dubfi.tests.test_inversion

Tests for Bayesian inversion.

Changed in version 0.1.1: (changed module path)

Added in version 0.1.0: (initial release)

Functions

gen_test_arrays(→ dict)	Generate some tests arrays.
compare_strict(→ int)	Compare cost function and its derivatives in different linear algebra implementations.
compare_strict_diag(→ int)	Compare cost function and its derivatives in dense and diagonal case.
run_test_dense(...)	Run inversion using dense matrix linear algebra implementation.
run_test_diagonal(...)	Run inversion using dense matrix linear algebra implementation.
run_test_sparse(...)	Run inversion using sparse (CSC) matrix linear algebra implementation.
run_test_mpi_defaults(...)	Run inversion using MPI linear algebra implementation.
run_test_mpi(...)	Run inversion using MPI linear algebra implementation.
compare_derivatives_hr(→ int)	Test derivatives of parameterized vector H and parametrized operator R.
compare_results(→ int)	Compare two inversion results.
profile_stats()	Show statistics from previously saved profiling data.
compare_InvertorOptimizer(→ int)	Test inversion by comparing inversion results for different linear algebra implementations.
compare_InvertorOptimizer_diag(→ int)	Test inversion by comparing inversion results for diagonal and CSC matrices.
test_compare_strict(invertor)	Unit test: fails if errors are encountered.
test_compare_strict_diag(invertor)	Unit test: fails if errors are encountered.
test_invertor()	Unit test: fails if errors are encountered.
test_invertor_diag()	Unit test: fails if errors are encountered.

Module Contents

dubfi.tests.test_inversion.gen_test_arrays(n: int, k: int, m: int, localization_scale: float = 10.0, b_prefactor: float = 0.1) → dict

Generate some tests arrays.

dubfi.tests.test_inversion.compare_strict(n: int = 117, k: int = 23, m: int = 17, tests: int = 10, norm_prefactor: float = 1.0, regularization: float = 1.0, localization_scale: float = 20.0, b_prefactor: float = 0.1, Invertor: type[dubfi.inversion.inversion.InvertorOptimizer] = InvertorOptimizer) → int

Compare cost function and its derivatives in different linear algebra implementations.

dubfi.tests.test_inversion.compare_strict_diag(n: int = 117, k: int = 23, m: int = 17, tests: int = 10, norm_prefactor: float = 1.0, regularization: float = 1.0, localization_scale: float = 0.01, b_prefactor: float = 0.1, Invertor: type[dubfi.inversion.inversion.InvertorOptimizer] = InvertorOptimizer) → int

Compare cost function and its derivatives in dense and diagonal case.

dubfi.tests.test_inversion.run_test_dense(test_arrays, regularization, norm_prefactor, tests=10, profiling=False) → tuple[dubfi.inversion.inversion.InversionResult, int]

Run inversion using dense matrix linear algebra implementation.

dubfi.tests.test_inversion.run_test_diagonal(test_arrays, regularization, norm_prefactor, tests=10, profiling=False) → tuple[dubfi.inversion.inversion.InversionResult, int]

Run inversion using dense matrix linear algebra implementation.

dubfi.tests.test_inversion.run_test_sparse(test_arrays, regularization, norm_prefactor, tests=10, profiling=False) → tuple[dubfi.inversion.inversion.InversionResult, int]

Run inversion using sparse (CSC) matrix linear algebra implementation.

dubfi.tests.test_inversion.run_test_mpi_defaults(n=349, k=23, m=17, **kwargs) → tuple[dubfi.inversion.inversion.InversionResult, int]

Run inversion using MPI linear algebra implementation.

dubfi.tests.test_inversion.run_test_mpi(test_arrays, regularization, norm_prefactor, localization_scale, tests=10, profiling=False) → tuple[dubfi.inversion.inversion.InversionResult, int]

Run inversion using MPI linear algebra implementation.

dubfi.tests.test_inversion.compare_derivatives_hr(h, r, states, **kwargs) → int

Test derivatives of parameterized vector H and parametrized operator R.

dubfi.tests.test_inversion.compare_results(res1: dubfi.inversion.inversion.InversionResult, res2: dubfi.inversion.inversion.InversionResult, label1: str, label2: str) → int

Compare two inversion results.

dubfi.tests.test_inversion.profile_stats()

Show statistics from previously saved profiling data.

dubfi.tests.test_inversion.compare_InvertorOptimizer(n: int = 401, k: int = 23, m: int = 17, tests: int = 2, profiling=False, norm_prefactor=0.5, regularization=1.0, localization_scale=20.0, b_prefactor=0.1) → int

Test inversion by comparing inversion results for different linear algebra implementations.

dubfi.tests.test_inversion.compare_InvertorOptimizer_diag(n: int = 401, k: int = 23, m: int = 17, tests: int = 2, profiling=False, norm_prefactor=0.5, regularization=1.0, localization_scale=0.01, b_prefactor=0.1) → int

Test inversion by comparing inversion results for diagonal and CSC matrices.

dubfi.tests.test_inversion.test_compare_strict(invertor: type[dubfi.inversion.inversion.InvertorOptimizer])

Unit test: fails if errors are encountered.

dubfi.tests.test_inversion.test_compare_strict_diag(invertor: type[dubfi.inversion.inversion.InvertorOptimizer])

Unit test: fails if errors are encountered.

dubfi.tests.test_inversion.test_invertor()

Unit test: fails if errors are encountered.

dubfi.tests.test_inversion.test_invertor_diag()

Unit test: fails if errors are encountered.