y, prop)#

This function is used to guess a peak with a given area ratio, which has been generated (after the implementation of the same method of EGH) completely by Cody AI.

guess_binary_peaks(x, y, p1, p2, guess_tau=False, debug=False, plot_info=None)#
x_from_height_ratio(ecurve, ratio, params)#
EGH_TAU_UPPER_LIMIT = 50#

This implemetation is based on the following paper A hybrid of exponential and gaussian functions as a simple model of asymmetric chromatographic peaks http://acadine.physics.jmu.edu/group/technical_notes/GC_peak_fitting/X_lan_Jorgenson.pdf

egh_no_affine(x, H=1, tR=0, sigma=1.0, tau=1.0, a=0)#
egh_params(x, params)#
egh_x_from_height_ratio(alpha, tR, sigma, tau)#
egha(x, H, tR, sigma, tau, a, raise_=False)#
egha_impl(x, H, tR, sigma, tau, a, raise_=False)#
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