gftools.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. |
FilterNegImag([threshold]) |
Return function to check if imaginary part is smaller than threshold. |
Mod_Averager(z_in, coeff, mod_fct, \*, …) |
Create function for averaging Pade scheme using mod_fct before the average. |
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. |
masked_coefficients(z, fct_z) |
Calculate coefficients but ignore extreme values. |
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. |