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<title>selfisys.selection_functions &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>
<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"><a class="reference internal" href="../../selfisys.prior.html">prior</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../selfisys.prior.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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.html#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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<h1>Source code for selfisys.selection_functions</h1><div class="highlight"><pre>
<span></span><span class="ch">#!/usr/bin/env python3</span>
<span class="c1"># ----------------------------------------------------------------------</span>
<span class="c1"># Copyright (C) 2024 Tristan Hoellinger</span>
<span class="c1"># Distributed under the GNU General Public License v3.0 (GPLv3).</span>
<span class="c1"># See the LICENSE file in the root directory for details.</span>
<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
<span class="c1"># ----------------------------------------------------------------------</span>
<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Tristan Hoellinger&quot;</span>
<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;0.1.0&quot;</span>
<span class="n">__date__</span> <span class="o">=</span> <span class="s2">&quot;2024&quot;</span>
<span class="n">__license__</span> <span class="o">=</span> <span class="s2">&quot;GPLv3&quot;</span>
<span class="sd">&quot;&quot;&quot;Selection functions to simulate galaxy populations.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">gc</span> <span class="kn">import</span> <span class="n">collect</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">h5py</span>
<div class="viewcode-block" id="LognormalSelection">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection">[docs]</a>
<span class="k">class</span> <span class="nc">LognormalSelection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Class to generate radial selection functions.&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">L</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">selection_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">survey_mask_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">local_select_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Initialise the LognormalSelection object.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> L : float</span>
<span class="sd"> Size of the simulation box (in Mpc/h). If not provided, it</span>
<span class="sd"> must be set before calling init_selection and using</span>
<span class="sd"> grid-dependent methods.</span>
<span class="sd"> selection_params : tuple of arrays</span>
<span class="sd"> Parameters for the selection functions (ss, mm, rr).</span>
<span class="sd"> Required for calling init_selection.</span>
<span class="sd"> survey_mask_path : str or None</span>
<span class="sd"> Path to the survey mask file. Required for calling</span>
<span class="sd"> init_selection.</span>
<span class="sd"> local_select_path : str</span>
<span class="sd"> Path where the selection function will be saved. Required</span>
<span class="sd"> for calling init_selection.</span>
<span class="sd"> size : int, optional</span>
<span class="sd"> Number of grid points along each axis. If not provided, it</span>
<span class="sd"> must be set before using grid-dependent methods.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">L</span>
<span class="bp">self</span><span class="o">.</span><span class="n">selection_params</span> <span class="o">=</span> <span class="n">selection_params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">survey_mask_path</span> <span class="o">=</span> <span class="n">survey_mask_path</span>
<span class="bp">self</span><span class="o">.</span><span class="n">local_select_path</span> <span class="o">=</span> <span class="n">local_select_path</span>
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span>
<div class="viewcode-block" id="LognormalSelection.r_grid">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.r_grid">[docs]</a>
<span class="k">def</span> <span class="nf">r_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Compute the grid of radial distances in the simulation box.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> ndarray</span>
<span class="sd"> 3D array of radial distances from the origin.</span>
<span class="sd"> Raises</span>
<span class="sd"> ------</span>
<span class="sd"> AttributeError</span>
<span class="sd"> If the &#39;size&#39; attribute is not defined.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
<span class="s2">&quot;The attribute &#39;size&#39; must be defined to compute the radial grid.&quot;</span>
<span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">L</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span><span class="s2">&quot;The attribute &#39;L&#39; must be defined to compute the radial grid.&quot;</span><span class="p">)</span>
<span class="n">range1d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">L</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">zz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">range1d</span><span class="p">,</span> <span class="n">range1d</span><span class="p">,</span> <span class="n">range1d</span><span class="p">)</span>
<span class="n">x0</span> <span class="o">=</span> <span class="n">y0</span> <span class="o">=</span> <span class="n">z0</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">xx</span> <span class="o">-</span> <span class="n">x0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">yy</span> <span class="o">-</span> <span class="n">y0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">zz</span> <span class="o">-</span> <span class="n">z0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1e-10</span>
<span class="k">return</span> <span class="n">r</span></div>
<div class="viewcode-block" id="LognormalSelection.one_lognormal">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.one_lognormal">[docs]</a>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">one_lognormal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">rescale</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Rescaled log-normal distribution.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x : ndarray</span>
<span class="sd"> Input array.</span>
<span class="sd"> std : float</span>
<span class="sd"> Standard deviation of the distribution.</span>
<span class="sd"> mean : float</span>
<span class="sd"> Mean of the distribution.</span>
<span class="sd"> rescale : float, optional</span>
<span class="sd"> Rescaling factor. If None, the distribution is normalised</span>
<span class="sd"> such that its maximum value is 1.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> ndarray</span>
<span class="sd"> Log-normal distribution evaluated at x.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">mean</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">std</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">mean</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
<span class="n">sig2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">std</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">mean</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">lognorm</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">sig2</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
<span class="o">-</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">sig2</span><span class="p">))</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">rescale</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">lognorm</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">lognorm</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">lognorm</span> <span class="o">*</span> <span class="n">rescale</span></div>
<div class="viewcode-block" id="LognormalSelection.multiple_lognormal">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.multiple_lognormal">[docs]</a>
<span class="k">def</span> <span class="nf">multiple_lognormal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">ss</span><span class="p">,</span> <span class="n">ll</span><span class="p">,</span> <span class="n">rr</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Compute multiple log-normal distributions.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x : ndarray</span>
<span class="sd"> Input array.</span>
<span class="sd"> mask : ndarray or None</span>
<span class="sd"> Survey mask C(n).</span>
<span class="sd"> ss : array_like</span>
<span class="sd"> Standard deviations for each distribution.</span>
<span class="sd"> ll : array_like</span>
<span class="sd"> Means for each distribution.</span>
<span class="sd"> rr : array_like</span>
<span class="sd"> Rescaling factors for each distribution.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of ndarray</span>
<span class="sd"> List of log-normal distributions.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">one_lognormal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="o">*</span> <span class="n">mask</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ss</span><span class="p">,</span> <span class="n">ll</span><span class="p">,</span> <span class="n">rr</span><span class="p">)]</span></div>
<div class="viewcode-block" id="LognormalSelection.one_lognormal_z">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.one_lognormal_z">[docs]</a>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">one_lognormal_z</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sig2</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">rescale</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Compute a log-normal distribution in redshift.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x : ndarray</span>
<span class="sd"> Input array.</span>
<span class="sd"> sig2 : float</span>
<span class="sd"> Variance of the distribution.</span>
<span class="sd"> mu : float</span>
<span class="sd"> Mean of the distribution.</span>
<span class="sd"> rescale : float, optional</span>
<span class="sd"> Rescaling factor.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> ndarray</span>
<span class="sd"> Log-normal distribution evaluated at x.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">lognorm</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">sig2</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
<span class="o">-</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">sig2</span><span class="p">))</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">lognorm</span> <span class="o">*</span> <span class="n">rescale</span> <span class="k">if</span> <span class="n">rescale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">lognorm</span></div>
<div class="viewcode-block" id="LognormalSelection.multiple_lognormal_z">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.multiple_lognormal_z">[docs]</a>
<span class="k">def</span> <span class="nf">multiple_lognormal_z</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">ss</span><span class="p">,</span> <span class="n">mm</span><span class="p">,</span> <span class="n">rr</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute multiple rescaled lognormal distributions as functions</span>
<span class="sd"> of redshift.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x : ndarray</span>
<span class="sd"> Input array (redshifts).</span>
<span class="sd"> mask : ndarray or None</span>
<span class="sd"> Survey mask C(n).</span>
<span class="sd"> ss : array_like</span>
<span class="sd"> Standard deviations of the lognormal distributions.</span>
<span class="sd"> mm : array_like</span>
<span class="sd"> Means of the lognormal distributions.</span>
<span class="sd"> rr : array_like</span>
<span class="sd"> Rescaling factors for each distribution.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of ndarray</span>
<span class="sd"> List of log-normal distributions.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">maxima</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ss</span><span class="p">,</span> <span class="n">mm</span><span class="p">,</span> <span class="n">rr</span><span class="p">):</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">m</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">s</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">m</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
<span class="n">sig2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">s</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">m</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">maxima</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">sig2</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">sig2</span><span class="p">)))</span>
<span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">one_lognormal_z</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sig2</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">rescale</span><span class="o">=</span><span class="n">r</span><span class="p">)</span> <span class="o">*</span> <span class="n">mask</span><span class="p">)</span>
<span class="nb">max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">maxima</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span> <span class="o">/</span> <span class="nb">max</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">res</span><span class="p">]</span>
<span class="k">return</span> <span class="n">res</span></div>
<div class="viewcode-block" id="LognormalSelection.lognormals_z_to_x">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.lognormals_z_to_x">[docs]</a>
<span class="k">def</span> <span class="nf">lognormals_z_to_x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xx</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">spline</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Convert log-normal distributions from redshift to distance.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> xx : array-like</span>
<span class="sd"> Comoving distances at which to evaluate the distributions.</span>
<span class="sd"> mask : ndarray or None</span>
<span class="sd"> Survey mask C(n).</span>
<span class="sd"> params : tuple of arrays</span>
<span class="sd"> Parameters for the distributions (ss, mm, rr).</span>
<span class="sd"> spline : UnivariateSpline</span>
<span class="sd"> Linear interpolator for the distance-redshift relation.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> tuple</span>
<span class="sd"> Tuple containing redshifts and list of distributions.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">ss</span><span class="p">,</span> <span class="n">mm</span><span class="p">,</span> <span class="n">rr</span> <span class="o">=</span> <span class="n">params</span>
<span class="n">zs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">spline</span><span class="p">(</span><span class="n">xx</span><span class="p">))</span>
<span class="n">res</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiple_lognormal_z</span><span class="p">(</span><span class="n">zs</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">ss</span><span class="p">,</span> <span class="n">mm</span><span class="p">,</span> <span class="n">rr</span><span class="p">)</span>
<span class="k">return</span> <span class="n">zs</span><span class="p">,</span> <span class="n">res</span></div>
<div class="viewcode-block" id="LognormalSelection.init_selection">
<a class="viewcode-back" href="../../selfisys.selection_functions.html#selfisys.selection_functions.LognormalSelection.init_selection">[docs]</a>
<span class="k">def</span> <span class="nf">init_selection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reset</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialise the radial selection functions.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> reset : bool, optional</span>
<span class="sd"> Whether to reset the selection function.</span>
<span class="sd"> Raises</span>
<span class="sd"> ------</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">survey_mask_path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">local_select_path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">]):</span>
<span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
<span class="s2">&quot;Some attributes are missing to initialise the selection function.&quot;</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">local_select_path</span><span class="p">)</span> <span class="ow">or</span> <span class="n">reset</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">UnivariateSpline</span>
<span class="kn">from</span> <span class="nn">classy</span> <span class="kn">import</span> <span class="n">Class</span>
<span class="kn">from</span> <span class="nn">astropy.cosmology</span> <span class="kn">import</span> <span class="n">FlatLambdaCDM</span>
<span class="kn">from</span> <span class="nn">selfisys.utils.tools</span> <span class="kn">import</span> <span class="n">cosmo_vector_to_class_dict</span>
<span class="kn">from</span> <span class="nn">selfisys.global_parameters</span> <span class="kn">import</span> <span class="n">omegas_gt</span>
<span class="kn">from</span> <span class="nn">selfisys.utils.plot_utils</span> <span class="kn">import</span> <span class="n">plot_selection_functions</span>
<span class="c1"># Redshift-distance relation</span>
<span class="n">redshifts_upper_bound</span> <span class="o">=</span> <span class="mf">3.0</span>
<span class="n">zz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">redshifts_upper_bound</span><span class="p">,</span> <span class="mi">10_000</span><span class="p">)</span>
<span class="n">cosmo</span> <span class="o">=</span> <span class="n">FlatLambdaCDM</span><span class="p">(</span><span class="n">H0</span><span class="o">=</span><span class="mi">100</span> <span class="o">*</span> <span class="n">omegas_gt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">Ob0</span><span class="o">=</span><span class="n">omegas_gt</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">Om0</span><span class="o">=</span><span class="n">omegas_gt</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">cosmo</span><span class="o">.</span><span class="n">comoving_distance</span><span class="p">(</span><span class="n">zz</span><span class="p">)</span><span class="o">.</span><span class="n">value</span> <span class="o">/</span> <span class="mf">1e3</span> <span class="c1"># -&gt; Gpc/h</span>
<span class="n">spline</span> <span class="o">=</span> <span class="n">UnivariateSpline</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">zz</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Plot the selection functions</span>
<span class="n">L</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">L</span> <span class="o">/</span> <span class="mf">1e3</span>
<span class="n">Lcorner</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="n">L</span>
<span class="n">zcorner</span> <span class="o">=</span> <span class="n">zz</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">d</span> <span class="o">-</span> <span class="n">Lcorner</span><span class="p">))]</span>
<span class="c1"># Get linear growth factor from CLASS</span>
<span class="n">cosmo_dict</span> <span class="o">=</span> <span class="n">cosmo_vector_to_class_dict</span><span class="p">(</span><span class="n">omegas_gt</span><span class="p">)</span>
<span class="n">cosmo_class</span> <span class="o">=</span> <span class="n">Class</span><span class="p">()</span>
<span class="n">cosmo_class</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">cosmo_dict</span><span class="p">)</span>
<span class="n">cosmo_class</span><span class="o">.</span><span class="n">compute</span><span class="p">()</span>
<span class="n">Dz</span> <span class="o">=</span> <span class="n">cosmo_class</span><span class="o">.</span><span class="n">get_background</span><span class="p">()[</span><span class="s2">&quot;gr.fac. D&quot;</span><span class="p">]</span>
<span class="n">redshifts</span> <span class="o">=</span> <span class="n">cosmo_class</span><span class="o">.</span><span class="n">get_background</span><span class="p">()[</span><span class="s2">&quot;z&quot;</span><span class="p">]</span>
<span class="n">cosmo_class</span><span class="o">.</span><span class="n">struct_cleanup</span><span class="p">()</span>
<span class="n">cosmo_class</span><span class="o">.</span><span class="n">empty</span><span class="p">()</span>
<span class="c1"># Define the axis for the plot</span>
<span class="n">xx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">Lcorner</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
<span class="n">zz</span><span class="p">,</span> <span class="n">res</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lognormals_z_to_x</span><span class="p">(</span>
<span class="n">xx</span><span class="p">,</span>
<span class="kc">None</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">selection_params</span><span class="p">,</span>
<span class="n">spline</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Call auxiliary plotting routine</span>
<span class="n">plot_selection_functions</span><span class="p">(</span>
<span class="n">xx</span><span class="p">,</span>
<span class="n">res</span><span class="p">,</span>
<span class="kc">None</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">selection_params</span><span class="p">,</span>
<span class="n">L</span><span class="p">,</span>
<span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="n">L</span><span class="p">,</span>
<span class="n">zz</span><span class="o">=</span><span class="n">zz</span><span class="p">,</span>
<span class="n">zcorner</span><span class="o">=</span><span class="n">zcorner</span><span class="p">,</span>
<span class="n">path</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_select_path</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;.png&quot;</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Compute the selection function and save it to disk</span>
<span class="n">survey_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">survey_mask_path</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">survey_mask_path</span> <span class="k">else</span> <span class="kc">None</span>
<span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">r_grid</span><span class="p">()</span> <span class="o">/</span> <span class="mf">1e3</span> <span class="c1"># Convert to Gpc/h</span>
<span class="n">_</span><span class="p">,</span> <span class="n">select_fct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lognormals_z_to_x</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">survey_mask</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">selection_params</span><span class="p">,</span> <span class="n">spline</span><span class="p">)</span>
<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">local_select_path</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s2">&quot;select_fct&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">select_fct</span><span class="p">)</span>
<span class="k">del</span> <span class="n">survey_mask</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">zz</span><span class="p">,</span> <span class="n">spline</span><span class="p">,</span> <span class="n">select_fct</span>
<span class="n">collect</span><span class="p">()</span></div>
</div>
</pre></div>
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