Changelog

2.1.2 (2025-07-10)

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  • Set up building of Conda packages for released versions. The packages are now available in the ‘krivenko’ channel.

2.1.1 (2025-06-30)

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  • Accept TRIQS 3.3 as compatible version.

  • Add generation of an easyconfig file as part of build process.

  • Minor coding style improvements and fixes.

2.1.0 (2023-10-03)

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2.0.0 (2022-06-27)

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Major changes and new features

  • Complete port to TRIQS 3.1 and Python 3.

  • Implementation of the Stochastic Optimization with Consistent Constraints (SOCC) proposed by Goulko et al in Phys. Rev. B 95, 014102 (2017). It includes three major pieces of functionality.

    • The Consistent Constraints update in the Markov chain used to accumulate particular solutions;

    • The Consistent Constraints protocol for constructing final solutions out of particular solutions;

    • The solution quality assessment technique implemented in a new Python module som.spectral_stats.

  • For consistency with MaxEnt and other stochastic continuation methods, the objective function of the optimization problem has been changed to the “goodness of fit” \(\chi^2\)-functional.

  • Adoption of the \(\chi^2\)-functional has made it possible to support user-supplied covariance matrices of input data as an alternative to simple estimated error bars (credits to @snirgaz for proposing this feature).

  • A new family of integral kernels for symmetric fermionic Green’s functions has been introduced. The corresponding observable is called FermionGfSymm.

  • The BosonAutoCorr kernels have been changed to more closely reproduce results of the BosonCorr kernels for the same input data. Both kernel families are defined on the whole energy axis and expect the same spectrum normalization constants now (before one had to divide the constants by 2 for BosonAutoCorr).

  • Projection of an observable onto a real frequency mesh can now be performed using binning (enabled by default). In this mode the projected observable is integrated over bins centered around points of the mesh.

  • Further MPI parallelization.

  • Massively reworked online documentation.

Python API changes

  • Following a convention change for TRIQS applications, the Python package of SOM has been renamed from pytriqs.applications.analytical_continuation.som to a laconic som.

  • Functionality of the run() method of SomCore has been split among a few new methods,

    • accumulate() – accumulate particular solutions;

    • adjust_f() – adjust the number of global updates F;

    • compute_final_solution() – construct the final solution using the standard SOM protocol;

    • compute_final_solution_cc() – construct the final solution using Goulko’s SOCC protocol.

    One may still call the deprecated run(), which is equivalent to calling accumulate() + compute_final_solution().

  • In recent versions of TRIQS it became impossible to use the << syntax to fill GF containers from user-defined Python objects. Furthermore, high-frequency tail data was separated from the GF containers. As a result, that syntax had to be abandoned in favor of a few free functions.

    • fill_refreq() – fill a real-frequency observable from a computed SOM solution;

    • compute_tail() – compute high-frequency tail coefficients from a computed SOM solution;

    • reconstruct() – reconstruct input from a computed SOM solution.

  • It is now possible to resume accumulation of particular solutions by calling SomCore.accumulate() multiple times, and to discard all accumulated solutions by calling SomCore.clear().

  • A handful of new properties and accessor methods have been added to SomCore.

  • The rectangle and configuration C++ objects are now exposed as Python classes Rectangle and Configuration. Configuration objects can be saved to/loaded from HDF5 archives.

  • Updated signature of som.count_good_solutions() to take both good_chi_abs and good_chi_rel (thresholds on \(\chi\) and \(\chi/\chi_\mathrm{min}\) for a solution to be considered good).

  • A new utility function som.estimate_boson_corr_spectrum_norms() has been added. Given a correlator of boson-like operators \(\chi\) defined on any supported mesh, it returns a list of spectrum normalization constants \(\mathcal{N} = \pi \chi(i\Omega = 0)\).

Build system and developer tools

  • Minimum required CMake version has been bumped to 3.12.4.

  • Structure of the project has been adjusted to follow conventions established by the app4triqs application template.

  • A Dockerfile has been added.

  • Files som.modulefile and somvars.sh are generated and installed as part of the build process.

  • New benchmarks: all_kernels, binning, consistent_constraints, bosonautocorr and fermiongfsymm.

  • The chi benchmark has been removed as it depended on the private triqs_ctseg code.

  • Support for C++ static analysis tools clang-tidy and cppcheck has been added.

  • A CMake option has been added to link libsom and unit tests to Clang sanitizers (AddressSanitizer and UndefinedBehaviorSanitizer).

  • C++/Python coding style is enforced with clang-format and flake8.

1.2 (2020-03-15)

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  • Improvements and small fixes in documentation.

  • Added a new histogram post-processing function, count_good_solutions().

  • Fixed a bug in update_glue_shift elementary update.

  • New benchmark all_kernels and fixes in the chi benchmark.

  • Added Travis CI config for continuous testing and documentation deployment.

  • Minor code improvements.

1.1 (2017-04-23)

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  • Massive extension of documentation.

  • adjust_f mode is disabled by default.

  • Fixed a critical bug in back_transform().

1.0 (2017-03-19)

  • First official release.

  • Supports 12 integral kernels:

    • 4 observable kinds (FermionGf, BosonCorr, BosonAutoCorr, and ZeroTemp);

    • 3 input meshes (imtime, imfreq, legendre).