dubfi.tests.test_inversion
==========================

.. py:module:: dubfi.tests.test_inversion

.. autoapi-nested-parse::

   Tests for Bayesian inversion.

   .. codeauthor:: Valentin Bruch, DWD <valentin.bruch@dwd.de>
   .. versionchanged:: 0.1.1 (changed module path)
   .. versionadded:: 0.1.0 (initial release)



Functions
---------

.. autoapisummary::

   dubfi.tests.test_inversion.gen_test_arrays
   dubfi.tests.test_inversion.compare_strict
   dubfi.tests.test_inversion.compare_strict_diag
   dubfi.tests.test_inversion.run_test_dense
   dubfi.tests.test_inversion.run_test_diagonal
   dubfi.tests.test_inversion.run_test_sparse
   dubfi.tests.test_inversion.run_test_mpi_defaults
   dubfi.tests.test_inversion.run_test_mpi
   dubfi.tests.test_inversion.compare_derivatives_hr
   dubfi.tests.test_inversion.compare_results
   dubfi.tests.test_inversion.profile_stats
   dubfi.tests.test_inversion.compare_InvertorOptimizer
   dubfi.tests.test_inversion.compare_InvertorOptimizer_diag
   dubfi.tests.test_inversion.test_compare_strict
   dubfi.tests.test_inversion.test_compare_strict_diag
   dubfi.tests.test_inversion.test_invertor
   dubfi.tests.test_inversion.test_invertor_diag


Module Contents
---------------

.. py:function:: gen_test_arrays(n, k, m, localization_scale = 10.0, b_prefactor = 0.1)

   Generate some tests arrays.


.. py:function:: compare_strict(n = 117, k = 23, m = 17, tests = 10, norm_prefactor=1.0, regularization=1.0, localization_scale=20.0, b_prefactor=0.1)

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


.. py:function:: compare_strict_diag(n = 117, k = 23, m = 17, tests = 10, norm_prefactor=1.0, regularization=1.0, localization_scale=0.01, b_prefactor=0.1)

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


.. py:function:: run_test_dense(test_arrays, regularization, norm_prefactor, tests=10, profiling=False)

   Run inversion using dense matrix linear algebra implementation.


.. py:function:: run_test_diagonal(test_arrays, regularization, norm_prefactor, tests=10, profiling=False)

   Run inversion using dense matrix linear algebra implementation.


.. py:function:: run_test_sparse(test_arrays, regularization, norm_prefactor, tests=10, profiling=False)

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


.. py:function:: run_test_mpi_defaults(n=349, k=23, m=17, **kwargs)

   Run inversion using MPI linear algebra implementation.


.. py:function:: run_test_mpi(test_arrays, regularization, norm_prefactor, localization_scale, tests=10, profiling=False)

   Run inversion using MPI linear algebra implementation.


.. py:function:: compare_derivatives_hr(h, r, states, **kwargs)

   Test derivatives of parameterized vector H and parametrized operator R.


.. py:function:: compare_results(res1, res2, label1, label2)

   Compare two inversion results.


.. py:function:: profile_stats()

   Show statistics from previously saved profiling data.


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

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


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

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


.. py:function:: test_compare_strict()

   Unit test: fails if errors are encountered.


.. py:function:: test_compare_strict_diag()

   Unit test: fails if errors are encountered.


.. py:function:: test_invertor()

   Unit test: fails if errors are encountered.


.. py:function:: test_invertor_diag()

   Unit test: fails if errors are encountered.