triqs_Nevanlinna
Install Nevanlinna
Packaged Versions of Nevanlinna
Ubuntu Debian packages
Compiling Nevanlinna from source
Prerequisites
Installation steps
Version compatibility
Custom CMake options
Documentation
Tutorials
Single impurity Anderson model with discrete bath
SIAM system parameters
Construct hybridization function and Weiss Field
Construct the AtomDiag Object and solve it
Calculate and plot the single-particle Green function and self-energy
Running the Nevanlinna analytic continuation
Continuing the self-energy
Comparison to Pade & MaxEnt
run Pade
run MaxEnt
plot results of continuation
\(\Sigma\)
comparison to Pade
GW self-energy of a 2D square lattice Hubbard model
Setup simple two orbital 2D Hubbard model on square lattice
plot dispersion along high-symmetry lines
GW in imaginary time
setup bare interaction
Run one GW loop
Plot results
GW on real frequency axis
Analytic continuation
Nevanlinna
Pade
MaxEnt
Comparison: Pade, Nevanlinna, Pade, and Maxent
Matrix valued: 2 orbital GW self-energy of a 2D square lattice Hubbard model
Setup simple two orbital 2D Hubbard model on square lattice
plot dispersion along high-symmetry lines
GW in imaginary time
setup bare interaction
Run one GW loop
Plot results
GW on real frequency axis
Analytic continuation
Nevanlinna
Pade
Comparison
Matrix valued continuation & Hardy optimization
Setup simple two orbital 2D Hubbard model on square lattice
plot dispersion along high-symmetry lines
Setup lattice Green’s function and calculate
\(G_{loc}\)
on Matsubara axis
calculate local Green’s function
Calculate reference data directly on real frequency axis
Analytic continuation with Pade, Nevanlinna, and MaxEnt
Pade
Nevanlinna & Caratheodory
run MaxEnt
Final plot for comparison
Hardy optimization
C++ reference manual
Python reference manual
triqs_Nevanlinna.solver
triqs_Nevanlinna.solver.Solver
Solver
triqs_Nevanlinna.solver.Solver.Pick_eigenvalues
triqs_Nevanlinna.solver.Solver.evaluate
triqs_Nevanlinna.solver.Solver.optimize
triqs_Nevanlinna.solver.Solver.solve
triqs_Nevanlinna.solver_core
triqs_Nevanlinna.solver_core.NevanlinnaParametersT
NevanlinnaParametersT
triqs_Nevanlinna.solver_core.NevanlinnaParametersT.kernel
triqs_Nevanlinna.solver_core.NevanlinnaParametersT.precision
triqs_Nevanlinna.solver_core.SolverCore
SolverCore
triqs_Nevanlinna.solver_core.SolverCore.Pick_eigenvalues
triqs_Nevanlinna.solver_core.SolverCore.evaluate
triqs_Nevanlinna.solver_core.SolverCore.solve
triqs_Nevanlinna.kernels
triqs_Nevanlinna.kernels_core
triqs_Nevanlinna.kernels_core.CaratheodoryKernel
CaratheodoryKernel
triqs_Nevanlinna.kernels_core.CaratheodoryKernel.Pick_eigenvalues
triqs_Nevanlinna.kernels_core.CaratheodoryKernel.evaluate
triqs_Nevanlinna.kernels_core.CaratheodoryKernel.get_Pick_eigenvalues
triqs_Nevanlinna.kernels_core.CaratheodoryKernel.init
triqs_Nevanlinna.kernels_core.NevanlinnaKernel
NevanlinnaKernel
triqs_Nevanlinna.kernels_core.NevanlinnaKernel.Pick_eigenvalues
triqs_Nevanlinna.kernels_core.NevanlinnaKernel.evaluate
triqs_Nevanlinna.kernels_core.NevanlinnaKernel.get_Pick_eigenvalues
triqs_Nevanlinna.kernels_core.NevanlinnaKernel.init
Reporting issues
Changelog
Version 4.0.0
General
cmake
jenkins
doc
Version 3.3.0
General
jenkins
cmake
Version 3.2.0
About Nevanlinna
triqs_Nevanlinna
triqs_Nevanlinna.solver.Solver.__len__
View page source
triqs_Nevanlinna.solver.Solver.__len__
Solver.
__len__
(
/
)
Return len(self).