gftool.pade

Pade analytic continuation for Green’s functions and self-energies.

The main aim of this module is to provide analytic continuation based on averaging over multiple Pade approximates (similar to [1]).

In most cases the following high level function should be used:

averaged, avg_no_neg_imag

Return one-shot analytic continuation evaluated at z.

Averager

Returns a function for repeated evaluation of the continued function.

References

1

Schött et al. “Analytic Continuation by Averaging Pade Approximants”. Phys Rev B 93, no. 7 (2016): 075104. https://doi.org/10.1103/PhysRevB.93.075104.

API

Functions

Averager(z_in, coeff, *, valid_pades, kind)	Create function for averaging Pade scheme.
FilterHighVariance([rel_num, abs_num])	Return function to filter continuations with highest variance.
FilterNegImag([threshold])	Return function to check if imaginary part is smaller than threshold.
FilterNegImagNum([abs_num, rel_num])	Return function to check how bad the imaginary part gets.
Mod_Averager(z_in, coeff, mod_fct, *, …[, …])	Create function for averaging Pade scheme using mod_fct before the average.
apply_filter(*filters, validity_iter)	Handle usage of filters for Pade.
averaged(z_out, z_in, *[, valid_z, fct_z, …])	Return the averaged Pade continuation with its variance.
avg_no_neg_imag(z_out, z_in, *[, valid_z, …])	Average Pade filtering approximants with non-negative imaginary part.
calc_iterator(z_out, z_in, coeff)	Calculate Pade continuation of function at points z_out.
coefficients(z, fct_z)	Calculate the coefficients for the Pade continuation.

Classes

KindGf(n_min, n_max)	Filter approximants such that the high-frequency behavior is \(1/ω\).
KindSelector(n_min, n_max)	Abstract filter class to determine high-frequency behavior of Pade.
KindSelf(n_min, n_max)	Filter approximants such that the high-frequency behavior is a constant.