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<title>prior &mdash; SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation</title>
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<p class="caption" role="heading"><span class="caption-text">API Documentation</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="selfisys.hiddenbox.html">hiddenbox</a><ul>
<li class="toctree-l2"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox"><code class="docutils literal notranslate"><span class="pre">HiddenBox</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.Npop"><code class="docutils literal notranslate"><span class="pre">HiddenBox.Npop</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.Ntimesteps"><code class="docutils literal notranslate"><span class="pre">HiddenBox.Ntimesteps</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.Psingle"><code class="docutils literal notranslate"><span class="pre">HiddenBox.Psingle</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.compute_pool"><code class="docutils literal notranslate"><span class="pre">HiddenBox.compute_pool()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.evaluate"><code class="docutils literal notranslate"><span class="pre">HiddenBox.evaluate()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.force_neglect_lightcone"><code class="docutils literal notranslate"><span class="pre">HiddenBox.force_neglect_lightcone</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.force_recompute_mocks"><code class="docutils literal notranslate"><span class="pre">HiddenBox.force_recompute_mocks</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.gravity_on"><code class="docutils literal notranslate"><span class="pre">HiddenBox.gravity_on</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.load_pool"><code class="docutils literal notranslate"><span class="pre">HiddenBox.load_pool()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.make_data"><code class="docutils literal notranslate"><span class="pre">HiddenBox.make_data()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.modified_selfi"><code class="docutils literal notranslate"><span class="pre">HiddenBox.modified_selfi</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.prefix_mocks"><code class="docutils literal notranslate"><span class="pre">HiddenBox.prefix_mocks</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.reset_survey"><code class="docutils literal notranslate"><span class="pre">HiddenBox.reset_survey()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.setup_only"><code class="docutils literal notranslate"><span class="pre">HiddenBox.setup_only</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.switch_recompute_pool"><code class="docutils literal notranslate"><span class="pre">HiddenBox.switch_recompute_pool()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.switch_setup"><code class="docutils literal notranslate"><span class="pre">HiddenBox.switch_setup()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.hiddenbox.html#selfisys.hiddenbox.HiddenBox.update"><code class="docutils literal notranslate"><span class="pre">HiddenBox.update()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="selfisys.normalise_hb.html">normalise_hb</a><ul>
<li class="toctree-l2"><a class="reference internal" href="selfisys.normalise_hb.html#selfisys.normalise_hb.define_normalisation"><code class="docutils literal notranslate"><span class="pre">define_normalisation()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.normalise_hb.html#selfisys.normalise_hb.worker_normalisation"><code class="docutils literal notranslate"><span class="pre">worker_normalisation()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.normalise_hb.html#selfisys.normalise_hb.worker_normalisation_public"><code class="docutils literal notranslate"><span class="pre">worker_normalisation_public()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.normalise_hb.html#selfisys.normalise_hb.worker_normalisation_wrapper"><code class="docutils literal notranslate"><span class="pre">worker_normalisation_wrapper()</span></code></a></li>
</ul>
</li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">prior</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#selfisys.prior.get_summary"><code class="docutils literal notranslate"><span class="pre">get_summary()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="#selfisys.prior.logposterior_hyperparameters_parallel"><code class="docutils literal notranslate"><span class="pre">logposterior_hyperparameters_parallel()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="#selfisys.prior.perform_prior_optimisation_and_plot"><code class="docutils literal notranslate"><span class="pre">perform_prior_optimisation_and_plot()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="#selfisys.prior.planck_prior"><code class="docutils literal notranslate"><span class="pre">planck_prior</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.mean"><code class="docutils literal notranslate"><span class="pre">planck_prior.mean</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.covariance"><code class="docutils literal notranslate"><span class="pre">planck_prior.covariance</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.inv_covariance"><code class="docutils literal notranslate"><span class="pre">planck_prior.inv_covariance</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.Nbin_max"><code class="docutils literal notranslate"><span class="pre">planck_prior.Nbin_max</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.Nbin_min"><code class="docutils literal notranslate"><span class="pre">planck_prior.Nbin_min</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.compute"><code class="docutils literal notranslate"><span class="pre">planck_prior.compute()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.load"><code class="docutils literal notranslate"><span class="pre">planck_prior.load()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.logpdf"><code class="docutils literal notranslate"><span class="pre">planck_prior.logpdf()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.sample"><code class="docutils literal notranslate"><span class="pre">planck_prior.sample()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="#selfisys.prior.planck_prior.save"><code class="docutils literal notranslate"><span class="pre">planck_prior.save()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#selfisys.prior.worker_class"><code class="docutils literal notranslate"><span class="pre">worker_class()</span></code></a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="selfisys.selection_functions.html">selection_functions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection"><code class="docutils literal notranslate"><span class="pre">LognormalSelection</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.init_selection"><code class="docutils literal notranslate"><span class="pre">LognormalSelection.init_selection()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.lognormals_z_to_x"><code class="docutils literal notranslate"><span class="pre">LognormalSelection.lognormals_z_to_x()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.multiple_lognormal"><code class="docutils literal notranslate"><span class="pre">LognormalSelection.multiple_lognormal()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.multiple_lognormal_z"><code class="docutils literal notranslate"><span class="pre">LognormalSelection.multiple_lognormal_z()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.one_lognormal"><code class="docutils literal notranslate"><span class="pre">LognormalSelection.one_lognormal()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.one_lognormal_z"><code class="docutils literal notranslate"><span class="pre">LognormalSelection.one_lognormal_z()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.r_grid"><code class="docutils literal notranslate"><span class="pre">LognormalSelection.r_grid()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="selfisys.selfi_interface.html">selfi_interface</a><ul>
<li class="toctree-l2"><a class="reference internal" href="selfisys.selfi_interface.html#selfisys.selfi_interface.PrintMessage"><code class="docutils literal notranslate"><span class="pre">PrintMessage()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.selfi_interface.html#selfisys.selfi_interface.indent"><code class="docutils literal notranslate"><span class="pre">indent()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.selfi_interface.html#selfisys.selfi_interface.unindent"><code class="docutils literal notranslate"><span class="pre">unindent()</span></code></a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="selfisys.sbmy_interface.html">sbmy_interface</a><ul>
<li class="toctree-l2"><a class="reference internal" href="selfisys.sbmy_interface.html#selfisys.sbmy_interface.compute_Phi"><code class="docutils literal notranslate"><span class="pre">compute_Phi()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.sbmy_interface.html#selfisys.sbmy_interface.generate_white_noise_Field"><code class="docutils literal notranslate"><span class="pre">generate_white_noise_Field()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.sbmy_interface.html#selfisys.sbmy_interface.get_power_spectrum_from_cosmo"><code class="docutils literal notranslate"><span class="pre">get_power_spectrum_from_cosmo()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.sbmy_interface.html#selfisys.sbmy_interface.handle_time_stepping"><code class="docutils literal notranslate"><span class="pre">handle_time_stepping()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.sbmy_interface.html#selfisys.sbmy_interface.setup_sbmy_parfiles"><code class="docutils literal notranslate"><span class="pre">setup_sbmy_parfiles()</span></code></a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="selfisys.grf.html">grf</a><ul>
<li class="toctree-l2"><a class="reference internal" href="selfisys.grf.html#selfisys.grf.primordial_grf"><code class="docutils literal notranslate"><span class="pre">primordial_grf()</span></code></a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="selfisys.utils.html">selfisys.utils package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#module-selfisys.utils.examples_utils">selfisys.utils.examples_utils module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.examples_utils.clear_large_plot"><code class="docutils literal notranslate"><span class="pre">clear_large_plot()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#module-selfisys.utils.logger">selfisys.utils.logger module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.CustomLogger"><code class="docutils literal notranslate"><span class="pre">CustomLogger</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.CustomLogger.diagnostic"><code class="docutils literal notranslate"><span class="pre">CustomLogger.diagnostic()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.CustomLoggerHandler"><code class="docutils literal notranslate"><span class="pre">CustomLoggerHandler</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.CustomLoggerHandler.emit"><code class="docutils literal notranslate"><span class="pre">CustomLoggerHandler.emit()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.INDENT"><code class="docutils literal notranslate"><span class="pre">INDENT()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.PrintDiagnostic"><code class="docutils literal notranslate"><span class="pre">PrintDiagnostic()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.PrintError"><code class="docutils literal notranslate"><span class="pre">PrintError()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.PrintInfo"><code class="docutils literal notranslate"><span class="pre">PrintInfo()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.PrintLeftType"><code class="docutils literal notranslate"><span class="pre">PrintLeftType()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.PrintWarning"><code class="docutils literal notranslate"><span class="pre">PrintWarning()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.UNINDENT"><code class="docutils literal notranslate"><span class="pre">UNINDENT()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.logger.getCustomLogger"><code class="docutils literal notranslate"><span class="pre">getCustomLogger()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#module-selfisys.utils.low_level">selfisys.utils.low_level module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.low_level.stderr_redirector"><code class="docutils literal notranslate"><span class="pre">stderr_redirector()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.low_level.stdout_redirector"><code class="docutils literal notranslate"><span class="pre">stdout_redirector()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#module-selfisys.utils.parser">selfisys.utils.parser module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.parser.bool_sh"><code class="docutils literal notranslate"><span class="pre">bool_sh()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.parser.check_files_exist"><code class="docutils literal notranslate"><span class="pre">check_files_exist()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.parser.intNone"><code class="docutils literal notranslate"><span class="pre">intNone()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.parser.joinstrs"><code class="docutils literal notranslate"><span class="pre">joinstrs()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.parser.joinstrs_only"><code class="docutils literal notranslate"><span class="pre">joinstrs_only()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.parser.none_or_bool_or_str"><code class="docutils literal notranslate"><span class="pre">none_or_bool_or_str()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.parser.safe_npload"><code class="docutils literal notranslate"><span class="pre">safe_npload()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#module-selfisys.utils.path_utils">selfisys.utils.path_utils module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.path_utils.file_names_evaluate"><code class="docutils literal notranslate"><span class="pre">file_names_evaluate()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.path_utils.get_file_names"><code class="docutils literal notranslate"><span class="pre">get_file_names()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#module-selfisys.utils.plot_examples">selfisys.utils.plot_examples module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_examples.plot_comoving_distance_redshift"><code class="docutils literal notranslate"><span class="pre">plot_comoving_distance_redshift()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_examples.plot_galaxy_field_slice"><code class="docutils literal notranslate"><span class="pre">plot_galaxy_field_slice()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_examples.plot_power_spectrum"><code class="docutils literal notranslate"><span class="pre">plot_power_spectrum()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_examples.plot_selection_functions_def_in_z"><code class="docutils literal notranslate"><span class="pre">plot_selection_functions_def_in_z()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_examples.redshift_distance_conversion"><code class="docutils literal notranslate"><span class="pre">redshift_distance_conversion()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_examples.relative_error_analysis"><code class="docutils literal notranslate"><span class="pre">relative_error_analysis()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="selfisys.utils.html#module-selfisys.utils.plot_params">selfisys.utils.plot_params module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_params.ScalarFormatterForceFormat_11"><code class="docutils literal notranslate"><span class="pre">ScalarFormatterForceFormat_11</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_params.ScalarFormatterForceFormat_11.get_offset"><code class="docutils literal notranslate"><span class="pre">ScalarFormatterForceFormat_11.get_offset()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_params.create_all_colormaps"><code class="docutils literal notranslate"><span class="pre">create_all_colormaps()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_utils.plot_C"><code class="docutils literal notranslate"><span class="pre">plot_C()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_utils.plot_mocks_compact"><code class="docutils literal notranslate"><span class="pre">plot_mocks_compact()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_utils.plot_observations"><code class="docutils literal notranslate"><span class="pre">plot_observations()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_utils.plot_prior_and_posterior_covariances"><code class="docutils literal notranslate"><span class="pre">plot_prior_and_posterior_covariances()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_utils.plot_reconstruction"><code class="docutils literal notranslate"><span class="pre">plot_reconstruction()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.plot_utils.plot_selection_functions"><code class="docutils literal notranslate"><span class="pre">plot_selection_functions()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.tools.cosmo_vector_to_Simbelmyne_dict"><code class="docutils literal notranslate"><span class="pre">cosmo_vector_to_Simbelmyne_dict()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.tools.cosmo_vector_to_class_dict"><code class="docutils literal notranslate"><span class="pre">cosmo_vector_to_class_dict()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.tools.none_or_bool_or_str"><code class="docutils literal notranslate"><span class="pre">none_or_bool_or_str()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="selfisys.utils.html#selfisys.utils.workers.evaluate_gradient_of_Symbelmyne"><code class="docutils literal notranslate"><span class="pre">evaluate_gradient_of_Symbelmyne()</span></code></a></li>
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<section id="module-selfisys.prior">
<span id="prior"></span><h1>prior<a class="headerlink" href="#module-selfisys.prior" title="Link to this heading"></a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="selfisys.prior.get_summary">
<span class="sig-prename descclassname"><span class="pre">selfisys.prior.</span></span><span class="sig-name descname"><span class="pre">get_summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalisation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#get_summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.get_summary" title="Link to this definition"></a></dt>
<dd><p>Compute a power-spectrum summary for given cosmological parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<em>array-like</em>) – Cosmological parameters [h, Omega_b, Omega_m, n_s, sigma_8].</p></li>
<li><p><strong>bins</strong> (<em>array-like</em>) – Wavenumber bins.</p></li>
<li><p><strong>normalisation</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em>) – Normalisation constant to scale the resulting spectrum.</p></li>
<li><p><strong>kmax</strong> (<em>float</em><em>, </em><em>optional</em>) – Maximum wavenumber for get_Pk.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>theta</strong> – The computed power-spectrum values, optionally normalised.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray</p>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>RuntimeError</strong> – If the power-spectrum computation fails unexpectedly.</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="selfisys.prior.logposterior_hyperparameters_parallel">
<span class="sig-prename descclassname"><span class="pre">selfisys.prior.</span></span><span class="sig-name descname"><span class="pre">logposterior_hyperparameters_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selfi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_fiducial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Nbin_min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Nbin_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_corr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_cv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#logposterior_hyperparameters_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.logposterior_hyperparameters_parallel" title="Link to this definition"></a></dt>
<dd><p>Compute the log-posterior for the hyperparameters of the prior from
[leclercq2019primordial], for use within the SelfiSys pipeline.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>selfi</strong> (<em>object</em>) – The selfi object.</p></li>
<li><p><strong>theta_fiducial</strong> (<em>ndarray</em>) – Fiducial spectrum.</p></li>
<li><p><strong>Nbin_min</strong> (<em>int</em>) – Minimum bin index for the wavenumber range.</p></li>
<li><p><strong>Nbin_max</strong> (<em>int</em>) – Maximum bin index for the wavenumber range.</p></li>
<li><p><strong>theta_norm</strong> (<em>float</em>) – Hyperparameter controlling the overall uncertainty.</p></li>
<li><p><strong>k_corr</strong> (<em>float</em>) – Hyperparameter controlling correlation scale.</p></li>
<li><p><strong>alpha_cv</strong> (<em>float</em>) – Cosmic variance strength.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>The log-posterior value for the given hyperparameters.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>float</p>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>RuntimeError</strong> – If the log-posterior computation fails unexpectedly.</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="selfisys.prior.perform_prior_optimisation_and_plot">
<span class="sig-prename descclassname"><span class="pre">selfisys.prior.</span></span><span class="sig-name descname"><span class="pre">perform_prior_optimisation_and_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selfi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_fiducial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_norm_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_norm_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_corr_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_corr_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.015</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_opt_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_opt_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_norm_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.04</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_norm_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_corr_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.012</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_corr_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meshsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Nbin_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Nbin_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_corr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.015</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.00065</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#perform_prior_optimisation_and_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.perform_prior_optimisation_and_plot" title="Link to this definition"></a></dt>
<dd><p>Optimise the hyperparameters for the selfi2019 prior (from
[leclercq2019primordial]).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>selfi</strong> (<em>object</em>) – The selfi object.</p></li>
<li><p><strong>theta_fiducial</strong> (<em>ndarray</em>) – Fiducial spectrum.</p></li>
<li><p><strong>theta_norm_mean</strong> (<em>float</em><em>, </em><em>optional</em>) – Mean of the Gaussian hyperprior on theta_norm. Default 0.1.</p></li>
<li><p><strong>theta_norm_std</strong> (<em>float</em><em>, </em><em>optional</em>) – Standard deviation of the hyperprior on theta_norm. Default 0.3.</p></li>
<li><p><strong>k_corr_mean</strong> (<em>float</em><em>, </em><em>optional</em>) – Mean of the Gaussian hyperprior on k_corr. Default 0.020.</p></li>
<li><p><strong>k_corr_std</strong> (<em>float</em><em>, </em><em>optional</em>) – Standard deviation of the hyperprior on k_corr. Default 0.015.</p></li>
<li><p><strong>k_opt_min</strong> (<em>float</em><em>, </em><em>optional</em>) – Minimum wavenumber for the prior optimisation. Default 0.0.</p></li>
<li><p><strong>k_opt_max</strong> (<em>float</em><em>, </em><em>optional</em>) – Maximum wavenumber for the prior optimisation. Default 1.4.</p></li>
<li><p><strong>theta_norm_min</strong> (<em>float</em><em>, </em><em>optional</em>) – Lower bound for theta_norm in the mesh. Default 0.04.</p></li>
<li><p><strong>theta_norm_max</strong> (<em>float</em><em>, </em><em>optional</em>) – Upper bound for theta_norm in the mesh. Default 0.12.</p></li>
<li><p><strong>k_corr_min</strong> (<em>float</em><em>, </em><em>optional</em>) – Lower bound for k_corr in the mesh. Default 0.012.</p></li>
<li><p><strong>k_corr_max</strong> (<em>float</em><em>, </em><em>optional</em>) – Upper bound for k_corr in the mesh. Default 0.02.</p></li>
<li><p><strong>meshsize</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of points in each dimension of the plot mesh. Default 30.</p></li>
<li><p><strong>Nbin_min</strong> (<em>int</em><em>, </em><em>optional</em>) – Minimum bin index for restricting the prior. Default 0.</p></li>
<li><p><strong>Nbin_max</strong> (<em>int</em><em>, </em><em>optional</em>) – Maximum bin index for restricting the prior. Default 100.</p></li>
<li><p><strong>theta_norm</strong> (<em>float</em><em>, </em><em>optional</em>) – Initial or default guess of theta_norm. Default 0.05.</p></li>
<li><p><strong>k_corr</strong> (<em>float</em><em>, </em><em>optional</em>) – Initial or default guess of k_corr. Default 0.015.</p></li>
<li><p><strong>alpha_cv</strong> (<em>float</em><em>, </em><em>optional</em>) – Cosmic variance term or similar. Default 0.00065.</p></li>
<li><p><strong>plot</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, generate and show/save a 2D contour plot. Default True.</p></li>
<li><p><strong>savepath</strong> (<em>str</em><em>, </em><em>optional</em>) – File path to save the plot. If None, the plot is displayed.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>(theta_norm, k_corr) after optimisation.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>tuple</p>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>OSError</strong> – If file operations fail during saving the prior or posterior.</p></li>
<li><p><strong>RuntimeError</strong> – If the optimisation fails unexpectedly.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">selfisys.prior.</span></span><span class="sig-name descname"><span class="pre">planck_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Omega_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Omega_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalisation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kmax</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthreads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EPS_K</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EPS_residual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#planck_prior"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.planck_prior" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Custom prior for the SelfiSys pipeline. This is the prior used in
[hoellinger2024diagnosing], based on the Planck 2018 cosmological
parameters.</p>
<p>This class provides methods to compute a power-spectrum prior from a
prior distribution of cosmological parameters, using a Gaussian fit.
See equation (7) in [hoellinger2024diagnosing].</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>Omega_mean</strong> (<em>array-like</em>) – Mean of the prior distribution on cosmological parameters.</p></li>
<li><p><strong>Omega_cov</strong> (<em>array-like</em>) – Covariance matrix of the prior distribution on cosmological
parameters.</p></li>
<li><p><strong>bins</strong> (<em>array-like</em>) – Wavenumbers where the power spectrum is evaluated.</p></li>
<li><p><strong>normalisation</strong> (<em>float</em><em> or </em><em>None</em>) – If not None, divide the power spectra by the normalisation.</p></li>
<li><p><strong>kmax</strong> (<em>float</em>) – Maximum wavenumber for computations.</p></li>
<li><p><strong>nsamples</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of samples drawn from the prior on the cosmological
parameters. Default is 10,000.</p></li>
<li><p><strong>nthreads</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of CPU threads for parallel tasks. Default is -1, that
is, auto-detect the number of available threads.</p></li>
<li><p><strong>EPS_K</strong> (<em>float</em><em>, </em><em>optional</em>) – Regularisation parameter for covariance inversion. Default 1e-7.</p></li>
<li><p><strong>EPS_residual</strong> (<em>float</em><em>, </em><em>optional</em>) – Additional cutoff for matrix inversion. Default 1e-3.</p></li>
<li><p><strong>filename</strong> (<em>str</em><em> or </em><em>None</em><em>, </em><em>optional</em>) – Path to a .npy file to store or load precomputed power spectra.</p></li>
</ul>
</dd>
</dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.mean">
<span class="sig-name descname"><span class="pre">mean</span></span><a class="headerlink" href="#selfisys.prior.planck_prior.mean" title="Link to this definition"></a></dt>
<dd><p>Mean of the computed power spectra.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.covariance">
<span class="sig-name descname"><span class="pre">covariance</span></span><a class="headerlink" href="#selfisys.prior.planck_prior.covariance" title="Link to this definition"></a></dt>
<dd><p>Covariance matrix of the computed power spectra.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.inv_covariance">
<span class="sig-name descname"><span class="pre">inv_covariance</span></span><a class="headerlink" href="#selfisys.prior.planck_prior.inv_covariance" title="Link to this definition"></a></dt>
<dd><p>Inverse of the covariance matrix.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="field-list simple">
<dt class="field-odd">Raises<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>OSError</strong> – If file reading or writing fails.</p></li>
<li><p><strong>RuntimeError</strong> – For unexpected HPC or multi-processing errors.</p></li>
</ul>
</dd>
</dl>
<dl class="py property">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.Nbin_max">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Nbin_max</span></span><a class="headerlink" href="#selfisys.prior.planck_prior.Nbin_max" title="Link to this definition"></a></dt>
<dd><p>Index of the maximal wavenumber given self.kmax.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.Nbin_min">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Nbin_min</span></span><a class="headerlink" href="#selfisys.prior.planck_prior.Nbin_min" title="Link to this definition"></a></dt>
<dd><p>Index of the minimal wavenumber given k_min.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.compute">
<span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#planck_prior.compute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.planck_prior.compute" title="Link to this definition"></a></dt>
<dd><p>Compute the prior (mean, covariance, and inverse covariance).</p>
<p>If <cite>self.filename</cite> exists, tries to load the prior. Otherwise,
samples from the prior distribution on cosmological parameters
and evaluates the power spectra in parallel.</p>
<dl class="field-list simple">
<dt class="field-odd">Raises<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>OSError</strong> – If self.filename is not writable/accessible.</p></li>
<li><p><strong>RuntimeError</strong> – If multi-processing or power-spectra computations fail.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.load">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#planck_prior.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.planck_prior.load" title="Link to this definition"></a></dt>
<dd><p>Load the prior from input file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>fname</strong> (<em>str</em>) – Input HDF5 filename.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>The prior object.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>prior</p>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>OSError</strong> – If the file cannot be read or is invalid.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.logpdf">
<span class="sig-name descname"><span class="pre">logpdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_covariance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_icov</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#planck_prior.logpdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.planck_prior.logpdf" title="Link to this definition"></a></dt>
<dd><p>Return the log prior probability at a given point in parameter
space.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>theta</strong> (<em>ndarray</em>) – Evaluation point in parameter space.</p></li>
<li><p><strong>theta_mean</strong> (<em>ndarray</em>) – Prior mean vector.</p></li>
<li><p><strong>theta_covariance</strong> (<em>ndarray</em>) – Prior covariance matrix.</p></li>
<li><p><strong>theta_icov</strong> (<em>ndarray</em>) – Inverse of the prior covariance matrix.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Log prior probability value.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.sample">
<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seedsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#planck_prior.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.planck_prior.sample" title="Link to this definition"></a></dt>
<dd><p>Draw a random sample from the prior distribution.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>seedsample</strong> (<em>int</em><em>, </em><em>optional</em>) – Seed for the random number generator.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A single sample from the prior distribution.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="selfisys.prior.planck_prior.save">
<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#planck_prior.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.planck_prior.save" title="Link to this definition"></a></dt>
<dd><p>Save the prior to an output file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>fname</strong> (<em>str</em>) – Output HDF5 filename to store the prior data.</p>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>OSError</strong> – If the file cannot be accessed or written.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="selfisys.prior.worker_class">
<span class="sig-prename descclassname"><span class="pre">selfisys.prior.</span></span><span class="sig-name descname"><span class="pre">worker_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/selfisys/prior.html#worker_class"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#selfisys.prior.worker_class" title="Link to this definition"></a></dt>
<dd><p>Worker function to compute power spectra with CLASS, compatible with
Python multiprocessing.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>params</strong> (<em>tuple</em>) – (x, bins, normalisation, kmax) where x is an array-like of
cosmological parameters, bins is the wavenumber array,
normalisation is a float or None, and kmax is a float.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>theta</strong> – Power-spectrum summary from <cite>get_summary</cite>.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray</p>
</dd>
</dl>
</dd></dl>
</section>
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