commit c0413846626982f771329078021271d2bcbbdd83 Author: hoellinger Date: Fri Jan 10 17:03:16 2025 +0100 Initial commit: v0.1.0 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..76f4a5c --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,58 @@ +# Contributing to SelfiSys + +We welcome contributions to the SelfiSys repository! Please follow these guidelines when contributing. + +--- + +## Reporting Issues + +If you find a bug or have a suggestion, please open an issue in the [GitHub repository](https://github.com/hoellin/selfisys_public). Include as much detail as possible: +- Steps to reproduce the issue +- Expected vs. actual behaviour +- Relevant error messages or logs (if applicable) +- Suggestions for improvement (if applicable) + +If you are unsure whether your issue is a bug or have questions about the code, you are welcome to open an issue for discussion. + +## Submitting Contributions + +1. Fork the repository and create a new branch for your changes. +2. Ensure your contributions are well-documented and adhere to the highest coding standards. +3. Test your changes thoroughly before submitting: + - Ensure Jupyter notebooks run without errors from top to bottom. + - Validate new functionality or fixes using appropriate test cases. +4. Before submitting a pull request, synchronise your fork with the main repository to incorporate upstream changes. + - Add the main repository as a remote. + ```bash + git remote add upstream https://github.com/hoellin/selfisys_public.git + ``` + - Fetch the latest changes from the upstream repository. + ```bash + git fetch upstream + ``` + - Merge the changes into your local repository. + ```bash + git merge upstream/main + ``` +5. Open a pull request describing your changes to the main repository. + +## Style Guidelines + +Follow best practices for Python coding and Jupyter notebooks. + +### Python code + +Refer to the [PEP 8 Style Guide](https://pep8.org/) for Python coding standards. + +### Jupyter Notebooks + +- Use clear and concise Markdown cells to explain code and results. +- Avoid leaving unnecessary output (e.g., debugging print statements). +- Ensure notebooks are runnable from top to bottom. +- For tips on creating clean and effective Jupyter notebooks, see [Jupyter Notebook Best Practices](https://realpython.com/jupyter-notebook-best-practices/). + +--- + +## Questions? + +If you have any questions or need further clarification, feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr). \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..9cecc1d --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + {one line to give the program's name and a brief idea of what it does.} + Copyright (C) {year} {name of author} + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + {project} Copyright (C) {year} {fullname} + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..e85d351 --- /dev/null +++ b/README.md @@ -0,0 +1,83 @@ +# SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys + +[![arXiv](https://img.shields.io/badge/astro--ph.CO-arxiv%3A2412.04443-B31B1B.svg?style=flat)](https://arxiv.org/abs/2412.04443) +[![GPLv3 license](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/hoellin/selfisys_public/blob/main/LICENSE) +[![GitHub version](https://img.shields.io/github/tag/hoellin/selfisys_public.svg?label=version)](https://github.com/hoellin/selfisys_public) +[![GitHub last commit](https://img.shields.io/github/last-commit/hoellin/selfisys_public.svg)](https://github.com/hoellin/selfisys_public/commits/main) + + +**SelfiSys** is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference. + +It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys. + +## Key Features + +- **Custom hidden-box forward models** + +We provide a `HiddenBox` class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver. + +- **Diagnosis of systematic effects** + +Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference. + +- **Cosmological inference** + +Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +--- + +## Documentation + +The documentation, including a detailed API reference, is available at [hoellin.github.io/selfisys_public](https://hoellin.github.io/selfisys_public/). + +For practical examples demonstrating how to use SelfiSys, visit the [SelfiSys Examples Repository](https://github.com/hoellin/selfisys_examples). + +## Contributors + +- **Tristan Hoellinger**, [tristan.hoellinger@iap.fr](mailto:tristan.hoellinger@iap.fr) + Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP). + +For information on contributing, refer to [CONTRIBUTING.md](CONTRIBUTING.md). + +## References + +If you use the SelfiSys package in your research, please cite the following paper and feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr) for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., 2024* +[arXiv:2412.04443](https://arxiv.org/abs/2412.04443) [[astro-ph.CO]](https://arxiv.org/abs/2412.04443) [[ADS]](https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443) [[pdf]](https://arxiv.org/pdf/2412.04443) + +BibTeX entry for citation: +```bibtex +@ARTICLE{hoellinger2024diagnosing, + author = {Hoellinger, Tristan and Leclercq, Florent}, + title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}", + journal = {arXiv e-prints}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2024, + month = dec, + eid = {arXiv:2412.04443}, + pages = {arXiv:2412.04443}, + doi = {10.48550/arXiv.2412.04443}, +archivePrefix = {arXiv}, + eprint = {2412.04443}, +primaryClass = {astro-ph.CO}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} +``` + +## Requirements + +The code is written in Python 3.10 and depends on the following packages: +- [`pySELFI`](https://pyselfi.readthedocs.io/en/latest/): Python implementation of the Simulator Expansion for Likelihood-Free Inference. +- [`Simbelmynë`](https://simbelmyne.readthedocs.io/en/latest/): A hierarchical probabilistic simulator for generating synthetic galaxy survey data. +- [`ELFI`](https://elfi.readthedocs.io/en/latest/): A statistical software package for likelihood-free inference, implementing in particular Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +A comprehensive list of dependencies, including version specifications to ensure reproducibility, will be provided in a yaml file, along with installation instructions, in a future release. + +--- + +## License + +This software is distributed under the GPLv3 Licence. Please review the [LICENSE](https://github.com/hoellin/selfisys_public/blob/main/LICENSE) file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence. \ No newline at end of file diff --git a/REFERENCES.md b/REFERENCES.md new file mode 100644 index 0000000..d211609 --- /dev/null +++ b/REFERENCES.md @@ -0,0 +1,44 @@ +# References + +If you use the SelfiSys package in your research, please cite the following paper and feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr) for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., 2024* +[arXiv:2412.04443](https://arxiv.org/abs/2412.04443) [[astro-ph.CO]](https://arxiv.org/abs/2412.04443) [[ADS]](https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443) [[pdf]](https://arxiv.org/pdf/2412.04443) + +BibTeX entry for citation: +```bibtex +@ARTICLE{hoellinger2024diagnosing, + author = {Hoellinger, Tristan and Leclercq, Florent}, + title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}", + journal = {arXiv e-prints}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2024, + month = dec, + eid = {arXiv:2412.04443}, + pages = {arXiv:2412.04443}, + doi = {10.48550/arXiv.2412.04443}, +archivePrefix = {arXiv}, + eprint = {2412.04443}, +primaryClass = {astro-ph.CO}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} +``` + +Other references relevant to the SelfiSys pipeline and cited in the code are listed below. + +- **[jasche2010bayesian]** +Jasche, J., Kitaura, F. S., Wandelt, B. D., and Enßlin, T. A., “Bayesian power-spectrum inference for large-scale structure data”, Monthly Notices of the Royal Astronomical Society, vol. 406, no. 1, OUP, pp. 60–85, 2010. [doi:10.1111/j.1365-2966.2010.16610.x](https://doi.org/10.48550/arXiv.0911.2493). + +- **[gil2015power]** +Gil-Marín, H., “The power spectrum and bispectrum of SDSS DR11 BOSS galaxies - I. Bias and gravity”, Monthly Notices of the Royal Astronomical Society, vol. 451, no. 1, OUP, pp. 539–580, 2015. [doi:10.1093/mnras/stv961](https://doi.org/10.48550/arXiv.1407.5668). + +- **[howlett2015clustering]** +Howlett, C., Ross, A. J., Samushia, L., Percival, W. J., and Manera, M., “The clustering of the SDSS main galaxy sample - II. Mock galaxy catalogues and a measurement of the growth of structure from redshift space distortions at z = 0.15”, Monthly Notices of the Royal Astronomical Society, vol. 449, no. 1, OUP, pp. 848–866, 2015. [doi:10.1093/mnras/stu2693](https://doi.org/10.48550/arXiv.1409.3238). + +- **[leclercq2019primordial]** +Florent Leclercq, Wolfgang Enzi, Jens Jasche, Alan Heavens, Primordial power spectrum and cosmology from black-box galaxy surveys, Monthly Notices of the Royal Astronomical Society, Volume 490, Issue 3, December 2019, Pages 4237–4253, [doi:10.1093/mnras/stz2718](https://doi.org/10.1093/mnras/stz2718). + +- **[hoellinger2024diagnosing]** +Hoellinger, T. and Leclercq, F., “Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum”, arXiv e-prints, Art. no. arXiv:2412.04443, 2024. [doi.org:10.48550/arXiv.2412.04443](https://doi.org/10.48550/arXiv.2412.04443). \ No newline at end of file diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html new file mode 100644 index 0000000..19271a6 --- /dev/null +++ b/docs/CONTRIBUTING.html @@ -0,0 +1,384 @@ + + + + + + + + + Contributing to SelfiSys — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Contributing to SelfiSys

+

We welcome contributions to the SelfiSys repository! Please follow these guidelines when contributing.

+
+
+

Reporting Issues

+

If you find a bug or have a suggestion, please open an issue in the GitHub repository. Include as much detail as possible:

+
    +

  • Steps to reproduce the issue

    • +

  • Expected vs. actual behaviour

    • +

  • Relevant error messages or logs (if applicable)

    • +

  • Suggestions for improvement (if applicable)

    • +
+

If you are unsure whether your issue is a bug or have questions about the code, you are welcome to open an issue for discussion.

+
+
+

Submitting Contributions

+
    +

  1. Fork the repository and create a new branch for your changes.

    2. +

  2. Ensure your contributions are well-documented and adhere to the highest coding standards.

    3. +

  3. Test your changes thoroughly before submitting:

    +
      +

    • Ensure Jupyter notebooks run without errors from top to bottom.

      • +

    • Validate new functionality or fixes using appropriate test cases.

      • +
    +
    4. +

  4. Before submitting a pull request, synchronise your fork with the main repository to incorporate upstream changes.

    +
      +

    • Add the main repository as a remote.

      +
      git remote add upstream https://github.com/hoellin/selfisys_public.git
+
      +
      +
      • +

    • Fetch the latest changes from the upstream repository.

      +
      git fetch upstream
+
      +
      +
      • +

    • Merge the changes into your local repository.

      +
      git merge upstream/main
+
      +
      +
      • +
    +
    5. +

  5. Open a pull request describing your changes to the main repository.

    6. +
+
+
+

Style Guidelines

+

Follow best practices for Python coding and Jupyter notebooks.

+
+

Python code

+

Refer to the PEP 8 Style Guide for Python coding standards.

+
+
+

Jupyter Notebooks

+
    +

  • Use clear and concise Markdown cells to explain code and results.

    • +

  • Avoid leaving unnecessary output (e.g., debugging print statements).

    • +

  • Ensure notebooks are runnable from top to bottom.

    • +

  • For tips on creating clean and effective Jupyter notebooks, see Jupyter Notebook Best Practices.

    • +
+
+
+
+
+

Questions?

+

If you have any questions or need further clarification, feel free to contact the authors.

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/README.html b/docs/README.html new file mode 100644 index 0000000..28e80c9 --- /dev/null +++ b/docs/README.html @@ -0,0 +1,387 @@ + + + + + + + + + SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys
    • +
  • + Edit on GitHub +
    • +
+
+
+
+
+ +
+

SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys

+

arXiv +GPLv3 license +GitHub version +GitHub last commit

+

SelfiSys is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference.

+

It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys.

+
+

Key Features

+
    +

  • Custom hidden-box forward models

    • +
+

We provide a HiddenBox class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver.

+
    +

  • Diagnosis of systematic effects

    • +
+

Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference.

+
    +

  • Cosmological inference

    • +
+

Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.

+
+
+
+

Documentation

+

The documentation, including a detailed API reference, is available at hoellin.github.io/selfisys_public.

+

For practical examples demonstrating how to use SelfiSys, visit the SelfiSys Examples Repository.

+
+
+

Contributors

+
    +

  • Tristan Hoellinger, tristan.hoellinger@iap.fr
    +Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP).

    • +
+

For information on contributing, refer to CONTRIBUTING.md.

+
+
+

References

+

If you use the SelfiSys package in your research, please cite the following paper and feel free to contact the authors for feedback, collaboration opportunities, or other inquiries.

+

Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum +Hoellinger, T. and Leclercq, F., 2024 +arXiv:2412.04443 [astro-ph.CO] [ADS] [pdf]

+

BibTeX entry for citation:

+
@ARTICLE{hoellinger2024diagnosing,
+      author = {Hoellinger, Tristan and Leclercq, Florent},
+      title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}",
+      journal = {arXiv e-prints},
+   keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
+         year = 2024,
+      month = dec,
+         eid = {arXiv:2412.04443},
+      pages = {arXiv:2412.04443},
+         doi = {10.48550/arXiv.2412.04443},
+archivePrefix = {arXiv},
+      eprint = {2412.04443},
+primaryClass = {astro-ph.CO},
+      adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+
+
+
+

Requirements

+

The code is written in Python 3.10 and depends on the following packages:

+
    +

  • pySELFI: Python implementation of the Simulator Expansion for Likelihood-Free Inference.

    • +

  • Simbelmynë: A hierarchical probabilistic simulator for generating synthetic galaxy survey data.

    • +

  • ELFI: A statistical software package for likelihood-free inference, implementing in particular Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.

    • +
+

A comprehensive list of dependencies, including version specifications to ensure reproducibility, will be provided in a yaml file, along with installation instructions, in a future release.

+
+
+
+

License

+

This software is distributed under the GPLv3 Licence. Please review the LICENSE file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence.

+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/REFERENCES.html b/docs/REFERENCES.html new file mode 100644 index 0000000..21c7a6c --- /dev/null +++ b/docs/REFERENCES.html @@ -0,0 +1,350 @@ + + + + + + + + + References — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

References

+

If you use the SelfiSys package in your research, please cite the following paper and feel free to contact the authors for feedback, collaboration opportunities, or other inquiries.

+

Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum +Hoellinger, T. and Leclercq, F., 2024 +arXiv:2412.04443 [astro-ph.CO] [ADS] [pdf]

+

BibTeX entry for citation:

+
@ARTICLE{hoellinger2024diagnosing,
+      author = {Hoellinger, Tristan and Leclercq, Florent},
+      title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}",
+      journal = {arXiv e-prints},
+   keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
+         year = 2024,
+      month = dec,
+         eid = {arXiv:2412.04443},
+      pages = {arXiv:2412.04443},
+         doi = {10.48550/arXiv.2412.04443},
+archivePrefix = {arXiv},
+      eprint = {2412.04443},
+primaryClass = {astro-ph.CO},
+      adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+
+

Other references relevant to the SelfiSys pipeline and cited in the code are listed below.

+
    +

  • [jasche2010bayesian] +Jasche, J., Kitaura, F. S., Wandelt, B. D., and Enßlin, T. A., “Bayesian power-spectrum inference for large-scale structure data”, Monthly Notices of the Royal Astronomical Society, vol. 406, no. 1, OUP, pp. 60–85, 2010. doi:10.1111/j.1365-2966.2010.16610.x.

    • +

  • [gil2015power] +Gil-Marín, H., “The power spectrum and bispectrum of SDSS DR11 BOSS galaxies - I. Bias and gravity”, Monthly Notices of the Royal Astronomical Society, vol. 451, no. 1, OUP, pp. 539–580, 2015. doi:10.1093/mnras/stv961.

    • +

  • [howlett2015clustering] +Howlett, C., Ross, A. J., Samushia, L., Percival, W. J., and Manera, M., “The clustering of the SDSS main galaxy sample - II. Mock galaxy catalogues and a measurement of the growth of structure from redshift space distortions at z = 0.15”, Monthly Notices of the Royal Astronomical Society, vol. 449, no. 1, OUP, pp. 848–866, 2015. doi:10.1093/mnras/stu2693.

    • +

  • [leclercq2019primordial] +Florent Leclercq, Wolfgang Enzi, Jens Jasche, Alan Heavens, Primordial power spectrum and cosmology from black-box galaxy surveys, Monthly Notices of the Royal Astronomical Society, Volume 490, Issue 3, December 2019, Pages 4237–4253, doi:10.1093/mnras/stz2718.

    • +

  • [hoellinger2024diagnosing] +Hoellinger, T. and Leclercq, F., “Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum”, arXiv e-prints, Art. no. arXiv:2412.04443, 2024. doi.org:10.48550/arXiv.2412.04443.

    • +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/index.html b/docs/_modules/index.html new file mode 100644 index 0000000..858d661 --- /dev/null +++ b/docs/_modules/index.html @@ -0,0 +1,326 @@ + + + + + + + + Overview: module code — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Overview: module code
    • +
  • +
    • +
+
+
+
+ +
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/grf.html b/docs/_modules/selfisys/grf.html new file mode 100644 index 0000000..bdca704 --- /dev/null +++ b/docs/_modules/selfisys/grf.html @@ -0,0 +1,423 @@ + + + + + + + + selfisys.grf — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.grf

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Tools for generating Gaussian random fields from given power spectra.
+"""
+
+from selfisys.utils.logger import getCustomLogger
+
+logger = getCustomLogger(__name__)
+
+
+

+[docs]
+def primordial_grf(
+    L,
+    N,
+    seedphases,
+    fname_powerspectrum,
+    fname_outputinitialdensity,
+    force_sim=False,
+    return_g=False,
+    verbose=0,
+):
+    """
+    Generate a Gaussian random field from a specified input power
+    spectrum.
+
+    Parameters
+    ----------
+    L : float
+        Side length of the simulation box in Mpc/h.
+    N : int
+        Grid resolution (number of cells per dimension).
+    seedphases : int
+        Seed for random phase generation (for reproducibility).
+    fname_powerspectrum : str
+        File path to the input power spectrum.
+    fname_outputinitialdensity : str
+        File path to store the generated initial density field.
+    force_sim : bool, optional
+        If True, regenerate the GRF even if the output file exists.
+        Default is False.
+    return_g : bool, optional
+        If True, return the GRF as a numpy array. Default is False.
+    verbose : int, optional
+        Verbosity level (0 = silent, 1 = progress, 2 = detailed).
+        Default is 0.
+
+    Raises
+    ------
+    OSError
+        If the power spectrum file cannot be read.
+    RuntimeError
+        If an unexpected error occurs during power spectrum reading.
+
+    Returns
+    -------
+    numpy.ndarray or None
+        The GRF data if `return_g` is True, otherwise None.
+    """
+    from os.path import exists
+    from gc import collect
+    from pysbmy.power import PowerSpectrum
+    from pysbmy.field import Field
+
+    # Skip simulation if output already exists and overwrite is not requested
+    if not force_sim and exists(fname_outputinitialdensity):
+        from pysbmy.field import read_basefield
+
+        if verbose > 0:
+            logger.info(f"{fname_outputinitialdensity} already exists. Skipping simulation.")
+        return read_basefield(fname_outputinitialdensity).data if return_g else None
+
+    # Read the power spectrum
+    try:
+        P = PowerSpectrum.read(fname_powerspectrum)
+    except OSError as e:
+        logger.error(f"Unable to read power spectrum file: {fname_powerspectrum}")
+        raise
+    except Exception as e:
+        logger.exception(f"Unexpected error while reading power spectrum: {e}")
+        raise
+
+    # Generate the Gaussian random field
+    if verbose > 1:
+        g = Field.GRF(L, L, L, 0, 0, 0, N, N, N, P, 1e3, seedphases)  # a_init = 1e3
+    else:
+        from selfisys.utils.low_level import stdout_redirector
+        from io import BytesIO
+
+        # Suppress standard output to avoid cluttering logs
+        with BytesIO() as f:
+            with stdout_redirector(f):
+                g = Field.GRF(L, L, L, 0, 0, 0, N, N, N, P, 1e3, seedphases)
+
+    # Write the field to disk
+    g.write(fname_outputinitialdensity)
+    field = g.data.copy() if return_g else None
+
+    # Free memory
+    del g
+    collect()
+
+    return field

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
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+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/hiddenbox.html b/docs/_modules/selfisys/hiddenbox.html new file mode 100644 index 0000000..5d63d1c --- /dev/null +++ b/docs/_modules/selfisys/hiddenbox.html @@ -0,0 +1,1848 @@ + + + + + + + + selfisys.hiddenbox — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.hiddenbox

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""This module provides the HiddenBox class, which is used to define
+standalone stochastic forward models of large-scale spectroscopic
+galaxy surveys.
+
+The HiddenBox class is compatible with the pySELFI package.
+"""
+
+import os
+from gc import collect
+from typing import Callable, Any, List, Optional, Union
+
+import h5py
+import numpy as np
+from selfisys.global_parameters import DEFAULT_VERBOSE_LEVEL
+from selfisys.utils.parser import joinstrs_only
+
+
+

+[docs]
+class HiddenBox:
+    """This class represents custom forward data model of large-scale
+    spectroscopic galaxy surveys."""
+
+    def __init__(
+        self,
+        k_s: Any,
+        P_ss_path: str,
+        Pbins_bnd: Any,
+        theta2P: Callable,
+        P: int,
+        size: int,
+        L: float,
+        G_sim_path: str,
+        G_ss_path: str,
+        Np0: int,
+        Npm0: int,
+        fsimdir: str,
+        noise_std: Optional[float] = None,
+        radial_selection: Optional[str] = None,
+        selection_params: Optional[List[Any]] = None,
+        observed_density: Optional[float] = None,
+        linear_bias: Optional[Union[float, List[float]]] = None,
+        norm_csts: Optional[Union[float, List[float]]] = None,
+        survey_mask_path: Optional[str] = None,
+        local_mask_prefix: Optional[str] = None,
+        sim_params: Optional[str] = None,
+        eff_redshifts: Optional[List[float]] = None,
+        TimeSteps: Optional[List[int]] = None,
+        TimeStepDistribution: Optional[str] = None,
+        seedphase: Optional[int] = None,
+        seednoise: Optional[int] = None,
+        fixnoise: bool = False,
+        seednorm: Optional[int] = None,
+        save_frequency: Optional[int] = None,
+        reset: bool = False,
+        verbosity: int = DEFAULT_VERBOSE_LEVEL,
+        **kwargs,
+    ):
+        """
+        Initialise the HiddenBox.
+
+        Parameters
+        ----------
+        k_s : array-like
+            Vector of input support wavenumbers.
+        P_ss_path : str
+            Path to the spectrum used to normalise the outputs.
+        Pbins_bnd : array-like
+            Vector of bin boundaries for the summary statistics.
+        theta2P : Callable
+            Function to convert theta vectors to initial power spectra.
+        P : int
+            Dimension of the output summary statistics.
+        size : int
+            Side length of the simulation box in voxels.
+        L : float
+            Side length of the simulation box in Mpc/h.
+        G_sim_path : str
+            Path to the simulation grid.
+        G_ss_path : str
+            Path to the summary grid.
+        Np0 : int
+            Number of dark matter particles per spatial dimension.
+        Npm0 : int
+            Side length of the particle-mesh grid in voxels.
+        fsimdir : str
+            Output directory.
+        noise_std : float, optional
+            Standard deviation for the Gaussian noise. Default is
+            `None`.
+        radial_selection : str, optional
+            Type of radial selection mask. Default is `None`,
+            corresponding to no radial selection.
+
+            Available options:
+                - 'multiple_lognormal': Use multiple log-normal radial
+                selection functions.
+        selection_params : list, optional
+            List of parameters for the radial selection mask, for each
+            population. Default is `None`.
+
+            For the 'multiple_lognormal' radial selection,
+            `selection_params` is a shape `(3, N_pop)` array comprising
+            the three following parameters for each population:
+                - selection_std : (float)
+                Standard deviation of the distribution, e.g.,
+                constant × (1 + z).
+                - selection_mean : (float)
+                Mean of the distribution in Gpc/h.
+                - selection_rescale : (float, optional)
+                Individually rescale the distributions by the given
+                value. If `None`, the global maximum is normalised to
+                `1`.
+        observed_density : float, optional
+            Mean galaxy density. Default is `None`.
+        linear_bias : float or list of float, optional
+            First-order linear galaxy biases. If `None`, use the dark
+            matter density to compute the summaries. Default is `None`.
+        norm_csts : float or list of float, optional
+            If not `None`, normalise the output of the hidden box
+            accordingly.
+
+            For `radial_selection == 'multiple_lognormal'`, `norm_csts`
+            must be a list of `N_pop` values. Default is `None`.
+        survey_mask_path : str, optional
+            If not `None`, apply the corresponding survey mask to the
+            observed field. Default is `None`.
+        local_mask_prefix : str, optional
+            Prefix for the local copy of the survey mask. If `None`, use
+            the default name. Default is `None`.
+        sim_params : str, optional
+            Set of simulation parameters to be used for Simbelmynë.
+            Check `setup_sbmy_parfiles` in `selfisys.sbmy_parser` for
+            details. Default is `None`.
+        eff_redshifts : list, optional
+            Effective redshifts for the time steps. Default is `None`.
+        TimeSteps : list, optional
+            Number of time steps to reach the corresponding effective
+            redshifts. Default is `None`.
+        TimeStepDistribution : str, optional
+            Path to the Simbelmynë time step distribution file. Default
+            is `None`.
+        seedphase : int, optional
+            Seed to generate the initial white noise realisation.
+            Default is `None`.
+        seednoise : int, optional
+            Initial state of the RNG to generate the noise sequence. If
+            `fixnoise` is True, the seed used in `compute_pool` is
+            always `seednoise`. If `fixnoise` is False, it is
+            `seednoise + i` for realisation `i`. Default is `None`.
+        fixnoise : bool, optional
+            Whether to fix the noise realisation. If `True`, always use
+            `seednoise`. Default is `False`.
+        seednorm : int, optional
+            Seed used for normalisation. Default is `None`.
+        save_frequency : int, optional
+            Save the outputs of the hidden box to disk every
+            `save_frequency` evaluations. Default is `None`.
+        reset : bool, optional
+            Whether to always force reset the survey box when the hidden
+            `HiddenBox` object is instantiated. Default is `False`.
+        verbosity : int, optional
+            Verbosity level of the hidden box. `0` is silent, `1` is
+            minimal, `2` is verbose. Default is `DEFAULT_VERBOSE_LEVEL`.
+        **kwargs
+            Additional optional keyword arguments.
+        """
+        # Mandatory attributes
+        self.k_s = k_s
+        self.P_ss_path = P_ss_path
+        self.Pbins_bnd = Pbins_bnd
+        self.theta2P = theta2P
+        self.P = P
+        self.size = size
+        self.L = L
+        self.G_sim_path = G_sim_path
+        self.G_ss_path = G_ss_path
+        self.Np0 = Np0
+        self.Npm0 = Npm0
+        self.fsimdir = fsimdir
+
+        # Optional attributes
+        self.noise_std = noise_std
+        self.radial_selection = radial_selection
+        self.selection_params = selection_params
+        self.observed_density = observed_density
+        self.linear_bias = linear_bias
+        self.norm_csts = norm_csts
+        self.survey_mask_path = survey_mask_path
+        self.local_mask_prefix = local_mask_prefix
+        self.sim_params = sim_params
+        self.eff_redshifts = eff_redshifts
+        self.TimeSteps = TimeSteps
+        self.TimeStepDistribution = TimeStepDistribution
+        self.seedphase = seedphase
+        self.seednoise = seednoise
+        self.fixnoise = fixnoise
+        self.seednorm = seednorm
+        self.save_frequency = save_frequency
+        self.reset = reset
+        self.verbosity = verbosity
+
+        # Additional attributes from kwargs
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+        # Compute default values
+        self._set_defaults()
+
+        # Create the window function W(n, r) = C(n) * R(r)
+        self._init_survey_mask()
+        self._init_radial_selection()
+
+

+[docs]
+    def reset_survey(self):
+        """Re-initialise the survey mask C(n) and radial selection
+        function R(r).
+        """
+        self._init_survey_mask(reset=True)
+        self._init_radial_selection(reset=True)

+
+
+

+[docs]
+    def update(self, **kwargs):
+        """Updates the given parameter(s) of the hidden box with the
+        given value(s).
+
+        Parameters
+        ----------
+        **kwargs : dict
+            dictionary of parameters to update
+
+        """
+        for key, value in kwargs.items():
+            setattr(self, key, value)

+
+
+

+[docs]
+    def make_data(
+        self,
+        cosmo,
+        id,
+        seedphase,
+        seednoise,
+        force_powerspectrum=False,
+        force_parfiles=False,
+        force_sim=False,
+        force_cosmo=False,
+        force_phase=False,
+        d=-1,
+        remove_sbmy=True,
+        verbosity=None,
+        return_g=False,
+        RSDs=True,
+        prefix_mocks=None,
+    ):
+        """Generate simulated data based on the given cosmological
+        parameters.
+
+        Parameters
+        ----------
+        cosmo : dict
+            Cosmological and infrastructure parameters.
+        id : int or str, optional
+            Identifier used as a suffix in the file names. Default is
+            `0`.
+        seedphase : int or list of int
+            Seed to generate the initial white noise in Fourier space.
+        seednoise : int or list of int
+            Seed to generate the noise realisation.
+        force_powerspectrum : bool, optional
+            Force recomputing the input spectrum. Default is `False`.
+        force_parfiles : bool, optional
+            Force recomputing the parameter files. Default is `False`.
+        force_sim : bool, optional
+            Force recomputing the simulation. Default is `False`.
+        force_cosmo : bool, optional
+            Force recomputing the cosmological parameters. Default is
+            `False`.
+        force_phase : bool, optional
+            Force recomputing the initial phase. Default is `False`.
+        d : int, optional
+            Direction in the parameter space. Default is `-1`.
+        remove_sbmy : bool, optional
+            Whether to remove most Simbelmynë output files after use for
+            disk space management. Default is `True`.
+        verbosity : int, optional
+            Verbosity level. If `None`, use self.verbosity. Default is
+            `None`.
+        return_g : bool, optional
+            Whether to return the full field alongside the summary.
+            Default is `False`.
+        RSDs : bool, optional
+            Whether to compute the redshift-space distortions. Default
+            is `True`.
+        prefix_mocks : str, optional
+            Prefix for the mock data. If None, use self.prefix_mocks.
+            Default is `None`.
+
+        Returns
+        -------
+        Phi : ndarray
+            Vector of summary statistics.
+        g : ndarray or list of ndarray or None
+            Observed field(s) if return_g is `True`; otherwise `None`.
+        """
+        from selfisys.utils.path_utils import get_file_names
+        from selfisys.sbmy_interface import get_power_spectrum_from_cosmo
+
+        self._PrintMessage(0, f"Making mock data...", verbosity)
+        self._indent()
+
+        names = get_file_names(
+            self.fsimdir,
+            id,
+            self.sim_params,
+            self.TimeSteps,
+            prefix_mocks or self.prefix_mocks,
+            self.gravity_on,
+            return_g,
+        )
+
+        # Save cosmological parameters
+        self._save_cosmo(cosmo, names["fname_cosmo"], force_cosmo)
+
+        # Generate the input initial matter power spectrum
+        self._PrintMessage(1, f"Computing initial power spectrum...", verbosity)
+        get_power_spectrum_from_cosmo(
+            self.L, self.size, cosmo, names["fname_power_spectrum"], force_powerspectrum
+        )
+        self._PrintMessage(1, f"Computing initial power spectrum done.", verbosity)
+
+        # Generate the simulated galaxy survey data
+        self._PrintMessage(1, f"Running the forward model...", verbosity)
+        Phi, g = self._aux_hiddenbox(
+            d,
+            seedphase,
+            seednoise,
+            names,
+            force_parfiles=force_parfiles,
+            force_sim=force_sim,
+            force_phase=force_phase,
+            return_g=return_g,
+            RSDs=RSDs,
+        )
+        self._PrintMessage(1, f"Running the forward model done.", verbosity)
+
+        if remove_sbmy and self.gravity_on:
+            self._clean_output(names["fname_outputinitialdensity"])
+
+        self._unindent()
+        self._PrintMessage(1, f"Making mock data done.", verbosity)
+
+        return (Phi, g) if return_g else Phi

+
+
+

+[docs]
+    def evaluate(
+        self,
+        theta,
+        d,
+        seedphase,
+        seednoise,
+        i=0,
+        N=0,
+        force_powerspectrum=False,
+        force_parfiles=False,
+        force_sim=False,
+        remove_sbmy=False,
+        theta_is_p=False,
+        simspath=None,
+        check_output=False,
+        RSDs=True,
+        abc=False,
+        cosmo_vect=None,
+    ):
+        """Evaluate the hidden box for a given input power spectrum.
+
+        The result is deterministic (the phase is fixed), except as it
+        is modified by nuisance parameters if any.
+
+        This routine is used by `pySELFI` to compute the gradient of the
+        hidden box with respect to the power spectrum, and by ABC-PMC to
+        evaluate the forward model.
+
+        Parameters
+        ----------
+        theta : ndarray
+            Power spectrum values at the support wavenumbers.
+        d : int
+            Direction in parameter space, from `0` to `S`.
+        seedphase : int or list of int
+            Seed to generate the initial white noise in Fourier space.
+        seednoise : int or list of int
+            Seed to generate the noise realisation.
+        i : int, optional
+            Current evaluation index of the hidden box. Default is `0`.
+        N : int, optional
+            Total number of evaluations of the hidden box. Default is
+            `0`.
+        force_powerspectrum : bool, optional
+            If `True`, force recomputation of the power spectrum at the
+            values of the Fourier grid. Default is `False`.
+        force_parfiles : bool, optional
+            If `True`, overwrite existing parameter files. Default is
+            `False`.
+        force_sim : bool, optional
+            If `True`, rerun the simulation even if the output density
+            already exists. Default is `False`.
+        remove_sbmy : bool, optional
+            If `True`, remove Simbelmynë output files from disk. Default
+            is `False`.
+        theta_is_p : bool, optional
+            Set to `True` if `theta` is already an unnormalised power
+            spectrum. Default is `False`.
+        simspath : str, optional
+            Path to the simulations directory. Default is `None`.
+        check_output : bool, optional
+            If `True`, check the integrity of the output file and
+            recompute if corrupted. Default is `False`.
+        RSDs : bool, optional
+            Whether to compute redshift-space distortions. Default is
+            `True`.
+        abc : bool or str, optional
+            If not False, remove most output files after evaluation.
+            Default is `False`.
+        cosmo_vect : ndarray, optional
+            Cosmological parameters. Required if `abc` is `True`.
+
+        Returns
+        -------
+        Phi : ndarray
+            Vector of summary statistics.
+        """
+        from selfisys.utils.path_utils import file_names_evaluate
+
+        if abc and cosmo_vect is None:
+            raise ValueError("cosmo_vect must be provided when using ABC.")
+        if not abc and cosmo_vect is not None:
+            raise ValueError("cosmo_vect must not be provided when not using ABC.")
+
+        if not theta_is_p:
+            self._PrintMessage(
+                1,
+                f"Direction {d}. Evaluating hidden box (index {i}/{N - 1})...",
+            )
+            self._indent()
+
+        simdir = simspath or self.fsimdir
+        simdir_d = os.path.join(simdir, joinstrs_only(["pool/", abc, "d", str(d)]))
+
+        # Get the file names for the current evaluation
+        names = file_names_evaluate(
+            simdir,
+            simdir_d,
+            d,
+            i,
+            self.sim_params,
+            self.TimeSteps,
+            self.prefix_mocks,
+            abc,
+            self.gravity_on,
+        )
+        fname_power_spectrum = names["fname_power_spectrum"]
+        fname_simparfile = names["fname_simparfile"]
+        fname_whitenoise = names["fname_whitenoise"]
+        fname_outputinitialdensity = names["fname_outputinitialdensity"]
+
+        # Interpolate the power spectrum values over the Fourier grid
+        self._PrintMessage(1, f"Interpolating spectrum over the Fourier grid...")
+        self._power_spectrum_from_theta(
+            theta, fname_power_spectrum, theta_is_p, force=force_powerspectrum
+        )
+        self._PrintMessage(1, f"Interpolating spectrum over the Fourier grid done.")
+
+        # Compute the simulated data
+        self._PrintMessage(1, f"Generating observed summary...")
+        Phi, _ = self._aux_hiddenbox(
+            d,
+            seedphase,
+            seednoise,
+            names,
+            force_parfiles,
+            force_sim=force_sim,
+            force_phase=False,
+            return_g=False,
+            check_output=check_output,
+            RSDs=RSDs,
+            sample=cosmo_vect,
+        )
+        self._PrintMessage(1, f"Generating observed summary done.")
+
+        # Clean up the output files
+        if remove_sbmy and self.gravity_on:
+            self._clean_output(fname_outputinitialdensity)
+        if abc:
+            self._clean_output(fname_power_spectrum)
+            self._clean_output(fname_simparfile)
+            self._clean_output(fname_whitenoise)
+            for f in os.listdir(simdir_d):
+                if self.TimeSteps is not None:
+                    if f.startswith(f"output_realdensity_d{d}_p{i}_{self.TimeSteps[0]}"):
+                        self._clean_output(os.path.join(simdir_d, f))
+                else:
+                    if f.startswith(f"output_realdensity_d{d}_p{i}"):
+                        self._clean_output(os.path.join(simdir_d, f))
+                if f.startswith(f"output_density_d{d}_p{i}"):
+                    self._clean_output(os.path.join(simdir_d, f))
+            if not self.gravity_on:
+                self._clean_output(fname_outputinitialdensity)
+
+        if not theta_is_p:
+            self._unindent()
+            self._PrintMessage(
+                1,
+                f"Direction {d}. Evaluation done (index {i}/{N - 1}).",
+            )
+
+        return Phi

+
+
+

+[docs]
+    def switch_recompute_pool(self, prefix_mocks=None):
+        """Toggle recomputation of the pool for future `compute_pool`
+        calls.
+
+        Parameters
+        ----------
+        prefix_mocks : str, optional
+            Prefix for the future simulation files. Default is `None`.
+        """
+        self._force_recompute_mocks = not self.force_recompute_mocks
+        self._prefix_mocks = prefix_mocks if prefix_mocks is not None else None

+
+
+

+[docs]
+    def switch_setup(self):
+        """Toggle the setup-only mode."""
+        self._setup_only = not self.setup_only

+
+
+

+[docs]
+    def compute_pool(
+        self,
+        theta,
+        d,
+        pool_fname,
+        N,
+        index=None,
+        force_powerspectrum=False,
+        force_parfiles=False,
+        force_sim=False,
+        remove_sbmy=False,
+        theta_is_p=False,
+        simspath=None,
+        bar=False,
+    ):
+        """Compute a pool of realisations of the hidden box compatible
+        with `pySELFI`.
+
+        Parameters
+        ----------
+        theta : ndarray
+            Power spectrum values at the support wavenumbers.
+        d : int
+            Direction in parameter space, from 0 to S.
+        pool_fname : str
+            Filename for the pool.
+        N : int
+            Number of realisations required at the given direction.
+        index : int, optional
+            Index of a single simulation to run. Default is `None`.
+        force_powerspectrum : bool, optional
+            If True, force recomputation of the power spectrum. Default
+            is `False`.
+        force_parfiles : bool, optional
+            If True, overwrite existing parameter files. Default is
+            `False`.
+        force_sim : bool, optional
+            If True, rerun the simulation even if the output density
+            already exists. Default is `False`.
+        remove_sbmy : bool, optional
+            If True, remove Simbelmynë output files from disk. Default
+            is `False`.
+        theta_is_p : bool, optional
+            Set to True when `theta` is already an unnormalised power
+            spectrum. Default is `False`.
+        simspath : str, optional
+            Path indicating where to store the simulations. Default is
+            `None`.
+        bar : bool, optional
+            If True, display a progress bar. Default is `False`.
+
+        Returns
+        -------
+        p : Pool
+            Simulation pool object.
+        """
+        import tqdm.auto as tqdm
+        from pyselfi.pool import pool as Pool
+
+        self._PrintMessage(1, f"Computing a pool of realisations of the hidden box...")
+
+        pool_fname = str(pool_fname)
+        if self.force_recompute_mocks:
+            if os.path.exists(pool_fname):
+                os.remove(pool_fname)
+
+        p = Pool(pool_fname, N, retro=False)
+        ids = list(range(N)) if index is None else [index]
+
+        def worker(i):
+            this_seedphase = self._get_current_seed(self.__global_seedphase, False, i)
+            this_seednoise = self._get_current_seed(self.__global_seednoise, self.fixnoise, i)
+            Phi = self.evaluate(
+                theta,
+                d,
+                this_seedphase,
+                this_seednoise,
+                i=i,
+                N=N,
+                force_powerspectrum=force_powerspectrum,
+                force_parfiles=force_parfiles,
+                force_sim=force_sim,
+                remove_sbmy=remove_sbmy,
+                theta_is_p=theta_is_p,
+                simspath=simspath,
+            )
+            p.add_sim(Phi, i)
+
+        iterator = tqdm.tqdm(ids, desc=f"Direction {d}/{self.S}") if bar else ids
+        for i in iterator:
+            worker(i)
+
+        if index is None:
+            p.load_sims()
+            p.save_all()
+        return p

+
+
+

+[docs]
+    def load_pool(self, pool_fname, N):
+        """Load a pool of realisations of the hidden box.
+
+        Parameters
+        ----------
+        pool_fname : str
+            Filename of the pool to load.
+        N : int
+            Number of realisations in the pool.
+
+        Returns
+        -------
+        p : Pool
+            Simulation pool object.
+        """
+        from pyselfi.pool import pool as Pool
+
+        pool_fname = str(pool_fname)
+        p = Pool(pool_fname, N, retro=False)
+        p.load_sims()
+        p.save_all()
+        return p

+
+
+    @property
+    def Npop(self):
+        """Number of populations."""
+        return self._Npop
+
+    @Npop.setter
+    def Npop(self, _):
+        """Compute the number of populations."""
+        if self.radial_selection == "multiple_lognormal":
+            if self.selection_params is not None:
+                self._Npop = len(self.selection_params[0])
+                if self.linear_bias is not None and len(self.linear_bias) != self._Npop:
+                    raise ValueError("Length of linear_bias must match the number of populations.")
+                if self.norm_csts is not None and len(self.norm_csts) != self._Npop:
+                    raise ValueError("Length of norm_csts must match the number of populations.")
+            else:
+                raise ValueError(
+                    "Selection parameters are required for multiple_lognormal radial selection."
+                )
+        else:
+            self._Npop = 1
+
+    @property
+    def gravity_on(self):
+        """Whether gravity is enabled."""
+        return self._gravity_on
+
+    @gravity_on.setter
+    def gravity_on(self, _):
+        """Compute and set the gravity status."""
+        if self.sim_params:
+            self._gravity_on = not self.sim_params.startswith("nograv")
+        else:
+            self._gravity_on = True
+
+    @property
+    def Ntimesteps(self):
+        """Number of time steps."""
+        return self._Ntimesteps
+
+    @Ntimesteps.setter
+    def Ntimesteps(self, _):
+        """Compute and set the number of time steps."""
+        if self.sim_params:
+            if self.sim_params.startswith("splitLPT"):
+                self._Ntimesteps = len(self.eff_redshifts)
+            elif self.sim_params.startswith("split"):
+                self._Ntimesteps = len(self.TimeStepDistribution)
+            elif self.sim_params.startswith("nograv"):
+                self._Ntimesteps = None
+            else:
+                self._Ntimesteps = None
+        else:
+            self._Ntimesteps = None
+
+    @property
+    def force_recompute_mocks(self):
+        """Whether to force recomputation of mocks."""
+        return self._force_recompute_mocks
+
+    @property
+    def setup_only(self):
+        """Whether to only set up the hidden box."""
+        return self._setup_only
+
+    @property
+    def prefix_mocks(self):
+        """Prefix for the mocks."""
+        return self._prefix_mocks
+
+    @property
+    def modified_selfi(self):
+        """Whether to use the modified selfi."""
+        return self._modified_selfi
+
+    @property
+    def force_neglect_lightcone(self):
+        """Whether to force neglecting the lightcone."""
+        return self._force_neglect_lightcone
+
+    @property
+    def Psingle(self):
+        """Number of summary statistics for each population."""
+        return self._Psingle
+
+    @Psingle.setter
+    def Psingle(self, _):
+        """Dimension of the summary statistics for a single population,
+        if relevant.
+        """
+        if self.radial_selection == "multiple_lognormal":
+            self._Psingle = self.P // self.Npop
+        else:
+            self._Psingle = self.P
+
+    @property
+    def __global_seedphase(self):
+        return self.seedphase
+
+    @property
+    def __global_seednoise(self):
+        return self.seednoise
+
+    @property
+    def __global_seednorm(self):
+        """Global seed for the normalisation constants."""
+        return self.seednorm
+
+    def _set_defaults(self):
+        """Set default values and ensure consistency of the hidden box
+        configuration.
+        """
+        if not hasattr(self, "modeldir"):
+            self.modeldir = os.path.join(self.fsimdir, "model")
+
+        self._force_recompute_mocks = False
+        self._setup_only = False
+        self._prefix_mocks = None
+        self._modified_selfi = True
+        self._force_neglect_lightcone = False
+
+        self.S = len(self.k_s)
+        # The following attributes are set by the setters
+        self.Ntimesteps = None
+        self.gravity_on = True
+        self.Npop = len(self.linear_bias) or 1
+        self.Psingle = None
+
+    def _init_survey_mask(self, reset=False):
+        """Initialise the survey mask and save it to disk in binary
+        format.
+
+        The survey mask C(n) represents the angular selection function
+        of the survey.
+        """
+        if self.local_mask_prefix:
+            mask_filename = f"{self.local_mask_prefix}_survey_mask_binary.h5"
+        else:
+            mask_filename = "survey_mask_binary.h5"
+        self.local_mask_path = os.path.join(self.modeldir, mask_filename)
+
+        if not os.path.exists(self.local_mask_path) or reset or self.reset:
+            if self.survey_mask_path is not None:
+                survey_mask = np.load(self.survey_mask_path)
+                survey_mask_binary = (survey_mask > 0).astype(int)
+                del survey_mask
+            else:
+                survey_mask_binary = np.ones([self.size] * 3, dtype=int)
+
+            with h5py.File(self.local_mask_path, "w") as f:
+                f.create_dataset("survey_mask_binary", data=survey_mask_binary)
+            del survey_mask_binary
+            collect()
+
+    def _init_radial_selection(self, reset=False):
+        """Initialise the radial selection function R(r) and save it to
+        disk.
+        """
+        if self.radial_selection == "multiple_lognormal":
+            from selfisys.selection_functions import LognormalSelection
+
+            # Set the normalisation constants
+            if self.norm_csts is None:
+                self.norm_csts = [1.0] * self.Npop
+
+            # Initialise the radial selection functions
+            if self.local_mask_prefix:
+                filename = f"{self.local_mask_prefix}_select_fct.h5"
+            else:
+                filename = "select_fct.h5"
+            self.local_select_path = os.path.join(self.modeldir, filename)
+            LogNorm = LognormalSelection(
+                self.L,
+                self.selection_params,
+                survey_mask_path=self.survey_mask_path,
+                local_select_path=self.local_select_path,
+                size=self.size,
+            )
+            LogNorm.init_selection(reset=self.reset or reset)
+            del LogNorm
+        elif self.radial_selection is None:
+            if self.norm_csts is None:
+                self.norm_csts = 1.0
+        else:
+            raise ValueError(
+                f"Unknown or unimplemented selection function: {self.radial_selection}"
+            )
+
+    def _PrintMessage(self, required_verbosity, message, verbosity=None):
+        """Print a message to standard output using PrintMessage from
+        pyselfi.utils.
+        """
+        from selfisys.selfi_interface import PrintMessage
+
+        PrintMessage(required_verbosity, message, verbosity or self.verbosity)
+
+    def _indent(self):
+        """Indents the standard output using INDENT from
+        pyselfi.utils.
+        """
+        from selfisys.selfi_interface import indent
+
+        indent()
+
+    def _unindent(self):
+        """Unindents the standard output using UNINDENT from
+        pyselfi.utils.
+        """
+        from selfisys.selfi_interface import unindent
+
+        unindent()
+
+    def _save_cosmo(self, cosmo, fname_cosmo, force_cosmo=False):
+        """Save cosmological parameters in JSON format.
+
+        Parameters
+        ----------
+        cosmo : dict
+            Cosmological parameters (and infrastructure parameters) to
+            be saved.
+        fname_cosmo : str
+            Name of the output JSON file.
+        force_cosmo : bool, optional
+            If True, overwrite the file if it already exists. Default is
+            `False`.
+        """
+        from os.path import exists
+
+        if not exists(fname_cosmo) or force_cosmo:
+            from json import dump
+
+            with open(fname_cosmo, "w") as fp:
+                dump(cosmo, fp)
+
+    def _add_noise(self, g, seednoise, field=None):
+        """Add noise to a realisation in physical space.
+
+        Parameters
+        ----------
+        g : ndarray
+            Field to which the noise is added (modified in place).
+        seednoise : int or list of int
+            Seed to generate the noise realisation.
+        field : ndarray, optional
+            Selection function to apply to the input field. Default is
+            `None`.
+
+        Returns
+        -------
+        None
+        """
+        if self.noise_std > 0:
+            from numpy import random, sqrt, ones_like
+            from h5py import File
+
+            if seednoise is not None:
+                rng = random.default_rng(seednoise)
+            else:
+                raise ValueError("Seednoise must be provided and cannot be None.")
+
+            if field is None:
+                field = ones_like(g)
+
+            N = self.observed_density if self.observed_density is not None else 1.0
+            noise = rng.normal(
+                size=(self.size, self.size, self.size),
+                scale=self.noise_std * sqrt(N * field),
+            )
+
+            with File(self.local_mask_path, "r") as f:
+                mask = f["survey_mask_binary"][:]
+                noise *= mask
+
+            g += noise
+            del noise
+            collect()
+
+    def _get_density_field(self, delta_g_dm, bias):
+        """Apply galaxy bias to a dark matter overdensity field.
+
+        Parameters
+        ----------
+        delta_g_dm : ndarray
+            Dark matter density contrast in physical space.
+        bias : float
+            Linear bias factor.
+
+        Returns
+        -------
+        delta_g : ndarray
+            Galaxy density or overdensity field.
+        """
+        if bias is None:
+            bias = 1.0
+        if not isinstance(bias, float):
+            raise TypeError("Bias must be a float.")
+
+        if self.observed_density is None:
+            delta_g = bias * delta_g_dm
+        else:
+            delta_g = self.observed_density * (1 + bias * delta_g_dm)
+
+        return delta_g
+
+    def _repaint_and_get_Phi(
+        self,
+        g_obj,
+        norm,
+        seednoise,
+        bias=None,
+        field=None,
+        return_g=False,
+        AliasingCorr=True,
+    ):
+        """Repaint a realisation in physical space and compute its
+        summary statistics.
+
+        Parameters
+        ----------
+        g_obj : Field
+            Input field object.
+        norm : ndarray
+            Normalisation constants for the summary statistics.
+        seednoise : int or list of int
+            Seed to generate the noise realisation.
+        bias : float, optional
+            Bias to apply to the input field. Default is `None`.
+        field : ndarray, optional
+            Selection function. Reused as output to save memory
+            allocations. Default is `None`.
+        return_g : bool, optional
+            If True, returns the full field. Default is `False`.
+        AliasingCorr : bool, optional
+            Whether to apply aliasing correction. Default is `True`.
+
+        Returns
+        -------
+        Phi : ndarray
+            Vector of summary statistics.
+        delta_g : ndarray or None
+            Realisation in physical space if return_g is True; None
+            otherwise.
+        """
+        import copy
+        from selfisys.sbmy_interface import compute_Phi
+
+        if bias is not None and not isinstance(bias, float):
+            raise TypeError("Bias must be a float.")
+
+        g_obj_local = copy.deepcopy(g_obj)
+        g_obj_local.data = self._get_density_field(g_obj_local.data, bias)
+        if field is not None:
+            g_obj_local.data *= field
+
+        self._add_noise(g_obj_local.data, seednoise=seednoise, field=field)
+        Phi = compute_Phi(
+            self.G_ss_path,
+            self.P_ss_path,
+            g_obj_local,
+            norm,
+            AliasingCorr,
+            self.verbosity,
+        )
+
+        delta_g = copy.deepcopy(g_obj_local.data) if return_g else None
+        del g_obj_local
+        collect()
+        return Phi, delta_g
+
+    def _apply_selection(self, fnames_outputdensity, seednoise, return_g=False, AliasingCorr=True):
+        """Apply the selection function to a realisation in physical
+        space.
+
+        Parameters
+        ----------
+        fnames_outputdensity : list of str
+            Filenames of the output density fields.
+        seednoise : int or list of int
+            Seed to generate the noise realisation.
+        return_g : bool, optional
+            If True, returns the full field(s). Default is `False`.
+        AliasingCorr : bool, optional
+            Whether to apply aliasing correction. Default is `True`.
+
+        Returns
+        -------
+        Phi_tot : ndarray
+            Concatenated summary statistics for each population.
+        gs : list of ndarray or None
+            List of full fields in physical space if return_g is True;
+            None otherwise.
+        """
+        from pysbmy.field import read_basefield
+
+        split = any(self.sim_params.startswith(s) for s in ["split", "custom"])
+        if not split:
+            g_obj = read_basefield(fnames_outputdensity[0])
+        elif self.force_neglect_lightcone:
+            g_obj = read_basefield(fnames_outputdensity[0])
+        if self.radial_selection is not None:
+            Phi_tot = []
+            gs = []
+            for ifct in range(self.Npop):
+                if split and not self.force_neglect_lightcone:
+                    g_obj = read_basefield(fnames_outputdensity[ifct])
+                with h5py.File(self.local_select_path, "r") as f:
+                    field = f["select_fct"][ifct].astype(float)
+                bias = float(self.linear_bias[ifct]) if self.linear_bias is not None else None
+                Phi, g_out = self._repaint_and_get_Phi(
+                    g_obj,
+                    self.norm_csts[ifct],
+                    seednoise=seednoise,
+                    bias=bias,
+                    field=field,
+                    return_g=return_g,
+                    AliasingCorr=AliasingCorr,
+                )
+                Phi_tot = np.concatenate([Phi_tot, Phi])
+                if return_g:
+                    gs.append(g_out)
+                del g_out
+            result = Phi_tot, gs if return_g else None
+        else:
+            from h5py import File
+
+            if self.local_mask_path is not None:
+                with File(self.local_mask_path, "r") as f:
+                    field = f["survey_mask_binary"][:]
+            else:
+                field = None
+            if not split:
+                bias = self.linear_bias if self.linear_bias is not None else None
+                Phi_tot, delta_g = self._repaint_and_get_Phi(
+                    g_obj,
+                    self.norm_csts,
+                    seednoise=seednoise,
+                    bias=bias,
+                    field=field,
+                    return_g=return_g,
+                    AliasingCorr=AliasingCorr,
+                )
+                result = (Phi_tot, [delta_g]) if return_g else (Phi_tot, None)
+            else:
+                Phi_tot = []
+                gs = []
+                for ifct in range(len(self.TimeSteps)):
+                    bias = (
+                        self.linear_bias
+                        if isinstance(self.linear_bias, float)
+                        else (self.linear_bias[ifct] if self.linear_bias is not None else None)
+                    )
+                    g_obj = read_basefield(fnames_outputdensity[ifct])
+                    Phi, g_out = self._repaint_and_get_Phi(
+                        g_obj,
+                        self.norm_csts,
+                        seednoise=seednoise,
+                        bias=bias,
+                        field=field,
+                        return_g=return_g,
+                        AliasingCorr=AliasingCorr,
+                    )
+                    Phi_tot = np.concatenate([Phi_tot, Phi])
+                    if return_g:
+                        gs.append(g_out)
+                del g_out
+                result = Phi_tot, gs if return_g else None
+        del field, g_obj
+        collect()
+        return result
+
+    def _setup_parfiles(
+        self,
+        d,
+        cosmology,
+        file_names,
+        force=False,
+    ):
+        """Sets up Simbelmynë parameter file given the necessary inputs
+        (please refer to the Simbelmynë documentation for more details).
+
+        Parameters
+        ----------
+        d : int
+            index giving the direction in parameter space:
+            -1 for mock data, 0 for the expansion point, or from 1 to S
+        cosmology : array, double, dimension=5
+            cosmological parameters
+        file_names : dict
+            Dictionary containing the names of the input and output
+            files.
+        force : bool, optional, default=False
+            overwrite if files already exists?
+
+        """
+        from selfisys.sbmy_interface import setup_sbmy_parfiles
+
+        hiddenbox_params = {
+            "Npop": self.Npop,
+            "TimeSteps": self.TimeSteps,
+            "eff_redshifts": self.eff_redshifts,
+            "sim_params": self.sim_params,
+            "Ntimesteps": self.Ntimesteps,
+            "TimeStepDistribution": self.TimeStepDistribution,
+            "modified_selfi": self.modified_selfi,
+            "Np0": self.Np0,
+            "Npm0": self.Npm0,
+            "size": self.size,
+            "L": self.L,
+            "fsimdir": self.fsimdir,
+        }
+        setup_sbmy_parfiles(
+            d,
+            cosmology,
+            file_names,
+            hiddenbox_params,
+            force,
+        )
+
+    def _run_sim(
+        self,
+        fname_simparfile,
+        fname_simlogs,
+        fnames_outputdensity,
+        force_sim=False,
+        check_output=False,
+    ):
+        """Run a simulation with Simbelmynë.
+
+        Parameters
+        ----------
+        fname_simparfile : str
+            Name of the input parameter file.
+        fname_simlogs : str
+            Name of the output Simbelmynë logs.
+        fnames_outputdensity : list of str
+            Names of the output density fields to be written.
+        force_sim : bool, optional
+            If True, force recomputation if output density already
+            exists.
+            Default is `False`.
+        check_output : bool, optional
+            If True, check the integrity of the output files and
+            recompute if corrupted. Default is `False`.
+        """
+        from glob import glob
+        from selfisys.utils.parser import check_files_exist
+
+        split = self.sim_params.startswith("split")
+        if not check_files_exist(fnames_outputdensity) or force_sim:
+            from pysbmy import pySbmy
+
+            if not split:
+                pySbmy(f"{fname_simparfile}_{self.Npop}.sbmy", fname_simlogs)
+            else:
+                for i in range(self.Ntimesteps):
+                    pySbmy(f"{fname_simparfile}_pop{i}.sbmy", fname_simlogs)
+        elif check_output:
+            from pysbmy.field import read_basefield
+
+            try:
+                for fname in fnames_outputdensity:
+                    g = read_basefield(fname)
+                del g
+                collect()
+            except Exception:
+                from pysbmy import pySbmy
+
+                for fname in fnames_outputdensity:
+                    os.remove(fname)
+                pySbmy(fname_simparfile, fname_simlogs)
+
+        # Workaround to remove unwanted Simbelmynë outputs from disk
+        temp_files = glob(os.path.join(self.fsimdir, "data", "cola_kick_*.h5"))
+        for f in temp_files:
+            os.remove(f)
+
+        if split:
+            temp_files = glob(os.path.join(self.fsimdir, "data", "cola_snapshot_*.h5"))
+            temp_files += glob(os.path.join(self.fsimdir, "data", "lpt_psi*.h5"))
+            for f in temp_files:
+                os.remove(f)
+
+    def _compute_mocks(
+        self,
+        seednoise,
+        fnames_input,
+        fname_mocks,
+        return_g,
+        fname_g=None,
+        AliasingCorr=True,
+    ):
+        """Apply galaxy bias, observational effects, and compute the
+        summary statistics.
+
+        Parameters
+        ----------
+        seednoise : int or list of int
+            Seed to generate the noise realisation.
+        fnames_input : list of str
+            Filenames of the input density fields.
+        fname_mocks : str
+            Filename to save the computed summary statistics.
+        return_g : bool
+            If True, return the observed field(s).
+        fname_g : str, optional
+            Filename to save the observed field(s) if return_g is True.
+        AliasingCorr : bool, optional
+            Whether to apply aliasing correction. Default is `True`.
+
+        Returns
+        -------
+        Phi : ndarray
+            Vector of summary statistics.
+        g_out : ndarray or list of ndarray or None
+            Observed field(s) if return_g is True; otherwise None.
+        """
+
+        if return_g and fname_g is None:
+            raise ValueError("Filename for the observed field must be provided.")
+
+        if (not os.path.exists(fname_mocks)) or self.force_recompute_mocks:
+            Phi, g_out = self._apply_selection(
+                fnames_input,
+                seednoise=seednoise,
+                return_g=return_g,
+                AliasingCorr=AliasingCorr,
+            )
+            with h5py.File(fname_mocks, "w") as f:
+                f.create_dataset("Phi", data=Phi)
+            if return_g:
+                with h5py.File(fname_g, "w") as f:
+                    f.create_dataset("g", data=g_out)
+        else:
+            self._PrintMessage(1, f"Using existing mock file: {fname_mocks}")
+            with h5py.File(fname_mocks, "r") as f:
+                Phi = f["Phi"][:]
+            if return_g:
+                with h5py.File(fname_g, "r") as f:
+                    g_out = f["g"][:]
+            else:
+                g_out = None
+
+        return Phi, g_out
+
+    def _sample_omega(self, seed):
+        """Sample cosmological parameters from the prior.
+
+        Parameters
+        ----------
+        seed : int or list of int
+            Seed for the random number generator.
+
+        Returns
+        -------
+        omega_sample : ndarray
+            Sampled cosmological parameters.
+        """
+        from selfisys.utils.tools import sample_omega_from_prior
+        from selfisys.global_parameters import planck_mean, planck_cov
+
+        ids = list(range(len(planck_mean)))
+        return sample_omega_from_prior(1, planck_mean, planck_cov, ids, seed=seed)[0]
+
+    def _aux_hiddenbox(
+        self,
+        d,
+        seedphase,
+        seednoise,
+        file_names,
+        force_parfiles=False,
+        force_sim=False,
+        force_phase=False,
+        return_g=False,
+        check_output=False,
+        RSDs=True,
+        sample=None,
+    ):
+        """Generate observations from the input initial matter power
+        spectrum.
+
+        Parameters
+        ----------
+        d : int
+            Index indicating the direction in parameter space (-1 for
+            mock data, 0 for the expansion point, or from 1 to S for the
+            gradient directions).
+        seedphase : int or list of int
+            Seed to generate the initial white noise in Fourier space.
+        seednoise : int or list of int
+            Seed to generate the observational noise realisation.
+        file_names : dict
+            Dictionary containing the names of the input and output
+            files.
+        force_parfiles : bool, optional
+            If True, force recomputation of the parameter files. Default
+            is `False`.
+        force_sim : bool, optional
+            If True, force recomputation of the simulation. Default is
+            `False`.
+        force_phase : bool, optional
+            If True, force recomputation of the phase. Default is
+            `False`.
+        return_g : bool, optional
+            If True, return the full realisation in physical space.
+            Default is `False`.
+        check_output : bool, optional
+            If True, check the integrity of the output file and
+            recompute if corrupted. Default is `False`.
+        RSDs : bool, optional
+            If True, include redshift-space distortions. Default is
+            `True`.
+        sample : ndarray, optional
+            Cosmological parameters sample. If `None`, sample from the
+            prior. Default is `None`.
+
+        Returns
+        -------
+        result : tuple
+            A tuple containing:
+            - Phi : (ndarray)
+                Summary statistics for each population, concatenated.
+            - g : (ndarray or list of ndarray or None)
+                List of observed fields if return_g is True; None
+                otherwise.
+        """
+        fname_power_spectrum = file_names["fname_power_spectrum"]
+        fname_simparfile = file_names["fname_simparfile"]
+        fname_whitenoise = file_names["fname_whitenoise"]
+        seedname_whitenoise = file_names["seedname_whitenoise"]
+        fname_outputinitialdensity = file_names["fname_outputinitialdensity"]
+        fnames_outputrealspacedensity = file_names["fnames_outputrealspacedensity"]
+        fnames_outputdensity = file_names["fnames_outputdensity"]
+        fname_simlogs = file_names["fname_simlogs"]
+        fname_mocks = file_names["fname_mocks"]
+        fname_g = file_names["fname_g"] if return_g else None
+
+        sample = self._sample_omega(seedphase) if sample is None else sample
+
+        if self.gravity_on:
+            from selfisys.sbmy_interface import generate_white_noise_Field
+
+            self._setup_parfiles(
+                d,
+                sample,
+                file_names,
+                force_parfiles,
+            )
+            generate_white_noise_Field(
+                self.L,
+                self.size,
+                seedphase,
+                fname_whitenoise,
+                seedname_whitenoise,
+                force_phase,
+            )
+
+        if not self.setup_only:
+            if self.gravity_on:
+                self._run_sim(
+                    fname_simparfile,
+                    fname_simlogs,
+                    fnames_outputdensity,
+                    force_sim,
+                    check_output,
+                )
+                fnames = fnames_outputdensity if RSDs else fnames_outputrealspacedensity
+            else:
+                from selfisys.grf import primordial_grf
+
+                primordial_grf(
+                    self.L,
+                    self.size,
+                    seedphase,
+                    fname_power_spectrum,
+                    fname_outputinitialdensity,
+                    force_sim,
+                    False,
+                    self.verbosity,
+                )
+                fnames = [fname_outputinitialdensity]
+
+            result = self._compute_mocks(seednoise, fnames, fname_mocks, return_g, fname_g)
+        else:
+            result = [], []  # lists are mandatory for compatibility
+
+        return result
+
+    def _power_spectrum_from_theta(
+        self, theta, fname_power_spectrum, theta_is_p=False, force=False
+    ):
+        """Compute the power spectrum values using spline interpolation.
+
+        Parameters
+        ----------
+        theta : ndarray
+            Vector of power spectrum values at the support wavenumbers.
+        fname_power_spectrum : str
+            Name of the input/output power spectrum file.
+        theta_is_p : bool, optional
+            If True, theta is already an unnormalised power spectrum.
+            Default is `False`.
+        force : bool, optional
+            If True, force recomputation. Default is `False`.
+        """
+        from pysbmy.power import PowerSpectrum
+
+        if (not os.path.exists(fname_power_spectrum)) or force:
+            from scipy.interpolate import InterpolatedUnivariateSpline
+            from pysbmy.power import FourierGrid
+
+            PP = theta if theta_is_p else self.theta2P(theta)
+            Spline = InterpolatedUnivariateSpline(self.k_s, PP, k=5)
+            G_sim = FourierGrid.read(self.G_sim_path)
+            power_spectrum = Spline(G_sim.k_modes)
+            power_spectrum[0] = 0.0
+            P = PowerSpectrum.from_FourierGrid(G_sim, powerspectrum=power_spectrum)
+            P.write(fname_power_spectrum)
+            del G_sim
+            collect()
+
+    def _clean_output(self, fname_output):
+        """Remove a file from disk if it exists.
+
+        Parameters
+        ----------
+        fname_output : str
+            Name of the file to remove.
+        """
+        if fname_output is not None and os.path.exists(fname_output):
+            os.remove(fname_output)
+
+    def _get_current_seed(self, parent_seed, fixed_seed, i):
+        """Return the current seed for the i-th realisation.
+
+        Parameters
+        ----------
+        parent_seed : int or list of int
+            The parent seed.
+        fixed_seed : bool
+            If True, use the parent seed directly.
+        i : int
+            Index of the current realisation.
+
+        Returns
+        -------
+        this_seed : int or list of int
+            The seed for the current realisation.
+        """
+        if fixed_seed:
+            this_seed = parent_seed
+        else:
+            this_seed = [i, parent_seed]
+        return this_seed

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/normalise_hb.html b/docs/_modules/selfisys/normalise_hb.html new file mode 100644 index 0000000..c5dac83 --- /dev/null +++ b/docs/_modules/selfisys/normalise_hb.html @@ -0,0 +1,596 @@ + + + + + + + + selfisys.normalise_hb — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.normalise_hb

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Tools to define normalisation constants for the hidden box."""
+
+import os
+import numpy as np
+from typing import Tuple, Dict, Any
+
+from selfisys.global_parameters import MIN_K_NORMALISATION
+from selfisys.hiddenbox import HiddenBox
+
+
+

+[docs]
+def worker_normalisation(
+    hidden_box: HiddenBox,
+    params: Tuple[Dict[str, Any], list, list, bool],
+) -> np.ndarray:
+    """Worker function to compute the normalisation constants,
+    compatible with Python multiprocessing.
+
+    Parameters
+    ----------
+    hidden_box : HiddenBox
+        Instance of the HiddenBox class.
+    params : tuple
+        A tuple containing (cosmo, seedphase, seednoise, force).
+
+    Returns
+    -------
+    phi : ndarray
+        Computed summary statistics.
+    """
+    (
+        cosmo,
+        seedphase,
+        seednoise,
+        force,
+    ) = params
+    name = (
+        "norm"
+        + "__"
+        + "_".join([str(int(s)) for s in seedphase])
+        + "__"
+        + "_".join([str(int(s)) for s in seednoise])
+    )
+
+    if hidden_box.verbosity > 1:
+        hidden_box._PrintMessage(1, "Running simulation...")
+        hidden_box._indent()
+        phi = hidden_box.make_data(
+            cosmo,
+            name,
+            seedphase,
+            seednoise,
+            force,
+            force,
+            force,
+            force,
+        )
+        hidden_box._unindent()
+    elif hidden_box.verbosity > 0:
+        from selfisys.utils.low_level import (
+            stdout_redirector,
+        )
+        from io import BytesIO
+
+        f = BytesIO()
+        with stdout_redirector(f):
+            phi = hidden_box.make_data(
+                cosmo,
+                name,
+                seedphase,
+                seednoise,
+                force,
+                force,
+                force,
+                force,
+            )
+        f.close()
+    else:
+        from selfisys.utils.low_level import (
+            stdout_redirector,
+            stderr_redirector,
+        )
+        from io import BytesIO
+
+        f = BytesIO()
+        g = BytesIO()
+        with stdout_redirector(f), stderr_redirector(g):
+            phi = hidden_box.make_data(
+                cosmo,
+                name,
+                seedphase,
+                seednoise,
+                force,
+                force,
+                force,
+                force,
+            )
+        f.close()
+        g.close()
+
+    return phi

+
+
+
+

+[docs]
+def worker_normalisation_wrapper(args):
+    """Wrapper function for the worker_normalisation function.
+
+    Parameters
+    ----------
+    args : tuple
+        A tuple containing (hidden_box, params).
+
+    Returns
+    -------
+    phi : ndarray
+        Computed summary statistics.
+    """
+    hidden_box, params = args
+    return worker_normalisation(hidden_box, params)

+
+
+
+

+[docs]
+def worker_normalisation_public(
+    hidden_box,
+    cosmo: Dict[str, Any],
+    N: int,
+    i: int,
+):
+    """Run the i-th simulation required to compute the normalisation
+    constants.
+
+    Parameters
+    ----------
+    hidden_box : HiddenBox
+        Instance of the HiddenBox class.
+    cosmo : dict
+        Cosmological and some infrastructure parameters.
+    N : int
+        Total number of realisations required.
+    i : int
+        Index of the simulation to be computed.
+    """
+    params = (
+        cosmo,
+        [
+            i,
+            hidden_box._HiddenBox__global_seednorm,
+        ],
+        [
+            i + N,
+            hidden_box._HiddenBox__global_seednorm,
+        ],
+        False,
+    )
+    worker_normalisation(hidden_box, params)

+
+
+
+

+[docs]
+def define_normalisation(
+    hidden_box: HiddenBox,
+    Pbins: np.ndarray,
+    cosmo: Dict[str, Any],
+    N: int,
+    min_k_norma: float = MIN_K_NORMALISATION,
+    npar: int = 1,
+    force: bool = False,
+) -> np.ndarray:
+    """Define the normalisation constants for the HiddenBox instance.
+
+    Parameters
+    ----------
+    hidden_box : HiddenBox
+        Instance of the HiddenBox class.
+    Pbins : ndarray
+        Array of P bin values.
+    cosmo : dict
+        Cosmological and infrastructure parameters.
+    N : int
+        Number of realisations required.
+    min_k_norma : float, optional
+        Minimum k value to compute the normalisation constants.
+    npar : int, optional
+        Number of parallel processes to use. Default is 1.
+    force : bool, optional
+        If True, force recomputation. Default is False.
+
+    Returns
+    -------
+    norm_csts : ndarray
+        Normalisation constants for the HiddenBox instance.
+    """
+    import tqdm.auto as tqdm
+    from multiprocessing import Pool
+
+    hidden_box._PrintMessage(
+        0,
+        "Defining normalisation constants...",
+    )
+    hidden_box._indent()
+    indices = np.where(Pbins > min_k_norma)
+    tasks = [
+        (
+            hidden_box,
+            (
+                cosmo,
+                [
+                    i,
+                    hidden_box._HiddenBox__global_seednorm,
+                ],
+                [
+                    i + N,
+                    hidden_box._HiddenBox__global_seednorm,
+                ],
+                force,
+            ),
+        )
+        for i in range(N)
+    ]
+
+    ncors = os.cpu_count()
+    nprocs = min(npar, ncors)
+
+    norm_csts_list = np.zeros((hidden_box._Npop, N))
+    if npar > 1:
+        with Pool(nprocs) as p:
+            for j, val in enumerate(
+                tqdm.tqdm(
+                    p.imap(
+                        worker_normalisation_wrapper,
+                        tasks,
+                    ),
+                    total=N,
+                )
+            ):
+                norm_csts_list[:, j] = np.array(
+                    [
+                        np.mean(
+                            val[i * hidden_box.Psingle : (i + 1) * hidden_box.Psingle][indices]
+                        )
+                        for i in range(hidden_box._Npop)
+                    ]
+                )
+    else:
+        for j, val in enumerate(
+            tqdm.tqdm(
+                map(
+                    worker_normalisation_wrapper,
+                    tasks,
+                ),
+                total=N,
+            )
+        ):
+            val = np.array(val)
+            norm_csts_list[:, j] = np.array(
+                [
+                    np.mean(val[i * hidden_box.Psingle : (i + 1) * hidden_box.Psingle][indices])
+                    for i in range(hidden_box._Npop)
+                ]
+            )
+    norm_csts = np.mean(norm_csts_list, axis=1)
+    hidden_box._unindent()
+    hidden_box._PrintMessage(
+        0,
+        "Defining normalisation constants done.",
+    )
+
+    return norm_csts

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/prior.html b/docs/_modules/selfisys/prior.html new file mode 100644 index 0000000..4cae76c --- /dev/null +++ b/docs/_modules/selfisys/prior.html @@ -0,0 +1,1028 @@ + + + + + + + + selfisys.prior — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.prior

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Priors for the SelfiSys pipeline. This module provides:
+- a Planck2018-based prior class (`planck_prior`) compatible with
+pySelfi, adapted for the logic of the SelfiSys pipeline;
+- wrappers for the selfi2019 prior from [leclercq2019primordial].
+
+
+Raises
+------
+OSError
+    If file or directory paths are inaccessible.
+RuntimeError
+    If unexpected HPC or multi-processing errors arise.
+"""
+
+import gc
+
+from selfisys.utils.logger import getCustomLogger
+
+logger = getCustomLogger(__name__)
+
+
+

+[docs]
+def get_summary(x, bins, normalisation=None, kmax=1.4):
+    """
+    Compute a power-spectrum summary for given cosmological parameters.
+
+    Parameters
+    ----------
+    x : array-like
+        Cosmological parameters [h, Omega_b, Omega_m, n_s, sigma_8].
+    bins : array-like
+        Wavenumber bins.
+    normalisation : float or None, optional
+        Normalisation constant to scale the resulting spectrum.
+    kmax : float, optional
+        Maximum wavenumber for get_Pk.
+
+    Returns
+    -------
+    theta : ndarray
+        The computed power-spectrum values, optionally normalised.
+
+    Raises
+    ------
+    RuntimeError
+        If the power-spectrum computation fails unexpectedly.
+    """
+    from numpy import array
+    from pysbmy.power import get_Pk
+    from selfisys.utils.tools import cosmo_vector_to_Simbelmyne_dict
+
+    try:
+        theta = get_Pk(bins, cosmo_vector_to_Simbelmyne_dict(x, kmax=kmax))
+        if normalisation is not None:
+            theta /= normalisation
+        return array(theta)
+    except Exception as e:
+        logger.critical("Unexpected error in get_summary: %s", str(e))
+        raise RuntimeError("Failed to compute power spectrum summary.") from e
+    finally:
+        gc.collect()

+
+
+
+

+[docs]
+def worker_class(params):
+    """
+    Worker function to compute power spectra with CLASS, compatible with
+    Python multiprocessing.
+
+    Parameters
+    ----------
+    params : tuple
+        (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.
+
+    Returns
+    -------
+    theta : ndarray
+        Power-spectrum summary from `get_summary`.
+    """
+    x, bins, normalisation, kmax = params
+    return get_summary(x, bins, normalisation, kmax)

+
+
+
+

+[docs]
+class planck_prior:
+    """
+    Custom prior for the SelfiSys pipeline. This is the prior used in
+    [hoellinger2024diagnosing], based on the Planck 2018 cosmological
+    parameters.
+
+    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].
+
+    Parameters
+    ----------
+    Omega_mean : array-like
+        Mean of the prior distribution on cosmological parameters.
+    Omega_cov : array-like
+        Covariance matrix of the prior distribution on cosmological
+        parameters.
+    bins : array-like
+        Wavenumbers where the power spectrum is evaluated.
+    normalisation : float or None
+        If not None, divide the power spectra by the normalisation.
+    kmax : float
+        Maximum wavenumber for computations.
+    nsamples : int, optional
+        Number of samples drawn from the prior on the cosmological
+        parameters. Default is 10,000.
+    nthreads : int, optional
+        Number of CPU threads for parallel tasks. Default is -1, that
+        is, auto-detect the number of available threads.
+    EPS_K : float, optional
+        Regularisation parameter for covariance inversion. Default 1e-7.
+    EPS_residual : float, optional
+        Additional cutoff for matrix inversion. Default 1e-3.
+    filename : str or None, optional
+        Path to a .npy file to store or load precomputed power spectra.
+
+    Attributes
+    ----------
+    mean : ndarray
+        Mean of the computed power spectra.
+    covariance : ndarray
+        Covariance matrix of the computed power spectra.
+    inv_covariance : ndarray
+        Inverse of the covariance matrix.
+
+    Raises
+    ------
+    OSError
+        If file reading or writing fails.
+    RuntimeError
+        For unexpected HPC or multi-processing errors.
+    """
+
+    def __init__(
+        self,
+        Omega_mean,
+        Omega_cov,
+        bins,
+        normalisation,
+        kmax,
+        nsamples=10000,
+        nthreads=-1,
+        EPS_K=1e-7,
+        EPS_residual=1e-3,
+        filename=None,
+    ):
+        from numpy import where
+        from multiprocessing import cpu_count
+
+        self.Omega_mean = Omega_mean
+        self.Omega_cov = Omega_cov
+        self.bins = bins
+        self.normalisation = normalisation
+        self.kmax = kmax
+        self.nsamples = nsamples
+        self.EPS_K = EPS_K
+        self.EPS_residual = EPS_residual
+        self.filename = filename
+
+        if nthreads == -1:
+            # Use #CPU - 1 or fallback to 1 if a single CPU is available
+            self.nthreads = cpu_count() - 1 or 1
+        else:
+            self.nthreads = nthreads
+
+        self._Nbin_min = where(self.bins >= 0.01)[0].min()
+        self._Nbin_max = where(self.bins <= self.kmax)[0].max() + 1
+
+        # Attributes set after compute()
+        self.mean = None
+        self.covariance = None
+        self.inv_covariance = None
+        self.thetas = None
+
+    @property
+    def Nbin_min(self, k_min):
+        """Index of the minimal wavenumber given k_min."""
+        return self._Nbin_min
+
+    @property
+    def Nbin_max(self, k_min):
+        """Index of the maximal wavenumber given self.kmax."""
+        return self._Nbin_max
+
+

+[docs]
+    def compute(self):
+        """
+        Compute the prior (mean, covariance, and inverse covariance).
+
+        If `self.filename` exists, tries to load the prior. Otherwise,
+        samples from the prior distribution on cosmological parameters
+        and evaluates the power spectra in parallel.
+
+        Raises
+        ------
+        OSError
+            If self.filename is not writable/accessible.
+        RuntimeError
+            If multi-processing or power-spectra computations fail.
+        """
+        from os.path import exists
+        import numpy as np
+
+        try:
+            if self.filename and exists(self.filename):
+                logger.info("Loading precomputed thetas from %s", self.filename)
+                self.thetas = np.load(self.filename)
+            else:
+                from time import time
+                from multiprocessing import Pool
+                import tqdm.auto as tqdm
+
+                logger.info("Sampling %d cosmological parameter sets...", self.nsamples)
+                OO = np.random.multivariate_normal(
+                    np.array(self.Omega_mean), np.array(self.Omega_cov), self.nsamples
+                )
+                eps = 1e-5
+                OO = np.clip(OO, eps, 1 - eps)
+
+                liste = [(o, self.bins, self.normalisation, self.kmax) for o in OO]
+
+                logger.info(
+                    "Computing prior power spectra in parallel using %d threads...", self.nthreads
+                )
+                start = time()
+                with Pool(self.nthreads) as pool:
+                    thetas = []
+                    for theta in tqdm.tqdm(pool.imap(worker_class, liste), total=len(liste)):
+                        thetas.append(theta)
+                thetas = np.array(thetas)
+                end = time()
+                logger.info("Done computing power spectra in %.2f seconds.", end - start)
+
+                self.thetas = thetas
+                if self.filename:
+                    logger.info("Saving thetas to %s", self.filename)
+                    np.save(self.filename, thetas)
+
+            # Compute stats
+            self.mean = np.mean(self.thetas, axis=0)
+            self.covariance = np.cov(self.thetas.T)
+
+            logger.info("Regularising and inverting the prior covariance matrix.")
+            from pyselfi.utils import regular_inv
+
+            self.inv_covariance = regular_inv(self.covariance, self.EPS_K, self.EPS_residual)
+
+        except OSError as e:
+            logger.error("File I/O error: %s", str(e))
+            raise
+        except Exception as e:
+            logger.critical("Error during prior computation: %s", str(e))
+            raise RuntimeError("planck_prior computation failed.") from e
+        finally:
+            gc.collect()

+
+
+

+[docs]
+    def logpdf(self, theta, theta_mean, theta_covariance, theta_icov):
+        """
+        Return the log prior probability at a given point in parameter
+        space.
+
+        Parameters
+        ----------
+        theta : ndarray
+            Evaluation point in parameter space.
+        theta_mean : ndarray
+            Prior mean vector.
+        theta_covariance : ndarray
+            Prior covariance matrix.
+        theta_icov : ndarray
+            Inverse of the prior covariance matrix.
+
+        Returns
+        -------
+        float
+            Log prior probability value.
+        """
+        import numpy as np
+
+        diff = theta - theta_mean
+        val = -0.5 * diff.dot(theta_icov).dot(diff)
+        val -= 0.5 * np.linalg.slogdet(2 * np.pi * theta_covariance)[1]
+        return val

+
+
+

+[docs]
+    def sample(self, seedsample=None):
+        """
+        Draw a random sample from the prior distribution.
+
+        Parameters
+        ----------
+        seedsample : int, optional
+            Seed for the random number generator.
+
+        Returns
+        -------
+        ndarray
+            A single sample from the prior distribution.
+        """
+        from numpy.random import seed, multivariate_normal
+
+        if seedsample is not None:
+            seed(seedsample)
+        return multivariate_normal(self.mean, self.covariance)

+
+
+

+[docs]
+    def save(self, fname):
+        """
+        Save the prior to an output file.
+
+        Parameters
+        ----------
+        fname : str
+            Output HDF5 filename to store the prior data.
+
+        Raises
+        ------
+        OSError
+            If the file cannot be accessed or written.
+        """
+        import h5py
+        from ctypes import c_double
+        from pyselfi.utils import PrintMessage, save_replace_dataset, save_replace_attr
+
+        try:
+            PrintMessage(3, f"Writing prior in data file '{fname}'...")
+            with h5py.File(fname, "r+") as hf:
+
+                def save_to_hf(name, data, **kwargs):
+                    save_replace_dataset(hf, f"/prior/{name}", data, dtype=c_double, **kwargs)
+
+                # Hyperparameters
+                save_to_hf("thetas", self.thetas, maxshape=(None, None))
+                save_to_hf("Omega_mean", self.Omega_mean, maxshape=(None,))
+                save_to_hf("Omega_cov", self.Omega_cov, maxshape=(None, None))
+                save_to_hf("bins", self.bins, maxshape=(None,))
+
+                save_replace_attr(hf, "/prior/normalisation", self.normalisation, dtype=c_double)
+                save_replace_attr(hf, "/prior/kmax", self.kmax, dtype=c_double)
+
+                # Mandatory attributes
+                save_to_hf("mean", self.mean, maxshape=(None,))
+                save_to_hf("covariance", self.covariance, maxshape=(None, None))
+                save_to_hf("inv_covariance", self.inv_covariance, maxshape=(None, None))
+
+            PrintMessage(3, f"Writing prior in data file '{fname}' done.")
+        except OSError as e:
+            logger.error("Failed to save prior to '%s': %s", fname, str(e))
+            raise
+        finally:
+            gc.collect()

+
+
+

+[docs]
+    @classmethod
+    def load(cls, fname):
+        """
+        Load the prior from input file.
+
+        Parameters
+        ----------
+        fname : str
+            Input HDF5 filename.
+
+        Returns
+        -------
+        prior
+            The prior object.
+
+        Raises
+        ------
+        OSError
+            If the file cannot be read or is invalid.
+        """
+        from h5py import File
+        from numpy import array
+        from ctypes import c_double
+        from pyselfi.utils import PrintMessage
+
+        try:
+            PrintMessage(3, f"Reading prior in data file '{fname}'...")
+            with File(fname, "r") as hf:
+                # Load constructor parameters
+                Omega_mean = array(hf.get("/prior/Omega_mean"), dtype=c_double)
+                Omega_cov = array(hf.get("/prior/Omega_cov"), dtype=c_double)
+                bins = array(hf.get("/prior/bins"), dtype=c_double)
+                normalisation = hf.attrs["/prior/normalisation"]
+                kmax = hf.attrs["/prior/kmax"]
+
+                # Instantiate class
+                prior = cls(Omega_mean, Omega_cov, bins, normalisation, kmax)
+
+                # Load mandatory arrays
+                prior.mean = array(hf.get("prior/mean"), dtype=c_double)
+                prior.covariance = array(hf.get("/prior/covariance"), dtype=c_double)
+                prior.inv_covariance = array(hf.get("/prior/inv_covariance"), dtype=c_double)
+
+            PrintMessage(3, f"Reading prior in data file '{fname}' done.")
+            return prior
+        except OSError as e:
+            logger.error("Failed to read prior from '%s': %s", fname, str(e))
+            raise
+        finally:
+            gc.collect()

+

+
+
+
+

+[docs]
+def logposterior_hyperparameters_parallel(
+    selfi,
+    theta_fiducial,
+    Nbin_min,
+    Nbin_max,
+    theta_norm,
+    k_corr,
+    alpha_cv,
+):
+    """
+    Compute the log-posterior for the hyperparameters of the prior from
+    [leclercq2019primordial], for use within the SelfiSys pipeline.
+
+    Parameters
+    ----------
+    selfi : object
+        The selfi object.
+    theta_fiducial : ndarray
+        Fiducial spectrum.
+    Nbin_min : int
+        Minimum bin index for the wavenumber range.
+    Nbin_max : int
+        Maximum bin index for the wavenumber range.
+    theta_norm : float
+        Hyperparameter controlling the overall uncertainty.
+    k_corr : float
+        Hyperparameter controlling correlation scale.
+    alpha_cv : float
+        Cosmic variance strength.
+
+    Returns
+    -------
+    float
+        The log-posterior value for the given hyperparameters.
+
+    Raises
+    ------
+    RuntimeError
+        If the log-posterior computation fails unexpectedly.
+    """
+    try:
+        return selfi.logposterior_hyperparameters(
+            theta_fiducial, Nbin_min, Nbin_max, theta_norm, k_corr, alpha_cv
+        )
+    except Exception as e:
+        logger.critical("Unexpected error in logposterior_hyperparameters_parallel: %s", str(e))
+        raise RuntimeError("logposterior_hyperparameters_parallel failed.") from e
+    finally:
+        gc.collect()

+
+
+
+

+[docs]
+def perform_prior_optimisation_and_plot(
+    selfi,
+    theta_fiducial,
+    theta_norm_mean=0.1,
+    theta_norm_std=0.3,
+    k_corr_mean=0.020,
+    k_corr_std=0.015,
+    k_opt_min=0.0,
+    k_opt_max=1.4,
+    theta_norm_min=0.04,
+    theta_norm_max=0.12,
+    k_corr_min=0.012,
+    k_corr_max=0.02,
+    meshsize=30,
+    Nbin_min=0,
+    Nbin_max=100,
+    theta_norm=0.05,
+    k_corr=0.015,
+    alpha_cv=0.00065,
+    plot=True,
+    savepath=None,
+):
+    """
+    Optimise the hyperparameters for the selfi2019 prior (from
+    [leclercq2019primordial]).
+
+    Parameters
+    ----------
+    selfi : object
+        The selfi object.
+    theta_fiducial : ndarray
+        Fiducial spectrum.
+    theta_norm_mean : float, optional
+        Mean of the Gaussian hyperprior on theta_norm. Default 0.1.
+    theta_norm_std : float, optional
+        Standard deviation of the hyperprior on theta_norm. Default 0.3.
+    k_corr_mean : float, optional
+        Mean of the Gaussian hyperprior on k_corr. Default 0.020.
+    k_corr_std : float, optional
+        Standard deviation of the hyperprior on k_corr. Default 0.015.
+    k_opt_min : float, optional
+        Minimum wavenumber for the prior optimisation. Default 0.0.
+    k_opt_max : float, optional
+        Maximum wavenumber for the prior optimisation. Default 1.4.
+    theta_norm_min : float, optional
+        Lower bound for theta_norm in the mesh. Default 0.04.
+    theta_norm_max : float, optional
+        Upper bound for theta_norm in the mesh. Default 0.12.
+    k_corr_min : float, optional
+        Lower bound for k_corr in the mesh. Default 0.012.
+    k_corr_max : float, optional
+        Upper bound for k_corr in the mesh. Default 0.02.
+    meshsize : int, optional
+        Number of points in each dimension of the plot mesh. Default 30.
+    Nbin_min : int, optional
+        Minimum bin index for restricting the prior. Default 0.
+    Nbin_max : int, optional
+        Maximum bin index for restricting the prior. Default 100.
+    theta_norm : float, optional
+        Initial or default guess of theta_norm. Default 0.05.
+    k_corr : float, optional
+        Initial or default guess of k_corr. Default 0.015.
+    alpha_cv : float, optional
+        Cosmic variance term or similar. Default 0.00065.
+    plot : bool, optional
+        If True, generate and show/save a 2D contour plot. Default True.
+    savepath : str, optional
+        File path to save the plot. If None, the plot is displayed.
+
+    Returns
+    -------
+    tuple
+        (theta_norm, k_corr) after optimisation.
+
+    Raises
+    ------
+    OSError
+        If file operations fail during saving the prior or posterior.
+    RuntimeError
+        If the optimisation fails unexpectedly.
+    """
+    try:
+        if plot:
+            from selfisys.utils.plot_utils import get_contours
+            from numpy import meshgrid, linspace, zeros, exp, array
+            from joblib import Parallel, delayed
+
+            logger.info("Preparing the hyperparameter grid for plotting (meshsize=%d).", meshsize)
+
+            X0, Y0 = meshgrid(
+                linspace(theta_norm_min, theta_norm_max, meshsize),
+                linspace(k_corr_min, k_corr_max, meshsize),
+            )
+            Z = zeros((meshsize, meshsize))
+
+            # Evaluate log-posterior on the grid in parallel
+            Z = array(
+                Parallel(n_jobs=-1)(
+                    delayed(logposterior_hyperparameters_parallel)(
+                        selfi,
+                        theta_fiducial,
+                        Nbin_min,
+                        Nbin_max,
+                        X0[i][j],
+                        Y0[i][j],
+                        alpha_cv,
+                    )
+                    for i in range(meshsize)
+                    for j in range(meshsize)
+                )
+            ).reshape(meshsize, meshsize)
+            Z -= Z.max()
+            Z = exp(Z)
+
+            Z_contours = get_contours(Z, meshsize)
+            logger.info("Grid evaluations complete.")
+
+        logger.info("Performing the prior hyperparameter optimisation...")
+        selfi.prior.theta_norm = theta_norm
+        selfi.prior.k_corr = k_corr
+        selfi.prior.alpha_cv = alpha_cv
+
+        # Perform the prior optimisation
+        x0 = [theta_norm, k_corr]
+        selfi.optimize_prior(
+            theta_fiducial,
+            k_opt_min,
+            k_opt_max,
+            x0=x0,
+            theta_norm_min=theta_norm_min,
+            theta_norm_max=theta_norm_max,
+            theta_norm_mean=theta_norm_mean,
+            theta_norm_std=theta_norm_std,
+            k_corr_min=k_corr_min,
+            k_corr_max=k_corr_max,
+            k_corr_mean=k_corr_mean,
+            k_corr_std=k_corr_std,
+            options={
+                "maxiter": 30,
+                "ftol": 1e-10,
+                "gtol": 1e-10,
+                "eps": 1e-6,
+                "disp": False,
+            },
+        )
+
+        logger.info("Saving prior and posterior after optimisation.")
+        selfi.save_prior()
+        selfi.save_posterior()
+
+        theta_norm = selfi.prior.theta_norm
+        k_corr = selfi.prior.k_corr
+
+        prior_theta_mean, prior_theta_covariance = selfi.prior.mean, selfi.prior.covariance
+        prior_theta_mean = prior_theta_mean[Nbin_min:Nbin_max]
+        prior_theta_covariance = prior_theta_covariance[Nbin_min:Nbin_max, Nbin_min:Nbin_max]
+
+        posterior_theta_mean, posterior_theta_covariance, posterior_theta_icov = (
+            selfi.restrict_posterior(Nbin_min, Nbin_max)
+        )
+
+        logger.info("Optimised hyperparameters: theta_norm=%.5f, k_corr=%.5f", theta_norm, k_corr)
+
+        if plot:
+            import matplotlib.pyplot as plt
+
+            fig, ax = plt.subplots(figsize=(6, 5))
+            ax.xaxis.set_ticks_position("both")
+            ax.yaxis.set_ticks_position("both")
+            ax.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax.xaxis.set_tick_params(which="major", length=6, labelsize=17)
+            ax.xaxis.set_tick_params(which="minor", length=4)
+            ax.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax.yaxis.set_tick_params(which="major", length=6, labelsize=17)
+
+            pcm = ax.pcolormesh(X0, Y0, Z, cmap="Greys", shading="gouraud")
+            ax.grid(linestyle=":")
+            ax.contour(
+                Z,
+                Z_contours,
+                extent=[theta_norm_min, theta_norm_max, k_corr_min, k_corr_max],
+                colors="C9",
+            )
+            ax.plot(
+                [x0[0], x0[0]],
+                [k_corr_min, k_corr_max],
+                color="C3",
+                linestyle=":",
+                label="Before optimisation",
+            )
+            ax.plot([theta_norm_min, theta_norm_max], [x0[1], x0[1]], color="C3", linestyle=":")
+            ax.plot(
+                [theta_norm, theta_norm],
+                [k_corr_min, k_corr_max],
+                linestyle="--",
+                color="C3",
+                label="After optimisation",
+            )
+            ax.plot([theta_norm_min, theta_norm_max], [k_corr, k_corr], linestyle="--", color="C3")
+
+            ax.set_xlabel(r"$\theta_\mathrm{norm}$", size=19)
+            ax.set_ylabel(r"$k_\mathrm{corr}$ [$h$/Mpc]", size=19)
+            ax.legend()
+
+            if savepath is None:
+                plt.show()
+            else:
+                fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True)
+                fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf")
+            plt.close(fig)
+
+        return theta_norm, k_corr
+
+    except OSError as e:
+        logger.error("File access or I/O error: %s", str(e))
+        raise
+    except Exception as e:
+        logger.critical("Unexpected error in perform_prior_optimisation_and_plot: %s", str(e))
+        raise RuntimeError("perform_prior_optimisation_and_plot failed.") from e
+    finally:
+        gc.collect()

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/sbmy_interface.html b/docs/_modules/selfisys/sbmy_interface.html new file mode 100644 index 0000000..82bf1ec --- /dev/null +++ b/docs/_modules/selfisys/sbmy_interface.html @@ -0,0 +1,1212 @@ + + + + + + + + selfisys.sbmy_interface — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.sbmy_interface

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Simbelmynë-related functions for the SelfiSys pipeline.
+"""
+
+import os
+import gc
+from typing import Optional, List, Tuple
+
+from selfisys.utils.logger import getCustomLogger, INDENT, UNINDENT
+
+logger = getCustomLogger(__name__)
+
+
+

+[docs]
+def get_power_spectrum_from_cosmo(
+    L,
+    size,
+    cosmo,
+    fname_power_spectrum,
+    force=False,
+):
+    """
+    Compute a power spectrum from cosmological parameters and save it to
+    disk.
+
+    Parameters
+    ----------
+    L : float
+        Size of the simulation box (in Mpc/h).
+    size : int
+        Number of grid points along each axis.
+    cosmo : dict
+        Cosmological parameters (and infrastructure parameters).
+    fname_power_spectrum : str
+        Name (including path) of the power spectrum file to read/write.
+    force : bool, optional
+        If True, forces recomputation even if the file exists. Default
+        is False.
+
+    Raises
+    ------
+    OSError
+        If file writing fails or the directory path is invalid.
+    RuntimeError
+        For unexpected issues during power spectrum computation.
+    """
+    if not os.path.exists(fname_power_spectrum) or force:
+        from pysbmy.power import PowerSpectrum
+
+        try:
+            logger.debug("Computing power spectrum for L=%.2f, size=%d", L, size)
+            P = PowerSpectrum(L, L, L, size, size, size, cosmo)
+            P.write(fname_power_spectrum)
+            logger.debug("Power spectrum written to %s", fname_power_spectrum)
+        except OSError as e:
+            logger.error("File write error at %s: %s", fname_power_spectrum, str(e))
+            raise
+        except Exception as e:
+            logger.critical("Unexpected error in power spectrum computation: %s", str(e))
+            raise RuntimeError("get_power_spectrum_from_cosmo failed.") from e
+        finally:
+            gc.collect()

+
+
+
+

+[docs]
+def compute_Phi(
+    G_ss_path,
+    P_ss_path,
+    g_obj,
+    norm,
+    AliasingCorr=True,
+    verbosity=1,
+):
+    """
+    Compute the summary statistics from a field object, based on a
+    provided summary-statistics Fourier grid and baseline spectrum.
+
+    Parameters
+    ----------
+    G_ss_path : str
+        Path to the FourierGrid file used for summary-statistics.
+    P_ss_path : str
+        Path to the baseline power spectrum file for normalisation.
+    g_obj : Field
+        Input field object from which to compute summary statistics.
+    norm : ndarray
+        Normalisation constants for the summary statistics.
+    AliasingCorr : bool, optional
+        Whether to apply aliasing correction. Default is True.
+    verbosity : int, optional
+        Verbosity level (0=quiet, 1=normal, 2=debug). Default 1.
+
+    Returns
+    -------
+    Phi : ndarray
+        Vector of summary statistics.
+
+    Raises
+    ------
+    OSError
+        If file reading fails at G_ss_path or P_ss_path.
+    RuntimeError
+        If unexpected issues occur during computation.
+    """
+    from pysbmy.correlations import get_autocorrelation
+    from pysbmy.power import FourierGrid, PowerSpectrum
+    from pysbmy import c_double
+    from io import BytesIO
+
+    try:
+        logger.debug("Reading FourierGrid from %s", G_ss_path)
+        G_ss = FourierGrid.read(G_ss_path)
+
+        if verbosity > 1:
+            Pk, _ = get_autocorrelation(g_obj, G_ss, AliasingCorr=AliasingCorr)
+        else:
+            from selfisys.utils.low_level import stdout_redirector
+
+            f = BytesIO()
+            with stdout_redirector(f):
+                Pk, _ = get_autocorrelation(g_obj, G_ss, AliasingCorr=AliasingCorr)
+            f.close()
+
+        logger.debug("Reading baseline PowerSpectrum from %s", P_ss_path)
+        P_ss = PowerSpectrum.read(P_ss_path)
+        Phi = Pk / (norm * P_ss.powerspectrum)
+
+        del G_ss, P_ss
+        gc.collect()
+        return Phi.astype(c_double)
+    except OSError as e:
+        logger.error("File not found or inaccessible: %s", str(e))
+        raise
+    except Exception as e:
+        logger.critical("Unexpected error in compute_Phi: %s", str(e))
+        raise RuntimeError("compute_Phi failed.") from e
+    finally:
+        gc.collect()

+
+
+
+

+[docs]
+def generate_white_noise_Field(
+    L,
+    size,
+    seedphase,
+    fname_whitenoise,
+    seedname_whitenoise,
+    force_phase=False,
+):
+    """
+    Generate a white noise realisation in physical space and write it to
+    disk.
+
+    Parameters
+    ----------
+    L : float
+        Size of the simulation box (in Mpc/h).
+    size : int
+        Number of grid points along each axis.
+    seedphase : int or list of int
+        User-provided seed to generate the initial white noise.
+    fname_whitenoise : str
+        File path to write the white noise realisation.
+    seedname_whitenoise : str
+        File path to write the seed state of the RNG.
+    force_phase : bool, optional
+        If True, forces regeneration of the random phases. Default is
+        False.
+
+    Raises
+    ------
+    OSError
+        If file writing fails or directory paths are invalid.
+    RuntimeError
+        For unexpected issues.
+    """
+    if not os.path.exists(fname_whitenoise) or force_phase:
+        import numpy as np
+        from pysbmy.field import BaseField
+
+        try:
+            logger.debug("Generating white noise for L=%.2f, size=%d", L, size)
+            rng = np.random.default_rng(seedphase)
+
+            logger.debug("Saving RNG state to %s", seedname_whitenoise)
+            np.save(seedname_whitenoise, rng.bit_generator.state)
+            with open(seedname_whitenoise + ".txt", "w") as f:
+                f.write(str(rng.bit_generator.state))
+
+            data = rng.standard_normal(size=size**3)
+            wn = BaseField(L, L, L, 0, 0, 0, 1, size, size, size, data)
+            del data
+
+            wn.write(fname_whitenoise)
+            logger.debug("White noise field written to %s", fname_whitenoise)
+            del wn
+        except OSError as e:
+            logger.error("Writing white noise failed at '%s': %s", fname_whitenoise, str(e))
+            raise
+        except Exception as e:
+            logger.critical("Unexpected error in generate_white_noise_Field: %s", str(e))
+            raise RuntimeError("generate_white_noise_Field failed.") from e
+        finally:
+            gc.collect()

+
+
+
+

+[docs]
+def setup_sbmy_parfiles(
+    d,
+    cosmology,
+    file_names,
+    hiddenbox_params,
+    force=False,
+):
+    """Set up Simbelmynë parameter file (please refer to the Simbelmynë
+    documentation for more details).
+
+    Parameters
+    ----------
+    d : int
+        Index (from 1 to S) specifying a direction in parameter space, 0
+        for the expansion point, or -1 for mock data.
+    cosmology : array, double, dimension=5
+        Cosmological parameters.
+    file_names : dict
+        Dictionary containing the names of the input/output files for
+        the simulation.
+    hiddenbox_params : dict
+        See the `HiddenBox` class for more details.
+    force : bool, optional, default=False
+        If True, forces recompute the simulation parameter files.
+
+    """
+    from os.path import exists
+
+    fname_simparfile = file_names["fname_simparfile"]
+    fname_power_spectrum = file_names["fname_power_spectrum"]
+    fname_whitenoise = file_names["fname_whitenoise"]
+    fname_outputinitialdensity = file_names["fname_outputinitialdensity"]
+    fnames_outputrealspacedensity = file_names["fnames_outputrealspacedensity"]
+    fnames_outputdensity = file_names["fnames_outputdensity"]
+    fnames_outputLPTdensity = file_names["fnames_outputLPTdensity"]
+
+    Npop = hiddenbox_params["Npop"]
+    Np0 = hiddenbox_params["Np0"]
+    Npm0 = hiddenbox_params["Npm0"]
+    size = hiddenbox_params["size"]
+    L = hiddenbox_params["L"]
+    Ntimesteps = hiddenbox_params["Ntimesteps"]
+    sim_params = hiddenbox_params["sim_params"]
+    eff_redshifts = hiddenbox_params["eff_redshifts"]
+    TimeSteps = hiddenbox_params["TimeSteps"]
+    TimeStepDistribution = hiddenbox_params["TimeStepDistribution"]
+    modified_selfi = hiddenbox_params["modified_selfi"]
+    fsimdir = hiddenbox_params["fsimdir"]
+
+    if not exists(fname_simparfile + "_{}.sbmy".format(Npop)) or force:
+        from pysbmy import param_file
+        from re import search
+        from selfisys.global_parameters import BASEID_OBS
+
+        if TimeSteps is not None and eff_redshifts is None:
+            raise ValueError("TimeSteps must be provided if eff_redshifts is None.")
+
+        regex = r"([a-zA-Z]+)(\d+)?([a-zA-Z]+)?(\d+)?([a-zA-Z]+)?"
+        m = search(regex, sim_params)
+        if m.group(1) == "std":
+            # Single LPT+COLA/PM Simbelmynë data card with linear time
+            # stepping
+            if m.group(2)[0] == "0":
+                RedshiftLPT = float("0." + m.group(2)[1:])
+            else:
+                RedshiftLPT = int(m.group(2))
+
+            RedshiftFCs = 0.0
+            WriteLPTSnapshot = 0
+            WriteLPTDensity = 0
+            match m.group(3):
+                case "RSD":
+                    ModulePMCOLA = 0
+                    EvolutionMode = 2
+                    NumberOfTimeSteps = 0
+                    RedshiftFCs = RedshiftLPT
+                    NonLinearRSD = 1
+                case "PM":
+                    ModulePMCOLA = 1
+                    EvolutionMode = 1
+                    NumberOfTimeSteps = m.group(4)
+                    NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1
+                case "COLA":
+                    ModulePMCOLA = 1
+                    EvolutionMode = 2
+                    NumberOfTimeSteps = m.group(4)
+                    NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1
+                case _:
+                    raise ValueError("sim_params = {} not valid".format(sim_params))
+            NumberOfTimeSteps = int(m.group(4)) if m.group(4) is not None else 0
+        elif m.group(1) == "custom":
+            # Single LPT+COLA/PM Simbelmynë card with user-provided time
+            # stepping object
+            RedshiftLPT = int(m.group(2))
+            match m.group(3):
+                case None:
+                    ModulePMCOLA = 0
+                    EvolutionMode = 2
+                case "PM":
+                    ModulePMCOLA = 1
+                    EvolutionMode = 1
+                    NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1
+                case "COLA":
+                    ModulePMCOLA = 1
+                    EvolutionMode = 2
+                    NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1
+                case _:
+                    raise ValueError("sim_params = {} not valid".format(sim_params))
+            if TimeStepDistribution is None:
+                raise ValueError("TimeStepDistribution must be provided for 'custom'.")
+        elif m.group(1) == "splitLPT":
+            # Use as many Simbelmynë data cards as there are populations
+            # of galaxies
+            if eff_redshifts is None:
+                raise ValueError("eff_redshifts must be provided for 'splitLPT'.")
+            elif len(eff_redshifts) != Ntimesteps:
+                raise ValueError("len(eff_redshifts) != Ntimesteps")
+        elif m.group(1) == "split":
+            # Use as many Simbelmynë data cards as there are populations
+            # of galaxies
+            if TimeStepDistribution is None:
+                raise ValueError("TimeStepDistribution must be for 'split'.")
+            if eff_redshifts is None:
+                raise ValueError("eff_redshifts must be provided for 'split'.")
+            elif len(eff_redshifts) != Ntimesteps:
+                raise ValueError("len(eff_redshifts) != Ntimesteps")
+
+            RedshiftLPT = int(m.group(2))
+            match m.group(3):
+                case "RSD":
+                    ModulePMCOLA = 1
+                    EvolutionMode = 2
+                    NonLinearRSD = 1
+                case _:
+                    raise ValueError("sim_params = {} not valid".format(sim_params))
+            NumberOfTimeSteps = int(m.group(4)) if m.group(4) is not None else 0
+        else:
+            raise ValueError("sim_params = {} not valid" + sim_params)
+
+        if sim_params[-3:] == BASEID_OBS:
+            from selfisys.global_parameters import (
+                h_obs as h,
+                Omega_b_obs as Omega_b,
+                Omega_m_obs as Omega_m,
+                nS_obs as nS,
+                sigma8_obs as sigma8,
+            )
+        else:
+            if modified_selfi:
+                # Treat the cosmological parameters as nuisance
+                # parameters within the hidden box forward model
+                h, Omega_b, Omega_m, nS, sigma8 = cosmology
+            else:
+                # Fix the fiducial cosmology within the hidden box
+                from selfisys.global_parameters import (
+                    h_planck as h,
+                    Omega_b_planck as Omega_b,
+                    Omega_m_planck as Omega_m,
+                    nS_planck as nS,
+                    sigma8_planck as sigma8,
+                )
+
+        if d < 0:  # -1 for mock data, -2 to recompute the observations
+            WriteInitialConditions = 1
+            WriteDensities = 1  # also write real space density fields
+        else:  # d=0 for expansion point or d>0 for the gradients
+            WriteInitialConditions = 0
+            WriteDensities = 1  # also write real space density fields
+
+        if m.group(1) == "std":
+            S = param_file(  ## Module LPT ##
+                ModuleLPT=1,
+                # Basic setup:
+                Particles=Np0,
+                Mesh=size,
+                BoxSize=L,
+                corner0=0.0,
+                corner1=0.0,
+                corner2=0.0,
+                # Initial conditions:
+                ICsMode=1,
+                WriteICsRngState=0,
+                WriteInitialConditions=WriteInitialConditions,
+                InputWhiteNoise=fname_whitenoise,
+                OutputInitialConditions=fname_outputinitialdensity,
+                # Power spectrum:
+                InputPowerSpectrum=fname_power_spectrum,
+                # Final conditions for LPT:
+                RedshiftLPT=RedshiftLPT,
+                WriteLPTSnapshot=WriteLPTSnapshot,
+                WriteLPTDensity=WriteLPTDensity,
+                OutputLPTDensity=fnames_outputLPTdensity,
+                ####################
+                ## Module PM/COLA ##
+                ####################
+                ModulePMCOLA=ModulePMCOLA,
+                EvolutionMode=EvolutionMode,  # 1 for PM, 2 for COLA
+                ParticleMesh=Npm0,
+                NumberOfTimeSteps=NumberOfTimeSteps,
+                # Final snapshot:
+                RedshiftFCs=RedshiftFCs,
+                WriteFinalSnapshot=0,
+                WriteFinalDensity=WriteDensities,
+                OutputFinalDensity=fnames_outputrealspacedensity[0],
+                #########
+                ## RSD ##
+                #########
+                ModuleRSD=1,
+                WriteIntermediaryRSD=0,
+                DoNonLinearMapping=NonLinearRSD,
+                WriteRSDensity=1,
+                OutputRSDensity=fnames_outputdensity[0],
+                #############################
+                ## Cosmological parameters ##
+                #############################
+                h=h,
+                Omega_q=1.0 - Omega_m,
+                Omega_b=Omega_b,
+                Omega_m=Omega_m,
+                Omega_k=0.0,
+                n_s=nS,
+                sigma8=sigma8,
+                w0_fld=-1.0,
+                wa_fld=0.0,
+            )
+            S.write(fname_simparfile + "_{}.sbmy".format(Npop))
+        elif m.group(1) == "custom":
+            RedshiftFCs = eff_redshifts
+            fname_outputdensity = (
+                fnames_outputdensity[0][: fnames_outputdensity[0].rfind("_")] + ".h5"
+            )
+            S = param_file(  ## Module LPT ##
+                ModuleLPT=1,
+                # Basic setup:
+                Particles=Np0,
+                Mesh=size,
+                BoxSize=L,
+                corner0=0.0,
+                corner1=0.0,
+                corner2=0.0,
+                # Initial conditions:
+                ICsMode=1,
+                WriteICsRngState=0,
+                WriteInitialConditions=WriteInitialConditions,
+                InputWhiteNoise=fname_whitenoise,
+                OutputInitialConditions=fname_outputinitialdensity,
+                # Power spectrum:
+                InputPowerSpectrum=fname_power_spectrum,
+                # Final conditions for LPT:
+                RedshiftLPT=RedshiftLPT,
+                WriteLPTSnapshot=0,
+                WriteLPTDensity=0,
+                ####################
+                ## Module PM/COLA ##
+                ####################
+                ModulePMCOLA=ModulePMCOLA,
+                EvolutionMode=EvolutionMode,  # 1 for PM, 2 for COLA
+                ParticleMesh=Npm0,
+                OutputKickBase=fsimdir + "/data/cola_kick_",
+                # Final snapshot:
+                RedshiftFCs=RedshiftFCs,
+                WriteFinalSnapshot=0,
+                WriteFinalDensity=0,
+                OutputFinalDensity=fnames_outputrealspacedensity[0],
+                # Intermediate snapshots:
+                WriteSnapshots=0,
+                WriteDensities=WriteDensities,
+                OutputDensitiesBase=fnames_outputrealspacedensity[0][
+                    : fnames_outputrealspacedensity[0].rfind("_")
+                ]
+                + "_",
+                OutputDensitiesExt=".h5",
+                ############################
+                ## Time step distribution ##
+                ############################
+                TimeStepDistribution=TimeStepDistribution,
+                ModifiedDiscretization=1,  # Modified KD discretisation
+                n_LPT=-2.5,  # Exponent for the Ansatz in KD operators
+                #########
+                ## RSD ##
+                #########
+                ModuleRSD=1,
+                WriteIntermediaryRSD=1,
+                DoNonLinearMapping=NonLinearRSD,
+                WriteRSDensity=1,
+                OutputRSDensity=fname_outputdensity,
+                #############################
+                ## Cosmological parameters ##
+                #############################
+                h=h,
+                Omega_q=1.0 - Omega_m,
+                Omega_b=Omega_b,
+                Omega_m=Omega_m,
+                Omega_k=0.0,
+                n_s=nS,
+                sigma8=sigma8,
+                w0_fld=-1.0,
+                wa_fld=0.0,
+            )
+            S.write(fname_simparfile + "_{}.sbmy".format(Npop))
+        elif m.group(1) == "split":
+            datadir = fsimdir + "/data/"
+            RedshiftFCs = eff_redshifts[0]
+
+            # Write the parameter file for the first simulation
+            S = param_file(
+                ################
+                ## Module LPT ##
+                ################
+                ModuleLPT=1,
+                # Basic setup:
+                Particles=Np0,
+                Mesh=size,
+                BoxSize=L,
+                corner0=0.0,
+                corner1=0.0,
+                corner2=0.0,
+                # Initial conditions:
+                ICsMode=1,
+                WriteICsRngState=0,
+                WriteInitialConditions=WriteInitialConditions,
+                InputWhiteNoise=fname_whitenoise,
+                OutputInitialConditions=fname_outputinitialdensity,
+                # Power spectrum:
+                InputPowerSpectrum=fname_power_spectrum,
+                # Final conditions for LPT:
+                RedshiftLPT=RedshiftLPT,
+                WriteLPTSnapshot=0,
+                WriteLPTDensity=0,
+                ####################
+                ## Module PM/COLA ##
+                ####################
+                ModulePMCOLA=ModulePMCOLA,
+                EvolutionMode=EvolutionMode,
+                ParticleMesh=Npm0,
+                OutputKickBase=datadir + "cola_kick_0_",
+                # Final snapshot:
+                RedshiftFCs=RedshiftFCs,
+                WriteFinalSnapshot=1,
+                OutputFinalSnapshot=datadir + "cola_snapshot_0.gadget3",
+                WriteFinalDensity=1,
+                OutputFinalDensity=fnames_outputrealspacedensity[0],
+                WriteLPTDisplacements=1,
+                OutputPsiLPT1=datadir + "lpt_psi1_0.h5",
+                OutputPsiLPT2=datadir + "lpt_psi2_0.h5",
+                ############################
+                ## Time step distribution ##
+                ############################
+                TimeStepDistribution=TimeStepDistribution[0],
+                ModifiedDiscretization=1,
+                #########
+                ## RSD ##
+                #########
+                ModuleRSD=1,
+                WriteIntermediaryRSD=0,
+                DoNonLinearMapping=NonLinearRSD,
+                WriteRSDensity=1,
+                OutputRSDensity=fnames_outputdensity[0],
+                #############################
+                ## Cosmological parameters ##
+                #############################
+                h=h,
+                Omega_q=1.0 - Omega_m,
+                Omega_b=Omega_b,
+                Omega_m=Omega_m,
+                Omega_k=0.0,
+                n_s=nS,
+                sigma8=sigma8,
+                w0_fld=-1.0,
+                wa_fld=0.0,
+            )
+            S.write(fname_simparfile + "_pop0.sbmy")
+
+            for i in range(1, Ntimesteps):
+                RedshiftFCs = eff_redshifts[i]
+
+                S = param_file(
+                    ModuleLPT=0,
+                    # Basic setup:
+                    Particles=Np0,
+                    Mesh=size,
+                    BoxSize=L,
+                    corner0=0.0,
+                    corner1=0.0,
+                    corner2=0.0,
+                    InputPsiLPT1=datadir + "lpt_psi1_0.h5",
+                    InputPsiLPT2=datadir + "lpt_psi2_0.h5",
+                    ####################
+                    ## Module PM/COLA ##
+                    ####################
+                    ModulePMCOLA=ModulePMCOLA,
+                    InputPMCOLASnapshot=datadir + "cola_snapshot_{:d}.gadget3".format(i - 1),
+                    EvolutionMode=EvolutionMode,
+                    ParticleMesh=Npm0,
+                    OutputKickBase=datadir + "cola_kick_{:d}_".format(i),
+                    # Final snapshot:
+                    RedshiftFCs=RedshiftFCs,
+                    WriteFinalSnapshot=1,
+                    OutputFinalSnapshot=datadir + "cola_snapshot_{:d}.gadget3".format(i),
+                    WriteFinalDensity=1,
+                    OutputFinalDensity=fnames_outputrealspacedensity[::-1][i],
+                    WriteLPTDisplacements=0,
+                    ############################
+                    ## Time step distribution ##
+                    ############################
+                    TimeStepDistribution=TimeStepDistribution[i],
+                    ModifiedDiscretization=1,
+                    #########
+                    ## RSD ##
+                    #########
+                    ModuleRSD=1,
+                    WriteIntermediaryRSD=0,
+                    DoNonLinearMapping=NonLinearRSD,
+                    WriteRSDensity=1,
+                    OutputRSDensity=fnames_outputdensity[i],
+                    #############################
+                    ## Cosmological parameters ##
+                    #############################
+                    h=h,
+                    Omega_q=1.0 - Omega_m,
+                    Omega_b=Omega_b,
+                    Omega_m=Omega_m,
+                    Omega_k=0.0,
+                    n_s=nS,
+                    sigma8=sigma8,
+                    w0_fld=-1.0,
+                    wa_fld=0.0,
+                )
+                S.write(fname_simparfile + "_pop{}.sbmy".format(i))
+        elif m.group(1) == "splitLPT":
+            datadir = fsimdir + "/data/"
+            RedshiftLPT = eff_redshifts[0]
+            RedshiftFCs = eff_redshifts[0]
+
+            # Write the parameter file for the first simulation
+            S = param_file(
+                ################
+                ## Module LPT ##
+                ################
+                ModuleLPT=1,
+                # Basic setup:
+                Particles=Np0,
+                Mesh=size,
+                BoxSize=L,
+                corner0=0.0,
+                corner1=0.0,
+                corner2=0.0,
+                # Initial conditions:
+                ICsMode=1,
+                WriteICsRngState=0,
+                InputWhiteNoise=fname_whitenoise,
+                OutputInitialConditions=fname_outputinitialdensity,
+                InputPowerSpectrum=fname_power_spectrum,
+                # Final conditions for LPT:
+                RedshiftLPT=RedshiftLPT,
+                WriteLPTSnapshot=0,
+                WriteLPTDensity=0,
+                # Final snapshot:
+                RedshiftFCs=RedshiftFCs,
+                WriteFinalDensity=0,
+                WriteLPTDisplacements=0,
+                #########
+                ## RSD ##
+                #########
+                ModuleRSD=1,
+                WriteIntermediaryRSD=0,
+                DoNonLinearMapping=NonLinearRSD,
+                WriteRSDensity=1,
+                OutputRSDensity=fnames_outputdensity[0],
+                #############################
+                ## Cosmological parameters ##
+                #############################
+                h=h,
+                Omega_q=1.0 - Omega_m,
+                Omega_b=Omega_b,
+                Omega_m=Omega_m,
+                Omega_k=0.0,
+                n_s=nS,
+                sigma8=sigma8,
+                w0_fld=-1.0,
+                wa_fld=0.0,
+            )
+            S.write(fname_simparfile + "_pop0.sbmy")
+
+            for i in range(1, Ntimesteps):
+                RedshiftLPT = eff_redshifts[i]
+                RedshiftFCs = eff_redshifts[i]
+
+                S = param_file(
+                    ################
+                    ## Module LPT ##
+                    ################
+                    ModuleLPT=1,
+                    # Basic setup:
+                    Particles=Np0,
+                    Mesh=size,
+                    BoxSize=L,
+                    corner0=0.0,
+                    corner1=0.0,
+                    corner2=0.0,
+                    # Initial conditions:
+                    ICsMode=1,
+                    WriteICsRngState=0,
+                    InputWhiteNoise=fname_whitenoise,
+                    OutputInitialConditions=fname_outputinitialdensity,
+                    InputPowerSpectrum=fname_power_spectrum,
+                    # Final conditions for LPT:
+                    RedshiftLPT=RedshiftLPT,
+                    WriteLPTDensity=0,
+                    WriteLPTDisplacements=0,
+                    #########
+                    ## RSD ##
+                    #########
+                    ModuleRSD=1,
+                    WriteIntermediaryRSD=0,
+                    DoNonLinearMapping=NonLinearRSD,
+                    WriteRSDensity=1,
+                    OutputRSDensity=fnames_outputdensity[i],
+                    #############################
+                    ## Cosmological parameters ##
+                    #############################
+                    h=h,
+                    Omega_q=1.0 - Omega_m,
+                    Omega_b=Omega_b,
+                    Omega_m=Omega_m,
+                    Omega_k=0.0,
+                    n_s=nS,
+                    sigma8=sigma8,
+                    w0_fld=-1.0,
+                    wa_fld=0.0,
+                )
+                S.write(fname_simparfile + "_pop{}.sbmy".format(i))

+
+
+
+

+[docs]
+def handle_time_stepping(
+    aa: List[float],
+    total_steps: int,
+    modeldir: str,
+    figuresdir: str,
+    sim_params: str,
+    force: bool = False,
+) -> Tuple[Optional[List[int]], Optional[float]]:
+    """
+    Create and merge individual time-stepping objects.
+
+    Parameters
+    ----------
+    aa : list of float
+        List of scale factors in ascending order.
+    total_steps : int
+        Total number of time steps to distribute among the provided
+        scale factors.
+    modeldir : str
+        Directory path to store generated time-stepping files.
+    figuresdir : str
+        Directory path to store time-stepping plots.
+    sim_params : str
+        Simulation parameter string (e.g., "custom", "std", "nograv").
+    force : bool, optional
+        Whether to force recompute the time-stepping files. Default is
+        False.
+
+    Returns
+    -------
+    merged_path : str
+        Path to the merged time-stepping file.
+    indices_steps_cumul : list of int or None
+        Cumulative indices for the distributed steps. Returns None if
+        using splitLPT or if `sim_params` indicates an alternative
+        strategy.
+    eff_redshifts : float or None
+        Effective redshift derived from the final scale factor in
+        'custom' or 'nograv' mode. None otherwise.
+
+    Raises
+    ------
+    NotImplementedError
+        If a unsupported time-stepping strategy is used.
+    OSError
+        If file or directory operations fail.
+    RuntimeError
+        If unexpected issues occur during time-stepping setup.
+    """
+    import numpy as np
+
+    from pysbmy.timestepping import StandardTimeStepping, read_timestepping
+    from selfisys.utils.plot_utils import reset_plotting, setup_plotting
+    from selfisys.utils.timestepping import merge_nTS
+
+    logger.info("Evaluating time-stepping strategy: %s", sim_params)
+
+    indices_steps_cumul = None
+    eff_redshifts = None
+
+    isstd = sim_params.startswith("std")
+    splitLPT = sim_params.startswith("splitLPT")
+
+    try:
+        # Case 1: standard approach with distributed steps
+        if not isstd and not splitLPT:
+            reset_plotting()  # Revert to default plotting style
+            merged_path = modeldir + "merged.h5"
+
+            # Create time-stepping
+            if not os.path.exists(merged_path) or force:
+                logger.info("Setting up time-stepping...")
+
+                # Distribute steps among the scale factors
+                nsteps = [
+                    round((aa[i + 1] - aa[i]) / (aa[-1] - aa[0]) * total_steps)
+                    for i in range(len(aa) - 1)
+                ]
+                # Adjust the largest gap if rounding caused a mismatch
+                if sum(nsteps) != total_steps:
+                    nsteps[nsteps.index(max(nsteps))] += total_steps - sum(nsteps)
+
+                indices_steps_cumul = list(np.cumsum(nsteps) - 1)
+                np.save(modeldir + "indices_steps_cumul.npy", indices_steps_cumul)
+
+                INDENT()
+                logger.diagnostic("Generating individual time-stepping objects...")
+
+                TS_paths = []
+                for i, (ai, af) in enumerate(zip(aa[:-1], aa[1:])):
+                    snapshots = np.full((nsteps[i]), False)
+                    snapshots[-1] = True  # Mark last step as a snapshot
+                    TS = StandardTimeStepping(ai, af, snapshots, 0)
+                    TS_path = modeldir + f"ts{i+1}.h5"
+                    TS.write(str(TS_path))
+                    TS_paths.append(TS_path)
+
+                # Ensure the timestepping object are readable and plot
+                for i, path_ts in enumerate(TS_paths):
+                    read_timestepping(str(path_ts)).plot(path=str(figuresdir + f"TS{i}.png"))
+                logger.diagnostic("Generating individual time-stepping objects done.")
+
+                logger.diagnostic("Merging time-stepping...")
+                merge_nTS([str(p) for p in TS_paths], merged_path)
+                TS_merged = read_timestepping(merged_path)
+                TS_merged.plot(path=str(figuresdir + "TS_merged.png"))
+
+                # Restore the project's plotting style
+                setup_plotting()
+                logger.diagnostic("Merging time-stepping done.")
+                UNINDENT()
+                logger.info("Setting up time-stepping done.")
+            else:
+                logger.diagnostic("Time-stepping objects already computed.")
+
+            # Evaluate final effective redshift
+            if sim_params.startswith("custom") or sim_params.startswith("nograv"):
+                eff_redshifts = 1 / aa[-1] - 1
+            else:
+                raise NotImplementedError("Time-stepping strategy not yet implemented.")
+
+        # Case 2: splitted
+        elif splitLPT:
+            indices_steps_cumul = [f"pop{i}" for i in range(1, len(aa))]
+            eff_redshifts = [1 / a - 1 for a in aa[1:]]
+
+        # Case 3: other
+        else:
+            logger.diagnostic("Standard time-stepping or no special distribution required.")
+
+    except OSError as e:
+        logger.error("File or directory access error in handle_time_stepping: %s", str(e))
+        raise
+    except Exception as e:
+        logger.critical("An error occurred during time-stepping setup: %s", str(e))
+        raise RuntimeError("Time-stepping setup failed.") from e
+    finally:
+        gc.collect()
+
+    return merged_path, indices_steps_cumul, eff_redshifts

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/selection_functions.html b/docs/_modules/selfisys/selection_functions.html new file mode 100644 index 0000000..48f0cc3 --- /dev/null +++ b/docs/_modules/selfisys/selection_functions.html @@ -0,0 +1,638 @@ + + + + + + + + selfisys.selection_functions — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.selection_functions

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Selection functions to simulate galaxy populations.
+"""
+
+import os
+from gc import collect
+import numpy as np
+import h5py
+
+
+

+[docs]
+class LognormalSelection:
+    """Class to generate radial selection functions."""
+
+    def __init__(
+        self,
+        L=None,
+        selection_params=None,
+        survey_mask_path=None,
+        local_select_path=None,
+        size=None,
+    ):
+        """
+        Initialise the LognormalSelection object.
+
+        Parameters
+        ----------
+        L : float
+            Size of the simulation box (in Mpc/h). If not provided, it
+            must be set before calling init_selection and using
+            grid-dependent methods.
+        selection_params : tuple of arrays
+            Parameters for the selection functions (ss, mm, rr).
+            Required for calling init_selection.
+        survey_mask_path : str or None
+            Path to the survey mask file. Required for calling
+            init_selection.
+        local_select_path : str
+            Path where the selection function will be saved. Required
+            for calling init_selection.
+        size : int, optional
+            Number of grid points along each axis. If not provided, it
+            must be set before using grid-dependent methods.
+        """
+        self.L = L
+        self.selection_params = selection_params
+        self.survey_mask_path = survey_mask_path
+        self.local_select_path = local_select_path
+        self.size = size
+
+

+[docs]
+    def r_grid(self):
+        """Compute the grid of radial distances in the simulation box.
+
+        Returns
+        -------
+        ndarray
+            3D array of radial distances from the origin.
+
+        Raises
+        ------
+        AttributeError
+            If the 'size' attribute is not defined.
+        """
+        if self.size is None:
+            raise AttributeError(
+                "The attribute 'size' must be defined to compute the radial grid."
+            )
+        if self.L is None:
+            raise AttributeError("The attribute 'L' must be defined to compute the radial grid.")
+
+        range1d = np.linspace(0, self.L, self.size, endpoint=False)
+        xx, yy, zz = np.meshgrid(range1d, range1d, range1d)
+        x0 = y0 = z0 = 0.0
+        r = np.sqrt((xx - x0) ** 2 + (yy - y0) ** 2 + (zz - z0) ** 2) + 1e-10
+        return r

+
+
+

+[docs]
+    @staticmethod
+    def one_lognormal(x, std, mean, rescale=None):
+        """Rescaled log-normal distribution.
+
+        Parameters
+        ----------
+        x : ndarray
+            Input array.
+        std : float
+            Standard deviation of the distribution.
+        mean : float
+            Mean of the distribution.
+        rescale : float, optional
+            Rescaling factor. If None, the distribution is normalised
+            such that its maximum value is 1.
+
+        Returns
+        -------
+        ndarray
+            Log-normal distribution evaluated at x.
+        """
+        mu = np.log(mean**2 / np.sqrt(std**2 + mean**2))
+        sig2 = np.log(1 + std**2 / mean**2)
+        lognorm = (1 / (np.sqrt(2 * np.pi) * np.sqrt(sig2) * x)) * np.exp(
+            -((np.log(x) - mu) ** 2 / (2 * sig2))
+        )
+        if rescale is None:
+            return lognorm / np.max(lognorm)
+        else:
+            return lognorm * rescale

+
+
+

+[docs]
+    def multiple_lognormal(self, x, mask, ss, ll, rr):
+        """Compute multiple log-normal distributions.
+
+        Parameters
+        ----------
+        x : ndarray
+            Input array.
+        mask : ndarray or None
+            Survey mask C(n).
+        ss : array_like
+            Standard deviations for each distribution.
+        ll : array_like
+            Means for each distribution.
+        rr : array_like
+            Rescaling factors for each distribution.
+
+        Returns
+        -------
+        list of ndarray
+            List of log-normal distributions.
+        """
+        if mask is None:
+            mask = np.ones_like(x)
+        return [self.one_lognormal(x, s, l, r) * mask for s, l, r in zip(ss, ll, rr)]

+
+
+

+[docs]
+    @staticmethod
+    def one_lognormal_z(x, sig2, mu, rescale=None):
+        """Compute a log-normal distribution in redshift.
+
+        Parameters
+        ----------
+        x : ndarray
+            Input array.
+        sig2 : float
+            Variance of the distribution.
+        mu : float
+            Mean of the distribution.
+        rescale : float, optional
+            Rescaling factor.
+
+        Returns
+        -------
+        ndarray
+            Log-normal distribution evaluated at x.
+        """
+        lognorm = (1 / (np.sqrt(2 * np.pi) * np.sqrt(sig2) * x)) * np.exp(
+            -((np.log(x) - mu) ** 2 / (2 * sig2))
+        )
+        return lognorm * rescale if rescale is not None else lognorm

+
+
+

+[docs]
+    def multiple_lognormal_z(self, x, mask, ss, mm, rr):
+        """
+        Compute multiple rescaled lognormal distributions as functions
+        of redshift.
+
+        Parameters
+        ----------
+        x : ndarray
+            Input array (redshifts).
+        mask : ndarray or None
+            Survey mask C(n).
+        ss : array_like
+            Standard deviations of the lognormal distributions.
+        mm : array_like
+            Means of the lognormal distributions.
+        rr : array_like
+            Rescaling factors for each distribution.
+
+        Returns
+        -------
+        list of ndarray
+            List of log-normal distributions.
+        """
+        if mask is None:
+            mask = np.ones_like(x)
+        res = []
+        maxima = []
+        for s, m, r in zip(ss, mm, rr):
+            mu = np.log(m**2 / np.sqrt(s**2 + m**2))
+            sig2 = np.log(1 + s**2 / m**2)
+            maxima.append(np.exp(sig2 / 2 - mu) / (np.sqrt(2 * np.pi * sig2)))
+            res.append(self.one_lognormal_z(x, sig2, mu, rescale=r) * mask)
+        max = np.max(maxima)
+        res = [r / max for r in res]
+        return res

+
+
+

+[docs]
+    def lognormals_z_to_x(self, xx, mask, params, spline):
+        """Convert log-normal distributions from redshift to distance.
+
+        Parameters
+        ----------
+        xx : array-like
+            Comoving distances at which to evaluate the distributions.
+        mask : ndarray or None
+            Survey mask C(n).
+        params : tuple of arrays
+            Parameters for the distributions (ss, mm, rr).
+        spline : UnivariateSpline
+            Linear interpolator for the distance-redshift relation.
+
+        Returns
+        -------
+        tuple
+            Tuple containing redshifts and list of distributions.
+        """
+        ss, mm, rr = params
+        zs = np.maximum(1e-4, spline(xx))
+        res = self.multiple_lognormal_z(zs, mask, ss, mm, rr)
+        return zs, res

+
+
+

+[docs]
+    def init_selection(self, reset=False):
+        """Initialise the radial selection functions.
+
+        Parameters
+        ----------
+        reset : bool, optional
+            Whether to reset the selection function.
+
+        Raises
+        ------
+
+        """
+        if any([self.survey_mask_path is None, self.local_select_path is None]):
+            raise AttributeError(
+                "Some attributes are missing to initialise the selection function."
+            )
+
+        if not os.path.exists(self.local_select_path) or reset:
+            from scipy.interpolate import UnivariateSpline
+            from classy import Class
+            from astropy.cosmology import FlatLambdaCDM
+            from selfisys.utils.tools import cosmo_vector_to_class_dict
+            from selfisys.global_parameters import omegas_gt
+            from selfisys.utils.plot_utils import plot_selection_functions
+
+            # Redshift-distance relation
+            redshifts_upper_bound = 3.0
+            zz = np.linspace(0, redshifts_upper_bound, 10_000)
+            cosmo = FlatLambdaCDM(H0=100 * omegas_gt[0], Ob0=omegas_gt[1], Om0=omegas_gt[2])
+            d = cosmo.comoving_distance(zz).value / 1e3  # -> Gpc/h
+            spline = UnivariateSpline(d, zz, k=1, s=0)
+
+            # Plot the selection functions
+            L = self.L / 1e3
+            Lcorner = np.sqrt(3) * L
+            zcorner = zz[np.argmin(np.abs(d - Lcorner))]
+
+            # Get linear growth factor from CLASS
+            cosmo_dict = cosmo_vector_to_class_dict(omegas_gt)
+            cosmo_class = Class()
+            cosmo_class.set(cosmo_dict)
+            cosmo_class.compute()
+            Dz = cosmo_class.get_background()["gr.fac. D"]
+            redshifts = cosmo_class.get_background()["z"]
+            cosmo_class.struct_cleanup()
+            cosmo_class.empty()
+
+            # Define the axis for the plot
+            xx = np.linspace(1e-5, Lcorner, 1000)
+            zz, res = self.lognormals_z_to_x(
+                xx,
+                None,
+                self.selection_params,
+                spline,
+            )
+
+            # Call auxiliary plotting routine
+            plot_selection_functions(
+                xx,
+                res,
+                None,
+                self.selection_params,
+                L,
+                np.sqrt(3) * L,
+                zz=zz,
+                zcorner=zcorner,
+                path=self.local_select_path[:-3] + ".png",
+            )
+
+            # Compute the selection function and save it to disk
+            survey_mask = np.load(self.survey_mask_path) if self.survey_mask_path else None
+            r = self.r_grid() / 1e3  # Convert to Gpc/h
+            _, select_fct = self.lognormals_z_to_x(r, survey_mask, self.selection_params, spline)
+            with h5py.File(self.local_select_path, "w") as f:
+                f.create_dataset("select_fct", data=select_fct)
+
+            del survey_mask, r, d, zz, spline, select_fct
+            collect()

+

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/selfi_interface.html b/docs/_modules/selfisys/selfi_interface.html new file mode 100644 index 0000000..80b1609 --- /dev/null +++ b/docs/_modules/selfisys/selfi_interface.html @@ -0,0 +1,367 @@ + + + + + + + + selfisys.selfi_interface — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.selfi_interface

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Provides simple wrappers around pyselfi.utils functions for the SelfiSys
+pipeline.
+"""
+
+
+

+[docs]
+def PrintMessage(required_verbosity: int, message: str, verbosity: int) -> None:
+    """
+    Print a message to standard output using pyselfi.utils.PrintMessage.
+
+    Parameters
+    ----------
+    required_verbosity : int
+        The verbosity level required to display the message.
+    message : str
+        The actual message to display.
+    verbosity : int
+        The current verbosity level (0=quiet, 1=normal, 2=debug).
+    """
+    from pyselfi.utils import PrintMessage as PSMessage
+
+    if verbosity >= required_verbosity:
+        PSMessage(3, message)

+
+
+
+

+[docs]
+def indent() -> None:
+    """Indent the standard output using pyselfi.utils."""
+    from pyselfi.utils import INDENT
+
+    INDENT()

+
+
+
+

+[docs]
+def unindent() -> None:
+    """Unindent the standard output using pyselfi.utils."""
+    from pyselfi.utils import UNINDENT
+
+    UNINDENT()

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/setup_model.html b/docs/_modules/selfisys/setup_model.html new file mode 100644 index 0000000..2946ad5 --- /dev/null +++ b/docs/_modules/selfisys/setup_model.html @@ -0,0 +1,645 @@ + + + + + + + + selfisys.setup_model — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.setup_model

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Set up parameters related to the grid and the fiducial power spectrum.
+"""
+
+import os.path
+import gc
+from typing import Optional, NamedTuple
+import numpy as np
+from h5py import File
+
+from pysbmy.power import PowerSpectrum, FourierGrid, get_Pk
+from selfisys.utils.logger import getCustomLogger
+from selfisys.utils.tools import get_k_max
+
+logger = getCustomLogger(__name__)
+
+
+

+[docs]
+class ModelSetup(NamedTuple):
+    size: int
+    L: float
+    P: int
+    S: int
+    G_sim_path: str
+    G_ss_path: str
+    Pbins_bnd: np.ndarray
+    Pbins: np.ndarray
+    k_s: np.ndarray
+    P_ss_obj_path: str
+    P_0: np.ndarray
+    planck_Pk: np.ndarray

+
+
+
+

+[docs]
+def setup_model(
+    workdir: str,
+    params_planck: dict,
+    params_P0: dict,
+    size: int = 256,
+    L: float = 3600.0,
+    S: int = 100,
+    N_exact: int = 8,
+    Pinit: int = 50,
+    trim_threshold: int = 100,
+    minval: Optional[float] = None,
+    maxval: Optional[float] = None,
+    force: bool = False,
+) -> ModelSetup:
+    """
+    Set up the model by computing or loading necessary grids and
+    parameters.
+
+    Parameters
+    ----------
+    workdir : str
+        Directory where the results will be stored.
+    params_planck : dict
+        Parameters for the Planck 2018 cosmology.
+    params_P0 : dict
+        Parameters for the normalisation power spectrum.
+    size : int
+        Number of elements in each direction of the box.
+    L : float
+        Comoving length of the box in Mpc/h.
+    S : int
+        Number of support wavenumbers for the input power spectra.
+    N_exact : int
+        Number of support wavenumbers matching the Fourier grid.
+    Pinit : int
+        Maximum number of bins for the summaries.
+    trim_threshold : int
+        Minimum number of modes required per bin.
+    minval : float, optional
+        Minimum k value for the summaries.
+    maxval : float, optional
+        Maximum k value for the summaries.
+    force : bool
+        If True, forces recomputation of the inputs.
+
+    Returns
+    -------
+    ModelSetup
+        A named tuple containing:
+        - size (int): Number of elements in each direction of the box.
+        - L (float): Comoving length of the box in Mpc/h.
+        - P (int): Number of bins for the summaries.
+        - S (int): Number of support wavenumbers for input powerspectra.
+        - G_sim_path (str): Path to the full Fourier grid file.
+        - G_ss_path (str): Path to the Fourier grid for summaries file.
+        - Pbins_bnd (np.ndarray): Boundaries of summary bins.
+        - Pbins (np.ndarray): Centres of the bins for the summaries.
+        - k_s (np.ndarray): Support wavenumbers for input power spectra.
+        - P_ss_obj_path (str): Path to the summary power spectrum file.
+        - P_0 (np.ndarray): Normalisation power spectrum values.
+        - planck_Pk (np.ndarray): Planck 2018 power spectrum values.
+    """
+    # Check input parameters
+    if N_exact < 0 or N_exact > S:
+        raise ValueError("Parameter 'N_exact' must be between 0 and 'S'.")
+
+    # Define file paths
+    G_sim_path = os.path.join(workdir, "G_sim.h5")
+    k_s_path = os.path.join(workdir, "k_s.npy")
+    G_ss_path = os.path.join(workdir, "G_ss.h5")
+    P_ss_obj_path = os.path.join(workdir, "P_ss_obj.h5")
+    P_0_path = os.path.join(workdir, "P_0.npy")
+    theta_planck_path = os.path.join(workdir, "theta_planck.npy")
+    Pbins_path = os.path.join(workdir, "Pbins.npy")
+    Pbins_bnd_path = os.path.join(workdir, "Pbins_bnd.npy")
+
+    # Compute or load the full Fourier grid
+    if not os.path.exists(G_sim_path) or force:
+        logger.info("Computing Fourier grid...")
+        G_sim = FourierGrid(L, L, L, size, size, size)
+        G_sim.write(G_sim_path)
+        logger.info("Computing Fourier grid done.")
+    else:
+        logger.info("Loading Fourier grid.")
+        G_sim = FourierGrid.read(G_sim_path)
+
+    # Determine minimum and maximum k values
+    if minval is None:
+        minval = np.min(G_sim.k_modes[G_sim.k_modes != 0])
+    if maxval is None:
+        maxval = np.pi * size / L  # 1D Nyquist frequency
+
+    # Compute or load support wavenumbers for the input power spectrum
+    if not os.path.exists(k_s_path) or force:
+        logger.diagnostic("Computing input power spectrum support wavenumbers...")
+        k_s = np.zeros(S)
+        sorted_knorms = np.sort(G_sim.k_modes.flatten())
+        unique_indices = np.unique(np.round(sorted_knorms, 5), return_index=True)[1]
+        sorted_knorms_corrected = sorted_knorms[unique_indices]
+        k_s[:N_exact] = sorted_knorms_corrected[1 : N_exact + 1]
+        k_s_max = get_k_max(L, size)
+        k_s[N_exact:] = np.logspace(
+            np.log10(sorted_knorms_corrected[N_exact]),
+            np.log10(k_s_max),
+            S - N_exact + 1,
+        )[1:]
+        np.save(k_s_path, k_s)
+        logger.diagnostic("Computing input power spectrum support wavenumbers done.")
+    else:
+        logger.diagnostic("Loading input power spectrum support wavenumbers.")
+        try:
+            k_s = np.load(k_s_path)
+        except (IOError, FileNotFoundError) as e:
+            logger.error(f"Failed to load k_s from {k_s_path}: {e}")
+            raise
+
+    # Initialise Pbins
+    Pbins_left_bnds_init = np.logspace(
+        np.log10(minval), np.log10(maxval), Pinit + 1, dtype=np.float32
+    )
+    Pbins_left_bnds_init = Pbins_left_bnds_init[:-1]
+
+    # Compute or load Fourier grid for the summaries
+    if not os.path.exists(G_ss_path) or force:
+        G_ss = FourierGrid(
+            L,
+            L,
+            L,
+            size,
+            size,
+            size,
+            k_modes=Pbins_left_bnds_init,
+            kmax=maxval,
+            trim_bins=True,
+            trim_threshold=trim_threshold,
+        )
+        G_ss.write(G_ss_path)
+    else:
+        G_ss = FourierGrid.read(G_ss_path)
+    P = G_ss.NUM_MODES
+
+    # Compute or load Pbins and Pbins_bnd
+    if not os.path.exists(Pbins_path) or not os.path.exists(Pbins_bnd_path) or force:
+        k_ss_max_offset = Pbins_left_bnds_init[-1] - Pbins_left_bnds_init[-2]
+        logger.diagnostic(f"k_ss_max_offset: {k_ss_max_offset:.5f}")
+        Pbins_bnd = G_ss.k_modes
+        Pbins_bnd = np.concatenate([Pbins_bnd, [Pbins_bnd[-1] + k_ss_max_offset]])
+        Pbins = (Pbins_bnd[1:] + Pbins_bnd[:-1]) / 2
+        np.save(Pbins_path, Pbins)
+        np.save(Pbins_bnd_path, Pbins_bnd)
+    else:
+        try:
+            Pbins = np.load(Pbins_path)
+            Pbins_bnd = np.load(Pbins_bnd_path)
+        except (IOError, FileNotFoundError) as e:
+            logger.error(f"Failed to load Pbins or Pbins_bnd: {e}")
+            raise
+
+    # Compute or load BBKS spectrum for normalisation
+    if not os.path.exists(P_0_path) or force:
+        P_0 = get_Pk(k_s, params_P0)
+        np.save(P_0_path, P_0)
+    else:
+        try:
+            P_0 = np.load(P_0_path)
+        except (IOError, FileNotFoundError) as e:
+            logger.error(f"Failed to load P_0 from {P_0_path}: {e}")
+            raise
+
+    if not os.path.exists(P_ss_obj_path) or force:
+        P_0_ss = get_Pk(G_ss.k_modes, params_P0)
+        P_ss_obj = PowerSpectrum.from_FourierGrid(G_ss, powerspectrum=P_0_ss, cosmo=params_P0)
+        P_ss_obj.write(P_ss_obj_path)
+    else:
+        P_ss_obj = PowerSpectrum.read(P_ss_obj_path)
+
+    # Compute or load Planck power spectrum
+    if not os.path.exists(theta_planck_path) or force:
+        planck_Pk = get_Pk(k_s, params_planck)
+        np.save(theta_planck_path, planck_Pk)
+    else:
+        try:
+            planck_Pk = np.load(theta_planck_path)
+        except (IOError, FileNotFoundError) as e:
+            logger.error(f"Failed to load theta_planck from {theta_planck_path}: {e}")
+            raise
+
+    # Clean up
+    del G_sim, G_ss, P_ss_obj, Pbins_left_bnds_init
+    gc.collect()
+
+    return ModelSetup(
+        size,
+        L,
+        P,
+        S,
+        G_sim_path,
+        G_ss_path,
+        Pbins_bnd,
+        Pbins,
+        k_s,
+        P_ss_obj_path,
+        P_0,
+        planck_Pk,
+    )

+
+
+
+

+[docs]
+def compute_alpha_cv(
+    workdir: str,
+    k_s: np.ndarray,
+    size: int,
+    L: float,
+    window_fct_path: Optional[str] = None,
+    force: bool = False,
+) -> None:
+    """
+    Compute the cosmic variance parameter alpha_cv.
+
+    Parameters
+    ----------
+    workdir : str
+        Directory where the results will be stored.
+    k_s : np.ndarray
+        Support wavenumbers.
+    size : int
+        Number of elements in each direction of the box.
+    L : float
+        Comoving length of the box in Mpc/h.
+    window_fct_path : str, optional
+        Path to the window function file.
+    force : bool
+        If True, forces recomputation of the inputs.
+
+    """
+    from scipy.optimize import curve_fit
+
+    alpha_cv_path = os.path.join(workdir, "alpha_cv.npy")
+    alpha_cv_eff_path = os.path.join(workdir, "alpha_cv_eff.npy")
+
+    if not os.path.exists(alpha_cv_path) or force:
+        logger.info("Computing cosmic variance alpha_cv...")
+        k_s_bnd = np.concatenate([k_s, [np.inf]])
+
+        G_sim = FourierGrid.read(os.path.join(workdir, "G_sim.h5")).k_modes.flatten()
+        knorms = np.sort(G_sim)
+
+        Nks, _ = np.histogram(knorms, bins=k_s_bnd)
+
+        del knorms, G_sim
+
+        nyquist_frequency = np.pi * size / L
+        idx_nyquist = np.searchsorted(k_s, nyquist_frequency)
+
+        def cubic_func(x, a):
+            return a * x**3
+
+        try:
+            popt, _ = curve_fit(cubic_func, k_s[:idx_nyquist], Nks[:idx_nyquist])
+        except RuntimeError as e:
+            logger.error(f"Curve fitting failed: {e}")
+            raise
+
+        alpha_cv = np.sqrt(1 / popt[0])
+        np.save(alpha_cv_path, alpha_cv)
+        logger.info(f"Computing cosmic variance alpha_cv done. alpha_cv = {alpha_cv}")
+
+        if window_fct_path is not None:
+            # Compute alpha_cv with approximate correction for the effective volume
+            nnz = 0
+            with File(window_fct_path, "r") as f:
+                for ipop in range(3):
+                    mask = f["select_fct"][:][ipop]
+                    nnz += np.sum(mask)
+            nnz_size = nnz ** (1 / 3.0)  # Side length of a cube containing nnz voxels
+            eff_L = nnz_size * L / size
+
+            alpha_cv_eff = alpha_cv * (L / eff_L) ** 1.5
+            logger.info(f"Effective length: {eff_L * 1e-3} Gpc/h")
+            logger.info(f"Effective volume: {(eff_L * 1e-3) ** 3} (Gpc/h)^3")
+            logger.info(f"alpha_cv_eff = {alpha_cv_eff}")
+            np.save(alpha_cv_eff_path, alpha_cv_eff)
+
+    gc.collect()

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/examples_utils.html b/docs/_modules/selfisys/utils/examples_utils.html new file mode 100644 index 0000000..34492ce --- /dev/null +++ b/docs/_modules/selfisys/utils/examples_utils.html @@ -0,0 +1,342 @@ + + + + + + + + selfisys.utils.examples_utils — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • selfisys.utils.examples_utils
    • +
  • +
    • +
+
+
+
+
+ +

Source code for selfisys.utils.examples_utils

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+This module provides utility functions for the examples.
+"""
+
+
+

+[docs]
+def clear_large_plot(fig):
+    """
+    Clear a figure to free up memory.
+
+    Parameters
+    ----------
+    fig : matplotlib.figure.Figure
+        The figure to clear.
+    """
+    from IPython.display import clear_output
+
+    del fig
+    clear_output()

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/logger.html b/docs/_modules/selfisys/utils/logger.html new file mode 100644 index 0000000..da214e0 --- /dev/null +++ b/docs/_modules/selfisys/utils/logger.html @@ -0,0 +1,572 @@ + + + + + + + + selfisys.utils.logger — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.logger

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Logger routines for the SelfiSys package.
+
+The printing routines and colours are adapted from the Simbelmynë
+comological solver (https://simbelmyne.readthedocs.io/en/latest).
+"""
+
+import sys
+from typing import cast
+import logging
+from selfisys import DEFAULT_VERBOSE_LEVEL
+
+# Global variables for fonts
+FONT_BOLDRED = "\033[1;31m"
+FONT_BOLDGREEN = "\033[1;32m"
+FONT_BOLDYELLOW = "\033[1;33m"
+FONT_BOLDCYAN = "\033[1;36m"
+FONT_BOLDGREY = "\033[1;37m"
+FONT_LIGHTPURPLE = "\033[38;5;147m"
+
+FONT_NORMAL = "\033[00m"
+
+# Global variables for verbosity
+ERROR_VERBOSITY = 0
+INFO_VERBOSITY = 1
+WARNING_VERBOSITY = 2
+DIAGNOSTIC_VERBOSITY = 3
+DEBUG_VERBOSITY = 4
+DIAGNOSTIC_LEVEL = 15
+logging.addLevelName(DIAGNOSTIC_LEVEL, "DIAGNOSTIC")
+
+G__ind__ = 0  # Global variable for logger indentation
+
+
+

+[docs]
+def INDENT():
+    """Indents the current level of outputs."""
+    global G__ind__
+    G__ind__ += 1
+    return G__ind__

+
+
+
+

+[docs]
+def UNINDENT():
+    """Unindents the current level of outputs."""
+    global G__ind__
+    G__ind__ -= 1
+    return G__ind__

+
+
+
+

+[docs]
+def PrintLeftType(message_type, FONT_COLOR):
+    """Prints the type of output to screen.
+
+    Parameters
+    ----------
+    message_type (string) : type of message
+    FONT_COLOR (string) : font color for this type of message
+
+    """
+    from time import localtime, strftime
+
+    sys.stdout.write(
+        "["
+        + strftime("%H:%M:%S", localtime())
+        + "|"
+        + FONT_COLOR
+        + message_type
+        + FONT_NORMAL
+        + "]"
+    )
+    sys.stdout.write("==" * G__ind__)
+    sys.stdout.write("|")

+
+
+
+

+[docs]
+def PrintInfo(message):
+    """Prints an information to screen.
+
+    Parameters
+    ----------
+    message (string) : message
+
+    """
+    if DEFAULT_VERBOSE_LEVEL >= INFO_VERBOSITY:
+        PrintLeftType("INFO      ", FONT_BOLDCYAN)
+        sys.stdout.write("{}\n".format(message))
+        sys.stdout.flush()

+
+
+
+

+[docs]
+def PrintDiagnostic(verbosity, message):
+    """Prints a diagnostic to screen.
+
+    Parameters
+    ----------
+    verbosity (int) : verbosity of the message
+    message (string) : message
+
+    """
+    if DEFAULT_VERBOSE_LEVEL >= verbosity:
+        PrintLeftType("DIAGNOSTIC", FONT_BOLDGREY)
+        sys.stdout.write("{}\n".format(message))

+
+
+
+

+[docs]
+def PrintWarning(message):
+    """Prints a warning to screen.
+
+    Parameters
+    ----------
+    message (string) : message
+
+    """
+    if DEFAULT_VERBOSE_LEVEL >= WARNING_VERBOSITY:
+        PrintLeftType("WARNING   ", FONT_BOLDYELLOW)
+        sys.stdout.write(FONT_BOLDYELLOW + message + FONT_NORMAL + "\n")

+
+
+
+

+[docs]
+def PrintError(message):
+    """Prints an error to screen.
+
+    Parameters
+    ----------
+    message (string) : message
+
+    """
+    if DEFAULT_VERBOSE_LEVEL >= ERROR_VERBOSITY:
+        PrintLeftType("ERROR     ", FONT_BOLDRED)
+        sys.stdout.write(FONT_BOLDRED + message + FONT_NORMAL + "\n")

+
+
+
+

+[docs]
+class CustomLoggerHandler(logging.Handler):
+    """
+    Custom logging handler to redirect Python logger messages to custom
+    print functions, with support for verbosity levels in debug
+    messages.
+    """
+
+

+[docs]
+    def emit(self, record):
+        """
+        Emit a log record.
+        """
+        try:
+            log_message = self.format(record)
+            log_level = record.levelno
+
+            if log_level >= logging.ERROR:
+                PrintError(log_message)
+            elif log_level >= logging.WARNING:
+                PrintWarning(log_message)
+            elif log_level >= logging.INFO:
+                PrintInfo(log_message)
+            elif log_level == DIAGNOSTIC_LEVEL:
+                # Retrieve verbosity level from the record
+                verbosity = getattr(record, "verbosity", DIAGNOSTIC_VERBOSITY)
+                PrintDiagnostic(verbosity=verbosity, message=log_message)
+            elif log_level >= logging.DEBUG:
+                PrintDiagnostic(verbosity=DEBUG_VERBOSITY, message=log_message)
+            else:
+                # Fallback for other levels
+                PrintInfo(log_message)
+        except Exception:
+            self.handleError(record)

+

+
+
+
+

+[docs]
+class CustomLogger(logging.Logger):
+    """
+    Custom logger class supporting custom verbosity levels in diagnostic
+    messages.
+    """
+
+

+[docs]
+    def diagnostic(self, msg, *args, verbosity=DIAGNOSTIC_VERBOSITY, **kwargs) -> None:
+        """
+        Log a message with DIAGNOSTIC level.
+
+        Parameters
+        ----------
+        msg : str
+            The message to log.
+        verbosity : int, optional
+            The verbosity level required to log this message.
+        """
+        if self.isEnabledFor(DIAGNOSTIC_LEVEL):
+            # Pass verbosity as part of the extra argument
+            extra = kwargs.get("extra", {})
+            extra["verbosity"] = verbosity
+            kwargs["extra"] = extra
+            self.log(DIAGNOSTIC_LEVEL, msg, *args, **kwargs)

+

+
+
+
+logging.setLoggerClass(CustomLogger)
+
+
+

+[docs]
+def getCustomLogger(name: str) -> CustomLogger:
+    """
+    Get as CustomLogger instance to use the custom printing routines.
+
+    Parameters
+    ----------
+    name : str
+        The name of the logger.
+
+    Returns
+    -------
+    logger : logging.Logger
+        The custom logger instance.
+    """
+    logging.setLoggerClass(CustomLogger)
+    logger = cast(CustomLogger, logging.getLogger(name))  # cast for type checkers and PyLance
+    logger.setLevel(logging.DEBUG)  # Set the desired base logging level
+
+    handler = CustomLoggerHandler()
+    formatter = logging.Formatter(f"{FONT_LIGHTPURPLE}(%(name)s){FONT_NORMAL} %(message)s")
+    handler.setFormatter(formatter)
+
+    # Attach the handler to the logger if not already present
+    if not logger.handlers:
+        logger.addHandler(handler)
+
+    return logger

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/low_level.html b/docs/_modules/selfisys/utils/low_level.html new file mode 100644 index 0000000..6651f24 --- /dev/null +++ b/docs/_modules/selfisys/utils/low_level.html @@ -0,0 +1,442 @@ + + + + + + + + selfisys.utils.low_level — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.low_level

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Tools to deal with low-level operations such as redirecting stdout from
+C code.
+"""
+
+from contextlib import contextmanager
+import platform
+import ctypes
+import io
+import os, sys
+import tempfile
+
+libc = ctypes.CDLL(None)
+if platform.system() == "Darwin":  # macOS
+    stdout_symbol = "__stdoutp"
+    stderr_symbol = "__stderrp"
+else:
+    stdout_symbol = "stdout"
+    stderr_symbol = "stderr"
+c_stdout = ctypes.c_void_p.in_dll(libc, stdout_symbol)
+c_stderr = ctypes.c_void_p.in_dll(libc, stderr_symbol)
+
+
+# Taken from:
+# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/
+

+[docs]
+@contextmanager
+def stdout_redirector(stream):
+    """A context manager that redirects stdout to the given stream. For
+    instance, this can be used to redirect C code stdout to None (to
+    avoid cluttering the log, e.g., when using tqdm).
+
+    Args:
+        stream (file-like object): The stream to which stdout should be
+            redirected.
+    Example:
+        >>> with stdout_redirector(stream):
+        >>>     print("Hello world!") # Will be printed to stream
+        >>>                           # instead of stdout.
+    """
+    # The original fd stdout points to. Usually 1 on POSIX systems.
+    original_stdout_fd = sys.stdout.fileno()
+
+    def _redirect_stdout(to_fd):
+        """Redirect stdout to the given file descriptor."""
+        # Flush the C-level buffer stdout
+        libc.fflush(c_stdout)
+        # Flush and close sys.stdout - also closes the file descriptor (fd)
+        sys.stdout.close()
+        # Make original_stdout_fd point to the same file as to_fd
+        os.dup2(to_fd, original_stdout_fd)
+        # Create a new sys.stdout that points to the redirected fd
+        sys.stdout = io.TextIOWrapper(os.fdopen(original_stdout_fd, "wb"))
+
+    # Save a copy of the original stdout fd in saved_stdout_fd
+    saved_stdout_fd = os.dup(original_stdout_fd)
+    try:
+        # Create a temporary file and redirect stdout to it
+        tfile = tempfile.TemporaryFile(mode="w+b")
+        _redirect_stdout(tfile.fileno())
+        # Yield to caller, then redirect stdout back to the saved fd
+        yield
+        _redirect_stdout(saved_stdout_fd)
+        # Copy contents of temporary file to the given stream
+        tfile.flush()
+        tfile.seek(0, io.SEEK_SET)
+        stream.write(tfile.read())
+    finally:
+        tfile.close()
+        os.close(saved_stdout_fd)

+
+
+
+# Adapted from:
+# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/
+

+[docs]
+@contextmanager
+def stderr_redirector(stream):
+    """A context manager that redirects stderr to the given stream.
+    For instance, this can be used to redirect C code stderr to None (to
+    avoid cluttering the log, e.g., when using tqdm).
+
+    Use with caution.
+
+    Args:
+        stream (file-like object): The stream to which stdout should be
+            redirected.
+    """
+    # The original fd stdout points to. Usually 1 on POSIX systems.
+    original_stderr_fd = sys.stderr.fileno()
+
+    def _redirect_stderr(to_fd):
+        """Redirect stderr to the given file descriptor."""
+        # Flush the C-level buffer stderr
+        libc.fflush(c_stderr)
+        # Flush and close sys.stderr - also closes the file descriptor (fd)
+        sys.stderr.close()
+        # Make original_stderr_fd point to the same file as to_fd
+        os.dup2(to_fd, original_stderr_fd)
+        # Create a new sys.stderr that points to the redirected fd
+        sys.stderr = io.TextIOWrapper(os.fdopen(original_stderr_fd, "wb"))
+
+    # Save a copy of the original stdout fd in saved_stdout_fd
+    saved_stderr_fd = os.dup(original_stderr_fd)
+    try:
+        # Create a temporary file and redirect stdout to it
+        tfile = tempfile.TemporaryFile(mode="w+b")
+        _redirect_stderr(tfile.fileno())
+        # Yield to caller, then redirect stdout back to the saved fd
+        yield
+        _redirect_stderr(saved_stderr_fd)
+        # Copy contents of temporary file to the given stream
+        tfile.flush()
+        tfile.seek(0, io.SEEK_SET)
+        stream.write(tfile.read())
+    finally:
+        tfile.close()
+        os.close(saved_stderr_fd)

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/parser.html b/docs/_modules/selfisys/utils/parser.html new file mode 100644 index 0000000..eba19e8 --- /dev/null +++ b/docs/_modules/selfisys/utils/parser.html @@ -0,0 +1,481 @@ + + + + + + + + selfisys.utils.parser — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.parser

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Utility functions for parsing command-line arguments.
+"""
+
+import os
+from argparse import ArgumentParser, ArgumentTypeError
+
+
+

+[docs]
+def joinstrs(list_of_strs):
+    """Join a list of strings into a single string.
+
+    Parameters
+    ----------
+    list_of_strs : list of str
+        List of strings to join.
+
+    Returns
+    -------
+    str
+        Concatenated string.
+    """
+    return "".join([str(x) for x in list_of_strs if x is not None])

+
+
+
+

+[docs]
+def joinstrs_only(list_of_strs):
+    """Join a list of strings into a single string, ignoring all
+    non-string elements such as None values.
+
+    Parameters
+    ----------
+    list_of_strs : list of str
+        List of strings to join.
+
+    Returns
+    -------
+    str
+        Concatenated string.
+    """
+    return "".join([str(x) for x in list_of_strs if type(x) == str])

+
+
+
+

+[docs]
+def check_files_exist(files):
+    """Check if all files in the list exist.
+
+    Parameters
+    ----------
+    files : list of str
+        List of file paths to check.
+
+    Returns
+    -------
+    bool
+        True if all files exist, False otherwise.
+    """
+
+    return all(os.path.exists(f) for f in files)

+
+
+
+

+[docs]
+def none_or_bool_or_str(value):
+    """Convert a string to None, bool, or str.
+
+    Parameters
+    ----------
+    value : str
+        String to convert.
+
+    Returns
+    -------
+    None, bool, or str
+        Converted value.
+    """
+    if value == "None" or value == None:
+        return None
+    elif value == "True":
+        return True
+    elif value == "False":
+        return False
+    return value

+
+
+
+

+[docs]
+def intNone(value):
+    """Convert a string to None or int.
+
+    Parameters
+    ----------
+    value : str
+        String to convert.
+
+    Returns
+    -------
+    None or int
+        Converted value.
+    """
+    if value == "None" or value == None:
+        return None
+    else:
+        return int(value)

+
+
+
+

+[docs]
+def safe_npload(path):
+    """Load a numpy array from a file.
+
+    Parameters
+    ----------
+    path : str
+        Path to the file to load.
+
+    Returns
+    -------
+    None or np.ndarray
+        Loaded array or None if the file does not exist.
+    """
+    import numpy as np
+
+    val = np.load(path, allow_pickle=True)
+    if val is None or val == "None" or val == None:
+        return None
+    else:
+        return val

+
+
+
+

+[docs]
+def bool_sh(value):
+    """Convert a string to a boolean.
+
+    Parameters
+    ----------
+    value : str
+        String to convert.
+
+    Returns
+    -------
+    bool
+        Converted value.
+    """
+    if value == "True":
+        return True
+    elif value == "False":
+        return False
+    else:
+        raise ArgumentTypeError("Boolean value expected.")

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/path_utils.html b/docs/_modules/selfisys/utils/path_utils.html new file mode 100644 index 0000000..85d318a --- /dev/null +++ b/docs/_modules/selfisys/utils/path_utils.html @@ -0,0 +1,525 @@ + + + + + + + + selfisys.utils.path_utils — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.path_utils

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Helper functions to handle paths and file names.
+"""
+
+
+import os
+
+
+def _get_prefix(prefix_mocks: str, suffix: str, sim_id=None, d=None, p=None) -> str:
+    """
+    Get file prefix.
+
+    Parameters
+    ----------
+    prefix_mocks : str | None
+        Prefix for the mock data files. If None, defaults to base
+        suffix.
+    suffix : str
+        Base suffix for the file name (e.g., "mocks" or "g").
+    sim_id : int, optional
+        Simulation ID. Used if d and p are not provided.
+    d : int, optional
+        Direction index.
+    p : int, optional
+        Simulation index. If both d and p are provided, they take
+        precedence over sim_id.
+
+    Returns
+    -------
+    str
+        Formatted file name string.
+    """
+    prefix = f"{prefix_mocks}_{suffix}" if prefix_mocks else suffix
+    if d is not None and p is not None:
+        return f"{prefix}_d{d}_p{p}.h5"
+    return f"{prefix}_{sim_id}.h5"
+
+
+

+[docs]
+def get_file_names(
+    fsimdir: str,
+    sim_id: int,
+    sim_params: str,
+    TimeSteps: list[int],
+    prefix_mocks: str,
+    gravity_on: bool = True,
+    return_g: bool = False,
+) -> dict:
+    """
+    Generate file paths for a given simulation ID and parameters.
+
+    Parameters
+    ----------
+    fsimdir : str
+        Path to the simulation directory.
+    sim_id : int
+        Simulation ID.
+    sim_params : str
+        Simulation parameters.
+    TimeSteps : list of int
+        List of time steps.
+    prefix_mocks : str | None
+        Prefix for mock data files. If None, defaults to "mocks".
+    gravity_on : bool, optional
+        Whether gravity is active. Default is True.
+    return_g : bool, optional
+        If True, return the file name for the observed galaxy field.
+        Default is False.
+
+    Returns
+    -------
+    dict
+        Dictionary containing simulation inputs / outputs file paths.
+    """
+    datadir = os.path.join(fsimdir, "data")
+    names = {
+        "fname_cosmo": os.path.join(datadir, f"input_cosmo_{sim_id}.json"),
+        "fname_power_spectrum": os.path.join(datadir, f"input_power_{sim_id}.h5"),
+        "fname_outputinitialdensity": os.path.join(datadir, f"initial_density_{sim_id}.h5"),
+        "fname_mocks": os.path.join(datadir, _get_prefix(prefix_mocks, "mocks", sim_id)),
+        "fname_g": (
+            os.path.join(datadir, _get_prefix(prefix_mocks, "g", sim_id)) if return_g else None
+        ),
+        "fname_simparfile": None,
+        "fname_whitenoise": None,
+        "seedname_whitenoise": None,
+        "fnames_outputLPTdensity": None,
+        "fnames_outputrealspacedensity": None,
+        "fnames_outputdensity": None,
+        "fname_simlogs": None,
+    }
+
+    if gravity_on:
+        names.update(
+            {
+                "fname_simparfile": os.path.join(datadir, f"sim_{sim_id}"),
+                "fname_whitenoise": os.path.join(
+                    datadir, f"initial_density_white_noise_{sim_id}.h5"
+                ),
+                "seedname_whitenoise": os.path.join(datadir, f"initial_density_wn_{sim_id}_seed"),
+                "fnames_outputLPTdensity": os.path.join(datadir, f"output_density_{sim_id}.h5"),
+                "fname_simlogs": os.path.join(datadir, f"logs_sim_{sim_id}.txt"),
+            }
+        )
+        if sim_params.startswith(("split", "custom")):
+            names["fnames_outputdensity"] = [
+                os.path.join(datadir, f"output_density_{sim_id}_{i}.h5") for i in TimeSteps[::-1]
+            ]
+            names["fnames_outputrealspacedensity"] = [
+                os.path.join(datadir, f"output_realdensity_{sim_id}_{i}.h5")
+                for i in TimeSteps[::-1]
+            ]
+        else:
+            names["fnames_outputdensity"] = [os.path.join(datadir, f"output_density_{sim_id}.h5")]
+            names["fnames_outputrealspacedensity"] = [
+                os.path.join(datadir, f"output_realdensity_{sim_id}.h5")
+            ]
+
+    return names

+
+
+
+

+[docs]
+def file_names_evaluate(
+    simdir: str,
+    sd: str,
+    d: int,
+    i: int,
+    sim_params: str,
+    TimeSteps: list[int],
+    prefix_mocks: str,
+    abc: bool = False,
+    gravity_on: bool = True,
+) -> dict:
+    """
+    Generate file paths for the given simulation id and parameters.
+
+    Parameters
+    ----------
+    simdir : str
+        Path to the simulation directory.
+    sd : str
+        Path to the simulation directory for the given direction.
+    d : int
+        Direction index (-1 for mock data, 0 for the expansion point, or
+        1 to S).
+    i : int
+        Simulation index.
+    sim_params : str
+        Simulation parameters.
+    TimeSteps : list of int
+        List of time steps.
+    prefix_mocks : str | None
+        Prefix for mock data files. If None, defaults to "mocks".
+    abc : bool, optional
+        If True, appends the ABC index to the white noise path.
+    gravity_on : bool, optional
+        Whether gravity is active. Default is True.
+
+    Returns
+    -------
+    dict
+        Dictionary containing simulation inputs / outputs file paths.
+    """
+    names = {
+        "fname_power_spectrum": os.path.join(sd, f"input_power_d{d}.h5"),
+        "fname_outputinitialdensity": os.path.join(sd, f"initial_density_d{d}_p{i}.h5"),
+        "fname_mocks": os.path.join(sd, _get_prefix(prefix_mocks, "mocks", d=d, p=i)),
+        "fname_simlogs": os.path.join(sd, f"logs_sim_d{d}_p{i}.txt"),
+        "fname_simparfile": None,
+        "fname_whitenoise": None,
+        "seedname_whitenoise": None,
+        "fnames_outputLPTdensity": None,
+        "fnames_outputrealspacedensity": None,
+        "fnames_outputdensity": None,
+        "fname_g": None,
+    }
+
+    if gravity_on:
+        dir_wn = os.path.join(simdir, "..", "wn") if not abc else os.path.join(simdir, "wn", abc)
+        names.update(
+            {
+                "fname_simparfile": os.path.join(sd, f"sim_d{d}_p{i}"),
+                "fname_whitenoise": os.path.join(dir_wn, f"initial_density_white_p{i}.h5"),
+                "seedname_whitenoise": os.path.join(dir_wn, f"initial_density_white_p{i}"),
+                "fnames_outputLPTdensity": os.path.join(sd, f"output_density_d{d}_p{i}.h5"),
+            }
+        )
+        if sim_params.startswith(("split", "custom")):
+            names["fnames_outputrealspacedensity"] = [
+                os.path.join(sd, f"output_realdensity_d{d}_p{i}_{j}.h5") for j in TimeSteps[::-1]
+            ]
+            names["fnames_outputdensity"] = [
+                os.path.join(sd, f"output_density_d{d}_p{i}_{j}.h5") for j in TimeSteps[::-1]
+            ]
+        else:
+            names["fnames_outputrealspacedensity"] = [
+                os.path.join(sd, f"output_realdensity_d{d}_p{i}.h5")
+            ]
+            names["fnames_outputdensity"] = [os.path.join(sd, f"output_density_d{d}_p{i}.h5")]
+
+    return names

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/plot_examples.html b/docs/_modules/selfisys/utils/plot_examples.html new file mode 100644 index 0000000..9e5d2fc --- /dev/null +++ b/docs/_modules/selfisys/utils/plot_examples.html @@ -0,0 +1,942 @@ + + + + + + + + selfisys.utils.plot_examples — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.plot_examples

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Visualisation utilities for the exploratory examples in SelfiSys.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from selfisys.utils.plot_params import *
+
+# Configure global plotting settings
+setup_plotting()
+
+
+

+[docs]
+def plot_power_spectrum(
+    G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, title=None, display=True
+):
+    """
+    Plot a power spectrum over Fourier modes, its linear interpolation
+    over specified support points, and a given binning for comparison.
+
+    Parameters
+    ----------
+    G_sim : pysbmy.power.FourierGrid
+        Fourier grid object containing the `k_modes` attribute.
+    true_P : pysbmy.power.PowerSpectrum
+        Power spectrum object containing the `powerspectrum` attribute.
+    k_s : array-like
+        Support points in k-space.
+    planck_Pk : array-like
+        Power spectrum values at the support points.
+    Pbins : array-like
+        Centres of the Φ bins in k-space.
+    Pbins_bnd : array-like
+        Boundaries of the Φ bins in k-space.
+    size : float
+        Box size in number of grid cells.
+    L : float
+        Box length in Mpc/h.
+    wd : str
+        Working directory path for saving the figure.
+    title : str, optional
+        Title for the figure. Default is None.
+    display : bool, optional
+        Whether to display the figure. Default is True.
+
+    Returns
+    -------
+    None
+    """
+    import os
+    from selfisys.utils.logger import PrintInfo
+
+    plt.figure(figsize=(15, 5))
+
+    # Plot power spectrum data
+    plt.plot(G_sim.k_modes, true_P.powerspectrum, label=r"$P(k)$ (over all modes)")
+    plt.plot(k_s, planck_Pk, label=r"$P(k)$ (binned–linear interpolation)", linestyle="dashed")
+
+    # Configure axes
+    plt.xlabel(r"$k\,[h/\mathrm{Mpc}]$")
+    plt.ylabel(r"$[{\rm Mpc}/h]^3$")
+    plt.xscale("log")
+    plt.yscale("log")
+    plt.xlim(np.clip(k_s.min() - 2e-4, 1e-4, None), k_s.max())
+    plt.ylim(1e1, 1e5)
+    plt.grid(which="both", axis="y")
+
+    # Plot vertical lines for support points and binning
+    plt.vlines(k_s[:-1], ymin=1e1, ymax=1e5, colors="green", linestyles="dotted", linewidth=0.6)
+    plt.axvline(
+        k_s[-1],
+        color="green",
+        linestyle="dotted",
+        linewidth=0.6,
+        label=r"$\boldsymbol{\uptheta}$ support points",
+    )
+    plt.vlines(
+        Pbins,
+        ymin=1e1,
+        ymax=5e2,
+        colors="red",
+        linestyles="dashed",
+        linewidth=0.5,
+        label=r"$\boldsymbol{\Phi}$ bin centres",
+    )
+    plt.vlines(
+        Pbins_bnd,
+        ymin=1e1,
+        ymax=1e2 / 2,
+        colors="blue",
+        linestyles="dashed",
+        linewidth=0.5,
+        label=r"$\boldsymbol{\Phi}$ bin boundaries",
+    )
+
+    # Plot the Nyquist frequency
+    nyquist_freq = np.pi * size / L
+    plt.axvline(
+        nyquist_freq, ymax=1 / 6.0, color="orange", linestyle="-", linewidth=2, label="Nyquist"
+    )
+
+    # Add legend, optional title, and save the figure
+    plt.legend(loc="upper center", bbox_to_anchor=(0.5, -0.2), ncol=3)
+    if title:
+        plt.title(title)
+    output_dir = os.path.join(wd, "Figures")
+    os.makedirs(output_dir, exist_ok=True)
+    output_path = os.path.join(output_dir, "summary.pdf")
+    plt.savefig(output_path, bbox_inches="tight")
+    PrintInfo(f"Figure saved to: {output_path}")
+
+    if display:
+        plt.show()
+    plt.close()

+
+
+
+

+[docs]
+def relative_error_analysis(
+    G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, display=True
+):
+    """
+    Compute and plot the relative error between the interpolated and
+    true power spectra.
+
+    Parameters
+    ----------
+    G_sim : pysbmy.power.FourierGrid
+        Fourier grid object containing the `k_modes` attribute.
+    true_P : pysbmy.power.PowerSpectrum
+        Power spectrum object containing the `powerspectrum` attribute.
+    k_s : array-like
+        Support points in k-space.
+    planck_Pk : array-like
+        Power spectrum values at the support points.
+    Pbins : array-like
+        Centres of the Φ bins in k-space.
+    Pbins_bnd : array-like
+        Boundaries of the Φ bins in k-space.
+    size : float
+        Box size in number of grid cells.
+    L : float
+        Box length in Mpc/h.
+    wd : str
+        Working directory path for saving the figure.
+    display : bool, optional
+        Whether to display the figure. Default is True.
+
+    Returns
+    -------
+    None
+    """
+    import os
+    from scipy.interpolate import InterpolatedUnivariateSpline
+    from selfisys.utils.logger import PrintInfo
+
+    # Interpolate the power spectrum
+    spline = InterpolatedUnivariateSpline(k_s, planck_Pk, k=5)
+    rec_Pk = spline(G_sim.k_modes[1:])
+    true_spectrum = true_P.powerspectrum[1:]
+    xx = G_sim.k_modes[1:]
+
+    # Compute relative errors
+    rel_err = (rec_Pk - true_spectrum) / true_spectrum
+    indices_all = slice(None)
+    indices_nyquist = np.where((xx >= k_s.min()) & (xx <= np.pi * size / L))[0]
+    indices_k2e1 = np.where(xx <= 2e-1)[0]
+
+    max_relerr = np.max(np.abs(rel_err[indices_all]))
+    max_relerr_nyquist = np.max(np.abs(rel_err[indices_nyquist]))
+    max_relerr_2e1 = np.max(np.abs(rel_err[indices_k2e1]))
+
+    # Create the figure
+    plt.figure(figsize=(15, 5))
+    plt.plot(
+        xx,
+        rel_err,
+        label=r"$\left(P_\textrm{interp}-P_{\mathrm{true}}\right)/P_{\mathrm{true}}$",
+    )
+    plt.xlabel(r"$k\,[h/\mathrm{Mpc}]$")
+    plt.ylabel("Relative error")
+    plt.xscale("log")
+    plt.xlim(np.clip(k_s.min() - 2e-4, 1e-4, None), k_s.max())
+    plt.ylim(-0.1, 0.1)
+    plt.grid(which="both", axis="y")
+
+    # Vertical lines for binning and support points
+    plt.axvline(
+        x=Pbins[0],
+        color="red",
+        linestyle="dashed",
+        linewidth=0.5,
+        label=r"$\boldsymbol\Phi$ bin centres",
+    )
+    plt.axvline(x=Pbins[-1], color="red", linestyle="dashed", linewidth=0.5)
+    for k in Pbins[1:-1]:
+        plt.axvline(x=k, ymax=1 / 6.0, color="red", linestyle="dashed", linewidth=0.5)
+    for k in k_s[:-1]:
+        plt.axvline(x=k, color="green", linestyle="dotted", linewidth=0.6)
+    plt.axvline(
+        x=k_s[-1],
+        color="green",
+        linestyle="dotted",
+        linewidth=0.6,
+        label=r"$\boldsymbol\uptheta$ support points",
+    )
+    plt.axvline(
+        x=Pbins_bnd[0],
+        ymax=1 / 3.0,
+        color="blue",
+        linestyle="dashed",
+        linewidth=0.5,
+        label=r"$\boldsymbol\Phi$ bin boundaries",
+    )
+    plt.axvline(x=Pbins_bnd[-1], ymax=1 / 3.0, color="blue", linestyle="dashed", linewidth=0.5)
+    for k in Pbins_bnd[1:-1]:
+        plt.axvline(x=k, ymax=1 / 12.0, color="blue", linestyle="dashed", linewidth=0.5)
+
+    # Nyquist and fundamental frequencies
+    plt.axvline(
+        x=2 * np.pi / L,
+        ymax=1 / 6.0,
+        color="orange",
+        linestyle="-",
+        linewidth=2,
+        label="Fundamental mode",
+    )
+    plt.axvline(
+        x=np.pi * size / L,
+        ymax=1 / 6.0,
+        color="orange",
+        linestyle="--",
+        linewidth=2,
+        label="Nyquist",
+    )
+
+    # Add title, legend, and save the figure
+    plt.legend(loc="upper center", bbox_to_anchor=(0.5, -0.2), ncol=3)
+    plt.title(
+        "Relative error between interpolated and true Planck 2018 power spectrum\n"
+        f"over the {G_sim.k_modes.size} modes of the Fourier grid (max: {max_relerr * 100:.3f}\\%)"
+    )
+    output_dir = os.path.join(wd, "Figures")
+    os.makedirs(output_dir, exist_ok=True)
+    output_path = os.path.join(output_dir, "summary_relerr.pdf")
+    plt.savefig(output_path, bbox_inches="tight")
+    PrintInfo(f"Figure saved to: {output_path}")
+
+    # Print summary of relative errors
+    PrintInfo(f"Max relative error over all support points: {max_relerr * 100:.3f}%")
+    PrintInfo(f"Max relative error up to 1D Nyquist frequency: {max_relerr_nyquist * 100:.3f}%")
+    PrintInfo(f"Max relative error up to k = 2e-1: {max_relerr_2e1 * 100:.3f}%")
+
+    if display:
+        plt.show()
+    plt.close()

+
+
+
+

+[docs]
+def plot_comoving_distance_redshift(
+    zz, cosmo, means_com, L, Lcorner, wd, colours_list=COLOUR_LIST, display=True
+):
+    """
+    Plot comoving distance as a function of redshift, highlighting key
+    scales.
+
+    Parameters
+    ----------
+    zz : array-like
+        Redshift range for the plot.
+    cosmo : astropy.cosmology object
+        Cosmology instance for calculating comoving distances.
+    means_com : array-like
+        Mean comoving distances of selection functions.
+    L : float
+        Box side length in Gpc/h.
+    Lcorner : float
+        Diagonal of the box (sqrt(3) * L) in Gpc/h.
+    wd : str
+        Working directory for saving figures.
+    colours_list : list
+        List of colours for selection function annotations.
+    display : bool, optional
+        Whether to display the figure. Default is True.
+    """
+    d = cosmo.comoving_distance(zz) / 1e3  # Convert to Gpc/h
+
+    plt.figure(figsize=(12, 5.2))
+    plt.plot(zz, d, label="Comoving distance")
+    plt.axhline(
+        L, color="black", linewidth=1, linestyle="--", label=rf"$L = {L:.2f}\textrm{{ Gpc}}/h$"
+    )
+    plt.axhline(
+        Lcorner,
+        color="orange",
+        linewidth=1,
+        linestyle="--",
+        label=rf"$L_\textrm{{corner}} = {Lcorner:.2f}\textrm{{ Gpc}}/h$",
+    )
+
+    # Annotate key redshifts
+    d_np = d.value
+    z_L = zz[np.argmin(np.abs(d_np - L))]
+    z_corner = zz[np.argmin(np.abs(d_np - Lcorner))]
+    plt.axvline(z_L, color="black", linewidth=0.5, alpha=0.5, linestyle="-")
+    plt.axvline(z_corner, color="orange", linewidth=0.5, alpha=0.5, linestyle="-")
+    plt.text(z_L, 1.07 * d_np.max(), rf"$z(L) = {z_L:.2f}$", fontsize=GLOBAL_FS_TINY - 2)
+    plt.text(
+        z_corner,
+        1.07 * d_np.max(),
+        rf"$z(\sqrt{{3}}\,L) = {z_corner:.2f}$",
+        fontsize=GLOBAL_FS_TINY - 2,
+    )
+
+    # Annotate the selection functions' means
+    z_means = np.array([zz[np.argmin(np.abs(d_np - m))] for m in means_com])
+    for i, z_mean in enumerate(z_means):
+        plt.axvline(z_mean, color=colours_list[i], linestyle="--", linewidth=1)
+        plt.text(
+            z_mean - 0.07,
+            L + 0.2,
+            rf"$z(\mu_{{{i+1}}} = {means_com[i]:.2f}) = {z_mean:.2f}$",
+            fontsize=GLOBAL_FS_TINY - 2,
+            rotation=90,
+        )
+
+    # Add labels, legend, and save the figure
+    plt.xlabel("Redshift $z$")
+    plt.ylabel(r"Comoving distance [Gpc$/h$]")
+    plt.grid(which="both", axis="both", linestyle="-", linewidth=0.3, color="gray", alpha=0.5)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(f"{wd}selection_functions_z.pdf", bbox_inches="tight", dpi=300)
+    plt.savefig(f"{wd}selection_functions_z.png", bbox_inches="tight", dpi=300, transparent=True)
+    if display:
+        plt.show()
+    plt.close()

+
+
+
+

+[docs]
+def redshift_distance_conversion(
+    zz, cosmo, means_com, L, Lcorner, xx, wd, colours_list=COLOUR_LIST, display=True
+):
+    """
+    Plot the conversion between comoving distance and redshift; return
+    the redshifts corresponding to the selection functions' means.
+
+    Parameters
+    ----------
+    zz : array-like
+        Redshift range for the plot.
+    cosmo : astropy.cosmology object
+        Cosmology instance for calculating comoving distances.
+    means_com : array-like
+        Mean comoving distances of selection functions.
+    L : float
+        Box side length in Gpc/h.
+    Lcorner : float
+        Diagonal of the box (sqrt(3) * L) in Gpc/h.
+    xx : array-like
+        Comoving distances at which to compute redshift.
+    wd : str
+        Working directory for saving figures.
+    colours_list : list
+        List of colours for selection function annotations.
+    display : bool, optional
+        Whether to display the figure. Default is True.
+
+    Returns
+    -------
+    spline : scipy.interpolate.UnivariateSpline
+        Linear interpolator to convert comoving distances to redshifts.
+    """
+    from scipy.interpolate import UnivariateSpline
+
+    # Convert comoving distances to redshifts using a linear interpolation
+    d_np = (cosmo.comoving_distance(zz) / 1e3).value  # Gpc/h
+    spline = UnivariateSpline(d_np, zz, k=1, s=0)
+    z_x = spline(xx)
+
+    plt.figure(figsize=(12, 5))
+    plt.plot(xx, z_x)
+
+    # Annotate key scales
+    plt.axvline(
+        L, color="black", linewidth=1, linestyle="--", label=rf"$L = {L:.2f}\textrm{{ Gpc}}/h$"
+    )
+    plt.axhline(spline(L), color="black", linewidth=1, linestyle="--")
+    plt.axvline(
+        Lcorner,
+        color="orange",
+        linewidth=1,
+        linestyle="--",
+        label=rf"$L_\textrm{{corner}} = {Lcorner:.2f}\textrm{{ Gpc}}/h$",
+    )
+    plt.axhline(spline(Lcorner), color="orange", linewidth=1, linestyle="--")
+    plt.text(L + 0.08, spline(L) - 0.14, rf"$z(L) = {spline(L):.2f}$", fontsize=GLOBAL_FS_TINY - 2)
+    plt.text(
+        Lcorner - 1.2,
+        spline(Lcorner) - 0.17,
+        rf"$z(\sqrt{{3}}\,L) = {spline(Lcorner):.2f}$",
+        fontsize=GLOBAL_FS_TINY - 2,
+    )
+
+    # Annotate the selection functions' means
+    z_means = spline(means_com)
+    for i, z_mean in enumerate(z_means):
+        plt.axvline(means_com[i], color=colours_list[i], linestyle="--", linewidth=1)
+        plt.axhline(z_mean, color=colours_list[i], linestyle="--", linewidth=1)
+        plt.text(
+            L + 0.08,
+            z_mean - 0.14,
+            rf"$z(\mu_{{{i+1}}} = {means_com[i]:.2f}) = {z_mean:.2f}$",
+            fontsize=GLOBAL_FS_TINY - 2,
+        )
+
+    # Add labels, legend, and save the figure
+    plt.xlabel(r"Comoving distance [Gpc$/h$]")
+    plt.ylabel("Redshift $z$")
+    plt.grid(which="both", axis="both", linestyle="-", linewidth=0.3, color="gray", alpha=0.5)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(f"{wd}redshift_distance_conversion.pdf", bbox_inches="tight", dpi=300)
+    if display:
+        plt.show()
+    plt.close()
+
+    return spline

+
+
+
+

+[docs]
+def plot_selection_functions_def_in_z(
+    xx_of_zs,
+    res,
+    res_mis,
+    z_means,
+    cosmo,
+    L,
+    stds_z,
+    wd,
+    display=True,
+):
+    """
+    Plot radial lognormal (in redshift) selection functions against
+    comoving distances.
+
+    Parameters
+    ----------
+    xx_of_zs : array-like
+        Comoving distances mapped from redshift.
+    res : list of array-like
+        Selection functions for the well-specified model.
+    res_mis : list of array-like
+        Selection functions for the mis-specified model.
+    z_means : array-like
+        Mean redshifts of every galaxy population.
+    cosmo : object
+        Cosmology object.
+    L : float
+        Box side length in comoving distance units.
+    stds_z : array-like
+        Standard deviations of redshift distributions.
+    wd : str
+        Working directory for saving figures.
+    display : bool, optional
+        Whether to display the figure. Default is True.
+
+    Returns
+    -------
+    None
+    """
+    from matplotlib.ticker import FormatStrFormatter
+
+    colours_list = COLOUR_LIST[: len(res)]
+
+    plt.figure(figsize=(10, 5))
+
+    # Plot well-specified selection functions
+    for i, r in enumerate(res):
+        plt.plot(xx_of_zs, r, color=colours_list[i])
+    plt.plot(xx_of_zs, res[-1], color="black", alpha=0, label="Model A")
+
+    # Plot mis-specified selection functions
+    for i, r_mis in enumerate(res_mis):
+        plt.plot(xx_of_zs, r_mis, linestyle="--", color=colours_list[i])
+    plt.plot(xx_of_zs, res_mis[-1], linestyle="--", color="black", alpha=0, label="Model B")
+
+    # Define x-ticks and labels
+    xticks = [0, np.sqrt(3) * L]
+    xtick_labels = [r"$0$", r"$\sqrt 3\,L \simeq {:.2f}$".format(np.sqrt(3) * L)]
+    plt.axvline(L, color="black", linestyle="-", linewidth=1, zorder=0)
+
+    # Annotate populations
+    for i, mean in enumerate(z_means):
+        std = stds_z[i]
+        mu = np.log(mean**2 / np.sqrt(mean**2 + std**2))
+        sig2 = np.log(1 + std**2 / mean**2)
+        mode = np.exp(mu - sig2)
+
+        dmode = cosmo.comoving_distance(mode).value / 1e3
+        dmean = cosmo.comoving_distance(mean).value / 1e3
+
+        xticks.extend([dmean])
+        xtick_labels.extend([f"{dmean:.2f}"])
+        plt.axvline(dmean, color=colours_list[i], linestyle="-.", linewidth=1)
+        plt.axvline(dmode, color=colours_list[i], linestyle="-", linewidth=1)
+        plt.axvline(
+            mode,
+            color=colours_list[i],
+            alpha=0,
+            linewidth=1,
+            label=f"Population {i+1}",
+        )
+
+    # Configure axes, labels, ticks, legend
+    plt.xlabel(r"$r\,[{\rm Gpc}/h]$", fontsize=GLOBAL_FS_LARGE)
+    plt.ylabel(r"$R_i(r)$", fontsize=GLOBAL_FS_LARGE)
+    plt.xticks(xticks, xtick_labels)
+    plt.tick_params(axis="x", which="major", size=8, labelsize=GLOBAL_FS_SMALL)
+    plt.tick_params(axis="y", which="major", size=8, labelsize=GLOBAL_FS_SMALL)
+    plt.grid(which="both", axis="both", linestyle="-", linewidth=0.4, color="gray", alpha=0.5)
+
+    maxs = [np.max(r) for r in res]
+    yticks = [0] + maxs
+    plt.yticks(yticks)
+    plt.gca().yaxis.set_major_formatter(FormatStrFormatter("%.2f"))
+
+    legend = plt.legend(frameon=True, loc="upper right", fontsize=GLOBAL_FS_LARGE)
+    legend.get_frame().set_edgecolor("white")
+    for lh in legend.legend_handles:
+        lh.set_alpha(1)
+
+    plt.tight_layout()
+    plt.savefig(f"{wd}selection_functions_com.pdf", bbox_inches="tight", dpi=300)
+    if display:
+        plt.show()
+    plt.close()

+
+
+
+

+[docs]
+def plot_galaxy_field_slice(g, size, L, wd, id_obs, limits="minmax", display=True):
+    """
+    Plot a 2D slice of the observed field.
+
+    Parameters
+    ----------
+    g : ndarray
+        2D array representing the observed field slice.
+    size : int
+        Number of grid points along each axis.
+    L : float
+        Size of the simulation box (in Mpc/h).
+    wd : str
+        Working directory for saving output files.
+    id_obs : int or str
+        Identifier for the observation, used in file naming.
+    limits : str, optional
+        Colormap scaling method. Options: 'minmax', 'truncate', 'max'.
+    display : bool, optional
+        Whether to display the figure. Default is True.
+    """
+    from mpl_toolkits.axes_grid1 import make_axes_locatable
+    from matplotlib import colors
+
+    # Define colormap and set scaling limits
+    GalaxyMap = create_colormap("GalaxyMap")
+
+    if limits == "max":
+        maxcol = np.max(np.abs(g))
+        mincol = -maxcol
+        cmap = GalaxyMap
+    elif limits == "truncate":
+        maxcol = np.min([np.max(-g), np.max(g)])
+        mincol = -maxcol
+        cmap = "PiYG"
+    elif limits == "minmax":
+        maxcol = np.max(g)
+        mincol = np.min(g)
+        cmap = GalaxyMap
+
+    divnorm = colors.TwoSlopeNorm(vmin=mincol, vcenter=0, vmax=maxcol)
+
+    # Plot
+    fig, ax = plt.subplots(figsize=(6, 6))
+    im = ax.imshow(g, norm=divnorm, cmap=cmap)
+    ax.invert_yaxis()  # Place origin at bottom-left
+    ax.spines[["top", "right", "left", "bottom"]].set_visible(False)
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="5%", pad=0.1)
+    cbar = fig.colorbar(im, cax=cax)
+    cbar.outline.set_visible(False)
+    ticks = [mincol, mincol / 2, 0, maxcol / 3, 2 * maxcol / 3, maxcol]
+    cbar.set_ticks(ticks)
+    cbar.set_ticklabels([f"{x:.2f}" for x in ticks], size=GLOBAL_FS_SMALL)
+    cbar.set_label(r"$\delta_\textrm{g}$", size=GLOBAL_FS)
+    ax.set_xticks(
+        [size * i / 4.0 for i in range(5)], [f"{L * 1e-3 * i / 4:.1f}" for i in range(5)]
+    )
+    ax.set_yticks(
+        [size * i / 4.0 for i in range(5)], [f"{L * 1e-3 * i / 4:.1f}" for i in range(5)]
+    )
+    ax.set_xlabel(r"Gpc/$h$", size=GLOBAL_FS)
+    ax.set_ylabel(r"Gpc/$h$", size=GLOBAL_FS)
+
+    # Save or display
+    if display:
+        plt.show()
+    else:
+        plt.savefig(f"{wd}Figures/g_{id_obs}.png", bbox_inches="tight", dpi=300)
+        plt.savefig(f"{wd}Figures/g_{id_obs}.pdf", bbox_inches="tight", dpi=300)
+    plt.close()

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/plot_params.html b/docs/_modules/selfisys/utils/plot_params.html new file mode 100644 index 0000000..ffbabfc --- /dev/null +++ b/docs/_modules/selfisys/utils/plot_params.html @@ -0,0 +1,662 @@ + + + + + + + + selfisys.utils.plot_params — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.plot_params

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Plotting utilities and custom colormaps for the SelfiSys package.
+
+This module provides custom Matplotlib settings, formatter classes, and
+colormaps used for visualising results in the SelfiSys project.
+"""
+
+
+# Global font sizes
+GLOBAL_FS = 20
+GLOBAL_FS_LARGE = 22
+GLOBAL_FS_XLARGE = 24
+GLOBAL_FS_SMALL = 18
+GLOBAL_FS_TINY = 16
+COLOUR_LIST = ["C4", "C5", "C6", "C7"]
+
+
+

+[docs]
+def reset_plotting():
+    import matplotlib as mpl
+
+    mpl.rcParams.update(mpl.rcParamsDefault)

+
+
+
+

+[docs]
+def setup_plotting():
+    """
+    Configure Matplotlib plotting settings for consistent appearance.
+    """
+    import matplotlib.pyplot as plt
+    import importlib.resources
+
+    with importlib.resources.open_text("selfisys", "preamble.tex") as f:
+        preamble = f.read()
+
+    # Dictionary with rcParams settings
+    rcparams = {
+        "font.family": "serif",
+        "font.size": GLOBAL_FS,  # Base font size
+        "axes.titlesize": GLOBAL_FS_XLARGE,
+        "axes.labelsize": GLOBAL_FS_LARGE,
+        "axes.linewidth": 1.0,
+        "xtick.labelsize": GLOBAL_FS_SMALL,
+        "ytick.labelsize": GLOBAL_FS_SMALL,
+        "xtick.major.width": 1.2,
+        "ytick.major.width": 1.2,
+        "xtick.minor.width": 1.0,
+        "ytick.minor.width": 1.0,
+        "xtick.direction": "in",
+        "ytick.direction": "in",
+        "xtick.major.pad": 5,
+        "xtick.minor.pad": 5,
+        "ytick.major.pad": 5,
+        "ytick.minor.pad": 5,
+        "legend.fontsize": GLOBAL_FS_SMALL,
+        "legend.title_fontsize": GLOBAL_FS_LARGE,
+        "figure.titlesize": GLOBAL_FS_XLARGE,
+        "figure.dpi": 300,
+        "grid.color": "gray",
+        "grid.linestyle": "dotted",
+        "grid.linewidth": 0.6,
+        "lines.linewidth": 2,
+        "lines.markersize": 8,
+        "text.usetex": True,
+        "text.latex.preamble": preamble,
+    }
+
+    # Update rcParams
+    plt.rcParams.update(rcparams)

+
+
+
+

+[docs]
+def dynamic_text_scaling(fig_height):
+    """
+    Dynamically scale text sizes based on the vertical height of the
+    figure.
+
+    Parameters
+    ----------
+    fig_height : float
+        Height of the figure in inches.
+
+    Returns
+    -------
+    dict
+        Dictionary of scaled font sizes for consistent appearance.
+    """
+    scaling_factor = fig_height / 6.0  # Reference height is 6 inches
+    return {
+        "font.size": GLOBAL_FS * scaling_factor,
+        "axes.titlesize": GLOBAL_FS_XLARGE * scaling_factor,
+        "axes.labelsize": GLOBAL_FS_LARGE * scaling_factor,
+        "xtick.labelsize": GLOBAL_FS_SMALL * scaling_factor,
+        "ytick.labelsize": GLOBAL_FS_SMALL * scaling_factor,
+        "legend.fontsize": GLOBAL_FS_SMALL * scaling_factor,
+        "legend.title_fontsize": GLOBAL_FS_LARGE * scaling_factor,
+        "figure.titlesize": GLOBAL_FS_XLARGE * scaling_factor,
+    }

+
+
+
+

+[docs]
+class ScalarFormatterForceFormat_11:
+    """
+    Custom scalar formatter to enforce a specific number format with an
+    offset.
+
+    This formatter displays tick labels with one decimal place and
+    includes the offset notation for powers of ten.
+    """
+
+    def __init__(self, useOffset=True, useMathText=True, useLocale=None):
+        from matplotlib.ticker import ScalarFormatter
+
+        self.formatter = ScalarFormatter(
+            useOffset=useOffset, useMathText=useMathText, useLocale=useLocale
+        )
+        self.formatter.set_powerlimits((0, 0))
+
+    def __call__(self, val, pos=None):
+        return self.formatter.__call__(val, pos)
+
+

+[docs]
+    def set_scientific(self, b):
+        self.formatter.set_scientific(b)

+
+
+

+[docs]
+    def set_useOffset(self, b):
+        self.formatter.set_useOffset(b)

+
+
+

+[docs]
+    def get_offset(self):
+        offset = self.formatter.get_offset()
+        if self.formatter.orderOfMagnitude != 0:
+            return r"$\times 10^{%+d}$" % self.formatter.orderOfMagnitude
+        else:
+            return r"$\times 10^{+0}$"

+

+
+
+
+

+[docs]
+def get_contours(Z, nBins, confLevels=(0.3173, 0.0455, 0.0027)):
+    """
+    Compute contour levels for given confidence levels.
+
+    Parameters
+    ----------
+    Z : ndarray
+        2D histogram or density estimate.
+    nBins : int
+        Number of bins along one axis.
+    confLevels : tuple of float
+        Confidence levels for which to compute contour levels.
+
+    Returns
+    -------
+    chainLevels : ndarray
+        Contour levels corresponding to the provided confidence levels.
+    """
+    import numpy as np
+
+    Z = Z / Z.sum()
+    nContourLevels = len(confLevels)
+    chainLevels = np.ones(nContourLevels + 1)
+    histOrdered = np.sort(Z.flat)
+    histCumulative = np.cumsum(histOrdered)
+    nBinsFlat = np.linspace(0.0, nBins**2, nBins**2)
+
+    for l in range(nContourLevels):
+        temp = np.interp(confLevels[l], histCumulative, nBinsFlat)
+        chainLevels[nContourLevels - 1 - l] = np.interp(temp, nBinsFlat, histOrdered)
+
+    return chainLevels

+
+
+
+

+[docs]
+def create_colormap(name):
+    """
+    Create a custom colormap based on the specified name.
+
+    Parameters
+    ----------
+    name : str
+        The name of the colormap to create.
+
+    Returns
+    -------
+    ListedColormap
+        The requested custom colormap.
+
+    Raises
+    ------
+    ValueError
+        If the specified colormap name is not recognised.
+    """
+    import numpy as np
+    from matplotlib import cm, colors, colormaps
+
+    if name == "GalaxyMap":
+        # Colormap for slices through galaxy density fields
+        Ndots = 2**13
+        stretch_top = 0.5
+        truncate_bottom = 0.0
+        stretch_bottom = 1.0
+
+        top = cm.get_cmap("RdPu", Ndots)
+        top = colors.LinearSegmentedColormap.from_list("", ["white", top(0.5), top(1.0)])
+        bottom = cm.get_cmap("Greens_r", Ndots)
+        bottom = colors.LinearSegmentedColormap.from_list("", [bottom(0), bottom(0.5), "white"])
+
+        interp_top = np.linspace(0, 1, Ndots) ** stretch_top
+        interp_bottom = np.linspace(truncate_bottom, 1, Ndots) ** stretch_bottom
+        cols_galaxy = np.vstack((bottom(interp_bottom), top(interp_top)))
+        return colors.ListedColormap(cols_galaxy, name="GalaxyMap")
+
+    elif name == "GradientMap":
+        # Colormap for gradient matrices
+        Ndots = 2**13
+        stretch_bottom = 6.0
+        stretch_top = 1 / 2.5
+        truncate_bottom = 0.35
+
+        bottom = cm.get_cmap("BuGn_r", Ndots)
+        top = cm.get_cmap("RdPu", Ndots)
+
+        interp_top = np.linspace(0, 1, Ndots) ** stretch_top
+        interp_bottom = np.linspace(truncate_bottom, 1, Ndots) ** stretch_bottom
+        newcolors = np.vstack((bottom(interp_bottom), top(interp_top)))
+        return colors.ListedColormap(newcolors, name="GradientMap")
+
+    elif name == "CovarianceMap":
+        # Colormap for the diagonal blocks of covariance matrices
+        Ndots = 2**15
+        stretch_top_1 = 0.3
+        stretch_top_2 = 1.0
+        stretch_bottom = 0.2
+        middle = 0.4  # Middle of the positive scale, between 0 and 1
+        cmap_name = "BrBG"
+        top = colormaps[cmap_name]
+        bottom = colormaps[cmap_name]
+
+        interp_top = np.concatenate(
+            (
+                middle * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_1 + 0.5,
+                (1 - middle) * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_2 + 0.5 + middle,
+            )
+        )
+        interp_bottom = np.linspace(0.0, 1.0, Ndots) ** stretch_bottom - 0.5
+        newcolors = np.vstack((bottom(interp_bottom), top(interp_top)))
+        return colors.ListedColormap(newcolors, name="CovarianceMap")
+
+    elif name == "FullCovarianceMap":
+        # Colormap for full covariance matrices
+        Ndots = 2**15
+        stretch_top_1 = 0.3
+        stretch_top_2 = 1.0
+        middle_top = 0.4  # Middle of the positive scale, between 0 and 1
+        stretch_bottom_1 = 1.0
+        stretch_bottom_2 = 5.0
+        middle_bottom = 0.7  # Middle of the negative scale, between 0 and 1
+        colname = "PRGn_r"  # Options: "PRGn", "PRGn_r", "BrBG", "PuOr"
+        top = colormaps[colname]
+        bottom = colormaps[colname]
+
+        interp_top = np.concatenate(
+            (
+                middle_top * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_1 + 0.5,
+                (1 - middle_top) * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_2
+                + 0.5
+                + middle_top,
+            )
+        )
+        interp_bottom = np.concatenate(
+            (
+                middle_bottom * np.linspace(0.0, 1, Ndots // 2) ** stretch_bottom_1 - 0.5,
+                (1 - middle_bottom) * np.linspace(0.0, 1, Ndots // 2) ** stretch_bottom_2
+                - 0.5
+                + middle_bottom,
+            )
+        )
+        newcolors = np.vstack((bottom(interp_bottom), top(interp_top)))
+        return colors.ListedColormap(newcolors, name="FullCovarianceMap")
+
+    elif name == "Blues_Reds":
+        # Additional colormap combining blues and reds
+        top = cm.get_cmap("Reds_r", 128)
+        bottom = cm.get_cmap("Blues", 128)
+        newcolors = np.vstack((top(np.linspace(0.7, 1, 128)), bottom(np.linspace(0, 1, 128))))
+        return colors.ListedColormap(newcolors, name="Blues_Reds")
+
+    elif name == "Purples_Oranges":
+        # Additional colormap combining purples and oranges
+        top = cm.get_cmap("Oranges_r", 128)
+        bottom = cm.get_cmap("Purples", 128)
+        newcolors = np.vstack((top(np.linspace(0.7, 1, 128)), bottom(np.linspace(0, 1, 128))))
+        return colors.ListedColormap(newcolors, name="Purples_Oranges")
+
+    else:
+        raise ValueError(f"Colormap '{name}' is not defined.")

+
+
+
+

+[docs]
+def create_all_colormaps():
+    """
+    Create all custom colormaps.
+
+    Returns
+    -------
+    colormaps : dict
+        Dictionary containing all custom colormaps.
+    """
+    colormaps_dict = {}
+    colormap_names = [
+        "GalaxyMap",
+        "GradientMap",
+        "CovarianceMap",
+        "FullCovarianceMap",
+        "Blues_Reds",
+        "Purples_Oranges",
+    ]
+    for name in colormap_names:
+        colormaps_dict[name] = create_colormap(name)
+    return colormaps_dict

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/plot_utils.html b/docs/_modules/selfisys/utils/plot_utils.html new file mode 100644 index 0000000..7dab39f --- /dev/null +++ b/docs/_modules/selfisys/utils/plot_utils.html @@ -0,0 +1,2741 @@ + + + + + + + + selfisys.utils.plot_utils — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.plot_utils

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Plotting routines for the SelfiSys project.
+"""
+
+import gc
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import gridspec, colors
+from matplotlib.ticker import FormatStrFormatter, ScalarFormatter
+from selfisys.utils.plot_params import *
+from selfisys.utils.logger import getCustomLogger
+
+logger = getCustomLogger(__name__)
+
+# Configure global plotting settings
+setup_plotting()
+
+
+

+[docs]
+def plot_selection_functions(
+    x,
+    res,
+    res_mis,
+    params,
+    L,
+    corner,
+    axis="com",
+    zz=None,
+    zcorner=None,
+    z_L=None,
+    path=None,
+    force_plot=False,
+    labsAB=True,
+):
+    """
+    Plot selection functions.
+
+    Parameters
+    ----------
+    x : array-like
+        x-axis values (e.g., comoving distances or redshifts).
+    res : list of array-like
+        Selection functions for Model A.
+    res_mis : list of array-like, optional
+        Selection functions for Model B (optional).
+    params : tuple of (array-like, array-like, array-like), optional
+        Standard deviations, means, and normalisation factors for the
+        multiple galaxy populations.
+    L : float
+        Box size.
+    corner : float
+        Diagonal box size.
+    axis : str, optional
+        x-axis type ('com' for comoving distance, 'redshift' for
+        redshift).
+    zz : array-like, optional
+        Mapping between comoving distances and redshifts.
+    zcorner : float, optional
+        Redshift corresponding to the diagonal box size.
+    z_L : float, optional
+        Redshift corresponding to the box side length.
+    path : str, optional
+        Path to save the output plot.
+    force_plot : bool, optional
+        If True, displays the plot even if a path is specified.
+    labsAB : bool, optional
+        If True, labels models as 'Model A' and 'Model B'.
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+    """
+    logger.info("Plotting selection functions...")
+
+    try:
+        colours_list = COLOUR_LIST[: len(res)]
+        plt.figure(figsize=(10, 5))
+
+        # Plot rescaled selection functions for Model A
+        for i, r in enumerate(res):
+            plt.plot(x, r, color=colours_list[i])  # , label=None)
+
+        # Plot rescaled selection functions for Model B, if provided
+        if res_mis is not None:
+            label_a = "Model A" if labsAB else None
+            plt.plot(x, res[-1], color="black", alpha=0, label=label_a)
+            for i, r_mis in enumerate(res_mis):
+                plt.plot(x, r_mis, linestyle="--", color=colours_list[i])
+            label_b = "Model B" if labsAB else None
+            plt.plot(x, res_mis[-1], linestyle="--", color="black", alpha=0, label=label_b)
+
+        # Configure x-axis ticks and labels
+        xticks = [0, corner]
+        xtick_labels = [r"$0$"]
+        if axis == "com" and corner is not None:
+            xtick_labels.append(r"$\sqrt 3\,L \simeq {:.2f}$".format(corner))
+        elif axis == "redshift" and corner is not None:
+            xtick_labels.append(r"$z(\sqrt 3\,L) \simeq {:.3f}$".format(corner))
+        if z_L is not None and L is not None:
+            xticks.insert(1, L)
+            xtick_labels.insert(1, r"$L=3.6$")
+
+        # Annotate populations
+        for i, mean in enumerate(params[1]):
+            mean_plt = x[np.argmin(np.abs(zz - mean))] if zz is not None else mean
+            plt.axvline(mean_plt, color=colours_list[i], linestyle="--", linewidth=1.5)
+            lab_pop = f"Population {i + 1}"
+            plt.axvline(mean_plt, color=colours_list[i], alpha=0, linewidth=2, label=lab_pop)
+            xticks.append(mean_plt)
+            xtick_labels.append(r"${:.2f}$".format(mean_plt))
+
+        # Set axis labels
+        xlabel = r"$r\,[{\rm Gpc}/h]$" if axis == "com" else r"$z$"
+        ylabel = r"$R_i(r)$" if zcorner is None else r"$R_i$"
+        plt.xlabel(xlabel, fontsize=GLOBAL_FS_LARGE)
+        plt.ylabel(ylabel, fontsize=GLOBAL_FS_LARGE)
+
+        # Configure ticks and grid
+        plt.xticks(xticks, xtick_labels)
+        plt.tick_params(axis="x", which="major", size=8, labelsize=GLOBAL_FS_SMALL)
+        plt.tick_params(axis="y", which="major", size=8, labelsize=GLOBAL_FS_SMALL)
+        plt.grid(which="both", axis="both", linestyle="-", linewidth=0.4, color="gray", alpha=0.5)
+
+        # Add legend, save and display plot
+        fs_legend = GLOBAL_FS_LARGE
+        loc_legend = (0.6, 0.35)
+        legend = plt.legend(frameon=True, loc=loc_legend, fontsize=fs_legend)
+        for handle in legend.legend_handles:
+            handle.set_alpha(1)
+
+        # Handle dual x-axes for redshift and comoving distance
+        if zcorner is not None:
+            ax2 = plt.gca().twiny()
+            ax2.set_xlabel(r"$z$", fontsize=GLOBAL_FS_LARGE)
+            zticks = (
+                np.concatenate([[0], [z_L], [zcorner], params[1]])
+                if z_L
+                else np.concatenate([[0], [zcorner], params[1]])
+            )
+            ax2.set_xticks(xticks)
+            ax2.set_xticklabels([r"${:.2f}$".format(z) for z in zticks])
+            ax2.tick_params(axis="x", which="major", size=8, labelsize=GLOBAL_FS_SMALL)
+            ax2.grid(
+                which="both", axis="both", linestyle="-", linewidth=0.4, color="gray", alpha=0.5
+            )
+
+        plt.tight_layout()
+
+        if path:
+            plt.savefig(path, bbox_inches="tight", dpi=300)
+            plt.savefig(path.replace(".png", ".pdf"), bbox_inches="tight", dpi=300)
+            logger.info("Figure saved to %s", path)
+        if not path or force_plot:
+            plt.show()
+        plt.close()
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e

+
+
+
+

+[docs]
+def plotly_3d(field, size=128, L=None, colormap="RdYlBu", limits="max"):
+    """
+    Create an interactive 3D plot of volume slices using Plotly.
+
+    Parameters
+    ----------
+    field : array-like
+        3D data field to visualise.
+    size : int, optional
+        Size of the field along one dimension. Default is 128.
+    L : float, optional
+        Physical size of the field in Mpc/h. Used for axis labels only.
+    colormap : str, optional
+        Colour map for visualisation. Default is 'RdYlBu'.
+    limits : str, optional
+        Colour scale limits ('max', 'truncate', or 'default'). Default
+        is 'max'.
+
+    Returns
+    -------
+    go.Figure
+        Plotly figure object.
+    """
+    import plotly.graph_objects as go
+
+    volume = field.T
+    rows, cols = volume[0].shape
+
+    # Define colour scale limits
+    if limits == "max":
+        maxcol = np.max(np.abs(volume))
+        mincol = -maxcol
+    elif limits == "truncate":
+        maxcol = min(np.max(-volume), np.max(volume))
+        mincol = -maxcol
+    else:
+        maxcol = np.max(volume)
+        mincol = np.min(volume)
+    midcol = np.mean(volume)
+
+    # Generate frames for the animation
+    nb_frames = size
+    frames = [
+        go.Frame(
+            data=go.Surface(
+                z=(size - k) * np.ones((rows, cols)),
+                surfacecolor=np.flipud(volume[cols - 1 - k]),
+                cmin=mincol,
+                cmid=midcol,
+                cmax=maxcol,
+            ),
+            name=str(k),  # Frames must be named for proper animation
+        )
+        for k in range(nb_frames)
+    ]
+
+    # Initial plot configuration
+    fig = go.Figure(
+        frames=frames,
+        data=go.Surface(
+            z=size * np.ones((rows, cols)),
+            surfacecolor=np.flipud(volume[cols // 2]),
+            colorscale=colormap,
+            cmin=mincol,
+            cmid=midcol,
+            cmax=maxcol,
+            colorbar=dict(thickness=20, ticklen=4),
+        ),
+    )
+
+    def frame_args(duration):
+        """Helper function to set animation frame arguments."""
+        return {
+            "frame": {"duration": duration},
+            "mode": "immediate",
+            "fromcurrent": True,
+            "transition": {"duration": duration, "easing": "linear"},
+        }
+
+    # Add animation slider
+    sliders = [
+        {
+            "pad": {"b": 10, "t": 60},
+            "len": 0.9,
+            "x": 0.1,
+            "y": 0,
+            "steps": [
+                {
+                    "args": [[f.name], frame_args(0)],
+                    "label": str(k),
+                    "method": "animate",
+                }
+                for k, f in enumerate(fig.frames)
+            ],
+        }
+    ]
+
+    # Configure layout with or without physical size
+    layout_config = dict(
+        title="Slices in density field",
+        width=600,
+        height=600,
+        scene=dict(
+            zaxis=dict(range=[0, size - 1], autorange=False),
+            xaxis_title="x [Mpc/h]",
+            yaxis_title="y [Mpc/h]",
+            zaxis_title="z [Mpc/h]",
+            aspectratio=dict(x=1, y=1, z=1),
+        ),
+        updatemenus=[
+            {
+                "buttons": [
+                    {"args": [None, frame_args(50)], "label": "&#9654;", "method": "animate"},
+                    {"args": [[None], frame_args(0)], "label": "&#9724;", "method": "animate"},
+                ],
+                "direction": "left",
+                "pad": {"r": 10, "t": 70},
+                "type": "buttons",
+                "x": 0.1,
+                "y": 0,
+            }
+        ],
+        sliders=sliders,
+    )
+    if L is not None:
+        layout_config["scene"]["xaxis"] = dict(
+            ticktext=[0, L / 2, L],
+            tickvals=[0, size / 2, size],
+            title="x [Mpc/h]",
+        )
+        layout_config["scene"]["yaxis"] = dict(
+            ticktext=[0, L / 2, L],
+            tickvals=[0, size / 2, size],
+            title="y [Mpc/h]",
+        )
+        layout_config["scene"]["zaxis"]["ticktext"] = [0, L / 2, L]
+        layout_config["scene"]["zaxis"]["tickvals"] = [0, size / 2, size]
+
+    fig.update_layout(**layout_config)
+    return fig

+
+
+
+

+[docs]
+def plot_observations(
+    k_s,
+    theta_gt,
+    planck_Pk_EH,
+    P_0,
+    Pbins,
+    phi_obs,
+    Npop,
+    path=None,
+    force_plot=False,
+):
+    """
+    Plot the observed power spectra and related quantities.
+
+    Parameters
+    ----------
+    k_s : ndarray
+        Array of wavenumbers.
+    theta_gt : ndarray
+        Ground truth theta values.
+    planck_Pk_EH : ndarray
+        Planck power spectrum values.
+    P_0 : float
+        Normalisation constant for power spectra.
+    Pbins : ndarray
+        Vector of bin boundaries for the summary statistics.
+    phi_obs : ndarray
+        Observed summaries.
+    Npop : int
+        Number of populations.
+    path : str, optional
+        Path to save the output plot.
+    force_plot : bool, optional
+        If True, displays the plot even if a path is specified.
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+
+    """
+    logger.info("Plotting observations...")
+
+    # Sanity checks
+    if len(k_s) == 0 or len(theta_gt) == 0 or len(planck_Pk_EH) == 0:
+        logger.warning("One or more input arrays are empty. The plot may be incomplete.")
+
+    if len(k_s) != len(theta_gt) or len(k_s) != len(planck_Pk_EH):
+        logger.error("Mismatch in array lengths. Plotting may not reflect all data.")
+
+    try:
+        _, ax1 = plt.subplots(figsize=(12, 5))
+
+        # Plot theta values
+        ax1.plot(k_s, theta_gt / P_0, label=r"$\boldsymbol{\uptheta}_{\mathrm{gt}}$", color="C0")
+        ax1.set_xscale("log")
+        ax1.semilogx(
+            k_s,
+            planck_Pk_EH / P_0,
+            label=r"$P_{\mathrm{Planck}}(k)/P_0(k)$",
+            color="C1",
+            lw=0.5,
+        )
+
+        ax1.set_xlabel(r"$k$ [$h$/Mpc]")
+        ax1.set_ylabel(r"$[{\mathrm{Mpc}}/h]^3$")
+        ax1.grid(which="both", axis="y", linestyle="dotted", linewidth=0.6)
+
+        # Vertical lines for theta support wavenumbers
+        for k in k_s[:-1]:
+            ax1.axvline(x=k, color="green", linestyle="dotted", linewidth=0.6)
+        ax1.axvline(
+            x=k_s[-1],
+            color="green",
+            linestyle="dotted",
+            linewidth=0.6,
+            label=r"$\boldsymbol{\uptheta}$ support wavenumbers",
+        )
+
+        # Vertical lines for Phi bin centres
+        ax2 = ax1.twinx()
+        ax1.axvline(x=Pbins[0], color="red", linestyle="dashed", linewidth=0.5)
+        ax2.axvline(
+            x=Pbins[-1],
+            color="red",
+            linestyle="dashed",
+            linewidth=0.5,
+            label=r"$\boldsymbol{\Phi}$-bins centres",
+        )
+        for k in Pbins[1:-1]:
+            ax1.axvline(x=k, ymax=0.167, color="red", linestyle="dashed", linewidth=0.5)
+
+        ax1.legend(loc="lower left", fontsize=GLOBAL_FS)
+        ax1.set_xlim(max(1e-4, k_s.min() - 2e-4), k_s.max())
+        ax1.set_ylim(7e-1, 1.6e0)
+
+        # Plot observations
+        len_obs = len(phi_obs) // Npop
+
+        if len(phi_obs) % Npop != 0:
+            logger.warning(
+                "Length of 'phi_obs' is not divisible by the number of populations. "
+                "Ensure input dimensions are consistent.",
+            )
+
+        for i in range(Npop):
+            ax2.plot(
+                Pbins,
+                phi_obs[i * len_obs : (i + 1) * len_obs],
+                marker="x",
+                label=r"$\boldsymbol{\Phi}_{\mathrm{obs}}$, population " + str(i),
+                linewidth=0.5,
+                color=COLOUR_LIST[i],
+            )
+
+        ax2.legend(loc="upper right", fontsize=GLOBAL_FS)
+
+        if path:
+            plt.savefig(path, bbox_inches="tight", dpi=300)
+            plt.savefig(path.replace(".png", ".pdf"), bbox_inches="tight", dpi=300)
+            logger.info("Figure saved to %s", path)
+        if not path or force_plot:
+            plt.show()
+        plt.close()
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e

+
+
+
+

+[docs]
+def plot_reconstruction(
+    k_s,
+    Pbins,
+    prior_theta_mean,
+    prior_theta_covariance,
+    posterior_theta_mean,
+    posterior_theta_covariance,
+    theta_gt,
+    P_0,
+    phi_obs=None,
+    suptitle=None,
+    theta_fid=None,
+    savepath=None,
+    force_plot=False,
+    legend_loc="upper right",
+    enforce_ylims=True,
+):
+    """
+    Plot the prior, posterior and ground truth power spectra.
+
+    Parameters
+    ----------
+    k_s : ndarray
+        Array of wavenumbers.
+    Pbins : ndarray
+        Vector of bin boundaries for the summary statistics.
+    prior_theta_mean : ndarray
+        Mean of the prior distribution.
+    prior_theta_covariance : ndarray
+        Covariance of the prior distribution.
+    posterior_theta_mean : ndarray
+        Mean of the posterior distribution.
+    posterior_theta_covariance : ndarray
+        Covariance of the posterior distribution.
+    theta_gt : ndarray
+        Ground truth power spectrum.
+    P_0 : float
+        Normalisation constant for the power spectrum.
+    phi_obs : ndarray, optional
+        Observed summaries.
+    suptitle : str, optional
+        Plot title. Leave empty for no title.
+    theta_fid : ndarray, optional
+        Fiducial theta for hyperparameter tuning (for some priors).
+    savepath : str or Path, optional
+        Path to save the plot.
+    force_plot : bool, optional
+        If True, displays the plot even if a path is specified.
+    legend_loc : str, optional
+        Location of the plot legend.
+    enforce_ylims : bool, optional
+        Enforce y-axis limits.
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+    """
+    logger.info("Generating power spectrum reconstruction plot.")
+
+    try:
+        fig, ax = plt.subplots(figsize=(14, 5))
+
+        # Prior
+        ax.plot(
+            k_s,
+            prior_theta_mean,
+            linestyle="-",
+            color="gold",
+            label="$\\boldsymbol{\\uptheta}_0$ (prior)",
+        )
+        ax.fill_between(
+            k_s,
+            prior_theta_mean - 2 * np.sqrt(np.diag(prior_theta_covariance)),
+            prior_theta_mean + 2 * np.sqrt(np.diag(prior_theta_covariance)),
+            color="gold",
+            alpha=0.2,
+        )
+
+        # Fiducial theta used for hyperparameter tuning with some priors
+        if theta_fid is not None:
+            ax.plot(
+                k_s,
+                theta_fid,
+                linestyle="--",
+                color="C1",
+                label="$\\boldsymbol{\\uptheta}_{\\mathrm{fid}}$ (fiducial)",
+            )
+
+        # Posterior
+        ax.plot(
+            k_s,
+            posterior_theta_mean,
+            color="C2",
+            label="$\\boldsymbol{\\upgamma}$ (reconstruction)",
+        )
+        ax.fill_between(
+            k_s,
+            posterior_theta_mean - 2 * np.sqrt(np.diag(posterior_theta_covariance)),
+            posterior_theta_mean + 2 * np.sqrt(np.diag(posterior_theta_covariance)),
+            color="C2",
+            alpha=0.35,
+        )
+
+        # Ground truth
+        ax.plot(
+            k_s,
+            theta_gt / P_0,
+            color="C0",
+            label="$\\boldsymbol{\\uptheta}_\\mathrm{gt}$ (groundtruth)",
+        )
+
+        # Plot the binning
+        ymin, ymax = ax.get_ylim()
+        for i in range(len(k_s)):
+            ax.axvline(
+                k_s[i],
+                ymin=ymin,
+                ymax=ymax,
+                linestyle=":",
+                linewidth=0.8,
+                color="green",
+                alpha=0.5,
+            )
+        ax.vlines(
+            Pbins, ymin=ymin, ymax=0.8, linestyle="--", linewidth=0.8, color="red", alpha=0.5
+        )
+
+        # Overlay observations if provided
+        if phi_obs is not None:
+            ax2 = ax.twinx()
+            ax2.plot(Pbins, phi_obs, "C3.-", label=r"$\Phi_{O}$ (observations)")
+            ax2.legend(loc="lower right", fontsize=GLOBAL_FS_LARGE)
+
+        ax.set_xlim([k_s.min() - 0.0001, k_s.max()])
+        ax.set_xscale("log")
+        ax.grid(visible=True, which="both", linestyle=":", color="grey")
+        ax.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax.xaxis.set_tick_params(which="minor", length=4)
+        ax.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        if enforce_ylims:
+            ax.set_ylim([0.85, 1.35])
+        ax.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_LARGE)
+        ax.set_ylabel("$\\theta(k) = P(k)/P_0(k)$", size=GLOBAL_FS_LARGE)
+        ax.legend(loc=legend_loc, fontsize=GLOBAL_FS_LARGE)
+
+        plt.suptitle(suptitle, fontsize=GLOBAL_FS_XLARGE) if suptitle else None
+
+        # Save / display
+        if savepath:
+            fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True)
+            fig.savefig(
+                savepath[:-4] + "_white.png",
+                bbox_inches="tight",
+                dpi=300,
+                format="png",
+                transparent=False,
+            )
+            fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf")
+            fig.suptitle("")
+            fig.savefig(
+                savepath[:-4] + "_notitle.png",
+                bbox_inches="tight",
+                dpi=300,
+                format="png",
+                transparent=True,
+            )
+            fig.savefig(
+                savepath[:-4] + "_white_notitle.png",
+                bbox_inches="tight",
+                dpi=300,
+                format="png",
+                transparent=False,
+            )
+            fig.savefig(
+                savepath[:-4] + "_notitle.pdf",
+                bbox_inches="tight",
+                dpi=300,
+                format="pdf",
+            )
+
+            logger.info("Figure saved to %s", savepath)
+
+        if force_plot or savepath is None:
+            plt.show()
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e
+    finally:
+        plt.close(fig)
+        del fig, ax
+        gc.collect()

+
+
+
+

+[docs]
+def plot_fisher(F0, params_names_fisher, title=None, path=None):
+    """
+    Plot the Fisher matrix as a heatmap.
+
+    Parameters
+    ----------
+    F0 : ndarray
+        Fisher matrix.
+    params_names_fisher : list of str
+        Names for the axes.
+    title : str, optional
+        Title of the plot. Default is "Fisher matrix".
+    path : str or Path, optional
+        Path to save the plot. If None, the plot is displayed.
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+    """
+    import seaborn as sns
+
+    logger.info("Generating Fisher matrix plot.")
+    try:
+        plt.figure(figsize=(10, 10))
+
+        # Normalisation for the colourmap
+        F0min, F0max = F0.min(), F0.max()
+        center = 0 if F0min < 0 and F0max > 0 else np.mean(F0)
+        divnorm = colors.TwoSlopeNorm(vmin=F0min, vcenter=center, vmax=F0max)
+
+        # Plot the Fisher matrix
+        sns.heatmap(
+            F0,
+            annot=True,
+            fmt=".2e",
+            cmap="RdBu_r",
+            norm=divnorm,
+            square=True,
+            cbar_kws={"shrink": 0.8},
+        )
+
+        plt.xticks(np.arange(len(params_names_fisher)) + 0.5, params_names_fisher, rotation=0)
+        plt.yticks(np.arange(len(params_names_fisher)) + 0.5, params_names_fisher, rotation=0)
+        plt.title(title or "Fisher matrix")
+
+        if path:
+            path = Path(path)
+            for fmt in ["png", "pdf"]:
+                plt.savefig(
+                    path.with_suffix(f".{fmt}"),
+                    bbox_inches="tight",
+                    dpi=300,
+                    format=fmt,
+                    transparent=True,
+                )
+
+            logger.info("Figure saved to %s", path)
+        else:
+            logger.info("Displaying plot.")
+            plt.show()
+
+    except Exception as e:
+        logger.critical("Unexpected error during Fisher matrix plotting: %s", str(e))
+        raise RuntimeError("Fisher matrix plotting failed.") from e
+    finally:
+        plt.close()
+        gc.collect()

+
+
+
+

+[docs]
+def plot_mocks_compact(
+    NORM,
+    N,
+    P,
+    Pbins,
+    phi_obs,
+    Phi_0,
+    f_0,
+    C_0,
+    suptitle=None,
+    force_plot=False,
+    savepath=None,
+):
+    """
+    Plot and compare observed and simulated power spectra.
+
+    Parameters
+    ----------
+    NORM : float
+        Normalisation factor for the observed spectra.
+    N : int
+        Number of mock data realisations.
+    P : int
+        Number of bins for the summaries.
+    Pbins : ndarray
+        Vector of bin boundaries for the summary statistics.
+    phi_obs : ndarray
+        Observed power spectrum.
+    Phi_0 : ndarray
+        Mock realisations of the power spectrum.
+    f_0 : ndarray
+        Mean power spectrum.
+    C_0 : ndarray
+        Covariance matrix of the mock summaries.
+    suptitle : str, optional
+        Title for the plot.
+    force_plot : bool, optional
+        If True, displays the plot even if a path is specified.
+    savepath : str or Path, optional
+        Path to save the plot.
+
+    Raises
+    ------
+    RuntimeError
+        If an unexpected error occurs during plotting.
+    """
+    logger.info("Plotting mock power spectra (compact)...")
+
+    alpha_mocks = 0.25
+    alpha_binning = 0.3
+
+    try:
+        Phi_0_full = Phi_0.copy()
+        phi_obs_full = phi_obs.copy()
+        f_0_full = f_0.copy()
+        C_0_full = C_0.copy()
+
+        idx = 0
+        Phi_0 = Phi_0[:, idx * P : (idx + 1) * P]
+        phi_obs = phi_obs[idx * P : (idx + 1) * P]
+        f_0 = f_0[idx * P : (idx + 1) * P]
+        C_0 = C_0[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P]
+
+        COLOUR_LIST_means = ["darkorchid", "saddlebrown", "mediumvioletred"]
+
+        fig = plt.figure(figsize=(10, 10))
+        gs0 = gridspec.GridSpec(
+            3,
+            2,
+            width_ratios=[1.0, 1.0],
+            height_ratios=[1.0, 1.0, 1.0],
+            wspace=0.0,
+            hspace=0.0,
+        )
+        gs0.update(right=1.0, left=0.0)
+        ax0 = plt.subplot(gs0[0, 0])
+        ax0b = plt.subplot(gs0[0, 1])
+        ax01 = plt.subplot(gs0[1, 0], sharex=ax0)
+        ax01b = plt.subplot(gs0[1, 1])
+        ax02 = plt.subplot(gs0[2, 0], sharex=ax0)
+        ax02b = plt.subplot(gs0[2, 1])
+        axx0x = [[ax01, ax01b], [ax02, ax02b]]
+
+        # Observed power spectrum (normalised)
+        ax0.semilogx(
+            Pbins,
+            phi_obs * NORM,
+            linewidth=2,
+            color="black",
+            label=r"$\boldsymbol{\Phi}_\mathrm{O}$",
+            zorder=3,
+        )
+
+        # Realisations at the expansion point
+        for i in range(N - 1):
+            ax0.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7)
+
+        # Average value
+        ax0.semilogx(
+            Pbins,
+            Phi_0[N - 1],
+            color="C7",
+            alpha=alpha_mocks,
+            linewidth=0.7,
+        )
+        ax0.semilogx(
+            Pbins,
+            f_0,
+            linewidth=2,
+            color=COLOUR_LIST_means[idx],
+            linestyle="--",
+            label=r"$\textbf{f}_0$",
+            zorder=2,
+        )
+
+        # 2-sigma intervals
+        ax0.fill_between(
+            Pbins,
+            f_0 - 2 * np.sqrt(np.diag(C_0)),
+            f_0 + 2 * np.sqrt(np.diag(C_0)),
+            color=COLOUR_LIST[idx],
+            alpha=0.4,
+            label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+            zorder=2,
+        )
+
+        # Plot the binning
+        (ymin, ymax) = ax0.get_ylim()
+        ax0.set_ylim([ymin, ymax])
+        for i in range(len(Pbins)):
+            ax0.plot(
+                (Pbins[i], Pbins[i]),
+                (ymin, ymax),
+                linestyle="--",
+                linewidth=0.8,
+                color="red",
+                alpha=alpha_binning,
+                zorder=1,
+            )
+
+        ax0.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+        ax0.set_ylabel("population 1", size=GLOBAL_FS)
+        ax0.legend(fontsize=GLOBAL_FS, loc="upper right")
+        ax0.xaxis.set_ticks_position("both")
+        ax0.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        for axis in ["top", "bottom", "left", "right"]:
+            ax0.spines[axis].set_linewidth(1.0)
+        ax0.xaxis.set_tick_params(which="major", length=6)
+        ax0.xaxis.set_tick_params(which="minor", length=4)
+        if suptitle is not None:
+            ax0.set_title(
+                r"$\boldsymbol{\Phi}$ (" + suptitle + ")", y=1.05, fontsize=GLOBAL_FS_LARGE
+            )
+        else:
+            ax0.set_title(r"$\boldsymbol{\Phi}$", y=1.05, fontsize=GLOBAL_FS_LARGE)
+        ax0.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+        ax0.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
+
+        # Same as above but normalise everything by the observations:
+        normalisation = phi_obs
+
+        ax0b.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+
+        # Observed power spectrum (normalised)
+        ax0b.semilogx(
+            Pbins,
+            NORM * phi_obs / normalisation,
+            linewidth=2,
+            color="black",
+            label=r"$\boldsymbol{\Phi}_\mathrm{O}$",
+            zorder=3,
+        )
+
+        for i in range(N - 1):
+            ax0b.semilogx(
+                Pbins,
+                Phi_0[i] / normalisation,
+                color="C7",
+                alpha=alpha_mocks,
+                linewidth=0.7,
+            )
+
+        ax0b.semilogx(
+            Pbins,
+            Phi_0[N - 1] / normalisation,
+            color="C7",
+            alpha=alpha_mocks,
+            linewidth=0.7,
+            label=r"$\boldsymbol{\Phi}_{\theta_0}$",
+        )
+
+        ax0b.semilogx(
+            Pbins,
+            f_0 / normalisation,
+            linewidth=2,
+            color=COLOUR_LIST_means[idx],
+            linestyle="--",
+            label=r"$\textbf{f}_0$",
+            zorder=2,
+        )
+
+        # 2-sigma intervals
+        ax0b.fill_between(
+            Pbins,
+            f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation,
+            f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation,
+            color=COLOUR_LIST[idx],
+            alpha=0.4,
+            label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+            zorder=2,
+        )
+
+        # Plot the binning
+        (ymin, ymax) = ax0b.get_ylim()
+        ax0b.set_ylim([ymin, ymax])
+        for i in range(len(Pbins)):
+            ax0b.plot(
+                (Pbins[i], Pbins[i]),
+                (ymin, ymax),
+                linestyle="--",
+                linewidth=0.8,
+                color="red",
+                alpha=alpha_binning,
+                zorder=1,
+            )
+
+        ax0b.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS)
+        ax0b.xaxis.set_ticks_position("both")
+        ax0b.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        for axis in ["top", "bottom", "left", "right"]:
+            ax0b.spines[axis].set_linewidth(1.0)
+        ax0b.xaxis.set_tick_params(which="major", length=6)
+        ax0b.xaxis.set_tick_params(which="minor", length=4)
+        ax0b.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+        ax0b.yaxis.tick_right()
+        if suptitle is not None:
+            ax0b.set_title(
+                r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$ (" + suptitle + ")",
+                y=1.05,
+                fontsize=GLOBAL_FS,
+            )
+        else:
+            ax0b.set_title(
+                r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$", y=1.05, fontsize=GLOBAL_FS
+            )
+
+        for ax, axb in axx0x:
+            idx += 1
+            Phi_0 = Phi_0_full[:, idx * P : (idx + 1) * P]
+            phi_obs = phi_obs_full[idx * P : (idx + 1) * P]
+            f_0 = f_0_full[idx * P : (idx + 1) * P]
+            C_0 = C_0_full[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P]
+
+            # Same plot but for the other axes:
+            ax.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+
+            # Observed power spectrum (normalised)
+            ax.semilogx(Pbins, phi_obs, linewidth=2, color="black", zorder=3)
+            for i in range(N - 1):
+                ax.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7)
+            ax.semilogx(Pbins, Phi_0[N - 1], color="C7", alpha=alpha_mocks, linewidth=0.7)
+            ax.semilogx(
+                Pbins,
+                f_0,
+                linewidth=2,
+                color=COLOUR_LIST_means[idx],
+                linestyle="--",
+                label=r"$\textbf{f}_0$",
+                zorder=2,
+            )
+
+            # 2-sigma intervals
+            ax.fill_between(
+                Pbins,
+                f_0 - 2 * np.sqrt(np.diag(C_0)),
+                f_0 + 2 * np.sqrt(np.diag(C_0)),
+                color=COLOUR_LIST[idx],
+                alpha=0.4,
+                label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+                zorder=2,
+            )
+
+            # Plot the binning:
+            (ymin, ymax) = ax.get_ylim()
+            ax.set_ylim([ymin, ymax])
+            for i in range(len(Pbins)):
+                ax.plot(
+                    (Pbins[i], Pbins[i]),
+                    (ymin, ymax),
+                    linestyle="--",
+                    linewidth=0.8,
+                    color="red",
+                    alpha=alpha_binning,
+                    zorder=1,
+                )
+            ax.set_ylabel("population " + str(idx + 1), size=GLOBAL_FS)
+            ax.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS)
+            ax.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8)
+            ax.xaxis.set_ticks_position("both")
+            ax.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+            ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
+            for axis in ["top", "bottom", "left", "right"]:
+                ax.spines[axis].set_linewidth(1.0)
+            ax.xaxis.set_tick_params(which="major", length=6)
+            ax.xaxis.set_tick_params(which="minor", length=4)
+            ax.legend(loc="upper right", fontsize=GLOBAL_FS)
+
+            # Normalise everything by the observations:
+            normalisation = phi_obs
+
+            axb.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+
+            # Observed power spectrum (normalised)
+            axb.semilogx(
+                Pbins,
+                phi_obs / normalisation,
+                linewidth=2,
+                color="black",
+                label=r"$\boldsymbol{\Phi}_\mathrm{O}$",
+                zorder=3,
+            )
+
+            for i in range(N - 1):
+                axb.semilogx(
+                    Pbins,
+                    Phi_0[i] / normalisation,
+                    color="C7",
+                    alpha=alpha_mocks,
+                    linewidth=0.7,
+                )
+
+            axb.semilogx(
+                Pbins,
+                Phi_0[N - 1] / normalisation,
+                color="C7",
+                alpha=alpha_mocks,
+                linewidth=0.7,
+                label=r"$\boldsymbol{\Phi}_{\theta_0}$",
+            )
+
+            axb.semilogx(
+                Pbins,
+                f_0 / normalisation,
+                linewidth=2,
+                color=COLOUR_LIST_means[idx],
+                linestyle="--",
+                label=r"$\textbf{f}_0$",
+                zorder=2,
+            )
+
+            # 2-sigma intervals
+            axb.fill_between(
+                Pbins,
+                f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation,
+                f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation,
+                color=COLOUR_LIST[idx],
+                alpha=0.4,
+                label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+                zorder=2,
+            )
+
+            # Plot the binning
+            (ymin, ymax) = axb.get_ylim()
+            axb.set_ylim([ymin, ymax])
+            for i in range(len(Pbins)):
+                axb.plot(
+                    (Pbins[i], Pbins[i]),
+                    (ymin, ymax),
+                    linestyle="--",
+                    linewidth=0.8,
+                    color="red",
+                    alpha=alpha_binning,
+                    zorder=1,
+                )
+
+            axb.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS)
+            axb.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8)
+            axb.xaxis.set_ticks_position("both")
+            axb.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            axb.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+            axb.yaxis.tick_right()
+            for axis in ["top", "bottom", "left", "right"]:
+                axb.spines[axis].set_linewidth(1.0)
+            axb.xaxis.set_tick_params(which="major", length=6)
+            axb.xaxis.set_tick_params(which="minor", length=4)
+
+        if savepath:
+            savepath = Path(savepath)
+            for fmt in ["png", "pdf"]:
+                fig.savefig(savepath.with_suffix(f".{fmt}"), bbox_inches="tight", dpi=300)
+            logger.info("Plot saved to %s", savepath)
+        if force_plot or not savepath:
+            plt.show()
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e
+    finally:
+        plt.close(fig)
+        del fig, ax0, ax0b, ax01, ax01b, ax02, ax02b
+        gc.collect()

+
+
+
+

+[docs]
+def plot_C(
+    C_0,
+    X,
+    Y,
+    Pbins,
+    CMap,
+    binning=True,
+    suptitle=None,
+    savepath=None,
+    force=False,
+):
+    """
+    Plot covariance matrix.
+
+    Parameters
+    ----------
+    C_0 : ndarray
+        Covariance matrix.
+    X : ndarray
+        X-axis grid for plotting.
+    Y : ndarray
+        Y-axis grid for plotting.
+    Pbins : ndarray
+        Vector of bin boundaries for the summary statistics.
+    CMap : str
+        Colormap for the plot.
+    binning : bool, optional
+        Whether to overlay bin lines on the plot. Default is True.
+    suptitle : str, optional
+        Title for the plot. If None, a default title is used.
+    savepath : str or Path, optional
+        Path to save the plot. If None, the plot is displayed.
+    force : bool, optional
+        If True, displays the plot even if savepath is specified.
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+    """
+
+    from itertools import product
+
+    logger.info("Plotting covariance matrix...")
+    try:
+        fig, axs = plt.subplots(3, 3, figsize=(13, 11))
+        P = len(Pbins)
+
+        # Determine vmin, vmax and central value for TwoSlopeNorm
+        vmin, vmax = C_0.min(), C_0.max()
+        centerval = 0 if vmin < 0 and vmax > 0 else np.mean(C_0)
+        divnorm = colors.TwoSlopeNorm(vmin=vmin, vcenter=centerval, vmax=vmax)
+
+        for i, j in product(range(3), range(3)):
+            C_0_ij = C_0[i * P : (i + 1) * P, j * P : (j + 1) * P]
+            imat = 2 - i  # Invert to place origin at bottom left
+            ax = axs[imat, j]
+            ax.set_aspect("equal")
+            ax.set_xscale("log")
+            ax.set_yscale("log")
+            ax.xaxis.set_ticks_position("both")
+            ax.yaxis.set_ticks_position("both")
+
+            # Plot the covariance matrix
+            im1 = ax.pcolormesh(X, Y, C_0_ij[:-1, :-1], shading="flat", norm=divnorm, cmap=CMap)
+
+            # Overlay the binning grid (if enabled)
+            if binning:
+                for n in range(len(Pbins)):
+                    ax.plot(
+                        (Pbins[n], Pbins[n]),
+                        (Pbins.min(), Pbins.max()),
+                        linestyle="--",
+                        linewidth=0.5,
+                        color="red",
+                        alpha=0.5,
+                    )
+                    ax.plot(
+                        (Pbins.min(), Pbins.max()),
+                        (Pbins[n], Pbins[n]),
+                        linestyle="--",
+                        linewidth=0.5,
+                        color="red",
+                        alpha=0.5,
+                    )
+
+            # Custom ticks for boundary axes
+            if i == 0:
+                ax.xaxis.set_tick_params(
+                    which="both", direction="in", width=1.0, labelsize=GLOBAL_FS
+                )
+                ax.xaxis.set_tick_params(which="major", length=6)
+                ax.xaxis.set_tick_params(which="minor", length=4)
+            else:
+                ax.set_xticks([])
+
+            if j == 0:
+                ax.yaxis.set_tick_params(
+                    which="both", direction="in", width=1.0, labelsize=GLOBAL_FS
+                )
+                ax.yaxis.set_tick_params(which="major", length=6)
+                ax.yaxis.set_tick_params(which="minor", length=4)
+            else:
+                ax.set_yticks([])
+
+        # Set title and adjust layout
+        suptitle = r"$\textbf{C}_0$" if suptitle is None else r"$\textbf{C}_0$ (" + suptitle + ")"
+        plt.suptitle(suptitle, y=0.94, x=0.45, size=GLOBAL_FS + GLOBAL_FS_XLARGE)
+        plt.subplots_adjust(wspace=0, hspace=0)
+
+        # Colourbar
+        cbar = fig.colorbar(
+            im1, ax=axs.ravel().tolist(), shrink=1, pad=0.009, aspect=40, orientation="vertical"
+        )
+        cbar.ax.tick_params(
+            axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS_LARGE
+        )
+        cbar.update_normal(im1)
+        cbar.mappable.set_clim(vmin=C_0[:-1, :-1].min(), vmax=C_0[:-1, :-1].max())
+
+        loc_xticks = np.concatenate([np.linspace(vmin, 0, 5), np.linspace(0, vmax, 5)[1:]])
+        val_xticks = np.round(loc_xticks, 2)
+        cbar.set_ticks(loc_xticks, labels=val_xticks)
+
+        # Axis labels
+        fig.text(0.45, 0.04, r"$k$ [$h$/Mpc]", ha="center", size=GLOBAL_FS_XLARGE)
+        fig.text(
+            0.04, 0.5, r"$k'$ [$h$/Mpc]", va="center", rotation="vertical", size=GLOBAL_FS_XLARGE
+        )
+
+        # Save / display
+        if savepath:
+            savepath = Path(savepath)
+            savepath.parent.mkdir(parents=True, exist_ok=True)
+            fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True)
+            fig.savefig(savepath.with_suffix(".pdf"), bbox_inches="tight", dpi=300)
+
+            logger.info("Plot saved to %s", savepath)
+
+        if force or not savepath:
+            plt.show()
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e
+    finally:
+        plt.close(fig)
+        del fig, axs, im1
+        gc.collect()

+
+
+
+

+[docs]
+def plot_prior_and_posterior_covariances(
+    X,
+    Y,
+    k_s,
+    prior_theta_covariance,
+    prior_covariance,
+    posterior_theta_covariance,
+    P_0,
+    force_plot=False,
+    suptitle="",
+    savepath=None,
+):
+    """
+    Plot prior and posterior covariance matrices.
+
+    Parameters
+    ----------
+    X : ndarray
+        X-axis grid.
+    Y : ndarray
+        Y-axis grid.
+    k_s : ndarray
+        Wavenumbers.
+    prior_theta_covariance : ndarray
+        Prior covariance matrix for normalised spectra.
+    prior_covariance : ndarray
+        Prior covariance matrix for unnormalised spectra.
+    posterior_theta_covariance : ndarray
+        Posterior covariance matrix for normalised spectra.
+    P_0 : ndarray
+        Fiducial power spectrum used for normalisation.
+    force_plot : bool, optional
+        Display plot even if savepath is set.
+    suptitle : str, optional
+        Title for the plot.
+    savepath : str or Path, optional
+        Path to save the plot. If None, the plot is displayed.
+    verbose : int, optional
+        Verbosity level (0=silent, 1=default, 2=detailed).
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+    """
+    from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+    logger.info("Plotting prior and posterior covariance matrices...")
+    try:
+        fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(figsize=(15, 14), nrows=2, ncols=2)
+        fig.suptitle(suptitle, fontsize=GLOBAL_FS_XLARGE, y=0.99)
+
+        # Covariance matrix of the prior (normalised spectra theta)
+        ax0.set_aspect("equal")
+        ax0.set_xscale("log")
+        ax0.set_yscale("log")
+        ax0.xaxis.set_ticks_position("both")
+        ax0.yaxis.set_ticks_position("both")
+        ax0.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax0.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax0.xaxis.set_tick_params(which="minor", length=4)
+        ax0.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax0.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax0.yaxis.set_tick_params(which="minor", length=4)
+        divider = make_axes_locatable(ax0)
+        ax0_cb = divider.new_horizontal(size="5%", pad=0.10)
+        im0 = ax0.pcolormesh(X, Y, prior_theta_covariance[:-1, :-1], cmap="Blues", shading="flat")
+        for i in range(len(k_s)):
+            ax0.plot(
+                (k_s[i], k_s[i]),
+                (k_s.min(), k_s.max()),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        for i in range(len(k_s)):
+            ax0.plot(
+                (k_s.min(), k_s.max()),
+                (k_s[i], k_s[i]),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        ax0.set_title("$\\textbf{S}$", size=GLOBAL_FS_XLARGE)
+        ax0.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE)
+        ax0.set_ylabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE)
+        fig.add_axes(ax0_cb)
+        cbar0 = fig.colorbar(im0, cax=ax0_cb)
+        cbar0.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS)
+
+        # Covariance matrix of the prior (unnormalized spectra)
+        ax1.set_aspect("equal")
+        ax1.set_xscale("log")
+        ax1.set_yscale("log")
+        ax1.xaxis.set_ticks_position("both")
+        ax1.yaxis.set_ticks_position("both")
+        ax1.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax1.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax1.xaxis.set_tick_params(which="minor", length=4)
+        ax1.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax1.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax1.yaxis.set_tick_params(which="minor", length=4)
+        divider = make_axes_locatable(ax1)
+        ax1_cb = divider.new_horizontal(size="5%", pad=0.10)
+        im1 = ax1.pcolormesh(X, Y, prior_covariance[:-1, :-1], cmap="Purples", shading="flat")
+        for i in range(len(k_s)):
+            ax1.plot(
+                (k_s[i], k_s[i]),
+                (k_s.min(), k_s.max()),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        for i in range(len(k_s)):
+            ax1.plot(
+                (k_s.min(), k_s.max()),
+                (k_s[i], k_s[i]),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        ax1.set_title(
+            "$\\mathrm{diag}(\\textbf{P}_0) \\cdot \\textbf{S} \\cdot \\mathrm{diag}(\\textbf{P}_0)$",
+            size=GLOBAL_FS_XLARGE,
+        )
+        ax1.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE)
+        ax1.set_ylabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE)
+        fig.add_axes(ax1_cb)
+        cbar1 = fig.colorbar(im1, cax=ax1_cb)
+        cbar1.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS)
+
+        posterior_covariance = np.diag(P_0).dot(posterior_theta_covariance).dot(np.diag(P_0))
+
+        # Covariance matrix of the posterior (normalised spectra theta)
+        ax2.set_aspect("equal")
+        ax2.set_xscale("log")
+        ax2.set_yscale("log")
+        ax2.xaxis.set_ticks_position("both")
+        ax2.yaxis.set_ticks_position("both")
+        ax2.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax2.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax2.xaxis.set_tick_params(which="minor", length=4)
+        ax2.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax2.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax2.yaxis.set_tick_params(which="minor", length=4)
+        divider = make_axes_locatable(ax2)
+        ax2_cb = divider.new_horizontal(size="5%", pad=0.10)
+        quantity = posterior_theta_covariance
+        vmin = quantity.min()
+        vmax = quantity.max()
+        if vmin < 0 and vmax > 0:
+            centerval = 0
+        else:
+            centerval = np.mean(quantity)
+        norm_posterior = colors.TwoSlopeNorm(
+            vmin=posterior_theta_covariance.min(),
+            vcenter=centerval,
+            vmax=posterior_theta_covariance.max(),
+        )
+        Blues_Reds = create_colormap("Blues_Reds")
+        im2 = ax2.pcolormesh(
+            X,
+            Y,
+            posterior_theta_covariance[:-1, :-1],
+            cmap=Blues_Reds,
+            norm=norm_posterior,
+            shading="flat",
+        )
+        for i in range(len(k_s)):
+            ax2.plot(
+                (k_s[i], k_s[i]),
+                (k_s.min(), k_s.max()),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        for i in range(len(k_s)):
+            ax2.plot(
+                (k_s.min(), k_s.max()),
+                (k_s[i], k_s[i]),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        ax2.set_title("$\\boldsymbol{\\Gamma}$", size=GLOBAL_FS_XLARGE)
+        ax2.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE)
+        ax2.set_ylabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE)
+        fig.add_axes(ax2_cb)
+        cbar2 = fig.colorbar(im2, cax=ax2_cb)
+        cbar2.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS)
+        cbar2.mappable.set_clim(
+            vmin=posterior_theta_covariance.min(), vmax=posterior_theta_covariance.max()
+        )
+        if posterior_theta_covariance.min() < 0 and posterior_theta_covariance.max() > 0:
+            ticks = np.concatenate(
+                [
+                    np.linspace(posterior_theta_covariance.min(), 0, 5),
+                    np.linspace(0, posterior_theta_covariance.max(), 5)[1:],
+                ]
+            )
+        else:
+            ticks = np.linspace(
+                posterior_theta_covariance.min(), posterior_theta_covariance.max(), 6
+            )
+        ticks_labels = ["{:.1e}".format(tick) for tick in ticks]
+        cbar2.set_ticks(ticks)
+        cbar2.set_ticklabels(ticks_labels)
+
+        # Covariance matrix of the posterior (unnormalised spectra)
+        ax3.set_aspect("equal")
+        ax3.set_xscale("log")
+        ax3.set_yscale("log")
+        ax3.xaxis.set_ticks_position("both")
+        ax3.yaxis.set_ticks_position("both")
+        ax3.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax3.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax3.xaxis.set_tick_params(which="minor", length=4)
+        ax3.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+        ax3.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS)
+        ax3.yaxis.set_tick_params(which="minor", length=4)
+        divider = make_axes_locatable(ax3)
+        ax3_cb = divider.new_horizontal(size="5%", pad=0.10)
+        quantity = posterior_covariance
+        vmin = quantity.min()
+        vmax = quantity.max()
+        if vmin < 0 and vmax > 0:
+            centerval = 0
+        else:
+            centerval = np.mean(quantity)
+        norm_posterior_spectrum = colors.TwoSlopeNorm(
+            vmin=posterior_covariance.min(),
+            vcenter=centerval,
+            vmax=posterior_covariance.max(),
+        )
+        Purples_Oranges = create_colormap("Purples_Oranges")
+        im3 = ax3.pcolormesh(
+            X,
+            Y,
+            posterior_covariance[:-1, :-1],
+            cmap=Purples_Oranges,
+            norm=norm_posterior_spectrum,
+            shading="flat",
+        )
+        for i in range(len(k_s)):
+            ax3.plot(
+                (k_s[i], k_s[i]),
+                (k_s.min(), k_s.max()),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        for i in range(len(k_s)):
+            ax3.plot(
+                (k_s.min(), k_s.max()),
+                (k_s[i], k_s[i]),
+                linestyle=":",
+                linewidth=0.5,
+                color="green",
+                alpha=0.5,
+            )
+        ax3.set_title(
+            "$\\mathrm{diag}(\\textbf{P}_0) \\cdot \\boldsymbol{\\Gamma} \\cdot \\mathrm{diag}(\\textbf{P}_0)$",
+            size=22,
+        )
+        ax3.set_xlabel("$k$ [$h$/Mpc]", size=22)
+        ax3.set_ylabel("$k$ [$h$/Mpc]", size=22)
+        fig.add_axes(ax3_cb)
+        cbar3 = fig.colorbar(im3, cax=ax3_cb)
+        cbar3.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=19)
+        cbar3.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=19)
+        cbar3.mappable.set_clim(vmin=posterior_covariance.min(), vmax=posterior_covariance.max())
+        if posterior_covariance.min() < 0 and posterior_covariance.max() > 0:
+            ticks = np.concatenate(
+                [
+                    np.linspace(posterior_covariance.min(), 0, 5),
+                    np.linspace(0, posterior_covariance.max(), 5)[1:],
+                ]
+            )
+        else:
+            ticks = np.linspace(posterior_covariance.min(), posterior_covariance.max(), 6)
+        ticks_labels = ["{:.1e}".format(tick) for tick in ticks]
+        cbar3.set_ticks(ticks)
+        cbar3.set_ticklabels(ticks_labels)
+
+        fig.tight_layout()
+        if savepath is not None:
+            fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True)
+            fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf")
+            fig.suptitle("")
+            fig.savefig(
+                savepath[:-4] + "_notitle.png",
+                bbox_inches="tight",
+                dpi=300,
+                format="png",
+                transparent=True,
+            )
+            fig.savefig(savepath[:-4] + "_notitle.pdf", bbox_inches="tight", dpi=300, format="pdf")
+            logger.info("Plot saved to %s", savepath)
+        if force_plot or not savepath:
+            plt.show()
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e
+    finally:
+        plt.close(fig)
+        del fig
+        gc.collect()

+
+
+
+

+[docs]
+def plot_gradients(
+    Pbins,
+    P,
+    df_16_full,
+    df_32_full,
+    df_48_full,
+    df_full,
+    k_s,
+    X,
+    Y,
+    fixscale=False,
+    force=False,
+    suptitle="",
+    savepath=None,
+):
+    """
+    Plot gradients.
+
+    Parameters
+    ----------
+    Pbins : ndarray
+        Vector of bin boundaries for the summary statistics.
+    P : int
+        Number of bins.
+    df_16_full : ndarray
+        Derivative with respect to the 16th input.
+    df_32_full : ndarray
+        Derivative with respect to the 32nd input.
+    df_48_full : ndarray
+        Derivative with respect to the 48th input.
+    df_full : ndarray
+        Full derivative.
+    k_s : ndarray
+        Wavenumbers.
+    X : ndarray
+        X-axis grid.
+    Y : ndarray
+        Y-axis grid.
+    fixscale : bool, optional
+        Fix the y-axis scale. Default is False.
+    force : bool, optional
+        Display plot even if savepath is set.
+    suptitle : str, optional
+        Title for the plot.
+    savepath : str or Path, optional
+        Path to save the plot. If None, the plot is displayed.
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+
+    """
+    logger.info("Plotting gradients...")
+
+    try:
+        fig = plt.figure(figsize=(15, 12))
+        fig.suptitle(suptitle, y=0.95, fontsize=22)
+
+        gs0 = gridspec.GridSpec(
+            3,
+            2,
+            width_ratios=[1.0, 0.5],
+            height_ratios=[1.0, 1.0, 1.0],
+            hspace=0.0,
+            wspace=0.2,
+        )
+        gs0.update(right=1.0, left=0.0)
+        ax00 = plt.subplot(gs0[0, 0])
+        ax01 = plt.subplot(gs0[1, 0], sharex=ax00)
+        ax02 = plt.subplot(gs0[2, 0], sharex=ax00)
+
+        gs1 = gridspec.GridSpec(
+            3,
+            2,
+            width_ratios=[1.0, 0.5],
+            height_ratios=[1.0, 1.0, 1.0],
+            hspace=0.0,
+            wspace=0.2,
+        )
+        gs1.update(top=0.881, bottom=0.112)
+        ax10 = plt.subplot(gs1[0, 1])
+        ax11 = plt.subplot(gs1[1, 1], sharex=ax10)
+        ax12 = plt.subplot(gs1[2, 1], sharex=ax10)
+
+        axx = [(ax00, ax10), (ax01, ax11), (ax02, ax12)]
+        for axs, idx in zip(axx, range(3)):
+            ax = axs[0]
+            df_16 = np.copy(df_16_full[idx * P : (idx + 1) * P])
+            df_32 = np.copy(df_32_full[idx * P : (idx + 1) * P])
+            df_48 = np.copy(df_48_full[idx * P : (idx + 1) * P])
+            df = df_full[idx * P : (idx + 1) * P]
+
+            # Plot the three selected components of the derivative:
+            ax.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+            ax.semilogx(Pbins, np.zeros_like(Pbins), linestyle=":", color="black")
+            ax.semilogx(
+                Pbins,
+                df_16,
+                linewidth=2,
+                linestyle="-",
+                color="C4",
+                label=r"$(\nabla \mathbf{f}_0)^\intercal_{16}$",
+                zorder=2,
+            )
+            ax.semilogx(
+                Pbins,
+                df_32,
+                linewidth=2,
+                linestyle="-",
+                color="C0",
+                label=r"$(\nabla \mathbf{f}_0)^\intercal_{32}$",
+                zorder=2,
+            )
+            ax.semilogx(
+                Pbins,
+                df_48,
+                linewidth=2,
+                linestyle="-",
+                color="C2",
+                label=r"$(\nabla \mathbf{f}_0)^\intercal_{48}$",
+                zorder=2,
+            )
+            if fixscale:
+                ymin = np.min([df_16, df_32, df_48]) - 1e-2
+                ymax = np.max([df_16, df_32, df_48]) + 1e-2
+            else:
+                (ymin, ymax) = ax.get_ylim()
+            ax.set_ylim([ymin, ymax])
+
+            # Binning
+            for i in range(len(Pbins)):
+                ax.plot(
+                    (Pbins[i], Pbins[i]),
+                    (ymin, ymax),
+                    linestyle="--",
+                    linewidth=0.8,
+                    color="red",
+                    alpha=0.5,
+                    zorder=1,
+                )
+            ax.yaxis.grid(linestyle=":", color="grey")
+            ax.set_ylabel("population " + str(idx + 1), size=21)
+            ax.xaxis.set_ticks_position("both")
+            ax.yaxis.set_ticks_position("both")
+            ax.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+            for axis in ["top", "bottom", "left", "right"]:
+                ax.spines[axis].set_linewidth(1.0)
+            ax.xaxis.set_tick_params(which="major", length=6)
+            ax.xaxis.set_tick_params(which="minor", length=4)
+            ax.yaxis.set_tick_params(which="major", length=6)
+
+            # Plot the full gradient
+            ax1 = axs[1]
+            ax1.set_xlim([k_s.min(), k_s.max()])
+            ax1.set_ylim([Pbins.min(), Pbins.max()])
+            ax1.set_xscale("log")
+            ax1.set_yscale("log")
+            ax1.xaxis.set_ticks_position("both")
+            ax1.yaxis.set_ticks_position("both")
+            ax1.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax1.xaxis.set_tick_params(which="major", length=6)
+            ax1.xaxis.set_tick_params(which="minor", length=4)
+            ax1.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax1.yaxis.set_tick_params(which="major", length=6)
+            ax1.yaxis.set_tick_params(which="minor", length=4)
+            quantity = df
+            vmin = quantity.min()
+            vmax = quantity.max()
+            if vmin < 0 and vmax > 0:
+                centerval = 0
+            else:
+                centerval = np.mean(quantity)
+            norm_grad = colors.TwoSlopeNorm(vmin=vmin, vcenter=centerval, vmax=vmax)
+            GradientMap = create_colormap("GradientMap")
+            im1 = ax1.pcolormesh(
+                X, Y, df[:-1, :-1], cmap=GradientMap, shading="flat", norm=norm_grad
+            )
+            ax1.plot(k_s, k_s, color="grey", linestyle="--")
+            for i in range(len(k_s)):
+                ax1.plot(
+                    (k_s[i], k_s[i]),
+                    (Pbins.min(), Pbins.max()),
+                    linestyle=":",
+                    linewidth=0.5,
+                    color="green",
+                    alpha=0.5,
+                )
+            for i in range(len(Pbins)):
+                ax1.plot(
+                    (k_s.min(), k_s.max()),
+                    (Pbins[i], Pbins[i]),
+                    linestyle="--",
+                    linewidth=0.5,
+                    color="red",
+                    alpha=0.5,
+                )
+            ax1.set_ylabel(r"$k$ [$h$/Mpc]", size=17)
+            cbar1 = fig.colorbar(im1, shrink=0.9, pad=0.01)
+            cbar1.ax.tick_params(axis="y", direction="in", width=1.0, length=6)
+            ticks = np.concatenate([np.linspace(df.min(), 0, 5), np.linspace(0, df.max(), 5)[1:]])
+            cbar1.set_ticks(ticks)
+
+            if idx == 0:
+                ax.legend(fontsize=21, loc="upper left")
+                ax1.set_title(r"Full gradient $\nabla \mathbf{f}_0$", size=21)
+            elif idx == 2:
+                ax.set_xlabel(r"$k$ [$h$/Mpc]", size=21)
+                ax1.set_xlabel(r"$k$ [$h$/Mpc]", size=21)
+
+        if savepath is None or force:
+            plt.show()
+        if savepath is not None:
+            fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True)
+            fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf")
+            fig.suptitle("")
+            fig.savefig(
+                savepath[:-4] + "_notitle.png",
+                bbox_inches="tight",
+                dpi=300,
+                format="png",
+                transparent=True,
+            )
+            fig.savefig(savepath[:-4] + "_notitle.pdf", bbox_inches="tight", dpi=300, format="pdf")
+
+            logger.info("Plot saved to %s", savepath)
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e
+    finally:
+        plt.close(fig)
+        del fig
+        gc.collect()

+
+
+
+

+[docs]
+def plot_mocks(
+    NORM,
+    N,
+    P,
+    Pbins,
+    phi_obs,
+    Phi_0,
+    f_0,
+    C_0,
+    X,
+    Y,
+    CMap,
+    suptitle=None,
+    force_plot=False,
+    savepath=None,
+):
+    """
+    Plot mocks.
+
+    Parameters
+    ----------
+    NORM : float
+        Normalisation factor.
+    N : int
+        Number of mocks.
+    P : int
+        Number of bins.
+    Pbins : ndarray
+        Vector of bin boundaries for the summary statistics.
+    phi_obs : ndarray
+        Observed power spectrum.
+    Phi_0 : ndarray
+        Mock power spectra.
+    f_0 : ndarray
+        Averaged power spectrum.
+    C_0 : ndarray
+        Covariance matrix.
+    X : ndarray
+        X-axis grid.
+    Y : ndarray
+        Y-axis grid.
+    CMap : str
+        Colormap.
+    suptitle : str, optional
+        Title for the plot.
+    force_plot : bool, optional
+        Display plot even if savepath is set.
+    savepath : str or Path, optional
+        Path to save the plot. If None, the plot is displayed.
+
+    Raises
+    ------
+    RuntimeError
+        If unexpected errors occur during plotting.
+
+    """
+    logger.info("Plotting mocks and intra-population covariance...")
+
+    alpha_mocks = 0.25
+    alpha_binning = 0.3
+    try:
+        Phi_0_full = Phi_0.copy()
+        phi_obs_full = phi_obs.copy()
+        f_0_full = f_0.copy()
+        C_0_full = C_0.copy()
+
+        idx = 0
+        Phi_0 = Phi_0[:, idx * P : (idx + 1) * P]
+        phi_obs = phi_obs[idx * P : (idx + 1) * P]
+        f_0 = f_0[idx * P : (idx + 1) * P]
+        C_0 = C_0[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P]
+
+        COLOUR_LIST_means = ["darkorchid", "saddlebrown", "mediumvioletred"]
+
+        fig = plt.figure(figsize=(15.5, 10))
+        gs0 = gridspec.GridSpec(
+            3,
+            3,
+            width_ratios=[1.0, 1.0, 1.0],
+            height_ratios=[1.0, 1.0, 1.0],
+            wspace=0.0,
+            hspace=0.0,
+        )
+        gs0.update(right=1.0, left=0.0)
+        ax0 = plt.subplot(gs0[0, 0])
+        ax0b = plt.subplot(gs0[0, 1])
+        ax01 = plt.subplot(gs0[1, 0], sharex=ax0)
+        ax01b = plt.subplot(gs0[1, 1])
+        ax02 = plt.subplot(gs0[2, 0], sharex=ax0)
+        ax02b = plt.subplot(gs0[2, 1])
+
+        axx0x = [[ax01, ax01b], [ax02, ax02b]]
+
+        # Observed power spectrum (normalised)
+        ax0.semilogx(
+            Pbins,
+            phi_obs * NORM,
+            linewidth=2,
+            color="black",
+            label=r"$\boldsymbol{\Phi}_\mathrm{O}$",
+            zorder=3,
+        )
+
+        # Plot the Ne realisations and the average value
+        for i in range(N - 1):
+            ax0.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7)
+        ax0.semilogx(
+            Pbins,
+            Phi_0[N - 1],
+            color="C7",
+            alpha=alpha_mocks,
+            linewidth=0.7,
+        )
+        ax0.semilogx(
+            Pbins,
+            f_0,
+            linewidth=2,
+            color=COLOUR_LIST_means[idx],
+            linestyle="--",
+            label=r"$\textbf{f}_0$",
+            zorder=2,
+        )
+
+        # 2-sigma intervals
+        ax0.fill_between(
+            Pbins,
+            f_0 - 2 * np.sqrt(np.diag(C_0)),
+            f_0 + 2 * np.sqrt(np.diag(C_0)),
+            color=COLOUR_LIST[idx],
+            alpha=0.4,
+            label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+            zorder=2,
+        )
+
+        # Plot the binning
+        (ymin, ymax) = ax0.get_ylim()
+        ax0.set_ylim([ymin, ymax])
+        for i in range(len(Pbins)):
+            ax0.plot(
+                (Pbins[i], Pbins[i]),
+                (ymin, ymax),
+                linestyle="--",
+                linewidth=0.8,
+                color="red",
+                alpha=alpha_binning,
+                zorder=1,
+            )
+
+        ax0.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+        ax0.set_ylabel("population 1", size=GLOBAL_FS)
+        ax0.legend(fontsize=GLOBAL_FS, loc="upper right")
+        ax0.xaxis.set_ticks_position("both")
+        ax0.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        for axis in ["top", "bottom", "left", "right"]:
+            ax0.spines[axis].set_linewidth(1.0)
+        ax0.xaxis.set_tick_params(which="major", length=6)
+        ax0.xaxis.set_tick_params(which="minor", length=4)
+        if suptitle is not None:
+            ax0.set_title(
+                r"$\boldsymbol{\Phi}$ (" + suptitle + ")", y=1.05, fontsize=GLOBAL_FS_LARGE
+            )
+        else:
+            ax0.set_title(r"$\boldsymbol{\Phi}$", y=1.05, fontsize=GLOBAL_FS_LARGE)
+        ax0.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+        ax0.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
+
+        # Same as above but normalise everything by the observations:
+        normalisation = phi_obs
+
+        ax0b.semilogx(
+            Pbins,
+            NORM * phi_obs / normalisation,
+            linewidth=2,
+            color="black",
+            label=r"$\boldsymbol{\Phi}_\mathrm{O}$",
+            zorder=3,
+        )
+
+        for i in range(N - 1):
+            ax0b.semilogx(
+                Pbins,
+                Phi_0[i] / normalisation,
+                color="C7",
+                alpha=alpha_mocks,
+                linewidth=0.7,
+            )
+
+        ax0b.semilogx(
+            Pbins,
+            Phi_0[N - 1] / normalisation,
+            color="C7",
+            alpha=alpha_mocks,
+            linewidth=0.7,
+            label=r"$\boldsymbol{\Phi}_{\theta_0}$",
+        )
+
+        ax0b.semilogx(
+            Pbins,
+            f_0 / normalisation,
+            linewidth=2,
+            color=COLOUR_LIST_means[idx],
+            linestyle="--",
+            label=r"$\textbf{f}_0$",
+            zorder=2,
+        )
+
+        # 2-sigma intervals
+        ax0b.fill_between(
+            Pbins,
+            f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation,
+            f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation,
+            color=COLOUR_LIST[idx],
+            alpha=0.4,
+            label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+            zorder=2,
+        )
+
+        # Plot the binning
+        (ymin, ymax) = ax0b.get_ylim()
+        ax0b.set_ylim([ymin, ymax])
+        for i in range(len(Pbins)):
+            ax0b.plot(
+                (Pbins[i], Pbins[i]),
+                (ymin, ymax),
+                linestyle="--",
+                linewidth=0.8,
+                color="red",
+                alpha=alpha_binning,
+                zorder=1,
+            )
+
+        ax0b.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+        ax0b.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS)
+        ax0b.xaxis.set_ticks_position("both")
+        ax0b.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+        for axis in ["top", "bottom", "left", "right"]:
+            ax0b.spines[axis].set_linewidth(1.0)
+        ax0b.xaxis.set_tick_params(which="major", length=6)
+        ax0b.xaxis.set_tick_params(which="minor", length=4)
+        ax0b.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+        ax0b.yaxis.tick_right()
+        if suptitle is not None:
+            ax0b.set_title(
+                r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$ (" + suptitle + ")",
+                y=1.05,
+                fontsize=GLOBAL_FS,
+            )
+        else:
+            ax0b.set_title(
+                r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$", y=1.05, fontsize=GLOBAL_FS
+            )
+
+        for ax, axb in axx0x:
+            idx += 1
+            Phi_0 = Phi_0_full[:, idx * P : (idx + 1) * P]
+            phi_obs = phi_obs_full[idx * P : (idx + 1) * P]
+            f_0 = f_0_full[idx * P : (idx + 1) * P]
+            C_0 = C_0_full[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P]
+
+            # Same plot as above but for the other axes:
+            ax.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+            ax.semilogx(Pbins, phi_obs, linewidth=2, color="black", zorder=3)
+            for i in range(N - 1):
+                ax.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7)
+            ax.semilogx(Pbins, Phi_0[N - 1], color="C7", alpha=alpha_mocks, linewidth=0.7)
+            ax.semilogx(
+                Pbins,
+                f_0,
+                linewidth=2,
+                color=COLOUR_LIST_means[idx],
+                linestyle="--",
+                label=r"$\textbf{f}_0$",
+                zorder=2,
+            )
+
+            # 2-sigma intervals
+            ax.fill_between(
+                Pbins,
+                f_0 - 2 * np.sqrt(np.diag(C_0)),
+                f_0 + 2 * np.sqrt(np.diag(C_0)),
+                color=COLOUR_LIST[idx],
+                alpha=0.4,
+                label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+                zorder=2,
+            )
+
+            # Plot the binning
+            (ymin, ymax) = ax.get_ylim()
+            ax.set_ylim([ymin, ymax])
+            for i in range(len(Pbins)):
+                ax.plot(
+                    (Pbins[i], Pbins[i]),
+                    (ymin, ymax),
+                    linestyle="--",
+                    linewidth=0.8,
+                    color="red",
+                    alpha=alpha_binning,
+                    zorder=1,
+                )
+            ax.set_ylabel("population " + str(idx + 1), size=GLOBAL_FS)
+            ax.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS)
+            ax.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8)
+            ax.xaxis.set_ticks_position("both")
+            ax.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+            ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
+            for axis in ["top", "bottom", "left", "right"]:
+                ax.spines[axis].set_linewidth(1.0)
+            ax.xaxis.set_tick_params(which="major", length=6)
+            ax.xaxis.set_tick_params(which="minor", length=4)
+            ax.legend(loc="upper right", fontsize=GLOBAL_FS)
+
+            # Same as above but normalise everything by the observations:
+            normalisation = phi_obs
+
+            # Observed power spectrum
+            axb.semilogx(
+                Pbins,
+                phi_obs / normalisation,
+                linewidth=2,
+                color="black",
+                label=r"$\boldsymbol{\Phi}_\mathrm{O}$",
+                zorder=3,
+            )
+
+            for i in range(N - 1):
+                axb.semilogx(
+                    Pbins,
+                    Phi_0[i] / normalisation,
+                    color="C7",
+                    alpha=alpha_mocks,
+                    linewidth=0.7,
+                )
+
+            axb.semilogx(
+                Pbins,
+                Phi_0[N - 1] / normalisation,
+                color="C7",
+                alpha=alpha_mocks,
+                linewidth=0.7,
+                label=r"$\boldsymbol{\Phi}_{\theta_0}$",
+            )
+            axb.semilogx(
+                Pbins,
+                f_0 / normalisation,
+                linewidth=2,
+                color=COLOUR_LIST_means[idx],
+                linestyle="--",
+                label=r"$\textbf{f}_0$",
+                zorder=2,
+            )
+
+            # 2-sigma intervals
+            axb.fill_between(
+                Pbins,
+                f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation,
+                f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation,
+                color=COLOUR_LIST[idx],
+                alpha=0.4,
+                label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$",
+                zorder=2,
+            )
+
+            # Plot the binning
+            (ymin, ymax) = axb.get_ylim()
+            axb.set_ylim([ymin, ymax])
+            for i in range(len(Pbins)):
+                axb.plot(
+                    (Pbins[i], Pbins[i]),
+                    (ymin, ymax),
+                    linestyle="--",
+                    linewidth=0.8,
+                    color="red",
+                    alpha=alpha_binning,
+                    zorder=1,
+                )
+
+            axb.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01])
+            axb.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS)
+            axb.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8)
+            axb.xaxis.set_ticks_position("both")
+            axb.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            axb.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+            axb.yaxis.tick_right()
+            for axis in ["top", "bottom", "left", "right"]:
+                axb.spines[axis].set_linewidth(1.0)
+            axb.xaxis.set_tick_params(which="major", length=6)
+            axb.xaxis.set_tick_params(which="minor", length=4)
+
+        axx1x = [plt.subplot(gs0[0, 2]), plt.subplot(gs0[1, 2]), plt.subplot(gs0[2, 2])]
+
+        # Diagonal blocks of the covariance matrix (intra-population covariance)
+        idx = 0
+        for ax1 in axx1x:
+            Phi_0 = Phi_0_full[:, idx * P : (idx + 1) * P]
+            phi_obs = phi_obs_full[idx * P : (idx + 1) * P]
+            f_0 = f_0_full[idx * P : (idx + 1) * P]
+            C_0 = C_0_full[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P]
+            idx += 1
+
+            C0min = C_0.min()
+            C0max = C_0.max()
+            if C0min < 0 and C0max > 0:
+                centerval = 0
+            else:
+                centerval = np.mean(C_0)
+            divnorm = colors.TwoSlopeNorm(vmin=C_0.min(), vcenter=centerval, vmax=C_0.max())
+
+            # Plot the current block of the covariance matrix
+            im1 = ax1.pcolormesh(X, Y, C_0[:-1, :-1], shading="flat", norm=divnorm, cmap=CMap)
+
+            # Plot the binning
+            for i in range(len(Pbins)):
+                ax1.plot(
+                    (Pbins[i], Pbins[i]),
+                    (Pbins.min(), Pbins.max()),
+                    linestyle="--",
+                    linewidth=0.5,
+                    color="red",
+                    alpha=0.5,
+                )
+            for i in range(len(Pbins)):
+                ax1.plot(
+                    (Pbins.min(), Pbins.max()),
+                    (Pbins[i], Pbins[i]),
+                    linestyle="--",
+                    linewidth=0.5,
+                    color="red",
+                    alpha=0.5,
+                )
+            if idx == 1:
+                ax1.set_title(r"diagonal blocks of $\textbf{C}_0$", size=GLOBAL_FS, y=1.05)
+            ax1.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS)
+            ax1.set_ylabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS_SMALL)
+
+            ax1.set_aspect("equal")
+            ax1.set_xscale("log")
+            ax1.set_yscale("log")
+            ax1.xaxis.set_ticks_position("both")
+            ax1.yaxis.set_ticks_position("both")
+            ax1.xaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax1.xaxis.set_tick_params(which="major", length=6)
+            ax1.xaxis.set_tick_params(which="minor", length=4)
+            ax1.yaxis.set_tick_params(which="both", direction="in", width=1.0)
+            ax1.yaxis.set_tick_params(which="major", length=6)
+            ax1.yaxis.set_tick_params(which="minor", length=4)
+            ax1.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8)
+            ax1.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY)
+
+            cbar1 = fig.colorbar(im1, shrink=0.9, pad=0.02, format="%.1e")
+            cbar1.ax.tick_params(axis="y", direction="in", width=1.0, length=4)
+            cbar1.ax.yaxis.set_tick_params(labelsize=GLOBAL_FS_SMALL)
+            cbar1.update_normal(im1)
+            vmin = C_0[:-1, :-1].min()
+            vmax = C_0[:-1, :-1].max()
+            cbar1.mappable.set_clim(vmin=vmin, vmax=vmax)
+            if vmin < 0 and vmax > 0:
+                ticks = np.concatenate(
+                    [
+                        np.linspace(vmin * 0.8, 0, 2),
+                        np.linspace(0, 0.8 * vmax, 2)[1:],
+                    ]
+                )
+            else:
+                ticks = np.linspace(vmin, vmax, 5)
+            cbar1.set_ticks(ticks)
+            formatter = ScalarFormatter(useMathText=True)
+            formatter.set_powerlimits((0, 0))
+            cbar1.ax.yaxis.set_major_formatter(formatter)
+            cbar1.ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f"))
+            cbar1.ax.yaxis.get_offset_text().set_fontsize(GLOBAL_FS_TINY)
+            cbar1.update_ticks()
+
+        if savepath is not None:
+            savepath = Path(savepath)
+            fig.savefig(savepath, bbox_inches="tight", transparent=True, dpi=300, format="png")
+            fig.savefig(savepath.with_suffix(".pdf"), bbox_inches="tight", dpi=300, format="pdf")
+            logger.info("Plot saved to %s", savepath)
+        if force_plot or not savepath:
+            plt.show()
+
+    except Exception as e:
+        logger.critical("Unexpected error during plotting: %s", str(e))
+        raise RuntimeError("Plotting failed.") from e

+
+
+
+

+[docs]
+def plot_histogram(
+    data,
+    recmahal,
+    suptitle,
+    savepath,
+    bins=30,
+    alpha=0.5,
+    color="blue",
+):
+    """
+    Plot a Mahalanobis distance histogram with key reference lines.
+
+    Parameters
+    ----------
+    data : array-like
+        Collection of Mahalanobis distances.
+    recmahal : float
+        Single Mahalanobis distance for the reconstruction
+        (e.g. posterior vs. prior mean).
+    suptitle : str
+        Figure title.
+    savepath : str
+        Full path (including filename) to save the resulting plot.
+    bins : int, optional
+        Number of bins for the histogram. Default is 30.
+    alpha : float, optional
+        Transparency level for the histogram bars. Default is 0.5.
+    color : str, optional
+        Colour of the histogram bars. Default is "blue".
+
+    Raises
+    ------
+    OSError
+        If the file cannot be saved to the specified path.
+    RuntimeError
+        For unexpected plotting failures.
+    """
+    try:
+        logger.debug("Starting plot_histogram with data of size %d.", len(data))
+
+        plt.figure(figsize=(8, 5))
+        plt.hist(data, bins=bins, alpha=alpha, color=color)
+
+        labrec = r"$d_\mathrm{M}(\boldsymbol{\gamma}_{\textrm{rec}}, \boldsymbol{\theta}_0 \mid \textbf{S})$"
+        plt.axvline(recmahal, color="black", linestyle="-", linewidth=2, label=labrec)
+
+        labx = r"$d_\mathrm{M}(\boldsymbol{\gamma}, \boldsymbol{\theta}_0 \mid \textbf{S})$"
+        labmean = r"$\langle d_\mathrm{M}(\boldsymbol{\gamma}, \boldsymbol{\theta}_0 \mid \textbf{S}) \rangle$"
+        plt.axvline(
+            np.mean(data),
+            color="tab:pink",
+            linestyle="-",
+            linewidth=2,
+            label=labmean,
+        )
+        std = np.std(data)
+        labstd = r"$\pm 1 \sigma$"
+        plt.axvline(
+            np.mean(data) + std,
+            color="pink",
+            linestyle="--",
+            linewidth=1,
+            label=labstd,
+        )
+        plt.axvline(
+            np.mean(data) - std,
+            color="pink",
+            linestyle="--",
+            linewidth=1,
+        )
+
+        plt.xlabel(labx, fontsize=GLOBAL_FS_LARGE)
+        plt.ylabel("Density", fontsize=GLOBAL_FS_LARGE)
+        plt.xticks(fontsize=GLOBAL_FS)
+        plt.yticks([])
+        plt.suptitle(suptitle, fontsize=GLOBAL_FS_XLARGE)
+        plt.legend(fontsize=GLOBAL_FS_LARGE)
+
+        plt.savefig(savepath, bbox_inches="tight", dpi=300, transparent=True)
+        plt.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300)
+        logger.info("Histogram plot saved to %s and %s.pdf", savepath, savepath[:-4])
+        plt.close()
+
+    except OSError as e:
+        logger.error("File saving failed at path '%s': %s", savepath, str(e))
+        raise
+    except Exception as e:
+        logger.critical("Unexpected error during histogram plotting: %s", str(e))
+        raise RuntimeError("plot_histogram failed.") from e
+    finally:
+        gc.collect()
+        logger.debug("plot_histogram: memory cleanup done.")

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/timestepping.html b/docs/_modules/selfisys/utils/timestepping.html new file mode 100644 index 0000000..dfcf431 --- /dev/null +++ b/docs/_modules/selfisys/utils/timestepping.html @@ -0,0 +1,389 @@ + + + + + + + + selfisys.utils.timestepping — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.timestepping

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""Tools for time-stepping.
+"""
+
+
+

+[docs]
+def merge_nTS(ts_path_list, merged_path):
+    """
+    Merge multiple time-stepping objects into a single file.
+
+    Parameters
+    ----------
+    ts_path_list : list of str
+        Paths to the individual time-stepping files to be merged.
+    merged_path : str
+        Path to save the merged time-stepping file.
+
+    Returns
+    -------
+    None
+    """
+    from h5py import File
+    from numpy import concatenate
+    from pysbmy.timestepping import read_timestepping
+
+    # Read individual time-stepping objects
+    ts = [read_timestepping(ts_path) for ts_path in ts_path_list]
+
+    with File(merged_path, "w") as hf:
+        # Write scalar attributes
+        hf.attrs["/info/scalars/nsteps"] = sum(tsi.nsteps for tsi in ts)
+        hf.attrs["/info/scalars/nkicks"] = sum(tsi.nkicks for tsi in ts)
+        hf.attrs["/info/scalars/ndrifts"] = sum(tsi.ndrifts for tsi in ts)
+        hf.attrs["/info/scalars/ai"] = ts[0].ai
+        hf.attrs["/info/scalars/af"] = ts[-1].af
+
+        # Merge and write datasets
+        hf.create_dataset("/scalars/forces", data=concatenate([tsi.forces for tsi in ts]))
+        hf.create_dataset("/scalars/snapshots", data=concatenate([tsi.snapshots for tsi in ts]))
+        hf.create_dataset("/scalars/aKickBeg", data=concatenate([tsi.aKickBeg for tsi in ts]))
+        hf.create_dataset("/scalars/aKickEnd", data=concatenate([tsi.aKickEnd for tsi in ts]))
+        hf.create_dataset("/scalars/aDriftBeg", data=concatenate([tsi.aDriftBeg for tsi in ts]))
+        hf.create_dataset("/scalars/aDriftEnd", data=concatenate([tsi.aDriftEnd for tsi in ts]))
+        hf.create_dataset("/scalars/aiKick", data=concatenate([tsi.aiKick for tsi in ts]))
+        hf.create_dataset("/scalars/afKick", data=concatenate([tsi.afKick for tsi in ts]))
+
+        # Handle `aDrift` merging with overlap adjustments
+        aDrift_data = concatenate(
+            [
+                [ts[0].aDrift[0]],  # Initial drift
+                concatenate(
+                    [concatenate([tsi.aDrift[1:], [tsi.aDrift[-1]]]) for tsi in ts[:-1]]
+                ),  # Intermediate drifts
+                ts[-1].aDrift[1:],  # Final drift
+            ]
+        )
+        hf.create_dataset("/scalars/aDrift", data=aDrift_data)
+
+        # Handle `aSnapshotSave` merging
+        aSnapshotSave_data = concatenate(
+            [ts[0].aSnapshotSave] + [tsi.aSnapshotSave[1:] for tsi in ts[1:]]
+        )
+        hf.create_dataset("/scalars/aSnapshotSave", data=aSnapshotSave_data)
+
+        hf.create_dataset("/scalars/aiDrift", data=concatenate([tsi.aiDrift for tsi in ts]))
+        hf.create_dataset("/scalars/afDrift", data=concatenate([tsi.afDrift for tsi in ts]))
+        hf.create_dataset("/scalars/aKick", data=concatenate([tsi.aKick for tsi in ts]))

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/tools.html b/docs/_modules/selfisys/utils/tools.html new file mode 100644 index 0000000..acc5ea6 --- /dev/null +++ b/docs/_modules/selfisys/utils/tools.html @@ -0,0 +1,644 @@ + + + + + + + + selfisys.utils.tools — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.tools

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Utilities for the SelfiSys package, including tools for cosmological
+parameter handling, power spectrum computations, and prior sampling.
+"""
+
+
+

+[docs]
+def none_or_bool_or_str(value):
+    """
+    Convert string representations of None, True, and False to their
+    respective Python objects; otherwise, return the input value.
+    """
+    if value == "None":
+        return None
+    if value == "True":
+        return True
+    if value == "False":
+        return False
+    return value

+
+
+
+

+[docs]
+def get_k_max(L, size):
+    """
+    Compute the maximum wavenumber for a given box size.
+
+    Parameters
+    ----------
+    L : float
+        Size of the box in Mpc/h.
+    size : int
+        Number of grid cells along each dimension.
+
+    Returns
+    -------
+    float
+        Maximum wavenumber in h/Mpc.
+    """
+    from numpy import pi, sqrt
+
+    return int(1e3 * sqrt(3) * pi * size / L + 1) * 1e-3

+
+
+
+

+[docs]
+def custom_stat(vec):
+    """
+    Compute a custom statistic for use with
+    `scipy.stats.binned_statistic`.
+
+    Assumes the data power spectrum is inverse-Gamma distributed (as in
+    [jasche2010bayesian] and [leclercq2019primordial]). Returns "NaN"
+    for vectors with insufficient elements, as expected by
+    `scipy.stats.binned_statistic`.
+
+    Parameters
+    ----------
+    vec : array-like
+        Input vector for computation.
+
+    Returns
+    -------
+    float or str
+        Custom statistic or NaN if input is invalid.
+    """
+    if len(vec) <= 2 or sum(vec) == 0:
+        return "NaN"
+    return sum(vec) / (len(vec) - 2)

+
+
+
+

+[docs]
+def cosmo_vector_to_Simbelmyne_dict(x, kmax=1.4):
+    """
+    Convert a vector of cosmological parameters into a dictionary
+    compatible with `pysbmy`.
+
+    Parameters
+    ----------
+    x : array-like
+        Vector of cosmological parameters.
+    kmax : float, optional
+        Maximum wavenumber for the power spectrum computation.
+
+    Returns
+    -------
+    dict
+        Dictionary of cosmological parameters compatible with `pysbmy`.
+    """
+    from selfisys.global_parameters import WHICH_SPECTRUM
+
+    return {
+        "h": x[0],
+        "Omega_r": 0.0,
+        "Omega_q": 1.0 - x[2],
+        "Omega_b": x[1],
+        "Omega_m": x[2],
+        "m_ncdm": 0.0,
+        "Omega_k": 0.0,
+        "tau_reio": 0.066,
+        "n_s": x[3],
+        "sigma8": x[4],
+        "w0_fld": -1.0,
+        "wa_fld": 0.0,
+        "k_max": kmax,
+        "WhichSpectrum": WHICH_SPECTRUM,
+    }

+
+
+
+

+[docs]
+def cosmo_vector_to_class_dict(x, lmax=2500, kmax=1.4):
+    """
+    Convert a vector of cosmological parameters into a dictionary
+    compatible with `classy`.
+
+    Parameters
+    ----------
+    x : array-like
+        Vector of cosmological parameters.
+    lmax : int, optional
+        Maximum multipole for the power spectrum computation.
+    kmax : float, optional
+        Maximum wavenumber for the power spectrum computation.
+
+    Returns
+    -------
+    dict
+        Dictionary of cosmological parameters compatible with `classy`.
+    """
+    return {
+        "output": "lCl mPk",
+        "l_max_scalars": lmax,
+        "lensing": "no",
+        "N_ncdm": 0,
+        "P_k_max_h/Mpc": kmax,
+        "h": x[0],
+        "Omega_b": x[1],
+        "Omega_m": x[2],
+        "n_s": x[3],
+        "sigma8": x[4],
+    }

+
+
+
+

+[docs]
+def params_ids_to_Simbelmyne_dict(params_vals, params_ids, fixed, kmax):
+    """
+    Convert a list of cosmological parameters into a dictionary
+    compatible with `pysbmy`.
+
+    Fixed parameters remain unchanged unless overridden by
+    `params_vals`.
+
+    Parameters
+    ----------
+    params_vals : array-like
+        Values of the parameters to be modified.
+    params_ids : array-like
+        Indices of the parameters to be modified.
+    fixed : array-like
+        Base values of the parameters.
+    kmax : float
+        Maximum wavenumber for the power spectrum computation.
+
+    Returns
+    -------
+    dict
+        Dictionary of cosmological parameters compatible with `pysbmy`.
+    """
+    from numpy import copy
+
+    x = copy(fixed)
+    x[params_ids] = params_vals
+    return cosmo_vector_to_Simbelmyne_dict(x, kmax=kmax)

+
+
+
+

+[docs]
+def get_summary(params_vals, params_ids, Omegas_fixed, bins, normalisation=None, kmax=1.4):
+    """
+    Compute the normalised power spectrum summary for a given parameter
+    set.
+
+    Parameters
+    ----------
+    params_vals : array-like
+        Parameter values to update.
+    params_ids : array-like
+        Indices of the parameters to update.
+    Omegas_fixed : array-like
+        Fixed base values of parameters.
+    bins : array-like
+        Power spectrum bins.
+    normalisation : float, optional
+        Normalisation factor for the summary.
+    kmax : float, optional
+        Maximum wavenumber for power spectrum computation.
+
+    Returns
+    -------
+    array
+        Normalised power spectrum summary.
+    """
+    from pysbmy.power import get_Pk
+    from numpy import array
+
+    phi = get_Pk(bins, params_ids_to_Simbelmyne_dict(params_vals, params_ids, Omegas_fixed, kmax))
+
+    return array(phi) / normalisation if normalisation else array(phi)

+
+
+
+

+[docs]
+def summary_to_score(params_ids, omega0, F0, F0_inv, f0, dw_f0, C0_inv, phi):
+    """
+    Compute the Fisher score.
+
+    Parameters
+    ----------
+    params_ids : array-like
+        Indices of the parameters.
+    omega0 : array-like
+        Cosmological parameters at the expansion point.
+    F0 : array-like
+        Fisher information matrix.
+    F0_inv : array-like
+        Inverse Fisher information matrix.
+    f0 : array-like
+        Mean model at the expansion point.
+    dw_f0 : array-like
+        Derivative of the mean model.
+    C0_inv : array-like
+        Inverse covariance matrix.
+    phi : array-like
+        Observed summary.
+
+    Returns
+    -------
+    array
+        Fisher score.
+    """
+    return omega0[params_ids] + F0_inv @ dw_f0.T @ C0_inv @ (phi - f0)

+
+
+
+

+[docs]
+def fisher_rao(Com, Com_obs, F0):
+    """
+    Compute the Fisher-Rao distance between two summaries.
+
+    Parameters
+    ----------
+    Com : array-like
+        Computed summary.
+    Com_obs : array-like
+        Observed summary.
+    F0 : array-like
+        Fisher information matrix.
+
+    Returns
+    -------
+    float
+        Fisher-Rao distance.
+    """
+    from numpy import sqrt
+
+    diff = Com - Com_obs
+    return sqrt(diff.T @ F0 @ diff)

+
+
+
+

+[docs]
+def sample_omega_from_prior(nsample, omega_mean, omega_cov, params_ids, seed=None):
+    """
+    Sample cosmological parameters from a prior distribution.
+
+    Ensures physical validity by clipping values to [eps, 1-eps].
+
+    Parameters
+    ----------
+    nsample : int
+        Number of samples to draw.
+    omega_mean : array-like
+        Prior mean vector.
+    omega_cov : array-like
+        Prior covariance matrix.
+    params_ids : array-like
+        Indices of the parameters to sample.
+    seed : int, optional
+        Seed for the random number generator.
+
+    Returns
+    -------
+    array
+        Sampled cosmological parameters.
+    """
+    from numpy import array, ix_, clip
+    from numpy.random import default_rng
+
+    if seed is None:
+        raise ValueError("A seed value is mandatory.")
+
+    rng = default_rng(seed)
+    OO_unbounded = rng.multivariate_normal(
+        array(omega_mean)[params_ids],
+        array(omega_cov)[ix_(params_ids, params_ids)],
+        nsample,
+    )
+    eps = 1e-5
+    return clip(OO_unbounded, eps, 1 - eps)

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/selfisys/utils/workers.html b/docs/_modules/selfisys/utils/workers.html new file mode 100644 index 0000000..f8347bc --- /dev/null +++ b/docs/_modules/selfisys/utils/workers.html @@ -0,0 +1,702 @@ + + + + + + + + selfisys.utils.workers — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for selfisys.utils.workers

+#!/usr/bin/env python3
+# ----------------------------------------------------------------------
+# Copyright (C) 2024 Tristan Hoellinger
+# Distributed under the GNU General Public License v3.0 (GPLv3).
+# See the LICENSE file in the root directory for details.
+# SPDX-License-Identifier: GPL-3.0-or-later
+# ----------------------------------------------------------------------
+
+__author__ = "Tristan Hoellinger"
+__version__ = "0.1.0"
+__date__ = "2024"
+__license__ = "GPLv3"
+
+"""
+Routines for parameter inference and gradient evaluation in the SelfiSys
+pipeline.
+"""
+
+import gc
+from typing import Any, Tuple, List
+
+from selfisys.utils.logger import getCustomLogger
+
+logger = getCustomLogger(__name__)
+
+
+

+[docs]
+def Simbelmyne_worker(args) -> Tuple[float, Any]:
+    """
+    Worker function used for implicit likelihood inference of
+    cosmological parameters.
+
+    Parameters
+    ----------
+    args : tuple
+        A tuple of arguments to be unpacked for the worker routine:
+        (index, param_val, param_id, fsimdir, k_s, Pbins_bnd,
+        selection_params, norm_csts, P_ss_obj_path, obs_density,
+        lin_bias, noise, survey_mask_path, G_sim_path, G_ss_path, Np0,
+        Npm0, seedphase_init, seednoise_init, size, L,
+        radial_selection, sim_params, wd, batch_idx, dbg, modeldir,
+        local_mask_prefix, TimeStepDistribution, indices_steps_cumul,
+        eff_redshifts, poolname_abc, setup_only, prefix_mocks).
+
+    Returns
+    -------
+    tuple
+        (param_val, Phi) where param_val is the parameter value used,
+        and Phi is the resulting summary from evaluating the model.
+
+    Raises
+    ------
+    OSError
+        If file I/O (reading or writing mock data) fails.
+    RuntimeError
+        For unexpected errors in the worker routine.
+    """
+    import os
+    from pathlib import Path
+
+    try:
+        (
+            index,
+            param_val,
+            param_id,
+            fsimdir,
+            k_s,
+            Pbins_bnd,
+            selection_params,
+            norm_csts,
+            P_ss_obj_path,
+            obs_density,
+            lin_bias,
+            noise,
+            survey_mask_path,
+            G_sim_path,
+            G_ss_path,
+            Np0,
+            Npm0,
+            seedphase_init,
+            seednoise_init,
+            size,
+            L,
+            radial_selection,
+            sim_params,
+            wd,
+            batch_idx,
+            dbg,
+            modeldir,
+            local_mask_prefix,
+            TimeStepDistribution,
+            indices_steps_cumul,
+            eff_redshifts,
+            poolname_abc,
+            setup_only,
+            prefix_mocks,
+        ) = args
+
+        spectrum_name = int(str(seedphase_init + index) + str(seednoise_init + index))
+        pooldir = (
+            fsimdir + "/pool/d" if not poolname_abc else fsimdir + "/pool/" + poolname_abc + "/d"
+        )
+        simdir_d = pooldir + str(spectrum_name) + "/"
+        Path(simdir_d).mkdir(parents=True, exist_ok=True)
+
+        if prefix_mocks is None:
+            fname_mocks = (
+                simdir_d + "mocks_d" + str(spectrum_name) + "_p" + str(batch_idx + index) + ".h5"
+            )
+        else:
+            fname_mocks = (
+                simdir_d
+                + prefix_mocks
+                + "_mocks_d"
+                + str(spectrum_name)
+                + "_p"
+                + str(batch_idx + index)
+                + ".h5"
+            )
+
+        if os.path.exists(fname_mocks):
+            from h5py import File
+
+            logger.debug("Mock file %s found, loading existing data...", fname_mocks)
+            with File(fname_mocks, "r") as f:
+                Phi = f["Phi"][:]
+        else:
+            logger.debug("No existing mock file at %s, generating new data...", fname_mocks)
+            from numpy.random import normal
+            from numpy import shape, max
+            from selfisys.global_parameters import BASELINE_SEEDNORM, omegas_gt
+            from selfisys.utils.tools import get_k_max
+            from selfisys.hiddenbox import HiddenBox
+            from selfisys.utils.tools import get_summary
+
+            P = len(Pbins_bnd) - 1
+            try:
+                BB_selfi = HiddenBox(
+                    k_s=k_s,
+                    P_ss_path=P_ss_obj_path,
+                    Pbins_bnd=Pbins_bnd,
+                    theta2P=None,
+                    P=P * shape(selection_params)[1],  # P * Npop
+                    size=size,
+                    L=L,
+                    G_sim_path=G_sim_path,
+                    G_ss_path=G_ss_path,
+                    Np0=Np0,
+                    Npm0=Npm0,
+                    fsimdir=wd[:-1],
+                    modeldir=modeldir,
+                    noise_std=noise,
+                    radial_selection=radial_selection,
+                    selection_params=selection_params,
+                    observed_density=obs_density,
+                    linear_bias=lin_bias,
+                    norm_csts=norm_csts,
+                    survey_mask_path=survey_mask_path,
+                    local_mask_prefix=local_mask_prefix,
+                    sim_params=sim_params,
+                    TimeStepDistribution=TimeStepDistribution,
+                    TimeSteps=indices_steps_cumul,
+                    eff_redshifts=eff_redshifts,
+                    seedphase=seedphase_init,
+                    seednoise=seednoise_init,
+                    fixnoise=False,
+                    seednorm=BASELINE_SEEDNORM,
+                    reset=False,
+                    save_frequency=5,
+                    verbosity=2,
+                )
+                k_max = get_k_max(L, size)
+            except Exception as e:
+                logger.critical("Error instantiating HiddenBox: %s", str(e))
+                raise RuntimeError("Failed to set up HiddenBox.") from e
+
+            # Evaluate the param -> 'theta' using some get_summary logic
+            try:
+                theta = get_summary(param_val, param_id, omegas_gt, k_s, kmax=k_max)
+            except Exception:
+                max_tries = 10
+                perturb_std = 1e-8
+                param_val_init = param_val
+                logger.warning(
+                    "get_summary failed for param_val=%s. Trying small perturbations...", param_val
+                )
+                for i in range(max_tries):
+                    param_val = normal(param_val_init, perturb_std)
+                    logger.diagnostic("Attempt #%d: param_val=%s", i + 1, param_val)
+                    try:
+                        theta = get_summary(param_val, param_id, omegas_gt, k_s, kmax=k_max)
+                        logger.diagnostic(
+                            "Success with param_val=%s on attempt #%d", param_val, i + 1
+                        )
+                        break
+                    except Exception:
+                        if i == max_tries - 1:
+                            logger.critical(
+                                "All attempts to get_summary failed for param_val=%s",
+                                param_val_init,
+                            )
+                            raise RuntimeError("get_summary repeatedly failed.")
+                        continue
+
+            from io import BytesIO
+            from selfisys.utils.low_level import stderr_redirector, stdout_redirector
+
+            cosmo_vect = omegas_gt
+            cosmo_vect[param_id] = param_val
+
+            logger.debug("Evaluating model with HPC redirection, setup_only=%s", setup_only)
+            f = BytesIO()
+            g = BytesIO()
+            try:
+                with stderr_redirector(f):
+                    with stdout_redirector(g):
+                        if setup_only:
+                            BB_selfi.switch_setup()
+                        else:
+                            BB_selfi.switch_recompute_pool(prefix_mocks=prefix_mocks)
+
+                        Phi = BB_selfi.evaluate(
+                            theta,
+                            spectrum_name,
+                            seedphase_init + index,
+                            seednoise_init + index,
+                            i=batch_idx + index,
+                            thetaIsP=True,
+                            remove_sbmy=True,
+                            force_powerspectrum=dbg,
+                            force_parfiles=dbg,
+                            check_output=dbg,
+                            abc=poolname_abc,
+                            cosmo_vect=cosmo_vect,
+                        )
+                        if setup_only:
+                            BB_selfi.switch_setup()
+                        else:
+                            BB_selfi.switch_recompute_pool(prefix_mocks=prefix_mocks)
+
+            except Exception as e:
+                logger.critical("Error while evaluating model: %s", str(e))
+                raise RuntimeError("Simbelmyne_worker model evaluation failed.") from e
+            finally:
+                g.close()
+                f.close()
+
+        logger.debug("Returning param_val=%s with resulting Phi of shape %s", param_val, Phi.shape)
+        return param_val, Phi
+
+    except OSError as e:
+        logger.error("File I/O error in Simbelmyne_worker: %s", str(e))
+        raise
+    except Exception as e:
+        logger.critical("Unexpected error in Simbelmyne_worker: %s", str(e))
+        raise RuntimeError("Simbelmyne_worker HPC run failed.") from e
+    finally:
+        gc.collect()

+
+
+
+

+[docs]
+def worker_gradient_Symbelmyne(
+    coeff: float,
+    delta_x: float,
+    omega,
+    param_index: int,
+    k_s,
+    delta: float,
+    kmax: float,
+):
+    """
+    Worker function for evaluating the gradient of the power spectrum
+    using finite differences.
+
+    Parameters
+    ----------
+    coeff : float
+        Coefficient for the finite difference.
+    delta_x : float
+        Step size in the parameter space.
+    omega : ndarray
+        Base cosmological parameter vector.
+    param_index : int
+        Index of the parameter being varied.
+    k_s : ndarray
+        Array of wavenumbers.
+    delta : float
+        Denominator for finite differences (scaled).
+    kmax : float
+        Maximum wavenumber for power spectrum.
+
+    Returns
+    -------
+    ndarray
+        The gradient of the power spectrum wrt the specified parameter.
+
+    Raises
+    ------
+    RuntimeError
+        If the gradient evaluation fails.
+    """
+    import numpy as np
+    from pysbmy.power import get_Pk
+    from selfisys.utils.tools import cosmo_vector_to_Simbelmyne_dict
+
+    omega_new = omega.copy()
+    try:
+        omega_new[param_index] += delta_x
+        ps = get_Pk(k_s, cosmo_vector_to_Simbelmyne_dict(omega_new, kmax=kmax))
+        contrib_to_grad = (coeff * ps) / delta
+        return np.array(contrib_to_grad)
+    except Exception as e:
+        logger.critical("Error in worker_gradient_Symbelmyne: %s", str(e))
+        raise RuntimeError("worker_gradient_Symbelmyne failed.") from e
+    finally:
+        gc.collect()

+
+
+
+

+[docs]
+def evaluate_gradient_of_Symbelmyne(
+    omega,
+    param_index: int,
+    k_s,
+    coeffs: List[float] = [2 / 3.0, -1 / 12.0],
+    deltas_x: List[float] = [0.01, 0.02],
+    delta: float = 1e-2,
+    kmax: float = 1.4,
+):
+    """
+    Estimate the gradient of CLASS with respect to the cosmological
+    parameters using central finite differences of arbitrary order.
+
+    Parameters
+    ----------
+    omega : ndarray
+        Base cosmological parameter vector.
+    param_index : int
+        Index of the parameter to differentiate against.
+    k_s : ndarray
+        Wavenumbers for the power spectrum.
+    coeffs : list of float, optional
+        Coefficients for the finite-difference scheme, typically
+        [2/3, -1/12] etc. Default is [2/3.0, -1/12.0].
+    deltas_x : list of float, optional
+        Step sizes. The corresponding negative steps are generated
+        automatically. Default is [0.01, 0.02].
+    delta : float, optional
+        Scale for the finite difference in the denominator. Default is
+        1e-2.
+    kmax : float, optional
+        Maximum wavenumber for the power spectrum. Default is 1.4.
+
+    Returns
+    -------
+    ndarray
+        The gradient of the power spectrum wrt the specified parameter.
+
+    Raises
+    ------
+    RuntimeError
+        If the gradient evaluation fails.
+    """
+    import numpy as np
+    from multiprocessing import Pool
+
+    try:
+        grad = np.zeros(len(k_s))
+        full_coeffs = np.concatenate((-np.array(coeffs)[::-1], coeffs))
+        deltas_x_full = np.concatenate((-np.array(deltas_x)[::-1], deltas_x))
+
+        tasks = [
+            (c, dx, omega, param_index, k_s, delta, kmax)
+            for c, dx in zip(full_coeffs, deltas_x_full)
+        ]
+        logger.diagnostic("Starting parallel HPC for gradient, tasks=%d", len(tasks))
+
+        with Pool() as mp_pool:
+            results = mp_pool.starmap(worker_gradient_Symbelmyne, tasks)
+            for contrib in results:
+                grad += contrib
+
+        logger.diagnostic("Gradient evaluation completed. Shape=%s", grad.shape)
+        return grad
+    except Exception as e:
+        logger.critical("Unexpected error in evaluate_gradient_of_Symbelmyne: %s", str(e))
+        raise RuntimeError("evaluate_gradient_of_Symbelmyne failed.") from e
+    finally:
+        gc.collect()

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_sources/CONTRIBUTING.md.txt b/docs/_sources/CONTRIBUTING.md.txt new file mode 100644 index 0000000..76f4a5c --- /dev/null +++ b/docs/_sources/CONTRIBUTING.md.txt @@ -0,0 +1,58 @@ +# Contributing to SelfiSys + +We welcome contributions to the SelfiSys repository! Please follow these guidelines when contributing. + +--- + +## Reporting Issues + +If you find a bug or have a suggestion, please open an issue in the [GitHub repository](https://github.com/hoellin/selfisys_public). Include as much detail as possible: +- Steps to reproduce the issue +- Expected vs. actual behaviour +- Relevant error messages or logs (if applicable) +- Suggestions for improvement (if applicable) + +If you are unsure whether your issue is a bug or have questions about the code, you are welcome to open an issue for discussion. + +## Submitting Contributions + +1. Fork the repository and create a new branch for your changes. +2. Ensure your contributions are well-documented and adhere to the highest coding standards. +3. Test your changes thoroughly before submitting: + - Ensure Jupyter notebooks run without errors from top to bottom. + - Validate new functionality or fixes using appropriate test cases. +4. Before submitting a pull request, synchronise your fork with the main repository to incorporate upstream changes. + - Add the main repository as a remote. + ```bash + git remote add upstream https://github.com/hoellin/selfisys_public.git + ``` + - Fetch the latest changes from the upstream repository. + ```bash + git fetch upstream + ``` + - Merge the changes into your local repository. + ```bash + git merge upstream/main + ``` +5. Open a pull request describing your changes to the main repository. + +## Style Guidelines + +Follow best practices for Python coding and Jupyter notebooks. + +### Python code + +Refer to the [PEP 8 Style Guide](https://pep8.org/) for Python coding standards. + +### Jupyter Notebooks + +- Use clear and concise Markdown cells to explain code and results. +- Avoid leaving unnecessary output (e.g., debugging print statements). +- Ensure notebooks are runnable from top to bottom. +- For tips on creating clean and effective Jupyter notebooks, see [Jupyter Notebook Best Practices](https://realpython.com/jupyter-notebook-best-practices/). + +--- + +## Questions? + +If you have any questions or need further clarification, feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr). \ No newline at end of file diff --git a/docs/_sources/README.md.txt b/docs/_sources/README.md.txt new file mode 100644 index 0000000..e85d351 --- /dev/null +++ b/docs/_sources/README.md.txt @@ -0,0 +1,83 @@ +# SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys + +[![arXiv](https://img.shields.io/badge/astro--ph.CO-arxiv%3A2412.04443-B31B1B.svg?style=flat)](https://arxiv.org/abs/2412.04443) +[![GPLv3 license](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/hoellin/selfisys_public/blob/main/LICENSE) +[![GitHub version](https://img.shields.io/github/tag/hoellin/selfisys_public.svg?label=version)](https://github.com/hoellin/selfisys_public) +[![GitHub last commit](https://img.shields.io/github/last-commit/hoellin/selfisys_public.svg)](https://github.com/hoellin/selfisys_public/commits/main) + + +**SelfiSys** is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference. + +It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys. + +## Key Features + +- **Custom hidden-box forward models** + +We provide a `HiddenBox` class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver. + +- **Diagnosis of systematic effects** + +Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference. + +- **Cosmological inference** + +Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +--- + +## Documentation + +The documentation, including a detailed API reference, is available at [hoellin.github.io/selfisys_public](https://hoellin.github.io/selfisys_public/). + +For practical examples demonstrating how to use SelfiSys, visit the [SelfiSys Examples Repository](https://github.com/hoellin/selfisys_examples). + +## Contributors + +- **Tristan Hoellinger**, [tristan.hoellinger@iap.fr](mailto:tristan.hoellinger@iap.fr) + Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP). + +For information on contributing, refer to [CONTRIBUTING.md](CONTRIBUTING.md). + +## References + +If you use the SelfiSys package in your research, please cite the following paper and feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr) for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., 2024* +[arXiv:2412.04443](https://arxiv.org/abs/2412.04443) [[astro-ph.CO]](https://arxiv.org/abs/2412.04443) [[ADS]](https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443) [[pdf]](https://arxiv.org/pdf/2412.04443) + +BibTeX entry for citation: +```bibtex +@ARTICLE{hoellinger2024diagnosing, + author = {Hoellinger, Tristan and Leclercq, Florent}, + title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}", + journal = {arXiv e-prints}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2024, + month = dec, + eid = {arXiv:2412.04443}, + pages = {arXiv:2412.04443}, + doi = {10.48550/arXiv.2412.04443}, +archivePrefix = {arXiv}, + eprint = {2412.04443}, +primaryClass = {astro-ph.CO}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} +``` + +## Requirements + +The code is written in Python 3.10 and depends on the following packages: +- [`pySELFI`](https://pyselfi.readthedocs.io/en/latest/): Python implementation of the Simulator Expansion for Likelihood-Free Inference. +- [`Simbelmynë`](https://simbelmyne.readthedocs.io/en/latest/): A hierarchical probabilistic simulator for generating synthetic galaxy survey data. +- [`ELFI`](https://elfi.readthedocs.io/en/latest/): A statistical software package for likelihood-free inference, implementing in particular Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +A comprehensive list of dependencies, including version specifications to ensure reproducibility, will be provided in a yaml file, along with installation instructions, in a future release. + +--- + +## License + +This software is distributed under the GPLv3 Licence. Please review the [LICENSE](https://github.com/hoellin/selfisys_public/blob/main/LICENSE) file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence. \ No newline at end of file diff --git a/docs/_sources/REFERENCES.md.txt b/docs/_sources/REFERENCES.md.txt new file mode 100644 index 0000000..d211609 --- /dev/null +++ b/docs/_sources/REFERENCES.md.txt @@ -0,0 +1,44 @@ +# References + +If you use the SelfiSys package in your research, please cite the following paper and feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr) for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., 2024* +[arXiv:2412.04443](https://arxiv.org/abs/2412.04443) [[astro-ph.CO]](https://arxiv.org/abs/2412.04443) [[ADS]](https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443) [[pdf]](https://arxiv.org/pdf/2412.04443) + +BibTeX entry for citation: +```bibtex +@ARTICLE{hoellinger2024diagnosing, + author = {Hoellinger, Tristan and Leclercq, Florent}, + title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}", + journal = {arXiv e-prints}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2024, + month = dec, + eid = {arXiv:2412.04443}, + pages = {arXiv:2412.04443}, + doi = {10.48550/arXiv.2412.04443}, +archivePrefix = {arXiv}, + eprint = {2412.04443}, +primaryClass = {astro-ph.CO}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} +``` + +Other references relevant to the SelfiSys pipeline and cited in the code are listed below. + +- **[jasche2010bayesian]** +Jasche, J., Kitaura, F. S., Wandelt, B. D., and Enßlin, T. A., “Bayesian power-spectrum inference for large-scale structure data”, Monthly Notices of the Royal Astronomical Society, vol. 406, no. 1, OUP, pp. 60–85, 2010. [doi:10.1111/j.1365-2966.2010.16610.x](https://doi.org/10.48550/arXiv.0911.2493). + +- **[gil2015power]** +Gil-Marín, H., “The power spectrum and bispectrum of SDSS DR11 BOSS galaxies - I. Bias and gravity”, Monthly Notices of the Royal Astronomical Society, vol. 451, no. 1, OUP, pp. 539–580, 2015. [doi:10.1093/mnras/stv961](https://doi.org/10.48550/arXiv.1407.5668). + +- **[howlett2015clustering]** +Howlett, C., Ross, A. J., Samushia, L., Percival, W. J., and Manera, M., “The clustering of the SDSS main galaxy sample - II. Mock galaxy catalogues and a measurement of the growth of structure from redshift space distortions at z = 0.15”, Monthly Notices of the Royal Astronomical Society, vol. 449, no. 1, OUP, pp. 848–866, 2015. [doi:10.1093/mnras/stu2693](https://doi.org/10.48550/arXiv.1409.3238). + +- **[leclercq2019primordial]** +Florent Leclercq, Wolfgang Enzi, Jens Jasche, Alan Heavens, Primordial power spectrum and cosmology from black-box galaxy surveys, Monthly Notices of the Royal Astronomical Society, Volume 490, Issue 3, December 2019, Pages 4237–4253, [doi:10.1093/mnras/stz2718](https://doi.org/10.1093/mnras/stz2718). + +- **[hoellinger2024diagnosing]** +Hoellinger, T. and Leclercq, F., “Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum”, arXiv e-prints, Art. no. arXiv:2412.04443, 2024. [doi.org:10.48550/arXiv.2412.04443](https://doi.org/10.48550/arXiv.2412.04443). \ No newline at end of file diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt new file mode 100644 index 0000000..26dda27 --- /dev/null +++ b/docs/_sources/index.rst.txt @@ -0,0 +1,95 @@ +SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys +=================================================================== + +.. image:: https://img.shields.io/badge/astro--ph.CO-arxiv%3A2412.04443-B31B1B.svg + :target: https://arxiv.org/abs/2412.04443 + :alt: arXiv + +.. image:: https://img.shields.io/github/v/tag/hoellin/selfisys_public.svg?label=version + :target: https://github.com/hoellin/selfisys_public/releases + :alt: GitHub Release + +.. image:: https://img.shields.io/github/last-commit/hoellin/selfisys_public + :target: https://github.com/hoellin/selfisys_public/commits/main + :alt: Last Commit + +.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg + :target: https://github.com/hoellin/selfisys_public/blob/main/LICENSE + :alt: License + +**SelfiSys** is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference. + +It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys. + +Key Features +------------ + +- **Custom hidden-box forward models** + We provide a `HiddenBox` class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver. +- **Diagnosis of systematic effects** + Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference. +- **Cosmological inference** + Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +For practical examples demonstrating how to use SelfiSys, visit the `SelfiSys Examples Repository `_. + +References +---------- + +If you use the SelfiSys package in your research, please cite the following paper and feel free to `contact the authors `_ for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., arXiv e-prints*, 2024 +`arXiv:2412.04443 `_ +`[astro-ph.CO] `_ +`[ADS] `_ +`[pdf] `_ + +Contributors +------------ + +- **Tristan Hoellinger** + `tristan.hoellinger@iap.fr `_ + + Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP). + +License +------- + +This software is distributed under the GPLv3 Licence. Please review the `LICENSE `_ file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence. + +Requirements +------------ + +The code is written in Python 3.10 and depends on the following packages: + +- `pySELFI `_: Python implementation of the Simulator Expansion for Likelihood-Free Inference. +- `Simbelmynë `_: A hierarchical probabilistic simulator for generating synthetic galaxy survey data. +- `ELFI `_: A statistical software package for likelihood-free inference, implementing Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +A comprehensive list of dependencies, along with installation instructions, will be provided in a future release. + +.. toctree:: + :maxdepth: 2 + :caption: API Documentation + + selfisys.hiddenbox + selfisys.normalise_hb + selfisys.prior + selfisys.selection_functions + selfisys.selfi_interface + selfisys.sbmy_interface + selfisys.grf + selfisys.utils + +.. toctree:: + :maxdepth: 2 + :caption: Contribute + + ../../CONTRIBUTING.md + +.. toctree:: + :maxdepth: 2 + :caption: References + + ../../REFERENCES.md \ No newline at end of file diff --git a/docs/_sources/selfisys.global_parameters.rst.txt b/docs/_sources/selfisys.global_parameters.rst.txt new file mode 100644 index 0000000..0efb881 --- /dev/null +++ b/docs/_sources/selfisys.global_parameters.rst.txt @@ -0,0 +1,7 @@ +global_parameters +================= + +.. automodule:: selfisys.global_parameters + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.grf.rst.txt b/docs/_sources/selfisys.grf.rst.txt new file mode 100644 index 0000000..d10cbf8 --- /dev/null +++ b/docs/_sources/selfisys.grf.rst.txt @@ -0,0 +1,7 @@ +grf +=== + +.. automodule:: selfisys.grf + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.hiddenbox.rst.txt b/docs/_sources/selfisys.hiddenbox.rst.txt new file mode 100644 index 0000000..292952c --- /dev/null +++ b/docs/_sources/selfisys.hiddenbox.rst.txt @@ -0,0 +1,7 @@ +hiddenbox +========= + +.. automodule:: selfisys.hiddenbox + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.normalise_hb.rst.txt b/docs/_sources/selfisys.normalise_hb.rst.txt new file mode 100644 index 0000000..108af97 --- /dev/null +++ b/docs/_sources/selfisys.normalise_hb.rst.txt @@ -0,0 +1,7 @@ +normalise_hb +============ + +.. automodule:: selfisys.normalise_hb + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.prior.rst.txt b/docs/_sources/selfisys.prior.rst.txt new file mode 100644 index 0000000..30a73e2 --- /dev/null +++ b/docs/_sources/selfisys.prior.rst.txt @@ -0,0 +1,7 @@ +prior +===== + +.. automodule:: selfisys.prior + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.rst.txt b/docs/_sources/selfisys.rst.txt new file mode 100644 index 0000000..1863c5a --- /dev/null +++ b/docs/_sources/selfisys.rst.txt @@ -0,0 +1,93 @@ +selfisys package +================ + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + selfisys.utils + +Submodules +---------- + +selfisys.global\_parameters module +---------------------------------- + +.. automodule:: selfisys.global_parameters + :members: + :undoc-members: + :show-inheritance: + +selfisys.grf module +------------------- + +.. automodule:: selfisys.grf + :members: + :undoc-members: + :show-inheritance: + +selfisys.hiddenbox module +------------------------- + +.. automodule:: selfisys.hiddenbox + :members: + :undoc-members: + :show-inheritance: + +selfisys.normalise\_hb module +----------------------------- + +.. automodule:: selfisys.normalise_hb + :members: + :undoc-members: + :show-inheritance: + +selfisys.prior module +--------------------- + +.. automodule:: selfisys.prior + :members: + :undoc-members: + :show-inheritance: + +selfisys.sbmy\_interface module +------------------------------- + +.. automodule:: selfisys.sbmy_interface + :members: + :undoc-members: + :show-inheritance: + +selfisys.selection\_functions module +------------------------------------ + +.. automodule:: selfisys.selection_functions + :members: + :undoc-members: + :show-inheritance: + +selfisys.selfi\_interface module +-------------------------------- + +.. automodule:: selfisys.selfi_interface + :members: + :undoc-members: + :show-inheritance: + +selfisys.setup\_model module +---------------------------- + +.. automodule:: selfisys.setup_model + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: selfisys + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.sbmy_interface.rst.txt b/docs/_sources/selfisys.sbmy_interface.rst.txt new file mode 100644 index 0000000..7f1f003 --- /dev/null +++ b/docs/_sources/selfisys.sbmy_interface.rst.txt @@ -0,0 +1,7 @@ +sbmy_interface +============== + +.. automodule:: selfisys.sbmy_interface + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.selection_functions.rst.txt b/docs/_sources/selfisys.selection_functions.rst.txt new file mode 100644 index 0000000..fdb56b4 --- /dev/null +++ b/docs/_sources/selfisys.selection_functions.rst.txt @@ -0,0 +1,7 @@ +selection_functions +=================== + +.. automodule:: selfisys.selection_functions + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.selfi_interface.rst.txt b/docs/_sources/selfisys.selfi_interface.rst.txt new file mode 100644 index 0000000..32b1adf --- /dev/null +++ b/docs/_sources/selfisys.selfi_interface.rst.txt @@ -0,0 +1,7 @@ +selfi_interface +=============== + +.. automodule:: selfisys.selfi_interface + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.setup_model.rst.txt b/docs/_sources/selfisys.setup_model.rst.txt new file mode 100644 index 0000000..a9036a8 --- /dev/null +++ b/docs/_sources/selfisys.setup_model.rst.txt @@ -0,0 +1,7 @@ +setup_model +=========== + +.. automodule:: selfisys.setup_model + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/selfisys.utils.rst.txt b/docs/_sources/selfisys.utils.rst.txt new file mode 100644 index 0000000..d5ac880 --- /dev/null +++ b/docs/_sources/selfisys.utils.rst.txt @@ -0,0 +1,101 @@ +selfisys.utils package +====================== + +Submodules +---------- + +selfisys.utils.examples\_utils module +------------------------------------- + +.. automodule:: selfisys.utils.examples_utils + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.logger module +---------------------------- + +.. automodule:: selfisys.utils.logger + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.low\_level module +-------------------------------- + +.. automodule:: selfisys.utils.low_level + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.parser module +---------------------------- + +.. automodule:: selfisys.utils.parser + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.path\_utils module +--------------------------------- + +.. automodule:: selfisys.utils.path_utils + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.plot\_examples module +------------------------------------ + +.. automodule:: selfisys.utils.plot_examples + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.plot\_params module +---------------------------------- + +.. automodule:: selfisys.utils.plot_params + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.plot\_utils module +--------------------------------- + +.. automodule:: selfisys.utils.plot_utils + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.timestepping module +---------------------------------- + +.. automodule:: selfisys.utils.timestepping + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.tools module +--------------------------- + +.. automodule:: selfisys.utils.tools + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.workers module +----------------------------- + +.. automodule:: selfisys.utils.workers + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: selfisys.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_static/_sphinx_javascript_frameworks_compat.js b/docs/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/docs/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return 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+ document.dispatchEvent(event); + }); +}); \ No newline at end of file diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js new file mode 100644 index 0000000..c7fe6c6 --- /dev/null +++ b/docs/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/_static/sphinx_highlight.js b/docs/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/docs/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/docs/genindex.html b/docs/genindex.html new file mode 100644 index 0000000..092946a --- /dev/null +++ b/docs/genindex.html @@ -0,0 +1,955 @@ + + + + + + + + Index — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
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© Copyright 2025, Tristan Hoellinger.

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+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
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  • SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys
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SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys

+arXiv + +GitHub Release + +Last Commit + +License + +

SelfiSys is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference.

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It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys.

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Key Features

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    Custom hidden-box forward models

    We provide a HiddenBox class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver.

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    Diagnosis of systematic effects

    Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference.

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    Cosmological inference

    Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.

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For practical examples demonstrating how to use SelfiSys, visit the SelfiSys Examples Repository.

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References

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If you use the SelfiSys package in your research, please cite the following paper and feel free to contact the authors for feedback, collaboration opportunities, or other inquiries.

+

Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum +Hoellinger, T. and Leclercq, F., arXiv e-prints, 2024 +arXiv:2412.04443 +[astro-ph.CO] +[ADS] +[pdf]

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Contributors

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  • Tristan Hoellinger +tristan.hoellinger@iap.fr

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    Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP).

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License

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This software is distributed under the GPLv3 Licence. Please review the LICENSE file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence.

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Requirements

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The code is written in Python 3.10 and depends on the following packages:

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  • pySELFI: Python implementation of the Simulator Expansion for Likelihood-Free Inference.

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  • Simbelmynë: A hierarchical probabilistic simulator for generating synthetic galaxy survey data.

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  • ELFI: A statistical software package for likelihood-free inference, implementing Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.

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A comprehensive list of dependencies, along with installation instructions, will be provided in a future release.

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API Documentation

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Contribute

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References

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+ s
+ selfisys +
    + selfisys.global_parameters +
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    + selfisys.normalise_hb +
    + selfisys.prior +
    + selfisys.sbmy_interface +
    + selfisys.selection_functions +
    + selfisys.selfi_interface +
    + selfisys.setup_model +
    + selfisys.utils +
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+ + + + + + + + + \ No newline at end of file diff --git a/docs/searchindex.js b/docs/searchindex.js new file mode 100644 index 0000000..2501d75 --- /dev/null +++ b/docs/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"API Documentation": [[3, null]], "Contribute": [[3, null]], "Contributing to SelfiSys": [[0, null]], "Contributors": [[1, "contributors"], [3, "contributors"]], "Documentation": [[1, "documentation"]], "Jupyter Notebooks": [[0, "jupyter-notebooks"]], "Key Features": [[1, "key-features"], [3, "key-features"]], "License": [[1, "license"], [3, "license"]], "Module contents": [[4, "module-selfisys"], [14, "module-selfisys.utils"]], "Python code": [[0, "python-code"]], "Questions?": [[0, "questions"]], "References": [[1, "references"], [2, null], [3, "references"], [3, null]], "Reporting Issues": [[0, "reporting-issues"]], "Requirements": [[1, "requirements"], [3, "requirements"]], "SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys": [[1, null], 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"grf": [4, 6], "guidelin": 0, "hiddenbox": [4, 7], "impact": [1, 3], "issu": 0, "jupyt": 0, "kei": [1, 3], "licens": [1, 3], "logger": 14, "low_level": 14, "modul": [4, 14], "normalise_hb": [4, 8], "notebook": 0, "packag": [4, 14], "parser": 14, "path_util": 14, "plot_exampl": 14, "plot_param": 14, "plot_util": 14, "prior": [4, 9], "python": 0, "question": 0, "refer": [1, 2, 3], "report": 0, "requir": [1, 3], "sbmy_interfac": [4, 10], "selection_funct": [4, 11], "selfi_interfac": [4, 12], "selfisi": [0, 1, 3, 4, 14], "setup_model": [4, 13], "style": 0, "submit": 0, "submodul": [4, 14], "subpackag": 4, "survei": [1, 3], "systemat": [1, 3], "timestep": 14, "tool": 14, "util": 14, "worker": 14}}) \ No newline at end of file diff --git a/docs/selfisys.global_parameters.html b/docs/selfisys.global_parameters.html new file mode 100644 index 0000000..e089cb2 --- /dev/null +++ b/docs/selfisys.global_parameters.html @@ -0,0 +1,311 @@ + + + + + + + + + global_parameters — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

global_parameters

+
+ + +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.grf.html b/docs/selfisys.grf.html new file mode 100644 index 0000000..e656583 --- /dev/null +++ b/docs/selfisys.grf.html @@ -0,0 +1,351 @@ + + + + + + + + + grf — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

grf

+
+
+selfisys.grf.primordial_grf(L, N, seedphases, fname_powerspectrum, fname_outputinitialdensity, force_sim=False, return_g=False, verbose=0)[source]
+

Generate a Gaussian random field from a specified input power +spectrum.

+
+
Parameters:
+
    +

  • L (float) – Side length of the simulation box in Mpc/h.

    • +

  • N (int) – Grid resolution (number of cells per dimension).

    • +

  • seedphases (int) – Seed for random phase generation (for reproducibility).

    • +

  • fname_powerspectrum (str) – File path to the input power spectrum.

    • +

  • fname_outputinitialdensity (str) – File path to store the generated initial density field.

    • +

  • force_sim (bool, optional) – If True, regenerate the GRF even if the output file exists. +Default is False.

    • +

  • return_g (bool, optional) – If True, return the GRF as a numpy array. Default is False.

    • +

  • verbose (int, optional) – Verbosity level (0 = silent, 1 = progress, 2 = detailed). +Default is 0.

    • +
+
+
Raises:
+
    +

  • OSError – If the power spectrum file cannot be read.

    • +

  • RuntimeError – If an unexpected error occurs during power spectrum reading.

    • +
+
+
Returns:
+

The GRF data if return_g is True, otherwise None.

+
+
Return type:
+

numpy.ndarray or None

+
+
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.hiddenbox.html b/docs/selfisys.hiddenbox.html new file mode 100644 index 0000000..7ec9369 --- /dev/null +++ b/docs/selfisys.hiddenbox.html @@ -0,0 +1,562 @@ + + + + + + + + + hiddenbox — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

hiddenbox

+
+
+class selfisys.hiddenbox.HiddenBox(k_s: Any, P_ss_path: str, Pbins_bnd: Any, theta2P: Callable, P: int, size: int, L: float, G_sim_path: str, G_ss_path: str, Np0: int, Npm0: int, fsimdir: str, noise_std: float | None = None, radial_selection: str | None = None, selection_params: List[Any] | None = None, observed_density: float | None = None, linear_bias: float | List[float] | None = None, norm_csts: float | List[float] | None = None, survey_mask_path: str | None = None, local_mask_prefix: str | None = None, sim_params: str | None = None, eff_redshifts: List[float] | None = None, TimeSteps: List[int] | None = None, TimeStepDistribution: str | None = None, seedphase: int | None = None, seednoise: int | None = None, fixnoise: bool = False, seednorm: int | None = None, save_frequency: int | None = None, reset: bool = False, verbosity: int = 2, **kwargs)[source]
+

Bases: object

+

This class represents custom forward data model of large-scale +spectroscopic galaxy surveys.

+
+
+property Npop
+

Number of populations.

+
+ +
+
+property Ntimesteps
+

Number of time steps.

+
+ +
+
+property Psingle
+

Number of summary statistics for each population.

+
+ +
+
+compute_pool(theta, d, pool_fname, N, index=None, force_powerspectrum=False, force_parfiles=False, force_sim=False, remove_sbmy=False, theta_is_p=False, simspath=None, bar=False)[source]
+

Compute a pool of realisations of the hidden box compatible +with pySELFI.

+
+
Parameters:
+
    +

  • theta (ndarray) – Power spectrum values at the support wavenumbers.

    • +

  • d (int) – Direction in parameter space, from 0 to S.

    • +

  • pool_fname (str) – Filename for the pool.

    • +

  • N (int) – Number of realisations required at the given direction.

    • +

  • index (int, optional) – Index of a single simulation to run. Default is None.

    • +

  • force_powerspectrum (bool, optional) – If True, force recomputation of the power spectrum. Default +is False.

    • +

  • force_parfiles (bool, optional) – If True, overwrite existing parameter files. Default is +False.

    • +

  • force_sim (bool, optional) – If True, rerun the simulation even if the output density +already exists. Default is False.

    • +

  • remove_sbmy (bool, optional) – If True, remove Simbelmynë output files from disk. Default +is False.

    • +

  • theta_is_p (bool, optional) – Set to True when theta is already an unnormalised power +spectrum. Default is False.

    • +

  • simspath (str, optional) – Path indicating where to store the simulations. Default is +None.

    • +

  • bar (bool, optional) – If True, display a progress bar. Default is False.

    • +
+
+
Returns:
+

p – Simulation pool object.

+
+
Return type:
+

Pool

+
+
+
+ +
+
+evaluate(theta, d, seedphase, seednoise, i=0, N=0, force_powerspectrum=False, force_parfiles=False, force_sim=False, remove_sbmy=False, theta_is_p=False, simspath=None, check_output=False, RSDs=True, abc=False, cosmo_vect=None)[source]
+

Evaluate the hidden box for a given input power spectrum.

+

The result is deterministic (the phase is fixed), except as it +is modified by nuisance parameters if any.

+

This routine is used by pySELFI to compute the gradient of the +hidden box with respect to the power spectrum, and by ABC-PMC to +evaluate the forward model.

+
+
Parameters:
+
    +

  • theta (ndarray) – Power spectrum values at the support wavenumbers.

    • +

  • d (int) – Direction in parameter space, from 0 to S.

    • +

  • seedphase (int or list of int) – Seed to generate the initial white noise in Fourier space.

    • +

  • seednoise (int or list of int) – Seed to generate the noise realisation.

    • +

  • i (int, optional) – Current evaluation index of the hidden box. Default is 0.

    • +

  • N (int, optional) – Total number of evaluations of the hidden box. Default is +0.

    • +

  • force_powerspectrum (bool, optional) – If True, force recomputation of the power spectrum at the +values of the Fourier grid. Default is False.

    • +

  • force_parfiles (bool, optional) – If True, overwrite existing parameter files. Default is +False.

    • +

  • force_sim (bool, optional) – If True, rerun the simulation even if the output density +already exists. Default is False.

    • +

  • remove_sbmy (bool, optional) – If True, remove Simbelmynë output files from disk. Default +is False.

    • +

  • theta_is_p (bool, optional) – Set to True if theta is already an unnormalised power +spectrum. Default is False.

    • +

  • simspath (str, optional) – Path to the simulations directory. Default is None.

    • +

  • check_output (bool, optional) – If True, check the integrity of the output file and +recompute if corrupted. Default is False.

    • +

  • RSDs (bool, optional) – Whether to compute redshift-space distortions. Default is +True.

    • +

  • abc (bool or str, optional) – If not False, remove most output files after evaluation. +Default is False.

    • +

  • cosmo_vect (ndarray, optional) – Cosmological parameters. Required if abc is True.

    • +
+
+
Returns:
+

Phi – Vector of summary statistics.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+property force_neglect_lightcone
+

Whether to force neglecting the lightcone.

+
+ +
+
+property force_recompute_mocks
+

Whether to force recomputation of mocks.

+
+ +
+
+property gravity_on
+

Whether gravity is enabled.

+
+ +
+
+load_pool(pool_fname, N)[source]
+

Load a pool of realisations of the hidden box.

+
+
Parameters:
+
    +

  • pool_fname (str) – Filename of the pool to load.

    • +

  • N (int) – Number of realisations in the pool.

    • +
+
+
Returns:
+

p – Simulation pool object.

+
+
Return type:
+

Pool

+
+
+
+ +
+
+make_data(cosmo, id, seedphase, seednoise, force_powerspectrum=False, force_parfiles=False, force_sim=False, force_cosmo=False, force_phase=False, d=-1, remove_sbmy=True, verbosity=None, return_g=False, RSDs=True, prefix_mocks=None)[source]
+

Generate simulated data based on the given cosmological +parameters.

+
+
Parameters:
+
    +

  • cosmo (dict) – Cosmological and infrastructure parameters.

    • +

  • id (int or str, optional) – Identifier used as a suffix in the file names. Default is +0.

    • +

  • seedphase (int or list of int) – Seed to generate the initial white noise in Fourier space.

    • +

  • seednoise (int or list of int) – Seed to generate the noise realisation.

    • +

  • force_powerspectrum (bool, optional) – Force recomputing the input spectrum. Default is False.

    • +

  • force_parfiles (bool, optional) – Force recomputing the parameter files. Default is False.

    • +

  • force_sim (bool, optional) – Force recomputing the simulation. Default is False.

    • +

  • force_cosmo (bool, optional) – Force recomputing the cosmological parameters. Default is +False.

    • +

  • force_phase (bool, optional) – Force recomputing the initial phase. Default is False.

    • +

  • d (int, optional) – Direction in the parameter space. Default is -1.

    • +

  • remove_sbmy (bool, optional) – Whether to remove most Simbelmynë output files after use for +disk space management. Default is True.

    • +

  • verbosity (int, optional) – Verbosity level. If None, use self.verbosity. Default is +None.

    • +

  • return_g (bool, optional) – Whether to return the full field alongside the summary. +Default is False.

    • +

  • RSDs (bool, optional) – Whether to compute the redshift-space distortions. Default +is True.

    • +

  • prefix_mocks (str, optional) – Prefix for the mock data. If None, use self.prefix_mocks. +Default is None.

    • +
+
+
Returns:
+

    +

  • Phi (ndarray) – Vector of summary statistics.

    • +

  • g (ndarray or list of ndarray or None) – Observed field(s) if return_g is True; otherwise None.

    • +
+

+
+
+
+ +
+
+property modified_selfi
+

Whether to use the modified selfi.

+
+ +
+
+property prefix_mocks
+

Prefix for the mocks.

+
+ +
+
+reset_survey()[source]
+

Re-initialise the survey mask C(n) and radial selection +function R(r).

+
+ +
+
+property setup_only
+

Whether to only set up the hidden box.

+
+ +
+
+switch_recompute_pool(prefix_mocks=None)[source]
+

Toggle recomputation of the pool for future compute_pool +calls.

+
+
Parameters:
+

prefix_mocks (str, optional) – Prefix for the future simulation files. Default is None.

+
+
+
+ +
+
+switch_setup()[source]
+

Toggle the setup-only mode.

+
+ +
+
+update(**kwargs)[source]
+

Updates the given parameter(s) of the hidden box with the +given value(s).

+
+
Parameters:
+

**kwargs (dict) – dictionary of parameters to update

+
+
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.html b/docs/selfisys.html new file mode 100644 index 0000000..febbc76 --- /dev/null +++ b/docs/selfisys.html @@ -0,0 +1,1570 @@ + + + + + + + + + selfisys package — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

selfisys package

+
+

Subpackages

+
+ +
+
+
+

Submodules

+
+
+

selfisys.global_parameters module

+
+
+

selfisys.grf module

+
+
+selfisys.grf.primordial_grf(L, N, seedphases, fname_powerspectrum, fname_outputinitialdensity, force_sim=False, return_g=False, verbose=0)[source]
+

Generate a Gaussian random field from a specified input power +spectrum.

+
+
Parameters:
+
    +

  • L (float) – Side length of the simulation box in Mpc/h.

    • +

  • N (int) – Grid resolution (number of cells per dimension).

    • +

  • seedphases (int) – Seed for random phase generation (for reproducibility).

    • +

  • fname_powerspectrum (str) – File path to the input power spectrum.

    • +

  • fname_outputinitialdensity (str) – File path to store the generated initial density field.

    • +

  • force_sim (bool, optional) – If True, regenerate the GRF even if the output file exists. +Default is False.

    • +

  • return_g (bool, optional) – If True, return the GRF as a numpy array. Default is False.

    • +

  • verbose (int, optional) – Verbosity level (0 = silent, 1 = progress, 2 = detailed). +Default is 0.

    • +
+
+
Raises:
+
    +

  • OSError – If the power spectrum file cannot be read.

    • +

  • RuntimeError – If an unexpected error occurs during power spectrum reading.

    • +
+
+
Returns:
+

The GRF data if return_g is True, otherwise None.

+
+
Return type:
+

numpy.ndarray or None

+
+
+
+ +
+
+

selfisys.hiddenbox module

+
+
+class selfisys.hiddenbox.HiddenBox(k_s: Any, P_ss_path: str, Pbins_bnd: Any, theta2P: Callable, P: int, size: int, L: float, G_sim_path: str, G_ss_path: str, Np0: int, Npm0: int, fsimdir: str, noise_std: float | None = None, radial_selection: str | None = None, selection_params: List[Any] | None = None, observed_density: float | None = None, linear_bias: float | List[float] | None = None, norm_csts: float | List[float] | None = None, survey_mask_path: str | None = None, local_mask_prefix: str | None = None, sim_params: str | None = None, eff_redshifts: List[float] | None = None, TimeSteps: List[int] | None = None, TimeStepDistribution: str | None = None, seedphase: int | None = None, seednoise: int | None = None, fixnoise: bool = False, seednorm: int | None = None, save_frequency: int | None = None, reset: bool = False, verbosity: int = 2, **kwargs)[source]
+

Bases: object

+

This class represents custom forward data model of large-scale +spectroscopic galaxy surveys.

+
+
+property Npop
+

Number of populations.

+
+ +
+
+property Ntimesteps
+

Number of time steps.

+
+ +
+
+property Psingle
+

Number of summary statistics for each population.

+
+ +
+
+compute_pool(theta, d, pool_fname, N, index=None, force_powerspectrum=False, force_parfiles=False, force_sim=False, remove_sbmy=False, theta_is_p=False, simspath=None, bar=False)[source]
+

Compute a pool of realisations of the hidden box compatible +with pySELFI.

+
+
Parameters:
+
    +

  • theta (ndarray) – Power spectrum values at the support wavenumbers.

    • +

  • d (int) – Direction in parameter space, from 0 to S.

    • +

  • pool_fname (str) – Filename for the pool.

    • +

  • N (int) – Number of realisations required at the given direction.

    • +

  • index (int, optional) – Index of a single simulation to run. Default is None.

    • +

  • force_powerspectrum (bool, optional) – If True, force recomputation of the power spectrum. Default +is False.

    • +

  • force_parfiles (bool, optional) – If True, overwrite existing parameter files. Default is +False.

    • +

  • force_sim (bool, optional) – If True, rerun the simulation even if the output density +already exists. Default is False.

    • +

  • remove_sbmy (bool, optional) – If True, remove Simbelmynë output files from disk. Default +is False.

    • +

  • theta_is_p (bool, optional) – Set to True when theta is already an unnormalised power +spectrum. Default is False.

    • +

  • simspath (str, optional) – Path indicating where to store the simulations. Default is +None.

    • +

  • bar (bool, optional) – If True, display a progress bar. Default is False.

    • +
+
+
Returns:
+

p – Simulation pool object.

+
+
Return type:
+

Pool

+
+
+
+ +
+
+evaluate(theta, d, seedphase, seednoise, i=0, N=0, force_powerspectrum=False, force_parfiles=False, force_sim=False, remove_sbmy=False, theta_is_p=False, simspath=None, check_output=False, RSDs=True, abc=False, cosmo_vect=None)[source]
+

Evaluate the hidden box for a given input power spectrum.

+

The result is deterministic (the phase is fixed), except as it +is modified by nuisance parameters if any.

+

This routine is used by pySELFI to compute the gradient of the +hidden box with respect to the power spectrum, and by ABC-PMC to +evaluate the forward model.

+
+
Parameters:
+
    +

  • theta (ndarray) – Power spectrum values at the support wavenumbers.

    • +

  • d (int) – Direction in parameter space, from 0 to S.

    • +

  • seedphase (int or list of int) – Seed to generate the initial white noise in Fourier space.

    • +

  • seednoise (int or list of int) – Seed to generate the noise realisation.

    • +

  • i (int, optional) – Current evaluation index of the hidden box. Default is 0.

    • +

  • N (int, optional) – Total number of evaluations of the hidden box. Default is +0.

    • +

  • force_powerspectrum (bool, optional) – If True, force recomputation of the power spectrum at the +values of the Fourier grid. Default is False.

    • +

  • force_parfiles (bool, optional) – If True, overwrite existing parameter files. Default is +False.

    • +

  • force_sim (bool, optional) – If True, rerun the simulation even if the output density +already exists. Default is False.

    • +

  • remove_sbmy (bool, optional) – If True, remove Simbelmynë output files from disk. Default +is False.

    • +

  • theta_is_p (bool, optional) – Set to True if theta is already an unnormalised power +spectrum. Default is False.

    • +

  • simspath (str, optional) – Path to the simulations directory. Default is None.

    • +

  • check_output (bool, optional) – If True, check the integrity of the output file and +recompute if corrupted. Default is False.

    • +

  • RSDs (bool, optional) – Whether to compute redshift-space distortions. Default is +True.

    • +

  • abc (bool or str, optional) – If not False, remove most output files after evaluation. +Default is False.

    • +

  • cosmo_vect (ndarray, optional) – Cosmological parameters. Required if abc is True.

    • +
+
+
Returns:
+

Phi – Vector of summary statistics.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+property force_neglect_lightcone
+

Whether to force neglecting the lightcone.

+
+ +
+
+property force_recompute_mocks
+

Whether to force recomputation of mocks.

+
+ +
+
+property gravity_on
+

Whether gravity is enabled.

+
+ +
+
+load_pool(pool_fname, N)[source]
+

Load a pool of realisations of the hidden box.

+
+
Parameters:
+
    +

  • pool_fname (str) – Filename of the pool to load.

    • +

  • N (int) – Number of realisations in the pool.

    • +
+
+
Returns:
+

p – Simulation pool object.

+
+
Return type:
+

Pool

+
+
+
+ +
+
+make_data(cosmo, id, seedphase, seednoise, force_powerspectrum=False, force_parfiles=False, force_sim=False, force_cosmo=False, force_phase=False, d=-1, remove_sbmy=True, verbosity=None, return_g=False, RSDs=True, prefix_mocks=None)[source]
+

Generate simulated data based on the given cosmological +parameters.

+
+
Parameters:
+
    +

  • cosmo (dict) – Cosmological and infrastructure parameters.

    • +

  • id (int or str, optional) – Identifier used as a suffix in the file names. Default is +0.

    • +

  • seedphase (int or list of int) – Seed to generate the initial white noise in Fourier space.

    • +

  • seednoise (int or list of int) – Seed to generate the noise realisation.

    • +

  • force_powerspectrum (bool, optional) – Force recomputing the input spectrum. Default is False.

    • +

  • force_parfiles (bool, optional) – Force recomputing the parameter files. Default is False.

    • +

  • force_sim (bool, optional) – Force recomputing the simulation. Default is False.

    • +

  • force_cosmo (bool, optional) – Force recomputing the cosmological parameters. Default is +False.

    • +

  • force_phase (bool, optional) – Force recomputing the initial phase. Default is False.

    • +

  • d (int, optional) – Direction in the parameter space. Default is -1.

    • +

  • remove_sbmy (bool, optional) – Whether to remove most Simbelmynë output files after use for +disk space management. Default is True.

    • +

  • verbosity (int, optional) – Verbosity level. If None, use self.verbosity. Default is +None.

    • +

  • return_g (bool, optional) – Whether to return the full field alongside the summary. +Default is False.

    • +

  • RSDs (bool, optional) – Whether to compute the redshift-space distortions. Default +is True.

    • +

  • prefix_mocks (str, optional) – Prefix for the mock data. If None, use self.prefix_mocks. +Default is None.

    • +
+
+
Returns:
+

    +

  • Phi (ndarray) – Vector of summary statistics.

    • +

  • g (ndarray or list of ndarray or None) – Observed field(s) if return_g is True; otherwise None.

    • +
+

+
+
+
+ +
+
+property modified_selfi
+

Whether to use the modified selfi.

+
+ +
+
+property prefix_mocks
+

Prefix for the mocks.

+
+ +
+
+reset_survey()[source]
+

Re-initialise the survey mask C(n) and radial selection +function R(r).

+
+ +
+
+property setup_only
+

Whether to only set up the hidden box.

+
+ +
+
+switch_recompute_pool(prefix_mocks=None)[source]
+

Toggle recomputation of the pool for future compute_pool +calls.

+
+
Parameters:
+

prefix_mocks (str, optional) – Prefix for the future simulation files. Default is None.

+
+
+
+ +
+
+switch_setup()[source]
+

Toggle the setup-only mode.

+
+ +
+
+update(**kwargs)[source]
+

Updates the given parameter(s) of the hidden box with the +given value(s).

+
+
Parameters:
+

**kwargs (dict) – dictionary of parameters to update

+
+
+
+ +
+ +
+
+

selfisys.normalise_hb module

+
+
+selfisys.normalise_hb.define_normalisation(hidden_box: HiddenBox, Pbins: ndarray, cosmo: Dict[str, Any], N: int, min_k_norma: float = 0.04, npar: int = 1, force: bool = False) → ndarray[source]
+

Define the normalisation constants for the HiddenBox instance.

+
+
Parameters:
+
    +

  • hidden_box (HiddenBox) – Instance of the HiddenBox class.

    • +

  • Pbins (ndarray) – Array of P bin values.

    • +

  • cosmo (dict) – Cosmological and infrastructure parameters.

    • +

  • N (int) – Number of realisations required.

    • +

  • min_k_norma (float, optional) – Minimum k value to compute the normalisation constants.

    • +

  • npar (int, optional) – Number of parallel processes to use. Default is 1.

    • +

  • force (bool, optional) – If True, force recomputation. Default is False.

    • +
+
+
Returns:
+

norm_csts – Normalisation constants for the HiddenBox instance.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+selfisys.normalise_hb.worker_normalisation(hidden_box: HiddenBox, params: Tuple[Dict[str, Any], list, list, bool]) → ndarray[source]
+

Worker function to compute the normalisation constants, +compatible with Python multiprocessing.

+
+
Parameters:
+
    +

  • hidden_box (HiddenBox) – Instance of the HiddenBox class.

    • +

  • params (tuple) – A tuple containing (cosmo, seedphase, seednoise, force).

    • +
+
+
Returns:
+

phi – Computed summary statistics.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+selfisys.normalise_hb.worker_normalisation_public(hidden_box, cosmo: Dict[str, Any], N: int, i: int)[source]
+

Run the i-th simulation required to compute the normalisation +constants.

+
+
Parameters:
+
    +

  • hidden_box (HiddenBox) – Instance of the HiddenBox class.

    • +

  • cosmo (dict) – Cosmological and some infrastructure parameters.

    • +

  • N (int) – Total number of realisations required.

    • +

  • i (int) – Index of the simulation to be computed.

    • +
+
+
+
+ +
+
+selfisys.normalise_hb.worker_normalisation_wrapper(args)[source]
+

Wrapper function for the worker_normalisation function.

+
+
Parameters:
+

args (tuple) – A tuple containing (hidden_box, params).

+
+
Returns:
+

phi – Computed summary statistics.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+

selfisys.prior module

+
+
+selfisys.prior.get_summary(x, bins, normalisation=None, kmax=1.4)[source]
+

Compute a power-spectrum summary for given cosmological parameters.

+
+
Parameters:
+
    +

  • x (array-like) – Cosmological parameters [h, Omega_b, Omega_m, n_s, sigma_8].

    • +

  • bins (array-like) – Wavenumber bins.

    • +

  • normalisation (float or None, optional) – Normalisation constant to scale the resulting spectrum.

    • +

  • kmax (float, optional) – Maximum wavenumber for get_Pk.

    • +
+
+
Returns:
+

theta – The computed power-spectrum values, optionally normalised.

+
+
Return type:
+

ndarray

+
+
Raises:
+

RuntimeError – If the power-spectrum computation fails unexpectedly.

+
+
+
+ +
+
+selfisys.prior.logposterior_hyperparameters_parallel(selfi, theta_fiducial, Nbin_min, Nbin_max, theta_norm, k_corr, alpha_cv)[source]
+

Compute the log-posterior for the hyperparameters of the prior from +[leclercq2019primordial], for use within the SelfiSys pipeline.

+
+
Parameters:
+
    +

  • selfi (object) – The selfi object.

    • +

  • theta_fiducial (ndarray) – Fiducial spectrum.

    • +

  • Nbin_min (int) – Minimum bin index for the wavenumber range.

    • +

  • Nbin_max (int) – Maximum bin index for the wavenumber range.

    • +

  • theta_norm (float) – Hyperparameter controlling the overall uncertainty.

    • +

  • k_corr (float) – Hyperparameter controlling correlation scale.

    • +

  • alpha_cv (float) – Cosmic variance strength.

    • +
+
+
Returns:
+

The log-posterior value for the given hyperparameters.

+
+
Return type:
+

float

+
+
Raises:
+

RuntimeError – If the log-posterior computation fails unexpectedly.

+
+
+
+ +
+
+selfisys.prior.perform_prior_optimisation_and_plot(selfi, theta_fiducial, theta_norm_mean=0.1, theta_norm_std=0.3, k_corr_mean=0.02, k_corr_std=0.015, k_opt_min=0.0, k_opt_max=1.4, theta_norm_min=0.04, theta_norm_max=0.12, k_corr_min=0.012, k_corr_max=0.02, meshsize=30, Nbin_min=0, Nbin_max=100, theta_norm=0.05, k_corr=0.015, alpha_cv=0.00065, plot=True, savepath=None)[source]
+

Optimise the hyperparameters for the selfi2019 prior (from +[leclercq2019primordial]).

+
+
Parameters:
+
    +

  • selfi (object) – The selfi object.

    • +

  • theta_fiducial (ndarray) – Fiducial spectrum.

    • +

  • theta_norm_mean (float, optional) – Mean of the Gaussian hyperprior on theta_norm. Default 0.1.

    • +

  • theta_norm_std (float, optional) – Standard deviation of the hyperprior on theta_norm. Default 0.3.

    • +

  • k_corr_mean (float, optional) – Mean of the Gaussian hyperprior on k_corr. Default 0.020.

    • +

  • k_corr_std (float, optional) – Standard deviation of the hyperprior on k_corr. Default 0.015.

    • +

  • k_opt_min (float, optional) – Minimum wavenumber for the prior optimisation. Default 0.0.

    • +

  • k_opt_max (float, optional) – Maximum wavenumber for the prior optimisation. Default 1.4.

    • +

  • theta_norm_min (float, optional) – Lower bound for theta_norm in the mesh. Default 0.04.

    • +

  • theta_norm_max (float, optional) – Upper bound for theta_norm in the mesh. Default 0.12.

    • +

  • k_corr_min (float, optional) – Lower bound for k_corr in the mesh. Default 0.012.

    • +

  • k_corr_max (float, optional) – Upper bound for k_corr in the mesh. Default 0.02.

    • +

  • meshsize (int, optional) – Number of points in each dimension of the plot mesh. Default 30.

    • +

  • Nbin_min (int, optional) – Minimum bin index for restricting the prior. Default 0.

    • +

  • Nbin_max (int, optional) – Maximum bin index for restricting the prior. Default 100.

    • +

  • theta_norm (float, optional) – Initial or default guess of theta_norm. Default 0.05.

    • +

  • k_corr (float, optional) – Initial or default guess of k_corr. Default 0.015.

    • +

  • alpha_cv (float, optional) – Cosmic variance term or similar. Default 0.00065.

    • +

  • plot (bool, optional) – If True, generate and show/save a 2D contour plot. Default True.

    • +

  • savepath (str, optional) – File path to save the plot. If None, the plot is displayed.

    • +
+
+
Returns:
+

(theta_norm, k_corr) after optimisation.

+
+
Return type:
+

tuple

+
+
Raises:
+
    +

  • OSError – If file operations fail during saving the prior or posterior.

    • +

  • RuntimeError – If the optimisation fails unexpectedly.

    • +
+
+
+
+ +
+
+class selfisys.prior.planck_prior(Omega_mean, Omega_cov, bins, normalisation, kmax, nsamples=10000, nthreads=-1, EPS_K=1e-07, EPS_residual=0.001, filename=None)[source]
+

Bases: object

+

Custom prior for the SelfiSys pipeline. This is the prior used in +[hoellinger2024diagnosing], based on the Planck 2018 cosmological +parameters.

+

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].

+
+
Parameters:
+
    +

  • Omega_mean (array-like) – Mean of the prior distribution on cosmological parameters.

    • +

  • Omega_cov (array-like) – Covariance matrix of the prior distribution on cosmological +parameters.

    • +

  • bins (array-like) – Wavenumbers where the power spectrum is evaluated.

    • +

  • normalisation (float or None) – If not None, divide the power spectra by the normalisation.

    • +

  • kmax (float) – Maximum wavenumber for computations.

    • +

  • nsamples (int, optional) – Number of samples drawn from the prior on the cosmological +parameters. Default is 10,000.

    • +

  • nthreads (int, optional) – Number of CPU threads for parallel tasks. Default is -1, that +is, auto-detect the number of available threads.

    • +

  • EPS_K (float, optional) – Regularisation parameter for covariance inversion. Default 1e-7.

    • +

  • EPS_residual (float, optional) – Additional cutoff for matrix inversion. Default 1e-3.

    • +

  • filename (str or None, optional) – Path to a .npy file to store or load precomputed power spectra.

    • +
+
+
+
+
+mean
+

Mean of the computed power spectra.

+
+
Type:
+

ndarray

+
+
+
+ +
+
+covariance
+

Covariance matrix of the computed power spectra.

+
+
Type:
+

ndarray

+
+
+
+ +
+
+inv_covariance
+

Inverse of the covariance matrix.

+
+
Type:
+

ndarray

+
+
+
+ +
+
Raises:
+
    +

  • OSError – If file reading or writing fails.

    • +

  • RuntimeError – For unexpected HPC or multi-processing errors.

    • +
+
+
+
+
+property Nbin_max
+

Index of the maximal wavenumber given self.kmax.

+
+ +
+
+property Nbin_min
+

Index of the minimal wavenumber given k_min.

+
+ +
+
+compute()[source]
+

Compute the prior (mean, covariance, and inverse covariance).

+

If self.filename exists, tries to load the prior. Otherwise, +samples from the prior distribution on cosmological parameters +and evaluates the power spectra in parallel.

+
+
Raises:
+
    +

  • OSError – If self.filename is not writable/accessible.

    • +

  • RuntimeError – If multi-processing or power-spectra computations fail.

    • +
+
+
+
+ +
+
+classmethod load(fname)[source]
+

Load the prior from input file.

+
+
Parameters:
+

fname (str) – Input HDF5 filename.

+
+
Returns:
+

The prior object.

+
+
Return type:
+

prior

+
+
Raises:
+

OSError – If the file cannot be read or is invalid.

+
+
+
+ +
+
+logpdf(theta, theta_mean, theta_covariance, theta_icov)[source]
+

Return the log prior probability at a given point in parameter +space.

+
+
Parameters:
+
    +

  • theta (ndarray) – Evaluation point in parameter space.

    • +

  • theta_mean (ndarray) – Prior mean vector.

    • +

  • theta_covariance (ndarray) – Prior covariance matrix.

    • +

  • theta_icov (ndarray) – Inverse of the prior covariance matrix.

    • +
+
+
Returns:
+

Log prior probability value.

+
+
Return type:
+

float

+
+
+
+ +
+
+sample(seedsample=None)[source]
+

Draw a random sample from the prior distribution.

+
+
Parameters:
+

seedsample (int, optional) – Seed for the random number generator.

+
+
Returns:
+

A single sample from the prior distribution.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+save(fname)[source]
+

Save the prior to an output file.

+
+
Parameters:
+

fname (str) – Output HDF5 filename to store the prior data.

+
+
Raises:
+

OSError – If the file cannot be accessed or written.

+
+
+
+ +
+ +
+
+selfisys.prior.worker_class(params)[source]
+

Worker function to compute power spectra with CLASS, compatible with +Python multiprocessing.

+
+
Parameters:
+

params (tuple) – (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.

+
+
Returns:
+

theta – Power-spectrum summary from get_summary.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+

selfisys.sbmy_interface module

+
+
+selfisys.sbmy_interface.compute_Phi(G_ss_path, P_ss_path, g_obj, norm, AliasingCorr=True, verbosity=1)[source]
+

Compute the summary statistics from a field object, based on a +provided summary-statistics Fourier grid and baseline spectrum.

+
+
Parameters:
+
    +

  • G_ss_path (str) – Path to the FourierGrid file used for summary-statistics.

    • +

  • P_ss_path (str) – Path to the baseline power spectrum file for normalisation.

    • +

  • g_obj (Field) – Input field object from which to compute summary statistics.

    • +

  • norm (ndarray) – Normalisation constants for the summary statistics.

    • +

  • AliasingCorr (bool, optional) – Whether to apply aliasing correction. Default is True.

    • +

  • verbosity (int, optional) – Verbosity level (0=quiet, 1=normal, 2=debug). Default 1.

    • +
+
+
Returns:
+

Phi – Vector of summary statistics.

+
+
Return type:
+

ndarray

+
+
Raises:
+
    +

  • OSError – If file reading fails at G_ss_path or P_ss_path.

    • +

  • RuntimeError – If unexpected issues occur during computation.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.generate_white_noise_Field(L, size, seedphase, fname_whitenoise, seedname_whitenoise, force_phase=False)[source]
+

Generate a white noise realisation in physical space and write it to +disk.

+
+
Parameters:
+
    +

  • L (float) – Size of the simulation box (in Mpc/h).

    • +

  • size (int) – Number of grid points along each axis.

    • +

  • seedphase (int or list of int) – User-provided seed to generate the initial white noise.

    • +

  • fname_whitenoise (str) – File path to write the white noise realisation.

    • +

  • seedname_whitenoise (str) – File path to write the seed state of the RNG.

    • +

  • force_phase (bool, optional) – If True, forces regeneration of the random phases. Default is +False.

    • +
+
+
Raises:
+
    +

  • OSError – If file writing fails or directory paths are invalid.

    • +

  • RuntimeError – For unexpected issues.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.get_power_spectrum_from_cosmo(L, size, cosmo, fname_power_spectrum, force=False)[source]
+

Compute a power spectrum from cosmological parameters and save it to +disk.

+
+
Parameters:
+
    +

  • L (float) – Size of the simulation box (in Mpc/h).

    • +

  • size (int) – Number of grid points along each axis.

    • +

  • cosmo (dict) – Cosmological parameters (and infrastructure parameters).

    • +

  • fname_power_spectrum (str) – Name (including path) of the power spectrum file to read/write.

    • +

  • force (bool, optional) – If True, forces recomputation even if the file exists. Default +is False.

    • +
+
+
Raises:
+
    +

  • OSError – If file writing fails or the directory path is invalid.

    • +

  • RuntimeError – For unexpected issues during power spectrum computation.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.handle_time_stepping(aa: List[float], total_steps: int, modeldir: str, figuresdir: str, sim_params: str, force: bool = False) → Tuple[List[int] | None, float | None][source]
+

Create and merge individual time-stepping objects.

+
+
Parameters:
+
    +

  • aa (list of float) – List of scale factors in ascending order.

    • +

  • total_steps (int) – Total number of time steps to distribute among the provided +scale factors.

    • +

  • modeldir (str) – Directory path to store generated time-stepping files.

    • +

  • figuresdir (str) – Directory path to store time-stepping plots.

    • +

  • sim_params (str) – Simulation parameter string (e.g., “custom”, “std”, “nograv”).

    • +

  • force (bool, optional) – Whether to force recompute the time-stepping files. Default is +False.

    • +
+
+
Returns:
+

    +

  • merged_path (str) – Path to the merged time-stepping file.

    • +

  • indices_steps_cumul (list of int or None) – Cumulative indices for the distributed steps. Returns None if +using splitLPT or if sim_params indicates an alternative +strategy.

    • +

  • eff_redshifts (float or None) – Effective redshift derived from the final scale factor in +‘custom’ or ‘nograv’ mode. None otherwise.

    • +
+

+
+
Raises:
+
    +

  • NotImplementedError – If a unsupported time-stepping strategy is used.

    • +

  • OSError – If file or directory operations fail.

    • +

  • RuntimeError – If unexpected issues occur during time-stepping setup.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.setup_sbmy_parfiles(d, cosmology, file_names, hiddenbox_params, force=False)[source]
+

Set up Simbelmynë parameter file (please refer to the Simbelmynë +documentation for more details).

+
+
Parameters:
+
    +

  • d (int) – Index (from 1 to S) specifying a direction in parameter space, 0 +for the expansion point, or -1 for mock data.

    • +

  • cosmology (array, double, dimension=5) – Cosmological parameters.

    • +

  • file_names (dict) – Dictionary containing the names of the input/output files for +the simulation.

    • +

  • hiddenbox_params (dict) – See the HiddenBox class for more details.

    • +

  • force (bool, optional, default=False) – If True, forces recompute the simulation parameter files.

    • +
+
+
+
+ +
+
+

selfisys.selection_functions module

+
+
+class selfisys.selection_functions.LognormalSelection(L=None, selection_params=None, survey_mask_path=None, local_select_path=None, size=None)[source]
+

Bases: object

+

Class to generate radial selection functions.

+
+
+init_selection(reset=False)[source]
+

Initialise the radial selection functions.

+
+
Parameters:
+

reset (bool, optional) – Whether to reset the selection function.

+
+
+
+ +
+
+lognormals_z_to_x(xx, mask, params, spline)[source]
+

Convert log-normal distributions from redshift to distance.

+
+
Parameters:
+
    +

  • xx (array-like) – Comoving distances at which to evaluate the distributions.

    • +

  • mask (ndarray or None) – Survey mask C(n).

    • +

  • params (tuple of arrays) – Parameters for the distributions (ss, mm, rr).

    • +

  • spline (UnivariateSpline) – Linear interpolator for the distance-redshift relation.

    • +
+
+
Returns:
+

Tuple containing redshifts and list of distributions.

+
+
Return type:
+

tuple

+
+
+
+ +
+
+multiple_lognormal(x, mask, ss, ll, rr)[source]
+

Compute multiple log-normal distributions.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array.

    • +

  • mask (ndarray or None) – Survey mask C(n).

    • +

  • ss (array_like) – Standard deviations for each distribution.

    • +

  • ll (array_like) – Means for each distribution.

    • +

  • rr (array_like) – Rescaling factors for each distribution.

    • +
+
+
Returns:
+

List of log-normal distributions.

+
+
Return type:
+

list of ndarray

+
+
+
+ +
+
+multiple_lognormal_z(x, mask, ss, mm, rr)[source]
+

Compute multiple rescaled lognormal distributions as functions +of redshift.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array (redshifts).

    • +

  • mask (ndarray or None) – Survey mask C(n).

    • +

  • ss (array_like) – Standard deviations of the lognormal distributions.

    • +

  • mm (array_like) – Means of the lognormal distributions.

    • +

  • rr (array_like) – Rescaling factors for each distribution.

    • +
+
+
Returns:
+

List of log-normal distributions.

+
+
Return type:
+

list of ndarray

+
+
+
+ +
+
+static one_lognormal(x, std, mean, rescale=None)[source]
+

Rescaled log-normal distribution.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array.

    • +

  • std (float) – Standard deviation of the distribution.

    • +

  • mean (float) – Mean of the distribution.

    • +

  • rescale (float, optional) – Rescaling factor. If None, the distribution is normalised +such that its maximum value is 1.

    • +
+
+
Returns:
+

Log-normal distribution evaluated at x.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+static one_lognormal_z(x, sig2, mu, rescale=None)[source]
+

Compute a log-normal distribution in redshift.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array.

    • +

  • sig2 (float) – Variance of the distribution.

    • +

  • mu (float) – Mean of the distribution.

    • +

  • rescale (float, optional) – Rescaling factor.

    • +
+
+
Returns:
+

Log-normal distribution evaluated at x.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+r_grid()[source]
+

Compute the grid of radial distances in the simulation box.

+
+
Returns:
+

3D array of radial distances from the origin.

+
+
Return type:
+

ndarray

+
+
Raises:
+

AttributeError – If the ‘size’ attribute is not defined.

+
+
+
+ +
+ +
+
+

selfisys.selfi_interface module

+
+
+selfisys.selfi_interface.PrintMessage(required_verbosity: int, message: str, verbosity: int) → None[source]
+

Print a message to standard output using pyselfi.utils.PrintMessage.

+
+
Parameters:
+
    +

  • required_verbosity (int) – The verbosity level required to display the message.

    • +

  • message (str) – The actual message to display.

    • +

  • verbosity (int) – The current verbosity level (0=quiet, 1=normal, 2=debug).

    • +
+
+
+
+ +
+
+selfisys.selfi_interface.indent() → None[source]
+

Indent the standard output using pyselfi.utils.

+
+ +
+
+selfisys.selfi_interface.unindent() → None[source]
+

Unindent the standard output using pyselfi.utils.

+
+ +
+
+

selfisys.setup_model module

+
+
+class selfisys.setup_model.ModelSetup(size, L, P, S, G_sim_path, G_ss_path, Pbins_bnd, Pbins, k_s, P_ss_obj_path, P_0, planck_Pk)[source]
+

Bases: NamedTuple

+
+
+G_sim_path: str
+

Alias for field number 4

+
+ +
+
+G_ss_path: str
+

Alias for field number 5

+
+ +
+
+L: float
+

Alias for field number 1

+
+ +
+
+P: int
+

Alias for field number 2

+
+ +
+
+P_0: ndarray
+

Alias for field number 10

+
+ +
+
+P_ss_obj_path: str
+

Alias for field number 9

+
+ +
+
+Pbins: ndarray
+

Alias for field number 7

+
+ +
+
+Pbins_bnd: ndarray
+

Alias for field number 6

+
+ +
+
+S: int
+

Alias for field number 3

+
+ +
+
+k_s: ndarray
+

Alias for field number 8

+
+ +
+
+planck_Pk: ndarray
+

Alias for field number 11

+
+ +
+
+size: int
+

Alias for field number 0

+
+ +
+ +
+
+selfisys.setup_model.compute_alpha_cv(workdir: str, k_s: ndarray, size: int, L: float, window_fct_path: str | None = None, force: bool = False) → None[source]
+

Compute the cosmic variance parameter alpha_cv.

+
+
Parameters:
+
    +

  • workdir (str) – Directory where the results will be stored.

    • +

  • k_s (np.ndarray) – Support wavenumbers.

    • +

  • size (int) – Number of elements in each direction of the box.

    • +

  • L (float) – Comoving length of the box in Mpc/h.

    • +

  • window_fct_path (str, optional) – Path to the window function file.

    • +

  • force (bool) – If True, forces recomputation of the inputs.

    • +
+
+
+
+ +
+
+selfisys.setup_model.setup_model(workdir: str, params_planck: dict, params_P0: dict, size: int = 256, L: float = 3600.0, S: int = 100, N_exact: int = 8, Pinit: int = 50, trim_threshold: int = 100, minval: float | None = None, maxval: float | None = None, force: bool = False) → ModelSetup[source]
+

Set up the model by computing or loading necessary grids and +parameters.

+
+
Parameters:
+
    +

  • workdir (str) – Directory where the results will be stored.

    • +

  • params_planck (dict) – Parameters for the Planck 2018 cosmology.

    • +

  • params_P0 (dict) – Parameters for the normalisation power spectrum.

    • +

  • size (int) – Number of elements in each direction of the box.

    • +

  • L (float) – Comoving length of the box in Mpc/h.

    • +

  • S (int) – Number of support wavenumbers for the input power spectra.

    • +

  • N_exact (int) – Number of support wavenumbers matching the Fourier grid.

    • +

  • Pinit (int) – Maximum number of bins for the summaries.

    • +

  • trim_threshold (int) – Minimum number of modes required per bin.

    • +

  • minval (float, optional) – Minimum k value for the summaries.

    • +

  • maxval (float, optional) – Maximum k value for the summaries.

    • +

  • force (bool) – If True, forces recomputation of the inputs.

    • +
+
+
Returns:
+

A named tuple containing: +- size (int): Number of elements in each direction of the box. +- L (float): Comoving length of the box in Mpc/h. +- P (int): Number of bins for the summaries. +- S (int): Number of support wavenumbers for input powerspectra. +- G_sim_path (str): Path to the full Fourier grid file. +- G_ss_path (str): Path to the Fourier grid for summaries file. +- Pbins_bnd (np.ndarray): Boundaries of summary bins. +- Pbins (np.ndarray): Centres of the bins for the summaries. +- k_s (np.ndarray): Support wavenumbers for input power spectra. +- P_ss_obj_path (str): Path to the summary power spectrum file. +- P_0 (np.ndarray): Normalisation power spectrum values. +- planck_Pk (np.ndarray): Planck 2018 power spectrum values.

+
+
Return type:
+

ModelSetup

+
+
+
+ +
+
+

Module contents

+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.normalise_hb.html b/docs/selfisys.normalise_hb.html new file mode 100644 index 0000000..9a0fbd0 --- /dev/null +++ b/docs/selfisys.normalise_hb.html @@ -0,0 +1,396 @@ + + + + + + + + + normalise_hb — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

normalise_hb

+
+
+selfisys.normalise_hb.define_normalisation(hidden_box: HiddenBox, Pbins: ndarray, cosmo: Dict[str, Any], N: int, min_k_norma: float = 0.04, npar: int = 1, force: bool = False) → ndarray[source]
+

Define the normalisation constants for the HiddenBox instance.

+
+
Parameters:
+
    +

  • hidden_box (HiddenBox) – Instance of the HiddenBox class.

    • +

  • Pbins (ndarray) – Array of P bin values.

    • +

  • cosmo (dict) – Cosmological and infrastructure parameters.

    • +

  • N (int) – Number of realisations required.

    • +

  • min_k_norma (float, optional) – Minimum k value to compute the normalisation constants.

    • +

  • npar (int, optional) – Number of parallel processes to use. Default is 1.

    • +

  • force (bool, optional) – If True, force recomputation. Default is False.

    • +
+
+
Returns:
+

norm_csts – Normalisation constants for the HiddenBox instance.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+selfisys.normalise_hb.worker_normalisation(hidden_box: HiddenBox, params: Tuple[Dict[str, Any], list, list, bool]) → ndarray[source]
+

Worker function to compute the normalisation constants, +compatible with Python multiprocessing.

+
+
Parameters:
+
    +

  • hidden_box (HiddenBox) – Instance of the HiddenBox class.

    • +

  • params (tuple) – A tuple containing (cosmo, seedphase, seednoise, force).

    • +
+
+
Returns:
+

phi – Computed summary statistics.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+selfisys.normalise_hb.worker_normalisation_public(hidden_box, cosmo: Dict[str, Any], N: int, i: int)[source]
+

Run the i-th simulation required to compute the normalisation +constants.

+
+
Parameters:
+
    +

  • hidden_box (HiddenBox) – Instance of the HiddenBox class.

    • +

  • cosmo (dict) – Cosmological and some infrastructure parameters.

    • +

  • N (int) – Total number of realisations required.

    • +

  • i (int) – Index of the simulation to be computed.

    • +
+
+
+
+ +
+
+selfisys.normalise_hb.worker_normalisation_wrapper(args)[source]
+

Wrapper function for the worker_normalisation function.

+
+
Parameters:
+

args (tuple) – A tuple containing (hidden_box, params).

+
+
Returns:
+

phi – Computed summary statistics.

+
+
Return type:
+

ndarray

+
+
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.prior.html b/docs/selfisys.prior.html new file mode 100644 index 0000000..ca36bc6 --- /dev/null +++ b/docs/selfisys.prior.html @@ -0,0 +1,610 @@ + + + + + + + + + prior — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

prior

+
+
+selfisys.prior.get_summary(x, bins, normalisation=None, kmax=1.4)[source]
+

Compute a power-spectrum summary for given cosmological parameters.

+
+
Parameters:
+
    +

  • x (array-like) – Cosmological parameters [h, Omega_b, Omega_m, n_s, sigma_8].

    • +

  • bins (array-like) – Wavenumber bins.

    • +

  • normalisation (float or None, optional) – Normalisation constant to scale the resulting spectrum.

    • +

  • kmax (float, optional) – Maximum wavenumber for get_Pk.

    • +
+
+
Returns:
+

theta – The computed power-spectrum values, optionally normalised.

+
+
Return type:
+

ndarray

+
+
Raises:
+

RuntimeError – If the power-spectrum computation fails unexpectedly.

+
+
+
+ +
+
+selfisys.prior.logposterior_hyperparameters_parallel(selfi, theta_fiducial, Nbin_min, Nbin_max, theta_norm, k_corr, alpha_cv)[source]
+

Compute the log-posterior for the hyperparameters of the prior from +[leclercq2019primordial], for use within the SelfiSys pipeline.

+
+
Parameters:
+
    +

  • selfi (object) – The selfi object.

    • +

  • theta_fiducial (ndarray) – Fiducial spectrum.

    • +

  • Nbin_min (int) – Minimum bin index for the wavenumber range.

    • +

  • Nbin_max (int) – Maximum bin index for the wavenumber range.

    • +

  • theta_norm (float) – Hyperparameter controlling the overall uncertainty.

    • +

  • k_corr (float) – Hyperparameter controlling correlation scale.

    • +

  • alpha_cv (float) – Cosmic variance strength.

    • +
+
+
Returns:
+

The log-posterior value for the given hyperparameters.

+
+
Return type:
+

float

+
+
Raises:
+

RuntimeError – If the log-posterior computation fails unexpectedly.

+
+
+
+ +
+
+selfisys.prior.perform_prior_optimisation_and_plot(selfi, theta_fiducial, theta_norm_mean=0.1, theta_norm_std=0.3, k_corr_mean=0.02, k_corr_std=0.015, k_opt_min=0.0, k_opt_max=1.4, theta_norm_min=0.04, theta_norm_max=0.12, k_corr_min=0.012, k_corr_max=0.02, meshsize=30, Nbin_min=0, Nbin_max=100, theta_norm=0.05, k_corr=0.015, alpha_cv=0.00065, plot=True, savepath=None)[source]
+

Optimise the hyperparameters for the selfi2019 prior (from +[leclercq2019primordial]).

+
+
Parameters:
+
    +

  • selfi (object) – The selfi object.

    • +

  • theta_fiducial (ndarray) – Fiducial spectrum.

    • +

  • theta_norm_mean (float, optional) – Mean of the Gaussian hyperprior on theta_norm. Default 0.1.

    • +

  • theta_norm_std (float, optional) – Standard deviation of the hyperprior on theta_norm. Default 0.3.

    • +

  • k_corr_mean (float, optional) – Mean of the Gaussian hyperprior on k_corr. Default 0.020.

    • +

  • k_corr_std (float, optional) – Standard deviation of the hyperprior on k_corr. Default 0.015.

    • +

  • k_opt_min (float, optional) – Minimum wavenumber for the prior optimisation. Default 0.0.

    • +

  • k_opt_max (float, optional) – Maximum wavenumber for the prior optimisation. Default 1.4.

    • +

  • theta_norm_min (float, optional) – Lower bound for theta_norm in the mesh. Default 0.04.

    • +

  • theta_norm_max (float, optional) – Upper bound for theta_norm in the mesh. Default 0.12.

    • +

  • k_corr_min (float, optional) – Lower bound for k_corr in the mesh. Default 0.012.

    • +

  • k_corr_max (float, optional) – Upper bound for k_corr in the mesh. Default 0.02.

    • +

  • meshsize (int, optional) – Number of points in each dimension of the plot mesh. Default 30.

    • +

  • Nbin_min (int, optional) – Minimum bin index for restricting the prior. Default 0.

    • +

  • Nbin_max (int, optional) – Maximum bin index for restricting the prior. Default 100.

    • +

  • theta_norm (float, optional) – Initial or default guess of theta_norm. Default 0.05.

    • +

  • k_corr (float, optional) – Initial or default guess of k_corr. Default 0.015.

    • +

  • alpha_cv (float, optional) – Cosmic variance term or similar. Default 0.00065.

    • +

  • plot (bool, optional) – If True, generate and show/save a 2D contour plot. Default True.

    • +

  • savepath (str, optional) – File path to save the plot. If None, the plot is displayed.

    • +
+
+
Returns:
+

(theta_norm, k_corr) after optimisation.

+
+
Return type:
+

tuple

+
+
Raises:
+
    +

  • OSError – If file operations fail during saving the prior or posterior.

    • +

  • RuntimeError – If the optimisation fails unexpectedly.

    • +
+
+
+
+ +
+
+class selfisys.prior.planck_prior(Omega_mean, Omega_cov, bins, normalisation, kmax, nsamples=10000, nthreads=-1, EPS_K=1e-07, EPS_residual=0.001, filename=None)[source]
+

Bases: object

+

Custom prior for the SelfiSys pipeline. This is the prior used in +[hoellinger2024diagnosing], based on the Planck 2018 cosmological +parameters.

+

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].

+
+
Parameters:
+
    +

  • Omega_mean (array-like) – Mean of the prior distribution on cosmological parameters.

    • +

  • Omega_cov (array-like) – Covariance matrix of the prior distribution on cosmological +parameters.

    • +

  • bins (array-like) – Wavenumbers where the power spectrum is evaluated.

    • +

  • normalisation (float or None) – If not None, divide the power spectra by the normalisation.

    • +

  • kmax (float) – Maximum wavenumber for computations.

    • +

  • nsamples (int, optional) – Number of samples drawn from the prior on the cosmological +parameters. Default is 10,000.

    • +

  • nthreads (int, optional) – Number of CPU threads for parallel tasks. Default is -1, that +is, auto-detect the number of available threads.

    • +

  • EPS_K (float, optional) – Regularisation parameter for covariance inversion. Default 1e-7.

    • +

  • EPS_residual (float, optional) – Additional cutoff for matrix inversion. Default 1e-3.

    • +

  • filename (str or None, optional) – Path to a .npy file to store or load precomputed power spectra.

    • +
+
+
+
+
+mean
+

Mean of the computed power spectra.

+
+
Type:
+

ndarray

+
+
+
+ +
+
+covariance
+

Covariance matrix of the computed power spectra.

+
+
Type:
+

ndarray

+
+
+
+ +
+
+inv_covariance
+

Inverse of the covariance matrix.

+
+
Type:
+

ndarray

+
+
+
+ +
+
Raises:
+
    +

  • OSError – If file reading or writing fails.

    • +

  • RuntimeError – For unexpected HPC or multi-processing errors.

    • +
+
+
+
+
+property Nbin_max
+

Index of the maximal wavenumber given self.kmax.

+
+ +
+
+property Nbin_min
+

Index of the minimal wavenumber given k_min.

+
+ +
+
+compute()[source]
+

Compute the prior (mean, covariance, and inverse covariance).

+

If self.filename exists, tries to load the prior. Otherwise, +samples from the prior distribution on cosmological parameters +and evaluates the power spectra in parallel.

+
+
Raises:
+
    +

  • OSError – If self.filename is not writable/accessible.

    • +

  • RuntimeError – If multi-processing or power-spectra computations fail.

    • +
+
+
+
+ +
+
+classmethod load(fname)[source]
+

Load the prior from input file.

+
+
Parameters:
+

fname (str) – Input HDF5 filename.

+
+
Returns:
+

The prior object.

+
+
Return type:
+

prior

+
+
Raises:
+

OSError – If the file cannot be read or is invalid.

+
+
+
+ +
+
+logpdf(theta, theta_mean, theta_covariance, theta_icov)[source]
+

Return the log prior probability at a given point in parameter +space.

+
+
Parameters:
+
    +

  • theta (ndarray) – Evaluation point in parameter space.

    • +

  • theta_mean (ndarray) – Prior mean vector.

    • +

  • theta_covariance (ndarray) – Prior covariance matrix.

    • +

  • theta_icov (ndarray) – Inverse of the prior covariance matrix.

    • +
+
+
Returns:
+

Log prior probability value.

+
+
Return type:
+

float

+
+
+
+ +
+
+sample(seedsample=None)[source]
+

Draw a random sample from the prior distribution.

+
+
Parameters:
+

seedsample (int, optional) – Seed for the random number generator.

+
+
Returns:
+

A single sample from the prior distribution.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+save(fname)[source]
+

Save the prior to an output file.

+
+
Parameters:
+

fname (str) – Output HDF5 filename to store the prior data.

+
+
Raises:
+

OSError – If the file cannot be accessed or written.

+
+
+
+ +
+ +
+
+selfisys.prior.worker_class(params)[source]
+

Worker function to compute power spectra with CLASS, compatible with +Python multiprocessing.

+
+
Parameters:
+

params (tuple) – (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.

+
+
Returns:
+

theta – Power-spectrum summary from get_summary.

+
+
Return type:
+

ndarray

+
+
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.sbmy_interface.html b/docs/selfisys.sbmy_interface.html new file mode 100644 index 0000000..69054e6 --- /dev/null +++ b/docs/selfisys.sbmy_interface.html @@ -0,0 +1,456 @@ + + + + + + + + + sbmy_interface — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

sbmy_interface

+
+
+selfisys.sbmy_interface.compute_Phi(G_ss_path, P_ss_path, g_obj, norm, AliasingCorr=True, verbosity=1)[source]
+

Compute the summary statistics from a field object, based on a +provided summary-statistics Fourier grid and baseline spectrum.

+
+
Parameters:
+
    +

  • G_ss_path (str) – Path to the FourierGrid file used for summary-statistics.

    • +

  • P_ss_path (str) – Path to the baseline power spectrum file for normalisation.

    • +

  • g_obj (Field) – Input field object from which to compute summary statistics.

    • +

  • norm (ndarray) – Normalisation constants for the summary statistics.

    • +

  • AliasingCorr (bool, optional) – Whether to apply aliasing correction. Default is True.

    • +

  • verbosity (int, optional) – Verbosity level (0=quiet, 1=normal, 2=debug). Default 1.

    • +
+
+
Returns:
+

Phi – Vector of summary statistics.

+
+
Return type:
+

ndarray

+
+
Raises:
+
    +

  • OSError – If file reading fails at G_ss_path or P_ss_path.

    • +

  • RuntimeError – If unexpected issues occur during computation.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.generate_white_noise_Field(L, size, seedphase, fname_whitenoise, seedname_whitenoise, force_phase=False)[source]
+

Generate a white noise realisation in physical space and write it to +disk.

+
+
Parameters:
+
    +

  • L (float) – Size of the simulation box (in Mpc/h).

    • +

  • size (int) – Number of grid points along each axis.

    • +

  • seedphase (int or list of int) – User-provided seed to generate the initial white noise.

    • +

  • fname_whitenoise (str) – File path to write the white noise realisation.

    • +

  • seedname_whitenoise (str) – File path to write the seed state of the RNG.

    • +

  • force_phase (bool, optional) – If True, forces regeneration of the random phases. Default is +False.

    • +
+
+
Raises:
+
    +

  • OSError – If file writing fails or directory paths are invalid.

    • +

  • RuntimeError – For unexpected issues.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.get_power_spectrum_from_cosmo(L, size, cosmo, fname_power_spectrum, force=False)[source]
+

Compute a power spectrum from cosmological parameters and save it to +disk.

+
+
Parameters:
+
    +

  • L (float) – Size of the simulation box (in Mpc/h).

    • +

  • size (int) – Number of grid points along each axis.

    • +

  • cosmo (dict) – Cosmological parameters (and infrastructure parameters).

    • +

  • fname_power_spectrum (str) – Name (including path) of the power spectrum file to read/write.

    • +

  • force (bool, optional) – If True, forces recomputation even if the file exists. Default +is False.

    • +
+
+
Raises:
+
    +

  • OSError – If file writing fails or the directory path is invalid.

    • +

  • RuntimeError – For unexpected issues during power spectrum computation.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.handle_time_stepping(aa: List[float], total_steps: int, modeldir: str, figuresdir: str, sim_params: str, force: bool = False) → Tuple[List[int] | None, float | None][source]
+

Create and merge individual time-stepping objects.

+
+
Parameters:
+
    +

  • aa (list of float) – List of scale factors in ascending order.

    • +

  • total_steps (int) – Total number of time steps to distribute among the provided +scale factors.

    • +

  • modeldir (str) – Directory path to store generated time-stepping files.

    • +

  • figuresdir (str) – Directory path to store time-stepping plots.

    • +

  • sim_params (str) – Simulation parameter string (e.g., “custom”, “std”, “nograv”).

    • +

  • force (bool, optional) – Whether to force recompute the time-stepping files. Default is +False.

    • +
+
+
Returns:
+

    +

  • merged_path (str) – Path to the merged time-stepping file.

    • +

  • indices_steps_cumul (list of int or None) – Cumulative indices for the distributed steps. Returns None if +using splitLPT or if sim_params indicates an alternative +strategy.

    • +

  • eff_redshifts (float or None) – Effective redshift derived from the final scale factor in +‘custom’ or ‘nograv’ mode. None otherwise.

    • +
+

+
+
Raises:
+
    +

  • NotImplementedError – If a unsupported time-stepping strategy is used.

    • +

  • OSError – If file or directory operations fail.

    • +

  • RuntimeError – If unexpected issues occur during time-stepping setup.

    • +
+
+
+
+ +
+
+selfisys.sbmy_interface.setup_sbmy_parfiles(d, cosmology, file_names, hiddenbox_params, force=False)[source]
+

Set up Simbelmynë parameter file (please refer to the Simbelmynë +documentation for more details).

+
+
Parameters:
+
    +

  • d (int) – Index (from 1 to S) specifying a direction in parameter space, 0 +for the expansion point, or -1 for mock data.

    • +

  • cosmology (array, double, dimension=5) – Cosmological parameters.

    • +

  • file_names (dict) – Dictionary containing the names of the input/output files for +the simulation.

    • +

  • hiddenbox_params (dict) – See the HiddenBox class for more details.

    • +

  • force (bool, optional, default=False) – If True, forces recompute the simulation parameter files.

    • +
+
+
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.selection_functions.html b/docs/selfisys.selection_functions.html new file mode 100644 index 0000000..d3f2c1a --- /dev/null +++ b/docs/selfisys.selection_functions.html @@ -0,0 +1,465 @@ + + + + + + + + + selection_functions — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

selection_functions

+
+
+class selfisys.selection_functions.LognormalSelection(L=None, selection_params=None, survey_mask_path=None, local_select_path=None, size=None)[source]
+

Bases: object

+

Class to generate radial selection functions.

+
+
+init_selection(reset=False)[source]
+

Initialise the radial selection functions.

+
+
Parameters:
+

reset (bool, optional) – Whether to reset the selection function.

+
+
+
+ +
+
+lognormals_z_to_x(xx, mask, params, spline)[source]
+

Convert log-normal distributions from redshift to distance.

+
+
Parameters:
+
    +

  • xx (array-like) – Comoving distances at which to evaluate the distributions.

    • +

  • mask (ndarray or None) – Survey mask C(n).

    • +

  • params (tuple of arrays) – Parameters for the distributions (ss, mm, rr).

    • +

  • spline (UnivariateSpline) – Linear interpolator for the distance-redshift relation.

    • +
+
+
Returns:
+

Tuple containing redshifts and list of distributions.

+
+
Return type:
+

tuple

+
+
+
+ +
+
+multiple_lognormal(x, mask, ss, ll, rr)[source]
+

Compute multiple log-normal distributions.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array.

    • +

  • mask (ndarray or None) – Survey mask C(n).

    • +

  • ss (array_like) – Standard deviations for each distribution.

    • +

  • ll (array_like) – Means for each distribution.

    • +

  • rr (array_like) – Rescaling factors for each distribution.

    • +
+
+
Returns:
+

List of log-normal distributions.

+
+
Return type:
+

list of ndarray

+
+
+
+ +
+
+multiple_lognormal_z(x, mask, ss, mm, rr)[source]
+

Compute multiple rescaled lognormal distributions as functions +of redshift.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array (redshifts).

    • +

  • mask (ndarray or None) – Survey mask C(n).

    • +

  • ss (array_like) – Standard deviations of the lognormal distributions.

    • +

  • mm (array_like) – Means of the lognormal distributions.

    • +

  • rr (array_like) – Rescaling factors for each distribution.

    • +
+
+
Returns:
+

List of log-normal distributions.

+
+
Return type:
+

list of ndarray

+
+
+
+ +
+
+static one_lognormal(x, std, mean, rescale=None)[source]
+

Rescaled log-normal distribution.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array.

    • +

  • std (float) – Standard deviation of the distribution.

    • +

  • mean (float) – Mean of the distribution.

    • +

  • rescale (float, optional) – Rescaling factor. If None, the distribution is normalised +such that its maximum value is 1.

    • +
+
+
Returns:
+

Log-normal distribution evaluated at x.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+static one_lognormal_z(x, sig2, mu, rescale=None)[source]
+

Compute a log-normal distribution in redshift.

+
+
Parameters:
+
    +

  • x (ndarray) – Input array.

    • +

  • sig2 (float) – Variance of the distribution.

    • +

  • mu (float) – Mean of the distribution.

    • +

  • rescale (float, optional) – Rescaling factor.

    • +
+
+
Returns:
+

Log-normal distribution evaluated at x.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+r_grid()[source]
+

Compute the grid of radial distances in the simulation box.

+
+
Returns:
+

3D array of radial distances from the origin.

+
+
Return type:
+

ndarray

+
+
Raises:
+

AttributeError – If the ‘size’ attribute is not defined.

+
+
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.selfi_interface.html b/docs/selfisys.selfi_interface.html new file mode 100644 index 0000000..beca3b0 --- /dev/null +++ b/docs/selfisys.selfi_interface.html @@ -0,0 +1,343 @@ + + + + + + + + + selfi_interface — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

selfi_interface

+
+
+selfisys.selfi_interface.PrintMessage(required_verbosity: int, message: str, verbosity: int) → None[source]
+

Print a message to standard output using pyselfi.utils.PrintMessage.

+
+
Parameters:
+
    +

  • required_verbosity (int) – The verbosity level required to display the message.

    • +

  • message (str) – The actual message to display.

    • +

  • verbosity (int) – The current verbosity level (0=quiet, 1=normal, 2=debug).

    • +
+
+
+
+ +
+
+selfisys.selfi_interface.indent() → None[source]
+

Indent the standard output using pyselfi.utils.

+
+ +
+
+selfisys.selfi_interface.unindent() → None[source]
+

Unindent the standard output using pyselfi.utils.

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.setup_model.html b/docs/selfisys.setup_model.html new file mode 100644 index 0000000..0c87232 --- /dev/null +++ b/docs/selfisys.setup_model.html @@ -0,0 +1,450 @@ + + + + + + + + + setup_model — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

setup_model

+
+
+class selfisys.setup_model.ModelSetup(size, L, P, S, G_sim_path, G_ss_path, Pbins_bnd, Pbins, k_s, P_ss_obj_path, P_0, planck_Pk)[source]
+

Bases: NamedTuple

+
+
+G_sim_path: str
+

Alias for field number 4

+
+ +
+
+G_ss_path: str
+

Alias for field number 5

+
+ +
+
+L: float
+

Alias for field number 1

+
+ +
+
+P: int
+

Alias for field number 2

+
+ +
+
+P_0: ndarray
+

Alias for field number 10

+
+ +
+
+P_ss_obj_path: str
+

Alias for field number 9

+
+ +
+
+Pbins: ndarray
+

Alias for field number 7

+
+ +
+
+Pbins_bnd: ndarray
+

Alias for field number 6

+
+ +
+
+S: int
+

Alias for field number 3

+
+ +
+
+k_s: ndarray
+

Alias for field number 8

+
+ +
+
+planck_Pk: ndarray
+

Alias for field number 11

+
+ +
+
+size: int
+

Alias for field number 0

+
+ +
+ +
+
+selfisys.setup_model.compute_alpha_cv(workdir: str, k_s: ndarray, size: int, L: float, window_fct_path: str | None = None, force: bool = False) → None[source]
+

Compute the cosmic variance parameter alpha_cv.

+
+
Parameters:
+
    +

  • workdir (str) – Directory where the results will be stored.

    • +

  • k_s (np.ndarray) – Support wavenumbers.

    • +

  • size (int) – Number of elements in each direction of the box.

    • +

  • L (float) – Comoving length of the box in Mpc/h.

    • +

  • window_fct_path (str, optional) – Path to the window function file.

    • +

  • force (bool) – If True, forces recomputation of the inputs.

    • +
+
+
+
+ +
+
+selfisys.setup_model.setup_model(workdir: str, params_planck: dict, params_P0: dict, size: int = 256, L: float = 3600.0, S: int = 100, N_exact: int = 8, Pinit: int = 50, trim_threshold: int = 100, minval: float | None = None, maxval: float | None = None, force: bool = False) → ModelSetup[source]
+

Set up the model by computing or loading necessary grids and +parameters.

+
+
Parameters:
+
    +

  • workdir (str) – Directory where the results will be stored.

    • +

  • params_planck (dict) – Parameters for the Planck 2018 cosmology.

    • +

  • params_P0 (dict) – Parameters for the normalisation power spectrum.

    • +

  • size (int) – Number of elements in each direction of the box.

    • +

  • L (float) – Comoving length of the box in Mpc/h.

    • +

  • S (int) – Number of support wavenumbers for the input power spectra.

    • +

  • N_exact (int) – Number of support wavenumbers matching the Fourier grid.

    • +

  • Pinit (int) – Maximum number of bins for the summaries.

    • +

  • trim_threshold (int) – Minimum number of modes required per bin.

    • +

  • minval (float, optional) – Minimum k value for the summaries.

    • +

  • maxval (float, optional) – Maximum k value for the summaries.

    • +

  • force (bool) – If True, forces recomputation of the inputs.

    • +
+
+
Returns:
+

A named tuple containing: +- size (int): Number of elements in each direction of the box. +- L (float): Comoving length of the box in Mpc/h. +- P (int): Number of bins for the summaries. +- S (int): Number of support wavenumbers for input powerspectra. +- G_sim_path (str): Path to the full Fourier grid file. +- G_ss_path (str): Path to the Fourier grid for summaries file. +- Pbins_bnd (np.ndarray): Boundaries of summary bins. +- Pbins (np.ndarray): Centres of the bins for the summaries. +- k_s (np.ndarray): Support wavenumbers for input power spectra. +- P_ss_obj_path (str): Path to the summary power spectrum file. +- P_0 (np.ndarray): Normalisation power spectrum values. +- planck_Pk (np.ndarray): Planck 2018 power spectrum values.

+
+
Return type:
+

ModelSetup

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2025, Tristan Hoellinger.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/selfisys.utils.html b/docs/selfisys.utils.html new file mode 100644 index 0000000..04ea229 --- /dev/null +++ b/docs/selfisys.utils.html @@ -0,0 +1,1566 @@ + + + + + + + + + selfisys.utils package — SelfiSys /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/ documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

selfisys.utils package

+
+

Submodules

+
+
+

selfisys.utils.examples_utils module

+
+
+selfisys.utils.examples_utils.clear_large_plot(fig)[source]
+

Clear a figure to free up memory.

+
+
Parameters:
+

fig (matplotlib.figure.Figure) – The figure to clear.

+
+
+
+ +
+
+

selfisys.utils.logger module

+
+
+class selfisys.utils.logger.CustomLogger(name, level=0)[source]
+

Bases: Logger

+

Custom logger class supporting custom verbosity levels in diagnostic +messages.

+
+
+diagnostic(msg, *args, verbosity=3, **kwargs) → None[source]
+

Log a message with DIAGNOSTIC level.

+
+
Parameters:
+
    +

  • msg (str) – The message to log.

    • +

  • verbosity (int, optional) – The verbosity level required to log this message.

    • +
+
+
+
+ +
+ +
+
+class selfisys.utils.logger.CustomLoggerHandler(level=0)[source]
+

Bases: Handler

+

Custom logging handler to redirect Python logger messages to custom +print functions, with support for verbosity levels in debug +messages.

+
+
+emit(record)[source]
+

Emit a log record.

+
+ +
+ +
+
+selfisys.utils.logger.INDENT()[source]
+

Indents the current level of outputs.

+
+ +
+
+selfisys.utils.logger.PrintDiagnostic(verbosity, message)[source]
+

Prints a diagnostic to screen.

+
+
Parameters:
+
    +

  • (int) (verbosity)

    • +

  • (string) (message)

    • +
+
+
+
+ +
+
+selfisys.utils.logger.PrintError(message)[source]
+

Prints an error to screen.

+
+
Parameters:
+

(string) (message)

+
+
+
+ +
+
+selfisys.utils.logger.PrintInfo(message)[source]
+

Prints an information to screen.

+
+
Parameters:
+

(string) (message)

+
+
+
+ +
+
+selfisys.utils.logger.PrintLeftType(message_type, FONT_COLOR)[source]
+

Prints the type of output to screen.

+
+
Parameters:
+
    +

  • (string) (FONT_COLOR)

    • +

  • (string)

    • +
+
+
+
+ +
+
+selfisys.utils.logger.PrintWarning(message)[source]
+

Prints a warning to screen.

+
+
Parameters:
+

(string) (message)

+
+
+
+ +
+
+selfisys.utils.logger.UNINDENT()[source]
+

Unindents the current level of outputs.

+
+ +
+
+selfisys.utils.logger.getCustomLogger(name: str) → CustomLogger[source]
+

Get as CustomLogger instance to use the custom printing routines.

+
+
Parameters:
+

name (str) – The name of the logger.

+
+
Returns:
+

logger – The custom logger instance.

+
+
Return type:
+

logging.Logger

+
+
+
+ +
+
+

selfisys.utils.low_level module

+
+
+selfisys.utils.low_level.stderr_redirector(stream)[source]
+

A context manager that redirects stderr to the given stream. +For instance, this can be used to redirect C code stderr to None (to +avoid cluttering the log, e.g., when using tqdm).

+

Use with caution.

+
+
Parameters:
+

stream (file-like object) – The stream to which stdout should be +redirected.

+
+
+
+ +
+
+selfisys.utils.low_level.stdout_redirector(stream)[source]
+

A context manager that redirects stdout to the given stream. For +instance, this can be used to redirect C code stdout to None (to +avoid cluttering the log, e.g., when using tqdm).

+
+
Parameters:
+

stream (file-like object) – The stream to which stdout should be +redirected.

+
+
+

Example

+
>>> with stdout_redirector(stream):
+>>>     print("Hello world!") # Will be printed to stream
+>>>                           # instead of stdout.
+
+
+
+ +
+
+

selfisys.utils.parser module

+
+
+selfisys.utils.parser.bool_sh(value)[source]
+

Convert a string to a boolean.

+
+
Parameters:
+

value (str) – String to convert.

+
+
Returns:
+

Converted value.

+
+
Return type:
+

bool

+
+
+
+ +
+
+selfisys.utils.parser.check_files_exist(files)[source]
+

Check if all files in the list exist.

+
+
Parameters:
+

files (list of str) – List of file paths to check.

+
+
Returns:
+

True if all files exist, False otherwise.

+
+
Return type:
+

bool

+
+
+
+ +
+
+selfisys.utils.parser.intNone(value)[source]
+

Convert a string to None or int.

+
+
Parameters:
+

value (str) – String to convert.

+
+
Returns:
+

Converted value.

+
+
Return type:
+

None or int

+
+
+
+ +
+
+selfisys.utils.parser.joinstrs(list_of_strs)[source]
+

Join a list of strings into a single string.

+
+
Parameters:
+

list_of_strs (list of str) – List of strings to join.

+
+
Returns:
+

Concatenated string.

+
+
Return type:
+

str

+
+
+
+ +
+
+selfisys.utils.parser.joinstrs_only(list_of_strs)[source]
+

Join a list of strings into a single string, ignoring all +non-string elements such as None values.

+
+
Parameters:
+

list_of_strs (list of str) – List of strings to join.

+
+
Returns:
+

Concatenated string.

+
+
Return type:
+

str

+
+
+
+ +
+
+selfisys.utils.parser.none_or_bool_or_str(value)[source]
+

Convert a string to None, bool, or str.

+
+
Parameters:
+

value (str) – String to convert.

+
+
Returns:
+

Converted value.

+
+
Return type:
+

None, bool, or str

+
+
+
+ +
+
+selfisys.utils.parser.safe_npload(path)[source]
+

Load a numpy array from a file.

+
+
Parameters:
+

path (str) – Path to the file to load.

+
+
Returns:
+

Loaded array or None if the file does not exist.

+
+
Return type:
+

None or np.ndarray

+
+
+
+ +
+
+

selfisys.utils.path_utils module

+
+
+selfisys.utils.path_utils.file_names_evaluate(simdir: str, sd: str, d: int, i: int, sim_params: str, TimeSteps: list[int], prefix_mocks: str, abc: bool = False, gravity_on: bool = True) → dict[source]
+

Generate file paths for the given simulation id and parameters.

+
+
Parameters:
+
    +

  • simdir (str) – Path to the simulation directory.

    • +

  • sd (str) – Path to the simulation directory for the given direction.

    • +

  • d (int) – Direction index (-1 for mock data, 0 for the expansion point, or +1 to S).

    • +

  • i (int) – Simulation index.

    • +

  • sim_params (str) – Simulation parameters.

    • +

  • TimeSteps (list of int) – List of time steps.

    • +

  • prefix_mocks (str | None) – Prefix for mock data files. If None, defaults to “mocks”.

    • +

  • abc (bool, optional) – If True, appends the ABC index to the white noise path.

    • +

  • gravity_on (bool, optional) – Whether gravity is active. Default is True.

    • +
+
+
Returns:
+

Dictionary containing simulation inputs / outputs file paths.

+
+
Return type:
+

dict

+
+
+
+ +
+
+selfisys.utils.path_utils.get_file_names(fsimdir: str, sim_id: int, sim_params: str, TimeSteps: list[int], prefix_mocks: str, gravity_on: bool = True, return_g: bool = False) → dict[source]
+

Generate file paths for a given simulation ID and parameters.

+
+
Parameters:
+
    +

  • fsimdir (str) – Path to the simulation directory.

    • +

  • sim_id (int) – Simulation ID.

    • +

  • sim_params (str) – Simulation parameters.

    • +

  • TimeSteps (list of int) – List of time steps.

    • +

  • prefix_mocks (str | None) – Prefix for mock data files. If None, defaults to “mocks”.

    • +

  • gravity_on (bool, optional) – Whether gravity is active. Default is True.

    • +

  • return_g (bool, optional) – If True, return the file name for the observed galaxy field. +Default is False.

    • +
+
+
Returns:
+

Dictionary containing simulation inputs / outputs file paths.

+
+
Return type:
+

dict

+
+
+
+ +
+
+

selfisys.utils.plot_examples module

+
+
+selfisys.utils.plot_examples.plot_comoving_distance_redshift(zz, cosmo, means_com, L, Lcorner, wd, colours_list=['C4', 'C5', 'C6', 'C7'], display=True)[source]
+

Plot comoving distance as a function of redshift, highlighting key +scales.

+
+
Parameters:
+
    +

  • zz (array-like) – Redshift range for the plot.

    • +

  • cosmo (astropy.cosmology object) – Cosmology instance for calculating comoving distances.

    • +

  • means_com (array-like) – Mean comoving distances of selection functions.

    • +

  • L (float) – Box side length in Gpc/h.

    • +

  • Lcorner (float) – Diagonal of the box (sqrt(3) * L) in Gpc/h.

    • +

  • wd (str) – Working directory for saving figures.

    • +

  • colours_list (list) – List of colours for selection function annotations.

    • +

  • display (bool, optional) – Whether to display the figure. Default is True.

    • +
+
+
+
+ +
+
+selfisys.utils.plot_examples.plot_galaxy_field_slice(g, size, L, wd, id_obs, limits='minmax', display=True)[source]
+

Plot a 2D slice of the observed field.

+
+
Parameters:
+
    +

  • g (ndarray) – 2D array representing the observed field slice.

    • +

  • size (int) – Number of grid points along each axis.

    • +

  • L (float) – Size of the simulation box (in Mpc/h).

    • +

  • wd (str) – Working directory for saving output files.

    • +

  • id_obs (int or str) – Identifier for the observation, used in file naming.

    • +

  • limits (str, optional) – Colormap scaling method. Options: ‘minmax’, ‘truncate’, ‘max’.

    • +

  • display (bool, optional) – Whether to display the figure. Default is True.

    • +
+
+
+
+ +
+
+selfisys.utils.plot_examples.plot_power_spectrum(G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, title=None, display=True)[source]
+

Plot a power spectrum over Fourier modes, its linear interpolation +over specified support points, and a given binning for comparison.

+
+
Parameters:
+
    +

  • G_sim (pysbmy.power.FourierGrid) – Fourier grid object containing the k_modes attribute.

    • +

  • true_P (pysbmy.power.PowerSpectrum) – Power spectrum object containing the powerspectrum attribute.

    • +

  • k_s (array-like) – Support points in k-space.

    • +

  • planck_Pk (array-like) – Power spectrum values at the support points.

    • +

  • Pbins (array-like) – Centres of the Φ bins in k-space.

    • +

  • Pbins_bnd (array-like) – Boundaries of the Φ bins in k-space.

    • +

  • size (float) – Box size in number of grid cells.

    • +

  • L (float) – Box length in Mpc/h.

    • +

  • wd (str) – Working directory path for saving the figure.

    • +

  • title (str, optional) – Title for the figure. Default is None.

    • +

  • display (bool, optional) – Whether to display the figure. Default is True.

    • +
+
+
Return type:
+

None

+
+
+
+ +
+
+selfisys.utils.plot_examples.plot_selection_functions_def_in_z(xx_of_zs, res, res_mis, z_means, cosmo, L, stds_z, wd, display=True)[source]
+

Plot radial lognormal (in redshift) selection functions against +comoving distances.

+
+
Parameters:
+
    +

  • xx_of_zs (array-like) – Comoving distances mapped from redshift.

    • +

  • res (list of array-like) – Selection functions for the well-specified model.

    • +

  • res_mis (list of array-like) – Selection functions for the mis-specified model.

    • +

  • z_means (array-like) – Mean redshifts of every galaxy population.

    • +

  • cosmo (object) – Cosmology object.

    • +

  • L (float) – Box side length in comoving distance units.

    • +

  • stds_z (array-like) – Standard deviations of redshift distributions.

    • +

  • wd (str) – Working directory for saving figures.

    • +

  • display (bool, optional) – Whether to display the figure. Default is True.

    • +
+
+
Return type:
+

None

+
+
+
+ +
+
+selfisys.utils.plot_examples.redshift_distance_conversion(zz, cosmo, means_com, L, Lcorner, xx, wd, colours_list=['C4', 'C5', 'C6', 'C7'], display=True)[source]
+

Plot the conversion between comoving distance and redshift; return +the redshifts corresponding to the selection functions’ means.

+
+
Parameters:
+
    +

  • zz (array-like) – Redshift range for the plot.

    • +

  • cosmo (astropy.cosmology object) – Cosmology instance for calculating comoving distances.

    • +

  • means_com (array-like) – Mean comoving distances of selection functions.

    • +

  • L (float) – Box side length in Gpc/h.

    • +

  • Lcorner (float) – Diagonal of the box (sqrt(3) * L) in Gpc/h.

    • +

  • xx (array-like) – Comoving distances at which to compute redshift.

    • +

  • wd (str) – Working directory for saving figures.

    • +

  • colours_list (list) – List of colours for selection function annotations.

    • +

  • display (bool, optional) – Whether to display the figure. Default is True.

    • +
+
+
Returns:
+

spline – Linear interpolator to convert comoving distances to redshifts.

+
+
Return type:
+

scipy.interpolate.UnivariateSpline

+
+
+
+ +
+
+selfisys.utils.plot_examples.relative_error_analysis(G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, display=True)[source]
+

Compute and plot the relative error between the interpolated and +true power spectra.

+
+
Parameters:
+
    +

  • G_sim (pysbmy.power.FourierGrid) – Fourier grid object containing the k_modes attribute.

    • +

  • true_P (pysbmy.power.PowerSpectrum) – Power spectrum object containing the powerspectrum attribute.

    • +

  • k_s (array-like) – Support points in k-space.

    • +

  • planck_Pk (array-like) – Power spectrum values at the support points.

    • +

  • Pbins (array-like) – Centres of the Φ bins in k-space.

    • +

  • Pbins_bnd (array-like) – Boundaries of the Φ bins in k-space.

    • +

  • size (float) – Box size in number of grid cells.

    • +

  • L (float) – Box length in Mpc/h.

    • +

  • wd (str) – Working directory path for saving the figure.

    • +

  • display (bool, optional) – Whether to display the figure. Default is True.

    • +
+
+
Return type:
+

None

+
+
+
+ +
+
+

selfisys.utils.plot_params module

+
+
+class selfisys.utils.plot_params.ScalarFormatterForceFormat_11(useOffset=True, useMathText=True, useLocale=None)[source]
+

Bases: object

+

Custom scalar formatter to enforce a specific number format with an +offset.

+

This formatter displays tick labels with one decimal place and +includes the offset notation for powers of ten.

+
+
+get_offset()[source]
+
+ +
+
+set_scientific(b)[source]
+
+ +
+
+set_useOffset(b)[source]
+
+ +
+ +
+
+selfisys.utils.plot_params.create_all_colormaps()[source]
+

Create all custom colormaps.

+
+
Returns:
+

colormaps – Dictionary containing all custom colormaps.

+
+
Return type:
+

dict

+
+
+
+ +
+
+selfisys.utils.plot_params.create_colormap(name)[source]
+

Create a custom colormap based on the specified name.

+
+
Parameters:
+

name (str) – The name of the colormap to create.

+
+
Returns:
+

The requested custom colormap.

+
+
Return type:
+

ListedColormap

+
+
Raises:
+

ValueError – If the specified colormap name is not recognised.

+
+
+
+ +
+
+selfisys.utils.plot_params.dynamic_text_scaling(fig_height)[source]
+

Dynamically scale text sizes based on the vertical height of the +figure.

+
+
Parameters:
+

fig_height (float) – Height of the figure in inches.

+
+
Returns:
+

Dictionary of scaled font sizes for consistent appearance.

+
+
Return type:
+

dict

+
+
+
+ +
+
+selfisys.utils.plot_params.get_contours(Z, nBins, confLevels=(0.3173, 0.0455, 0.0027))[source]
+

Compute contour levels for given confidence levels.

+
+
Parameters:
+
    +

  • Z (ndarray) – 2D histogram or density estimate.

    • +

  • nBins (int) – Number of bins along one axis.

    • +

  • confLevels (tuple of float) – Confidence levels for which to compute contour levels.

    • +
+
+
Returns:
+

chainLevels – Contour levels corresponding to the provided confidence levels.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+selfisys.utils.plot_params.reset_plotting()[source]
+
+ +
+
+selfisys.utils.plot_params.setup_plotting()[source]
+

Configure Matplotlib plotting settings for consistent appearance.

+
+ +
+
+

selfisys.utils.plot_utils module

+
+
+selfisys.utils.plot_utils.plot_C(C_0, X, Y, Pbins, CMap, binning=True, suptitle=None, savepath=None, force=False)[source]
+

Plot covariance matrix.

+
+
Parameters:
+
    +

  • C_0 (ndarray) – Covariance matrix.

    • +

  • X (ndarray) – X-axis grid for plotting.

    • +

  • Y (ndarray) – Y-axis grid for plotting.

    • +

  • Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

    • +

  • CMap (str) – Colormap for the plot.

    • +

  • binning (bool, optional) – Whether to overlay bin lines on the plot. Default is True.

    • +

  • suptitle (str, optional) – Title for the plot. If None, a default title is used.

    • +

  • savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

    • +

  • force (bool, optional) – If True, displays the plot even if savepath is specified.

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_fisher(F0, params_names_fisher, title=None, path=None)[source]
+

Plot the Fisher matrix as a heatmap.

+
+
Parameters:
+
    +

  • F0 (ndarray) – Fisher matrix.

    • +

  • params_names_fisher (list of str) – Names for the axes.

    • +

  • title (str, optional) – Title of the plot. Default is “Fisher matrix”.

    • +

  • path (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_gradients(Pbins, P, df_16_full, df_32_full, df_48_full, df_full, k_s, X, Y, fixscale=False, force=False, suptitle='', savepath=None)[source]
+

Plot gradients.

+
+
Parameters:
+
    +

  • Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

    • +

  • P (int) – Number of bins.

    • +

  • df_16_full (ndarray) – Derivative with respect to the 16th input.

    • +

  • df_32_full (ndarray) – Derivative with respect to the 32nd input.

    • +

  • df_48_full (ndarray) – Derivative with respect to the 48th input.

    • +

  • df_full (ndarray) – Full derivative.

    • +

  • k_s (ndarray) – Wavenumbers.

    • +

  • X (ndarray) – X-axis grid.

    • +

  • Y (ndarray) – Y-axis grid.

    • +

  • fixscale (bool, optional) – Fix the y-axis scale. Default is False.

    • +

  • force (bool, optional) – Display plot even if savepath is set.

    • +

  • suptitle (str, optional) – Title for the plot.

    • +

  • savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_histogram(data, recmahal, suptitle, savepath, bins=30, alpha=0.5, color='blue')[source]
+

Plot a Mahalanobis distance histogram with key reference lines.

+
+
Parameters:
+
    +

  • data (array-like) – Collection of Mahalanobis distances.

    • +

  • recmahal (float) – Single Mahalanobis distance for the reconstruction +(e.g. posterior vs. prior mean).

    • +

  • suptitle (str) – Figure title.

    • +

  • savepath (str) – Full path (including filename) to save the resulting plot.

    • +

  • bins (int, optional) – Number of bins for the histogram. Default is 30.

    • +

  • alpha (float, optional) – Transparency level for the histogram bars. Default is 0.5.

    • +

  • color (str, optional) – Colour of the histogram bars. Default is “blue”.

    • +
+
+
Raises:
+
    +

  • OSError – If the file cannot be saved to the specified path.

    • +

  • RuntimeError – For unexpected plotting failures.

    • +
+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_mocks(NORM, N, P, Pbins, phi_obs, Phi_0, f_0, C_0, X, Y, CMap, suptitle=None, force_plot=False, savepath=None)[source]
+

Plot mocks.

+
+
Parameters:
+
    +

  • NORM (float) – Normalisation factor.

    • +

  • N (int) – Number of mocks.

    • +

  • P (int) – Number of bins.

    • +

  • Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

    • +

  • phi_obs (ndarray) – Observed power spectrum.

    • +

  • Phi_0 (ndarray) – Mock power spectra.

    • +

  • f_0 (ndarray) – Averaged power spectrum.

    • +

  • C_0 (ndarray) – Covariance matrix.

    • +

  • X (ndarray) – X-axis grid.

    • +

  • Y (ndarray) – Y-axis grid.

    • +

  • CMap (str) – Colormap.

    • +

  • suptitle (str, optional) – Title for the plot.

    • +

  • force_plot (bool, optional) – Display plot even if savepath is set.

    • +

  • savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_mocks_compact(NORM, N, P, Pbins, phi_obs, Phi_0, f_0, C_0, suptitle=None, force_plot=False, savepath=None)[source]
+

Plot and compare observed and simulated power spectra.

+
+
Parameters:
+
    +

  • NORM (float) – Normalisation factor for the observed spectra.

    • +

  • N (int) – Number of mock data realisations.

    • +

  • P (int) – Number of bins for the summaries.

    • +

  • Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

    • +

  • phi_obs (ndarray) – Observed power spectrum.

    • +

  • Phi_0 (ndarray) – Mock realisations of the power spectrum.

    • +

  • f_0 (ndarray) – Mean power spectrum.

    • +

  • C_0 (ndarray) – Covariance matrix of the mock summaries.

    • +

  • suptitle (str, optional) – Title for the plot.

    • +

  • force_plot (bool, optional) – If True, displays the plot even if a path is specified.

    • +

  • savepath (str or Path, optional) – Path to save the plot.

    • +
+
+
Raises:
+

RuntimeError – If an unexpected error occurs during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_observations(k_s, theta_gt, planck_Pk_EH, P_0, Pbins, phi_obs, Npop, path=None, force_plot=False)[source]
+

Plot the observed power spectra and related quantities.

+
+
Parameters:
+
    +

  • k_s (ndarray) – Array of wavenumbers.

    • +

  • theta_gt (ndarray) – Ground truth theta values.

    • +

  • planck_Pk_EH (ndarray) – Planck power spectrum values.

    • +

  • P_0 (float) – Normalisation constant for power spectra.

    • +

  • Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

    • +

  • phi_obs (ndarray) – Observed summaries.

    • +

  • Npop (int) – Number of populations.

    • +

  • path (str, optional) – Path to save the output plot.

    • +

  • force_plot (bool, optional) – If True, displays the plot even if a path is specified.

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_prior_and_posterior_covariances(X, Y, k_s, prior_theta_covariance, prior_covariance, posterior_theta_covariance, P_0, force_plot=False, suptitle='', savepath=None)[source]
+

Plot prior and posterior covariance matrices.

+
+
Parameters:
+
    +

  • X (ndarray) – X-axis grid.

    • +

  • Y (ndarray) – Y-axis grid.

    • +

  • k_s (ndarray) – Wavenumbers.

    • +

  • prior_theta_covariance (ndarray) – Prior covariance matrix for normalised spectra.

    • +

  • prior_covariance (ndarray) – Prior covariance matrix for unnormalised spectra.

    • +

  • posterior_theta_covariance (ndarray) – Posterior covariance matrix for normalised spectra.

    • +

  • P_0 (ndarray) – Fiducial power spectrum used for normalisation.

    • +

  • force_plot (bool, optional) – Display plot even if savepath is set.

    • +

  • suptitle (str, optional) – Title for the plot.

    • +

  • savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

    • +

  • verbose (int, optional) – Verbosity level (0=silent, 1=default, 2=detailed).

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_reconstruction(k_s, Pbins, prior_theta_mean, prior_theta_covariance, posterior_theta_mean, posterior_theta_covariance, theta_gt, P_0, phi_obs=None, suptitle=None, theta_fid=None, savepath=None, force_plot=False, legend_loc='upper right', enforce_ylims=True)[source]
+

Plot the prior, posterior and ground truth power spectra.

+
+
Parameters:
+
    +

  • k_s (ndarray) – Array of wavenumbers.

    • +

  • Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

    • +

  • prior_theta_mean (ndarray) – Mean of the prior distribution.

    • +

  • prior_theta_covariance (ndarray) – Covariance of the prior distribution.

    • +

  • posterior_theta_mean (ndarray) – Mean of the posterior distribution.

    • +

  • posterior_theta_covariance (ndarray) – Covariance of the posterior distribution.

    • +

  • theta_gt (ndarray) – Ground truth power spectrum.

    • +

  • P_0 (float) – Normalisation constant for the power spectrum.

    • +

  • phi_obs (ndarray, optional) – Observed summaries.

    • +

  • suptitle (str, optional) – Plot title. Leave empty for no title.

    • +

  • theta_fid (ndarray, optional) – Fiducial theta for hyperparameter tuning (for some priors).

    • +

  • savepath (str or Path, optional) – Path to save the plot.

    • +

  • force_plot (bool, optional) – If True, displays the plot even if a path is specified.

    • +

  • legend_loc (str, optional) – Location of the plot legend.

    • +

  • enforce_ylims (bool, optional) – Enforce y-axis limits.

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plot_selection_functions(x, res, res_mis, params, L, corner, axis='com', zz=None, zcorner=None, z_L=None, path=None, force_plot=False, labsAB=True)[source]
+

Plot selection functions.

+
+
Parameters:
+
    +

  • x (array-like) – x-axis values (e.g., comoving distances or redshifts).

    • +

  • res (list of array-like) – Selection functions for Model A.

    • +

  • res_mis (list of array-like, optional) – Selection functions for Model B (optional).

    • +

  • params (tuple of (array-like, array-like, array-like), optional) – Standard deviations, means, and normalisation factors for the +multiple galaxy populations.

    • +

  • L (float) – Box size.

    • +

  • corner (float) – Diagonal box size.

    • +

  • axis (str, optional) – x-axis type (‘com’ for comoving distance, ‘redshift’ for +redshift).

    • +

  • zz (array-like, optional) – Mapping between comoving distances and redshifts.

    • +

  • zcorner (float, optional) – Redshift corresponding to the diagonal box size.

    • +

  • z_L (float, optional) – Redshift corresponding to the box side length.

    • +

  • path (str, optional) – Path to save the output plot.

    • +

  • force_plot (bool, optional) – If True, displays the plot even if a path is specified.

    • +

  • labsAB (bool, optional) – If True, labels models as ‘Model A’ and ‘Model B’.

    • +
+
+
Raises:
+

RuntimeError – If unexpected errors occur during plotting.

+
+
+
+ +
+
+selfisys.utils.plot_utils.plotly_3d(field, size=128, L=None, colormap='RdYlBu', limits='max')[source]
+

Create an interactive 3D plot of volume slices using Plotly.

+
+
Parameters:
+
    +

  • field (array-like) – 3D data field to visualise.

    • +

  • size (int, optional) – Size of the field along one dimension. Default is 128.

    • +

  • L (float, optional) – Physical size of the field in Mpc/h. Used for axis labels only.

    • +

  • colormap (str, optional) – Colour map for visualisation. Default is ‘RdYlBu’.

    • +

  • limits (str, optional) – Colour scale limits (‘max’, ‘truncate’, or ‘default’). Default +is ‘max’.

    • +
+
+
Returns:
+

Plotly figure object.

+
+
Return type:
+

go.Figure

+
+
+
+ +
+
+

selfisys.utils.timestepping module

+
+
+selfisys.utils.timestepping.merge_nTS(ts_path_list, merged_path)[source]
+

Merge multiple time-stepping objects into a single file.

+
+
Parameters:
+
    +

  • ts_path_list (list of str) – Paths to the individual time-stepping files to be merged.

    • +

  • merged_path (str) – Path to save the merged time-stepping file.

    • +
+
+
Return type:
+

None

+
+
+
+ +
+
+

selfisys.utils.tools module

+
+
+selfisys.utils.tools.cosmo_vector_to_Simbelmyne_dict(x, kmax=1.4)[source]
+

Convert a vector of cosmological parameters into a dictionary +compatible with pysbmy.

+
+
Parameters:
+
    +

  • x (array-like) – Vector of cosmological parameters.

    • +

  • kmax (float, optional) – Maximum wavenumber for the power spectrum computation.

    • +
+
+
Returns:
+

Dictionary of cosmological parameters compatible with pysbmy.

+
+
Return type:
+

dict

+
+
+
+ +
+
+selfisys.utils.tools.cosmo_vector_to_class_dict(x, lmax=2500, kmax=1.4)[source]
+

Convert a vector of cosmological parameters into a dictionary +compatible with classy.

+
+
Parameters:
+
    +

  • x (array-like) – Vector of cosmological parameters.

    • +

  • lmax (int, optional) – Maximum multipole for the power spectrum computation.

    • +

  • kmax (float, optional) – Maximum wavenumber for the power spectrum computation.

    • +
+
+
Returns:
+

Dictionary of cosmological parameters compatible with classy.

+
+
Return type:
+

dict

+
+
+
+ +
+
+selfisys.utils.tools.custom_stat(vec)[source]
+

Compute a custom statistic for use with +scipy.stats.binned_statistic.

+

Assumes the data power spectrum is inverse-Gamma distributed (as in +[jasche2010bayesian] and [leclercq2019primordial]). Returns “NaN” +for vectors with insufficient elements, as expected by +scipy.stats.binned_statistic.

+
+
Parameters:
+

vec (array-like) – Input vector for computation.

+
+
Returns:
+

Custom statistic or NaN if input is invalid.

+
+
Return type:
+

float or str

+
+
+
+ +
+
+selfisys.utils.tools.fisher_rao(Com, Com_obs, F0)[source]
+

Compute the Fisher-Rao distance between two summaries.

+
+
Parameters:
+
    +

  • Com (array-like) – Computed summary.

    • +

  • Com_obs (array-like) – Observed summary.

    • +

  • F0 (array-like) – Fisher information matrix.

    • +
+
+
Returns:
+

Fisher-Rao distance.

+
+
Return type:
+

float

+
+
+
+ +
+
+selfisys.utils.tools.get_k_max(L, size)[source]
+

Compute the maximum wavenumber for a given box size.

+
+
Parameters:
+
    +

  • L (float) – Size of the box in Mpc/h.

    • +

  • size (int) – Number of grid cells along each dimension.

    • +
+
+
Returns:
+

Maximum wavenumber in h/Mpc.

+
+
Return type:
+

float

+
+
+
+ +
+
+selfisys.utils.tools.get_summary(params_vals, params_ids, Omegas_fixed, bins, normalisation=None, kmax=1.4)[source]
+

Compute the normalised power spectrum summary for a given parameter +set.

+
+
Parameters:
+
    +

  • params_vals (array-like) – Parameter values to update.

    • +

  • params_ids (array-like) – Indices of the parameters to update.

    • +

  • Omegas_fixed (array-like) – Fixed base values of parameters.

    • +

  • bins (array-like) – Power spectrum bins.

    • +

  • normalisation (float, optional) – Normalisation factor for the summary.

    • +

  • kmax (float, optional) – Maximum wavenumber for power spectrum computation.

    • +
+
+
Returns:
+

Normalised power spectrum summary.

+
+
Return type:
+

array

+
+
+
+ +
+
+selfisys.utils.tools.none_or_bool_or_str(value)[source]
+

Convert string representations of None, True, and False to their +respective Python objects; otherwise, return the input value.

+
+ +
+
+selfisys.utils.tools.params_ids_to_Simbelmyne_dict(params_vals, params_ids, fixed, kmax)[source]
+

Convert a list of cosmological parameters into a dictionary +compatible with pysbmy.

+

Fixed parameters remain unchanged unless overridden by +params_vals.

+
+
Parameters:
+
    +

  • params_vals (array-like) – Values of the parameters to be modified.

    • +

  • params_ids (array-like) – Indices of the parameters to be modified.

    • +

  • fixed (array-like) – Base values of the parameters.

    • +

  • kmax (float) – Maximum wavenumber for the power spectrum computation.

    • +
+
+
Returns:
+

Dictionary of cosmological parameters compatible with pysbmy.

+
+
Return type:
+

dict

+
+
+
+ +
+
+selfisys.utils.tools.sample_omega_from_prior(nsample, omega_mean, omega_cov, params_ids, seed=None)[source]
+

Sample cosmological parameters from a prior distribution.

+

Ensures physical validity by clipping values to [eps, 1-eps].

+
+
Parameters:
+
    +

  • nsample (int) – Number of samples to draw.

    • +

  • omega_mean (array-like) – Prior mean vector.

    • +

  • omega_cov (array-like) – Prior covariance matrix.

    • +

  • params_ids (array-like) – Indices of the parameters to sample.

    • +

  • seed (int, optional) – Seed for the random number generator.

    • +
+
+
Returns:
+

Sampled cosmological parameters.

+
+
Return type:
+

array

+
+
+
+ +
+
+selfisys.utils.tools.summary_to_score(params_ids, omega0, F0, F0_inv, f0, dw_f0, C0_inv, phi)[source]
+

Compute the Fisher score.

+
+
Parameters:
+
    +

  • params_ids (array-like) – Indices of the parameters.

    • +

  • omega0 (array-like) – Cosmological parameters at the expansion point.

    • +

  • F0 (array-like) – Fisher information matrix.

    • +

  • F0_inv (array-like) – Inverse Fisher information matrix.

    • +

  • f0 (array-like) – Mean model at the expansion point.

    • +

  • dw_f0 (array-like) – Derivative of the mean model.

    • +

  • C0_inv (array-like) – Inverse covariance matrix.

    • +

  • phi (array-like) – Observed summary.

    • +
+
+
Returns:
+

Fisher score.

+
+
Return type:
+

array

+
+
+
+ +
+
+

selfisys.utils.workers module

+
+
+selfisys.utils.workers.Simbelmyne_worker(args) → Tuple[float, Any][source]
+

Worker function used for implicit likelihood inference of +cosmological parameters.

+
+
Parameters:
+

args (tuple) – A tuple of arguments to be unpacked for the worker routine: +(index, param_val, param_id, fsimdir, k_s, Pbins_bnd, +selection_params, norm_csts, P_ss_obj_path, obs_density, +lin_bias, noise, survey_mask_path, G_sim_path, G_ss_path, Np0, +Npm0, seedphase_init, seednoise_init, size, L, +radial_selection, sim_params, wd, batch_idx, dbg, modeldir, +local_mask_prefix, TimeStepDistribution, indices_steps_cumul, +eff_redshifts, poolname_abc, setup_only, prefix_mocks).

+
+
Returns:
+

(param_val, Phi) where param_val is the parameter value used, +and Phi is the resulting summary from evaluating the model.

+
+
Return type:
+

tuple

+
+
Raises:
+
    +

  • OSError – If file I/O (reading or writing mock data) fails.

    • +

  • RuntimeError – For unexpected errors in the worker routine.

    • +
+
+
+
+ +
+
+selfisys.utils.workers.evaluate_gradient_of_Symbelmyne(omega, param_index: int, k_s, coeffs: List[float] = [0.6666666666666666, -0.08333333333333333], deltas_x: List[float] = [0.01, 0.02], delta: float = 0.01, kmax: float = 1.4)[source]
+

Estimate the gradient of CLASS with respect to the cosmological +parameters using central finite differences of arbitrary order.

+
+
Parameters:
+
    +

  • omega (ndarray) – Base cosmological parameter vector.

    • +

  • param_index (int) – Index of the parameter to differentiate against.

    • +

  • k_s (ndarray) – Wavenumbers for the power spectrum.

    • +

  • coeffs (list of float, optional) – Coefficients for the finite-difference scheme, typically +[2/3, -1/12] etc. Default is [2/3.0, -1/12.0].

    • +

  • deltas_x (list of float, optional) – Step sizes. The corresponding negative steps are generated +automatically. Default is [0.01, 0.02].

    • +

  • delta (float, optional) – Scale for the finite difference in the denominator. Default is +1e-2.

    • +

  • kmax (float, optional) – Maximum wavenumber for the power spectrum. Default is 1.4.

    • +
+
+
Returns:
+

The gradient of the power spectrum wrt the specified parameter.

+
+
Return type:
+

ndarray

+
+
Raises:
+

RuntimeError – If the gradient evaluation fails.

+
+
+
+ +
+
+selfisys.utils.workers.worker_gradient_Symbelmyne(coeff: float, delta_x: float, omega, param_index: int, k_s, delta: float, kmax: float)[source]
+

Worker function for evaluating the gradient of the power spectrum +using finite differences.

+
+
Parameters:
+
    +

  • coeff (float) – Coefficient for the finite difference.

    • +

  • delta_x (float) – Step size in the parameter space.

    • +

  • omega (ndarray) – Base cosmological parameter vector.

    • +

  • param_index (int) – Index of the parameter being varied.

    • +

  • k_s (ndarray) – Array of wavenumbers.

    • +

  • delta (float) – Denominator for finite differences (scaled).

    • +

  • kmax (float) – Maximum wavenumber for power spectrum.

    • +
+
+
Returns:
+

The gradient of the power spectrum wrt the specified parameter.

+
+
Return type:
+

ndarray

+
+
Raises:
+

RuntimeError – If the gradient evaluation fails.

+
+
+
+ +
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/source/CONTRIBUTING.md b/docs/source/CONTRIBUTING.md new file mode 100644 index 0000000..76f4a5c --- /dev/null +++ b/docs/source/CONTRIBUTING.md @@ -0,0 +1,58 @@ +# Contributing to SelfiSys + +We welcome contributions to the SelfiSys repository! Please follow these guidelines when contributing. + +--- + +## Reporting Issues + +If you find a bug or have a suggestion, please open an issue in the [GitHub repository](https://github.com/hoellin/selfisys_public). Include as much detail as possible: +- Steps to reproduce the issue +- Expected vs. actual behaviour +- Relevant error messages or logs (if applicable) +- Suggestions for improvement (if applicable) + +If you are unsure whether your issue is a bug or have questions about the code, you are welcome to open an issue for discussion. + +## Submitting Contributions + +1. Fork the repository and create a new branch for your changes. +2. Ensure your contributions are well-documented and adhere to the highest coding standards. +3. Test your changes thoroughly before submitting: + - Ensure Jupyter notebooks run without errors from top to bottom. + - Validate new functionality or fixes using appropriate test cases. +4. Before submitting a pull request, synchronise your fork with the main repository to incorporate upstream changes. + - Add the main repository as a remote. + ```bash + git remote add upstream https://github.com/hoellin/selfisys_public.git + ``` + - Fetch the latest changes from the upstream repository. + ```bash + git fetch upstream + ``` + - Merge the changes into your local repository. + ```bash + git merge upstream/main + ``` +5. Open a pull request describing your changes to the main repository. + +## Style Guidelines + +Follow best practices for Python coding and Jupyter notebooks. + +### Python code + +Refer to the [PEP 8 Style Guide](https://pep8.org/) for Python coding standards. + +### Jupyter Notebooks + +- Use clear and concise Markdown cells to explain code and results. +- Avoid leaving unnecessary output (e.g., debugging print statements). +- Ensure notebooks are runnable from top to bottom. +- For tips on creating clean and effective Jupyter notebooks, see [Jupyter Notebook Best Practices](https://realpython.com/jupyter-notebook-best-practices/). + +--- + +## Questions? + +If you have any questions or need further clarification, feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr). \ No newline at end of file diff --git a/docs/source/README.md b/docs/source/README.md new file mode 100644 index 0000000..e85d351 --- /dev/null +++ b/docs/source/README.md @@ -0,0 +1,83 @@ +# SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys + +[![arXiv](https://img.shields.io/badge/astro--ph.CO-arxiv%3A2412.04443-B31B1B.svg?style=flat)](https://arxiv.org/abs/2412.04443) +[![GPLv3 license](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/hoellin/selfisys_public/blob/main/LICENSE) +[![GitHub version](https://img.shields.io/github/tag/hoellin/selfisys_public.svg?label=version)](https://github.com/hoellin/selfisys_public) +[![GitHub last commit](https://img.shields.io/github/last-commit/hoellin/selfisys_public.svg)](https://github.com/hoellin/selfisys_public/commits/main) + + +**SelfiSys** is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference. + +It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys. + +## Key Features + +- **Custom hidden-box forward models** + +We provide a `HiddenBox` class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver. + +- **Diagnosis of systematic effects** + +Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference. + +- **Cosmological inference** + +Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +--- + +## Documentation + +The documentation, including a detailed API reference, is available at [hoellin.github.io/selfisys_public](https://hoellin.github.io/selfisys_public/). + +For practical examples demonstrating how to use SelfiSys, visit the [SelfiSys Examples Repository](https://github.com/hoellin/selfisys_examples). + +## Contributors + +- **Tristan Hoellinger**, [tristan.hoellinger@iap.fr](mailto:tristan.hoellinger@iap.fr) + Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP). + +For information on contributing, refer to [CONTRIBUTING.md](CONTRIBUTING.md). + +## References + +If you use the SelfiSys package in your research, please cite the following paper and feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr) for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., 2024* +[arXiv:2412.04443](https://arxiv.org/abs/2412.04443) [[astro-ph.CO]](https://arxiv.org/abs/2412.04443) [[ADS]](https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443) [[pdf]](https://arxiv.org/pdf/2412.04443) + +BibTeX entry for citation: +```bibtex +@ARTICLE{hoellinger2024diagnosing, + author = {Hoellinger, Tristan and Leclercq, Florent}, + title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}", + journal = {arXiv e-prints}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2024, + month = dec, + eid = {arXiv:2412.04443}, + pages = {arXiv:2412.04443}, + doi = {10.48550/arXiv.2412.04443}, +archivePrefix = {arXiv}, + eprint = {2412.04443}, +primaryClass = {astro-ph.CO}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} +``` + +## Requirements + +The code is written in Python 3.10 and depends on the following packages: +- [`pySELFI`](https://pyselfi.readthedocs.io/en/latest/): Python implementation of the Simulator Expansion for Likelihood-Free Inference. +- [`Simbelmynë`](https://simbelmyne.readthedocs.io/en/latest/): A hierarchical probabilistic simulator for generating synthetic galaxy survey data. +- [`ELFI`](https://elfi.readthedocs.io/en/latest/): A statistical software package for likelihood-free inference, implementing in particular Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +A comprehensive list of dependencies, including version specifications to ensure reproducibility, will be provided in a yaml file, along with installation instructions, in a future release. + +--- + +## License + +This software is distributed under the GPLv3 Licence. Please review the [LICENSE](https://github.com/hoellin/selfisys_public/blob/main/LICENSE) file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence. \ No newline at end of file diff --git a/docs/source/REFERENCES.md b/docs/source/REFERENCES.md new file mode 100644 index 0000000..d211609 --- /dev/null +++ b/docs/source/REFERENCES.md @@ -0,0 +1,44 @@ +# References + +If you use the SelfiSys package in your research, please cite the following paper and feel free to [contact the authors](mailto:tristan.hoellinger@iap.fr) for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., 2024* +[arXiv:2412.04443](https://arxiv.org/abs/2412.04443) [[astro-ph.CO]](https://arxiv.org/abs/2412.04443) [[ADS]](https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443) [[pdf]](https://arxiv.org/pdf/2412.04443) + +BibTeX entry for citation: +```bibtex +@ARTICLE{hoellinger2024diagnosing, + author = {Hoellinger, Tristan and Leclercq, Florent}, + title = "{Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum}", + journal = {arXiv e-prints}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2024, + month = dec, + eid = {arXiv:2412.04443}, + pages = {arXiv:2412.04443}, + doi = {10.48550/arXiv.2412.04443}, +archivePrefix = {arXiv}, + eprint = {2412.04443}, +primaryClass = {astro-ph.CO}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241204443H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} +``` + +Other references relevant to the SelfiSys pipeline and cited in the code are listed below. + +- **[jasche2010bayesian]** +Jasche, J., Kitaura, F. S., Wandelt, B. D., and Enßlin, T. A., “Bayesian power-spectrum inference for large-scale structure data”, Monthly Notices of the Royal Astronomical Society, vol. 406, no. 1, OUP, pp. 60–85, 2010. [doi:10.1111/j.1365-2966.2010.16610.x](https://doi.org/10.48550/arXiv.0911.2493). + +- **[gil2015power]** +Gil-Marín, H., “The power spectrum and bispectrum of SDSS DR11 BOSS galaxies - I. Bias and gravity”, Monthly Notices of the Royal Astronomical Society, vol. 451, no. 1, OUP, pp. 539–580, 2015. [doi:10.1093/mnras/stv961](https://doi.org/10.48550/arXiv.1407.5668). + +- **[howlett2015clustering]** +Howlett, C., Ross, A. J., Samushia, L., Percival, W. J., and Manera, M., “The clustering of the SDSS main galaxy sample - II. Mock galaxy catalogues and a measurement of the growth of structure from redshift space distortions at z = 0.15”, Monthly Notices of the Royal Astronomical Society, vol. 449, no. 1, OUP, pp. 848–866, 2015. [doi:10.1093/mnras/stu2693](https://doi.org/10.48550/arXiv.1409.3238). + +- **[leclercq2019primordial]** +Florent Leclercq, Wolfgang Enzi, Jens Jasche, Alan Heavens, Primordial power spectrum and cosmology from black-box galaxy surveys, Monthly Notices of the Royal Astronomical Society, Volume 490, Issue 3, December 2019, Pages 4237–4253, [doi:10.1093/mnras/stz2718](https://doi.org/10.1093/mnras/stz2718). + +- **[hoellinger2024diagnosing]** +Hoellinger, T. and Leclercq, F., “Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum”, arXiv e-prints, Art. no. arXiv:2412.04443, 2024. [doi.org:10.48550/arXiv.2412.04443](https://doi.org/10.48550/arXiv.2412.04443). \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..9fdcf3a --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,114 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "SelfiSys" +copyright = "2025, Tristan Hoellinger" +author = "Tristan Hoellinger" +release = ( + "/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/SELFI/selfisys/src/" +) + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.autodoc", # Automatically generate documentation from docstrings. + "sphinx.ext.viewcode", # Adds links to highlighted source code. + "sphinx.ext.napoleon", # Supports Google and NumPy-style docstrings. + "myst_parser", # Markdown support. + "sphinx.ext.autosummary", # Generate summary tables for API docs. + "sphinx.ext.mathjax", # For rendering mathematical equations using MathJax. + "sphinx.ext.todo", # Support for TODO notes. +] + +templates_path = ["_templates"] +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "sphinx_rtd_theme" +html_static_path = ["_static"] +html_extra_path = ["_static"] + +html_theme_options = { + "collapse_navigation": False, + "sticky_navigation": True, + "navigation_depth": 4, + "style_external_links": True, + "titles_only": False, +} + +# Add GitHub link +html_context = { + "display_github": True, # Enables the link + "github_user": "hoellin", # GitHub username + "github_repo": "selfisys_public", # Repository name + "github_version": "main", # Branch name + "conf_py_path": "/docs/source/", # Path to the documentation directory in the repo +} + +import os +import sys +import shutil + +# Add the project's source path to sys.path +sys.path.insert(0, os.path.abspath("../../src")) + +# Copy Markdown files from parent directory +markdown_files = ["README.md", "CONTRIBUTING.md", "REFERENCES.md"] +for md_file in markdown_files: + src = os.path.abspath(f"../../{md_file}") # Adjust the path if necessary + dst = os.path.join(os.path.dirname(__file__), os.path.basename(md_file)) + try: + if os.path.exists(src) and not os.path.exists(dst): + shutil.copy(src, dst) + print(f"Copied {src} to {dst}") + except Exception as e: + print(f"Error copying {md_file}: {e}") + + +def generate_rst_files(): + """ + Automatically generate .rst files for standalone Python modules. + Avoids regeneration if files already exist. + """ + source_dir = os.path.abspath("src/selfisys") # Adjust to your source directory + output_dir = os.path.join(os.path.dirname(__file__)) # docs/source directory + + # Ensure source_dir exists + if not os.path.isdir(source_dir): + raise FileNotFoundError(f"The source directory '{source_dir}' does not exist.") + + # List all Python files in the source directory (excluding __init__.py) + for filename in os.listdir(source_dir): + if filename.endswith(".py") and filename != "__init__.py": + module_name = filename[:-3] # Remove .py extension + output_file = os.path.join(output_dir, f"selfisys.{module_name}.rst") + + # Skip regeneration if the file already exists + if os.path.exists(output_file): + continue + + # Write the .rst file content + with open(output_file, "w") as f: + # Module header + f.write(f"{module_name}\n") + f.write("=" * len(module_name) + "\n\n") + f.write(f".. automodule:: selfisys.{module_name}\n") + f.write(" :members:\n") + f.write(" :undoc-members:\n") + f.write(" :show-inheritance:\n") + + print(f"Generated {output_file}") + + +# Register a Sphinx hook to generate .rst files before the build +def setup(app): + app.connect("builder-inited", lambda app: generate_rst_files()) diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..26dda27 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,95 @@ +SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys +=================================================================== + +.. image:: https://img.shields.io/badge/astro--ph.CO-arxiv%3A2412.04443-B31B1B.svg + :target: https://arxiv.org/abs/2412.04443 + :alt: arXiv + +.. image:: https://img.shields.io/github/v/tag/hoellin/selfisys_public.svg?label=version + :target: https://github.com/hoellin/selfisys_public/releases + :alt: GitHub Release + +.. image:: https://img.shields.io/github/last-commit/hoellin/selfisys_public + :target: https://github.com/hoellin/selfisys_public/commits/main + :alt: Last Commit + +.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg + :target: https://github.com/hoellin/selfisys_public/blob/main/LICENSE + :alt: License + +**SelfiSys** is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference. + +It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys. + +Key Features +------------ + +- **Custom hidden-box forward models** + We provide a `HiddenBox` class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver. +- **Diagnosis of systematic effects** + Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference. +- **Cosmological inference** + Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +For practical examples demonstrating how to use SelfiSys, visit the `SelfiSys Examples Repository `_. + +References +---------- + +If you use the SelfiSys package in your research, please cite the following paper and feel free to `contact the authors `_ for feedback, collaboration opportunities, or other inquiries. + +**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum** +*Hoellinger, T. and Leclercq, F., arXiv e-prints*, 2024 +`arXiv:2412.04443 `_ +`[astro-ph.CO] `_ +`[ADS] `_ +`[pdf] `_ + +Contributors +------------ + +- **Tristan Hoellinger** + `tristan.hoellinger@iap.fr `_ + + Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP). + +License +------- + +This software is distributed under the GPLv3 Licence. Please review the `LICENSE `_ file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence. + +Requirements +------------ + +The code is written in Python 3.10 and depends on the following packages: + +- `pySELFI `_: Python implementation of the Simulator Expansion for Likelihood-Free Inference. +- `Simbelmynë `_: A hierarchical probabilistic simulator for generating synthetic galaxy survey data. +- `ELFI `_: A statistical software package for likelihood-free inference, implementing Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler. + +A comprehensive list of dependencies, along with installation instructions, will be provided in a future release. + +.. toctree:: + :maxdepth: 2 + :caption: API Documentation + + selfisys.hiddenbox + selfisys.normalise_hb + selfisys.prior + selfisys.selection_functions + selfisys.selfi_interface + selfisys.sbmy_interface + selfisys.grf + selfisys.utils + +.. toctree:: + :maxdepth: 2 + :caption: Contribute + + ../../CONTRIBUTING.md + +.. toctree:: + :maxdepth: 2 + :caption: References + + ../../REFERENCES.md \ No newline at end of file diff --git a/docs/source/selfisys.global_parameters.rst b/docs/source/selfisys.global_parameters.rst new file mode 100644 index 0000000..0efb881 --- /dev/null +++ b/docs/source/selfisys.global_parameters.rst @@ -0,0 +1,7 @@ +global_parameters +================= + +.. automodule:: selfisys.global_parameters + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.grf.rst b/docs/source/selfisys.grf.rst new file mode 100644 index 0000000..d10cbf8 --- /dev/null +++ b/docs/source/selfisys.grf.rst @@ -0,0 +1,7 @@ +grf +=== + +.. automodule:: selfisys.grf + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.hiddenbox.rst b/docs/source/selfisys.hiddenbox.rst new file mode 100644 index 0000000..292952c --- /dev/null +++ b/docs/source/selfisys.hiddenbox.rst @@ -0,0 +1,7 @@ +hiddenbox +========= + +.. automodule:: selfisys.hiddenbox + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.normalise_hb.rst b/docs/source/selfisys.normalise_hb.rst new file mode 100644 index 0000000..108af97 --- /dev/null +++ b/docs/source/selfisys.normalise_hb.rst @@ -0,0 +1,7 @@ +normalise_hb +============ + +.. automodule:: selfisys.normalise_hb + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.prior.rst b/docs/source/selfisys.prior.rst new file mode 100644 index 0000000..30a73e2 --- /dev/null +++ b/docs/source/selfisys.prior.rst @@ -0,0 +1,7 @@ +prior +===== + +.. automodule:: selfisys.prior + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.rst b/docs/source/selfisys.rst new file mode 100644 index 0000000..1863c5a --- /dev/null +++ b/docs/source/selfisys.rst @@ -0,0 +1,93 @@ +selfisys package +================ + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + selfisys.utils + +Submodules +---------- + +selfisys.global\_parameters module +---------------------------------- + +.. automodule:: selfisys.global_parameters + :members: + :undoc-members: + :show-inheritance: + +selfisys.grf module +------------------- + +.. automodule:: selfisys.grf + :members: + :undoc-members: + :show-inheritance: + +selfisys.hiddenbox module +------------------------- + +.. automodule:: selfisys.hiddenbox + :members: + :undoc-members: + :show-inheritance: + +selfisys.normalise\_hb module +----------------------------- + +.. automodule:: selfisys.normalise_hb + :members: + :undoc-members: + :show-inheritance: + +selfisys.prior module +--------------------- + +.. automodule:: selfisys.prior + :members: + :undoc-members: + :show-inheritance: + +selfisys.sbmy\_interface module +------------------------------- + +.. automodule:: selfisys.sbmy_interface + :members: + :undoc-members: + :show-inheritance: + +selfisys.selection\_functions module +------------------------------------ + +.. automodule:: selfisys.selection_functions + :members: + :undoc-members: + :show-inheritance: + +selfisys.selfi\_interface module +-------------------------------- + +.. automodule:: selfisys.selfi_interface + :members: + :undoc-members: + :show-inheritance: + +selfisys.setup\_model module +---------------------------- + +.. automodule:: selfisys.setup_model + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: selfisys + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.sbmy_interface.rst b/docs/source/selfisys.sbmy_interface.rst new file mode 100644 index 0000000..7f1f003 --- /dev/null +++ b/docs/source/selfisys.sbmy_interface.rst @@ -0,0 +1,7 @@ +sbmy_interface +============== + +.. automodule:: selfisys.sbmy_interface + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.selection_functions.rst b/docs/source/selfisys.selection_functions.rst new file mode 100644 index 0000000..fdb56b4 --- /dev/null +++ b/docs/source/selfisys.selection_functions.rst @@ -0,0 +1,7 @@ +selection_functions +=================== + +.. automodule:: selfisys.selection_functions + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.selfi_interface.rst b/docs/source/selfisys.selfi_interface.rst new file mode 100644 index 0000000..32b1adf --- /dev/null +++ b/docs/source/selfisys.selfi_interface.rst @@ -0,0 +1,7 @@ +selfi_interface +=============== + +.. automodule:: selfisys.selfi_interface + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.setup_model.rst b/docs/source/selfisys.setup_model.rst new file mode 100644 index 0000000..a9036a8 --- /dev/null +++ b/docs/source/selfisys.setup_model.rst @@ -0,0 +1,7 @@ +setup_model +=========== + +.. automodule:: selfisys.setup_model + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/selfisys.utils.rst b/docs/source/selfisys.utils.rst new file mode 100644 index 0000000..d5ac880 --- /dev/null +++ b/docs/source/selfisys.utils.rst @@ -0,0 +1,101 @@ +selfisys.utils package +====================== + +Submodules +---------- + +selfisys.utils.examples\_utils module +------------------------------------- + +.. automodule:: selfisys.utils.examples_utils + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.logger module +---------------------------- + +.. automodule:: selfisys.utils.logger + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.low\_level module +-------------------------------- + +.. automodule:: selfisys.utils.low_level + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.parser module +---------------------------- + +.. automodule:: selfisys.utils.parser + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.path\_utils module +--------------------------------- + +.. automodule:: selfisys.utils.path_utils + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.plot\_examples module +------------------------------------ + +.. automodule:: selfisys.utils.plot_examples + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.plot\_params module +---------------------------------- + +.. automodule:: selfisys.utils.plot_params + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.plot\_utils module +--------------------------------- + +.. automodule:: selfisys.utils.plot_utils + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.timestepping module +---------------------------------- + +.. automodule:: selfisys.utils.timestepping + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.tools module +--------------------------- + +.. automodule:: selfisys.utils.tools + :members: + :undoc-members: + :show-inheritance: + +selfisys.utils.workers module +----------------------------- + +.. automodule:: selfisys.utils.workers + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: selfisys.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/src/selfisys/__init__.py b/src/selfisys/__init__.py new file mode 100644 index 0000000..a421b31 --- /dev/null +++ b/src/selfisys/__init__.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +SelfiSys Package + +A Python package for diagnosing systematic effects in field-based, +implicit likelihood inference (ILI) of cosmological parameters from +large-scale spectroscopic galaxy surveys. The diagnostic utilises the +initial matter power spectrum inferred with pySELFI. + +Key functionalities: +- Setup custom models of realistic spectroscopic galaxy surveys, +- Diagnosis of systematic effects model using the initial matter power +spectrum inferred with pySELFI (https://pyselfi.readthedocs.io/), +- Perform inference of cosmological parameters using Approximate +Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler +using ELFI (https://elfi.readthedocs.io/). +""" + +from .global_parameters import * diff --git a/src/selfisys/global_parameters.py b/src/selfisys/global_parameters.py new file mode 100644 index 0000000..011d6cd --- /dev/null +++ b/src/selfisys/global_parameters.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Global parameters for this project.""" + +import os +from pathlib import Path +import numpy as np + +WHICH_SPECTRUM = "class" # available options are "eh" and "class" + +# Load global paths from environment variables +ROOT_PATH = os.getenv("SELFISYS_ROOT_PATH") +if ROOT_PATH is None: + raise EnvironmentError("Please set the 'SELFISYS_ROOT_PATH' environment variable.") +OUTPUT_PATH = os.getenv("SELFISYS_OUTPUT_PATH") +if OUTPUT_PATH is None: + raise EnvironmentError("Please set the 'SELFISYS_OUTPUT_PATH' environment variable.") + +# Default verbose level +# 0: errors only, 1: info, 2: warnings+, 3: all diagnostics, 4+: debug +DEFAULT_VERBOSE_LEVEL = 2 + +# Baseline seeds for reproducibility +BASELINE_SEEDNORM = 100030898 +BASELINE_SEEDNOISE = 200030898 +BASELINE_SEEDPHASE = 300030898 +SEEDPHASE_OBS = 100030896 +SEEDNOISE_OBS = 100030897 + +# Fiducial cosmological parameters +h_planck = 0.6766 +Omega_b_planck = 0.02242 / h_planck**2 +Omega_m_planck = 0.3111 +nS_planck = 0.9665 +sigma8_planck = 0.8102 + +planck_mean = np.array([h_planck, Omega_b_planck, Omega_m_planck, nS_planck, sigma8_planck]) +planck_cov = np.diag(np.array([0.0042, 0.00030, 0.0056, 0.0038, 0.0060]) ** 2) + +# Mock unknown ground truth parameters for consistency checks +h_obs = 0.679187146124996 +Omega_b_obs = 0.0487023481098232 +Omega_m_obs = 0.3053714257403574 +nS_obs = 0.9638467785003454 +sigma8_obs = 0.8210464735135183 + +omegas_gt = np.array([h_obs, Omega_b_obs, Omega_m_obs, nS_obs, sigma8_obs]) + +# Mapping from cosmological parameter names to corresponding indices +cosmo_params_names = [r"$h$", r"$\Omega_b$", r"$\Omega_m$", r"$n_S$", r"$\sigma_8$"] +cosmo_params_name_to_idx = {"h": 0, "Omega_b": 1, "Omega_m": 2, "n_s": 3, "sigma8": 4} + +# Minimum k value used in the normalisation of the summaries +MIN_K_NORMALISATION = 4e-2 + +params_planck_kmax_missing = { + "h": h_planck, + "Omega_r": 0.0, + "Omega_q": 1.0 - Omega_m_planck, + "Omega_b": Omega_b_planck, + "Omega_m": Omega_m_planck, + "m_ncdm": 0.0, + "Omega_k": 0.0, + "tau_reio": 0.066, + "n_s": nS_planck, + "sigma8": sigma8_planck, + "w0_fld": -1.0, + "wa_fld": 0.0, + "WhichSpectrum": WHICH_SPECTRUM, +} + +params_BBKS_kmax_missing = { + "h": h_planck, + "Omega_r": 0.0, + "Omega_q": 1.0 - Omega_m_planck, + "Omega_b": Omega_b_planck, + "Omega_m": Omega_m_planck, + "m_ncdm": 0.0, + "Omega_k": 0.0, + "tau_reio": 0.066, + "n_s": nS_planck, + "sigma8": sigma8_planck, + "w0_fld": -1.0, + "wa_fld": 0.0, + "WhichSpectrum": "BBKS", +} + +params_cosmo_obs_kmax_missing = { + "h": h_obs, + "Omega_r": 0.0, + "Omega_q": 1.0 - Omega_m_obs, + "Omega_b": Omega_b_obs, + "Omega_m": Omega_m_obs, + "m_ncdm": 0.0, + "Omega_k": 0.0, + "tau_reio": 0.066, + "n_s": nS_obs, + "sigma8": sigma8_obs, + "w0_fld": -1.0, + "wa_fld": 0.0, + "WhichSpectrum": WHICH_SPECTRUM, +} + +# Default hyperparameters for the wiggle-less prior from [leclercq2019primordial]. +THETA_NORM_GUESS = 0.05 +K_CORR_GUESS = 0.01 + +# Base ID for the observations +BASEID_OBS = "obs" diff --git a/src/selfisys/grf.py b/src/selfisys/grf.py new file mode 100644 index 0000000..0e18a2c --- /dev/null +++ b/src/selfisys/grf.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Tools for generating Gaussian random fields from given power spectra. +""" + +from selfisys.utils.logger import getCustomLogger + +logger = getCustomLogger(__name__) + + +def primordial_grf( + L, + N, + seedphases, + fname_powerspectrum, + fname_outputinitialdensity, + force_sim=False, + return_g=False, + verbose=0, +): + """ + Generate a Gaussian random field from a specified input power + spectrum. + + Parameters + ---------- + L : float + Side length of the simulation box in Mpc/h. + N : int + Grid resolution (number of cells per dimension). + seedphases : int + Seed for random phase generation (for reproducibility). + fname_powerspectrum : str + File path to the input power spectrum. + fname_outputinitialdensity : str + File path to store the generated initial density field. + force_sim : bool, optional + If True, regenerate the GRF even if the output file exists. + Default is False. + return_g : bool, optional + If True, return the GRF as a numpy array. Default is False. + verbose : int, optional + Verbosity level (0 = silent, 1 = progress, 2 = detailed). + Default is 0. + + Raises + ------ + OSError + If the power spectrum file cannot be read. + RuntimeError + If an unexpected error occurs during power spectrum reading. + + Returns + ------- + numpy.ndarray or None + The GRF data if `return_g` is True, otherwise None. + """ + from os.path import exists + from gc import collect + from pysbmy.power import PowerSpectrum + from pysbmy.field import Field + + # Skip simulation if output already exists and overwrite is not requested + if not force_sim and exists(fname_outputinitialdensity): + from pysbmy.field import read_basefield + + if verbose > 0: + logger.info(f"{fname_outputinitialdensity} already exists. Skipping simulation.") + return read_basefield(fname_outputinitialdensity).data if return_g else None + + # Read the power spectrum + try: + P = PowerSpectrum.read(fname_powerspectrum) + except OSError as e: + logger.error(f"Unable to read power spectrum file: {fname_powerspectrum}") + raise + except Exception as e: + logger.exception(f"Unexpected error while reading power spectrum: {e}") + raise + + # Generate the Gaussian random field + if verbose > 1: + g = Field.GRF(L, L, L, 0, 0, 0, N, N, N, P, 1e3, seedphases) # a_init = 1e3 + else: + from selfisys.utils.low_level import stdout_redirector + from io import BytesIO + + # Suppress standard output to avoid cluttering logs + with BytesIO() as f: + with stdout_redirector(f): + g = Field.GRF(L, L, L, 0, 0, 0, N, N, N, P, 1e3, seedphases) + + # Write the field to disk + g.write(fname_outputinitialdensity) + field = g.data.copy() if return_g else None + + # Free memory + del g + collect() + + return field diff --git a/src/selfisys/hiddenbox.py b/src/selfisys/hiddenbox.py new file mode 100644 index 0000000..7b0d83e --- /dev/null +++ b/src/selfisys/hiddenbox.py @@ -0,0 +1,1513 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""This module provides the HiddenBox class, which is used to define +standalone stochastic forward models of large-scale spectroscopic +galaxy surveys. + +The HiddenBox class is compatible with the pySELFI package. +""" + +import os +from gc import collect +from typing import Callable, Any, List, Optional, Union + +import h5py +import numpy as np +from selfisys.global_parameters import DEFAULT_VERBOSE_LEVEL +from selfisys.utils.parser import joinstrs_only + + +class HiddenBox: + """This class represents custom forward data model of large-scale + spectroscopic galaxy surveys.""" + + def __init__( + self, + k_s: Any, + P_ss_path: str, + Pbins_bnd: Any, + theta2P: Callable, + P: int, + size: int, + L: float, + G_sim_path: str, + G_ss_path: str, + Np0: int, + Npm0: int, + fsimdir: str, + noise_std: Optional[float] = None, + radial_selection: Optional[str] = None, + selection_params: Optional[List[Any]] = None, + observed_density: Optional[float] = None, + linear_bias: Optional[Union[float, List[float]]] = None, + norm_csts: Optional[Union[float, List[float]]] = None, + survey_mask_path: Optional[str] = None, + local_mask_prefix: Optional[str] = None, + sim_params: Optional[str] = None, + eff_redshifts: Optional[List[float]] = None, + TimeSteps: Optional[List[int]] = None, + TimeStepDistribution: Optional[str] = None, + seedphase: Optional[int] = None, + seednoise: Optional[int] = None, + fixnoise: bool = False, + seednorm: Optional[int] = None, + save_frequency: Optional[int] = None, + reset: bool = False, + verbosity: int = DEFAULT_VERBOSE_LEVEL, + **kwargs, + ): + """ + Initialise the HiddenBox. + + Parameters + ---------- + k_s : array-like + Vector of input support wavenumbers. + P_ss_path : str + Path to the spectrum used to normalise the outputs. + Pbins_bnd : array-like + Vector of bin boundaries for the summary statistics. + theta2P : Callable + Function to convert theta vectors to initial power spectra. + P : int + Dimension of the output summary statistics. + size : int + Side length of the simulation box in voxels. + L : float + Side length of the simulation box in Mpc/h. + G_sim_path : str + Path to the simulation grid. + G_ss_path : str + Path to the summary grid. + Np0 : int + Number of dark matter particles per spatial dimension. + Npm0 : int + Side length of the particle-mesh grid in voxels. + fsimdir : str + Output directory. + noise_std : float, optional + Standard deviation for the Gaussian noise. Default is + `None`. + radial_selection : str, optional + Type of radial selection mask. Default is `None`, + corresponding to no radial selection. + + Available options: + - 'multiple_lognormal': Use multiple log-normal radial + selection functions. + selection_params : list, optional + List of parameters for the radial selection mask, for each + population. Default is `None`. + + For the 'multiple_lognormal' radial selection, + `selection_params` is a shape `(3, N_pop)` array comprising + the three following parameters for each population: + - selection_std : (float) + Standard deviation of the distribution, e.g., + constant × (1 + z). + - selection_mean : (float) + Mean of the distribution in Gpc/h. + - selection_rescale : (float, optional) + Individually rescale the distributions by the given + value. If `None`, the global maximum is normalised to + `1`. + observed_density : float, optional + Mean galaxy density. Default is `None`. + linear_bias : float or list of float, optional + First-order linear galaxy biases. If `None`, use the dark + matter density to compute the summaries. Default is `None`. + norm_csts : float or list of float, optional + If not `None`, normalise the output of the hidden box + accordingly. + + For `radial_selection == 'multiple_lognormal'`, `norm_csts` + must be a list of `N_pop` values. Default is `None`. + survey_mask_path : str, optional + If not `None`, apply the corresponding survey mask to the + observed field. Default is `None`. + local_mask_prefix : str, optional + Prefix for the local copy of the survey mask. If `None`, use + the default name. Default is `None`. + sim_params : str, optional + Set of simulation parameters to be used for Simbelmynë. + Check `setup_sbmy_parfiles` in `selfisys.sbmy_parser` for + details. Default is `None`. + eff_redshifts : list, optional + Effective redshifts for the time steps. Default is `None`. + TimeSteps : list, optional + Number of time steps to reach the corresponding effective + redshifts. Default is `None`. + TimeStepDistribution : str, optional + Path to the Simbelmynë time step distribution file. Default + is `None`. + seedphase : int, optional + Seed to generate the initial white noise realisation. + Default is `None`. + seednoise : int, optional + Initial state of the RNG to generate the noise sequence. If + `fixnoise` is True, the seed used in `compute_pool` is + always `seednoise`. If `fixnoise` is False, it is + `seednoise + i` for realisation `i`. Default is `None`. + fixnoise : bool, optional + Whether to fix the noise realisation. If `True`, always use + `seednoise`. Default is `False`. + seednorm : int, optional + Seed used for normalisation. Default is `None`. + save_frequency : int, optional + Save the outputs of the hidden box to disk every + `save_frequency` evaluations. Default is `None`. + reset : bool, optional + Whether to always force reset the survey box when the hidden + `HiddenBox` object is instantiated. Default is `False`. + verbosity : int, optional + Verbosity level of the hidden box. `0` is silent, `1` is + minimal, `2` is verbose. Default is `DEFAULT_VERBOSE_LEVEL`. + **kwargs + Additional optional keyword arguments. + """ + # Mandatory attributes + self.k_s = k_s + self.P_ss_path = P_ss_path + self.Pbins_bnd = Pbins_bnd + self.theta2P = theta2P + self.P = P + self.size = size + self.L = L + self.G_sim_path = G_sim_path + self.G_ss_path = G_ss_path + self.Np0 = Np0 + self.Npm0 = Npm0 + self.fsimdir = fsimdir + + # Optional attributes + self.noise_std = noise_std + self.radial_selection = radial_selection + self.selection_params = selection_params + self.observed_density = observed_density + self.linear_bias = linear_bias + self.norm_csts = norm_csts + self.survey_mask_path = survey_mask_path + self.local_mask_prefix = local_mask_prefix + self.sim_params = sim_params + self.eff_redshifts = eff_redshifts + self.TimeSteps = TimeSteps + self.TimeStepDistribution = TimeStepDistribution + self.seedphase = seedphase + self.seednoise = seednoise + self.fixnoise = fixnoise + self.seednorm = seednorm + self.save_frequency = save_frequency + self.reset = reset + self.verbosity = verbosity + + # Additional attributes from kwargs + for key, value in kwargs.items(): + setattr(self, key, value) + + # Compute default values + self._set_defaults() + + # Create the window function W(n, r) = C(n) * R(r) + self._init_survey_mask() + self._init_radial_selection() + + def reset_survey(self): + """Re-initialise the survey mask C(n) and radial selection + function R(r). + """ + self._init_survey_mask(reset=True) + self._init_radial_selection(reset=True) + + def update(self, **kwargs): + """Updates the given parameter(s) of the hidden box with the + given value(s). + + Parameters + ---------- + **kwargs : dict + dictionary of parameters to update + + """ + for key, value in kwargs.items(): + setattr(self, key, value) + + def make_data( + self, + cosmo, + id, + seedphase, + seednoise, + force_powerspectrum=False, + force_parfiles=False, + force_sim=False, + force_cosmo=False, + force_phase=False, + d=-1, + remove_sbmy=True, + verbosity=None, + return_g=False, + RSDs=True, + prefix_mocks=None, + ): + """Generate simulated data based on the given cosmological + parameters. + + Parameters + ---------- + cosmo : dict + Cosmological and infrastructure parameters. + id : int or str, optional + Identifier used as a suffix in the file names. Default is + `0`. + seedphase : int or list of int + Seed to generate the initial white noise in Fourier space. + seednoise : int or list of int + Seed to generate the noise realisation. + force_powerspectrum : bool, optional + Force recomputing the input spectrum. Default is `False`. + force_parfiles : bool, optional + Force recomputing the parameter files. Default is `False`. + force_sim : bool, optional + Force recomputing the simulation. Default is `False`. + force_cosmo : bool, optional + Force recomputing the cosmological parameters. Default is + `False`. + force_phase : bool, optional + Force recomputing the initial phase. Default is `False`. + d : int, optional + Direction in the parameter space. Default is `-1`. + remove_sbmy : bool, optional + Whether to remove most Simbelmynë output files after use for + disk space management. Default is `True`. + verbosity : int, optional + Verbosity level. If `None`, use self.verbosity. Default is + `None`. + return_g : bool, optional + Whether to return the full field alongside the summary. + Default is `False`. + RSDs : bool, optional + Whether to compute the redshift-space distortions. Default + is `True`. + prefix_mocks : str, optional + Prefix for the mock data. If None, use self.prefix_mocks. + Default is `None`. + + Returns + ------- + Phi : ndarray + Vector of summary statistics. + g : ndarray or list of ndarray or None + Observed field(s) if return_g is `True`; otherwise `None`. + """ + from selfisys.utils.path_utils import get_file_names + from selfisys.sbmy_interface import get_power_spectrum_from_cosmo + + self._PrintMessage(0, f"Making mock data...", verbosity) + self._indent() + + names = get_file_names( + self.fsimdir, + id, + self.sim_params, + self.TimeSteps, + prefix_mocks or self.prefix_mocks, + self.gravity_on, + return_g, + ) + + # Save cosmological parameters + self._save_cosmo(cosmo, names["fname_cosmo"], force_cosmo) + + # Generate the input initial matter power spectrum + self._PrintMessage(1, f"Computing initial power spectrum...", verbosity) + get_power_spectrum_from_cosmo( + self.L, self.size, cosmo, names["fname_power_spectrum"], force_powerspectrum + ) + self._PrintMessage(1, f"Computing initial power spectrum done.", verbosity) + + # Generate the simulated galaxy survey data + self._PrintMessage(1, f"Running the forward model...", verbosity) + Phi, g = self._aux_hiddenbox( + d, + seedphase, + seednoise, + names, + force_parfiles=force_parfiles, + force_sim=force_sim, + force_phase=force_phase, + return_g=return_g, + RSDs=RSDs, + ) + self._PrintMessage(1, f"Running the forward model done.", verbosity) + + if remove_sbmy and self.gravity_on: + self._clean_output(names["fname_outputinitialdensity"]) + + self._unindent() + self._PrintMessage(1, f"Making mock data done.", verbosity) + + return (Phi, g) if return_g else Phi + + def evaluate( + self, + theta, + d, + seedphase, + seednoise, + i=0, + N=0, + force_powerspectrum=False, + force_parfiles=False, + force_sim=False, + remove_sbmy=False, + theta_is_p=False, + simspath=None, + check_output=False, + RSDs=True, + abc=False, + cosmo_vect=None, + ): + """Evaluate the hidden box for a given input power spectrum. + + The result is deterministic (the phase is fixed), except as it + is modified by nuisance parameters if any. + + This routine is used by `pySELFI` to compute the gradient of the + hidden box with respect to the power spectrum, and by ABC-PMC to + evaluate the forward model. + + Parameters + ---------- + theta : ndarray + Power spectrum values at the support wavenumbers. + d : int + Direction in parameter space, from `0` to `S`. + seedphase : int or list of int + Seed to generate the initial white noise in Fourier space. + seednoise : int or list of int + Seed to generate the noise realisation. + i : int, optional + Current evaluation index of the hidden box. Default is `0`. + N : int, optional + Total number of evaluations of the hidden box. Default is + `0`. + force_powerspectrum : bool, optional + If `True`, force recomputation of the power spectrum at the + values of the Fourier grid. Default is `False`. + force_parfiles : bool, optional + If `True`, overwrite existing parameter files. Default is + `False`. + force_sim : bool, optional + If `True`, rerun the simulation even if the output density + already exists. Default is `False`. + remove_sbmy : bool, optional + If `True`, remove Simbelmynë output files from disk. Default + is `False`. + theta_is_p : bool, optional + Set to `True` if `theta` is already an unnormalised power + spectrum. Default is `False`. + simspath : str, optional + Path to the simulations directory. Default is `None`. + check_output : bool, optional + If `True`, check the integrity of the output file and + recompute if corrupted. Default is `False`. + RSDs : bool, optional + Whether to compute redshift-space distortions. Default is + `True`. + abc : bool or str, optional + If not False, remove most output files after evaluation. + Default is `False`. + cosmo_vect : ndarray, optional + Cosmological parameters. Required if `abc` is `True`. + + Returns + ------- + Phi : ndarray + Vector of summary statistics. + """ + from selfisys.utils.path_utils import file_names_evaluate + + if abc and cosmo_vect is None: + raise ValueError("cosmo_vect must be provided when using ABC.") + if not abc and cosmo_vect is not None: + raise ValueError("cosmo_vect must not be provided when not using ABC.") + + if not theta_is_p: + self._PrintMessage( + 1, + f"Direction {d}. Evaluating hidden box (index {i}/{N - 1})...", + ) + self._indent() + + simdir = simspath or self.fsimdir + simdir_d = os.path.join(simdir, joinstrs_only(["pool/", abc, "d", str(d)])) + + # Get the file names for the current evaluation + names = file_names_evaluate( + simdir, + simdir_d, + d, + i, + self.sim_params, + self.TimeSteps, + self.prefix_mocks, + abc, + self.gravity_on, + ) + fname_power_spectrum = names["fname_power_spectrum"] + fname_simparfile = names["fname_simparfile"] + fname_whitenoise = names["fname_whitenoise"] + fname_outputinitialdensity = names["fname_outputinitialdensity"] + + # Interpolate the power spectrum values over the Fourier grid + self._PrintMessage(1, f"Interpolating spectrum over the Fourier grid...") + self._power_spectrum_from_theta( + theta, fname_power_spectrum, theta_is_p, force=force_powerspectrum + ) + self._PrintMessage(1, f"Interpolating spectrum over the Fourier grid done.") + + # Compute the simulated data + self._PrintMessage(1, f"Generating observed summary...") + Phi, _ = self._aux_hiddenbox( + d, + seedphase, + seednoise, + names, + force_parfiles, + force_sim=force_sim, + force_phase=False, + return_g=False, + check_output=check_output, + RSDs=RSDs, + sample=cosmo_vect, + ) + self._PrintMessage(1, f"Generating observed summary done.") + + # Clean up the output files + if remove_sbmy and self.gravity_on: + self._clean_output(fname_outputinitialdensity) + if abc: + self._clean_output(fname_power_spectrum) + self._clean_output(fname_simparfile) + self._clean_output(fname_whitenoise) + for f in os.listdir(simdir_d): + if self.TimeSteps is not None: + if f.startswith(f"output_realdensity_d{d}_p{i}_{self.TimeSteps[0]}"): + self._clean_output(os.path.join(simdir_d, f)) + else: + if f.startswith(f"output_realdensity_d{d}_p{i}"): + self._clean_output(os.path.join(simdir_d, f)) + if f.startswith(f"output_density_d{d}_p{i}"): + self._clean_output(os.path.join(simdir_d, f)) + if not self.gravity_on: + self._clean_output(fname_outputinitialdensity) + + if not theta_is_p: + self._unindent() + self._PrintMessage( + 1, + f"Direction {d}. Evaluation done (index {i}/{N - 1}).", + ) + + return Phi + + def switch_recompute_pool(self, prefix_mocks=None): + """Toggle recomputation of the pool for future `compute_pool` + calls. + + Parameters + ---------- + prefix_mocks : str, optional + Prefix for the future simulation files. Default is `None`. + """ + self._force_recompute_mocks = not self.force_recompute_mocks + self._prefix_mocks = prefix_mocks if prefix_mocks is not None else None + + def switch_setup(self): + """Toggle the setup-only mode.""" + self._setup_only = not self.setup_only + + def compute_pool( + self, + theta, + d, + pool_fname, + N, + index=None, + force_powerspectrum=False, + force_parfiles=False, + force_sim=False, + remove_sbmy=False, + theta_is_p=False, + simspath=None, + bar=False, + ): + """Compute a pool of realisations of the hidden box compatible + with `pySELFI`. + + Parameters + ---------- + theta : ndarray + Power spectrum values at the support wavenumbers. + d : int + Direction in parameter space, from 0 to S. + pool_fname : str + Filename for the pool. + N : int + Number of realisations required at the given direction. + index : int, optional + Index of a single simulation to run. Default is `None`. + force_powerspectrum : bool, optional + If True, force recomputation of the power spectrum. Default + is `False`. + force_parfiles : bool, optional + If True, overwrite existing parameter files. Default is + `False`. + force_sim : bool, optional + If True, rerun the simulation even if the output density + already exists. Default is `False`. + remove_sbmy : bool, optional + If True, remove Simbelmynë output files from disk. Default + is `False`. + theta_is_p : bool, optional + Set to True when `theta` is already an unnormalised power + spectrum. Default is `False`. + simspath : str, optional + Path indicating where to store the simulations. Default is + `None`. + bar : bool, optional + If True, display a progress bar. Default is `False`. + + Returns + ------- + p : Pool + Simulation pool object. + """ + import tqdm.auto as tqdm + from pyselfi.pool import pool as Pool + + self._PrintMessage(1, f"Computing a pool of realisations of the hidden box...") + + pool_fname = str(pool_fname) + if self.force_recompute_mocks: + if os.path.exists(pool_fname): + os.remove(pool_fname) + + p = Pool(pool_fname, N, retro=False) + ids = list(range(N)) if index is None else [index] + + def worker(i): + this_seedphase = self._get_current_seed(self.__global_seedphase, False, i) + this_seednoise = self._get_current_seed(self.__global_seednoise, self.fixnoise, i) + Phi = self.evaluate( + theta, + d, + this_seedphase, + this_seednoise, + i=i, + N=N, + force_powerspectrum=force_powerspectrum, + force_parfiles=force_parfiles, + force_sim=force_sim, + remove_sbmy=remove_sbmy, + theta_is_p=theta_is_p, + simspath=simspath, + ) + p.add_sim(Phi, i) + + iterator = tqdm.tqdm(ids, desc=f"Direction {d}/{self.S}") if bar else ids + for i in iterator: + worker(i) + + if index is None: + p.load_sims() + p.save_all() + return p + + def load_pool(self, pool_fname, N): + """Load a pool of realisations of the hidden box. + + Parameters + ---------- + pool_fname : str + Filename of the pool to load. + N : int + Number of realisations in the pool. + + Returns + ------- + p : Pool + Simulation pool object. + """ + from pyselfi.pool import pool as Pool + + pool_fname = str(pool_fname) + p = Pool(pool_fname, N, retro=False) + p.load_sims() + p.save_all() + return p + + @property + def Npop(self): + """Number of populations.""" + return self._Npop + + @Npop.setter + def Npop(self, _): + """Compute the number of populations.""" + if self.radial_selection == "multiple_lognormal": + if self.selection_params is not None: + self._Npop = len(self.selection_params[0]) + if self.linear_bias is not None and len(self.linear_bias) != self._Npop: + raise ValueError("Length of linear_bias must match the number of populations.") + if self.norm_csts is not None and len(self.norm_csts) != self._Npop: + raise ValueError("Length of norm_csts must match the number of populations.") + else: + raise ValueError( + "Selection parameters are required for multiple_lognormal radial selection." + ) + else: + self._Npop = 1 + + @property + def gravity_on(self): + """Whether gravity is enabled.""" + return self._gravity_on + + @gravity_on.setter + def gravity_on(self, _): + """Compute and set the gravity status.""" + if self.sim_params: + self._gravity_on = not self.sim_params.startswith("nograv") + else: + self._gravity_on = True + + @property + def Ntimesteps(self): + """Number of time steps.""" + return self._Ntimesteps + + @Ntimesteps.setter + def Ntimesteps(self, _): + """Compute and set the number of time steps.""" + if self.sim_params: + if self.sim_params.startswith("splitLPT"): + self._Ntimesteps = len(self.eff_redshifts) + elif self.sim_params.startswith("split"): + self._Ntimesteps = len(self.TimeStepDistribution) + elif self.sim_params.startswith("nograv"): + self._Ntimesteps = None + else: + self._Ntimesteps = None + else: + self._Ntimesteps = None + + @property + def force_recompute_mocks(self): + """Whether to force recomputation of mocks.""" + return self._force_recompute_mocks + + @property + def setup_only(self): + """Whether to only set up the hidden box.""" + return self._setup_only + + @property + def prefix_mocks(self): + """Prefix for the mocks.""" + return self._prefix_mocks + + @property + def modified_selfi(self): + """Whether to use the modified selfi.""" + return self._modified_selfi + + @property + def force_neglect_lightcone(self): + """Whether to force neglecting the lightcone.""" + return self._force_neglect_lightcone + + @property + def Psingle(self): + """Number of summary statistics for each population.""" + return self._Psingle + + @Psingle.setter + def Psingle(self, _): + """Dimension of the summary statistics for a single population, + if relevant. + """ + if self.radial_selection == "multiple_lognormal": + self._Psingle = self.P // self.Npop + else: + self._Psingle = self.P + + @property + def __global_seedphase(self): + return self.seedphase + + @property + def __global_seednoise(self): + return self.seednoise + + @property + def __global_seednorm(self): + """Global seed for the normalisation constants.""" + return self.seednorm + + def _set_defaults(self): + """Set default values and ensure consistency of the hidden box + configuration. + """ + if not hasattr(self, "modeldir"): + self.modeldir = os.path.join(self.fsimdir, "model") + + self._force_recompute_mocks = False + self._setup_only = False + self._prefix_mocks = None + self._modified_selfi = True + self._force_neglect_lightcone = False + + self.S = len(self.k_s) + # The following attributes are set by the setters + self.Ntimesteps = None + self.gravity_on = True + self.Npop = len(self.linear_bias) or 1 + self.Psingle = None + + def _init_survey_mask(self, reset=False): + """Initialise the survey mask and save it to disk in binary + format. + + The survey mask C(n) represents the angular selection function + of the survey. + """ + if self.local_mask_prefix: + mask_filename = f"{self.local_mask_prefix}_survey_mask_binary.h5" + else: + mask_filename = "survey_mask_binary.h5" + self.local_mask_path = os.path.join(self.modeldir, mask_filename) + + if not os.path.exists(self.local_mask_path) or reset or self.reset: + if self.survey_mask_path is not None: + survey_mask = np.load(self.survey_mask_path) + survey_mask_binary = (survey_mask > 0).astype(int) + del survey_mask + else: + survey_mask_binary = np.ones([self.size] * 3, dtype=int) + + with h5py.File(self.local_mask_path, "w") as f: + f.create_dataset("survey_mask_binary", data=survey_mask_binary) + del survey_mask_binary + collect() + + def _init_radial_selection(self, reset=False): + """Initialise the radial selection function R(r) and save it to + disk. + """ + if self.radial_selection == "multiple_lognormal": + from selfisys.selection_functions import LognormalSelection + + # Set the normalisation constants + if self.norm_csts is None: + self.norm_csts = [1.0] * self.Npop + + # Initialise the radial selection functions + if self.local_mask_prefix: + filename = f"{self.local_mask_prefix}_select_fct.h5" + else: + filename = "select_fct.h5" + self.local_select_path = os.path.join(self.modeldir, filename) + LogNorm = LognormalSelection( + self.L, + self.selection_params, + survey_mask_path=self.survey_mask_path, + local_select_path=self.local_select_path, + size=self.size, + ) + LogNorm.init_selection(reset=self.reset or reset) + del LogNorm + elif self.radial_selection is None: + if self.norm_csts is None: + self.norm_csts = 1.0 + else: + raise ValueError( + f"Unknown or unimplemented selection function: {self.radial_selection}" + ) + + def _PrintMessage(self, required_verbosity, message, verbosity=None): + """Print a message to standard output using PrintMessage from + pyselfi.utils. + """ + from selfisys.selfi_interface import PrintMessage + + PrintMessage(required_verbosity, message, verbosity or self.verbosity) + + def _indent(self): + """Indents the standard output using INDENT from + pyselfi.utils. + """ + from selfisys.selfi_interface import indent + + indent() + + def _unindent(self): + """Unindents the standard output using UNINDENT from + pyselfi.utils. + """ + from selfisys.selfi_interface import unindent + + unindent() + + def _save_cosmo(self, cosmo, fname_cosmo, force_cosmo=False): + """Save cosmological parameters in JSON format. + + Parameters + ---------- + cosmo : dict + Cosmological parameters (and infrastructure parameters) to + be saved. + fname_cosmo : str + Name of the output JSON file. + force_cosmo : bool, optional + If True, overwrite the file if it already exists. Default is + `False`. + """ + from os.path import exists + + if not exists(fname_cosmo) or force_cosmo: + from json import dump + + with open(fname_cosmo, "w") as fp: + dump(cosmo, fp) + + def _add_noise(self, g, seednoise, field=None): + """Add noise to a realisation in physical space. + + Parameters + ---------- + g : ndarray + Field to which the noise is added (modified in place). + seednoise : int or list of int + Seed to generate the noise realisation. + field : ndarray, optional + Selection function to apply to the input field. Default is + `None`. + + Returns + ------- + None + """ + if self.noise_std > 0: + from numpy import random, sqrt, ones_like + from h5py import File + + if seednoise is not None: + rng = random.default_rng(seednoise) + else: + raise ValueError("Seednoise must be provided and cannot be None.") + + if field is None: + field = ones_like(g) + + N = self.observed_density if self.observed_density is not None else 1.0 + noise = rng.normal( + size=(self.size, self.size, self.size), + scale=self.noise_std * sqrt(N * field), + ) + + with File(self.local_mask_path, "r") as f: + mask = f["survey_mask_binary"][:] + noise *= mask + + g += noise + del noise + collect() + + def _get_density_field(self, delta_g_dm, bias): + """Apply galaxy bias to a dark matter overdensity field. + + Parameters + ---------- + delta_g_dm : ndarray + Dark matter density contrast in physical space. + bias : float + Linear bias factor. + + Returns + ------- + delta_g : ndarray + Galaxy density or overdensity field. + """ + if bias is None: + bias = 1.0 + if not isinstance(bias, float): + raise TypeError("Bias must be a float.") + + if self.observed_density is None: + delta_g = bias * delta_g_dm + else: + delta_g = self.observed_density * (1 + bias * delta_g_dm) + + return delta_g + + def _repaint_and_get_Phi( + self, + g_obj, + norm, + seednoise, + bias=None, + field=None, + return_g=False, + AliasingCorr=True, + ): + """Repaint a realisation in physical space and compute its + summary statistics. + + Parameters + ---------- + g_obj : Field + Input field object. + norm : ndarray + Normalisation constants for the summary statistics. + seednoise : int or list of int + Seed to generate the noise realisation. + bias : float, optional + Bias to apply to the input field. Default is `None`. + field : ndarray, optional + Selection function. Reused as output to save memory + allocations. Default is `None`. + return_g : bool, optional + If True, returns the full field. Default is `False`. + AliasingCorr : bool, optional + Whether to apply aliasing correction. Default is `True`. + + Returns + ------- + Phi : ndarray + Vector of summary statistics. + delta_g : ndarray or None + Realisation in physical space if return_g is True; None + otherwise. + """ + import copy + from selfisys.sbmy_interface import compute_Phi + + if bias is not None and not isinstance(bias, float): + raise TypeError("Bias must be a float.") + + g_obj_local = copy.deepcopy(g_obj) + g_obj_local.data = self._get_density_field(g_obj_local.data, bias) + if field is not None: + g_obj_local.data *= field + + self._add_noise(g_obj_local.data, seednoise=seednoise, field=field) + Phi = compute_Phi( + self.G_ss_path, + self.P_ss_path, + g_obj_local, + norm, + AliasingCorr, + self.verbosity, + ) + + delta_g = copy.deepcopy(g_obj_local.data) if return_g else None + del g_obj_local + collect() + return Phi, delta_g + + def _apply_selection(self, fnames_outputdensity, seednoise, return_g=False, AliasingCorr=True): + """Apply the selection function to a realisation in physical + space. + + Parameters + ---------- + fnames_outputdensity : list of str + Filenames of the output density fields. + seednoise : int or list of int + Seed to generate the noise realisation. + return_g : bool, optional + If True, returns the full field(s). Default is `False`. + AliasingCorr : bool, optional + Whether to apply aliasing correction. Default is `True`. + + Returns + ------- + Phi_tot : ndarray + Concatenated summary statistics for each population. + gs : list of ndarray or None + List of full fields in physical space if return_g is True; + None otherwise. + """ + from pysbmy.field import read_basefield + + split = any(self.sim_params.startswith(s) for s in ["split", "custom"]) + if not split: + g_obj = read_basefield(fnames_outputdensity[0]) + elif self.force_neglect_lightcone: + g_obj = read_basefield(fnames_outputdensity[0]) + if self.radial_selection is not None: + Phi_tot = [] + gs = [] + for ifct in range(self.Npop): + if split and not self.force_neglect_lightcone: + g_obj = read_basefield(fnames_outputdensity[ifct]) + with h5py.File(self.local_select_path, "r") as f: + field = f["select_fct"][ifct].astype(float) + bias = float(self.linear_bias[ifct]) if self.linear_bias is not None else None + Phi, g_out = self._repaint_and_get_Phi( + g_obj, + self.norm_csts[ifct], + seednoise=seednoise, + bias=bias, + field=field, + return_g=return_g, + AliasingCorr=AliasingCorr, + ) + Phi_tot = np.concatenate([Phi_tot, Phi]) + if return_g: + gs.append(g_out) + del g_out + result = Phi_tot, gs if return_g else None + else: + from h5py import File + + if self.local_mask_path is not None: + with File(self.local_mask_path, "r") as f: + field = f["survey_mask_binary"][:] + else: + field = None + if not split: + bias = self.linear_bias if self.linear_bias is not None else None + Phi_tot, delta_g = self._repaint_and_get_Phi( + g_obj, + self.norm_csts, + seednoise=seednoise, + bias=bias, + field=field, + return_g=return_g, + AliasingCorr=AliasingCorr, + ) + result = (Phi_tot, [delta_g]) if return_g else (Phi_tot, None) + else: + Phi_tot = [] + gs = [] + for ifct in range(len(self.TimeSteps)): + bias = ( + self.linear_bias + if isinstance(self.linear_bias, float) + else (self.linear_bias[ifct] if self.linear_bias is not None else None) + ) + g_obj = read_basefield(fnames_outputdensity[ifct]) + Phi, g_out = self._repaint_and_get_Phi( + g_obj, + self.norm_csts, + seednoise=seednoise, + bias=bias, + field=field, + return_g=return_g, + AliasingCorr=AliasingCorr, + ) + Phi_tot = np.concatenate([Phi_tot, Phi]) + if return_g: + gs.append(g_out) + del g_out + result = Phi_tot, gs if return_g else None + del field, g_obj + collect() + return result + + def _setup_parfiles( + self, + d, + cosmology, + file_names, + force=False, + ): + """Sets up Simbelmynë parameter file given the necessary inputs + (please refer to the Simbelmynë documentation for more details). + + Parameters + ---------- + d : int + index giving the direction in parameter space: + -1 for mock data, 0 for the expansion point, or from 1 to S + cosmology : array, double, dimension=5 + cosmological parameters + file_names : dict + Dictionary containing the names of the input and output + files. + force : bool, optional, default=False + overwrite if files already exists? + + """ + from selfisys.sbmy_interface import setup_sbmy_parfiles + + hiddenbox_params = { + "Npop": self.Npop, + "TimeSteps": self.TimeSteps, + "eff_redshifts": self.eff_redshifts, + "sim_params": self.sim_params, + "Ntimesteps": self.Ntimesteps, + "TimeStepDistribution": self.TimeStepDistribution, + "modified_selfi": self.modified_selfi, + "Np0": self.Np0, + "Npm0": self.Npm0, + "size": self.size, + "L": self.L, + "fsimdir": self.fsimdir, + } + setup_sbmy_parfiles( + d, + cosmology, + file_names, + hiddenbox_params, + force, + ) + + def _run_sim( + self, + fname_simparfile, + fname_simlogs, + fnames_outputdensity, + force_sim=False, + check_output=False, + ): + """Run a simulation with Simbelmynë. + + Parameters + ---------- + fname_simparfile : str + Name of the input parameter file. + fname_simlogs : str + Name of the output Simbelmynë logs. + fnames_outputdensity : list of str + Names of the output density fields to be written. + force_sim : bool, optional + If True, force recomputation if output density already + exists. + Default is `False`. + check_output : bool, optional + If True, check the integrity of the output files and + recompute if corrupted. Default is `False`. + """ + from glob import glob + from selfisys.utils.parser import check_files_exist + + split = self.sim_params.startswith("split") + if not check_files_exist(fnames_outputdensity) or force_sim: + from pysbmy import pySbmy + + if not split: + pySbmy(f"{fname_simparfile}_{self.Npop}.sbmy", fname_simlogs) + else: + for i in range(self.Ntimesteps): + pySbmy(f"{fname_simparfile}_pop{i}.sbmy", fname_simlogs) + elif check_output: + from pysbmy.field import read_basefield + + try: + for fname in fnames_outputdensity: + g = read_basefield(fname) + del g + collect() + except Exception: + from pysbmy import pySbmy + + for fname in fnames_outputdensity: + os.remove(fname) + pySbmy(fname_simparfile, fname_simlogs) + + # Workaround to remove unwanted Simbelmynë outputs from disk + temp_files = glob(os.path.join(self.fsimdir, "data", "cola_kick_*.h5")) + for f in temp_files: + os.remove(f) + + if split: + temp_files = glob(os.path.join(self.fsimdir, "data", "cola_snapshot_*.h5")) + temp_files += glob(os.path.join(self.fsimdir, "data", "lpt_psi*.h5")) + for f in temp_files: + os.remove(f) + + def _compute_mocks( + self, + seednoise, + fnames_input, + fname_mocks, + return_g, + fname_g=None, + AliasingCorr=True, + ): + """Apply galaxy bias, observational effects, and compute the + summary statistics. + + Parameters + ---------- + seednoise : int or list of int + Seed to generate the noise realisation. + fnames_input : list of str + Filenames of the input density fields. + fname_mocks : str + Filename to save the computed summary statistics. + return_g : bool + If True, return the observed field(s). + fname_g : str, optional + Filename to save the observed field(s) if return_g is True. + AliasingCorr : bool, optional + Whether to apply aliasing correction. Default is `True`. + + Returns + ------- + Phi : ndarray + Vector of summary statistics. + g_out : ndarray or list of ndarray or None + Observed field(s) if return_g is True; otherwise None. + """ + + if return_g and fname_g is None: + raise ValueError("Filename for the observed field must be provided.") + + if (not os.path.exists(fname_mocks)) or self.force_recompute_mocks: + Phi, g_out = self._apply_selection( + fnames_input, + seednoise=seednoise, + return_g=return_g, + AliasingCorr=AliasingCorr, + ) + with h5py.File(fname_mocks, "w") as f: + f.create_dataset("Phi", data=Phi) + if return_g: + with h5py.File(fname_g, "w") as f: + f.create_dataset("g", data=g_out) + else: + self._PrintMessage(1, f"Using existing mock file: {fname_mocks}") + with h5py.File(fname_mocks, "r") as f: + Phi = f["Phi"][:] + if return_g: + with h5py.File(fname_g, "r") as f: + g_out = f["g"][:] + else: + g_out = None + + return Phi, g_out + + def _sample_omega(self, seed): + """Sample cosmological parameters from the prior. + + Parameters + ---------- + seed : int or list of int + Seed for the random number generator. + + Returns + ------- + omega_sample : ndarray + Sampled cosmological parameters. + """ + from selfisys.utils.tools import sample_omega_from_prior + from selfisys.global_parameters import planck_mean, planck_cov + + ids = list(range(len(planck_mean))) + return sample_omega_from_prior(1, planck_mean, planck_cov, ids, seed=seed)[0] + + def _aux_hiddenbox( + self, + d, + seedphase, + seednoise, + file_names, + force_parfiles=False, + force_sim=False, + force_phase=False, + return_g=False, + check_output=False, + RSDs=True, + sample=None, + ): + """Generate observations from the input initial matter power + spectrum. + + Parameters + ---------- + d : int + Index indicating the direction in parameter space (-1 for + mock data, 0 for the expansion point, or from 1 to S for the + gradient directions). + seedphase : int or list of int + Seed to generate the initial white noise in Fourier space. + seednoise : int or list of int + Seed to generate the observational noise realisation. + file_names : dict + Dictionary containing the names of the input and output + files. + force_parfiles : bool, optional + If True, force recomputation of the parameter files. Default + is `False`. + force_sim : bool, optional + If True, force recomputation of the simulation. Default is + `False`. + force_phase : bool, optional + If True, force recomputation of the phase. Default is + `False`. + return_g : bool, optional + If True, return the full realisation in physical space. + Default is `False`. + check_output : bool, optional + If True, check the integrity of the output file and + recompute if corrupted. Default is `False`. + RSDs : bool, optional + If True, include redshift-space distortions. Default is + `True`. + sample : ndarray, optional + Cosmological parameters sample. If `None`, sample from the + prior. Default is `None`. + + Returns + ------- + result : tuple + A tuple containing: + - Phi : (ndarray) + Summary statistics for each population, concatenated. + - g : (ndarray or list of ndarray or None) + List of observed fields if return_g is True; None + otherwise. + """ + fname_power_spectrum = file_names["fname_power_spectrum"] + fname_simparfile = file_names["fname_simparfile"] + fname_whitenoise = file_names["fname_whitenoise"] + seedname_whitenoise = file_names["seedname_whitenoise"] + fname_outputinitialdensity = file_names["fname_outputinitialdensity"] + fnames_outputrealspacedensity = file_names["fnames_outputrealspacedensity"] + fnames_outputdensity = file_names["fnames_outputdensity"] + fname_simlogs = file_names["fname_simlogs"] + fname_mocks = file_names["fname_mocks"] + fname_g = file_names["fname_g"] if return_g else None + + sample = self._sample_omega(seedphase) if sample is None else sample + + if self.gravity_on: + from selfisys.sbmy_interface import generate_white_noise_Field + + self._setup_parfiles( + d, + sample, + file_names, + force_parfiles, + ) + generate_white_noise_Field( + self.L, + self.size, + seedphase, + fname_whitenoise, + seedname_whitenoise, + force_phase, + ) + + if not self.setup_only: + if self.gravity_on: + self._run_sim( + fname_simparfile, + fname_simlogs, + fnames_outputdensity, + force_sim, + check_output, + ) + fnames = fnames_outputdensity if RSDs else fnames_outputrealspacedensity + else: + from selfisys.grf import primordial_grf + + primordial_grf( + self.L, + self.size, + seedphase, + fname_power_spectrum, + fname_outputinitialdensity, + force_sim, + False, + self.verbosity, + ) + fnames = [fname_outputinitialdensity] + + result = self._compute_mocks(seednoise, fnames, fname_mocks, return_g, fname_g) + else: + result = [], [] # lists are mandatory for compatibility + + return result + + def _power_spectrum_from_theta( + self, theta, fname_power_spectrum, theta_is_p=False, force=False + ): + """Compute the power spectrum values using spline interpolation. + + Parameters + ---------- + theta : ndarray + Vector of power spectrum values at the support wavenumbers. + fname_power_spectrum : str + Name of the input/output power spectrum file. + theta_is_p : bool, optional + If True, theta is already an unnormalised power spectrum. + Default is `False`. + force : bool, optional + If True, force recomputation. Default is `False`. + """ + from pysbmy.power import PowerSpectrum + + if (not os.path.exists(fname_power_spectrum)) or force: + from scipy.interpolate import InterpolatedUnivariateSpline + from pysbmy.power import FourierGrid + + PP = theta if theta_is_p else self.theta2P(theta) + Spline = InterpolatedUnivariateSpline(self.k_s, PP, k=5) + G_sim = FourierGrid.read(self.G_sim_path) + power_spectrum = Spline(G_sim.k_modes) + power_spectrum[0] = 0.0 + P = PowerSpectrum.from_FourierGrid(G_sim, powerspectrum=power_spectrum) + P.write(fname_power_spectrum) + del G_sim + collect() + + def _clean_output(self, fname_output): + """Remove a file from disk if it exists. + + Parameters + ---------- + fname_output : str + Name of the file to remove. + """ + if fname_output is not None and os.path.exists(fname_output): + os.remove(fname_output) + + def _get_current_seed(self, parent_seed, fixed_seed, i): + """Return the current seed for the i-th realisation. + + Parameters + ---------- + parent_seed : int or list of int + The parent seed. + fixed_seed : bool + If True, use the parent seed directly. + i : int + Index of the current realisation. + + Returns + ------- + this_seed : int or list of int + The seed for the current realisation. + """ + if fixed_seed: + this_seed = parent_seed + else: + this_seed = [i, parent_seed] + return this_seed diff --git a/src/selfisys/normalise_hb.py b/src/selfisys/normalise_hb.py new file mode 100644 index 0000000..a4c11f1 --- /dev/null +++ b/src/selfisys/normalise_hb.py @@ -0,0 +1,276 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Tools to define normalisation constants for the hidden box.""" + +import os +import numpy as np +from typing import Tuple, Dict, Any + +from selfisys.global_parameters import MIN_K_NORMALISATION +from selfisys.hiddenbox import HiddenBox + + +def worker_normalisation( + hidden_box: HiddenBox, + params: Tuple[Dict[str, Any], list, list, bool], +) -> np.ndarray: + """Worker function to compute the normalisation constants, + compatible with Python multiprocessing. + + Parameters + ---------- + hidden_box : HiddenBox + Instance of the HiddenBox class. + params : tuple + A tuple containing (cosmo, seedphase, seednoise, force). + + Returns + ------- + phi : ndarray + Computed summary statistics. + """ + ( + cosmo, + seedphase, + seednoise, + force, + ) = params + name = ( + "norm" + + "__" + + "_".join([str(int(s)) for s in seedphase]) + + "__" + + "_".join([str(int(s)) for s in seednoise]) + ) + + if hidden_box.verbosity > 1: + hidden_box._PrintMessage(1, "Running simulation...") + hidden_box._indent() + phi = hidden_box.make_data( + cosmo, + name, + seedphase, + seednoise, + force, + force, + force, + force, + ) + hidden_box._unindent() + elif hidden_box.verbosity > 0: + from selfisys.utils.low_level import ( + stdout_redirector, + ) + from io import BytesIO + + f = BytesIO() + with stdout_redirector(f): + phi = hidden_box.make_data( + cosmo, + name, + seedphase, + seednoise, + force, + force, + force, + force, + ) + f.close() + else: + from selfisys.utils.low_level import ( + stdout_redirector, + stderr_redirector, + ) + from io import BytesIO + + f = BytesIO() + g = BytesIO() + with stdout_redirector(f), stderr_redirector(g): + phi = hidden_box.make_data( + cosmo, + name, + seedphase, + seednoise, + force, + force, + force, + force, + ) + f.close() + g.close() + + return phi + + +def worker_normalisation_wrapper(args): + """Wrapper function for the worker_normalisation function. + + Parameters + ---------- + args : tuple + A tuple containing (hidden_box, params). + + Returns + ------- + phi : ndarray + Computed summary statistics. + """ + hidden_box, params = args + return worker_normalisation(hidden_box, params) + + +def worker_normalisation_public( + hidden_box, + cosmo: Dict[str, Any], + N: int, + i: int, +): + """Run the i-th simulation required to compute the normalisation + constants. + + Parameters + ---------- + hidden_box : HiddenBox + Instance of the HiddenBox class. + cosmo : dict + Cosmological and some infrastructure parameters. + N : int + Total number of realisations required. + i : int + Index of the simulation to be computed. + """ + params = ( + cosmo, + [ + i, + hidden_box._HiddenBox__global_seednorm, + ], + [ + i + N, + hidden_box._HiddenBox__global_seednorm, + ], + False, + ) + worker_normalisation(hidden_box, params) + + +def define_normalisation( + hidden_box: HiddenBox, + Pbins: np.ndarray, + cosmo: Dict[str, Any], + N: int, + min_k_norma: float = MIN_K_NORMALISATION, + npar: int = 1, + force: bool = False, +) -> np.ndarray: + """Define the normalisation constants for the HiddenBox instance. + + Parameters + ---------- + hidden_box : HiddenBox + Instance of the HiddenBox class. + Pbins : ndarray + Array of P bin values. + cosmo : dict + Cosmological and infrastructure parameters. + N : int + Number of realisations required. + min_k_norma : float, optional + Minimum k value to compute the normalisation constants. + npar : int, optional + Number of parallel processes to use. Default is 1. + force : bool, optional + If True, force recomputation. Default is False. + + Returns + ------- + norm_csts : ndarray + Normalisation constants for the HiddenBox instance. + """ + import tqdm.auto as tqdm + from multiprocessing import Pool + + hidden_box._PrintMessage( + 0, + "Defining normalisation constants...", + ) + hidden_box._indent() + indices = np.where(Pbins > min_k_norma) + tasks = [ + ( + hidden_box, + ( + cosmo, + [ + i, + hidden_box._HiddenBox__global_seednorm, + ], + [ + i + N, + hidden_box._HiddenBox__global_seednorm, + ], + force, + ), + ) + for i in range(N) + ] + + ncors = os.cpu_count() + nprocs = min(npar, ncors) + + norm_csts_list = np.zeros((hidden_box._Npop, N)) + if npar > 1: + with Pool(nprocs) as p: + for j, val in enumerate( + tqdm.tqdm( + p.imap( + worker_normalisation_wrapper, + tasks, + ), + total=N, + ) + ): + norm_csts_list[:, j] = np.array( + [ + np.mean( + val[i * hidden_box.Psingle : (i + 1) * hidden_box.Psingle][indices] + ) + for i in range(hidden_box._Npop) + ] + ) + else: + for j, val in enumerate( + tqdm.tqdm( + map( + worker_normalisation_wrapper, + tasks, + ), + total=N, + ) + ): + val = np.array(val) + norm_csts_list[:, j] = np.array( + [ + np.mean(val[i * hidden_box.Psingle : (i + 1) * hidden_box.Psingle][indices]) + for i in range(hidden_box._Npop) + ] + ) + norm_csts = np.mean(norm_csts_list, axis=1) + hidden_box._unindent() + hidden_box._PrintMessage( + 0, + "Defining normalisation constants done.", + ) + + return norm_csts diff --git a/src/selfisys/pipeline/step0a.py b/src/selfisys/pipeline/step0a.py new file mode 100644 index 0000000..aa2726f --- /dev/null +++ b/src/selfisys/pipeline/step0a.py @@ -0,0 +1,480 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Step 0a of the SELFI pipeline. + +We use a blackbox forward data model with physics relying on COLA +simulations (using the Simbelmynë hierarchical probabilistic simulator). + +This step generates the Simbelmynë parameter files needed for +normalising the blackbox and computes the white noise fields. At this +stage, the only simulation performed is to compute the ground truth +spectrum. +""" + +import gc +import pickle +import numpy as np + +from os.path import exists +from pathlib import Path + +from selfisys.utils.parser import ArgumentParser, none_or_bool_or_str, bool_sh, intNone +from selfisys.global_parameters import * +from selfisys.setup_model import * +from selfisys.hiddenbox import HiddenBox +from selfisys.utils.tools import get_k_max +from selfisys.sbmy_interface import handle_time_stepping +from selfisys.utils.plot_utils import setup_plotting +from selfisys.normalise_hb import worker_normalisation_public +from selfisys.utils.logger import getCustomLogger, INDENT, UNINDENT + +logger = getCustomLogger(__name__) + +""" +Below is the core logic of step 0a. + +Raises +------ +OSError + If file or directory access fails. +RuntimeError + If unexpected issues occur (e.g., plotting or data generation + failures). +""" + +parser = ArgumentParser( + description=( + "Run the first step of the SelfiSys pipeline. " + "Generates Simbelmynë parameter files for blackbox normalisation." + ) +) + +parser.add_argument( + "--wd_ext", + type=str, + help=( + "Name of the working directory (relative to ROOT_PATH in " + "`../global_parameters.py`), ending with a slash." + ), +) +parser.add_argument( + "--name", + type=str, + default="std", + help=( + "Suffix to the working directory for this run. " + "White noise fields are shared between runs irrespective of name." + ), +) +parser.add_argument( + "--total_steps", + type=int, + default=None, + help="Number of timesteps.", +) +parser.add_argument( + "--aa", + type=float, + nargs="*", + default=None, + help="List of scale factors at which to synchronise kicks and drifts.", +) +parser.add_argument( + "--size", + type=int, + default=512, + help="Number of grid points in each direction.", +) +parser.add_argument( + "--Np0", + type=intNone, + default=1024, + help="Number of dark matter particles along each axis.", +) +parser.add_argument( + "--Npm0", + type=intNone, + default=1024, + help="Number of particle-mesh cells along each axis.", +) +parser.add_argument( + "--L", + type=int, + default=3600, + help="Size of the simulation box in Mpc/h.", +) +parser.add_argument( + "--S", + type=int, + default=64, + help="Number of support wavenumbers for the initial matter power spectrum.", +) +parser.add_argument( + "--Pinit", + type=int, + default=50, + help=( + "Max number of bins for summaries. Actual count may be smaller since it is automatically " + "tuned to ensure that each bin contains a sufficient number of modes." + ), +) +parser.add_argument( + "--Nnorm", + type=int, + default=10, + help="Number of simulations for summary normalisation.", +) +parser.add_argument( + "--Ne", + type=int, + default=300, + help="Number of simulations at the expansion point for blackbox linearisation.", +) +parser.add_argument( + "--Ns", + type=int, + default=10, + help="Number of simulations for each gradient component at the expansion point.", +) +parser.add_argument( + "--Delta_theta", + type=float, + default=1e-2, + help="Finite difference step size for gradient computation.", +) +parser.add_argument( + "--OUTDIR", + type=str, + help="Absolute path to the output directory.", +) +parser.add_argument( + "--prior", + type=str, + default="planck2018", + help='Prior type (e.g. "selfi2019", "planck2018", "planck2018_cv").', +) +parser.add_argument( + "--nsamples_prior", + type=int, + default=int(5e4), + help=( + "Number of samples for computing the prior on the initial power spectrum " + "(when using planck2018[_cv])." + ), +) +parser.add_argument( + "--radial_selection", + type=none_or_bool_or_str, + default="multiple_lognormal", + help=( + "Radial selection function. " + 'Set to "multiple_lognormal" for multi-population lognormal selection.' + ), +) +parser.add_argument( + "--selection_params", + type=float, + nargs="*", + help="Parameters for the radial selection function (see hiddenbox.py).", +) +parser.add_argument( + "--survey_mask_path", + type=none_or_bool_or_str, + default=None, + help="Absolute path to the survey mask (if any).", +) +parser.add_argument( + "--sim_params", + type=none_or_bool_or_str, + default=None, + help="Parameters for the gravity solver.", +) +parser.add_argument( + "--lin_bias", + type=float, + nargs="*", + help="Linear biases.", +) +parser.add_argument( + "--obs_density", + type=none_or_bool_or_str, + default=None, + help="Observed density.", +) +parser.add_argument( + "--noise", + type=float, + default=0.1, + help="Noise level.", +) +parser.add_argument( + "--force", + type=bool_sh, + default=False, + help="Force recomputations if True.", +) + +args = parser.parse_args() + +if __name__ == "__main__": + try: + wd_ext = args.wd_ext + name = args.name + total_steps = args.total_steps + aa = args.aa + size = args.size + Np0 = args.Np0 + Npm0 = args.Npm0 + L = args.L + S = args.S + Pinit = args.Pinit + Nnorm = args.Nnorm + Ne = args.Ne + Ns = args.Ns + Delta_theta = args.Delta_theta + OUTDIR = args.OUTDIR + prior_type = args.prior + nsamples_prior = int(args.nsamples_prior) + radial_selection = args.radial_selection + if radial_selection == "multiple_lognormal": + selection_params = np.reshape(np.array(args.selection_params), (3, -1)) + else: + logger.error("Radial selection not yet implemented.") + raise NotImplementedError("Only 'multiple_lognormal' is supported at present.") + survey_mask_path = args.survey_mask_path + sim_params = args.sim_params + isstd = sim_params[:3] == "std" + splitLPT = sim_params[:8] == "splitLPT" + gravity_on = sim_params[:6] != "nograv" + if isinstance(args.lin_bias, list): + lin_bias = np.array(args.lin_bias) + else: + lin_bias = args.lin_bias + Npop = len(lin_bias) if isinstance(lin_bias, np.ndarray) else 1 + obs_density = args.obs_density + noise = args.noise + force = args.force + + # Configure plotting aesthetics for consistent visualisation + setup_plotting() + + # Create directories + wd_noname = f"{OUTDIR}{wd_ext}{size}{int(L)}{Pinit}{Nnorm}/" + wd = wd_noname + name + "/" + modeldir = wd + "model/" + figuresdir = wd + "Figures/" + + Path(wd + "RESULTS/").mkdir(parents=True, exist_ok=True) + Path(modeldir).mkdir(parents=True, exist_ok=True) + Path(wd_noname + "wn/").mkdir(parents=True, exist_ok=True) + Path(wd + "data/").mkdir(parents=True, exist_ok=True) + Path(figuresdir).mkdir(parents=True, exist_ok=True) + Path(wd + "pool/").mkdir(parents=True, exist_ok=True) + Path(wd + "score_compression/").mkdir(parents=True, exist_ok=True) + + for d in range(S + 1): + dirsims = wd + f"pool/d{d}/" + Path(dirsims).mkdir(parents=True, exist_ok=True) + + np.save(modeldir + "radial_selection.npy", radial_selection) + np.save(modeldir + "selection_params.npy", selection_params) + np.save(modeldir + "lin_bias.npy", lin_bias) + np.save(modeldir + "obs_density.npy", obs_density) + np.save(modeldir + "noise.npy", noise) + + logger.info("Setting up model parameters...") + + k_max = get_k_max(L, size) # k_max in h/Mpc + logger.info("Maximum wavenumber: k_max = %f", k_max) + # Cosmo at the expansion point: + params_planck = params_planck_kmax_missing.copy() + params_planck["k_max"] = k_max + # Fiducial BBKS spectrum for normalisation: + params_BBKS = params_BBKS_kmax_missing.copy() + params_BBKS["k_max"] = k_max + # Observed cosmology: + params_cosmo_obs = params_cosmo_obs_kmax_missing.copy() + params_cosmo_obs["k_max"] = k_max + + params = setup_model( + workdir=modeldir, + params_planck=params_planck, + params_P0=params_BBKS, + size=size, + L=L, + S=S, + Pinit=Pinit, + force=True, + ) + gc.collect() + + ( + size, + L, + P, + S, + G_sim_path, + G_ss_path, + Pbins_bnd, + Pbins, + k_s, + P_ss_obj_path, + P_0, + planck_Pk_EH, + ) = params + + other_params = { + "size": size, + "P": P, + "Np0": Np0, + "Npm0": Npm0, + "L": L, + "S": S, + "total_steps": total_steps, + "aa": aa, + "G_sim_path": G_sim_path, + "G_ss_path": G_ss_path, + "P_ss_obj_path": P_ss_obj_path, + "Pinit": Pinit, + "Nnorm": Nnorm, + "Ne": Ne, + "Ns": Ns, + "Delta_theta": Delta_theta, + "sim_params": sim_params, + } + with open(modeldir + "other_params.pkl", "wb") as f: + pickle.dump(other_params, f) + + # Save a human readable record of the parameters + with open(wd + "params.txt", "w") as f: + f.write("Parameters for this run:\n") + f.write("size: " + str(size) + "\n") + f.write("Np0: " + str(Np0) + "\n") + f.write("Npm0: " + str(Npm0) + "\n") + f.write("L: " + str(L) + "\n") + f.write("S: " + str(S) + "\n") + f.write("Pinit: " + str(Pinit) + "\n") + f.write("P: " + str(P) + "\n") + f.write("Nnorm: " + str(Nnorm) + "\n") + f.write("total_steps: " + str(total_steps) + "\n") + f.write("aa: " + str(aa) + "\n") + f.write("Ne: " + str(Ne) + "\n") + f.write("Ns: " + str(Ns) + "\n") + f.write("Delta_theta: " + str(Delta_theta) + "\n") + f.write("OUTDIR: " + OUTDIR + "\n") + f.write("prior_type: " + prior_type + "\n") + f.write("nsamples_prior: " + str(nsamples_prior) + "\n") + f.write("radial_selection: " + str(radial_selection) + "\n") + f.write("selection_params:\n" + str(selection_params) + "\n") + f.write("survey_mask_path: " + str(survey_mask_path) + "\n") + f.write("lin_bias: " + str(lin_bias) + "\n") + f.write("obs_density: " + str(obs_density) + "\n") + f.write("noise: " + str(noise) + "\n") + f.write("sim_params: " + str(sim_params) + "\n") + + logger.info("Setting up model parameters done.") + + logger.info("Generating ground truth spectrum...") + gt_path = modeldir + "theta_gt.npy" + if not exists(gt_path) or force: + from pysbmy.power import get_Pk + + theta_gt = get_Pk(k_s, params_cosmo_obs) + np.save(gt_path, theta_gt) + del theta_gt + logger.info("Generating ground truth spectrum done.") + + def theta2P(theta): + return theta * P_0 + + merged_path, indices_steps_cumul, eff_redshifts = handle_time_stepping( + aa=aa, + total_steps=total_steps, + modeldir=modeldir, + figuresdir=figuresdir, + sim_params=sim_params, + force=force, + ) + + # Instantiate the HiddenBox object + logger.info("Instantiating the HiddenBox...") + HB_selfi = HiddenBox( + k_s=k_s, + P_ss_path=P_ss_obj_path, + Pbins_bnd=Pbins_bnd, + theta2P=theta2P, + P=P * Npop, + size=size, + L=L, + G_sim_path=G_sim_path, + G_ss_path=G_ss_path, + Np0=Np0, + Npm0=Npm0, + fsimdir=wd[:-1], + noise_std=noise, + radial_selection=radial_selection, + selection_params=selection_params, + observed_density=obs_density, + linear_bias=lin_bias, + norm_csts=None, + survey_mask_path=survey_mask_path, + local_mask_prefix=None, + sim_params=sim_params, + TimeStepDistribution=merged_path, + TimeSteps=indices_steps_cumul, + eff_redshifts=eff_redshifts, + seedphase=BASELINE_SEEDPHASE, + seednoise=BASELINE_SEEDNOISE, + fixnoise=False, + seednorm=BASELINE_SEEDNORM, + reset=True, + save_frequency=5, + ) + logger.info("Instantiating the HiddenBox done.") + + logger.info("Generating Simbelmynë parameter files for normalisation...") + if gravity_on: + HB_selfi.switch_setup() + INDENT() + for i in range(Nnorm): + logger.diagnostic("Setting Simbelmynë file %d/%d...", i + 1, Nnorm, verbosity=1) + worker_normalisation_public(HB_selfi, params_planck, Nnorm, i) + logger.diagnostic("Setting Simbelmynë file %d/%d done.", i + 1, Nnorm, verbosity=1) + if gravity_on: + HB_selfi.switch_setup() + + if prior_type == "selfi2019": + logger.diagnostic("Computing cosmic variance alpha_cv...") + compute_alpha_cv( + workdir=modeldir, + k_s=k_s, + size=size, + L=L, + window_fct_path=wd[:-1] + "/model/select_fct.h5", + force=True, + ) + logger.diagnostic("Computing cosmic variance alpha_cv done.") + UNINDENT() + logger.info("Generating Simbelmynë parameter files for normalisation done.") + + except OSError as e: + logger.error("Directory or file access error: %s", str(e)) + raise + except Exception as e: + logger.critical("An unexpected error occurred: %s", str(e)) + raise RuntimeError("Pipeline step 0a failed.") from e + finally: + gc.collect() + logger.info("step 0a of the SelfiSys pipeline: done.") diff --git a/src/selfisys/pipeline/step0b.py b/src/selfisys/pipeline/step0b.py new file mode 100644 index 0000000..cb09dd6 --- /dev/null +++ b/src/selfisys/pipeline/step0b.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Step 0b of the SelfiSys pipeline. + +Run all the Simbelmynë simulations required to normalise the HiddenBox. + +This script invokes the pySbmy interface to launch Simbelmynë +simulations in parallel. +""" + +import os +from pathlib import Path +import gc +import numpy as np + +from selfisys.utils.parser import ArgumentParser, bool_sh +from selfisys.utils.logger import getCustomLogger + +logger = getCustomLogger(__name__) + +parser = ArgumentParser(description="Run Simbelmynë for step 0b of the SelfiSys pipeline.") +parser.add_argument("--pool_path", type=str, help="Path to the pool of simulations.") +parser.add_argument("--ii", type=int, nargs="+", help="Indices of simulations to run.") +parser.add_argument( + "--npar", type=int, help="Number of simulations to run in parallel.", default=4 +) +parser.add_argument("--force", type=bool_sh, help="Force the computations.", default=False) +args = parser.parse_args() + +pool_path = args.pool_path +npar = args.npar +force = args.force +ii = np.array(args.ii, dtype=int) + +# If 'ii' is [-1], find simulation indices from the pool directory +if len(ii) == 1 and ii[0] == -1: + try: + ii = np.array( + [ + int(f.split("norm__")[1].split("_")[0]) + for f in os.listdir(pool_path) + if f.startswith("sim_norm") and f.endswith(".sbmy") + ], + dtype=int, + ) + except OSError as e: + logger.error("Failed to list files in '%s': %s", pool_path, str(e)) + raise + +nsim = len(ii) + + +def worker_norm(i): + """ + Run a Simbelmynë simulation to normalise the HiddenBox. + + Parameters + ---------- + i : int + Index specifying which simulation file to run. + + Raises + ------ + OSError + If file or directory access fails. + RuntimeError + If pySbmy encounters an unexpected error or the simulation fails. + """ + from pysbmy import pySbmy + from selfisys.utils.low_level import stdout_redirector, stderr_redirector + from io import BytesIO + + file_prefix = "sim_norm__" + str(i) + try: + # Find the simulation file corresponding to this index + suffix = [str(f) for f in os.listdir(pool_path) if f.startswith(file_prefix)][0] + fname_simparfile = Path(pool_path) / suffix + + # Derive output and logs filenames + base_out = suffix.split(".")[0].split("sim_")[1] + fname_output = Path(pool_path) / f"output_density_{base_out}.h5" + fname_simlogs = Path(pool_path) / f"{file_prefix}.txt" + + # Skip if the output file already exists + if fname_output.exists() and not force: + logger.info( + "Output file %s already exists, skipping simulation index %d...", fname_output, i + ) + return + + # Suppress or capture stdout/stderr for live monitoring purposes + f = BytesIO() + g = BytesIO() + with stdout_redirector(f): + with stderr_redirector(g): + pySbmy(str(fname_simparfile), str(fname_simlogs)) + g.close() + f.close() + + except OSError as e: + logger.error("File or directory access error while running index %d: %s", i, str(e)) + raise + except Exception as e: + logger.critical("Unexpected error in worker_norm (index %d): %s", i, str(e)) + raise RuntimeError(f"Simulation index {i} failed.") from e + + +if __name__ == "__main__": + from tqdm import tqdm + from multiprocessing import Pool + + try: + logger.info("Running the simulations to normalise the HiddenBox...") + with Pool(processes=npar) as pool: + for _ in tqdm(pool.imap(worker_norm, ii), total=nsim): + pass + logger.info("Running the simulations to normalise the HiddenBox done.") + + except OSError as e: + logger.error("Pool or directory error: %s", str(e)) + raise + except Exception as e: + logger.critical("An unexpected error occurred during step 0b: %s", str(e)) + raise RuntimeError("Step 0b failed.") from e + finally: + gc.collect() + logger.info("step 0b of the SelfiSys pipeline: done.") diff --git a/src/selfisys/pipeline/step0c.py b/src/selfisys/pipeline/step0c.py new file mode 100644 index 0000000..e9fbdc3 --- /dev/null +++ b/src/selfisys/pipeline/step0c.py @@ -0,0 +1,242 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Step 0c of the SelfiSys pipeline. + +Compute the normalisation constants (based on the simulations performed +in step 0b using LPT or COLA) for the SelfiSys pipeline. +""" + +import gc +import numpy as np + +from selfisys.utils.parser import ArgumentParser, none_or_bool_or_str, bool_sh, safe_npload +from selfisys.global_parameters import * +from selfisys.utils.tools import get_k_max +from selfisys.utils.logger import getCustomLogger, INDENT, UNINDENT + +logger = getCustomLogger(__name__) + +parser = ArgumentParser( + description=( + "Step 0c of the SelfiSys pipeline. " + "Compute the normalisation constants based on the simulations performed in step 0b." + ) +) +parser.add_argument("--wd", type=str, help="Absolute path of the working directory.") +parser.add_argument( + "--npar_norm", + type=int, + help=( + "Number of simulations to load in parallel when computing the summaries. " + "Note that the overdensity fields were already computed at step 0b." + ), +) +parser.add_argument( + "--survey_mask_path", + type=none_or_bool_or_str, + default=None, + help="Path to the survey mask for the well-specified model.", +) +parser.add_argument( + "--effective_volume", + type=bool_sh, + default=False, + help="Use the effective volume to compute alpha_cv.", +) +parser.add_argument( + "--norm_csts_path", + type=none_or_bool_or_str, + default=None, + help="Path to external normalisation constants. Mandatory for test_gravity=True.", +) +parser.add_argument( + "--force", + type=bool_sh, + default=False, + help="Force the recomputation of the mocks.", +) + +args = parser.parse_args() + +wd = args.wd +npar_norm = args.npar_norm +survey_mask_path = args.survey_mask_path +effective_volume = args.effective_volume +norm_csts_path = args.norm_csts_path +force = args.force + +modeldir = wd + "model/" + +# Consistency check: 'npar_norm' and 'norm_csts_path' are mutually exclusive +if not (npar_norm is None) ^ (norm_csts_path is None): + raise ValueError("npar_norm and norm_csts_path are mutually exclusive.") + +if __name__ == "__main__": + try: + # If the user normalisation constants are provided, load them + if norm_csts_path is not None: + INDENT() + logger.info("Loading normalisation constants...") + if not exists(norm_csts_path): + raise ValueError("Normalisation constants not found.") + else: + norm_csts = np.load(norm_csts_path) + np.save(modeldir + "norm_csts.npy", norm_csts) + logger.info( + "External normalisation constants loaded and saved to model directory." + ) + UNINDENT() + else: + # Otherwise, compute normalisation constants from simulation data + from os.path import exists + import pickle + from pysbmy.timestepping import read_timestepping + from selfisys.hiddenbox import HiddenBox + from selfisys.normalise_hb import define_normalisation + + logger.info("Loading main parameters from 'other_params.pkl'...") + with open(modeldir + "other_params.pkl", "rb") as f: + other_params = pickle.load(f) + + size = other_params["size"] + Np0 = other_params["Np0"] + Npm0 = other_params["Npm0"] + L = other_params["L"] + S = other_params["S"] + total_steps = other_params["total_steps"] + aa = other_params["aa"] + P = other_params["P"] + G_sim_path = other_params["G_sim_path"] + G_ss_path = other_params["G_ss_path"] + P_ss_obj_path = other_params["P_ss_obj_path"] + Nnorm = other_params["Nnorm"] + sim_params = other_params["sim_params"] + + isstd = sim_params[:3] == "std" + + # Load radial selection + radial_selection = np.load(modeldir + "radial_selection.npy", allow_pickle=True) + if radial_selection is None: + radial_selection = None + selection_params = np.load(modeldir + "selection_params.npy") + lin_bias = np.load(modeldir + "lin_bias.npy") + Npop = len(lin_bias) if isinstance(lin_bias, np.ndarray) else 1 + obs_density = safe_npload(modeldir + "obs_density.npy") + noise = np.load(modeldir + "noise.npy") + + k_max = get_k_max(L, size) # k_max in h/Mpc + # Cosmology at the expansion point: + params_planck = params_planck_kmax_missing.copy() + params_planck["k_max"] = k_max + + Pbins_bnd = np.load(modeldir + "Pbins_bnd.npy") + Pbins = np.load(modeldir + "Pbins.npy") + k_s = np.load(modeldir + "k_s.npy") + P_0 = np.load(modeldir + "P_0.npy") + + def theta2P(theta): + return theta * P_0 + + # Set up the merged time-stepping if needed + if not isstd: + logger.info("Setting up time-stepping...") + nsteps = [ + round((aa[i + 1] - aa[i]) / (aa[-1] - aa[0]) * total_steps) + for i in range(len(aa) - 1) + ] + if sum(nsteps) != total_steps: + nsteps[nsteps.index(max(nsteps))] += total_steps - sum(nsteps) + indices_steps_cumul = list(np.cumsum(nsteps) - 1) + merged_path = modeldir + "merged.h5" + TS_merged = read_timestepping(merged_path) + + if sim_params.startswith("custom") or sim_params.startswith("nograv"): + TimeStepDistribution = merged_path + eff_redshifts = 1 / aa[-1] - 1 + else: + raise NotImplementedError("Time-stepping strategy not yet implemented.") + logger.info("Setting up time-stepping done.") + else: + TimeStepDistribution = None + eff_redshifts = None + indices_steps_cumul = None + + logger.info("Instantiating the HiddenBox for normalisation constants...") + HB_selfi = HiddenBox( + k_s=k_s, + P_ss_path=P_ss_obj_path, + Pbins_bnd=Pbins_bnd, + theta2P=theta2P, + P=P * Npop, + size=size, + L=L, + G_sim_path=G_sim_path, + G_ss_path=G_ss_path, + Np0=Np0, + Npm0=Npm0, + fsimdir=wd[:-1], + noise_std=noise, + radial_selection=radial_selection, + selection_params=selection_params, + observed_density=obs_density, + linear_bias=lin_bias, + norm_csts=None, + survey_mask_path=survey_mask_path, + local_mask_prefix=None, + sim_params=sim_params, + TimeStepDistribution=TimeStepDistribution, + TimeSteps=indices_steps_cumul, + eff_redshifts=eff_redshifts, + seedphase=BASELINE_SEEDPHASE, + seednoise=BASELINE_SEEDNOISE, + fixnoise=False, + seednorm=BASELINE_SEEDNORM, + reset=False, + save_frequency=5, + ) + logger.info("Instantiating the HiddenBox for normalisation constants done.") + + # Compute normalisation constants + if not exists(modeldir + "norm_csts.npy") or force: + if force: + HB_selfi.switch_recompute_pool() + norm_csts = define_normalisation( + HB_selfi, + Pbins, + params_planck, + Nnorm, + min_k_norma=MIN_K_NORMALISATION, + npar=1, + force=force, + ) + if force: + HB_selfi.switch_recompute_pool() + np.save(modeldir + "norm_csts.npy", norm_csts) + logger.info("Normalisation constants computed and saved.") + else: + logger.info("Normalisation constants already exist, skipping re-computation.") + norm_csts = np.load(modeldir + "norm_csts.npy") + + logger.info("Normalisation constants: %s", norm_csts) + + except OSError as e: + logger.error("File or directory access error in step 0c: %s", str(e)) + raise + except Exception as e: + logger.critical("Unexpected error occurred in step 0c: %s", str(e)) + raise RuntimeError("Step 0c failed.") from e + finally: + gc.collect() + logger.info("step 0c of the SelfiSys pipeline: done.") diff --git a/src/selfisys/pipeline/step0d.py b/src/selfisys/pipeline/step0d.py new file mode 100644 index 0000000..04d5a23 --- /dev/null +++ b/src/selfisys/pipeline/step0d.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Step 0d of the SelfiSys pipeline. + +Generate the observations using the ground truth cosmology. +""" + +import pickle +import gc +from os.path import exists +import numpy as np + +from selfisys.utils.parser import ( + ArgumentParser, + none_or_bool_or_str, + bool_sh, + joinstrs, + safe_npload, +) +from selfisys.global_parameters import * +from selfisys.utils.tools import get_k_max +from selfisys.hiddenbox import HiddenBox +from selfisys.utils.logger import getCustomLogger, INDENT, UNINDENT + +logger = getCustomLogger(__name__) + +parser = ArgumentParser( + description=( + "Step 0d of the SelfiSys pipeline. " + "Generate the observations using the ground truth cosmology." + ) +) +parser.add_argument("--wd", type=str, help="Absolute path of the working directory.") +parser.add_argument( + "--prefix_mocks", + type=none_or_bool_or_str, + default=None, + help="Prefix for the mock files.", +) +parser.add_argument( + "--survey_mask_path", + type=none_or_bool_or_str, + default=None, + help="Path to the survey mask for the well-specified model.", +) +parser.add_argument( + "--effective_volume", + type=bool_sh, + default=False, + help="Use the effective volume to compute alpha_cv.", +) +parser.add_argument( + "--name_obs", + type=none_or_bool_or_str, + default=None, + help="Prefix for the observation files. If None, uses default name. " + "Can be used for different data vectors.", +) +parser.add_argument( + "--reset_window_function", + type=bool_sh, + default=False, + help="Reset the window function.", +) +parser.add_argument( + "--neglect_lightcone", + type=bool_sh, + default=False, + help="Neglect lightcone effects even if snapshots at multiple redshifts are available.", +) +parser.add_argument( + "--force_obs", + type=bool_sh, + default=False, + help="Recompute the observations (e.g., to try a new cosmology).", +) +parser.add_argument( + "--copy_obs_from", + type=none_or_bool_or_str, + default=None, + help="Copy the observations from another project.", +) +parser.add_argument( + "--copy_fields", + type=bool_sh, + default=False, + help="Copy the fields from another project.", +) +parser.add_argument( + "--save_g", + type=bool_sh, + default=False, + help="Save the observed fields (g).", +) + +args = parser.parse_args() + +wd = args.wd +survey_mask_path = args.survey_mask_path +effective_volume = args.effective_volume +prefix_mocks = args.prefix_mocks +name_obs = "_" + args.name_obs if args.name_obs is not None else None +local_mask_prefix = args.name_obs if args.name_obs is not None else None +reset_window_function = args.reset_window_function +neglect_lightcone = args.neglect_lightcone +force_obs = args.force_obs +copy_obs_from = args.copy_obs_from +copy_fields = args.copy_fields +save_g = args.save_g + +if copy_obs_from is not None and name_obs is None: + raise ValueError( + "If you want to copy the observations from another project, " + "you must specify a name for the observation files." + ) +if copy_fields and copy_obs_from is None: + raise ValueError( + "If you want to copy the fields from another project, " + "you must specify the project to copy from." + ) + +if __name__ == "__main__": + from pysbmy.timestepping import read_timestepping + + try: + logger.info("Starting Step 0d of the SelfiSys pipeline.") + logger.info("Setting up main parameters...") + + modeldir = wd + "model/" + datadir = wd + "data/" + + # Copy the observations from another directory if specified + if copy_obs_from is not None: + from glob import glob + import shutil + + logger.info("Copying observations from: %s", copy_obs_from) + INDENT() + theta_gt_path = joinstrs([copy_obs_from, "model/theta_gt", name_obs, ".npy"]) + phi_obs_path = joinstrs([copy_obs_from, "model/phi_obs", name_obs, ".npy"]) + field_prefix = joinstrs([copy_obs_from, "data/output_density_obs", name_obs, "_"]) + + if not exists(theta_gt_path): + raise FileNotFoundError(f"{theta_gt_path} not found. Check the path.") + if not exists(phi_obs_path): + raise FileNotFoundError(f"{phi_obs_path} not found. Check the path.") + if len(glob(field_prefix + "*")) == 0: + raise FileNotFoundError( + f"No files starting with {field_prefix} found. Check the path." + ) + + logger.diagnostic("Copying theta_gt and phi_obs files...") + shutil.copy(theta_gt_path, f"{modeldir}theta_gt{name_obs}.npy") + shutil.copy(phi_obs_path, f"{modeldir}phi_obs{name_obs}.npy") + logger.diagnostic("Copying theta_gt and phi_obs files done.") + + if copy_fields: + logger.diagnostic("Copying full fields...") + for file in glob(field_prefix + "*"): + shutil.copy(file, datadir) + logger.diagnostic("Copying full fields done.") + UNINDENT() + else: + # Generating new observations + if prefix_mocks is not None: + modeldir_refined = modeldir + prefix_mocks + "/" + else: + modeldir_refined = modeldir + + with open(modeldir + "other_params.pkl", "rb") as f: + other_params = pickle.load(f) + size = other_params["size"] + Np0 = other_params["Np0"] + Npm0 = other_params["Npm0"] + L = other_params["L"] + S = other_params["S"] + total_steps = other_params["total_steps"] + aa = other_params["aa"] + P = other_params["P"] + G_sim_path = other_params["G_sim_path"] + G_ss_path = other_params["G_ss_path"] + P_ss_obj_path = other_params["P_ss_obj_path"] + sim_params_base = other_params["sim_params"] + + isstd = sim_params_base[:3] == "std" + if isstd and copy_obs_from is None: + # Workaround so that observations can be computed + sim_params_base = sim_params_base + "0" + sim_params = sim_params_base + BASEID_OBS + + radial_selection = safe_npload(modeldir + "radial_selection.npy") + selection_params = np.load(modeldir + "selection_params.npy") + lin_bias = np.load(modeldir + "lin_bias.npy") + Npop = len(lin_bias) if isinstance(lin_bias, np.ndarray) else 1 + obs_density = safe_npload(modeldir + "obs_density.npy") + noise = np.load(modeldir + "noise.npy") + + k_max = get_k_max(L, size) # k_max in h/Mpc + params_cosmo_obs = params_cosmo_obs_kmax_missing.copy() + params_cosmo_obs["k_max"] = k_max + + logger.diagnostic("Loading main parameters.") + Pbins_bnd = np.load(modeldir + "Pbins_bnd.npy") + Pbins = np.load(modeldir + "Pbins.npy") + k_s = np.load(modeldir + "k_s.npy") + P_0 = np.load(modeldir + "P_0.npy") + + def theta2P(theta): + return theta * P_0 + + # Setup time-stepping if needed + if not isstd: + logger.info("Setting up the time-stepping for non-standard approach...") + nsteps = [ + round((aa[i + 1] - aa[i]) / (aa[-1] - aa[0]) * total_steps) + for i in range(len(aa) - 1) + ] + if sum(nsteps) != total_steps: + nsteps[nsteps.index(max(nsteps))] += total_steps - sum(nsteps) + indices_steps_cumul = list(np.cumsum(nsteps) - 1) + merged_path = modeldir + "merged.h5" + TS_merged = read_timestepping(merged_path) + + if sim_params[:6] in ["custom", "nograv"]: + TimeStepDistribution = merged_path + eff_redshifts = 1 / aa[-1] - 1 + else: + raise NotImplementedError("Time-stepping strategy not yet implemented.") + logger.info("Setting up the time-stepping for non-standard approach done.") + else: + TimeStepDistribution = None + eff_redshifts = None + indices_steps_cumul = None + + logger.info("Instantiating the HiddenBox...") + # Load normalisation constants + if not exists(modeldir + "norm_csts.npy"): + raise ValueError("Normalisation constants not found.") + norm_csts = np.load(modeldir + "norm_csts.npy") + + BB_selfi = HiddenBox( + k_s=k_s, + P_ss_path=P_ss_obj_path, + Pbins_bnd=Pbins_bnd, + theta2P=theta2P, + P=P * Npop, + size=size, + L=L, + G_sim_path=G_sim_path, + G_ss_path=G_ss_path, + Np0=Np0, + Npm0=Npm0, + fsimdir=wd[:-1], + modeldir=modeldir_refined, + noise_std=noise, + radial_selection=radial_selection, + selection_params=selection_params, + observed_density=obs_density, + linear_bias=lin_bias, + norm_csts=norm_csts, + survey_mask_path=survey_mask_path, + local_mask_prefix=local_mask_prefix, + sim_params=sim_params, + TimeStepDistribution=TimeStepDistribution, + TimeSteps=indices_steps_cumul, + eff_redshifts=eff_redshifts, + seedphase=BASELINE_SEEDPHASE, + seednoise=BASELINE_SEEDNOISE, + fixnoise=False, + seednorm=BASELINE_SEEDNORM, + reset=reset_window_function, + save_frequency=5, + ) + logger.info("Instantiating the HiddenBox done.") + + # Generate the ground truth spectrum + if force_obs or not exists(joinstrs([modeldir, "theta_gt", name_obs, ".npy"])): + logger.info("Generating ground truth spectrum for Step 0d.") + from pysbmy.power import get_Pk + + theta_gt = get_Pk(k_s, params_cosmo_obs) + np.save(joinstrs([modeldir, "theta_gt", name_obs]), theta_gt) + logger.info("Generating ground truth spectrum for Step 0d done.") + + logger.info("Generating observations...") + phi_obs_path = joinstrs([modeldir, "phi_obs", name_obs, ".npy"]) + if not exists(phi_obs_path) or force_obs: + if neglect_lightcone: + BB_selfi.update(_force_neglect_lightcone=True) + d_obs = -1 + BB_selfi.switch_recompute_pool() + res = BB_selfi.make_data( + cosmo=params_cosmo_obs, + id=joinstrs([BASEID_OBS, name_obs]), + seedphase=SEEDPHASE_OBS, + seednoise=SEEDNOISE_OBS, + d=d_obs, + force_powerspectrum=force_obs, + force_parfiles=force_obs, + force_sim=force_obs, + force_cosmo=force_obs, + return_g=save_g, + ) + BB_selfi.switch_recompute_pool() + + if save_g: + phi_obs, _ = res + else: + phi_obs = res + + np.save(phi_obs_path, phi_obs) + logger.info("Generating observations done.") + + except OSError as e: + logger.error("File or directory access error during Step 0d: %s", str(e)) + raise + except Exception as e: + logger.critical("Unexpected error occurred in Step 0d: %s", str(e)) + raise RuntimeError("Step 0d failed.") from e + finally: + gc.collect() + logger.info("Step 0d of the SelfiSys pipeline: done.") diff --git a/src/selfisys/pipeline/step0e.py b/src/selfisys/pipeline/step0e.py new file mode 100644 index 0000000..9fbf867 --- /dev/null +++ b/src/selfisys/pipeline/step0e.py @@ -0,0 +1,551 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Step 0e of the SelfiSys pipeline. + +Generate all the Simbelmyne parameter files to run the simulations at +the expansion point, in all parameter space directions, in order to +linearise the HiddenBox. + +Unless the forward model is run in no-gravity mode, the only simulation +actually run here is the one to generate the prior on the initial +spectrum (if using planck2018[_cv]), based on cosmological parameters +drawn from the prior. +""" + +import gc +import pickle +import numpy as np +from pathlib import Path +from os.path import exists + +from selfisys.utils.parser import ( + ArgumentParser, + none_or_bool_or_str, + bool_sh, + joinstrs, + safe_npload, +) +from selfisys.global_parameters import * +from selfisys.utils.tools import get_k_max +from selfisys.hiddenbox import HiddenBox +from selfisys.utils.logger import getCustomLogger, INDENT, UNINDENT + +logger = getCustomLogger(__name__) + + +def worker_fct(params): + """ + Run a simulation in parallel to linearise the HiddenBox. + + Parameters + ---------- + params : tuple + A tuple containing (x, index, selfi_object): + x : int or float + Direction index (1..S) or 0 for the expansion point. + index : int or None + Simulation index for the expansion point. Expect None if + direction x is not 0. + selfi_object : object + Instance of the selfi object. + + Returns + ------- + int + Returns 0 on successful completion. + """ + from io import BytesIO + from selfisys.utils.low_level import stdout_redirector + import gc + + x, idx, selfi_object = params + logger.debug("Running simulation: x=%s, idx=%s", x, idx) + + # Capture output to avoid cluttering logs + f = BytesIO() + with stdout_redirector(f): + selfi_object.run_simulations(d=x, p=idx) + f.close() + + # Release memory + del selfi_object + gc.collect() + return 0 + + +parser = ArgumentParser( + description=( + "Step 0e of the SelfiSys pipeline. " + "Generate all the required Simbelmyne parameter files for the simulations " + "at the expansion point, and compute the prior on the initial spectrum." + ) +) +parser.add_argument("--wd", type=str, help="Absolute path of the working directory.") +parser.add_argument( + "--N_THREADS", + type=int, + default=64, + help=( + "Number of threads for computing the prior. Also serves as the number of " + "parameter files to generate in parallel (note that a distinct HiddenBox " + "object is instantiated for each)." + ), +) +parser.add_argument( + "--prior", + type=str, + default="planck2018", + help=( + "Prior for the parameters. Possible values: " + '"selfi2019" (as in [leclercq2019primordial]), ' + '"planck2018" (Planck 2018 cosmology), ' + '"planck2018_cv" (Planck 2018 + cosmic variance).' + ), +) +parser.add_argument( + "--nsamples_prior", + type=int, + default=int(1e4), + help=( + "Number of samples (drawn from the prior on cosmology) to compute the prior " + "on the primordial power spectrum (when using planck2018[_cv])." + ), +) +parser.add_argument( + "--survey_mask_path", + type=none_or_bool_or_str, + default=None, + help="Path to the survey mask for the well-specified model.", +) +parser.add_argument( + "--name_obs", + type=none_or_bool_or_str, + default=None, + help=( + "Prefix for the observation files. If None, uses a default name. " + "Can be used to work with different data vectors." + ), +) +parser.add_argument( + "--effective_volume", + type=bool_sh, + default=False, + help="Use the effective volume to compute alpha_cv.", +) +parser.add_argument( + "--force_recompute_prior", + type=bool_sh, + default=False, + help="Force overwriting the prior.", +) +parser.add_argument( + "--Ne", + type=int, + default=None, + help=( + "Number of simulations to keep at the expansion point. " + "If None, uses the value from the prior steps." + ), +) +parser.add_argument( + "--Ns", + type=int, + default=None, + help=( + "Number of simulations for each gradient component. " + "If None, uses the value from the prior steps." + ), +) + +args = parser.parse_args() + +wd = args.wd +N_THREADS = args.N_THREADS +prior_type = args.prior +nsamples_prior = int(args.nsamples_prior) +survey_mask_path = args.survey_mask_path +name_obs = "_" + args.name_obs if args.name_obs is not None else None +local_mask_prefix = args.name_obs if args.name_obs is not None else None +effective_volume = args.effective_volume +force_recompute_prior = args.force_recompute_prior + +modeldir = wd + "model/" +prior_dir = ROOT_PATH + "data/stored_priors/" +Path(prior_dir).mkdir(parents=True, exist_ok=True) + +P_0 = np.load(modeldir + "P_0.npy") + + +def theta2P(theta): + """ + Convert dimensionless theta to physical P(k). + + Parameters + ---------- + theta : ndarray + The dimensionless power-spectrum values. + + Returns + ------- + ndarray + The physical power-spectrum values. + """ + return theta * P_0 + + +if __name__ == "__main__": + from pysbmy.timestepping import read_timestepping + from os.path import exists + from selfisys.hiddenbox import HiddenBox + + try: + logger.diagnostic("Setting up main parameters...") + + with open(modeldir + "other_params.pkl", "rb") as f: + other_params = pickle.load(f) + size = other_params["size"] + Np0 = other_params["Np0"] + Npm0 = other_params["Npm0"] + L = other_params["L"] + S = other_params["S"] + total_steps = other_params["total_steps"] + aa = other_params["aa"] + P = other_params["P"] + G_sim_path = other_params["G_sim_path"] + G_ss_path = other_params["G_ss_path"] + P_ss_obj_path = other_params["P_ss_obj_path"] + Ne = other_params["Ne"] if args.Ne is None else args.Ne + Ns = other_params["Ns"] if args.Ns is None else args.Ns + Delta_theta = other_params["Delta_theta"] + sim_params = other_params["sim_params"] + + isstd = sim_params[:3] == "std" + splitLPT = sim_params[:8] == "splitLPT" + gravity_on = sim_params[:6] != "nograv" + + radial_selection = safe_npload(modeldir + "radial_selection.npy") + selection_params = np.load(modeldir + "selection_params.npy") + lin_bias = np.load(modeldir + "lin_bias.npy") + Npop = len(lin_bias) if isinstance(lin_bias, np.ndarray) else 1 + obs_density = safe_npload(modeldir + "obs_density.npy") + noise = np.load(modeldir + "noise.npy") + + k_max = get_k_max(L, size) # k_max in h/Mpc + + Pbins_bnd = np.load(modeldir + "Pbins_bnd.npy") + Pbins = np.load(modeldir + "Pbins.npy") + k_s = np.load(modeldir + "k_s.npy") + planck_Pk_EH = np.load(modeldir + "theta_planck.npy") + + INDENT() + if isstd: + TimeStepDistribution = None + eff_redshifts = None + TimeSteps = None + elif splitLPT: + TimeStepDistribution = None + TimeSteps = [f"pop{i}" for i in range(1, len(aa))] + eff_redshifts = [1 / a - 1 for a in aa[1:]] + else: + logger.info("Setting up the time-stepping...") + nsteps = [ + round((aa[i + 1] - aa[i]) / (aa[-1] - aa[0]) * total_steps) + for i in range(len(aa) - 1) + ] + if sum(nsteps) != total_steps: + nsteps[nsteps.index(max(nsteps))] += total_steps - sum(nsteps) + TimeSteps = list(np.cumsum(nsteps) - 1) + merged_path = modeldir + "merged.h5" + TS_merged = read_timestepping(merged_path) + + if sim_params.startswith("custom") or sim_params.startswith("nograv"): + TimeStepDistribution = merged_path + eff_redshifts = 1 / aa[-1] - 1 + else: + raise NotImplementedError("Time-stepping strategy not yet implemented.") + logger.info("Time-stepping setup done.") + UNINDENT() + logger.diagnostic("Setting up main parameters done.") + + # Normalisation constants + logger.diagnostic("Loading normalisation constants...") + norm_csts_path = modeldir + "norm_csts.npy" + if not exists(norm_csts_path): + raise ValueError( + "Normalisation constants not found. Please run steps 0c and 0d before 0e." + ) + norm_csts = np.load(norm_csts_path) + logger.diagnostic("Normalisation constants loaded.") + + logger.info("Instantiating the HiddenBox...") + HB_selfi = HiddenBox( + k_s=k_s, + P_ss_path=P_ss_obj_path, + Pbins_bnd=Pbins_bnd, + theta2P=theta2P, + P=P * Npop, + size=size, + L=L, + G_sim_path=G_sim_path, + G_ss_path=G_ss_path, + Np0=Np0, + Npm0=Npm0, + fsimdir=wd[:-1], + noise_std=noise, + radial_selection=radial_selection, + selection_params=selection_params, + observed_density=obs_density, + linear_bias=lin_bias, + norm_csts=norm_csts, + survey_mask_path=survey_mask_path, + local_mask_prefix=local_mask_prefix, + sim_params=sim_params, + TimeStepDistribution=TimeStepDistribution, + TimeSteps=TimeSteps, + eff_redshifts=eff_redshifts, + seedphase=BASELINE_SEEDPHASE, + seednoise=BASELINE_SEEDNOISE, + fixnoise=False, + seednorm=BASELINE_SEEDNORM, + reset=False, + save_frequency=5, + ) + logger.info("HiddenBox instantiated successfully.") + + logger.diagnostic("Loading the ground truth spectrum...") + if not exists(modeldir + "theta_gt.npy"): + raise ValueError("Ground truth cosmology not found.") + theta_gt = np.load(modeldir + "theta_gt.npy") + logger.diagnostic("Ground truth spectrum loaded.") + + logger.diagnostic("Loading observations...") + if not exists(joinstrs([modeldir, "phi_obs", name_obs, ".npy"])): + raise ValueError("Observation data not found.") + phi_obs = np.load(joinstrs([modeldir, "phi_obs", name_obs, ".npy"])) + logger.diagnostic("Observations loaded.") + + logger.info("Setting up the prior and instantiating the selfi object...") + fname_results = wd + "RESULTS/res.h5" + pool_prefix = wd + "pool/pool_res_dir_" + pool_suffix = ".h5" + + from pyselfi.power_spectrum.selfi import power_spectrum_selfi + + if prior_type == "selfi2019": + from pyselfi.power_spectrum.prior import power_spectrum_prior + + theta_0 = np.ones(S) + if effective_volume: + alpha_cv = np.load(modeldir + "alpha_cv_eff.npy") + else: + alpha_cv = np.load(modeldir + "alpha_cv.npy") + + prior = power_spectrum_prior( + k_s, theta_0, THETA_NORM_GUESS, K_CORR_GUESS, alpha_cv, False + ) + selfi = power_spectrum_selfi( + fname_results, + pool_prefix, + pool_suffix, + prior, + HB_selfi, + theta_0, + Ne, + Ns, + Delta_theta, + phi_obs, + ) + + selfi.prior.theta_norm = THETA_NORM_GUESS + selfi.prior.k_corr = K_CORR_GUESS + selfi.prior.alpha_cv = alpha_cv + elif prior_type.startswith("planck2018"): + from selfisys.prior import planck_prior + + theta_planck = np.load(modeldir + "theta_planck.npy") + theta_0 = theta_planck / P_0 + + prior = planck_prior( + planck_mean, + planck_cov, + k_s, + P_0, + k_max, + nsamples=nsamples_prior, + nthreads=N_THREADS, + filename=( + prior_dir + + f"planck_prior_S{S}_L{L}_size{size}_" + + f"{nsamples_prior}_{WHICH_SPECTRUM}.npy" + ), + ) + selfi = power_spectrum_selfi( + fname_results, + pool_prefix, + pool_suffix, + prior, + HB_selfi, + theta_0, + Ne, + Ns, + Delta_theta, + phi_obs, + ) + else: + raise ValueError(f"Unknown prior type: {prior_type}") + + logger.info("Prior and selfi object created successfully.") + + # Plot the observed summaries + logger.info("Plotting the observed summaries...") + import matplotlib.pyplot as plt + + fig, ax1 = plt.subplots(figsize=(15, 5)) + ax1.plot(k_s, theta_gt / P_0, label=r"$\theta_{\mathrm{gt}}$", color="C0") + ax1.set_xscale("log") + ax1.semilogx( + k_s, + planck_Pk_EH / P_0, + label=r"$P_{\mathrm{Planck}}(k)/P_0(k)$", + color="C1", + lw=0.5, + ) + ax1.set_xlabel("$k$ [$h$/Mpc]") + ax1.set_ylabel("$[\\mathrm{Mpc}/h]^3$") + ax1.grid(which="both", axis="y", linestyle="dotted", linewidth=0.6) + for kk in k_s[:-1]: + ax1.axvline(x=kk, color="green", linestyle="dotted", linewidth=0.6) + ax1.axvline( + x=k_s[-1], + color="green", + linestyle="dotted", + linewidth=0.6, + label=r"$\theta$-bins boundaries", + ) + ax1.axvline(x=Pbins[0], color="red", linestyle="dashed", linewidth=0.5) + ax1.axvline(x=Pbins[-1], color="red", linestyle="dashed", linewidth=0.5) + for kk in Pbins[1:-2]: + ax1.axvline(x=kk, ymax=0.167, color="red", linestyle="dashed", linewidth=0.5) + ax1.legend(loc=2) + ax1.set_xlim(max(1e-4, k_s.min() - 2e-4), k_s.max()) + ax1.set_ylim(7e-1, 1.6e0) + + ax2 = ax1.twinx() + ax2.axvline( + x=Pbins[-2], + ymax=0.333, + color="red", + linestyle="dashed", + linewidth=0.5, + label=r"$\psi$-bins centers", + ) + len_obs = len(phi_obs) // np.shape(selection_params)[1] + cols = ["C4", "C5", "C6", "C7"] + for i in range(np.shape(selection_params)[1]): + ax2.plot( + Pbins, + phi_obs[i * len_obs : (i + 1) * len_obs], + marker="x", + label=rf"Summary $\psi_{{\mathrm{{obs}}}},$ pop {i}", + linewidth=0.5, + color=cols[i % len(cols)], + ) + ax2.legend(loc=1) + ax2.set_ylabel("Summary values") + plt.title( + "Observations generated with the ground truth cosmology and well-specified models" + ) + plt.savefig(wd + "Figures/summary_obs_step0e.pdf", bbox_inches="tight", dpi=300) + plt.close() + logger.info("Plotting the observed summaries done.") + + logger.info("Loading or computing prior...") + error_str_prior = ( + "Error while computing the prior. For OOM issues, a fix might be to set " + "os.environ['OMP_NUM_THREADS'] = '1'. Otherwise, refer to the error message." + ) + + if not prior_type.startswith("selfi2019"): + if not force_recompute_prior: + try: + selfi.prior = selfi.prior.load(selfi.fname) + logger.info("Prior loaded from file.") + except: + logger.info("Prior not found in %s, recomputing...", selfi.fname) + try: + selfi.compute_prior() + selfi.save_prior() + selfi.prior = selfi.prior.load(selfi.fname) + except: + logger.critical(error_str_prior) + raise RuntimeError("Prior computation failed.") + logger.info("Prior computed and saved.") + else: + logger.info("Forcing recomputation of the prior (user request).") + selfi.compute_prior() + selfi.save_prior() + selfi.prior = selfi.prior.load(selfi.fname) + else: + selfi.compute_prior() + selfi.save_prior() + selfi.load_prior() + + from os import cpu_count + import tqdm.auto as tqdm + from multiprocessing import Pool + + HB_selfi.switch_recompute_pool() + if gravity_on: + HB_selfi.switch_setup() + + list_part_1 = [[0, idx, selfi] for idx in range(Ne)] + list_part_2 = [[x, None, selfi] for x in range(1, S + 1)] + + ncors = cpu_count() + nprocess = min(N_THREADS, ncors, len(list_part_1[1:]) + len(list_part_2)) + logger.info("Using %d processes to generate Simbelmynë parameter files.", nprocess) + gc.collect() + + # Generate parameter files for estimating f0 + logger.info("Generating parameter files for estimating f0...") + # First poke the HiddenBox once to avoid Pool access issues + worker_fct(list_part_1[0]) + with Pool(processes=nprocess) as mp_pool: + pool_results_1 = mp_pool.map(worker_fct, list_part_1[1:]) + for _ in tqdm.tqdm(pool_results_1, total=len(list_part_1[1:])): + pass + logger.info("Generating parameter files for the estimation of f0 done.") + + # Generate parameter files for estimating the gradient + logger.info("Generating parameter files for the gradient...") + with Pool(processes=nprocess) as mp_pool: + pool_results_2 = mp_pool.map(worker_fct, list_part_2) + for _ in tqdm.tqdm(pool_results_2, total=len(list_part_2)): + pass + logger.info("Generating parameter files for the gradient done.") + + if gravity_on: + HB_selfi.switch_setup() + HB_selfi.switch_recompute_pool() + + except OSError as e: + logger.error("File or directory access error during Step 0e: %s", str(e)) + raise + except Exception as e: + logger.critical("Unexpected error occurred in Step 0e: %s", str(e)) + raise RuntimeError("Step 0e failed.") from e + finally: + gc.collect() + logger.info("Step 0e of the SelfiSys pipeline: done.") diff --git a/src/selfisys/pipeline/step1_2.py b/src/selfisys/pipeline/step1_2.py new file mode 100644 index 0000000..4059a5e --- /dev/null +++ b/src/selfisys/pipeline/step1_2.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Steps 1 and 2 of the SelfiSys pipeline. + +Run all the Simbelmynë simulations needed to linearise the HiddenBox, +using the .sbmy files generated in step 0. It can run sequentially or in +parallel, depending on the --npar argument. + +Raises +------ +OSError + If file or directory paths are inaccessible. +RuntimeError + If unexpected HPC or PySbmy issues occur. +""" + +import os +import gc +import numpy as np + +from selfisys.utils.parser import ArgumentParser, none_or_bool_or_str, bool_sh +from selfisys.utils.logger import getCustomLogger + + +logger = getCustomLogger(__name__) + + +parser = ArgumentParser( + description="Run the Simbelmynë simulations required to linearise the HiddenBox." +) +parser.add_argument("--pool_path", type=str, help="Path to the pool of simulations.") +parser.add_argument("--directions", type=int, nargs="+", help="List of directions.") +parser.add_argument("--pp", type=int, nargs="+", help="List of simulation indices p.") +parser.add_argument("--Npop", type=int, help="Number of populations.", default=None) +parser.add_argument("--npar", type=int, help="Number of sim to run in parallel.", default=8) +parser.add_argument( + "--sim_params", + type=none_or_bool_or_str, + default=None, + help="Parameters for the simulations, e.g., 'splitLPT', 'custom19COLA20' etc.", +) +parser.add_argument("--force", type=bool_sh, help="Force computations.", default=False) + +args = parser.parse_args() +pool_path = args.pool_path +sim_params = args.sim_params +splitLPT = sim_params[:8] == "splitLPT" if sim_params is not None else False +force = args.force +directions = np.array(args.directions, dtype=int) +pp = np.array(args.pp, dtype=int) +Npop = args.Npop +npar = args.npar + + +def run_sim(val): + """ + Execute a single Simbelmynë simulation. + + Parameters + ---------- + val : tuple + A tuple (d, p, ipop) containing: + d : int + Direction index. + p : int + Simulation index. + ipop : str or None + Population identifier for splitLPT (e.g. 'pop0', 'pop1', + etc), None for other approaches. + + Raises + ------ + OSError + If the .sbmy file or output path is invalid. + RuntimeError + If the simulation fails unexpectedly. + """ + from pysbmy import pySbmy + + d, p, ipop = val + dirpath = f"{pool_path}d{d}/" + if ipop is not None: + fname_simparfile = f"{dirpath}sim_d{d}_p{p}_{ipop}.sbmy" + else: + fname_simparfile = f"{dirpath}sim_d{d}_p{p}.sbmy" + fname_output = f"{dirpath}output_density_d{d}_p{p}.h5" + fname_simlogs = f"{dirpath}logs_sim_d{d}_p{p}.txt" + + if os.path.isfile(fname_output) and not force: + logger.info("Output file %s already exists, skipping...", fname_output) + gc.collect() + else: + from io import BytesIO + from selfisys.utils.low_level import stdout_redirector, stderr_redirector + + logger.debug("Running Simbelmynë for d=%d, p=%d, ipop=%s", d, p, ipop) + # sys.stdout.flush() + f = BytesIO() + g = BytesIO() + with stdout_redirector(f): + with stderr_redirector(g): + pySbmy(fname_simparfile, fname_simlogs) + g.close() + f.close() + # sys.stdout.flush() + gc.collect() + logger.debug("Simbelmynë run completed for d=%d, p=%d, ipop=%s", d, p, ipop) + + +if len(pp) == 1 and pp[0] == -1: + # If simulation indices are not specified, find them in the + # pool_path directory + if splitLPT: + raise ValueError("pp = -1 not supported with splitLPT.") + + pp = np.array( + [ + int(f.split("_")[2].split(".")[0][1:]) + for f in os.listdir(f"{pool_path}d{directions[0]}") + if f.startswith("sim_d") and f.endswith(".sbmy") + ], + dtype=int, + ) + + +if __name__ == "__main__": + import tqdm.auto as tqdm + from itertools import product + + try: + if splitLPT: + if Npop is None: + raise ValueError("Npop must be specified when using splitLPT mode.") + pops = [f"pop{i}" for i in range(Npop)] + vals = list(product(directions, pp, pops)) + else: + vals = list(product(directions, pp, [Npop])) + + nsim = len(vals) + logger.info("Found %d simulation tasks to run.", nsim) + + if npar > 1: + from multiprocessing import Pool + + logger.info("Running simulations using %d processes in parallel.", npar) + with Pool(processes=npar) as mp_pool: + for _ in tqdm.tqdm(mp_pool.imap(run_sim, vals), total=nsim): + pass + logger.info("Running simulations done.") + else: + logger.info("Running simulations sequentially...") + for _ in tqdm.tqdm(map(run_sim, vals), total=nsim): + pass + logger.info("Running simulations done.") + + except OSError as e: + logger.error("File or directory access error: %s", str(e)) + raise + except Exception as e: + logger.critical("Unexpected error in step X: %s", str(e)) + raise RuntimeError("Simulations failed.") from e + finally: + gc.collect() + logger.info("All simulations completed successfully.") diff --git a/src/selfisys/pipeline/step3.py b/src/selfisys/pipeline/step3.py new file mode 100644 index 0000000..da7449a --- /dev/null +++ b/src/selfisys/pipeline/step3.py @@ -0,0 +1,1187 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Third step of the SelfiSys pipeline. + +Run the initial matter power spectrum inference, using the simulations +performed in previous steps. + +Raises +------ +OSError + If file or directory paths are inaccessible. +RuntimeError + If unexpected HPC or PySbmy issues occur. +""" + +import gc + +from selfisys.utils.parser import ( + ArgumentParser, + none_or_bool_or_str, + bool_sh, + joinstrs, + safe_npload, +) +from selfisys.utils.logger import getCustomLogger, INDENT, UNINDENT + +logger = getCustomLogger(__name__) + + +def worker_fct(params): + """ + Run a Simbelmynë simulation. + + Parameters + ---------- + params : tuple + (x, index, selfi_object) + x : int or float + Direction index (1..S), or 0 for the expansion point. + index : int or None + Simulation index for the expansion point. + selfi_object : object + Instance of the selfi object. + + Returns + ------- + int + Returns 0 on successful completion. + + Raises + ------ + OSError + If file/directory access fails for .sbmy or logs. + RuntimeError + If the simulation fails unexpectedly. + """ + from io import BytesIO + from selfisys.utils.low_level import stdout_redirector, stderr_redirector + + x, index, selfi_object = params + + # Check consistency + if x != 0 and index is not None: + raise ValueError("Expansion point is not 0 but index is not None.") + + logger.debug("Running simulation: offset=%s, index=%s", x, index) + + # Suppress console output to keep logs clean. Use with caution. + f = BytesIO() + g = BytesIO() + with stdout_redirector(f): + with stderr_redirector(g): + selfi_object.run_simulations(d=x, p=index) + g.close() + f.close() + + del selfi_object + gc.collect() + return 0 + + +from pathlib import Path +from os.path import exists +import numpy as np + +from selfisys.global_parameters import * +from selfisys.utils.tools import get_k_max + +parser = ArgumentParser( + description=( + "Third step of the SelfiSys pipeline. Run the initial matter power spectrum inference." + ) +) +parser.add_argument("--wd", type=str, help="Absolute path of the working directory.") +parser.add_argument("--N_THREADS", type=int, help="1 direction per thread.", default=64) +parser.add_argument( + "--N_THREADS_PRIOR", type=int, help="Number of threads for computing the prior.", default=64 +) +parser.add_argument( + "--prior", + type=str, + default="planck2018", + help=( + "Prior for the parameters. Possible values:\n" + ' - "selfi2019": prior used in [leclercq2019primordial]\n' + ' - "planck2018": sampling from Planck 2018 cosmology\n' + ' - "planck2018_cv": Planck 2018 + cosmic variance.\n' + "Note: 'selfi2019' and 'planck2018_cv' have not been checked with the latest code " + "version. Use at your own risk." + ), +) +parser.add_argument( + "--nsamples_prior", + type=int, + default=int(1e4), + help="Number of samples to compute planck2018[_cv].", +) +parser.add_argument( + "--survey_mask_path", + type=none_or_bool_or_str, + default=None, + help="Path to the survey mask.", +) +parser.add_argument( + "--effective_volume", + type=bool_sh, + default=False, + help="Use the effective volume to compute alpha_cv.", +) +parser.add_argument( + "--name_obs", + type=none_or_bool_or_str, + default=None, + help=( + "Prefix for the observation file. If None, uses default name. " + "Enables working with different data vectors." + ), +) +parser.add_argument( + "--params_obs", + type=none_or_bool_or_str, + default=None, + help="Recompute observations with the specified parameters.", +) +parser.add_argument( + "--force_obs", + type=bool_sh, + default=False, + help="Force re-computation of the observations.", +) +parser.add_argument( + "--recompute_obs_mock", + type=bool_sh, + default=False, + help="Recompute the observational part of the data vector.", +) +parser.add_argument( + "--time_steps_obs", + type=int, + nargs="*", + default=None, + help=( + "Suffixes of the fields to use for recomputing the observational part of the data vector. " + "Ignored if `recompute_obs_mock=False`." + ), +) +parser.add_argument( + "--force_recompute_prior", + type=bool_sh, + default=False, + help="Force overwriting the prior.", +) +parser.add_argument( + "--update_obs_phase", + type=bool_sh, + default=False, + help="Change the phase for observations.", +) +parser.add_argument( + "--recompute_mocks", + type=none_or_bool_or_str, + default=False, + help=( + "Recompute all mocks in the inference phase, without affecting the dark matter fields. " + "Possible values: True, 'gradients', 'list', 'listdd', 'list_exp'." + ), +) +parser.add_argument( + "--list_of_pps", + type=float, + nargs="*", + default=None, + help="Indices to recompute mocks for gradient, if 'list' is chosen.", +) +parser.add_argument("--pp_min", type=int, help="Min index for recompute_mocks in gradient mode.") +parser.add_argument("--pp_max", type=int, help="Max index for recompute_mocks in gradient mode.") +parser.add_argument("--dd_min", type=int, help="Min index for recompute_mocks in listdd mode.") +parser.add_argument("--dd_max", type=int, help="Max index for recompute_mocks in listdd mode.") +parser.add_argument( + "--perform_score_compression", + type=bool_sh, + default=False, + help="Perform score compression stage.", +) +parser.add_argument( + "--force_score_compression", + type=bool_sh, + default=False, + help="Force re-computation of the score compression.", +) +parser.add_argument( + "--test_gravity", + type=bool_sh, + default=False, + help="Flag to test gravity parameters. If True, norm_csts_path must be given.", +) +parser.add_argument( + "--neglect_lightcone", + type=bool_sh, + default=False, + help="Neglect lightcone effects even if multiple snapshots are available.", +) +parser.add_argument( + "--norm_csts_path", + type=none_or_bool_or_str, + default=None, + help="Path to external normalisation constants (needed for test_gravity=True).", +) +parser.add_argument( + "--Ne", + type=int, + default=None, + help="Number of simulations at the expansion point (override if not None).", +) +parser.add_argument( + "--Ns", + type=int, + default=None, + help="Number of simulations per gradient component (override if not None).", +) +parser.add_argument( + "--prefix_mocks", + type=none_or_bool_or_str, + default=None, + help="Prefix for mock files, if any.", +) +parser.add_argument( + "--selection_params", + type=float, + nargs="*", + default=None, + help="Selection function parameters for the well-specified model.", +) +parser.add_argument( + "--reset_window_function", + type=bool_sh, + default=False, + help="Reset the window function if True.", +) +parser.add_argument( + "--obs_density", + type=float, + default=None, + help="Observed density override for the well-specified model.", +) +parser.add_argument( + "--lin_bias", + type=float, + nargs="*", + default=None, + help="Linear bias override; uses stored value otherwise.", +) +parser.add_argument( + "--figdir_suffix", + type=none_or_bool_or_str, + default=None, + help="Suffix for the figures directory.", +) +parser.add_argument( + "--noise_dbg", + type=float, + default=None, + help="Manually specify noise level (for debugging). " + "Normalisation constants won't reflect this override: use with caution.", +) + +args = parser.parse_args() + +wd = args.wd +N_THREADS = args.N_THREADS +N_THREADS_PRIOR = args.N_THREADS_PRIOR +prior_type = args.prior +nsamples_prior = int(args.nsamples_prior) +survey_mask_path = args.survey_mask_path +effective_volume = args.effective_volume +force_recompute_prior = args.force_recompute_prior +update_obs_phase = args.update_obs_phase +recompute_mocks = args.recompute_mocks +list_of_pps = args.list_of_pps +pp_min = args.pp_min +pp_max = args.pp_max +ddmin = args.dd_min +ddmax = args.dd_max +prefix_mocks = args.prefix_mocks +params_obs = args.params_obs +name_obs = "_" + args.name_obs if args.name_obs is not None else None +force_obs = args.force_obs +recompute_obs_mock = args.recompute_obs_mock if not force_obs else True +time_steps_obs = args.time_steps_obs +local_mask_prefix = args.name_obs if args.name_obs is not None else None +reset_window_function = args.reset_window_function +perform_score_compression = args.perform_score_compression +force_score_compression = args.force_score_compression +test_gravity = args.test_gravity +neglect_lightcone = args.neglect_lightcone +norm_csts_path = args.norm_csts_path +figdir_suffix = args.figdir_suffix + "/" if args.figdir_suffix is not None else "" +noise_dbg = args.noise_dbg + +# Consistency checks +if pp_min is not None or pp_max is not None: + if pp_min is None or pp_max is None: + raise ValueError("both pp_min and pp_max should be specified if one of them is.") + elif list_of_pps is not None: + raise ValueError("pp_min and pp_max should not be specified if list_of_pps is specified.") + else: + list_of_pps = range(pp_min, pp_max + 1) + +if list_of_pps is not None and recompute_mocks: + if recompute_mocks[:4] != "list": + raise ValueError("To use list_of_pps, set recompute_mocks to 'list'.") + +if test_gravity: + if force_obs: + raise ValueError("test_gravity and force_obs cannot both be True.") + if norm_csts_path is None: + raise ValueError("norm_csts_path should be specified if test_gravity is True.") + +if time_steps_obs is not None and not recompute_obs_mock: + raise ValueError( + "time_steps_obs can't be specified if recompute_obs_mock is False. " + "If you want to recompute observations, set recompute_obs_mock=True." + ) + +# Load parameters +modeldir = wd + "model/" +if prefix_mocks is not None: + modeldir_refined = modeldir + prefix_mocks + "/" +else: + modeldir_refined = modeldir +if args.selection_params is None: + selection_params = np.load(modeldir + "selection_params.npy") +else: + selection_params = np.reshape(np.array(args.selection_params), (3, -1)) + +if args.norm_csts_path is None: + norm_csts_path = modeldir + "norm_csts.npy" + +radial_selection = safe_npload(modeldir + "radial_selection.npy") + +# If user hasn't specified new obs_density, load it from file +if args.obs_density is None: + obs_density = safe_npload(modeldir + "obs_density.npy") +else: + obs_density = args.obs_density + +# If user hasn't specified new linear bias, load it from file +if args.lin_bias is None: + lin_bias = np.load(modeldir + "lin_bias.npy") +else: + if not (recompute_mocks or force_obs): + raise ValueError( + "lin_bias shouldn't be specified if neither recompute_mocks nor force_obs is True." + ) + lin_bias = args.lin_bias + if not isinstance(lin_bias, float): + lin_bias = np.array(args.lin_bias) + +Npop = len(lin_bias) if isinstance(lin_bias, np.ndarray) else 1 + +Path(wd + "Figures/").mkdir(exist_ok=True) +modeldir_obs = wd + "model/" + +if prefix_mocks is not None: + resultsdir = wd + "RESULTS/" + prefix_mocks + "/" + figuresdir = joinstrs([wd, "Figures/", prefix_mocks, "/", figdir_suffix]) + resultsdir_obs = wd + "RESULTS/" + prefix_mocks + "/" + scoredir = wd + "score_compression/" + prefix_mocks + "/" + fname_results = wd + "RESULTS/" + prefix_mocks + "/res.h5" +else: + resultsdir = wd + "RESULTS/" + figuresdir = joinstrs([wd, "Figures/", figdir_suffix]) + resultsdir_obs = wd + "RESULTS/" + scoredir = wd + "score_compression/" + fname_results = wd + "RESULTS/res.h5" + +Path(resultsdir).mkdir(parents=True, exist_ok=True) +Path(figuresdir).mkdir(parents=True, exist_ok=True) +Path(resultsdir_obs).mkdir(parents=True, exist_ok=True) +Path(modeldir_obs).mkdir(parents=True, exist_ok=True) +Path(modeldir_refined).mkdir(parents=True, exist_ok=True) +Path(scoredir).mkdir(parents=True, exist_ok=True) + +logger.diagnostic("> Loading normalisation constants from: %s", norm_csts_path) +if not exists(norm_csts_path): + raise ValueError("Normalisation constants not found. Please run steps 0, 1, 2 before step 3.") +else: + norm_csts = np.load(norm_csts_path) +logger.diagnostic("Normalisation constants loaded successfully.") + +# Write all parameters to a text file for reference +with open(figuresdir + "parameters.txt", "w") as f: + f.write("wd = " + wd + "\n") + f.write("N_THREADS = " + str(N_THREADS) + "\n") + f.write("N_THREADS_PRIOR = " + str(N_THREADS_PRIOR) + "\n") + f.write("prior_type = " + prior_type + "\n") + f.write("nsamples_prior = " + str(nsamples_prior) + "\n") + f.write("survey_mask_path = " + str(survey_mask_path) + "\n") + f.write("effective_volume = " + str(effective_volume) + "\n") + f.write("force_recompute_prior = " + str(force_recompute_prior) + "\n") + f.write("update_obs_phase = " + str(update_obs_phase) + "\n") + f.write("recompute_mocks = " + str(recompute_mocks) + "\n") + f.write("list_of_pps = " + str(list_of_pps) + "\n") + f.write("pp_min = " + str(pp_min) + "\n") + f.write("pp_max = " + str(pp_max) + "\n") + f.write("dd_min = " + str(ddmin) + "\n") + f.write("dd_max = " + str(ddmax) + "\n") + f.write("prefix_mocks = " + str(prefix_mocks) + "\n") + f.write("params_obs = " + str(params_obs) + "\n") + f.write("name_obs = " + str(name_obs) + "\n") + f.write("force_obs = " + str(force_obs) + "\n") + f.write("recompute_obs_mock = " + str(recompute_obs_mock) + "\n") + f.write("local_mask_prefix = " + str(local_mask_prefix) + "\n") + f.write("reset_window_function = " + str(reset_window_function) + "\n") + f.write("perform_score_compression = " + str(perform_score_compression) + "\n") + f.write("force_score_compression = " + str(force_score_compression) + "\n") + f.write("test_gravity = " + str(test_gravity) + "\n") + f.write("norm_csts_path = " + str(norm_csts_path) + "\n") + f.write("norm_csts = " + str(norm_csts) + "\n") + f.write("noise_dbg = " + str(noise_dbg) + "\n") + +P_0 = np.load(modeldir + "P_0.npy") + + +def theta2P(theta): + """ + Convert dimensionless theta to physical P(k). + + Parameters + ---------- + theta : ndarray + Dimensionless power-spectrum values. + + Returns + ------- + ndarray + Physical power-spectrum values (P_0 multiplied). + """ + return theta * P_0 + + +if __name__ == "__main__": + import gc + from pickle import load + from pysbmy.timestepping import read_timestepping + from selfisys.hiddenbox import HiddenBox + + try: + logger.diagnostic( + "Loading main parameters from other_params.pkl in modeldir: %s", modeldir + ) + with open(modeldir + "other_params.pkl", "rb") as f: + other_params = load(f) + size = other_params["size"] + Np0 = other_params["Np0"] + Npm0 = other_params["Npm0"] + L = other_params["L"] + S = other_params["S"] + total_steps = other_params["total_steps"] + aa = other_params["aa"] + P = other_params["P"] + G_sim_path = other_params["G_sim_path"] + G_ss_path = other_params["G_ss_path"] + P_ss_obj_path = other_params["P_ss_obj_path"] + Pinit = other_params["Pinit"] + Ne = other_params["Ne"] if args.Ne is None else args.Ne + Ns = other_params["Ns"] if args.Ns is None else args.Ns + Delta_theta = other_params["Delta_theta"] + sim_params = other_params["sim_params"] + isstd = sim_params[:3] == "std" + splitLPT = sim_params[:8] == "splitLPT" + + noise = np.load(modeldir + "noise.npy") if noise_dbg is None else noise_dbg + + k_max = get_k_max(L, size) # k_max in h/Mpc + + params_planck_EH = params_planck_kmax_missing.copy() + params_planck_EH["k_max"] = k_max + params_cosmo_obs = params_cosmo_obs_kmax_missing.copy() + params_cosmo_obs["k_max"] = k_max + + Pbins_bnd = np.load(modeldir + "Pbins_bnd.npy") + Pbins = np.load(modeldir + "Pbins.npy") + k_s = np.load(modeldir + "k_s.npy") + planck_Pk = np.load(modeldir + "theta_planck.npy") + + logger.diagnostic("Successfully loaded input data.") + + if isstd: + TimeStepDistribution = None + eff_redshifts = None + TimeSteps = None + elif splitLPT: + TimeStepDistribution = None + TimeSteps = [f"pop{i}" for i in range(1, len(aa))] + eff_redshifts = [1 / a - 1 for a in aa[1:]] + else: + logger.info("Setting up time-stepping...") + nsteps = [ + round((aa[i + 1] - aa[i]) / (aa[-1] - aa[0]) * total_steps) + for i in range(len(aa) - 1) + ] + if sum(nsteps) != total_steps: + nsteps[nsteps.index(max(nsteps))] += total_steps - sum(nsteps) + TimeSteps = list(np.cumsum(nsteps) - 1) + merged_path = modeldir + "merged.h5" + TS_merged = read_timestepping(merged_path) + if sim_params.startswith("custom") or sim_params.startswith("nograv"): + TimeStepDistribution = merged_path + eff_redshifts = 1 / aa[-1] - 1 + else: + raise NotImplementedError("Time-stepping strategy not yet implemented.") + logger.info("Time-stepping setup done.") + + logger.info("Instantiating the HiddenBox...") + BB_selfi = HiddenBox( + k_s=k_s, + P_ss_path=P_ss_obj_path, + Pbins_bnd=Pbins_bnd, + theta2P=theta2P, + P=P * Npop, + size=size, + L=L, + G_sim_path=G_sim_path, + G_ss_path=G_ss_path, + Np0=Np0, + Npm0=Npm0, + fsimdir=wd[:-1], + modeldir=modeldir_refined, + noise_std=noise, + radial_selection=radial_selection, + selection_params=selection_params, + observed_density=obs_density, + linear_bias=lin_bias, + norm_csts=norm_csts, + survey_mask_path=survey_mask_path, + local_mask_prefix=local_mask_prefix, + sim_params=sim_params, + TimeStepDistribution=TimeStepDistribution, + TimeSteps=TimeSteps, + eff_redshifts=eff_redshifts, + seedphase=BASELINE_SEEDPHASE, + seednoise=BASELINE_SEEDNOISE, + fixnoise=False, + seednorm=BASELINE_SEEDNORM, + reset=reset_window_function, + save_frequency=5, + ) + logger.info("HiddenBox instantiated successfully.") + + modeldir_obs = wd + "model/" + if force_obs: + from pysbmy.power import get_Pk + + logger.info("Forceful re-computation of the ground truth.") + theta_gt = get_Pk(k_s, params_cosmo_obs) + np.save(joinstrs([modeldir_obs, "theta_gt", name_obs]), theta_gt) + logger.info("Ground truth recomputed successfully.") + elif not exists(joinstrs([modeldir_obs, "theta_gt", name_obs, ".npy"])): + raise ValueError("Ground truth cosmology not found. Please run prior steps.") + else: + theta_gt = np.load(joinstrs([modeldir_obs, "theta_gt", name_obs, ".npy"])) + + if ( + not exists(joinstrs([modeldir, "phi_obs", name_obs, ".npy"])) + and not recompute_obs_mock + ): + raise ValueError( + "Observations not found. Please re-run previous steps or set --recompute_obs_mock." + ) + elif recompute_obs_mock: + logger.info("Recomputing the observed data vector...") + if exists(joinstrs([modeldir_obs, "phi_obs", name_obs, ".npy"])) and not ( + update_obs_phase + ): + d_obs = -2 + else: + d_obs = -1 + if time_steps_obs is not None: + BB_selfi.update(TimeSteps=time_steps_obs) + if params_obs is not None: + BB_selfi.update(sim_params=params_obs) + + BB_selfi.switch_recompute_pool() + phi_obs = BB_selfi.make_data( + cosmo=params_cosmo_obs, + id=joinstrs([BASEID_OBS, name_obs]), + seedphase=SEEDPHASE_OBS, + seednoise=SEEDNOISE_OBS, + d=d_obs, + force_powerspectrum=force_obs, + force_parfiles=force_obs, + force_sim=force_obs, + force_cosmo=force_obs, + ) + BB_selfi.switch_recompute_pool() + + if params_obs is not None: + BB_selfi.update(sim_params=sim_params) + if time_steps_obs is not None: + BB_selfi.update(TimeSteps=TimeSteps) + np.save(joinstrs([modeldir_obs, "phi_obs", name_obs, ".npy"]), phi_obs) + logger.info("Observations recomputed successfully.") + else: + phi_obs = np.load(joinstrs([modeldir_obs, "phi_obs", name_obs, ".npy"])) + + logger.info("Setting up the prior and instantiating the selfi object...") + pool_prefix = wd + "pool/pool_res_dir_" + pool_suffix = ".h5" + from pyselfi.power_spectrum.selfi import power_spectrum_selfi + + if prior_type == "selfi2019": + from pyselfi.power_spectrum.prior import power_spectrum_prior + + theta_0 = np.ones(S) + if effective_volume: + alpha_cv = np.load(modeldir + "alpha_cv_eff.npy") + else: + alpha_cv = np.load(modeldir + "alpha_cv.npy") + + prior = power_spectrum_prior( + k_s, theta_0, THETA_NORM_GUESS, K_CORR_GUESS, alpha_cv, False + ) + selfi = power_spectrum_selfi( + fname_results, + pool_prefix, + pool_suffix, + prior, + BB_selfi, + theta_0, + Ne, + Ns, + Delta_theta, + phi_obs, + ) + selfi.prior.theta_norm = THETA_NORM_GUESS + selfi.prior.k_corr = K_CORR_GUESS + selfi.prior.alpha_cv = alpha_cv + elif prior_type.startswith("planck2018"): + from selfisys.prior import planck_prior + + theta_planck = np.load(modeldir + "theta_planck.npy") + theta_0 = theta_planck / P_0 + + prior = planck_prior( + planck_mean, + planck_cov, + k_s, + P_0, + k_max, + nsamples=nsamples_prior, + nthreads=N_THREADS_PRIOR, + filename=joinstrs( + [ + ROOT_PATH, + "data/stored_priors/planck_prior_S", + str(S), + "_L", + str(L), + "_size", + str(size), + "_", + str(nsamples_prior), + "_", + str(WHICH_SPECTRUM), + ".npy", + ] + ), + ) + selfi = power_spectrum_selfi( + fname_results, + pool_prefix, + pool_suffix, + prior, + BB_selfi, + theta_0, + Ne, + Ns, + Delta_theta, + phi_obs, + ) + else: + raise ValueError(f"Unknown prior type: {prior_type}") + logger.info("Prior set up and selfi object instantiated successfully.") + + if prior_type != "selfi2019": + logger.info("Computing / loading prior...") + if not force_recompute_prior: + try: + selfi.prior = selfi.prior.load(selfi.fname) + except: + logger.warning( + "Prior not found in %s, computing from scratch. Possible incomplete step 1?", + selfi.fname, + ) + try: + logger.info("Computing prior from scratch.") + selfi.compute_prior() + selfi.save_prior() + selfi.prior = selfi.prior.load(selfi.fname) + except: + logger.error( + "Error whilst recomputing the prior. For OOM or another error encountered while computing prior. " + "For OOM issues, a simple fix might be setting os.environ['OMP_NUM_THREADS'] = '1'. " + "Otherwise, refer the the error message." + ) + else: + try: + logger.warning("Forcefully recomputing prior from scratch: %s", selfi.fname) + selfi.compute_prior() + selfi.save_prior() + selfi.prior = selfi.prior.load(selfi.fname) + except: + logger.error( + "OOM or another error while forcing prior computation. " + "For OOM issues, a simple fix might be setting os.environ['OMP_NUM_THREADS'] = '1'. " + "Otherwise, refer the the error message." + ) + logger.info("Prior computed / loaded successfully.") + else: + selfi.compute_prior() + selfi.save_prior() + selfi.load_prior() + + if recompute_mocks: + if neglect_lightcone: + BB_selfi.update(_force_neglect_lightcone=True) + logger.info("Recomputing mocks...") + INDENT() + + from multiprocessing import Pool + from os import cpu_count + import tqdm.auto as tqdm + + if recompute_mocks is True: + list_part_1 = [[0, idx, selfi] for idx in range(Ne)] + list_part_2 = [[x, None, selfi] for x in range(1, S + 1)] + liste = list_part_1 + list_part_2 + elif recompute_mocks == "gradients": + liste = [[x, None, selfi] for x in range(1, S + 1)] + elif recompute_mocks == "list": + liste = [[x, p, selfi] for x in range(1, S + 1) for p in list_of_pps] + elif recompute_mocks == "listdd": + liste = [[d, None, selfi] for d in range(ddmin, ddmax + 1)] + elif recompute_mocks == "list_exp": + liste = [[0, p, selfi] for p in list_of_pps] + else: + raise ValueError("recompute_mocks can't be {}".format(recompute_mocks)) + + ncors = cpu_count() + nprocess = min(N_THREADS, ncors, len(liste)) + logger.info("Using %s processes to compute the mocks", nprocess) + gc.collect() + BB_selfi.switch_recompute_pool(prefix_mocks=prefix_mocks) + with Pool(processes=nprocess) as mp_pool: + import tqdm.auto as tqdm + + pool = mp_pool.imap(worker_fct, liste) + for contrib_to_grad in tqdm.tqdm(pool, total=len(liste)): + pass + BB_selfi.switch_recompute_pool(prefix_mocks=None) + UNINDENT() + logger.info("Mocks recomputed successfully.") + + logger.diagnostic("Loading simulations...") + BB_selfi.update(_prefix_mocks=prefix_mocks) + selfi.likelihood.switch_bar() + selfi.run_simulations(Ne=Ne, Ns=Ns) # > Load < the simulations + selfi.likelihood.switch_bar() + BB_selfi.update(_prefix_mocks=None) + logger.diagnostic("Simulations loaded successfully.") + + from selfisys.utils.plot_utils import * + + logger.diagnostic("Plotting the observed summaries...") + plot_observations( + k_s, + theta_gt, + planck_Pk, + P_0, + Pbins, + phi_obs, + np.shape(selection_params)[1], + path=figuresdir + f"observations_{name_obs}.png", + ) + gc.collect() + logger.diagnostic("Plotting the observed summaries done.") + + logger.info("Running the inference...") + INDENT() + logger.info("Computing likelihoods and posteriors...") + selfi.compute_likelihood() + selfi.save_likelihood() + selfi.load_likelihood() + selfi.compute_posterior() + selfi.save_posterior() + selfi.load_posterior() + logger.info("Computing likelihoods and posteriors done.") + + logger.diagnostic("Preparing SELFI outputs for plotting...") + C_0 = selfi.likelihood.C_0 + grad_f = selfi.likelihood.grad_f + Phi_0 = selfi.likelihood.Phi_0.Phi + f_0 = selfi.likelihood.f_0 + f_16 = selfi.likelihood.f_8 + f_32 = selfi.likelihood.f_16 + f_48 = selfi.likelihood.f_24 + # f_16 = selfi.likelihood.f_16 # DBG. TODO: put this back. + # f_32 = selfi.likelihood.f_32 + # f_48 = selfi.likelihood.f_48 + grad_f_16 = (f_16 - f_0) / Delta_theta + grad_f_32 = (f_32 - f_0) / Delta_theta + grad_f_48 = (f_48 - f_0) / Delta_theta + X0, Y0 = np.meshgrid(Pbins, Pbins) + X1, Y1 = np.meshgrid(k_s, Pbins) + N = Ne + + np.save(resultsdir + "Phi_0.npy", Phi_0) + np.save(resultsdir + "grad_f.npy", grad_f) + np.save(resultsdir + "f_0.npy", f_0) + np.save(resultsdir + "f_16.npy", f_16) + np.save(resultsdir + "f_32.npy", f_32) + np.save(resultsdir + "f_48.npy", f_48) + np.save(resultsdir + "C_0.npy", C_0) + logger.diagnostic("Preparing SELFI outputs for plotting done.") + + logger.diagnostic("Plotting the mocks and covariance matrices...") + CovarianceMap = create_colormap("CovarianceMap") + plot_mocks( + 1, + N, + P, + Pbins, + phi_obs, + Phi_0, + np.mean(Phi_0, axis=0), + C_0, + X0, + Y0, + CovarianceMap, + savepath=figuresdir + "covariance_matrix.png", + ) + plot_mocks_compact( + 1, + N, + P, + Pbins, + phi_obs, + Phi_0, + np.mean(Phi_0, axis=0), + C_0, + savepath=figuresdir + "mocks.pdf", + ) + logger.diagnostic("Plotting the mocks and covariance matrices done.") + + logger.diagnostic("Plotting the full covariance matrix...") + FullCovarianceMap = create_colormap("FullCovarianceMap") + plot_C( + C_0, + X0, + Y0, + Pbins, + FullCovarianceMap, + binning=False, + suptitle="Full covariance matrix", + savepath=figuresdir + "full_covariance_matrix.png", + ) + logger.diagnostic("Plotting the full covariance matrix done.") + + logger.diagnostic("Plotting the estimated gradients...") + plot_gradients( + Pbins, + P, + grad_f_16, + grad_f_32, + grad_f_48, + grad_f, + k_s, + X1, + Y1, + fixscale=True, + suptitle="Estimated gradients at expansion point for all populations of galaxies", + savepath=figuresdir + "gradients.png", + ) + logger.diagnostic("Plotting the estimated gradients done.") + + if prior_type == "selfi2019": + # Optimise the prior hyperparameters + theta_norm_min = 0.01 + theta_norm_max = 0.1 + k_corr_min = 0.005 + k_corr_max = 0.015 + + theta_norm = THETA_NORM_GUESS + k_corr = K_CORR_GUESS + # The routine `Nbin_min` in "power_spectrum/prior.py" finds + # the index of the minimal wavenumber given k_min, that is, + # minimal index such that k_s[Nbin_min] >= k_min + Nbin_min, Nbin_max = selfi.prior.Nbin_min(0), selfi.prior.Nbin_max(k_max) + + from selfisys.prior import perform_prior_optimisation_and_plot + + theta_fiducial = planck_Pk / P_0 + logger.info("Performing hyperparameters optimisation for the prior...") + theta_norm, k_corr = perform_prior_optimisation_and_plot( + selfi, + theta_fiducial, + k_opt_min=0.0, + k_opt_max=max(k_s), + theta_norm_mean=0.2, + theta_norm_std=0.3, + k_corr_mean=0.020, + k_corr_std=0.015, + theta_norm_min=theta_norm_min, + theta_norm_max=theta_norm_max, + k_corr_min=k_corr_min, + k_corr_max=k_corr_max, + meshsize=50, + Nbin_min=Nbin_min, + Nbin_max=Nbin_max, + theta_norm=theta_norm, + k_corr=k_corr, + alpha_cv=alpha_cv, + plot=True, + verbose=False, + savepath=wd + "Figures/prior_optimisation.png", + ) + logger.info("Performing hyperparameters optimisation for the prior done.") + + prior_theta_mean = selfi.prior.mean + prior_theta_covariance = selfi.prior.covariance + Nbin_min, Nbin_max = 0, len(k_s) # keep all scales + k_s = k_s[Nbin_min:Nbin_max] + P_0 = P_0[Nbin_min:Nbin_max] + prior_theta_mean = prior_theta_mean[Nbin_min:Nbin_max] + prior_theta_covariance = prior_theta_covariance[Nbin_min:Nbin_max, Nbin_min:Nbin_max] + posterior_theta_mean, posterior_theta_covariance, posterior_theta_icov = ( + selfi.restrict_posterior(Nbin_min, Nbin_max) + ) + + X2, Y2 = np.meshgrid(k_s, k_s) + prior_covariance = np.diag(P_0).dot(prior_theta_covariance).dot(np.diag(P_0)) + np.save(resultsdir + "prior_theta_mean.npy", prior_theta_mean) + np.save(resultsdir + "prior_theta_covariance.npy", prior_theta_covariance) + np.save(resultsdir_obs + "posterior_theta_mean.npy", posterior_theta_mean) + np.save(resultsdir_obs + "posterior_theta_covariance.npy", posterior_theta_covariance) + + logger.diagnostic("Plotting the prior and posterior...") + plot_prior_and_posterior_covariances( + X2, + Y2, + k_s, + prior_theta_covariance, + prior_covariance, + posterior_theta_covariance, + P_0, + suptitle="Prior and posterior covariance matrices", + savepath=figuresdir + "prior_and_posterior_covariances.png", + ) + logger.diagnostic("Plotting the prior and posterior done.") + + logger.diagnostic("Plotting the reconstruction...") + plot_reconstruction( + k_s, + Pbins, + prior_theta_mean, + prior_theta_covariance, + posterior_theta_mean, + posterior_theta_covariance, + theta_gt, + P_0, + suptitle="Posterior primordial matter power spectrum", + savepath=figuresdir + "reconstruction.png", + ) + logger.diagnostic("Plotting the reconstruction done.") + UNINDENT() + logger.info("Inference done.") + + if perform_score_compression: + logger.info("Performing score compression...") + INDENT() + + from selfisys.utils.tools import * + from selfisys.utils.workers import evaluate_gradient_of_Symbelmyne + + logger.info("Computing the gradient of CLASS wrt the cosmological parameters...") + delta = 1e-3 + if not exists(wd + "score_compression/grads_class.npy") or force_score_compression: + coeffs = [4 / 5, -1 / 5, 4 / 105, -1 / 280] + + grad = np.zeros((len(planck_mean), len(k_s))) + for i in range(len(planck_mean)): + if i == 0: # workaround to correctly evaluate the gradient with respect to h + delta *= 10 + logger.diagnostic("Evaluating gradient of CLASS wrt parameter %d" % i) + deltas_x = delta * np.linspace(1, len(coeffs), len(coeffs)) + grad[i, :] = evaluate_gradient_of_Symbelmyne( + planck_mean, + i, + k_s, + coeffs=coeffs, + deltas_x=deltas_x, + delta=delta, + kmax=max(k_s), + ) + if i == 0: + delta /= 10 + np.save(wd + "score_compression/grads_class.npy", grad) + else: + grad = np.load(wd + "score_compression/grads_class.npy") + logger.info("Computing the gradient of CLASS wrt the cosmological parameters done.") + + grad_class = grad.T + + if not exists(wd + "score_compression/planck_Pk.npy") or force_score_compression: + from pysbmy.power import get_Pk + + logger.info("Computing the Planck spectrum...") + planck_Pk = get_Pk(k_s, params_planck_EH) + np.save(wd + "score_compression/planck_Pk", planck_Pk) + logger.info("Computing the Planck spectrum done.") + else: + planck_Pk = np.load(wd + "score_compression/planck_Pk.npy") + + logger.diagnostic("Plotting the gradients of CLASS...") + plt.figure(figsize=(14, 10)) + names_of_parameters = [ + r"$h$", + r"$\Omega_b$", + r"$\Omega_m$", + r"$n_s$", + r"$\sigma_8$", + ] + fig, ax = plt.subplots(3, 2, figsize=(14, 15)) + u, v = (-1, -1) + ax[u, v].loglog(k_s, planck_Pk) + ax[u, v].set_xlabel(r"$k$ [h/Mpc]") + ax[u, v].set_ylabel(r"$P(k)$") + ax[u, v].set_title(r"$P=\mathcal{T}(\omega_{\rm Planck})$") + for k in k_s: + ax[u, v].axvline(k, color="k", alpha=0.1, linewidth=0.5) + for i in range(len(planck_mean)): + u = i // 2 + v = i % 2 + for k in k_s: + ax[u, v].axvline(k, color="k", alpha=0.1, linewidth=0.5) + ax[u, v].plot(k_s, grad[i]) + ax[u, v].set_xscale("log") + ax[u, v].set_xlabel(r"$k$ [h/Mpc]") + ax[u, v].set_ylabel(r"$\partial P(k)/\partial$" + names_of_parameters[i]) + ax[u, v].set_title("Gradient wrt " + names_of_parameters[i]) + plt.suptitle("Gradient of Simbelmynë wrt cosmological parameters", fontsize=20) + plt.tight_layout() + plt.savefig( + figuresdir + "gradient_class.png", + bbox_inches="tight", + dpi=300, + transparent=True, + ) + plt.savefig(figuresdir + "gradient_class.pdf", bbox_inches="tight", dpi=300) + plt.close() + logger.diagnostic("Plotting the gradients of CLASS done.") + + if prefix_mocks is None: + logger.info("Computing Fisher matrix for the well specified model...") + else: + logger.info( + "Computing Fisher matrix for the misspecified model %s...", prefix_mocks + ) + params_ids_fisher = np.linspace(0, 4, 5, dtype=int) + + dw_f0 = selfi.likelihood.grad_f.dot(grad_class)[:, params_ids_fisher] + C0_inv = np.linalg.inv(selfi.likelihood.C_0) + F0 = dw_f0.T.dot(C0_inv).dot(dw_f0) + + if not exists(scoredir + "dw_f0.npy") or force_score_compression: + np.save(scoredir + "dw_f0.npy", dw_f0) + if not exists(scoredir + "C0_inv.npy") or force_score_compression: + np.save(scoredir + "C0_inv.npy", C0_inv) + if not exists(scoredir + "F0.npy") or force_score_compression: + np.save(scoredir + "F0.npy", F0) + + f0 = selfi.likelihood.f_0 + np.save(scoredir + "f0_expansion.npy", f0) + + plot_fisher( + F0, + names_of_parameters, + title="Fisher matrix", + path=figuresdir + "fisher.png", + ) + logger.info("Fisher matrix for the well specified model done.") + UNINDENT() + logger.info("Score compression done.") + + logger.info("Computing additional statistics...") + INDENT() + + logger.info("Computing the Mahalanobis distances...") + INDENT() + prior_theta_icov = selfi.prior.inv_covariance + diff = posterior_theta_mean - prior_theta_mean + Mahalanobis_distance = np.sqrt(diff.dot(prior_theta_icov).dot(diff)) + logger.info( + "Mahalanobis distance between the posterior and the prior: %s", Mahalanobis_distance + ) + np.savetxt(resultsdir_obs + "Mahalanobis_distances.txt", [Mahalanobis_distance]) + + diff = theta_gt / P_0 - posterior_theta_mean + Mahalanobis_distance_gt = np.sqrt(diff.dot(posterior_theta_icov).dot(diff)) + logger.info( + "Mahalanobis distance between the groundtruth and the posterior: %s", + Mahalanobis_distance_gt, + ) + np.savetxt(resultsdir_obs + "Mahalanobis_distances_gt.txt", [Mahalanobis_distance_gt]) + + NMahal = 2000 + logger.info( + "Computing the mean Mahalanobis distance between the prior and %s samples...", NMahal + ) + Mahalanobis_distances = [] + for _ in range(NMahal): + theta = np.random.multivariate_normal(prior_theta_mean, prior_theta_covariance) + diff = theta - prior_theta_mean + mahal = np.sqrt(diff.dot(prior_theta_icov).dot(diff)) + Mahalanobis_distances.append(mahal) + mean_Mahalanobis_distance = np.mean(Mahalanobis_distances) + logger.info( + "Mean Mahalanobis distance between the prior and %s samples: %s", + NMahal, + mean_Mahalanobis_distance, + ) + np.savetxt(resultsdir_obs + "mean_Mahalanobis_distances.txt", [mean_Mahalanobis_distance]) + UNINDENT() + logger.info("Computing the Mahalanobis distances done.") + + logger.diagnostic("Plotting the Mahalanobis distances...") + plot_histogram( + Mahalanobis_distances, + Mahalanobis_distance, + suptitle="Mahalanobis distances between the prior and samples", + savepath=figuresdir + "Mahalanobis_distances.png", + ) + logger.diagnostic("Plotting the Mahalanobis distances done.") + UNINDENT() + + except OSError as e: + logger.error("Directory or file access error: %s", str(e)) + raise + except Exception as e: + logger.critical("An unexpected error occurred: %s", str(e)) + raise RuntimeError("Pipeline step 3 failed.") from e + finally: + gc.collect() + logger.info("step 3 of the SelfiSys pipeline: done.") diff --git a/src/selfisys/preamble.tex b/src/selfisys/preamble.tex new file mode 100644 index 0000000..4202bf6 --- /dev/null +++ b/src/selfisys/preamble.tex @@ -0,0 +1,14 @@ +% ---------------------------------------------------------------------- +% Copyright (C) 2024 Tristan Hoellinger +% Distributed under the GNU General Public License v3.0 (GPLv3). +% See the LICENSE file in the root directory for details. +% SPDX-License-Identifier: GPL-3.0-or-later +% ---------------------------------------------------------------------- + +% Author: Tristan Hoellinger +% Version: 0.1.0 +% Date: 2024 +% License: GPLv3 + +\usepackage{amsmath,amsfonts,amssymb,amsthm} +\usepackage{upgreek} diff --git a/src/selfisys/prior.py b/src/selfisys/prior.py new file mode 100644 index 0000000..61ee2e4 --- /dev/null +++ b/src/selfisys/prior.py @@ -0,0 +1,690 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Priors for the SelfiSys pipeline. This module provides: +- a Planck2018-based prior class (`planck_prior`) compatible with +pySelfi, adapted for the logic of the SelfiSys pipeline; +- wrappers for the selfi2019 prior from [leclercq2019primordial]. + + +Raises +------ +OSError + If file or directory paths are inaccessible. +RuntimeError + If unexpected HPC or multi-processing errors arise. +""" + +import gc + +from selfisys.utils.logger import getCustomLogger + +logger = getCustomLogger(__name__) + + +def get_summary(x, bins, normalisation=None, kmax=1.4): + """ + Compute a power-spectrum summary for given cosmological parameters. + + Parameters + ---------- + x : array-like + Cosmological parameters [h, Omega_b, Omega_m, n_s, sigma_8]. + bins : array-like + Wavenumber bins. + normalisation : float or None, optional + Normalisation constant to scale the resulting spectrum. + kmax : float, optional + Maximum wavenumber for get_Pk. + + Returns + ------- + theta : ndarray + The computed power-spectrum values, optionally normalised. + + Raises + ------ + RuntimeError + If the power-spectrum computation fails unexpectedly. + """ + from numpy import array + from pysbmy.power import get_Pk + from selfisys.utils.tools import cosmo_vector_to_Simbelmyne_dict + + try: + theta = get_Pk(bins, cosmo_vector_to_Simbelmyne_dict(x, kmax=kmax)) + if normalisation is not None: + theta /= normalisation + return array(theta) + except Exception as e: + logger.critical("Unexpected error in get_summary: %s", str(e)) + raise RuntimeError("Failed to compute power spectrum summary.") from e + finally: + gc.collect() + + +def worker_class(params): + """ + Worker function to compute power spectra with CLASS, compatible with + Python multiprocessing. + + Parameters + ---------- + params : tuple + (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. + + Returns + ------- + theta : ndarray + Power-spectrum summary from `get_summary`. + """ + x, bins, normalisation, kmax = params + return get_summary(x, bins, normalisation, kmax) + + +class planck_prior: + """ + Custom prior for the SelfiSys pipeline. This is the prior used in + [hoellinger2024diagnosing], based on the Planck 2018 cosmological + parameters. + + 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]. + + Parameters + ---------- + Omega_mean : array-like + Mean of the prior distribution on cosmological parameters. + Omega_cov : array-like + Covariance matrix of the prior distribution on cosmological + parameters. + bins : array-like + Wavenumbers where the power spectrum is evaluated. + normalisation : float or None + If not None, divide the power spectra by the normalisation. + kmax : float + Maximum wavenumber for computations. + nsamples : int, optional + Number of samples drawn from the prior on the cosmological + parameters. Default is 10,000. + nthreads : int, optional + Number of CPU threads for parallel tasks. Default is -1, that + is, auto-detect the number of available threads. + EPS_K : float, optional + Regularisation parameter for covariance inversion. Default 1e-7. + EPS_residual : float, optional + Additional cutoff for matrix inversion. Default 1e-3. + filename : str or None, optional + Path to a .npy file to store or load precomputed power spectra. + + Attributes + ---------- + mean : ndarray + Mean of the computed power spectra. + covariance : ndarray + Covariance matrix of the computed power spectra. + inv_covariance : ndarray + Inverse of the covariance matrix. + + Raises + ------ + OSError + If file reading or writing fails. + RuntimeError + For unexpected HPC or multi-processing errors. + """ + + def __init__( + self, + Omega_mean, + Omega_cov, + bins, + normalisation, + kmax, + nsamples=10000, + nthreads=-1, + EPS_K=1e-7, + EPS_residual=1e-3, + filename=None, + ): + from numpy import where + from multiprocessing import cpu_count + + self.Omega_mean = Omega_mean + self.Omega_cov = Omega_cov + self.bins = bins + self.normalisation = normalisation + self.kmax = kmax + self.nsamples = nsamples + self.EPS_K = EPS_K + self.EPS_residual = EPS_residual + self.filename = filename + + if nthreads == -1: + # Use #CPU - 1 or fallback to 1 if a single CPU is available + self.nthreads = cpu_count() - 1 or 1 + else: + self.nthreads = nthreads + + self._Nbin_min = where(self.bins >= 0.01)[0].min() + self._Nbin_max = where(self.bins <= self.kmax)[0].max() + 1 + + # Attributes set after compute() + self.mean = None + self.covariance = None + self.inv_covariance = None + self.thetas = None + + @property + def Nbin_min(self, k_min): + """Index of the minimal wavenumber given k_min.""" + return self._Nbin_min + + @property + def Nbin_max(self, k_min): + """Index of the maximal wavenumber given self.kmax.""" + return self._Nbin_max + + def compute(self): + """ + Compute the prior (mean, covariance, and inverse covariance). + + If `self.filename` exists, tries to load the prior. Otherwise, + samples from the prior distribution on cosmological parameters + and evaluates the power spectra in parallel. + + Raises + ------ + OSError + If self.filename is not writable/accessible. + RuntimeError + If multi-processing or power-spectra computations fail. + """ + from os.path import exists + import numpy as np + + try: + if self.filename and exists(self.filename): + logger.info("Loading precomputed thetas from %s", self.filename) + self.thetas = np.load(self.filename) + else: + from time import time + from multiprocessing import Pool + import tqdm.auto as tqdm + + logger.info("Sampling %d cosmological parameter sets...", self.nsamples) + OO = np.random.multivariate_normal( + np.array(self.Omega_mean), np.array(self.Omega_cov), self.nsamples + ) + eps = 1e-5 + OO = np.clip(OO, eps, 1 - eps) + + liste = [(o, self.bins, self.normalisation, self.kmax) for o in OO] + + logger.info( + "Computing prior power spectra in parallel using %d threads...", self.nthreads + ) + start = time() + with Pool(self.nthreads) as pool: + thetas = [] + for theta in tqdm.tqdm(pool.imap(worker_class, liste), total=len(liste)): + thetas.append(theta) + thetas = np.array(thetas) + end = time() + logger.info("Done computing power spectra in %.2f seconds.", end - start) + + self.thetas = thetas + if self.filename: + logger.info("Saving thetas to %s", self.filename) + np.save(self.filename, thetas) + + # Compute stats + self.mean = np.mean(self.thetas, axis=0) + self.covariance = np.cov(self.thetas.T) + + logger.info("Regularising and inverting the prior covariance matrix.") + from pyselfi.utils import regular_inv + + self.inv_covariance = regular_inv(self.covariance, self.EPS_K, self.EPS_residual) + + except OSError as e: + logger.error("File I/O error: %s", str(e)) + raise + except Exception as e: + logger.critical("Error during prior computation: %s", str(e)) + raise RuntimeError("planck_prior computation failed.") from e + finally: + gc.collect() + + def logpdf(self, theta, theta_mean, theta_covariance, theta_icov): + """ + Return the log prior probability at a given point in parameter + space. + + Parameters + ---------- + theta : ndarray + Evaluation point in parameter space. + theta_mean : ndarray + Prior mean vector. + theta_covariance : ndarray + Prior covariance matrix. + theta_icov : ndarray + Inverse of the prior covariance matrix. + + Returns + ------- + float + Log prior probability value. + """ + import numpy as np + + diff = theta - theta_mean + val = -0.5 * diff.dot(theta_icov).dot(diff) + val -= 0.5 * np.linalg.slogdet(2 * np.pi * theta_covariance)[1] + return val + + def sample(self, seedsample=None): + """ + Draw a random sample from the prior distribution. + + Parameters + ---------- + seedsample : int, optional + Seed for the random number generator. + + Returns + ------- + ndarray + A single sample from the prior distribution. + """ + from numpy.random import seed, multivariate_normal + + if seedsample is not None: + seed(seedsample) + return multivariate_normal(self.mean, self.covariance) + + def save(self, fname): + """ + Save the prior to an output file. + + Parameters + ---------- + fname : str + Output HDF5 filename to store the prior data. + + Raises + ------ + OSError + If the file cannot be accessed or written. + """ + import h5py + from ctypes import c_double + from pyselfi.utils import PrintMessage, save_replace_dataset, save_replace_attr + + try: + PrintMessage(3, f"Writing prior in data file '{fname}'...") + with h5py.File(fname, "r+") as hf: + + def save_to_hf(name, data, **kwargs): + save_replace_dataset(hf, f"/prior/{name}", data, dtype=c_double, **kwargs) + + # Hyperparameters + save_to_hf("thetas", self.thetas, maxshape=(None, None)) + save_to_hf("Omega_mean", self.Omega_mean, maxshape=(None,)) + save_to_hf("Omega_cov", self.Omega_cov, maxshape=(None, None)) + save_to_hf("bins", self.bins, maxshape=(None,)) + + save_replace_attr(hf, "/prior/normalisation", self.normalisation, dtype=c_double) + save_replace_attr(hf, "/prior/kmax", self.kmax, dtype=c_double) + + # Mandatory attributes + save_to_hf("mean", self.mean, maxshape=(None,)) + save_to_hf("covariance", self.covariance, maxshape=(None, None)) + save_to_hf("inv_covariance", self.inv_covariance, maxshape=(None, None)) + + PrintMessage(3, f"Writing prior in data file '{fname}' done.") + except OSError as e: + logger.error("Failed to save prior to '%s': %s", fname, str(e)) + raise + finally: + gc.collect() + + @classmethod + def load(cls, fname): + """ + Load the prior from input file. + + Parameters + ---------- + fname : str + Input HDF5 filename. + + Returns + ------- + prior + The prior object. + + Raises + ------ + OSError + If the file cannot be read or is invalid. + """ + from h5py import File + from numpy import array + from ctypes import c_double + from pyselfi.utils import PrintMessage + + try: + PrintMessage(3, f"Reading prior in data file '{fname}'...") + with File(fname, "r") as hf: + # Load constructor parameters + Omega_mean = array(hf.get("/prior/Omega_mean"), dtype=c_double) + Omega_cov = array(hf.get("/prior/Omega_cov"), dtype=c_double) + bins = array(hf.get("/prior/bins"), dtype=c_double) + normalisation = hf.attrs["/prior/normalisation"] + kmax = hf.attrs["/prior/kmax"] + + # Instantiate class + prior = cls(Omega_mean, Omega_cov, bins, normalisation, kmax) + + # Load mandatory arrays + prior.mean = array(hf.get("prior/mean"), dtype=c_double) + prior.covariance = array(hf.get("/prior/covariance"), dtype=c_double) + prior.inv_covariance = array(hf.get("/prior/inv_covariance"), dtype=c_double) + + PrintMessage(3, f"Reading prior in data file '{fname}' done.") + return prior + except OSError as e: + logger.error("Failed to read prior from '%s': %s", fname, str(e)) + raise + finally: + gc.collect() + + +def logposterior_hyperparameters_parallel( + selfi, + theta_fiducial, + Nbin_min, + Nbin_max, + theta_norm, + k_corr, + alpha_cv, +): + """ + Compute the log-posterior for the hyperparameters of the prior from + [leclercq2019primordial], for use within the SelfiSys pipeline. + + Parameters + ---------- + selfi : object + The selfi object. + theta_fiducial : ndarray + Fiducial spectrum. + Nbin_min : int + Minimum bin index for the wavenumber range. + Nbin_max : int + Maximum bin index for the wavenumber range. + theta_norm : float + Hyperparameter controlling the overall uncertainty. + k_corr : float + Hyperparameter controlling correlation scale. + alpha_cv : float + Cosmic variance strength. + + Returns + ------- + float + The log-posterior value for the given hyperparameters. + + Raises + ------ + RuntimeError + If the log-posterior computation fails unexpectedly. + """ + try: + return selfi.logposterior_hyperparameters( + theta_fiducial, Nbin_min, Nbin_max, theta_norm, k_corr, alpha_cv + ) + except Exception as e: + logger.critical("Unexpected error in logposterior_hyperparameters_parallel: %s", str(e)) + raise RuntimeError("logposterior_hyperparameters_parallel failed.") from e + finally: + gc.collect() + + +def perform_prior_optimisation_and_plot( + selfi, + theta_fiducial, + theta_norm_mean=0.1, + theta_norm_std=0.3, + k_corr_mean=0.020, + k_corr_std=0.015, + k_opt_min=0.0, + k_opt_max=1.4, + theta_norm_min=0.04, + theta_norm_max=0.12, + k_corr_min=0.012, + k_corr_max=0.02, + meshsize=30, + Nbin_min=0, + Nbin_max=100, + theta_norm=0.05, + k_corr=0.015, + alpha_cv=0.00065, + plot=True, + savepath=None, +): + """ + Optimise the hyperparameters for the selfi2019 prior (from + [leclercq2019primordial]). + + Parameters + ---------- + selfi : object + The selfi object. + theta_fiducial : ndarray + Fiducial spectrum. + theta_norm_mean : float, optional + Mean of the Gaussian hyperprior on theta_norm. Default 0.1. + theta_norm_std : float, optional + Standard deviation of the hyperprior on theta_norm. Default 0.3. + k_corr_mean : float, optional + Mean of the Gaussian hyperprior on k_corr. Default 0.020. + k_corr_std : float, optional + Standard deviation of the hyperprior on k_corr. Default 0.015. + k_opt_min : float, optional + Minimum wavenumber for the prior optimisation. Default 0.0. + k_opt_max : float, optional + Maximum wavenumber for the prior optimisation. Default 1.4. + theta_norm_min : float, optional + Lower bound for theta_norm in the mesh. Default 0.04. + theta_norm_max : float, optional + Upper bound for theta_norm in the mesh. Default 0.12. + k_corr_min : float, optional + Lower bound for k_corr in the mesh. Default 0.012. + k_corr_max : float, optional + Upper bound for k_corr in the mesh. Default 0.02. + meshsize : int, optional + Number of points in each dimension of the plot mesh. Default 30. + Nbin_min : int, optional + Minimum bin index for restricting the prior. Default 0. + Nbin_max : int, optional + Maximum bin index for restricting the prior. Default 100. + theta_norm : float, optional + Initial or default guess of theta_norm. Default 0.05. + k_corr : float, optional + Initial or default guess of k_corr. Default 0.015. + alpha_cv : float, optional + Cosmic variance term or similar. Default 0.00065. + plot : bool, optional + If True, generate and show/save a 2D contour plot. Default True. + savepath : str, optional + File path to save the plot. If None, the plot is displayed. + + Returns + ------- + tuple + (theta_norm, k_corr) after optimisation. + + Raises + ------ + OSError + If file operations fail during saving the prior or posterior. + RuntimeError + If the optimisation fails unexpectedly. + """ + try: + if plot: + from selfisys.utils.plot_utils import get_contours + from numpy import meshgrid, linspace, zeros, exp, array + from joblib import Parallel, delayed + + logger.info("Preparing the hyperparameter grid for plotting (meshsize=%d).", meshsize) + + X0, Y0 = meshgrid( + linspace(theta_norm_min, theta_norm_max, meshsize), + linspace(k_corr_min, k_corr_max, meshsize), + ) + Z = zeros((meshsize, meshsize)) + + # Evaluate log-posterior on the grid in parallel + Z = array( + Parallel(n_jobs=-1)( + delayed(logposterior_hyperparameters_parallel)( + selfi, + theta_fiducial, + Nbin_min, + Nbin_max, + X0[i][j], + Y0[i][j], + alpha_cv, + ) + for i in range(meshsize) + for j in range(meshsize) + ) + ).reshape(meshsize, meshsize) + Z -= Z.max() + Z = exp(Z) + + Z_contours = get_contours(Z, meshsize) + logger.info("Grid evaluations complete.") + + logger.info("Performing the prior hyperparameter optimisation...") + selfi.prior.theta_norm = theta_norm + selfi.prior.k_corr = k_corr + selfi.prior.alpha_cv = alpha_cv + + # Perform the prior optimisation + x0 = [theta_norm, k_corr] + selfi.optimize_prior( + theta_fiducial, + k_opt_min, + k_opt_max, + x0=x0, + theta_norm_min=theta_norm_min, + theta_norm_max=theta_norm_max, + theta_norm_mean=theta_norm_mean, + theta_norm_std=theta_norm_std, + k_corr_min=k_corr_min, + k_corr_max=k_corr_max, + k_corr_mean=k_corr_mean, + k_corr_std=k_corr_std, + options={ + "maxiter": 30, + "ftol": 1e-10, + "gtol": 1e-10, + "eps": 1e-6, + "disp": False, + }, + ) + + logger.info("Saving prior and posterior after optimisation.") + selfi.save_prior() + selfi.save_posterior() + + theta_norm = selfi.prior.theta_norm + k_corr = selfi.prior.k_corr + + prior_theta_mean, prior_theta_covariance = selfi.prior.mean, selfi.prior.covariance + prior_theta_mean = prior_theta_mean[Nbin_min:Nbin_max] + prior_theta_covariance = prior_theta_covariance[Nbin_min:Nbin_max, Nbin_min:Nbin_max] + + posterior_theta_mean, posterior_theta_covariance, posterior_theta_icov = ( + selfi.restrict_posterior(Nbin_min, Nbin_max) + ) + + logger.info("Optimised hyperparameters: theta_norm=%.5f, k_corr=%.5f", theta_norm, k_corr) + + if plot: + import matplotlib.pyplot as plt + + fig, ax = plt.subplots(figsize=(6, 5)) + ax.xaxis.set_ticks_position("both") + ax.yaxis.set_ticks_position("both") + ax.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax.xaxis.set_tick_params(which="major", length=6, labelsize=17) + ax.xaxis.set_tick_params(which="minor", length=4) + ax.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax.yaxis.set_tick_params(which="major", length=6, labelsize=17) + + pcm = ax.pcolormesh(X0, Y0, Z, cmap="Greys", shading="gouraud") + ax.grid(linestyle=":") + ax.contour( + Z, + Z_contours, + extent=[theta_norm_min, theta_norm_max, k_corr_min, k_corr_max], + colors="C9", + ) + ax.plot( + [x0[0], x0[0]], + [k_corr_min, k_corr_max], + color="C3", + linestyle=":", + label="Before optimisation", + ) + ax.plot([theta_norm_min, theta_norm_max], [x0[1], x0[1]], color="C3", linestyle=":") + ax.plot( + [theta_norm, theta_norm], + [k_corr_min, k_corr_max], + linestyle="--", + color="C3", + label="After optimisation", + ) + ax.plot([theta_norm_min, theta_norm_max], [k_corr, k_corr], linestyle="--", color="C3") + + ax.set_xlabel(r"$\theta_\mathrm{norm}$", size=19) + ax.set_ylabel(r"$k_\mathrm{corr}$ [$h$/Mpc]", size=19) + ax.legend() + + if savepath is None: + plt.show() + else: + fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True) + fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf") + plt.close(fig) + + return theta_norm, k_corr + + except OSError as e: + logger.error("File access or I/O error: %s", str(e)) + raise + except Exception as e: + logger.critical("Unexpected error in perform_prior_optimisation_and_plot: %s", str(e)) + raise RuntimeError("perform_prior_optimisation_and_plot failed.") from e + finally: + gc.collect() diff --git a/src/selfisys/sbmy_interface.py b/src/selfisys/sbmy_interface.py new file mode 100644 index 0000000..9546cb4 --- /dev/null +++ b/src/selfisys/sbmy_interface.py @@ -0,0 +1,889 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Simbelmynë-related functions for the SelfiSys pipeline. +""" + +import os +import gc +from typing import Optional, List, Tuple + +from selfisys.utils.logger import getCustomLogger, INDENT, UNINDENT + +logger = getCustomLogger(__name__) + + +def get_power_spectrum_from_cosmo( + L, + size, + cosmo, + fname_power_spectrum, + force=False, +): + """ + Compute a power spectrum from cosmological parameters and save it to + disk. + + Parameters + ---------- + L : float + Size of the simulation box (in Mpc/h). + size : int + Number of grid points along each axis. + cosmo : dict + Cosmological parameters (and infrastructure parameters). + fname_power_spectrum : str + Name (including path) of the power spectrum file to read/write. + force : bool, optional + If True, forces recomputation even if the file exists. Default + is False. + + Raises + ------ + OSError + If file writing fails or the directory path is invalid. + RuntimeError + For unexpected issues during power spectrum computation. + """ + if not os.path.exists(fname_power_spectrum) or force: + from pysbmy.power import PowerSpectrum + + try: + logger.debug("Computing power spectrum for L=%.2f, size=%d", L, size) + P = PowerSpectrum(L, L, L, size, size, size, cosmo) + P.write(fname_power_spectrum) + logger.debug("Power spectrum written to %s", fname_power_spectrum) + except OSError as e: + logger.error("File write error at %s: %s", fname_power_spectrum, str(e)) + raise + except Exception as e: + logger.critical("Unexpected error in power spectrum computation: %s", str(e)) + raise RuntimeError("get_power_spectrum_from_cosmo failed.") from e + finally: + gc.collect() + + +def compute_Phi( + G_ss_path, + P_ss_path, + g_obj, + norm, + AliasingCorr=True, + verbosity=1, +): + """ + Compute the summary statistics from a field object, based on a + provided summary-statistics Fourier grid and baseline spectrum. + + Parameters + ---------- + G_ss_path : str + Path to the FourierGrid file used for summary-statistics. + P_ss_path : str + Path to the baseline power spectrum file for normalisation. + g_obj : Field + Input field object from which to compute summary statistics. + norm : ndarray + Normalisation constants for the summary statistics. + AliasingCorr : bool, optional + Whether to apply aliasing correction. Default is True. + verbosity : int, optional + Verbosity level (0=quiet, 1=normal, 2=debug). Default 1. + + Returns + ------- + Phi : ndarray + Vector of summary statistics. + + Raises + ------ + OSError + If file reading fails at G_ss_path or P_ss_path. + RuntimeError + If unexpected issues occur during computation. + """ + from pysbmy.correlations import get_autocorrelation + from pysbmy.power import FourierGrid, PowerSpectrum + from pysbmy import c_double + from io import BytesIO + + try: + logger.debug("Reading FourierGrid from %s", G_ss_path) + G_ss = FourierGrid.read(G_ss_path) + + if verbosity > 1: + Pk, _ = get_autocorrelation(g_obj, G_ss, AliasingCorr=AliasingCorr) + else: + from selfisys.utils.low_level import stdout_redirector + + f = BytesIO() + with stdout_redirector(f): + Pk, _ = get_autocorrelation(g_obj, G_ss, AliasingCorr=AliasingCorr) + f.close() + + logger.debug("Reading baseline PowerSpectrum from %s", P_ss_path) + P_ss = PowerSpectrum.read(P_ss_path) + Phi = Pk / (norm * P_ss.powerspectrum) + + del G_ss, P_ss + gc.collect() + return Phi.astype(c_double) + except OSError as e: + logger.error("File not found or inaccessible: %s", str(e)) + raise + except Exception as e: + logger.critical("Unexpected error in compute_Phi: %s", str(e)) + raise RuntimeError("compute_Phi failed.") from e + finally: + gc.collect() + + +def generate_white_noise_Field( + L, + size, + seedphase, + fname_whitenoise, + seedname_whitenoise, + force_phase=False, +): + """ + Generate a white noise realisation in physical space and write it to + disk. + + Parameters + ---------- + L : float + Size of the simulation box (in Mpc/h). + size : int + Number of grid points along each axis. + seedphase : int or list of int + User-provided seed to generate the initial white noise. + fname_whitenoise : str + File path to write the white noise realisation. + seedname_whitenoise : str + File path to write the seed state of the RNG. + force_phase : bool, optional + If True, forces regeneration of the random phases. Default is + False. + + Raises + ------ + OSError + If file writing fails or directory paths are invalid. + RuntimeError + For unexpected issues. + """ + if not os.path.exists(fname_whitenoise) or force_phase: + import numpy as np + from pysbmy.field import BaseField + + try: + logger.debug("Generating white noise for L=%.2f, size=%d", L, size) + rng = np.random.default_rng(seedphase) + + logger.debug("Saving RNG state to %s", seedname_whitenoise) + np.save(seedname_whitenoise, rng.bit_generator.state) + with open(seedname_whitenoise + ".txt", "w") as f: + f.write(str(rng.bit_generator.state)) + + data = rng.standard_normal(size=size**3) + wn = BaseField(L, L, L, 0, 0, 0, 1, size, size, size, data) + del data + + wn.write(fname_whitenoise) + logger.debug("White noise field written to %s", fname_whitenoise) + del wn + except OSError as e: + logger.error("Writing white noise failed at '%s': %s", fname_whitenoise, str(e)) + raise + except Exception as e: + logger.critical("Unexpected error in generate_white_noise_Field: %s", str(e)) + raise RuntimeError("generate_white_noise_Field failed.") from e + finally: + gc.collect() + + +def setup_sbmy_parfiles( + d, + cosmology, + file_names, + hiddenbox_params, + force=False, +): + """Set up Simbelmynë parameter file (please refer to the Simbelmynë + documentation for more details). + + Parameters + ---------- + d : int + Index (from 1 to S) specifying a direction in parameter space, 0 + for the expansion point, or -1 for mock data. + cosmology : array, double, dimension=5 + Cosmological parameters. + file_names : dict + Dictionary containing the names of the input/output files for + the simulation. + hiddenbox_params : dict + See the `HiddenBox` class for more details. + force : bool, optional, default=False + If True, forces recompute the simulation parameter files. + + """ + from os.path import exists + + fname_simparfile = file_names["fname_simparfile"] + fname_power_spectrum = file_names["fname_power_spectrum"] + fname_whitenoise = file_names["fname_whitenoise"] + fname_outputinitialdensity = file_names["fname_outputinitialdensity"] + fnames_outputrealspacedensity = file_names["fnames_outputrealspacedensity"] + fnames_outputdensity = file_names["fnames_outputdensity"] + fnames_outputLPTdensity = file_names["fnames_outputLPTdensity"] + + Npop = hiddenbox_params["Npop"] + Np0 = hiddenbox_params["Np0"] + Npm0 = hiddenbox_params["Npm0"] + size = hiddenbox_params["size"] + L = hiddenbox_params["L"] + Ntimesteps = hiddenbox_params["Ntimesteps"] + sim_params = hiddenbox_params["sim_params"] + eff_redshifts = hiddenbox_params["eff_redshifts"] + TimeSteps = hiddenbox_params["TimeSteps"] + TimeStepDistribution = hiddenbox_params["TimeStepDistribution"] + modified_selfi = hiddenbox_params["modified_selfi"] + fsimdir = hiddenbox_params["fsimdir"] + + if not exists(fname_simparfile + "_{}.sbmy".format(Npop)) or force: + from pysbmy import param_file + from re import search + from selfisys.global_parameters import BASEID_OBS + + if TimeSteps is not None and eff_redshifts is None: + raise ValueError("TimeSteps must be provided if eff_redshifts is None.") + + regex = r"([a-zA-Z]+)(\d+)?([a-zA-Z]+)?(\d+)?([a-zA-Z]+)?" + m = search(regex, sim_params) + if m.group(1) == "std": + # Single LPT+COLA/PM Simbelmynë data card with linear time + # stepping + if m.group(2)[0] == "0": + RedshiftLPT = float("0." + m.group(2)[1:]) + else: + RedshiftLPT = int(m.group(2)) + + RedshiftFCs = 0.0 + WriteLPTSnapshot = 0 + WriteLPTDensity = 0 + match m.group(3): + case "RSD": + ModulePMCOLA = 0 + EvolutionMode = 2 + NumberOfTimeSteps = 0 + RedshiftFCs = RedshiftLPT + NonLinearRSD = 1 + case "PM": + ModulePMCOLA = 1 + EvolutionMode = 1 + NumberOfTimeSteps = m.group(4) + NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1 + case "COLA": + ModulePMCOLA = 1 + EvolutionMode = 2 + NumberOfTimeSteps = m.group(4) + NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1 + case _: + raise ValueError("sim_params = {} not valid".format(sim_params)) + NumberOfTimeSteps = int(m.group(4)) if m.group(4) is not None else 0 + elif m.group(1) == "custom": + # Single LPT+COLA/PM Simbelmynë card with user-provided time + # stepping object + RedshiftLPT = int(m.group(2)) + match m.group(3): + case None: + ModulePMCOLA = 0 + EvolutionMode = 2 + case "PM": + ModulePMCOLA = 1 + EvolutionMode = 1 + NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1 + case "COLA": + ModulePMCOLA = 1 + EvolutionMode = 2 + NonLinearRSD = 0 if (m.group(5) and m.group(5)[:3] == "lin") else 1 + case _: + raise ValueError("sim_params = {} not valid".format(sim_params)) + if TimeStepDistribution is None: + raise ValueError("TimeStepDistribution must be provided for 'custom'.") + elif m.group(1) == "splitLPT": + # Use as many Simbelmynë data cards as there are populations + # of galaxies + if eff_redshifts is None: + raise ValueError("eff_redshifts must be provided for 'splitLPT'.") + elif len(eff_redshifts) != Ntimesteps: + raise ValueError("len(eff_redshifts) != Ntimesteps") + elif m.group(1) == "split": + # Use as many Simbelmynë data cards as there are populations + # of galaxies + if TimeStepDistribution is None: + raise ValueError("TimeStepDistribution must be for 'split'.") + if eff_redshifts is None: + raise ValueError("eff_redshifts must be provided for 'split'.") + elif len(eff_redshifts) != Ntimesteps: + raise ValueError("len(eff_redshifts) != Ntimesteps") + + RedshiftLPT = int(m.group(2)) + match m.group(3): + case "RSD": + ModulePMCOLA = 1 + EvolutionMode = 2 + NonLinearRSD = 1 + case _: + raise ValueError("sim_params = {} not valid".format(sim_params)) + NumberOfTimeSteps = int(m.group(4)) if m.group(4) is not None else 0 + else: + raise ValueError("sim_params = {} not valid" + sim_params) + + if sim_params[-3:] == BASEID_OBS: + from selfisys.global_parameters import ( + h_obs as h, + Omega_b_obs as Omega_b, + Omega_m_obs as Omega_m, + nS_obs as nS, + sigma8_obs as sigma8, + ) + else: + if modified_selfi: + # Treat the cosmological parameters as nuisance + # parameters within the hidden box forward model + h, Omega_b, Omega_m, nS, sigma8 = cosmology + else: + # Fix the fiducial cosmology within the hidden box + from selfisys.global_parameters import ( + h_planck as h, + Omega_b_planck as Omega_b, + Omega_m_planck as Omega_m, + nS_planck as nS, + sigma8_planck as sigma8, + ) + + if d < 0: # -1 for mock data, -2 to recompute the observations + WriteInitialConditions = 1 + WriteDensities = 1 # also write real space density fields + else: # d=0 for expansion point or d>0 for the gradients + WriteInitialConditions = 0 + WriteDensities = 1 # also write real space density fields + + if m.group(1) == "std": + S = param_file( ## Module LPT ## + ModuleLPT=1, + # Basic setup: + Particles=Np0, + Mesh=size, + BoxSize=L, + corner0=0.0, + corner1=0.0, + corner2=0.0, + # Initial conditions: + ICsMode=1, + WriteICsRngState=0, + WriteInitialConditions=WriteInitialConditions, + InputWhiteNoise=fname_whitenoise, + OutputInitialConditions=fname_outputinitialdensity, + # Power spectrum: + InputPowerSpectrum=fname_power_spectrum, + # Final conditions for LPT: + RedshiftLPT=RedshiftLPT, + WriteLPTSnapshot=WriteLPTSnapshot, + WriteLPTDensity=WriteLPTDensity, + OutputLPTDensity=fnames_outputLPTdensity, + #################### + ## Module PM/COLA ## + #################### + ModulePMCOLA=ModulePMCOLA, + EvolutionMode=EvolutionMode, # 1 for PM, 2 for COLA + ParticleMesh=Npm0, + NumberOfTimeSteps=NumberOfTimeSteps, + # Final snapshot: + RedshiftFCs=RedshiftFCs, + WriteFinalSnapshot=0, + WriteFinalDensity=WriteDensities, + OutputFinalDensity=fnames_outputrealspacedensity[0], + ######### + ## RSD ## + ######### + ModuleRSD=1, + WriteIntermediaryRSD=0, + DoNonLinearMapping=NonLinearRSD, + WriteRSDensity=1, + OutputRSDensity=fnames_outputdensity[0], + ############################# + ## Cosmological parameters ## + ############################# + h=h, + Omega_q=1.0 - Omega_m, + Omega_b=Omega_b, + Omega_m=Omega_m, + Omega_k=0.0, + n_s=nS, + sigma8=sigma8, + w0_fld=-1.0, + wa_fld=0.0, + ) + S.write(fname_simparfile + "_{}.sbmy".format(Npop)) + elif m.group(1) == "custom": + RedshiftFCs = eff_redshifts + fname_outputdensity = ( + fnames_outputdensity[0][: fnames_outputdensity[0].rfind("_")] + ".h5" + ) + S = param_file( ## Module LPT ## + ModuleLPT=1, + # Basic setup: + Particles=Np0, + Mesh=size, + BoxSize=L, + corner0=0.0, + corner1=0.0, + corner2=0.0, + # Initial conditions: + ICsMode=1, + WriteICsRngState=0, + WriteInitialConditions=WriteInitialConditions, + InputWhiteNoise=fname_whitenoise, + OutputInitialConditions=fname_outputinitialdensity, + # Power spectrum: + InputPowerSpectrum=fname_power_spectrum, + # Final conditions for LPT: + RedshiftLPT=RedshiftLPT, + WriteLPTSnapshot=0, + WriteLPTDensity=0, + #################### + ## Module PM/COLA ## + #################### + ModulePMCOLA=ModulePMCOLA, + EvolutionMode=EvolutionMode, # 1 for PM, 2 for COLA + ParticleMesh=Npm0, + OutputKickBase=fsimdir + "/data/cola_kick_", + # Final snapshot: + RedshiftFCs=RedshiftFCs, + WriteFinalSnapshot=0, + WriteFinalDensity=0, + OutputFinalDensity=fnames_outputrealspacedensity[0], + # Intermediate snapshots: + WriteSnapshots=0, + WriteDensities=WriteDensities, + OutputDensitiesBase=fnames_outputrealspacedensity[0][ + : fnames_outputrealspacedensity[0].rfind("_") + ] + + "_", + OutputDensitiesExt=".h5", + ############################ + ## Time step distribution ## + ############################ + TimeStepDistribution=TimeStepDistribution, + ModifiedDiscretization=1, # Modified KD discretisation + n_LPT=-2.5, # Exponent for the Ansatz in KD operators + ######### + ## RSD ## + ######### + ModuleRSD=1, + WriteIntermediaryRSD=1, + DoNonLinearMapping=NonLinearRSD, + WriteRSDensity=1, + OutputRSDensity=fname_outputdensity, + ############################# + ## Cosmological parameters ## + ############################# + h=h, + Omega_q=1.0 - Omega_m, + Omega_b=Omega_b, + Omega_m=Omega_m, + Omega_k=0.0, + n_s=nS, + sigma8=sigma8, + w0_fld=-1.0, + wa_fld=0.0, + ) + S.write(fname_simparfile + "_{}.sbmy".format(Npop)) + elif m.group(1) == "split": + datadir = fsimdir + "/data/" + RedshiftFCs = eff_redshifts[0] + + # Write the parameter file for the first simulation + S = param_file( + ################ + ## Module LPT ## + ################ + ModuleLPT=1, + # Basic setup: + Particles=Np0, + Mesh=size, + BoxSize=L, + corner0=0.0, + corner1=0.0, + corner2=0.0, + # Initial conditions: + ICsMode=1, + WriteICsRngState=0, + WriteInitialConditions=WriteInitialConditions, + InputWhiteNoise=fname_whitenoise, + OutputInitialConditions=fname_outputinitialdensity, + # Power spectrum: + InputPowerSpectrum=fname_power_spectrum, + # Final conditions for LPT: + RedshiftLPT=RedshiftLPT, + WriteLPTSnapshot=0, + WriteLPTDensity=0, + #################### + ## Module PM/COLA ## + #################### + ModulePMCOLA=ModulePMCOLA, + EvolutionMode=EvolutionMode, + ParticleMesh=Npm0, + OutputKickBase=datadir + "cola_kick_0_", + # Final snapshot: + RedshiftFCs=RedshiftFCs, + WriteFinalSnapshot=1, + OutputFinalSnapshot=datadir + "cola_snapshot_0.gadget3", + WriteFinalDensity=1, + OutputFinalDensity=fnames_outputrealspacedensity[0], + WriteLPTDisplacements=1, + OutputPsiLPT1=datadir + "lpt_psi1_0.h5", + OutputPsiLPT2=datadir + "lpt_psi2_0.h5", + ############################ + ## Time step distribution ## + ############################ + TimeStepDistribution=TimeStepDistribution[0], + ModifiedDiscretization=1, + ######### + ## RSD ## + ######### + ModuleRSD=1, + WriteIntermediaryRSD=0, + DoNonLinearMapping=NonLinearRSD, + WriteRSDensity=1, + OutputRSDensity=fnames_outputdensity[0], + ############################# + ## Cosmological parameters ## + ############################# + h=h, + Omega_q=1.0 - Omega_m, + Omega_b=Omega_b, + Omega_m=Omega_m, + Omega_k=0.0, + n_s=nS, + sigma8=sigma8, + w0_fld=-1.0, + wa_fld=0.0, + ) + S.write(fname_simparfile + "_pop0.sbmy") + + for i in range(1, Ntimesteps): + RedshiftFCs = eff_redshifts[i] + + S = param_file( + ModuleLPT=0, + # Basic setup: + Particles=Np0, + Mesh=size, + BoxSize=L, + corner0=0.0, + corner1=0.0, + corner2=0.0, + InputPsiLPT1=datadir + "lpt_psi1_0.h5", + InputPsiLPT2=datadir + "lpt_psi2_0.h5", + #################### + ## Module PM/COLA ## + #################### + ModulePMCOLA=ModulePMCOLA, + InputPMCOLASnapshot=datadir + "cola_snapshot_{:d}.gadget3".format(i - 1), + EvolutionMode=EvolutionMode, + ParticleMesh=Npm0, + OutputKickBase=datadir + "cola_kick_{:d}_".format(i), + # Final snapshot: + RedshiftFCs=RedshiftFCs, + WriteFinalSnapshot=1, + OutputFinalSnapshot=datadir + "cola_snapshot_{:d}.gadget3".format(i), + WriteFinalDensity=1, + OutputFinalDensity=fnames_outputrealspacedensity[::-1][i], + WriteLPTDisplacements=0, + ############################ + ## Time step distribution ## + ############################ + TimeStepDistribution=TimeStepDistribution[i], + ModifiedDiscretization=1, + ######### + ## RSD ## + ######### + ModuleRSD=1, + WriteIntermediaryRSD=0, + DoNonLinearMapping=NonLinearRSD, + WriteRSDensity=1, + OutputRSDensity=fnames_outputdensity[i], + ############################# + ## Cosmological parameters ## + ############################# + h=h, + Omega_q=1.0 - Omega_m, + Omega_b=Omega_b, + Omega_m=Omega_m, + Omega_k=0.0, + n_s=nS, + sigma8=sigma8, + w0_fld=-1.0, + wa_fld=0.0, + ) + S.write(fname_simparfile + "_pop{}.sbmy".format(i)) + elif m.group(1) == "splitLPT": + datadir = fsimdir + "/data/" + RedshiftLPT = eff_redshifts[0] + RedshiftFCs = eff_redshifts[0] + + # Write the parameter file for the first simulation + S = param_file( + ################ + ## Module LPT ## + ################ + ModuleLPT=1, + # Basic setup: + Particles=Np0, + Mesh=size, + BoxSize=L, + corner0=0.0, + corner1=0.0, + corner2=0.0, + # Initial conditions: + ICsMode=1, + WriteICsRngState=0, + InputWhiteNoise=fname_whitenoise, + OutputInitialConditions=fname_outputinitialdensity, + InputPowerSpectrum=fname_power_spectrum, + # Final conditions for LPT: + RedshiftLPT=RedshiftLPT, + WriteLPTSnapshot=0, + WriteLPTDensity=0, + # Final snapshot: + RedshiftFCs=RedshiftFCs, + WriteFinalDensity=0, + WriteLPTDisplacements=0, + ######### + ## RSD ## + ######### + ModuleRSD=1, + WriteIntermediaryRSD=0, + DoNonLinearMapping=NonLinearRSD, + WriteRSDensity=1, + OutputRSDensity=fnames_outputdensity[0], + ############################# + ## Cosmological parameters ## + ############################# + h=h, + Omega_q=1.0 - Omega_m, + Omega_b=Omega_b, + Omega_m=Omega_m, + Omega_k=0.0, + n_s=nS, + sigma8=sigma8, + w0_fld=-1.0, + wa_fld=0.0, + ) + S.write(fname_simparfile + "_pop0.sbmy") + + for i in range(1, Ntimesteps): + RedshiftLPT = eff_redshifts[i] + RedshiftFCs = eff_redshifts[i] + + S = param_file( + ################ + ## Module LPT ## + ################ + ModuleLPT=1, + # Basic setup: + Particles=Np0, + Mesh=size, + BoxSize=L, + corner0=0.0, + corner1=0.0, + corner2=0.0, + # Initial conditions: + ICsMode=1, + WriteICsRngState=0, + InputWhiteNoise=fname_whitenoise, + OutputInitialConditions=fname_outputinitialdensity, + InputPowerSpectrum=fname_power_spectrum, + # Final conditions for LPT: + RedshiftLPT=RedshiftLPT, + WriteLPTDensity=0, + WriteLPTDisplacements=0, + ######### + ## RSD ## + ######### + ModuleRSD=1, + WriteIntermediaryRSD=0, + DoNonLinearMapping=NonLinearRSD, + WriteRSDensity=1, + OutputRSDensity=fnames_outputdensity[i], + ############################# + ## Cosmological parameters ## + ############################# + h=h, + Omega_q=1.0 - Omega_m, + Omega_b=Omega_b, + Omega_m=Omega_m, + Omega_k=0.0, + n_s=nS, + sigma8=sigma8, + w0_fld=-1.0, + wa_fld=0.0, + ) + S.write(fname_simparfile + "_pop{}.sbmy".format(i)) + + +def handle_time_stepping( + aa: List[float], + total_steps: int, + modeldir: str, + figuresdir: str, + sim_params: str, + force: bool = False, +) -> Tuple[Optional[List[int]], Optional[float]]: + """ + Create and merge individual time-stepping objects. + + Parameters + ---------- + aa : list of float + List of scale factors in ascending order. + total_steps : int + Total number of time steps to distribute among the provided + scale factors. + modeldir : str + Directory path to store generated time-stepping files. + figuresdir : str + Directory path to store time-stepping plots. + sim_params : str + Simulation parameter string (e.g., "custom", "std", "nograv"). + force : bool, optional + Whether to force recompute the time-stepping files. Default is + False. + + Returns + ------- + merged_path : str + Path to the merged time-stepping file. + indices_steps_cumul : list of int or None + Cumulative indices for the distributed steps. Returns None if + using splitLPT or if `sim_params` indicates an alternative + strategy. + eff_redshifts : float or None + Effective redshift derived from the final scale factor in + 'custom' or 'nograv' mode. None otherwise. + + Raises + ------ + NotImplementedError + If a unsupported time-stepping strategy is used. + OSError + If file or directory operations fail. + RuntimeError + If unexpected issues occur during time-stepping setup. + """ + import numpy as np + + from pysbmy.timestepping import StandardTimeStepping, read_timestepping + from selfisys.utils.plot_utils import reset_plotting, setup_plotting + from selfisys.utils.timestepping import merge_nTS + + logger.info("Evaluating time-stepping strategy: %s", sim_params) + + indices_steps_cumul = None + eff_redshifts = None + + isstd = sim_params.startswith("std") + splitLPT = sim_params.startswith("splitLPT") + + try: + # Case 1: standard approach with distributed steps + if not isstd and not splitLPT: + reset_plotting() # Revert to default plotting style + merged_path = modeldir + "merged.h5" + + # Create time-stepping + if not os.path.exists(merged_path) or force: + logger.info("Setting up time-stepping...") + + # Distribute steps among the scale factors + nsteps = [ + round((aa[i + 1] - aa[i]) / (aa[-1] - aa[0]) * total_steps) + for i in range(len(aa) - 1) + ] + # Adjust the largest gap if rounding caused a mismatch + if sum(nsteps) != total_steps: + nsteps[nsteps.index(max(nsteps))] += total_steps - sum(nsteps) + + indices_steps_cumul = list(np.cumsum(nsteps) - 1) + np.save(modeldir + "indices_steps_cumul.npy", indices_steps_cumul) + + INDENT() + logger.diagnostic("Generating individual time-stepping objects...") + + TS_paths = [] + for i, (ai, af) in enumerate(zip(aa[:-1], aa[1:])): + snapshots = np.full((nsteps[i]), False) + snapshots[-1] = True # Mark last step as a snapshot + TS = StandardTimeStepping(ai, af, snapshots, 0) + TS_path = modeldir + f"ts{i+1}.h5" + TS.write(str(TS_path)) + TS_paths.append(TS_path) + + # Ensure the timestepping object are readable and plot + for i, path_ts in enumerate(TS_paths): + read_timestepping(str(path_ts)).plot(path=str(figuresdir + f"TS{i}.png")) + logger.diagnostic("Generating individual time-stepping objects done.") + + logger.diagnostic("Merging time-stepping...") + merge_nTS([str(p) for p in TS_paths], merged_path) + TS_merged = read_timestepping(merged_path) + TS_merged.plot(path=str(figuresdir + "TS_merged.png")) + + # Restore the project's plotting style + setup_plotting() + logger.diagnostic("Merging time-stepping done.") + UNINDENT() + logger.info("Setting up time-stepping done.") + else: + logger.diagnostic("Time-stepping objects already computed.") + + # Evaluate final effective redshift + if sim_params.startswith("custom") or sim_params.startswith("nograv"): + eff_redshifts = 1 / aa[-1] - 1 + else: + raise NotImplementedError("Time-stepping strategy not yet implemented.") + + # Case 2: splitted + elif splitLPT: + indices_steps_cumul = [f"pop{i}" for i in range(1, len(aa))] + eff_redshifts = [1 / a - 1 for a in aa[1:]] + + # Case 3: other + else: + logger.diagnostic("Standard time-stepping or no special distribution required.") + + except OSError as e: + logger.error("File or directory access error in handle_time_stepping: %s", str(e)) + raise + except Exception as e: + logger.critical("An error occurred during time-stepping setup: %s", str(e)) + raise RuntimeError("Time-stepping setup failed.") from e + finally: + gc.collect() + + return merged_path, indices_steps_cumul, eff_redshifts diff --git a/src/selfisys/selection_functions.py b/src/selfisys/selection_functions.py new file mode 100644 index 0000000..513d1e3 --- /dev/null +++ b/src/selfisys/selection_functions.py @@ -0,0 +1,306 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Selection functions to simulate galaxy populations. +""" + +import os +from gc import collect +import numpy as np +import h5py + + +class LognormalSelection: + """Class to generate radial selection functions.""" + + def __init__( + self, + L=None, + selection_params=None, + survey_mask_path=None, + local_select_path=None, + size=None, + ): + """ + Initialise the LognormalSelection object. + + Parameters + ---------- + L : float + Size of the simulation box (in Mpc/h). If not provided, it + must be set before calling init_selection and using + grid-dependent methods. + selection_params : tuple of arrays + Parameters for the selection functions (ss, mm, rr). + Required for calling init_selection. + survey_mask_path : str or None + Path to the survey mask file. Required for calling + init_selection. + local_select_path : str + Path where the selection function will be saved. Required + for calling init_selection. + size : int, optional + Number of grid points along each axis. If not provided, it + must be set before using grid-dependent methods. + """ + self.L = L + self.selection_params = selection_params + self.survey_mask_path = survey_mask_path + self.local_select_path = local_select_path + self.size = size + + def r_grid(self): + """Compute the grid of radial distances in the simulation box. + + Returns + ------- + ndarray + 3D array of radial distances from the origin. + + Raises + ------ + AttributeError + If the 'size' attribute is not defined. + """ + if self.size is None: + raise AttributeError( + "The attribute 'size' must be defined to compute the radial grid." + ) + if self.L is None: + raise AttributeError("The attribute 'L' must be defined to compute the radial grid.") + + range1d = np.linspace(0, self.L, self.size, endpoint=False) + xx, yy, zz = np.meshgrid(range1d, range1d, range1d) + x0 = y0 = z0 = 0.0 + r = np.sqrt((xx - x0) ** 2 + (yy - y0) ** 2 + (zz - z0) ** 2) + 1e-10 + return r + + @staticmethod + def one_lognormal(x, std, mean, rescale=None): + """Rescaled log-normal distribution. + + Parameters + ---------- + x : ndarray + Input array. + std : float + Standard deviation of the distribution. + mean : float + Mean of the distribution. + rescale : float, optional + Rescaling factor. If None, the distribution is normalised + such that its maximum value is 1. + + Returns + ------- + ndarray + Log-normal distribution evaluated at x. + """ + mu = np.log(mean**2 / np.sqrt(std**2 + mean**2)) + sig2 = np.log(1 + std**2 / mean**2) + lognorm = (1 / (np.sqrt(2 * np.pi) * np.sqrt(sig2) * x)) * np.exp( + -((np.log(x) - mu) ** 2 / (2 * sig2)) + ) + if rescale is None: + return lognorm / np.max(lognorm) + else: + return lognorm * rescale + + def multiple_lognormal(self, x, mask, ss, ll, rr): + """Compute multiple log-normal distributions. + + Parameters + ---------- + x : ndarray + Input array. + mask : ndarray or None + Survey mask C(n). + ss : array_like + Standard deviations for each distribution. + ll : array_like + Means for each distribution. + rr : array_like + Rescaling factors for each distribution. + + Returns + ------- + list of ndarray + List of log-normal distributions. + """ + if mask is None: + mask = np.ones_like(x) + return [self.one_lognormal(x, s, l, r) * mask for s, l, r in zip(ss, ll, rr)] + + @staticmethod + def one_lognormal_z(x, sig2, mu, rescale=None): + """Compute a log-normal distribution in redshift. + + Parameters + ---------- + x : ndarray + Input array. + sig2 : float + Variance of the distribution. + mu : float + Mean of the distribution. + rescale : float, optional + Rescaling factor. + + Returns + ------- + ndarray + Log-normal distribution evaluated at x. + """ + lognorm = (1 / (np.sqrt(2 * np.pi) * np.sqrt(sig2) * x)) * np.exp( + -((np.log(x) - mu) ** 2 / (2 * sig2)) + ) + return lognorm * rescale if rescale is not None else lognorm + + def multiple_lognormal_z(self, x, mask, ss, mm, rr): + """ + Compute multiple rescaled lognormal distributions as functions + of redshift. + + Parameters + ---------- + x : ndarray + Input array (redshifts). + mask : ndarray or None + Survey mask C(n). + ss : array_like + Standard deviations of the lognormal distributions. + mm : array_like + Means of the lognormal distributions. + rr : array_like + Rescaling factors for each distribution. + + Returns + ------- + list of ndarray + List of log-normal distributions. + """ + if mask is None: + mask = np.ones_like(x) + res = [] + maxima = [] + for s, m, r in zip(ss, mm, rr): + mu = np.log(m**2 / np.sqrt(s**2 + m**2)) + sig2 = np.log(1 + s**2 / m**2) + maxima.append(np.exp(sig2 / 2 - mu) / (np.sqrt(2 * np.pi * sig2))) + res.append(self.one_lognormal_z(x, sig2, mu, rescale=r) * mask) + max = np.max(maxima) + res = [r / max for r in res] + return res + + def lognormals_z_to_x(self, xx, mask, params, spline): + """Convert log-normal distributions from redshift to distance. + + Parameters + ---------- + xx : array-like + Comoving distances at which to evaluate the distributions. + mask : ndarray or None + Survey mask C(n). + params : tuple of arrays + Parameters for the distributions (ss, mm, rr). + spline : UnivariateSpline + Linear interpolator for the distance-redshift relation. + + Returns + ------- + tuple + Tuple containing redshifts and list of distributions. + """ + ss, mm, rr = params + zs = np.maximum(1e-4, spline(xx)) + res = self.multiple_lognormal_z(zs, mask, ss, mm, rr) + return zs, res + + def init_selection(self, reset=False): + """Initialise the radial selection functions. + + Parameters + ---------- + reset : bool, optional + Whether to reset the selection function. + + Raises + ------ + + """ + if any([self.survey_mask_path is None, self.local_select_path is None]): + raise AttributeError( + "Some attributes are missing to initialise the selection function." + ) + + if not os.path.exists(self.local_select_path) or reset: + from scipy.interpolate import UnivariateSpline + from classy import Class + from astropy.cosmology import FlatLambdaCDM + from selfisys.utils.tools import cosmo_vector_to_class_dict + from selfisys.global_parameters import omegas_gt + from selfisys.utils.plot_utils import plot_selection_functions + + # Redshift-distance relation + redshifts_upper_bound = 3.0 + zz = np.linspace(0, redshifts_upper_bound, 10_000) + cosmo = FlatLambdaCDM(H0=100 * omegas_gt[0], Ob0=omegas_gt[1], Om0=omegas_gt[2]) + d = cosmo.comoving_distance(zz).value / 1e3 # -> Gpc/h + spline = UnivariateSpline(d, zz, k=1, s=0) + + # Plot the selection functions + L = self.L / 1e3 + Lcorner = np.sqrt(3) * L + zcorner = zz[np.argmin(np.abs(d - Lcorner))] + + # Get linear growth factor from CLASS + cosmo_dict = cosmo_vector_to_class_dict(omegas_gt) + cosmo_class = Class() + cosmo_class.set(cosmo_dict) + cosmo_class.compute() + Dz = cosmo_class.get_background()["gr.fac. D"] + redshifts = cosmo_class.get_background()["z"] + cosmo_class.struct_cleanup() + cosmo_class.empty() + + # Define the axis for the plot + xx = np.linspace(1e-5, Lcorner, 1000) + zz, res = self.lognormals_z_to_x( + xx, + None, + self.selection_params, + spline, + ) + + # Call auxiliary plotting routine + plot_selection_functions( + xx, + res, + None, + self.selection_params, + L, + np.sqrt(3) * L, + zz=zz, + zcorner=zcorner, + path=self.local_select_path[:-3] + ".png", + ) + + # Compute the selection function and save it to disk + survey_mask = np.load(self.survey_mask_path) if self.survey_mask_path else None + r = self.r_grid() / 1e3 # Convert to Gpc/h + _, select_fct = self.lognormals_z_to_x(r, survey_mask, self.selection_params, spline) + with h5py.File(self.local_select_path, "w") as f: + f.create_dataset("select_fct", data=select_fct) + + del survey_mask, r, d, zz, spline, select_fct + collect() diff --git a/src/selfisys/selfi_interface.py b/src/selfisys/selfi_interface.py new file mode 100644 index 0000000..499f807 --- /dev/null +++ b/src/selfisys/selfi_interface.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Provides simple wrappers around pyselfi.utils functions for the SelfiSys +pipeline. +""" + + +def PrintMessage(required_verbosity: int, message: str, verbosity: int) -> None: + """ + Print a message to standard output using pyselfi.utils.PrintMessage. + + Parameters + ---------- + required_verbosity : int + The verbosity level required to display the message. + message : str + The actual message to display. + verbosity : int + The current verbosity level (0=quiet, 1=normal, 2=debug). + """ + from pyselfi.utils import PrintMessage as PSMessage + + if verbosity >= required_verbosity: + PSMessage(3, message) + + +def indent() -> None: + """Indent the standard output using pyselfi.utils.""" + from pyselfi.utils import INDENT + + INDENT() + + +def unindent() -> None: + """Unindent the standard output using pyselfi.utils.""" + from pyselfi.utils import UNINDENT + + UNINDENT() diff --git a/src/selfisys/setup_model.py b/src/selfisys/setup_model.py new file mode 100644 index 0000000..a0eddc4 --- /dev/null +++ b/src/selfisys/setup_model.py @@ -0,0 +1,328 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Set up parameters related to the grid and the fiducial power spectrum. +""" + +import os.path +import gc +from typing import Optional, NamedTuple +import numpy as np +from h5py import File + +from pysbmy.power import PowerSpectrum, FourierGrid, get_Pk +from selfisys.utils.logger import getCustomLogger +from selfisys.utils.tools import get_k_max + +logger = getCustomLogger(__name__) + + +class ModelSetup(NamedTuple): + size: int + L: float + P: int + S: int + G_sim_path: str + G_ss_path: str + Pbins_bnd: np.ndarray + Pbins: np.ndarray + k_s: np.ndarray + P_ss_obj_path: str + P_0: np.ndarray + planck_Pk: np.ndarray + + +def setup_model( + workdir: str, + params_planck: dict, + params_P0: dict, + size: int = 256, + L: float = 3600.0, + S: int = 100, + N_exact: int = 8, + Pinit: int = 50, + trim_threshold: int = 100, + minval: Optional[float] = None, + maxval: Optional[float] = None, + force: bool = False, +) -> ModelSetup: + """ + Set up the model by computing or loading necessary grids and + parameters. + + Parameters + ---------- + workdir : str + Directory where the results will be stored. + params_planck : dict + Parameters for the Planck 2018 cosmology. + params_P0 : dict + Parameters for the normalisation power spectrum. + size : int + Number of elements in each direction of the box. + L : float + Comoving length of the box in Mpc/h. + S : int + Number of support wavenumbers for the input power spectra. + N_exact : int + Number of support wavenumbers matching the Fourier grid. + Pinit : int + Maximum number of bins for the summaries. + trim_threshold : int + Minimum number of modes required per bin. + minval : float, optional + Minimum k value for the summaries. + maxval : float, optional + Maximum k value for the summaries. + force : bool + If True, forces recomputation of the inputs. + + Returns + ------- + ModelSetup + A named tuple containing: + - size (int): Number of elements in each direction of the box. + - L (float): Comoving length of the box in Mpc/h. + - P (int): Number of bins for the summaries. + - S (int): Number of support wavenumbers for input powerspectra. + - G_sim_path (str): Path to the full Fourier grid file. + - G_ss_path (str): Path to the Fourier grid for summaries file. + - Pbins_bnd (np.ndarray): Boundaries of summary bins. + - Pbins (np.ndarray): Centres of the bins for the summaries. + - k_s (np.ndarray): Support wavenumbers for input power spectra. + - P_ss_obj_path (str): Path to the summary power spectrum file. + - P_0 (np.ndarray): Normalisation power spectrum values. + - planck_Pk (np.ndarray): Planck 2018 power spectrum values. + """ + # Check input parameters + if N_exact < 0 or N_exact > S: + raise ValueError("Parameter 'N_exact' must be between 0 and 'S'.") + + # Define file paths + G_sim_path = os.path.join(workdir, "G_sim.h5") + k_s_path = os.path.join(workdir, "k_s.npy") + G_ss_path = os.path.join(workdir, "G_ss.h5") + P_ss_obj_path = os.path.join(workdir, "P_ss_obj.h5") + P_0_path = os.path.join(workdir, "P_0.npy") + theta_planck_path = os.path.join(workdir, "theta_planck.npy") + Pbins_path = os.path.join(workdir, "Pbins.npy") + Pbins_bnd_path = os.path.join(workdir, "Pbins_bnd.npy") + + # Compute or load the full Fourier grid + if not os.path.exists(G_sim_path) or force: + logger.info("Computing Fourier grid...") + G_sim = FourierGrid(L, L, L, size, size, size) + G_sim.write(G_sim_path) + logger.info("Computing Fourier grid done.") + else: + logger.info("Loading Fourier grid.") + G_sim = FourierGrid.read(G_sim_path) + + # Determine minimum and maximum k values + if minval is None: + minval = np.min(G_sim.k_modes[G_sim.k_modes != 0]) + if maxval is None: + maxval = np.pi * size / L # 1D Nyquist frequency + + # Compute or load support wavenumbers for the input power spectrum + if not os.path.exists(k_s_path) or force: + logger.diagnostic("Computing input power spectrum support wavenumbers...") + k_s = np.zeros(S) + sorted_knorms = np.sort(G_sim.k_modes.flatten()) + unique_indices = np.unique(np.round(sorted_knorms, 5), return_index=True)[1] + sorted_knorms_corrected = sorted_knorms[unique_indices] + k_s[:N_exact] = sorted_knorms_corrected[1 : N_exact + 1] + k_s_max = get_k_max(L, size) + k_s[N_exact:] = np.logspace( + np.log10(sorted_knorms_corrected[N_exact]), + np.log10(k_s_max), + S - N_exact + 1, + )[1:] + np.save(k_s_path, k_s) + logger.diagnostic("Computing input power spectrum support wavenumbers done.") + else: + logger.diagnostic("Loading input power spectrum support wavenumbers.") + try: + k_s = np.load(k_s_path) + except (IOError, FileNotFoundError) as e: + logger.error(f"Failed to load k_s from {k_s_path}: {e}") + raise + + # Initialise Pbins + Pbins_left_bnds_init = np.logspace( + np.log10(minval), np.log10(maxval), Pinit + 1, dtype=np.float32 + ) + Pbins_left_bnds_init = Pbins_left_bnds_init[:-1] + + # Compute or load Fourier grid for the summaries + if not os.path.exists(G_ss_path) or force: + G_ss = FourierGrid( + L, + L, + L, + size, + size, + size, + k_modes=Pbins_left_bnds_init, + kmax=maxval, + trim_bins=True, + trim_threshold=trim_threshold, + ) + G_ss.write(G_ss_path) + else: + G_ss = FourierGrid.read(G_ss_path) + P = G_ss.NUM_MODES + + # Compute or load Pbins and Pbins_bnd + if not os.path.exists(Pbins_path) or not os.path.exists(Pbins_bnd_path) or force: + k_ss_max_offset = Pbins_left_bnds_init[-1] - Pbins_left_bnds_init[-2] + logger.diagnostic(f"k_ss_max_offset: {k_ss_max_offset:.5f}") + Pbins_bnd = G_ss.k_modes + Pbins_bnd = np.concatenate([Pbins_bnd, [Pbins_bnd[-1] + k_ss_max_offset]]) + Pbins = (Pbins_bnd[1:] + Pbins_bnd[:-1]) / 2 + np.save(Pbins_path, Pbins) + np.save(Pbins_bnd_path, Pbins_bnd) + else: + try: + Pbins = np.load(Pbins_path) + Pbins_bnd = np.load(Pbins_bnd_path) + except (IOError, FileNotFoundError) as e: + logger.error(f"Failed to load Pbins or Pbins_bnd: {e}") + raise + + # Compute or load BBKS spectrum for normalisation + if not os.path.exists(P_0_path) or force: + P_0 = get_Pk(k_s, params_P0) + np.save(P_0_path, P_0) + else: + try: + P_0 = np.load(P_0_path) + except (IOError, FileNotFoundError) as e: + logger.error(f"Failed to load P_0 from {P_0_path}: {e}") + raise + + if not os.path.exists(P_ss_obj_path) or force: + P_0_ss = get_Pk(G_ss.k_modes, params_P0) + P_ss_obj = PowerSpectrum.from_FourierGrid(G_ss, powerspectrum=P_0_ss, cosmo=params_P0) + P_ss_obj.write(P_ss_obj_path) + else: + P_ss_obj = PowerSpectrum.read(P_ss_obj_path) + + # Compute or load Planck power spectrum + if not os.path.exists(theta_planck_path) or force: + planck_Pk = get_Pk(k_s, params_planck) + np.save(theta_planck_path, planck_Pk) + else: + try: + planck_Pk = np.load(theta_planck_path) + except (IOError, FileNotFoundError) as e: + logger.error(f"Failed to load theta_planck from {theta_planck_path}: {e}") + raise + + # Clean up + del G_sim, G_ss, P_ss_obj, Pbins_left_bnds_init + gc.collect() + + return ModelSetup( + size, + L, + P, + S, + G_sim_path, + G_ss_path, + Pbins_bnd, + Pbins, + k_s, + P_ss_obj_path, + P_0, + planck_Pk, + ) + + +def compute_alpha_cv( + workdir: str, + k_s: np.ndarray, + size: int, + L: float, + window_fct_path: Optional[str] = None, + force: bool = False, +) -> None: + """ + Compute the cosmic variance parameter alpha_cv. + + Parameters + ---------- + workdir : str + Directory where the results will be stored. + k_s : np.ndarray + Support wavenumbers. + size : int + Number of elements in each direction of the box. + L : float + Comoving length of the box in Mpc/h. + window_fct_path : str, optional + Path to the window function file. + force : bool + If True, forces recomputation of the inputs. + + """ + from scipy.optimize import curve_fit + + alpha_cv_path = os.path.join(workdir, "alpha_cv.npy") + alpha_cv_eff_path = os.path.join(workdir, "alpha_cv_eff.npy") + + if not os.path.exists(alpha_cv_path) or force: + logger.info("Computing cosmic variance alpha_cv...") + k_s_bnd = np.concatenate([k_s, [np.inf]]) + + G_sim = FourierGrid.read(os.path.join(workdir, "G_sim.h5")).k_modes.flatten() + knorms = np.sort(G_sim) + + Nks, _ = np.histogram(knorms, bins=k_s_bnd) + + del knorms, G_sim + + nyquist_frequency = np.pi * size / L + idx_nyquist = np.searchsorted(k_s, nyquist_frequency) + + def cubic_func(x, a): + return a * x**3 + + try: + popt, _ = curve_fit(cubic_func, k_s[:idx_nyquist], Nks[:idx_nyquist]) + except RuntimeError as e: + logger.error(f"Curve fitting failed: {e}") + raise + + alpha_cv = np.sqrt(1 / popt[0]) + np.save(alpha_cv_path, alpha_cv) + logger.info(f"Computing cosmic variance alpha_cv done. alpha_cv = {alpha_cv}") + + if window_fct_path is not None: + # Compute alpha_cv with approximate correction for the effective volume + nnz = 0 + with File(window_fct_path, "r") as f: + for ipop in range(3): + mask = f["select_fct"][:][ipop] + nnz += np.sum(mask) + nnz_size = nnz ** (1 / 3.0) # Side length of a cube containing nnz voxels + eff_L = nnz_size * L / size + + alpha_cv_eff = alpha_cv * (L / eff_L) ** 1.5 + logger.info(f"Effective length: {eff_L * 1e-3} Gpc/h") + logger.info(f"Effective volume: {(eff_L * 1e-3) ** 3} (Gpc/h)^3") + logger.info(f"alpha_cv_eff = {alpha_cv_eff}") + np.save(alpha_cv_eff_path, alpha_cv_eff) + + gc.collect() diff --git a/src/selfisys/utils/__init__.py b/src/selfisys/utils/__init__.py new file mode 100644 index 0000000..5e681d3 --- /dev/null +++ b/src/selfisys/utils/__init__.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + + +"""Utility functions for the SelfiSys pipeline. +""" diff --git a/src/selfisys/utils/examples_utils.py b/src/selfisys/utils/examples_utils.py new file mode 100644 index 0000000..e239392 --- /dev/null +++ b/src/selfisys/utils/examples_utils.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +This module provides utility functions for the examples. +""" + + +def clear_large_plot(fig): + """ + Clear a figure to free up memory. + + Parameters + ---------- + fig : matplotlib.figure.Figure + The figure to clear. + """ + from IPython.display import clear_output + + del fig + clear_output() diff --git a/src/selfisys/utils/logger.py b/src/selfisys/utils/logger.py new file mode 100644 index 0000000..d84589f --- /dev/null +++ b/src/selfisys/utils/logger.py @@ -0,0 +1,228 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Logger routines for the SelfiSys package. + +The printing routines and colours are adapted from the Simbelmynë +comological solver (https://simbelmyne.readthedocs.io/en/latest). +""" + +import sys +from typing import cast +import logging +from selfisys import DEFAULT_VERBOSE_LEVEL + +# Global variables for fonts +FONT_BOLDRED = "\033[1;31m" +FONT_BOLDGREEN = "\033[1;32m" +FONT_BOLDYELLOW = "\033[1;33m" +FONT_BOLDCYAN = "\033[1;36m" +FONT_BOLDGREY = "\033[1;37m" +FONT_LIGHTPURPLE = "\033[38;5;147m" + +FONT_NORMAL = "\033[00m" + +# Global variables for verbosity +ERROR_VERBOSITY = 0 +INFO_VERBOSITY = 1 +WARNING_VERBOSITY = 2 +DIAGNOSTIC_VERBOSITY = 3 +DEBUG_VERBOSITY = 4 +DIAGNOSTIC_LEVEL = 15 +logging.addLevelName(DIAGNOSTIC_LEVEL, "DIAGNOSTIC") + +G__ind__ = 0 # Global variable for logger indentation + + +def INDENT(): + """Indents the current level of outputs.""" + global G__ind__ + G__ind__ += 1 + return G__ind__ + + +def UNINDENT(): + """Unindents the current level of outputs.""" + global G__ind__ + G__ind__ -= 1 + return G__ind__ + + +def PrintLeftType(message_type, FONT_COLOR): + """Prints the type of output to screen. + + Parameters + ---------- + message_type (string) : type of message + FONT_COLOR (string) : font color for this type of message + + """ + from time import localtime, strftime + + sys.stdout.write( + "[" + + strftime("%H:%M:%S", localtime()) + + "|" + + FONT_COLOR + + message_type + + FONT_NORMAL + + "]" + ) + sys.stdout.write("==" * G__ind__) + sys.stdout.write("|") + + +def PrintInfo(message): + """Prints an information to screen. + + Parameters + ---------- + message (string) : message + + """ + if DEFAULT_VERBOSE_LEVEL >= INFO_VERBOSITY: + PrintLeftType("INFO ", FONT_BOLDCYAN) + sys.stdout.write("{}\n".format(message)) + sys.stdout.flush() + + +def PrintDiagnostic(verbosity, message): + """Prints a diagnostic to screen. + + Parameters + ---------- + verbosity (int) : verbosity of the message + message (string) : message + + """ + if DEFAULT_VERBOSE_LEVEL >= verbosity: + PrintLeftType("DIAGNOSTIC", FONT_BOLDGREY) + sys.stdout.write("{}\n".format(message)) + + +def PrintWarning(message): + """Prints a warning to screen. + + Parameters + ---------- + message (string) : message + + """ + if DEFAULT_VERBOSE_LEVEL >= WARNING_VERBOSITY: + PrintLeftType("WARNING ", FONT_BOLDYELLOW) + sys.stdout.write(FONT_BOLDYELLOW + message + FONT_NORMAL + "\n") + + +def PrintError(message): + """Prints an error to screen. + + Parameters + ---------- + message (string) : message + + """ + if DEFAULT_VERBOSE_LEVEL >= ERROR_VERBOSITY: + PrintLeftType("ERROR ", FONT_BOLDRED) + sys.stdout.write(FONT_BOLDRED + message + FONT_NORMAL + "\n") + + +class CustomLoggerHandler(logging.Handler): + """ + Custom logging handler to redirect Python logger messages to custom + print functions, with support for verbosity levels in debug + messages. + """ + + def emit(self, record): + """ + Emit a log record. + """ + try: + log_message = self.format(record) + log_level = record.levelno + + if log_level >= logging.ERROR: + PrintError(log_message) + elif log_level >= logging.WARNING: + PrintWarning(log_message) + elif log_level >= logging.INFO: + PrintInfo(log_message) + elif log_level == DIAGNOSTIC_LEVEL: + # Retrieve verbosity level from the record + verbosity = getattr(record, "verbosity", DIAGNOSTIC_VERBOSITY) + PrintDiagnostic(verbosity=verbosity, message=log_message) + elif log_level >= logging.DEBUG: + PrintDiagnostic(verbosity=DEBUG_VERBOSITY, message=log_message) + else: + # Fallback for other levels + PrintInfo(log_message) + except Exception: + self.handleError(record) + + +class CustomLogger(logging.Logger): + """ + Custom logger class supporting custom verbosity levels in diagnostic + messages. + """ + + def diagnostic(self, msg, *args, verbosity=DIAGNOSTIC_VERBOSITY, **kwargs) -> None: + """ + Log a message with DIAGNOSTIC level. + + Parameters + ---------- + msg : str + The message to log. + verbosity : int, optional + The verbosity level required to log this message. + """ + if self.isEnabledFor(DIAGNOSTIC_LEVEL): + # Pass verbosity as part of the extra argument + extra = kwargs.get("extra", {}) + extra["verbosity"] = verbosity + kwargs["extra"] = extra + self.log(DIAGNOSTIC_LEVEL, msg, *args, **kwargs) + + +logging.setLoggerClass(CustomLogger) + + +def getCustomLogger(name: str) -> CustomLogger: + """ + Get as CustomLogger instance to use the custom printing routines. + + Parameters + ---------- + name : str + The name of the logger. + + Returns + ------- + logger : logging.Logger + The custom logger instance. + """ + logging.setLoggerClass(CustomLogger) + logger = cast(CustomLogger, logging.getLogger(name)) # cast for type checkers and PyLance + logger.setLevel(logging.DEBUG) # Set the desired base logging level + + handler = CustomLoggerHandler() + formatter = logging.Formatter(f"{FONT_LIGHTPURPLE}(%(name)s){FONT_NORMAL} %(message)s") + handler.setFormatter(formatter) + + # Attach the handler to the logger if not already present + if not logger.handlers: + logger.addHandler(handler) + + return logger diff --git a/src/selfisys/utils/low_level.py b/src/selfisys/utils/low_level.py new file mode 100644 index 0000000..2b437c5 --- /dev/null +++ b/src/selfisys/utils/low_level.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Tools to deal with low-level operations such as redirecting stdout from +C code. +""" + +from contextlib import contextmanager +import platform +import ctypes +import io +import os, sys +import tempfile + +libc = ctypes.CDLL(None) +if platform.system() == "Darwin": # macOS + stdout_symbol = "__stdoutp" + stderr_symbol = "__stderrp" +else: + stdout_symbol = "stdout" + stderr_symbol = "stderr" +c_stdout = ctypes.c_void_p.in_dll(libc, stdout_symbol) +c_stderr = ctypes.c_void_p.in_dll(libc, stderr_symbol) + + +# Taken from: +# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/ +@contextmanager +def stdout_redirector(stream): + """A context manager that redirects stdout to the given stream. For + instance, this can be used to redirect C code stdout to None (to + avoid cluttering the log, e.g., when using tqdm). + + Args: + stream (file-like object): The stream to which stdout should be + redirected. + Example: + >>> with stdout_redirector(stream): + >>> print("Hello world!") # Will be printed to stream + >>> # instead of stdout. + """ + # The original fd stdout points to. Usually 1 on POSIX systems. + original_stdout_fd = sys.stdout.fileno() + + def _redirect_stdout(to_fd): + """Redirect stdout to the given file descriptor.""" + # Flush the C-level buffer stdout + libc.fflush(c_stdout) + # Flush and close sys.stdout - also closes the file descriptor (fd) + sys.stdout.close() + # Make original_stdout_fd point to the same file as to_fd + os.dup2(to_fd, original_stdout_fd) + # Create a new sys.stdout that points to the redirected fd + sys.stdout = io.TextIOWrapper(os.fdopen(original_stdout_fd, "wb")) + + # Save a copy of the original stdout fd in saved_stdout_fd + saved_stdout_fd = os.dup(original_stdout_fd) + try: + # Create a temporary file and redirect stdout to it + tfile = tempfile.TemporaryFile(mode="w+b") + _redirect_stdout(tfile.fileno()) + # Yield to caller, then redirect stdout back to the saved fd + yield + _redirect_stdout(saved_stdout_fd) + # Copy contents of temporary file to the given stream + tfile.flush() + tfile.seek(0, io.SEEK_SET) + stream.write(tfile.read()) + finally: + tfile.close() + os.close(saved_stdout_fd) + + +# Adapted from: +# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/ +@contextmanager +def stderr_redirector(stream): + """A context manager that redirects stderr to the given stream. + For instance, this can be used to redirect C code stderr to None (to + avoid cluttering the log, e.g., when using tqdm). + + Use with caution. + + Args: + stream (file-like object): The stream to which stdout should be + redirected. + """ + # The original fd stdout points to. Usually 1 on POSIX systems. + original_stderr_fd = sys.stderr.fileno() + + def _redirect_stderr(to_fd): + """Redirect stderr to the given file descriptor.""" + # Flush the C-level buffer stderr + libc.fflush(c_stderr) + # Flush and close sys.stderr - also closes the file descriptor (fd) + sys.stderr.close() + # Make original_stderr_fd point to the same file as to_fd + os.dup2(to_fd, original_stderr_fd) + # Create a new sys.stderr that points to the redirected fd + sys.stderr = io.TextIOWrapper(os.fdopen(original_stderr_fd, "wb")) + + # Save a copy of the original stdout fd in saved_stdout_fd + saved_stderr_fd = os.dup(original_stderr_fd) + try: + # Create a temporary file and redirect stdout to it + tfile = tempfile.TemporaryFile(mode="w+b") + _redirect_stderr(tfile.fileno()) + # Yield to caller, then redirect stdout back to the saved fd + yield + _redirect_stderr(saved_stderr_fd) + # Copy contents of temporary file to the given stream + tfile.flush() + tfile.seek(0, io.SEEK_SET) + stream.write(tfile.read()) + finally: + tfile.close() + os.close(saved_stderr_fd) diff --git a/src/selfisys/utils/parser.py b/src/selfisys/utils/parser.py new file mode 100644 index 0000000..2310d8e --- /dev/null +++ b/src/selfisys/utils/parser.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Utility functions for parsing command-line arguments. +""" + +import os +from argparse import ArgumentParser, ArgumentTypeError + + +def joinstrs(list_of_strs): + """Join a list of strings into a single string. + + Parameters + ---------- + list_of_strs : list of str + List of strings to join. + + Returns + ------- + str + Concatenated string. + """ + return "".join([str(x) for x in list_of_strs if x is not None]) + + +def joinstrs_only(list_of_strs): + """Join a list of strings into a single string, ignoring all + non-string elements such as None values. + + Parameters + ---------- + list_of_strs : list of str + List of strings to join. + + Returns + ------- + str + Concatenated string. + """ + return "".join([str(x) for x in list_of_strs if type(x) == str]) + + +def check_files_exist(files): + """Check if all files in the list exist. + + Parameters + ---------- + files : list of str + List of file paths to check. + + Returns + ------- + bool + True if all files exist, False otherwise. + """ + + return all(os.path.exists(f) for f in files) + + +def none_or_bool_or_str(value): + """Convert a string to None, bool, or str. + + Parameters + ---------- + value : str + String to convert. + + Returns + ------- + None, bool, or str + Converted value. + """ + if value == "None" or value == None: + return None + elif value == "True": + return True + elif value == "False": + return False + return value + + +def intNone(value): + """Convert a string to None or int. + + Parameters + ---------- + value : str + String to convert. + + Returns + ------- + None or int + Converted value. + """ + if value == "None" or value == None: + return None + else: + return int(value) + + +def safe_npload(path): + """Load a numpy array from a file. + + Parameters + ---------- + path : str + Path to the file to load. + + Returns + ------- + None or np.ndarray + Loaded array or None if the file does not exist. + """ + import numpy as np + + val = np.load(path, allow_pickle=True) + if val is None or val == "None" or val == None: + return None + else: + return val + + +def bool_sh(value): + """Convert a string to a boolean. + + Parameters + ---------- + value : str + String to convert. + + Returns + ------- + bool + Converted value. + """ + if value == "True": + return True + elif value == "False": + return False + else: + raise ArgumentTypeError("Boolean value expected.") diff --git a/src/selfisys/utils/path_utils.py b/src/selfisys/utils/path_utils.py new file mode 100644 index 0000000..306d2af --- /dev/null +++ b/src/selfisys/utils/path_utils.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Helper functions to handle paths and file names. +""" + + +import os + + +def _get_prefix(prefix_mocks: str, suffix: str, sim_id=None, d=None, p=None) -> str: + """ + Get file prefix. + + Parameters + ---------- + prefix_mocks : str | None + Prefix for the mock data files. If None, defaults to base + suffix. + suffix : str + Base suffix for the file name (e.g., "mocks" or "g"). + sim_id : int, optional + Simulation ID. Used if d and p are not provided. + d : int, optional + Direction index. + p : int, optional + Simulation index. If both d and p are provided, they take + precedence over sim_id. + + Returns + ------- + str + Formatted file name string. + """ + prefix = f"{prefix_mocks}_{suffix}" if prefix_mocks else suffix + if d is not None and p is not None: + return f"{prefix}_d{d}_p{p}.h5" + return f"{prefix}_{sim_id}.h5" + + +def get_file_names( + fsimdir: str, + sim_id: int, + sim_params: str, + TimeSteps: list[int], + prefix_mocks: str, + gravity_on: bool = True, + return_g: bool = False, +) -> dict: + """ + Generate file paths for a given simulation ID and parameters. + + Parameters + ---------- + fsimdir : str + Path to the simulation directory. + sim_id : int + Simulation ID. + sim_params : str + Simulation parameters. + TimeSteps : list of int + List of time steps. + prefix_mocks : str | None + Prefix for mock data files. If None, defaults to "mocks". + gravity_on : bool, optional + Whether gravity is active. Default is True. + return_g : bool, optional + If True, return the file name for the observed galaxy field. + Default is False. + + Returns + ------- + dict + Dictionary containing simulation inputs / outputs file paths. + """ + datadir = os.path.join(fsimdir, "data") + names = { + "fname_cosmo": os.path.join(datadir, f"input_cosmo_{sim_id}.json"), + "fname_power_spectrum": os.path.join(datadir, f"input_power_{sim_id}.h5"), + "fname_outputinitialdensity": os.path.join(datadir, f"initial_density_{sim_id}.h5"), + "fname_mocks": os.path.join(datadir, _get_prefix(prefix_mocks, "mocks", sim_id)), + "fname_g": ( + os.path.join(datadir, _get_prefix(prefix_mocks, "g", sim_id)) if return_g else None + ), + "fname_simparfile": None, + "fname_whitenoise": None, + "seedname_whitenoise": None, + "fnames_outputLPTdensity": None, + "fnames_outputrealspacedensity": None, + "fnames_outputdensity": None, + "fname_simlogs": None, + } + + if gravity_on: + names.update( + { + "fname_simparfile": os.path.join(datadir, f"sim_{sim_id}"), + "fname_whitenoise": os.path.join( + datadir, f"initial_density_white_noise_{sim_id}.h5" + ), + "seedname_whitenoise": os.path.join(datadir, f"initial_density_wn_{sim_id}_seed"), + "fnames_outputLPTdensity": os.path.join(datadir, f"output_density_{sim_id}.h5"), + "fname_simlogs": os.path.join(datadir, f"logs_sim_{sim_id}.txt"), + } + ) + if sim_params.startswith(("split", "custom")): + names["fnames_outputdensity"] = [ + os.path.join(datadir, f"output_density_{sim_id}_{i}.h5") for i in TimeSteps[::-1] + ] + names["fnames_outputrealspacedensity"] = [ + os.path.join(datadir, f"output_realdensity_{sim_id}_{i}.h5") + for i in TimeSteps[::-1] + ] + else: + names["fnames_outputdensity"] = [os.path.join(datadir, f"output_density_{sim_id}.h5")] + names["fnames_outputrealspacedensity"] = [ + os.path.join(datadir, f"output_realdensity_{sim_id}.h5") + ] + + return names + + +def file_names_evaluate( + simdir: str, + sd: str, + d: int, + i: int, + sim_params: str, + TimeSteps: list[int], + prefix_mocks: str, + abc: bool = False, + gravity_on: bool = True, +) -> dict: + """ + Generate file paths for the given simulation id and parameters. + + Parameters + ---------- + simdir : str + Path to the simulation directory. + sd : str + Path to the simulation directory for the given direction. + d : int + Direction index (-1 for mock data, 0 for the expansion point, or + 1 to S). + i : int + Simulation index. + sim_params : str + Simulation parameters. + TimeSteps : list of int + List of time steps. + prefix_mocks : str | None + Prefix for mock data files. If None, defaults to "mocks". + abc : bool, optional + If True, appends the ABC index to the white noise path. + gravity_on : bool, optional + Whether gravity is active. Default is True. + + Returns + ------- + dict + Dictionary containing simulation inputs / outputs file paths. + """ + names = { + "fname_power_spectrum": os.path.join(sd, f"input_power_d{d}.h5"), + "fname_outputinitialdensity": os.path.join(sd, f"initial_density_d{d}_p{i}.h5"), + "fname_mocks": os.path.join(sd, _get_prefix(prefix_mocks, "mocks", d=d, p=i)), + "fname_simlogs": os.path.join(sd, f"logs_sim_d{d}_p{i}.txt"), + "fname_simparfile": None, + "fname_whitenoise": None, + "seedname_whitenoise": None, + "fnames_outputLPTdensity": None, + "fnames_outputrealspacedensity": None, + "fnames_outputdensity": None, + "fname_g": None, + } + + if gravity_on: + dir_wn = os.path.join(simdir, "..", "wn") if not abc else os.path.join(simdir, "wn", abc) + names.update( + { + "fname_simparfile": os.path.join(sd, f"sim_d{d}_p{i}"), + "fname_whitenoise": os.path.join(dir_wn, f"initial_density_white_p{i}.h5"), + "seedname_whitenoise": os.path.join(dir_wn, f"initial_density_white_p{i}"), + "fnames_outputLPTdensity": os.path.join(sd, f"output_density_d{d}_p{i}.h5"), + } + ) + if sim_params.startswith(("split", "custom")): + names["fnames_outputrealspacedensity"] = [ + os.path.join(sd, f"output_realdensity_d{d}_p{i}_{j}.h5") for j in TimeSteps[::-1] + ] + names["fnames_outputdensity"] = [ + os.path.join(sd, f"output_density_d{d}_p{i}_{j}.h5") for j in TimeSteps[::-1] + ] + else: + names["fnames_outputrealspacedensity"] = [ + os.path.join(sd, f"output_realdensity_d{d}_p{i}.h5") + ] + names["fnames_outputdensity"] = [os.path.join(sd, f"output_density_d{d}_p{i}.h5")] + + return names diff --git a/src/selfisys/utils/plot_examples.py b/src/selfisys/utils/plot_examples.py new file mode 100644 index 0000000..6c98be0 --- /dev/null +++ b/src/selfisys/utils/plot_examples.py @@ -0,0 +1,616 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Visualisation utilities for the exploratory examples in SelfiSys. +""" + +import numpy as np +import matplotlib.pyplot as plt +from selfisys.utils.plot_params import * + +# Configure global plotting settings +setup_plotting() + + +def plot_power_spectrum( + G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, title=None, display=True +): + """ + Plot a power spectrum over Fourier modes, its linear interpolation + over specified support points, and a given binning for comparison. + + Parameters + ---------- + G_sim : pysbmy.power.FourierGrid + Fourier grid object containing the `k_modes` attribute. + true_P : pysbmy.power.PowerSpectrum + Power spectrum object containing the `powerspectrum` attribute. + k_s : array-like + Support points in k-space. + planck_Pk : array-like + Power spectrum values at the support points. + Pbins : array-like + Centres of the Φ bins in k-space. + Pbins_bnd : array-like + Boundaries of the Φ bins in k-space. + size : float + Box size in number of grid cells. + L : float + Box length in Mpc/h. + wd : str + Working directory path for saving the figure. + title : str, optional + Title for the figure. Default is None. + display : bool, optional + Whether to display the figure. Default is True. + + Returns + ------- + None + """ + import os + from selfisys.utils.logger import PrintInfo + + plt.figure(figsize=(15, 5)) + + # Plot power spectrum data + plt.plot(G_sim.k_modes, true_P.powerspectrum, label=r"$P(k)$ (over all modes)") + plt.plot(k_s, planck_Pk, label=r"$P(k)$ (binned–linear interpolation)", linestyle="dashed") + + # Configure axes + plt.xlabel(r"$k\,[h/\mathrm{Mpc}]$") + plt.ylabel(r"$[{\rm Mpc}/h]^3$") + plt.xscale("log") + plt.yscale("log") + plt.xlim(np.clip(k_s.min() - 2e-4, 1e-4, None), k_s.max()) + plt.ylim(1e1, 1e5) + plt.grid(which="both", axis="y") + + # Plot vertical lines for support points and binning + plt.vlines(k_s[:-1], ymin=1e1, ymax=1e5, colors="green", linestyles="dotted", linewidth=0.6) + plt.axvline( + k_s[-1], + color="green", + linestyle="dotted", + linewidth=0.6, + label=r"$\boldsymbol{\uptheta}$ support points", + ) + plt.vlines( + Pbins, + ymin=1e1, + ymax=5e2, + colors="red", + linestyles="dashed", + linewidth=0.5, + label=r"$\boldsymbol{\Phi}$ bin centres", + ) + plt.vlines( + Pbins_bnd, + ymin=1e1, + ymax=1e2 / 2, + colors="blue", + linestyles="dashed", + linewidth=0.5, + label=r"$\boldsymbol{\Phi}$ bin boundaries", + ) + + # Plot the Nyquist frequency + nyquist_freq = np.pi * size / L + plt.axvline( + nyquist_freq, ymax=1 / 6.0, color="orange", linestyle="-", linewidth=2, label="Nyquist" + ) + + # Add legend, optional title, and save the figure + plt.legend(loc="upper center", bbox_to_anchor=(0.5, -0.2), ncol=3) + if title: + plt.title(title) + output_dir = os.path.join(wd, "Figures") + os.makedirs(output_dir, exist_ok=True) + output_path = os.path.join(output_dir, "summary.pdf") + plt.savefig(output_path, bbox_inches="tight") + PrintInfo(f"Figure saved to: {output_path}") + + if display: + plt.show() + plt.close() + + +def relative_error_analysis( + G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, display=True +): + """ + Compute and plot the relative error between the interpolated and + true power spectra. + + Parameters + ---------- + G_sim : pysbmy.power.FourierGrid + Fourier grid object containing the `k_modes` attribute. + true_P : pysbmy.power.PowerSpectrum + Power spectrum object containing the `powerspectrum` attribute. + k_s : array-like + Support points in k-space. + planck_Pk : array-like + Power spectrum values at the support points. + Pbins : array-like + Centres of the Φ bins in k-space. + Pbins_bnd : array-like + Boundaries of the Φ bins in k-space. + size : float + Box size in number of grid cells. + L : float + Box length in Mpc/h. + wd : str + Working directory path for saving the figure. + display : bool, optional + Whether to display the figure. Default is True. + + Returns + ------- + None + """ + import os + from scipy.interpolate import InterpolatedUnivariateSpline + from selfisys.utils.logger import PrintInfo + + # Interpolate the power spectrum + spline = InterpolatedUnivariateSpline(k_s, planck_Pk, k=5) + rec_Pk = spline(G_sim.k_modes[1:]) + true_spectrum = true_P.powerspectrum[1:] + xx = G_sim.k_modes[1:] + + # Compute relative errors + rel_err = (rec_Pk - true_spectrum) / true_spectrum + indices_all = slice(None) + indices_nyquist = np.where((xx >= k_s.min()) & (xx <= np.pi * size / L))[0] + indices_k2e1 = np.where(xx <= 2e-1)[0] + + max_relerr = np.max(np.abs(rel_err[indices_all])) + max_relerr_nyquist = np.max(np.abs(rel_err[indices_nyquist])) + max_relerr_2e1 = np.max(np.abs(rel_err[indices_k2e1])) + + # Create the figure + plt.figure(figsize=(15, 5)) + plt.plot( + xx, + rel_err, + label=r"$\left(P_\textrm{interp}-P_{\mathrm{true}}\right)/P_{\mathrm{true}}$", + ) + plt.xlabel(r"$k\,[h/\mathrm{Mpc}]$") + plt.ylabel("Relative error") + plt.xscale("log") + plt.xlim(np.clip(k_s.min() - 2e-4, 1e-4, None), k_s.max()) + plt.ylim(-0.1, 0.1) + plt.grid(which="both", axis="y") + + # Vertical lines for binning and support points + plt.axvline( + x=Pbins[0], + color="red", + linestyle="dashed", + linewidth=0.5, + label=r"$\boldsymbol\Phi$ bin centres", + ) + plt.axvline(x=Pbins[-1], color="red", linestyle="dashed", linewidth=0.5) + for k in Pbins[1:-1]: + plt.axvline(x=k, ymax=1 / 6.0, color="red", linestyle="dashed", linewidth=0.5) + for k in k_s[:-1]: + plt.axvline(x=k, color="green", linestyle="dotted", linewidth=0.6) + plt.axvline( + x=k_s[-1], + color="green", + linestyle="dotted", + linewidth=0.6, + label=r"$\boldsymbol\uptheta$ support points", + ) + plt.axvline( + x=Pbins_bnd[0], + ymax=1 / 3.0, + color="blue", + linestyle="dashed", + linewidth=0.5, + label=r"$\boldsymbol\Phi$ bin boundaries", + ) + plt.axvline(x=Pbins_bnd[-1], ymax=1 / 3.0, color="blue", linestyle="dashed", linewidth=0.5) + for k in Pbins_bnd[1:-1]: + plt.axvline(x=k, ymax=1 / 12.0, color="blue", linestyle="dashed", linewidth=0.5) + + # Nyquist and fundamental frequencies + plt.axvline( + x=2 * np.pi / L, + ymax=1 / 6.0, + color="orange", + linestyle="-", + linewidth=2, + label="Fundamental mode", + ) + plt.axvline( + x=np.pi * size / L, + ymax=1 / 6.0, + color="orange", + linestyle="--", + linewidth=2, + label="Nyquist", + ) + + # Add title, legend, and save the figure + plt.legend(loc="upper center", bbox_to_anchor=(0.5, -0.2), ncol=3) + plt.title( + "Relative error between interpolated and true Planck 2018 power spectrum\n" + f"over the {G_sim.k_modes.size} modes of the Fourier grid (max: {max_relerr * 100:.3f}\\%)" + ) + output_dir = os.path.join(wd, "Figures") + os.makedirs(output_dir, exist_ok=True) + output_path = os.path.join(output_dir, "summary_relerr.pdf") + plt.savefig(output_path, bbox_inches="tight") + PrintInfo(f"Figure saved to: {output_path}") + + # Print summary of relative errors + PrintInfo(f"Max relative error over all support points: {max_relerr * 100:.3f}%") + PrintInfo(f"Max relative error up to 1D Nyquist frequency: {max_relerr_nyquist * 100:.3f}%") + PrintInfo(f"Max relative error up to k = 2e-1: {max_relerr_2e1 * 100:.3f}%") + + if display: + plt.show() + plt.close() + + +def plot_comoving_distance_redshift( + zz, cosmo, means_com, L, Lcorner, wd, colours_list=COLOUR_LIST, display=True +): + """ + Plot comoving distance as a function of redshift, highlighting key + scales. + + Parameters + ---------- + zz : array-like + Redshift range for the plot. + cosmo : astropy.cosmology object + Cosmology instance for calculating comoving distances. + means_com : array-like + Mean comoving distances of selection functions. + L : float + Box side length in Gpc/h. + Lcorner : float + Diagonal of the box (sqrt(3) * L) in Gpc/h. + wd : str + Working directory for saving figures. + colours_list : list + List of colours for selection function annotations. + display : bool, optional + Whether to display the figure. Default is True. + """ + d = cosmo.comoving_distance(zz) / 1e3 # Convert to Gpc/h + + plt.figure(figsize=(12, 5.2)) + plt.plot(zz, d, label="Comoving distance") + plt.axhline( + L, color="black", linewidth=1, linestyle="--", label=rf"$L = {L:.2f}\textrm{{ Gpc}}/h$" + ) + plt.axhline( + Lcorner, + color="orange", + linewidth=1, + linestyle="--", + label=rf"$L_\textrm{{corner}} = {Lcorner:.2f}\textrm{{ Gpc}}/h$", + ) + + # Annotate key redshifts + d_np = d.value + z_L = zz[np.argmin(np.abs(d_np - L))] + z_corner = zz[np.argmin(np.abs(d_np - Lcorner))] + plt.axvline(z_L, color="black", linewidth=0.5, alpha=0.5, linestyle="-") + plt.axvline(z_corner, color="orange", linewidth=0.5, alpha=0.5, linestyle="-") + plt.text(z_L, 1.07 * d_np.max(), rf"$z(L) = {z_L:.2f}$", fontsize=GLOBAL_FS_TINY - 2) + plt.text( + z_corner, + 1.07 * d_np.max(), + rf"$z(\sqrt{{3}}\,L) = {z_corner:.2f}$", + fontsize=GLOBAL_FS_TINY - 2, + ) + + # Annotate the selection functions' means + z_means = np.array([zz[np.argmin(np.abs(d_np - m))] for m in means_com]) + for i, z_mean in enumerate(z_means): + plt.axvline(z_mean, color=colours_list[i], linestyle="--", linewidth=1) + plt.text( + z_mean - 0.07, + L + 0.2, + rf"$z(\mu_{{{i+1}}} = {means_com[i]:.2f}) = {z_mean:.2f}$", + fontsize=GLOBAL_FS_TINY - 2, + rotation=90, + ) + + # Add labels, legend, and save the figure + plt.xlabel("Redshift $z$") + plt.ylabel(r"Comoving distance [Gpc$/h$]") + plt.grid(which="both", axis="both", linestyle="-", linewidth=0.3, color="gray", alpha=0.5) + plt.legend() + plt.tight_layout() + plt.savefig(f"{wd}selection_functions_z.pdf", bbox_inches="tight", dpi=300) + plt.savefig(f"{wd}selection_functions_z.png", bbox_inches="tight", dpi=300, transparent=True) + if display: + plt.show() + plt.close() + + +def redshift_distance_conversion( + zz, cosmo, means_com, L, Lcorner, xx, wd, colours_list=COLOUR_LIST, display=True +): + """ + Plot the conversion between comoving distance and redshift; return + the redshifts corresponding to the selection functions' means. + + Parameters + ---------- + zz : array-like + Redshift range for the plot. + cosmo : astropy.cosmology object + Cosmology instance for calculating comoving distances. + means_com : array-like + Mean comoving distances of selection functions. + L : float + Box side length in Gpc/h. + Lcorner : float + Diagonal of the box (sqrt(3) * L) in Gpc/h. + xx : array-like + Comoving distances at which to compute redshift. + wd : str + Working directory for saving figures. + colours_list : list + List of colours for selection function annotations. + display : bool, optional + Whether to display the figure. Default is True. + + Returns + ------- + spline : scipy.interpolate.UnivariateSpline + Linear interpolator to convert comoving distances to redshifts. + """ + from scipy.interpolate import UnivariateSpline + + # Convert comoving distances to redshifts using a linear interpolation + d_np = (cosmo.comoving_distance(zz) / 1e3).value # Gpc/h + spline = UnivariateSpline(d_np, zz, k=1, s=0) + z_x = spline(xx) + + plt.figure(figsize=(12, 5)) + plt.plot(xx, z_x) + + # Annotate key scales + plt.axvline( + L, color="black", linewidth=1, linestyle="--", label=rf"$L = {L:.2f}\textrm{{ Gpc}}/h$" + ) + plt.axhline(spline(L), color="black", linewidth=1, linestyle="--") + plt.axvline( + Lcorner, + color="orange", + linewidth=1, + linestyle="--", + label=rf"$L_\textrm{{corner}} = {Lcorner:.2f}\textrm{{ Gpc}}/h$", + ) + plt.axhline(spline(Lcorner), color="orange", linewidth=1, linestyle="--") + plt.text(L + 0.08, spline(L) - 0.14, rf"$z(L) = {spline(L):.2f}$", fontsize=GLOBAL_FS_TINY - 2) + plt.text( + Lcorner - 1.2, + spline(Lcorner) - 0.17, + rf"$z(\sqrt{{3}}\,L) = {spline(Lcorner):.2f}$", + fontsize=GLOBAL_FS_TINY - 2, + ) + + # Annotate the selection functions' means + z_means = spline(means_com) + for i, z_mean in enumerate(z_means): + plt.axvline(means_com[i], color=colours_list[i], linestyle="--", linewidth=1) + plt.axhline(z_mean, color=colours_list[i], linestyle="--", linewidth=1) + plt.text( + L + 0.08, + z_mean - 0.14, + rf"$z(\mu_{{{i+1}}} = {means_com[i]:.2f}) = {z_mean:.2f}$", + fontsize=GLOBAL_FS_TINY - 2, + ) + + # Add labels, legend, and save the figure + plt.xlabel(r"Comoving distance [Gpc$/h$]") + plt.ylabel("Redshift $z$") + plt.grid(which="both", axis="both", linestyle="-", linewidth=0.3, color="gray", alpha=0.5) + plt.legend() + plt.tight_layout() + plt.savefig(f"{wd}redshift_distance_conversion.pdf", bbox_inches="tight", dpi=300) + if display: + plt.show() + plt.close() + + return spline + + +def plot_selection_functions_def_in_z( + xx_of_zs, + res, + res_mis, + z_means, + cosmo, + L, + stds_z, + wd, + display=True, +): + """ + Plot radial lognormal (in redshift) selection functions against + comoving distances. + + Parameters + ---------- + xx_of_zs : array-like + Comoving distances mapped from redshift. + res : list of array-like + Selection functions for the well-specified model. + res_mis : list of array-like + Selection functions for the mis-specified model. + z_means : array-like + Mean redshifts of every galaxy population. + cosmo : object + Cosmology object. + L : float + Box side length in comoving distance units. + stds_z : array-like + Standard deviations of redshift distributions. + wd : str + Working directory for saving figures. + display : bool, optional + Whether to display the figure. Default is True. + + Returns + ------- + None + """ + from matplotlib.ticker import FormatStrFormatter + + colours_list = COLOUR_LIST[: len(res)] + + plt.figure(figsize=(10, 5)) + + # Plot well-specified selection functions + for i, r in enumerate(res): + plt.plot(xx_of_zs, r, color=colours_list[i]) + plt.plot(xx_of_zs, res[-1], color="black", alpha=0, label="Model A") + + # Plot mis-specified selection functions + for i, r_mis in enumerate(res_mis): + plt.plot(xx_of_zs, r_mis, linestyle="--", color=colours_list[i]) + plt.plot(xx_of_zs, res_mis[-1], linestyle="--", color="black", alpha=0, label="Model B") + + # Define x-ticks and labels + xticks = [0, np.sqrt(3) * L] + xtick_labels = [r"$0$", r"$\sqrt 3\,L \simeq {:.2f}$".format(np.sqrt(3) * L)] + plt.axvline(L, color="black", linestyle="-", linewidth=1, zorder=0) + + # Annotate populations + for i, mean in enumerate(z_means): + std = stds_z[i] + mu = np.log(mean**2 / np.sqrt(mean**2 + std**2)) + sig2 = np.log(1 + std**2 / mean**2) + mode = np.exp(mu - sig2) + + dmode = cosmo.comoving_distance(mode).value / 1e3 + dmean = cosmo.comoving_distance(mean).value / 1e3 + + xticks.extend([dmean]) + xtick_labels.extend([f"{dmean:.2f}"]) + plt.axvline(dmean, color=colours_list[i], linestyle="-.", linewidth=1) + plt.axvline(dmode, color=colours_list[i], linestyle="-", linewidth=1) + plt.axvline( + mode, + color=colours_list[i], + alpha=0, + linewidth=1, + label=f"Population {i+1}", + ) + + # Configure axes, labels, ticks, legend + plt.xlabel(r"$r\,[{\rm Gpc}/h]$", fontsize=GLOBAL_FS_LARGE) + plt.ylabel(r"$R_i(r)$", fontsize=GLOBAL_FS_LARGE) + plt.xticks(xticks, xtick_labels) + plt.tick_params(axis="x", which="major", size=8, labelsize=GLOBAL_FS_SMALL) + plt.tick_params(axis="y", which="major", size=8, labelsize=GLOBAL_FS_SMALL) + plt.grid(which="both", axis="both", linestyle="-", linewidth=0.4, color="gray", alpha=0.5) + + maxs = [np.max(r) for r in res] + yticks = [0] + maxs + plt.yticks(yticks) + plt.gca().yaxis.set_major_formatter(FormatStrFormatter("%.2f")) + + legend = plt.legend(frameon=True, loc="upper right", fontsize=GLOBAL_FS_LARGE) + legend.get_frame().set_edgecolor("white") + for lh in legend.legend_handles: + lh.set_alpha(1) + + plt.tight_layout() + plt.savefig(f"{wd}selection_functions_com.pdf", bbox_inches="tight", dpi=300) + if display: + plt.show() + plt.close() + + +def plot_galaxy_field_slice(g, size, L, wd, id_obs, limits="minmax", display=True): + """ + Plot a 2D slice of the observed field. + + Parameters + ---------- + g : ndarray + 2D array representing the observed field slice. + size : int + Number of grid points along each axis. + L : float + Size of the simulation box (in Mpc/h). + wd : str + Working directory for saving output files. + id_obs : int or str + Identifier for the observation, used in file naming. + limits : str, optional + Colormap scaling method. Options: 'minmax', 'truncate', 'max'. + display : bool, optional + Whether to display the figure. Default is True. + """ + from mpl_toolkits.axes_grid1 import make_axes_locatable + from matplotlib import colors + + # Define colormap and set scaling limits + GalaxyMap = create_colormap("GalaxyMap") + + if limits == "max": + maxcol = np.max(np.abs(g)) + mincol = -maxcol + cmap = GalaxyMap + elif limits == "truncate": + maxcol = np.min([np.max(-g), np.max(g)]) + mincol = -maxcol + cmap = "PiYG" + elif limits == "minmax": + maxcol = np.max(g) + mincol = np.min(g) + cmap = GalaxyMap + + divnorm = colors.TwoSlopeNorm(vmin=mincol, vcenter=0, vmax=maxcol) + + # Plot + fig, ax = plt.subplots(figsize=(6, 6)) + im = ax.imshow(g, norm=divnorm, cmap=cmap) + ax.invert_yaxis() # Place origin at bottom-left + ax.spines[["top", "right", "left", "bottom"]].set_visible(False) + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size="5%", pad=0.1) + cbar = fig.colorbar(im, cax=cax) + cbar.outline.set_visible(False) + ticks = [mincol, mincol / 2, 0, maxcol / 3, 2 * maxcol / 3, maxcol] + cbar.set_ticks(ticks) + cbar.set_ticklabels([f"{x:.2f}" for x in ticks], size=GLOBAL_FS_SMALL) + cbar.set_label(r"$\delta_\textrm{g}$", size=GLOBAL_FS) + ax.set_xticks( + [size * i / 4.0 for i in range(5)], [f"{L * 1e-3 * i / 4:.1f}" for i in range(5)] + ) + ax.set_yticks( + [size * i / 4.0 for i in range(5)], [f"{L * 1e-3 * i / 4:.1f}" for i in range(5)] + ) + ax.set_xlabel(r"Gpc/$h$", size=GLOBAL_FS) + ax.set_ylabel(r"Gpc/$h$", size=GLOBAL_FS) + + # Save or display + if display: + plt.show() + else: + plt.savefig(f"{wd}Figures/g_{id_obs}.png", bbox_inches="tight", dpi=300) + plt.savefig(f"{wd}Figures/g_{id_obs}.pdf", bbox_inches="tight", dpi=300) + plt.close() diff --git a/src/selfisys/utils/plot_params.py b/src/selfisys/utils/plot_params.py new file mode 100644 index 0000000..dc96735 --- /dev/null +++ b/src/selfisys/utils/plot_params.py @@ -0,0 +1,324 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Plotting utilities and custom colormaps for the SelfiSys package. + +This module provides custom Matplotlib settings, formatter classes, and +colormaps used for visualising results in the SelfiSys project. +""" + + +# Global font sizes +GLOBAL_FS = 20 +GLOBAL_FS_LARGE = 22 +GLOBAL_FS_XLARGE = 24 +GLOBAL_FS_SMALL = 18 +GLOBAL_FS_TINY = 16 +COLOUR_LIST = ["C4", "C5", "C6", "C7"] + + +def reset_plotting(): + import matplotlib as mpl + + mpl.rcParams.update(mpl.rcParamsDefault) + + +def setup_plotting(): + """ + Configure Matplotlib plotting settings for consistent appearance. + """ + import matplotlib.pyplot as plt + import importlib.resources + + with importlib.resources.open_text("selfisys", "preamble.tex") as f: + preamble = f.read() + + # Dictionary with rcParams settings + rcparams = { + "font.family": "serif", + "font.size": GLOBAL_FS, # Base font size + "axes.titlesize": GLOBAL_FS_XLARGE, + "axes.labelsize": GLOBAL_FS_LARGE, + "axes.linewidth": 1.0, + "xtick.labelsize": GLOBAL_FS_SMALL, + "ytick.labelsize": GLOBAL_FS_SMALL, + "xtick.major.width": 1.2, + "ytick.major.width": 1.2, + "xtick.minor.width": 1.0, + "ytick.minor.width": 1.0, + "xtick.direction": "in", + "ytick.direction": "in", + "xtick.major.pad": 5, + "xtick.minor.pad": 5, + "ytick.major.pad": 5, + "ytick.minor.pad": 5, + "legend.fontsize": GLOBAL_FS_SMALL, + "legend.title_fontsize": GLOBAL_FS_LARGE, + "figure.titlesize": GLOBAL_FS_XLARGE, + "figure.dpi": 300, + "grid.color": "gray", + "grid.linestyle": "dotted", + "grid.linewidth": 0.6, + "lines.linewidth": 2, + "lines.markersize": 8, + "text.usetex": True, + "text.latex.preamble": preamble, + } + + # Update rcParams + plt.rcParams.update(rcparams) + + +def dynamic_text_scaling(fig_height): + """ + Dynamically scale text sizes based on the vertical height of the + figure. + + Parameters + ---------- + fig_height : float + Height of the figure in inches. + + Returns + ------- + dict + Dictionary of scaled font sizes for consistent appearance. + """ + scaling_factor = fig_height / 6.0 # Reference height is 6 inches + return { + "font.size": GLOBAL_FS * scaling_factor, + "axes.titlesize": GLOBAL_FS_XLARGE * scaling_factor, + "axes.labelsize": GLOBAL_FS_LARGE * scaling_factor, + "xtick.labelsize": GLOBAL_FS_SMALL * scaling_factor, + "ytick.labelsize": GLOBAL_FS_SMALL * scaling_factor, + "legend.fontsize": GLOBAL_FS_SMALL * scaling_factor, + "legend.title_fontsize": GLOBAL_FS_LARGE * scaling_factor, + "figure.titlesize": GLOBAL_FS_XLARGE * scaling_factor, + } + + +class ScalarFormatterForceFormat_11: + """ + Custom scalar formatter to enforce a specific number format with an + offset. + + This formatter displays tick labels with one decimal place and + includes the offset notation for powers of ten. + """ + + def __init__(self, useOffset=True, useMathText=True, useLocale=None): + from matplotlib.ticker import ScalarFormatter + + self.formatter = ScalarFormatter( + useOffset=useOffset, useMathText=useMathText, useLocale=useLocale + ) + self.formatter.set_powerlimits((0, 0)) + + def __call__(self, val, pos=None): + return self.formatter.__call__(val, pos) + + def set_scientific(self, b): + self.formatter.set_scientific(b) + + def set_useOffset(self, b): + self.formatter.set_useOffset(b) + + def get_offset(self): + offset = self.formatter.get_offset() + if self.formatter.orderOfMagnitude != 0: + return r"$\times 10^{%+d}$" % self.formatter.orderOfMagnitude + else: + return r"$\times 10^{+0}$" + + +def get_contours(Z, nBins, confLevels=(0.3173, 0.0455, 0.0027)): + """ + Compute contour levels for given confidence levels. + + Parameters + ---------- + Z : ndarray + 2D histogram or density estimate. + nBins : int + Number of bins along one axis. + confLevels : tuple of float + Confidence levels for which to compute contour levels. + + Returns + ------- + chainLevels : ndarray + Contour levels corresponding to the provided confidence levels. + """ + import numpy as np + + Z = Z / Z.sum() + nContourLevels = len(confLevels) + chainLevels = np.ones(nContourLevels + 1) + histOrdered = np.sort(Z.flat) + histCumulative = np.cumsum(histOrdered) + nBinsFlat = np.linspace(0.0, nBins**2, nBins**2) + + for l in range(nContourLevels): + temp = np.interp(confLevels[l], histCumulative, nBinsFlat) + chainLevels[nContourLevels - 1 - l] = np.interp(temp, nBinsFlat, histOrdered) + + return chainLevels + + +def create_colormap(name): + """ + Create a custom colormap based on the specified name. + + Parameters + ---------- + name : str + The name of the colormap to create. + + Returns + ------- + ListedColormap + The requested custom colormap. + + Raises + ------ + ValueError + If the specified colormap name is not recognised. + """ + import numpy as np + from matplotlib import cm, colors, colormaps + + if name == "GalaxyMap": + # Colormap for slices through galaxy density fields + Ndots = 2**13 + stretch_top = 0.5 + truncate_bottom = 0.0 + stretch_bottom = 1.0 + + top = cm.get_cmap("RdPu", Ndots) + top = colors.LinearSegmentedColormap.from_list("", ["white", top(0.5), top(1.0)]) + bottom = cm.get_cmap("Greens_r", Ndots) + bottom = colors.LinearSegmentedColormap.from_list("", [bottom(0), bottom(0.5), "white"]) + + interp_top = np.linspace(0, 1, Ndots) ** stretch_top + interp_bottom = np.linspace(truncate_bottom, 1, Ndots) ** stretch_bottom + cols_galaxy = np.vstack((bottom(interp_bottom), top(interp_top))) + return colors.ListedColormap(cols_galaxy, name="GalaxyMap") + + elif name == "GradientMap": + # Colormap for gradient matrices + Ndots = 2**13 + stretch_bottom = 6.0 + stretch_top = 1 / 2.5 + truncate_bottom = 0.35 + + bottom = cm.get_cmap("BuGn_r", Ndots) + top = cm.get_cmap("RdPu", Ndots) + + interp_top = np.linspace(0, 1, Ndots) ** stretch_top + interp_bottom = np.linspace(truncate_bottom, 1, Ndots) ** stretch_bottom + newcolors = np.vstack((bottom(interp_bottom), top(interp_top))) + return colors.ListedColormap(newcolors, name="GradientMap") + + elif name == "CovarianceMap": + # Colormap for the diagonal blocks of covariance matrices + Ndots = 2**15 + stretch_top_1 = 0.3 + stretch_top_2 = 1.0 + stretch_bottom = 0.2 + middle = 0.4 # Middle of the positive scale, between 0 and 1 + cmap_name = "BrBG" + top = colormaps[cmap_name] + bottom = colormaps[cmap_name] + + interp_top = np.concatenate( + ( + middle * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_1 + 0.5, + (1 - middle) * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_2 + 0.5 + middle, + ) + ) + interp_bottom = np.linspace(0.0, 1.0, Ndots) ** stretch_bottom - 0.5 + newcolors = np.vstack((bottom(interp_bottom), top(interp_top))) + return colors.ListedColormap(newcolors, name="CovarianceMap") + + elif name == "FullCovarianceMap": + # Colormap for full covariance matrices + Ndots = 2**15 + stretch_top_1 = 0.3 + stretch_top_2 = 1.0 + middle_top = 0.4 # Middle of the positive scale, between 0 and 1 + stretch_bottom_1 = 1.0 + stretch_bottom_2 = 5.0 + middle_bottom = 0.7 # Middle of the negative scale, between 0 and 1 + colname = "PRGn_r" # Options: "PRGn", "PRGn_r", "BrBG", "PuOr" + top = colormaps[colname] + bottom = colormaps[colname] + + interp_top = np.concatenate( + ( + middle_top * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_1 + 0.5, + (1 - middle_top) * np.linspace(0.0, 1, Ndots // 2) ** stretch_top_2 + + 0.5 + + middle_top, + ) + ) + interp_bottom = np.concatenate( + ( + middle_bottom * np.linspace(0.0, 1, Ndots // 2) ** stretch_bottom_1 - 0.5, + (1 - middle_bottom) * np.linspace(0.0, 1, Ndots // 2) ** stretch_bottom_2 + - 0.5 + + middle_bottom, + ) + ) + newcolors = np.vstack((bottom(interp_bottom), top(interp_top))) + return colors.ListedColormap(newcolors, name="FullCovarianceMap") + + elif name == "Blues_Reds": + # Additional colormap combining blues and reds + top = cm.get_cmap("Reds_r", 128) + bottom = cm.get_cmap("Blues", 128) + newcolors = np.vstack((top(np.linspace(0.7, 1, 128)), bottom(np.linspace(0, 1, 128)))) + return colors.ListedColormap(newcolors, name="Blues_Reds") + + elif name == "Purples_Oranges": + # Additional colormap combining purples and oranges + top = cm.get_cmap("Oranges_r", 128) + bottom = cm.get_cmap("Purples", 128) + newcolors = np.vstack((top(np.linspace(0.7, 1, 128)), bottom(np.linspace(0, 1, 128)))) + return colors.ListedColormap(newcolors, name="Purples_Oranges") + + else: + raise ValueError(f"Colormap '{name}' is not defined.") + + +def create_all_colormaps(): + """ + Create all custom colormaps. + + Returns + ------- + colormaps : dict + Dictionary containing all custom colormaps. + """ + colormaps_dict = {} + colormap_names = [ + "GalaxyMap", + "GradientMap", + "CovarianceMap", + "FullCovarianceMap", + "Blues_Reds", + "Purples_Oranges", + ] + for name in colormap_names: + colormaps_dict[name] = create_colormap(name) + return colormaps_dict diff --git a/src/selfisys/utils/plot_utils.py b/src/selfisys/utils/plot_utils.py new file mode 100644 index 0000000..baec3f4 --- /dev/null +++ b/src/selfisys/utils/plot_utils.py @@ -0,0 +1,2400 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Plotting routines for the SelfiSys project. +""" + +import gc +from pathlib import Path +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import gridspec, colors +from matplotlib.ticker import FormatStrFormatter, ScalarFormatter +from selfisys.utils.plot_params import * +from selfisys.utils.logger import getCustomLogger + +logger = getCustomLogger(__name__) + +# Configure global plotting settings +setup_plotting() + + +def plot_selection_functions( + x, + res, + res_mis, + params, + L, + corner, + axis="com", + zz=None, + zcorner=None, + z_L=None, + path=None, + force_plot=False, + labsAB=True, +): + """ + Plot selection functions. + + Parameters + ---------- + x : array-like + x-axis values (e.g., comoving distances or redshifts). + res : list of array-like + Selection functions for Model A. + res_mis : list of array-like, optional + Selection functions for Model B (optional). + params : tuple of (array-like, array-like, array-like), optional + Standard deviations, means, and normalisation factors for the + multiple galaxy populations. + L : float + Box size. + corner : float + Diagonal box size. + axis : str, optional + x-axis type ('com' for comoving distance, 'redshift' for + redshift). + zz : array-like, optional + Mapping between comoving distances and redshifts. + zcorner : float, optional + Redshift corresponding to the diagonal box size. + z_L : float, optional + Redshift corresponding to the box side length. + path : str, optional + Path to save the output plot. + force_plot : bool, optional + If True, displays the plot even if a path is specified. + labsAB : bool, optional + If True, labels models as 'Model A' and 'Model B'. + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + """ + logger.info("Plotting selection functions...") + + try: + colours_list = COLOUR_LIST[: len(res)] + plt.figure(figsize=(10, 5)) + + # Plot rescaled selection functions for Model A + for i, r in enumerate(res): + plt.plot(x, r, color=colours_list[i]) # , label=None) + + # Plot rescaled selection functions for Model B, if provided + if res_mis is not None: + label_a = "Model A" if labsAB else None + plt.plot(x, res[-1], color="black", alpha=0, label=label_a) + for i, r_mis in enumerate(res_mis): + plt.plot(x, r_mis, linestyle="--", color=colours_list[i]) + label_b = "Model B" if labsAB else None + plt.plot(x, res_mis[-1], linestyle="--", color="black", alpha=0, label=label_b) + + # Configure x-axis ticks and labels + xticks = [0, corner] + xtick_labels = [r"$0$"] + if axis == "com" and corner is not None: + xtick_labels.append(r"$\sqrt 3\,L \simeq {:.2f}$".format(corner)) + elif axis == "redshift" and corner is not None: + xtick_labels.append(r"$z(\sqrt 3\,L) \simeq {:.3f}$".format(corner)) + if z_L is not None and L is not None: + xticks.insert(1, L) + xtick_labels.insert(1, r"$L=3.6$") + + # Annotate populations + for i, mean in enumerate(params[1]): + mean_plt = x[np.argmin(np.abs(zz - mean))] if zz is not None else mean + plt.axvline(mean_plt, color=colours_list[i], linestyle="--", linewidth=1.5) + lab_pop = f"Population {i + 1}" + plt.axvline(mean_plt, color=colours_list[i], alpha=0, linewidth=2, label=lab_pop) + xticks.append(mean_plt) + xtick_labels.append(r"${:.2f}$".format(mean_plt)) + + # Set axis labels + xlabel = r"$r\,[{\rm Gpc}/h]$" if axis == "com" else r"$z$" + ylabel = r"$R_i(r)$" if zcorner is None else r"$R_i$" + plt.xlabel(xlabel, fontsize=GLOBAL_FS_LARGE) + plt.ylabel(ylabel, fontsize=GLOBAL_FS_LARGE) + + # Configure ticks and grid + plt.xticks(xticks, xtick_labels) + plt.tick_params(axis="x", which="major", size=8, labelsize=GLOBAL_FS_SMALL) + plt.tick_params(axis="y", which="major", size=8, labelsize=GLOBAL_FS_SMALL) + plt.grid(which="both", axis="both", linestyle="-", linewidth=0.4, color="gray", alpha=0.5) + + # Add legend, save and display plot + fs_legend = GLOBAL_FS_LARGE + loc_legend = (0.6, 0.35) + legend = plt.legend(frameon=True, loc=loc_legend, fontsize=fs_legend) + for handle in legend.legend_handles: + handle.set_alpha(1) + + # Handle dual x-axes for redshift and comoving distance + if zcorner is not None: + ax2 = plt.gca().twiny() + ax2.set_xlabel(r"$z$", fontsize=GLOBAL_FS_LARGE) + zticks = ( + np.concatenate([[0], [z_L], [zcorner], params[1]]) + if z_L + else np.concatenate([[0], [zcorner], params[1]]) + ) + ax2.set_xticks(xticks) + ax2.set_xticklabels([r"${:.2f}$".format(z) for z in zticks]) + ax2.tick_params(axis="x", which="major", size=8, labelsize=GLOBAL_FS_SMALL) + ax2.grid( + which="both", axis="both", linestyle="-", linewidth=0.4, color="gray", alpha=0.5 + ) + + plt.tight_layout() + + if path: + plt.savefig(path, bbox_inches="tight", dpi=300) + plt.savefig(path.replace(".png", ".pdf"), bbox_inches="tight", dpi=300) + logger.info("Figure saved to %s", path) + if not path or force_plot: + plt.show() + plt.close() + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + + +def plotly_3d(field, size=128, L=None, colormap="RdYlBu", limits="max"): + """ + Create an interactive 3D plot of volume slices using Plotly. + + Parameters + ---------- + field : array-like + 3D data field to visualise. + size : int, optional + Size of the field along one dimension. Default is 128. + L : float, optional + Physical size of the field in Mpc/h. Used for axis labels only. + colormap : str, optional + Colour map for visualisation. Default is 'RdYlBu'. + limits : str, optional + Colour scale limits ('max', 'truncate', or 'default'). Default + is 'max'. + + Returns + ------- + go.Figure + Plotly figure object. + """ + import plotly.graph_objects as go + + volume = field.T + rows, cols = volume[0].shape + + # Define colour scale limits + if limits == "max": + maxcol = np.max(np.abs(volume)) + mincol = -maxcol + elif limits == "truncate": + maxcol = min(np.max(-volume), np.max(volume)) + mincol = -maxcol + else: + maxcol = np.max(volume) + mincol = np.min(volume) + midcol = np.mean(volume) + + # Generate frames for the animation + nb_frames = size + frames = [ + go.Frame( + data=go.Surface( + z=(size - k) * np.ones((rows, cols)), + surfacecolor=np.flipud(volume[cols - 1 - k]), + cmin=mincol, + cmid=midcol, + cmax=maxcol, + ), + name=str(k), # Frames must be named for proper animation + ) + for k in range(nb_frames) + ] + + # Initial plot configuration + fig = go.Figure( + frames=frames, + data=go.Surface( + z=size * np.ones((rows, cols)), + surfacecolor=np.flipud(volume[cols // 2]), + colorscale=colormap, + cmin=mincol, + cmid=midcol, + cmax=maxcol, + colorbar=dict(thickness=20, ticklen=4), + ), + ) + + def frame_args(duration): + """Helper function to set animation frame arguments.""" + return { + "frame": {"duration": duration}, + "mode": "immediate", + "fromcurrent": True, + "transition": {"duration": duration, "easing": "linear"}, + } + + # Add animation slider + sliders = [ + { + "pad": {"b": 10, "t": 60}, + "len": 0.9, + "x": 0.1, + "y": 0, + "steps": [ + { + "args": [[f.name], frame_args(0)], + "label": str(k), + "method": "animate", + } + for k, f in enumerate(fig.frames) + ], + } + ] + + # Configure layout with or without physical size + layout_config = dict( + title="Slices in density field", + width=600, + height=600, + scene=dict( + zaxis=dict(range=[0, size - 1], autorange=False), + xaxis_title="x [Mpc/h]", + yaxis_title="y [Mpc/h]", + zaxis_title="z [Mpc/h]", + aspectratio=dict(x=1, y=1, z=1), + ), + updatemenus=[ + { + "buttons": [ + {"args": [None, frame_args(50)], "label": "▶", "method": "animate"}, + {"args": [[None], frame_args(0)], "label": "◼", "method": "animate"}, + ], + "direction": "left", + "pad": {"r": 10, "t": 70}, + "type": "buttons", + "x": 0.1, + "y": 0, + } + ], + sliders=sliders, + ) + if L is not None: + layout_config["scene"]["xaxis"] = dict( + ticktext=[0, L / 2, L], + tickvals=[0, size / 2, size], + title="x [Mpc/h]", + ) + layout_config["scene"]["yaxis"] = dict( + ticktext=[0, L / 2, L], + tickvals=[0, size / 2, size], + title="y [Mpc/h]", + ) + layout_config["scene"]["zaxis"]["ticktext"] = [0, L / 2, L] + layout_config["scene"]["zaxis"]["tickvals"] = [0, size / 2, size] + + fig.update_layout(**layout_config) + return fig + + +def plot_observations( + k_s, + theta_gt, + planck_Pk_EH, + P_0, + Pbins, + phi_obs, + Npop, + path=None, + force_plot=False, +): + """ + Plot the observed power spectra and related quantities. + + Parameters + ---------- + k_s : ndarray + Array of wavenumbers. + theta_gt : ndarray + Ground truth theta values. + planck_Pk_EH : ndarray + Planck power spectrum values. + P_0 : float + Normalisation constant for power spectra. + Pbins : ndarray + Vector of bin boundaries for the summary statistics. + phi_obs : ndarray + Observed summaries. + Npop : int + Number of populations. + path : str, optional + Path to save the output plot. + force_plot : bool, optional + If True, displays the plot even if a path is specified. + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + + """ + logger.info("Plotting observations...") + + # Sanity checks + if len(k_s) == 0 or len(theta_gt) == 0 or len(planck_Pk_EH) == 0: + logger.warning("One or more input arrays are empty. The plot may be incomplete.") + + if len(k_s) != len(theta_gt) or len(k_s) != len(planck_Pk_EH): + logger.error("Mismatch in array lengths. Plotting may not reflect all data.") + + try: + _, ax1 = plt.subplots(figsize=(12, 5)) + + # Plot theta values + ax1.plot(k_s, theta_gt / P_0, label=r"$\boldsymbol{\uptheta}_{\mathrm{gt}}$", color="C0") + ax1.set_xscale("log") + ax1.semilogx( + k_s, + planck_Pk_EH / P_0, + label=r"$P_{\mathrm{Planck}}(k)/P_0(k)$", + color="C1", + lw=0.5, + ) + + ax1.set_xlabel(r"$k$ [$h$/Mpc]") + ax1.set_ylabel(r"$[{\mathrm{Mpc}}/h]^3$") + ax1.grid(which="both", axis="y", linestyle="dotted", linewidth=0.6) + + # Vertical lines for theta support wavenumbers + for k in k_s[:-1]: + ax1.axvline(x=k, color="green", linestyle="dotted", linewidth=0.6) + ax1.axvline( + x=k_s[-1], + color="green", + linestyle="dotted", + linewidth=0.6, + label=r"$\boldsymbol{\uptheta}$ support wavenumbers", + ) + + # Vertical lines for Phi bin centres + ax2 = ax1.twinx() + ax1.axvline(x=Pbins[0], color="red", linestyle="dashed", linewidth=0.5) + ax2.axvline( + x=Pbins[-1], + color="red", + linestyle="dashed", + linewidth=0.5, + label=r"$\boldsymbol{\Phi}$-bins centres", + ) + for k in Pbins[1:-1]: + ax1.axvline(x=k, ymax=0.167, color="red", linestyle="dashed", linewidth=0.5) + + ax1.legend(loc="lower left", fontsize=GLOBAL_FS) + ax1.set_xlim(max(1e-4, k_s.min() - 2e-4), k_s.max()) + ax1.set_ylim(7e-1, 1.6e0) + + # Plot observations + len_obs = len(phi_obs) // Npop + + if len(phi_obs) % Npop != 0: + logger.warning( + "Length of 'phi_obs' is not divisible by the number of populations. " + "Ensure input dimensions are consistent.", + ) + + for i in range(Npop): + ax2.plot( + Pbins, + phi_obs[i * len_obs : (i + 1) * len_obs], + marker="x", + label=r"$\boldsymbol{\Phi}_{\mathrm{obs}}$, population " + str(i), + linewidth=0.5, + color=COLOUR_LIST[i], + ) + + ax2.legend(loc="upper right", fontsize=GLOBAL_FS) + + if path: + plt.savefig(path, bbox_inches="tight", dpi=300) + plt.savefig(path.replace(".png", ".pdf"), bbox_inches="tight", dpi=300) + logger.info("Figure saved to %s", path) + if not path or force_plot: + plt.show() + plt.close() + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + + +def plot_reconstruction( + k_s, + Pbins, + prior_theta_mean, + prior_theta_covariance, + posterior_theta_mean, + posterior_theta_covariance, + theta_gt, + P_0, + phi_obs=None, + suptitle=None, + theta_fid=None, + savepath=None, + force_plot=False, + legend_loc="upper right", + enforce_ylims=True, +): + """ + Plot the prior, posterior and ground truth power spectra. + + Parameters + ---------- + k_s : ndarray + Array of wavenumbers. + Pbins : ndarray + Vector of bin boundaries for the summary statistics. + prior_theta_mean : ndarray + Mean of the prior distribution. + prior_theta_covariance : ndarray + Covariance of the prior distribution. + posterior_theta_mean : ndarray + Mean of the posterior distribution. + posterior_theta_covariance : ndarray + Covariance of the posterior distribution. + theta_gt : ndarray + Ground truth power spectrum. + P_0 : float + Normalisation constant for the power spectrum. + phi_obs : ndarray, optional + Observed summaries. + suptitle : str, optional + Plot title. Leave empty for no title. + theta_fid : ndarray, optional + Fiducial theta for hyperparameter tuning (for some priors). + savepath : str or Path, optional + Path to save the plot. + force_plot : bool, optional + If True, displays the plot even if a path is specified. + legend_loc : str, optional + Location of the plot legend. + enforce_ylims : bool, optional + Enforce y-axis limits. + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + """ + logger.info("Generating power spectrum reconstruction plot.") + + try: + fig, ax = plt.subplots(figsize=(14, 5)) + + # Prior + ax.plot( + k_s, + prior_theta_mean, + linestyle="-", + color="gold", + label="$\\boldsymbol{\\uptheta}_0$ (prior)", + ) + ax.fill_between( + k_s, + prior_theta_mean - 2 * np.sqrt(np.diag(prior_theta_covariance)), + prior_theta_mean + 2 * np.sqrt(np.diag(prior_theta_covariance)), + color="gold", + alpha=0.2, + ) + + # Fiducial theta used for hyperparameter tuning with some priors + if theta_fid is not None: + ax.plot( + k_s, + theta_fid, + linestyle="--", + color="C1", + label="$\\boldsymbol{\\uptheta}_{\\mathrm{fid}}$ (fiducial)", + ) + + # Posterior + ax.plot( + k_s, + posterior_theta_mean, + color="C2", + label="$\\boldsymbol{\\upgamma}$ (reconstruction)", + ) + ax.fill_between( + k_s, + posterior_theta_mean - 2 * np.sqrt(np.diag(posterior_theta_covariance)), + posterior_theta_mean + 2 * np.sqrt(np.diag(posterior_theta_covariance)), + color="C2", + alpha=0.35, + ) + + # Ground truth + ax.plot( + k_s, + theta_gt / P_0, + color="C0", + label="$\\boldsymbol{\\uptheta}_\\mathrm{gt}$ (groundtruth)", + ) + + # Plot the binning + ymin, ymax = ax.get_ylim() + for i in range(len(k_s)): + ax.axvline( + k_s[i], + ymin=ymin, + ymax=ymax, + linestyle=":", + linewidth=0.8, + color="green", + alpha=0.5, + ) + ax.vlines( + Pbins, ymin=ymin, ymax=0.8, linestyle="--", linewidth=0.8, color="red", alpha=0.5 + ) + + # Overlay observations if provided + if phi_obs is not None: + ax2 = ax.twinx() + ax2.plot(Pbins, phi_obs, "C3.-", label=r"$\Phi_{O}$ (observations)") + ax2.legend(loc="lower right", fontsize=GLOBAL_FS_LARGE) + + ax.set_xlim([k_s.min() - 0.0001, k_s.max()]) + ax.set_xscale("log") + ax.grid(visible=True, which="both", linestyle=":", color="grey") + ax.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax.xaxis.set_tick_params(which="minor", length=4) + ax.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + if enforce_ylims: + ax.set_ylim([0.85, 1.35]) + ax.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_LARGE) + ax.set_ylabel("$\\theta(k) = P(k)/P_0(k)$", size=GLOBAL_FS_LARGE) + ax.legend(loc=legend_loc, fontsize=GLOBAL_FS_LARGE) + + plt.suptitle(suptitle, fontsize=GLOBAL_FS_XLARGE) if suptitle else None + + # Save / display + if savepath: + fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True) + fig.savefig( + savepath[:-4] + "_white.png", + bbox_inches="tight", + dpi=300, + format="png", + transparent=False, + ) + fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf") + fig.suptitle("") + fig.savefig( + savepath[:-4] + "_notitle.png", + bbox_inches="tight", + dpi=300, + format="png", + transparent=True, + ) + fig.savefig( + savepath[:-4] + "_white_notitle.png", + bbox_inches="tight", + dpi=300, + format="png", + transparent=False, + ) + fig.savefig( + savepath[:-4] + "_notitle.pdf", + bbox_inches="tight", + dpi=300, + format="pdf", + ) + + logger.info("Figure saved to %s", savepath) + + if force_plot or savepath is None: + plt.show() + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + finally: + plt.close(fig) + del fig, ax + gc.collect() + + +def plot_fisher(F0, params_names_fisher, title=None, path=None): + """ + Plot the Fisher matrix as a heatmap. + + Parameters + ---------- + F0 : ndarray + Fisher matrix. + params_names_fisher : list of str + Names for the axes. + title : str, optional + Title of the plot. Default is "Fisher matrix". + path : str or Path, optional + Path to save the plot. If None, the plot is displayed. + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + """ + import seaborn as sns + + logger.info("Generating Fisher matrix plot.") + try: + plt.figure(figsize=(10, 10)) + + # Normalisation for the colourmap + F0min, F0max = F0.min(), F0.max() + center = 0 if F0min < 0 and F0max > 0 else np.mean(F0) + divnorm = colors.TwoSlopeNorm(vmin=F0min, vcenter=center, vmax=F0max) + + # Plot the Fisher matrix + sns.heatmap( + F0, + annot=True, + fmt=".2e", + cmap="RdBu_r", + norm=divnorm, + square=True, + cbar_kws={"shrink": 0.8}, + ) + + plt.xticks(np.arange(len(params_names_fisher)) + 0.5, params_names_fisher, rotation=0) + plt.yticks(np.arange(len(params_names_fisher)) + 0.5, params_names_fisher, rotation=0) + plt.title(title or "Fisher matrix") + + if path: + path = Path(path) + for fmt in ["png", "pdf"]: + plt.savefig( + path.with_suffix(f".{fmt}"), + bbox_inches="tight", + dpi=300, + format=fmt, + transparent=True, + ) + + logger.info("Figure saved to %s", path) + else: + logger.info("Displaying plot.") + plt.show() + + except Exception as e: + logger.critical("Unexpected error during Fisher matrix plotting: %s", str(e)) + raise RuntimeError("Fisher matrix plotting failed.") from e + finally: + plt.close() + gc.collect() + + +def plot_mocks_compact( + NORM, + N, + P, + Pbins, + phi_obs, + Phi_0, + f_0, + C_0, + suptitle=None, + force_plot=False, + savepath=None, +): + """ + Plot and compare observed and simulated power spectra. + + Parameters + ---------- + NORM : float + Normalisation factor for the observed spectra. + N : int + Number of mock data realisations. + P : int + Number of bins for the summaries. + Pbins : ndarray + Vector of bin boundaries for the summary statistics. + phi_obs : ndarray + Observed power spectrum. + Phi_0 : ndarray + Mock realisations of the power spectrum. + f_0 : ndarray + Mean power spectrum. + C_0 : ndarray + Covariance matrix of the mock summaries. + suptitle : str, optional + Title for the plot. + force_plot : bool, optional + If True, displays the plot even if a path is specified. + savepath : str or Path, optional + Path to save the plot. + + Raises + ------ + RuntimeError + If an unexpected error occurs during plotting. + """ + logger.info("Plotting mock power spectra (compact)...") + + alpha_mocks = 0.25 + alpha_binning = 0.3 + + try: + Phi_0_full = Phi_0.copy() + phi_obs_full = phi_obs.copy() + f_0_full = f_0.copy() + C_0_full = C_0.copy() + + idx = 0 + Phi_0 = Phi_0[:, idx * P : (idx + 1) * P] + phi_obs = phi_obs[idx * P : (idx + 1) * P] + f_0 = f_0[idx * P : (idx + 1) * P] + C_0 = C_0[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P] + + COLOUR_LIST_means = ["darkorchid", "saddlebrown", "mediumvioletred"] + + fig = plt.figure(figsize=(10, 10)) + gs0 = gridspec.GridSpec( + 3, + 2, + width_ratios=[1.0, 1.0], + height_ratios=[1.0, 1.0, 1.0], + wspace=0.0, + hspace=0.0, + ) + gs0.update(right=1.0, left=0.0) + ax0 = plt.subplot(gs0[0, 0]) + ax0b = plt.subplot(gs0[0, 1]) + ax01 = plt.subplot(gs0[1, 0], sharex=ax0) + ax01b = plt.subplot(gs0[1, 1]) + ax02 = plt.subplot(gs0[2, 0], sharex=ax0) + ax02b = plt.subplot(gs0[2, 1]) + axx0x = [[ax01, ax01b], [ax02, ax02b]] + + # Observed power spectrum (normalised) + ax0.semilogx( + Pbins, + phi_obs * NORM, + linewidth=2, + color="black", + label=r"$\boldsymbol{\Phi}_\mathrm{O}$", + zorder=3, + ) + + # Realisations at the expansion point + for i in range(N - 1): + ax0.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7) + + # Average value + ax0.semilogx( + Pbins, + Phi_0[N - 1], + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + ) + ax0.semilogx( + Pbins, + f_0, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + ax0.fill_between( + Pbins, + f_0 - 2 * np.sqrt(np.diag(C_0)), + f_0 + 2 * np.sqrt(np.diag(C_0)), + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning + (ymin, ymax) = ax0.get_ylim() + ax0.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + ax0.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + + ax0.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + ax0.set_ylabel("population 1", size=GLOBAL_FS) + ax0.legend(fontsize=GLOBAL_FS, loc="upper right") + ax0.xaxis.set_ticks_position("both") + ax0.xaxis.set_tick_params(which="both", direction="in", width=1.0) + for axis in ["top", "bottom", "left", "right"]: + ax0.spines[axis].set_linewidth(1.0) + ax0.xaxis.set_tick_params(which="major", length=6) + ax0.xaxis.set_tick_params(which="minor", length=4) + if suptitle is not None: + ax0.set_title( + r"$\boldsymbol{\Phi}$ (" + suptitle + ")", y=1.05, fontsize=GLOBAL_FS_LARGE + ) + else: + ax0.set_title(r"$\boldsymbol{\Phi}$", y=1.05, fontsize=GLOBAL_FS_LARGE) + ax0.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + ax0.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) + + # Same as above but normalise everything by the observations: + normalisation = phi_obs + + ax0b.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + + # Observed power spectrum (normalised) + ax0b.semilogx( + Pbins, + NORM * phi_obs / normalisation, + linewidth=2, + color="black", + label=r"$\boldsymbol{\Phi}_\mathrm{O}$", + zorder=3, + ) + + for i in range(N - 1): + ax0b.semilogx( + Pbins, + Phi_0[i] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + ) + + ax0b.semilogx( + Pbins, + Phi_0[N - 1] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + label=r"$\boldsymbol{\Phi}_{\theta_0}$", + ) + + ax0b.semilogx( + Pbins, + f_0 / normalisation, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + ax0b.fill_between( + Pbins, + f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation, + f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation, + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning + (ymin, ymax) = ax0b.get_ylim() + ax0b.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + ax0b.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + + ax0b.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS) + ax0b.xaxis.set_ticks_position("both") + ax0b.xaxis.set_tick_params(which="both", direction="in", width=1.0) + for axis in ["top", "bottom", "left", "right"]: + ax0b.spines[axis].set_linewidth(1.0) + ax0b.xaxis.set_tick_params(which="major", length=6) + ax0b.xaxis.set_tick_params(which="minor", length=4) + ax0b.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + ax0b.yaxis.tick_right() + if suptitle is not None: + ax0b.set_title( + r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$ (" + suptitle + ")", + y=1.05, + fontsize=GLOBAL_FS, + ) + else: + ax0b.set_title( + r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$", y=1.05, fontsize=GLOBAL_FS + ) + + for ax, axb in axx0x: + idx += 1 + Phi_0 = Phi_0_full[:, idx * P : (idx + 1) * P] + phi_obs = phi_obs_full[idx * P : (idx + 1) * P] + f_0 = f_0_full[idx * P : (idx + 1) * P] + C_0 = C_0_full[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P] + + # Same plot but for the other axes: + ax.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + + # Observed power spectrum (normalised) + ax.semilogx(Pbins, phi_obs, linewidth=2, color="black", zorder=3) + for i in range(N - 1): + ax.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7) + ax.semilogx(Pbins, Phi_0[N - 1], color="C7", alpha=alpha_mocks, linewidth=0.7) + ax.semilogx( + Pbins, + f_0, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + ax.fill_between( + Pbins, + f_0 - 2 * np.sqrt(np.diag(C_0)), + f_0 + 2 * np.sqrt(np.diag(C_0)), + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning: + (ymin, ymax) = ax.get_ylim() + ax.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + ax.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + ax.set_ylabel("population " + str(idx + 1), size=GLOBAL_FS) + ax.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS) + ax.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8) + ax.xaxis.set_ticks_position("both") + ax.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) + for axis in ["top", "bottom", "left", "right"]: + ax.spines[axis].set_linewidth(1.0) + ax.xaxis.set_tick_params(which="major", length=6) + ax.xaxis.set_tick_params(which="minor", length=4) + ax.legend(loc="upper right", fontsize=GLOBAL_FS) + + # Normalise everything by the observations: + normalisation = phi_obs + + axb.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + + # Observed power spectrum (normalised) + axb.semilogx( + Pbins, + phi_obs / normalisation, + linewidth=2, + color="black", + label=r"$\boldsymbol{\Phi}_\mathrm{O}$", + zorder=3, + ) + + for i in range(N - 1): + axb.semilogx( + Pbins, + Phi_0[i] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + ) + + axb.semilogx( + Pbins, + Phi_0[N - 1] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + label=r"$\boldsymbol{\Phi}_{\theta_0}$", + ) + + axb.semilogx( + Pbins, + f_0 / normalisation, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + axb.fill_between( + Pbins, + f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation, + f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation, + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning + (ymin, ymax) = axb.get_ylim() + axb.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + axb.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + + axb.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS) + axb.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8) + axb.xaxis.set_ticks_position("both") + axb.xaxis.set_tick_params(which="both", direction="in", width=1.0) + axb.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + axb.yaxis.tick_right() + for axis in ["top", "bottom", "left", "right"]: + axb.spines[axis].set_linewidth(1.0) + axb.xaxis.set_tick_params(which="major", length=6) + axb.xaxis.set_tick_params(which="minor", length=4) + + if savepath: + savepath = Path(savepath) + for fmt in ["png", "pdf"]: + fig.savefig(savepath.with_suffix(f".{fmt}"), bbox_inches="tight", dpi=300) + logger.info("Plot saved to %s", savepath) + if force_plot or not savepath: + plt.show() + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + finally: + plt.close(fig) + del fig, ax0, ax0b, ax01, ax01b, ax02, ax02b + gc.collect() + + +def plot_C( + C_0, + X, + Y, + Pbins, + CMap, + binning=True, + suptitle=None, + savepath=None, + force=False, +): + """ + Plot covariance matrix. + + Parameters + ---------- + C_0 : ndarray + Covariance matrix. + X : ndarray + X-axis grid for plotting. + Y : ndarray + Y-axis grid for plotting. + Pbins : ndarray + Vector of bin boundaries for the summary statistics. + CMap : str + Colormap for the plot. + binning : bool, optional + Whether to overlay bin lines on the plot. Default is True. + suptitle : str, optional + Title for the plot. If None, a default title is used. + savepath : str or Path, optional + Path to save the plot. If None, the plot is displayed. + force : bool, optional + If True, displays the plot even if savepath is specified. + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + """ + + from itertools import product + + logger.info("Plotting covariance matrix...") + try: + fig, axs = plt.subplots(3, 3, figsize=(13, 11)) + P = len(Pbins) + + # Determine vmin, vmax and central value for TwoSlopeNorm + vmin, vmax = C_0.min(), C_0.max() + centerval = 0 if vmin < 0 and vmax > 0 else np.mean(C_0) + divnorm = colors.TwoSlopeNorm(vmin=vmin, vcenter=centerval, vmax=vmax) + + for i, j in product(range(3), range(3)): + C_0_ij = C_0[i * P : (i + 1) * P, j * P : (j + 1) * P] + imat = 2 - i # Invert to place origin at bottom left + ax = axs[imat, j] + ax.set_aspect("equal") + ax.set_xscale("log") + ax.set_yscale("log") + ax.xaxis.set_ticks_position("both") + ax.yaxis.set_ticks_position("both") + + # Plot the covariance matrix + im1 = ax.pcolormesh(X, Y, C_0_ij[:-1, :-1], shading="flat", norm=divnorm, cmap=CMap) + + # Overlay the binning grid (if enabled) + if binning: + for n in range(len(Pbins)): + ax.plot( + (Pbins[n], Pbins[n]), + (Pbins.min(), Pbins.max()), + linestyle="--", + linewidth=0.5, + color="red", + alpha=0.5, + ) + ax.plot( + (Pbins.min(), Pbins.max()), + (Pbins[n], Pbins[n]), + linestyle="--", + linewidth=0.5, + color="red", + alpha=0.5, + ) + + # Custom ticks for boundary axes + if i == 0: + ax.xaxis.set_tick_params( + which="both", direction="in", width=1.0, labelsize=GLOBAL_FS + ) + ax.xaxis.set_tick_params(which="major", length=6) + ax.xaxis.set_tick_params(which="minor", length=4) + else: + ax.set_xticks([]) + + if j == 0: + ax.yaxis.set_tick_params( + which="both", direction="in", width=1.0, labelsize=GLOBAL_FS + ) + ax.yaxis.set_tick_params(which="major", length=6) + ax.yaxis.set_tick_params(which="minor", length=4) + else: + ax.set_yticks([]) + + # Set title and adjust layout + suptitle = r"$\textbf{C}_0$" if suptitle is None else r"$\textbf{C}_0$ (" + suptitle + ")" + plt.suptitle(suptitle, y=0.94, x=0.45, size=GLOBAL_FS + GLOBAL_FS_XLARGE) + plt.subplots_adjust(wspace=0, hspace=0) + + # Colourbar + cbar = fig.colorbar( + im1, ax=axs.ravel().tolist(), shrink=1, pad=0.009, aspect=40, orientation="vertical" + ) + cbar.ax.tick_params( + axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS_LARGE + ) + cbar.update_normal(im1) + cbar.mappable.set_clim(vmin=C_0[:-1, :-1].min(), vmax=C_0[:-1, :-1].max()) + + loc_xticks = np.concatenate([np.linspace(vmin, 0, 5), np.linspace(0, vmax, 5)[1:]]) + val_xticks = np.round(loc_xticks, 2) + cbar.set_ticks(loc_xticks, labels=val_xticks) + + # Axis labels + fig.text(0.45, 0.04, r"$k$ [$h$/Mpc]", ha="center", size=GLOBAL_FS_XLARGE) + fig.text( + 0.04, 0.5, r"$k'$ [$h$/Mpc]", va="center", rotation="vertical", size=GLOBAL_FS_XLARGE + ) + + # Save / display + if savepath: + savepath = Path(savepath) + savepath.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True) + fig.savefig(savepath.with_suffix(".pdf"), bbox_inches="tight", dpi=300) + + logger.info("Plot saved to %s", savepath) + + if force or not savepath: + plt.show() + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + finally: + plt.close(fig) + del fig, axs, im1 + gc.collect() + + +def plot_prior_and_posterior_covariances( + X, + Y, + k_s, + prior_theta_covariance, + prior_covariance, + posterior_theta_covariance, + P_0, + force_plot=False, + suptitle="", + savepath=None, +): + """ + Plot prior and posterior covariance matrices. + + Parameters + ---------- + X : ndarray + X-axis grid. + Y : ndarray + Y-axis grid. + k_s : ndarray + Wavenumbers. + prior_theta_covariance : ndarray + Prior covariance matrix for normalised spectra. + prior_covariance : ndarray + Prior covariance matrix for unnormalised spectra. + posterior_theta_covariance : ndarray + Posterior covariance matrix for normalised spectra. + P_0 : ndarray + Fiducial power spectrum used for normalisation. + force_plot : bool, optional + Display plot even if savepath is set. + suptitle : str, optional + Title for the plot. + savepath : str or Path, optional + Path to save the plot. If None, the plot is displayed. + verbose : int, optional + Verbosity level (0=silent, 1=default, 2=detailed). + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + """ + from mpl_toolkits.axes_grid1 import make_axes_locatable + + logger.info("Plotting prior and posterior covariance matrices...") + try: + fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(figsize=(15, 14), nrows=2, ncols=2) + fig.suptitle(suptitle, fontsize=GLOBAL_FS_XLARGE, y=0.99) + + # Covariance matrix of the prior (normalised spectra theta) + ax0.set_aspect("equal") + ax0.set_xscale("log") + ax0.set_yscale("log") + ax0.xaxis.set_ticks_position("both") + ax0.yaxis.set_ticks_position("both") + ax0.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax0.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax0.xaxis.set_tick_params(which="minor", length=4) + ax0.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax0.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax0.yaxis.set_tick_params(which="minor", length=4) + divider = make_axes_locatable(ax0) + ax0_cb = divider.new_horizontal(size="5%", pad=0.10) + im0 = ax0.pcolormesh(X, Y, prior_theta_covariance[:-1, :-1], cmap="Blues", shading="flat") + for i in range(len(k_s)): + ax0.plot( + (k_s[i], k_s[i]), + (k_s.min(), k_s.max()), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + for i in range(len(k_s)): + ax0.plot( + (k_s.min(), k_s.max()), + (k_s[i], k_s[i]), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + ax0.set_title("$\\textbf{S}$", size=GLOBAL_FS_XLARGE) + ax0.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE) + ax0.set_ylabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE) + fig.add_axes(ax0_cb) + cbar0 = fig.colorbar(im0, cax=ax0_cb) + cbar0.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS) + + # Covariance matrix of the prior (unnormalized spectra) + ax1.set_aspect("equal") + ax1.set_xscale("log") + ax1.set_yscale("log") + ax1.xaxis.set_ticks_position("both") + ax1.yaxis.set_ticks_position("both") + ax1.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax1.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax1.xaxis.set_tick_params(which="minor", length=4) + ax1.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax1.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax1.yaxis.set_tick_params(which="minor", length=4) + divider = make_axes_locatable(ax1) + ax1_cb = divider.new_horizontal(size="5%", pad=0.10) + im1 = ax1.pcolormesh(X, Y, prior_covariance[:-1, :-1], cmap="Purples", shading="flat") + for i in range(len(k_s)): + ax1.plot( + (k_s[i], k_s[i]), + (k_s.min(), k_s.max()), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + for i in range(len(k_s)): + ax1.plot( + (k_s.min(), k_s.max()), + (k_s[i], k_s[i]), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + ax1.set_title( + "$\\mathrm{diag}(\\textbf{P}_0) \\cdot \\textbf{S} \\cdot \\mathrm{diag}(\\textbf{P}_0)$", + size=GLOBAL_FS_XLARGE, + ) + ax1.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE) + ax1.set_ylabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE) + fig.add_axes(ax1_cb) + cbar1 = fig.colorbar(im1, cax=ax1_cb) + cbar1.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS) + + posterior_covariance = np.diag(P_0).dot(posterior_theta_covariance).dot(np.diag(P_0)) + + # Covariance matrix of the posterior (normalised spectra theta) + ax2.set_aspect("equal") + ax2.set_xscale("log") + ax2.set_yscale("log") + ax2.xaxis.set_ticks_position("both") + ax2.yaxis.set_ticks_position("both") + ax2.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax2.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax2.xaxis.set_tick_params(which="minor", length=4) + ax2.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax2.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax2.yaxis.set_tick_params(which="minor", length=4) + divider = make_axes_locatable(ax2) + ax2_cb = divider.new_horizontal(size="5%", pad=0.10) + quantity = posterior_theta_covariance + vmin = quantity.min() + vmax = quantity.max() + if vmin < 0 and vmax > 0: + centerval = 0 + else: + centerval = np.mean(quantity) + norm_posterior = colors.TwoSlopeNorm( + vmin=posterior_theta_covariance.min(), + vcenter=centerval, + vmax=posterior_theta_covariance.max(), + ) + Blues_Reds = create_colormap("Blues_Reds") + im2 = ax2.pcolormesh( + X, + Y, + posterior_theta_covariance[:-1, :-1], + cmap=Blues_Reds, + norm=norm_posterior, + shading="flat", + ) + for i in range(len(k_s)): + ax2.plot( + (k_s[i], k_s[i]), + (k_s.min(), k_s.max()), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + for i in range(len(k_s)): + ax2.plot( + (k_s.min(), k_s.max()), + (k_s[i], k_s[i]), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + ax2.set_title("$\\boldsymbol{\\Gamma}$", size=GLOBAL_FS_XLARGE) + ax2.set_xlabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE) + ax2.set_ylabel("$k$ [$h$/Mpc]", size=GLOBAL_FS_XLARGE) + fig.add_axes(ax2_cb) + cbar2 = fig.colorbar(im2, cax=ax2_cb) + cbar2.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=GLOBAL_FS) + cbar2.mappable.set_clim( + vmin=posterior_theta_covariance.min(), vmax=posterior_theta_covariance.max() + ) + if posterior_theta_covariance.min() < 0 and posterior_theta_covariance.max() > 0: + ticks = np.concatenate( + [ + np.linspace(posterior_theta_covariance.min(), 0, 5), + np.linspace(0, posterior_theta_covariance.max(), 5)[1:], + ] + ) + else: + ticks = np.linspace( + posterior_theta_covariance.min(), posterior_theta_covariance.max(), 6 + ) + ticks_labels = ["{:.1e}".format(tick) for tick in ticks] + cbar2.set_ticks(ticks) + cbar2.set_ticklabels(ticks_labels) + + # Covariance matrix of the posterior (unnormalised spectra) + ax3.set_aspect("equal") + ax3.set_xscale("log") + ax3.set_yscale("log") + ax3.xaxis.set_ticks_position("both") + ax3.yaxis.set_ticks_position("both") + ax3.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax3.xaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax3.xaxis.set_tick_params(which="minor", length=4) + ax3.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax3.yaxis.set_tick_params(which="major", length=6, labelsize=GLOBAL_FS) + ax3.yaxis.set_tick_params(which="minor", length=4) + divider = make_axes_locatable(ax3) + ax3_cb = divider.new_horizontal(size="5%", pad=0.10) + quantity = posterior_covariance + vmin = quantity.min() + vmax = quantity.max() + if vmin < 0 and vmax > 0: + centerval = 0 + else: + centerval = np.mean(quantity) + norm_posterior_spectrum = colors.TwoSlopeNorm( + vmin=posterior_covariance.min(), + vcenter=centerval, + vmax=posterior_covariance.max(), + ) + Purples_Oranges = create_colormap("Purples_Oranges") + im3 = ax3.pcolormesh( + X, + Y, + posterior_covariance[:-1, :-1], + cmap=Purples_Oranges, + norm=norm_posterior_spectrum, + shading="flat", + ) + for i in range(len(k_s)): + ax3.plot( + (k_s[i], k_s[i]), + (k_s.min(), k_s.max()), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + for i in range(len(k_s)): + ax3.plot( + (k_s.min(), k_s.max()), + (k_s[i], k_s[i]), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + ax3.set_title( + "$\\mathrm{diag}(\\textbf{P}_0) \\cdot \\boldsymbol{\\Gamma} \\cdot \\mathrm{diag}(\\textbf{P}_0)$", + size=22, + ) + ax3.set_xlabel("$k$ [$h$/Mpc]", size=22) + ax3.set_ylabel("$k$ [$h$/Mpc]", size=22) + fig.add_axes(ax3_cb) + cbar3 = fig.colorbar(im3, cax=ax3_cb) + cbar3.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=19) + cbar3.ax.tick_params(axis="y", direction="in", width=1.0, length=6, labelsize=19) + cbar3.mappable.set_clim(vmin=posterior_covariance.min(), vmax=posterior_covariance.max()) + if posterior_covariance.min() < 0 and posterior_covariance.max() > 0: + ticks = np.concatenate( + [ + np.linspace(posterior_covariance.min(), 0, 5), + np.linspace(0, posterior_covariance.max(), 5)[1:], + ] + ) + else: + ticks = np.linspace(posterior_covariance.min(), posterior_covariance.max(), 6) + ticks_labels = ["{:.1e}".format(tick) for tick in ticks] + cbar3.set_ticks(ticks) + cbar3.set_ticklabels(ticks_labels) + + fig.tight_layout() + if savepath is not None: + fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True) + fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf") + fig.suptitle("") + fig.savefig( + savepath[:-4] + "_notitle.png", + bbox_inches="tight", + dpi=300, + format="png", + transparent=True, + ) + fig.savefig(savepath[:-4] + "_notitle.pdf", bbox_inches="tight", dpi=300, format="pdf") + logger.info("Plot saved to %s", savepath) + if force_plot or not savepath: + plt.show() + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + finally: + plt.close(fig) + del fig + gc.collect() + + +def plot_gradients( + Pbins, + P, + df_16_full, + df_32_full, + df_48_full, + df_full, + k_s, + X, + Y, + fixscale=False, + force=False, + suptitle="", + savepath=None, +): + """ + Plot gradients. + + Parameters + ---------- + Pbins : ndarray + Vector of bin boundaries for the summary statistics. + P : int + Number of bins. + df_16_full : ndarray + Derivative with respect to the 16th input. + df_32_full : ndarray + Derivative with respect to the 32nd input. + df_48_full : ndarray + Derivative with respect to the 48th input. + df_full : ndarray + Full derivative. + k_s : ndarray + Wavenumbers. + X : ndarray + X-axis grid. + Y : ndarray + Y-axis grid. + fixscale : bool, optional + Fix the y-axis scale. Default is False. + force : bool, optional + Display plot even if savepath is set. + suptitle : str, optional + Title for the plot. + savepath : str or Path, optional + Path to save the plot. If None, the plot is displayed. + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + + """ + logger.info("Plotting gradients...") + + try: + fig = plt.figure(figsize=(15, 12)) + fig.suptitle(suptitle, y=0.95, fontsize=22) + + gs0 = gridspec.GridSpec( + 3, + 2, + width_ratios=[1.0, 0.5], + height_ratios=[1.0, 1.0, 1.0], + hspace=0.0, + wspace=0.2, + ) + gs0.update(right=1.0, left=0.0) + ax00 = plt.subplot(gs0[0, 0]) + ax01 = plt.subplot(gs0[1, 0], sharex=ax00) + ax02 = plt.subplot(gs0[2, 0], sharex=ax00) + + gs1 = gridspec.GridSpec( + 3, + 2, + width_ratios=[1.0, 0.5], + height_ratios=[1.0, 1.0, 1.0], + hspace=0.0, + wspace=0.2, + ) + gs1.update(top=0.881, bottom=0.112) + ax10 = plt.subplot(gs1[0, 1]) + ax11 = plt.subplot(gs1[1, 1], sharex=ax10) + ax12 = plt.subplot(gs1[2, 1], sharex=ax10) + + axx = [(ax00, ax10), (ax01, ax11), (ax02, ax12)] + for axs, idx in zip(axx, range(3)): + ax = axs[0] + df_16 = np.copy(df_16_full[idx * P : (idx + 1) * P]) + df_32 = np.copy(df_32_full[idx * P : (idx + 1) * P]) + df_48 = np.copy(df_48_full[idx * P : (idx + 1) * P]) + df = df_full[idx * P : (idx + 1) * P] + + # Plot the three selected components of the derivative: + ax.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + ax.semilogx(Pbins, np.zeros_like(Pbins), linestyle=":", color="black") + ax.semilogx( + Pbins, + df_16, + linewidth=2, + linestyle="-", + color="C4", + label=r"$(\nabla \mathbf{f}_0)^\intercal_{16}$", + zorder=2, + ) + ax.semilogx( + Pbins, + df_32, + linewidth=2, + linestyle="-", + color="C0", + label=r"$(\nabla \mathbf{f}_0)^\intercal_{32}$", + zorder=2, + ) + ax.semilogx( + Pbins, + df_48, + linewidth=2, + linestyle="-", + color="C2", + label=r"$(\nabla \mathbf{f}_0)^\intercal_{48}$", + zorder=2, + ) + if fixscale: + ymin = np.min([df_16, df_32, df_48]) - 1e-2 + ymax = np.max([df_16, df_32, df_48]) + 1e-2 + else: + (ymin, ymax) = ax.get_ylim() + ax.set_ylim([ymin, ymax]) + + # Binning + for i in range(len(Pbins)): + ax.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=0.5, + zorder=1, + ) + ax.yaxis.grid(linestyle=":", color="grey") + ax.set_ylabel("population " + str(idx + 1), size=21) + ax.xaxis.set_ticks_position("both") + ax.yaxis.set_ticks_position("both") + ax.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax.yaxis.set_tick_params(which="both", direction="in", width=1.0) + for axis in ["top", "bottom", "left", "right"]: + ax.spines[axis].set_linewidth(1.0) + ax.xaxis.set_tick_params(which="major", length=6) + ax.xaxis.set_tick_params(which="minor", length=4) + ax.yaxis.set_tick_params(which="major", length=6) + + # Plot the full gradient + ax1 = axs[1] + ax1.set_xlim([k_s.min(), k_s.max()]) + ax1.set_ylim([Pbins.min(), Pbins.max()]) + ax1.set_xscale("log") + ax1.set_yscale("log") + ax1.xaxis.set_ticks_position("both") + ax1.yaxis.set_ticks_position("both") + ax1.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax1.xaxis.set_tick_params(which="major", length=6) + ax1.xaxis.set_tick_params(which="minor", length=4) + ax1.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax1.yaxis.set_tick_params(which="major", length=6) + ax1.yaxis.set_tick_params(which="minor", length=4) + quantity = df + vmin = quantity.min() + vmax = quantity.max() + if vmin < 0 and vmax > 0: + centerval = 0 + else: + centerval = np.mean(quantity) + norm_grad = colors.TwoSlopeNorm(vmin=vmin, vcenter=centerval, vmax=vmax) + GradientMap = create_colormap("GradientMap") + im1 = ax1.pcolormesh( + X, Y, df[:-1, :-1], cmap=GradientMap, shading="flat", norm=norm_grad + ) + ax1.plot(k_s, k_s, color="grey", linestyle="--") + for i in range(len(k_s)): + ax1.plot( + (k_s[i], k_s[i]), + (Pbins.min(), Pbins.max()), + linestyle=":", + linewidth=0.5, + color="green", + alpha=0.5, + ) + for i in range(len(Pbins)): + ax1.plot( + (k_s.min(), k_s.max()), + (Pbins[i], Pbins[i]), + linestyle="--", + linewidth=0.5, + color="red", + alpha=0.5, + ) + ax1.set_ylabel(r"$k$ [$h$/Mpc]", size=17) + cbar1 = fig.colorbar(im1, shrink=0.9, pad=0.01) + cbar1.ax.tick_params(axis="y", direction="in", width=1.0, length=6) + ticks = np.concatenate([np.linspace(df.min(), 0, 5), np.linspace(0, df.max(), 5)[1:]]) + cbar1.set_ticks(ticks) + + if idx == 0: + ax.legend(fontsize=21, loc="upper left") + ax1.set_title(r"Full gradient $\nabla \mathbf{f}_0$", size=21) + elif idx == 2: + ax.set_xlabel(r"$k$ [$h$/Mpc]", size=21) + ax1.set_xlabel(r"$k$ [$h$/Mpc]", size=21) + + if savepath is None or force: + plt.show() + if savepath is not None: + fig.savefig(savepath, bbox_inches="tight", dpi=300, format="png", transparent=True) + fig.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300, format="pdf") + fig.suptitle("") + fig.savefig( + savepath[:-4] + "_notitle.png", + bbox_inches="tight", + dpi=300, + format="png", + transparent=True, + ) + fig.savefig(savepath[:-4] + "_notitle.pdf", bbox_inches="tight", dpi=300, format="pdf") + + logger.info("Plot saved to %s", savepath) + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + finally: + plt.close(fig) + del fig + gc.collect() + + +def plot_mocks( + NORM, + N, + P, + Pbins, + phi_obs, + Phi_0, + f_0, + C_0, + X, + Y, + CMap, + suptitle=None, + force_plot=False, + savepath=None, +): + """ + Plot mocks. + + Parameters + ---------- + NORM : float + Normalisation factor. + N : int + Number of mocks. + P : int + Number of bins. + Pbins : ndarray + Vector of bin boundaries for the summary statistics. + phi_obs : ndarray + Observed power spectrum. + Phi_0 : ndarray + Mock power spectra. + f_0 : ndarray + Averaged power spectrum. + C_0 : ndarray + Covariance matrix. + X : ndarray + X-axis grid. + Y : ndarray + Y-axis grid. + CMap : str + Colormap. + suptitle : str, optional + Title for the plot. + force_plot : bool, optional + Display plot even if savepath is set. + savepath : str or Path, optional + Path to save the plot. If None, the plot is displayed. + + Raises + ------ + RuntimeError + If unexpected errors occur during plotting. + + """ + logger.info("Plotting mocks and intra-population covariance...") + + alpha_mocks = 0.25 + alpha_binning = 0.3 + try: + Phi_0_full = Phi_0.copy() + phi_obs_full = phi_obs.copy() + f_0_full = f_0.copy() + C_0_full = C_0.copy() + + idx = 0 + Phi_0 = Phi_0[:, idx * P : (idx + 1) * P] + phi_obs = phi_obs[idx * P : (idx + 1) * P] + f_0 = f_0[idx * P : (idx + 1) * P] + C_0 = C_0[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P] + + COLOUR_LIST_means = ["darkorchid", "saddlebrown", "mediumvioletred"] + + fig = plt.figure(figsize=(15.5, 10)) + gs0 = gridspec.GridSpec( + 3, + 3, + width_ratios=[1.0, 1.0, 1.0], + height_ratios=[1.0, 1.0, 1.0], + wspace=0.0, + hspace=0.0, + ) + gs0.update(right=1.0, left=0.0) + ax0 = plt.subplot(gs0[0, 0]) + ax0b = plt.subplot(gs0[0, 1]) + ax01 = plt.subplot(gs0[1, 0], sharex=ax0) + ax01b = plt.subplot(gs0[1, 1]) + ax02 = plt.subplot(gs0[2, 0], sharex=ax0) + ax02b = plt.subplot(gs0[2, 1]) + + axx0x = [[ax01, ax01b], [ax02, ax02b]] + + # Observed power spectrum (normalised) + ax0.semilogx( + Pbins, + phi_obs * NORM, + linewidth=2, + color="black", + label=r"$\boldsymbol{\Phi}_\mathrm{O}$", + zorder=3, + ) + + # Plot the Ne realisations and the average value + for i in range(N - 1): + ax0.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7) + ax0.semilogx( + Pbins, + Phi_0[N - 1], + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + ) + ax0.semilogx( + Pbins, + f_0, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + ax0.fill_between( + Pbins, + f_0 - 2 * np.sqrt(np.diag(C_0)), + f_0 + 2 * np.sqrt(np.diag(C_0)), + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning + (ymin, ymax) = ax0.get_ylim() + ax0.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + ax0.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + + ax0.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + ax0.set_ylabel("population 1", size=GLOBAL_FS) + ax0.legend(fontsize=GLOBAL_FS, loc="upper right") + ax0.xaxis.set_ticks_position("both") + ax0.xaxis.set_tick_params(which="both", direction="in", width=1.0) + for axis in ["top", "bottom", "left", "right"]: + ax0.spines[axis].set_linewidth(1.0) + ax0.xaxis.set_tick_params(which="major", length=6) + ax0.xaxis.set_tick_params(which="minor", length=4) + if suptitle is not None: + ax0.set_title( + r"$\boldsymbol{\Phi}$ (" + suptitle + ")", y=1.05, fontsize=GLOBAL_FS_LARGE + ) + else: + ax0.set_title(r"$\boldsymbol{\Phi}$", y=1.05, fontsize=GLOBAL_FS_LARGE) + ax0.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + ax0.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) + + # Same as above but normalise everything by the observations: + normalisation = phi_obs + + ax0b.semilogx( + Pbins, + NORM * phi_obs / normalisation, + linewidth=2, + color="black", + label=r"$\boldsymbol{\Phi}_\mathrm{O}$", + zorder=3, + ) + + for i in range(N - 1): + ax0b.semilogx( + Pbins, + Phi_0[i] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + ) + + ax0b.semilogx( + Pbins, + Phi_0[N - 1] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + label=r"$\boldsymbol{\Phi}_{\theta_0}$", + ) + + ax0b.semilogx( + Pbins, + f_0 / normalisation, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + ax0b.fill_between( + Pbins, + f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation, + f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation, + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning + (ymin, ymax) = ax0b.get_ylim() + ax0b.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + ax0b.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + + ax0b.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + ax0b.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS) + ax0b.xaxis.set_ticks_position("both") + ax0b.xaxis.set_tick_params(which="both", direction="in", width=1.0) + for axis in ["top", "bottom", "left", "right"]: + ax0b.spines[axis].set_linewidth(1.0) + ax0b.xaxis.set_tick_params(which="major", length=6) + ax0b.xaxis.set_tick_params(which="minor", length=4) + ax0b.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + ax0b.yaxis.tick_right() + if suptitle is not None: + ax0b.set_title( + r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$ (" + suptitle + ")", + y=1.05, + fontsize=GLOBAL_FS, + ) + else: + ax0b.set_title( + r"$\boldsymbol{\Phi}/\boldsymbol{\Phi}_\mathrm{O}$", y=1.05, fontsize=GLOBAL_FS + ) + + for ax, axb in axx0x: + idx += 1 + Phi_0 = Phi_0_full[:, idx * P : (idx + 1) * P] + phi_obs = phi_obs_full[idx * P : (idx + 1) * P] + f_0 = f_0_full[idx * P : (idx + 1) * P] + C_0 = C_0_full[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P] + + # Same plot as above but for the other axes: + ax.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + ax.semilogx(Pbins, phi_obs, linewidth=2, color="black", zorder=3) + for i in range(N - 1): + ax.semilogx(Pbins, Phi_0[i], color="C7", alpha=alpha_mocks, linewidth=0.7) + ax.semilogx(Pbins, Phi_0[N - 1], color="C7", alpha=alpha_mocks, linewidth=0.7) + ax.semilogx( + Pbins, + f_0, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + ax.fill_between( + Pbins, + f_0 - 2 * np.sqrt(np.diag(C_0)), + f_0 + 2 * np.sqrt(np.diag(C_0)), + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning + (ymin, ymax) = ax.get_ylim() + ax.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + ax.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + ax.set_ylabel("population " + str(idx + 1), size=GLOBAL_FS) + ax.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS) + ax.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8) + ax.xaxis.set_ticks_position("both") + ax.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) + for axis in ["top", "bottom", "left", "right"]: + ax.spines[axis].set_linewidth(1.0) + ax.xaxis.set_tick_params(which="major", length=6) + ax.xaxis.set_tick_params(which="minor", length=4) + ax.legend(loc="upper right", fontsize=GLOBAL_FS) + + # Same as above but normalise everything by the observations: + normalisation = phi_obs + + # Observed power spectrum + axb.semilogx( + Pbins, + phi_obs / normalisation, + linewidth=2, + color="black", + label=r"$\boldsymbol{\Phi}_\mathrm{O}$", + zorder=3, + ) + + for i in range(N - 1): + axb.semilogx( + Pbins, + Phi_0[i] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + ) + + axb.semilogx( + Pbins, + Phi_0[N - 1] / normalisation, + color="C7", + alpha=alpha_mocks, + linewidth=0.7, + label=r"$\boldsymbol{\Phi}_{\theta_0}$", + ) + axb.semilogx( + Pbins, + f_0 / normalisation, + linewidth=2, + color=COLOUR_LIST_means[idx], + linestyle="--", + label=r"$\textbf{f}_0$", + zorder=2, + ) + + # 2-sigma intervals + axb.fill_between( + Pbins, + f_0 / normalisation - 2 * np.sqrt(np.diag(C_0)) / normalisation, + f_0 / normalisation + 2 * np.sqrt(np.diag(C_0)) / normalisation, + color=COLOUR_LIST[idx], + alpha=0.4, + label=r"2 $\sqrt{\mathrm{diag}(\textbf{C}_0)}$", + zorder=2, + ) + + # Plot the binning + (ymin, ymax) = axb.get_ylim() + axb.set_ylim([ymin, ymax]) + for i in range(len(Pbins)): + axb.plot( + (Pbins[i], Pbins[i]), + (ymin, ymax), + linestyle="--", + linewidth=0.8, + color="red", + alpha=alpha_binning, + zorder=1, + ) + + axb.set_xlim([Pbins.min() - 0.0001, Pbins.max() + 0.01]) + axb.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS) + axb.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8) + axb.xaxis.set_ticks_position("both") + axb.xaxis.set_tick_params(which="both", direction="in", width=1.0) + axb.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + axb.yaxis.tick_right() + for axis in ["top", "bottom", "left", "right"]: + axb.spines[axis].set_linewidth(1.0) + axb.xaxis.set_tick_params(which="major", length=6) + axb.xaxis.set_tick_params(which="minor", length=4) + + axx1x = [plt.subplot(gs0[0, 2]), plt.subplot(gs0[1, 2]), plt.subplot(gs0[2, 2])] + + # Diagonal blocks of the covariance matrix (intra-population covariance) + idx = 0 + for ax1 in axx1x: + Phi_0 = Phi_0_full[:, idx * P : (idx + 1) * P] + phi_obs = phi_obs_full[idx * P : (idx + 1) * P] + f_0 = f_0_full[idx * P : (idx + 1) * P] + C_0 = C_0_full[idx * P : (idx + 1) * P, idx * P : (idx + 1) * P] + idx += 1 + + C0min = C_0.min() + C0max = C_0.max() + if C0min < 0 and C0max > 0: + centerval = 0 + else: + centerval = np.mean(C_0) + divnorm = colors.TwoSlopeNorm(vmin=C_0.min(), vcenter=centerval, vmax=C_0.max()) + + # Plot the current block of the covariance matrix + im1 = ax1.pcolormesh(X, Y, C_0[:-1, :-1], shading="flat", norm=divnorm, cmap=CMap) + + # Plot the binning + for i in range(len(Pbins)): + ax1.plot( + (Pbins[i], Pbins[i]), + (Pbins.min(), Pbins.max()), + linestyle="--", + linewidth=0.5, + color="red", + alpha=0.5, + ) + for i in range(len(Pbins)): + ax1.plot( + (Pbins.min(), Pbins.max()), + (Pbins[i], Pbins[i]), + linestyle="--", + linewidth=0.5, + color="red", + alpha=0.5, + ) + if idx == 1: + ax1.set_title(r"diagonal blocks of $\textbf{C}_0$", size=GLOBAL_FS, y=1.05) + ax1.set_xlabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS) + ax1.set_ylabel(r"$k$ [$h$/Mpc]", size=GLOBAL_FS_SMALL) + + ax1.set_aspect("equal") + ax1.set_xscale("log") + ax1.set_yscale("log") + ax1.xaxis.set_ticks_position("both") + ax1.yaxis.set_ticks_position("both") + ax1.xaxis.set_tick_params(which="both", direction="in", width=1.0) + ax1.xaxis.set_tick_params(which="major", length=6) + ax1.xaxis.set_tick_params(which="minor", length=4) + ax1.yaxis.set_tick_params(which="both", direction="in", width=1.0) + ax1.yaxis.set_tick_params(which="major", length=6) + ax1.yaxis.set_tick_params(which="minor", length=4) + ax1.tick_params(axis="x", which="major", labelsize=GLOBAL_FS_SMALL, pad=8) + ax1.tick_params(axis="y", which="major", labelsize=GLOBAL_FS_TINY) + + cbar1 = fig.colorbar(im1, shrink=0.9, pad=0.02, format="%.1e") + cbar1.ax.tick_params(axis="y", direction="in", width=1.0, length=4) + cbar1.ax.yaxis.set_tick_params(labelsize=GLOBAL_FS_SMALL) + cbar1.update_normal(im1) + vmin = C_0[:-1, :-1].min() + vmax = C_0[:-1, :-1].max() + cbar1.mappable.set_clim(vmin=vmin, vmax=vmax) + if vmin < 0 and vmax > 0: + ticks = np.concatenate( + [ + np.linspace(vmin * 0.8, 0, 2), + np.linspace(0, 0.8 * vmax, 2)[1:], + ] + ) + else: + ticks = np.linspace(vmin, vmax, 5) + cbar1.set_ticks(ticks) + formatter = ScalarFormatter(useMathText=True) + formatter.set_powerlimits((0, 0)) + cbar1.ax.yaxis.set_major_formatter(formatter) + cbar1.ax.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) + cbar1.ax.yaxis.get_offset_text().set_fontsize(GLOBAL_FS_TINY) + cbar1.update_ticks() + + if savepath is not None: + savepath = Path(savepath) + fig.savefig(savepath, bbox_inches="tight", transparent=True, dpi=300, format="png") + fig.savefig(savepath.with_suffix(".pdf"), bbox_inches="tight", dpi=300, format="pdf") + logger.info("Plot saved to %s", savepath) + if force_plot or not savepath: + plt.show() + + except Exception as e: + logger.critical("Unexpected error during plotting: %s", str(e)) + raise RuntimeError("Plotting failed.") from e + + +def plot_histogram( + data, + recmahal, + suptitle, + savepath, + bins=30, + alpha=0.5, + color="blue", +): + """ + Plot a Mahalanobis distance histogram with key reference lines. + + Parameters + ---------- + data : array-like + Collection of Mahalanobis distances. + recmahal : float + Single Mahalanobis distance for the reconstruction + (e.g. posterior vs. prior mean). + suptitle : str + Figure title. + savepath : str + Full path (including filename) to save the resulting plot. + bins : int, optional + Number of bins for the histogram. Default is 30. + alpha : float, optional + Transparency level for the histogram bars. Default is 0.5. + color : str, optional + Colour of the histogram bars. Default is "blue". + + Raises + ------ + OSError + If the file cannot be saved to the specified path. + RuntimeError + For unexpected plotting failures. + """ + try: + logger.debug("Starting plot_histogram with data of size %d.", len(data)) + + plt.figure(figsize=(8, 5)) + plt.hist(data, bins=bins, alpha=alpha, color=color) + + labrec = r"$d_\mathrm{M}(\boldsymbol{\gamma}_{\textrm{rec}}, \boldsymbol{\theta}_0 \mid \textbf{S})$" + plt.axvline(recmahal, color="black", linestyle="-", linewidth=2, label=labrec) + + labx = r"$d_\mathrm{M}(\boldsymbol{\gamma}, \boldsymbol{\theta}_0 \mid \textbf{S})$" + labmean = r"$\langle d_\mathrm{M}(\boldsymbol{\gamma}, \boldsymbol{\theta}_0 \mid \textbf{S}) \rangle$" + plt.axvline( + np.mean(data), + color="tab:pink", + linestyle="-", + linewidth=2, + label=labmean, + ) + std = np.std(data) + labstd = r"$\pm 1 \sigma$" + plt.axvline( + np.mean(data) + std, + color="pink", + linestyle="--", + linewidth=1, + label=labstd, + ) + plt.axvline( + np.mean(data) - std, + color="pink", + linestyle="--", + linewidth=1, + ) + + plt.xlabel(labx, fontsize=GLOBAL_FS_LARGE) + plt.ylabel("Density", fontsize=GLOBAL_FS_LARGE) + plt.xticks(fontsize=GLOBAL_FS) + plt.yticks([]) + plt.suptitle(suptitle, fontsize=GLOBAL_FS_XLARGE) + plt.legend(fontsize=GLOBAL_FS_LARGE) + + plt.savefig(savepath, bbox_inches="tight", dpi=300, transparent=True) + plt.savefig(savepath[:-4] + ".pdf", bbox_inches="tight", dpi=300) + logger.info("Histogram plot saved to %s and %s.pdf", savepath, savepath[:-4]) + plt.close() + + except OSError as e: + logger.error("File saving failed at path '%s': %s", savepath, str(e)) + raise + except Exception as e: + logger.critical("Unexpected error during histogram plotting: %s", str(e)) + raise RuntimeError("plot_histogram failed.") from e + finally: + gc.collect() + logger.debug("plot_histogram: memory cleanup done.") diff --git a/src/selfisys/utils/timestepping.py b/src/selfisys/utils/timestepping.py new file mode 100644 index 0000000..49e6aa2 --- /dev/null +++ b/src/selfisys/utils/timestepping.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +"""Tools for time-stepping. +""" + + +def merge_nTS(ts_path_list, merged_path): + """ + Merge multiple time-stepping objects into a single file. + + Parameters + ---------- + ts_path_list : list of str + Paths to the individual time-stepping files to be merged. + merged_path : str + Path to save the merged time-stepping file. + + Returns + ------- + None + """ + from h5py import File + from numpy import concatenate + from pysbmy.timestepping import read_timestepping + + # Read individual time-stepping objects + ts = [read_timestepping(ts_path) for ts_path in ts_path_list] + + with File(merged_path, "w") as hf: + # Write scalar attributes + hf.attrs["/info/scalars/nsteps"] = sum(tsi.nsteps for tsi in ts) + hf.attrs["/info/scalars/nkicks"] = sum(tsi.nkicks for tsi in ts) + hf.attrs["/info/scalars/ndrifts"] = sum(tsi.ndrifts for tsi in ts) + hf.attrs["/info/scalars/ai"] = ts[0].ai + hf.attrs["/info/scalars/af"] = ts[-1].af + + # Merge and write datasets + hf.create_dataset("/scalars/forces", data=concatenate([tsi.forces for tsi in ts])) + hf.create_dataset("/scalars/snapshots", data=concatenate([tsi.snapshots for tsi in ts])) + hf.create_dataset("/scalars/aKickBeg", data=concatenate([tsi.aKickBeg for tsi in ts])) + hf.create_dataset("/scalars/aKickEnd", data=concatenate([tsi.aKickEnd for tsi in ts])) + hf.create_dataset("/scalars/aDriftBeg", data=concatenate([tsi.aDriftBeg for tsi in ts])) + hf.create_dataset("/scalars/aDriftEnd", data=concatenate([tsi.aDriftEnd for tsi in ts])) + hf.create_dataset("/scalars/aiKick", data=concatenate([tsi.aiKick for tsi in ts])) + hf.create_dataset("/scalars/afKick", data=concatenate([tsi.afKick for tsi in ts])) + + # Handle `aDrift` merging with overlap adjustments + aDrift_data = concatenate( + [ + [ts[0].aDrift[0]], # Initial drift + concatenate( + [concatenate([tsi.aDrift[1:], [tsi.aDrift[-1]]]) for tsi in ts[:-1]] + ), # Intermediate drifts + ts[-1].aDrift[1:], # Final drift + ] + ) + hf.create_dataset("/scalars/aDrift", data=aDrift_data) + + # Handle `aSnapshotSave` merging + aSnapshotSave_data = concatenate( + [ts[0].aSnapshotSave] + [tsi.aSnapshotSave[1:] for tsi in ts[1:]] + ) + hf.create_dataset("/scalars/aSnapshotSave", data=aSnapshotSave_data) + + hf.create_dataset("/scalars/aiDrift", data=concatenate([tsi.aiDrift for tsi in ts])) + hf.create_dataset("/scalars/afDrift", data=concatenate([tsi.afDrift for tsi in ts])) + hf.create_dataset("/scalars/aKick", data=concatenate([tsi.aKick for tsi in ts])) diff --git a/src/selfisys/utils/tools.py b/src/selfisys/utils/tools.py new file mode 100644 index 0000000..fc6a18f --- /dev/null +++ b/src/selfisys/utils/tools.py @@ -0,0 +1,306 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Utilities for the SelfiSys package, including tools for cosmological +parameter handling, power spectrum computations, and prior sampling. +""" + + +def none_or_bool_or_str(value): + """ + Convert string representations of None, True, and False to their + respective Python objects; otherwise, return the input value. + """ + if value == "None": + return None + if value == "True": + return True + if value == "False": + return False + return value + + +def get_k_max(L, size): + """ + Compute the maximum wavenumber for a given box size. + + Parameters + ---------- + L : float + Size of the box in Mpc/h. + size : int + Number of grid cells along each dimension. + + Returns + ------- + float + Maximum wavenumber in h/Mpc. + """ + from numpy import pi, sqrt + + return int(1e3 * sqrt(3) * pi * size / L + 1) * 1e-3 + + +def custom_stat(vec): + """ + Compute a custom statistic for use with + `scipy.stats.binned_statistic`. + + Assumes the data power spectrum is inverse-Gamma distributed (as in + [jasche2010bayesian] and [leclercq2019primordial]). Returns "NaN" + for vectors with insufficient elements, as expected by + `scipy.stats.binned_statistic`. + + Parameters + ---------- + vec : array-like + Input vector for computation. + + Returns + ------- + float or str + Custom statistic or NaN if input is invalid. + """ + if len(vec) <= 2 or sum(vec) == 0: + return "NaN" + return sum(vec) / (len(vec) - 2) + + +def cosmo_vector_to_Simbelmyne_dict(x, kmax=1.4): + """ + Convert a vector of cosmological parameters into a dictionary + compatible with `pysbmy`. + + Parameters + ---------- + x : array-like + Vector of cosmological parameters. + kmax : float, optional + Maximum wavenumber for the power spectrum computation. + + Returns + ------- + dict + Dictionary of cosmological parameters compatible with `pysbmy`. + """ + from selfisys.global_parameters import WHICH_SPECTRUM + + return { + "h": x[0], + "Omega_r": 0.0, + "Omega_q": 1.0 - x[2], + "Omega_b": x[1], + "Omega_m": x[2], + "m_ncdm": 0.0, + "Omega_k": 0.0, + "tau_reio": 0.066, + "n_s": x[3], + "sigma8": x[4], + "w0_fld": -1.0, + "wa_fld": 0.0, + "k_max": kmax, + "WhichSpectrum": WHICH_SPECTRUM, + } + + +def cosmo_vector_to_class_dict(x, lmax=2500, kmax=1.4): + """ + Convert a vector of cosmological parameters into a dictionary + compatible with `classy`. + + Parameters + ---------- + x : array-like + Vector of cosmological parameters. + lmax : int, optional + Maximum multipole for the power spectrum computation. + kmax : float, optional + Maximum wavenumber for the power spectrum computation. + + Returns + ------- + dict + Dictionary of cosmological parameters compatible with `classy`. + """ + return { + "output": "lCl mPk", + "l_max_scalars": lmax, + "lensing": "no", + "N_ncdm": 0, + "P_k_max_h/Mpc": kmax, + "h": x[0], + "Omega_b": x[1], + "Omega_m": x[2], + "n_s": x[3], + "sigma8": x[4], + } + + +def params_ids_to_Simbelmyne_dict(params_vals, params_ids, fixed, kmax): + """ + Convert a list of cosmological parameters into a dictionary + compatible with `pysbmy`. + + Fixed parameters remain unchanged unless overridden by + `params_vals`. + + Parameters + ---------- + params_vals : array-like + Values of the parameters to be modified. + params_ids : array-like + Indices of the parameters to be modified. + fixed : array-like + Base values of the parameters. + kmax : float + Maximum wavenumber for the power spectrum computation. + + Returns + ------- + dict + Dictionary of cosmological parameters compatible with `pysbmy`. + """ + from numpy import copy + + x = copy(fixed) + x[params_ids] = params_vals + return cosmo_vector_to_Simbelmyne_dict(x, kmax=kmax) + + +def get_summary(params_vals, params_ids, Omegas_fixed, bins, normalisation=None, kmax=1.4): + """ + Compute the normalised power spectrum summary for a given parameter + set. + + Parameters + ---------- + params_vals : array-like + Parameter values to update. + params_ids : array-like + Indices of the parameters to update. + Omegas_fixed : array-like + Fixed base values of parameters. + bins : array-like + Power spectrum bins. + normalisation : float, optional + Normalisation factor for the summary. + kmax : float, optional + Maximum wavenumber for power spectrum computation. + + Returns + ------- + array + Normalised power spectrum summary. + """ + from pysbmy.power import get_Pk + from numpy import array + + phi = get_Pk(bins, params_ids_to_Simbelmyne_dict(params_vals, params_ids, Omegas_fixed, kmax)) + + return array(phi) / normalisation if normalisation else array(phi) + + +def summary_to_score(params_ids, omega0, F0, F0_inv, f0, dw_f0, C0_inv, phi): + """ + Compute the Fisher score. + + Parameters + ---------- + params_ids : array-like + Indices of the parameters. + omega0 : array-like + Cosmological parameters at the expansion point. + F0 : array-like + Fisher information matrix. + F0_inv : array-like + Inverse Fisher information matrix. + f0 : array-like + Mean model at the expansion point. + dw_f0 : array-like + Derivative of the mean model. + C0_inv : array-like + Inverse covariance matrix. + phi : array-like + Observed summary. + + Returns + ------- + array + Fisher score. + """ + return omega0[params_ids] + F0_inv @ dw_f0.T @ C0_inv @ (phi - f0) + + +def fisher_rao(Com, Com_obs, F0): + """ + Compute the Fisher-Rao distance between two summaries. + + Parameters + ---------- + Com : array-like + Computed summary. + Com_obs : array-like + Observed summary. + F0 : array-like + Fisher information matrix. + + Returns + ------- + float + Fisher-Rao distance. + """ + from numpy import sqrt + + diff = Com - Com_obs + return sqrt(diff.T @ F0 @ diff) + + +def sample_omega_from_prior(nsample, omega_mean, omega_cov, params_ids, seed=None): + """ + Sample cosmological parameters from a prior distribution. + + Ensures physical validity by clipping values to [eps, 1-eps]. + + Parameters + ---------- + nsample : int + Number of samples to draw. + omega_mean : array-like + Prior mean vector. + omega_cov : array-like + Prior covariance matrix. + params_ids : array-like + Indices of the parameters to sample. + seed : int, optional + Seed for the random number generator. + + Returns + ------- + array + Sampled cosmological parameters. + """ + from numpy import array, ix_, clip + from numpy.random import default_rng + + if seed is None: + raise ValueError("A seed value is mandatory.") + + rng = default_rng(seed) + OO_unbounded = rng.multivariate_normal( + array(omega_mean)[params_ids], + array(omega_cov)[ix_(params_ids, params_ids)], + nsample, + ) + eps = 1e-5 + return clip(OO_unbounded, eps, 1 - eps) diff --git a/src/selfisys/utils/workers.py b/src/selfisys/utils/workers.py new file mode 100644 index 0000000..e532030 --- /dev/null +++ b/src/selfisys/utils/workers.py @@ -0,0 +1,385 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Routines for parameter inference and gradient evaluation in the SelfiSys +pipeline. +""" + +import gc +from typing import Any, Tuple, List + +from selfisys.utils.logger import getCustomLogger + +logger = getCustomLogger(__name__) + + +def Simbelmyne_worker(args) -> Tuple[float, Any]: + """ + Worker function used for implicit likelihood inference of + cosmological parameters. + + Parameters + ---------- + args : tuple + A tuple of arguments to be unpacked for the worker routine: + (index, param_val, param_id, fsimdir, k_s, Pbins_bnd, + selection_params, norm_csts, P_ss_obj_path, obs_density, + lin_bias, noise, survey_mask_path, G_sim_path, G_ss_path, Np0, + Npm0, seedphase_init, seednoise_init, size, L, + radial_selection, sim_params, wd, batch_idx, dbg, modeldir, + local_mask_prefix, TimeStepDistribution, indices_steps_cumul, + eff_redshifts, poolname_abc, setup_only, prefix_mocks). + + Returns + ------- + tuple + (param_val, Phi) where param_val is the parameter value used, + and Phi is the resulting summary from evaluating the model. + + Raises + ------ + OSError + If file I/O (reading or writing mock data) fails. + RuntimeError + For unexpected errors in the worker routine. + """ + import os + from pathlib import Path + + try: + ( + index, + param_val, + param_id, + fsimdir, + k_s, + Pbins_bnd, + selection_params, + norm_csts, + P_ss_obj_path, + obs_density, + lin_bias, + noise, + survey_mask_path, + G_sim_path, + G_ss_path, + Np0, + Npm0, + seedphase_init, + seednoise_init, + size, + L, + radial_selection, + sim_params, + wd, + batch_idx, + dbg, + modeldir, + local_mask_prefix, + TimeStepDistribution, + indices_steps_cumul, + eff_redshifts, + poolname_abc, + setup_only, + prefix_mocks, + ) = args + + spectrum_name = int(str(seedphase_init + index) + str(seednoise_init + index)) + pooldir = ( + fsimdir + "/pool/d" if not poolname_abc else fsimdir + "/pool/" + poolname_abc + "/d" + ) + simdir_d = pooldir + str(spectrum_name) + "/" + Path(simdir_d).mkdir(parents=True, exist_ok=True) + + if prefix_mocks is None: + fname_mocks = ( + simdir_d + "mocks_d" + str(spectrum_name) + "_p" + str(batch_idx + index) + ".h5" + ) + else: + fname_mocks = ( + simdir_d + + prefix_mocks + + "_mocks_d" + + str(spectrum_name) + + "_p" + + str(batch_idx + index) + + ".h5" + ) + + if os.path.exists(fname_mocks): + from h5py import File + + logger.debug("Mock file %s found, loading existing data...", fname_mocks) + with File(fname_mocks, "r") as f: + Phi = f["Phi"][:] + else: + logger.debug("No existing mock file at %s, generating new data...", fname_mocks) + from numpy.random import normal + from numpy import shape, max + from selfisys.global_parameters import BASELINE_SEEDNORM, omegas_gt + from selfisys.utils.tools import get_k_max + from selfisys.hiddenbox import HiddenBox + from selfisys.utils.tools import get_summary + + P = len(Pbins_bnd) - 1 + try: + BB_selfi = HiddenBox( + k_s=k_s, + P_ss_path=P_ss_obj_path, + Pbins_bnd=Pbins_bnd, + theta2P=None, + P=P * shape(selection_params)[1], # P * Npop + size=size, + L=L, + G_sim_path=G_sim_path, + G_ss_path=G_ss_path, + Np0=Np0, + Npm0=Npm0, + fsimdir=wd[:-1], + modeldir=modeldir, + noise_std=noise, + radial_selection=radial_selection, + selection_params=selection_params, + observed_density=obs_density, + linear_bias=lin_bias, + norm_csts=norm_csts, + survey_mask_path=survey_mask_path, + local_mask_prefix=local_mask_prefix, + sim_params=sim_params, + TimeStepDistribution=TimeStepDistribution, + TimeSteps=indices_steps_cumul, + eff_redshifts=eff_redshifts, + seedphase=seedphase_init, + seednoise=seednoise_init, + fixnoise=False, + seednorm=BASELINE_SEEDNORM, + reset=False, + save_frequency=5, + verbosity=2, + ) + k_max = get_k_max(L, size) + except Exception as e: + logger.critical("Error instantiating HiddenBox: %s", str(e)) + raise RuntimeError("Failed to set up HiddenBox.") from e + + # Evaluate the param -> 'theta' using some get_summary logic + try: + theta = get_summary(param_val, param_id, omegas_gt, k_s, kmax=k_max) + except Exception: + max_tries = 10 + perturb_std = 1e-8 + param_val_init = param_val + logger.warning( + "get_summary failed for param_val=%s. Trying small perturbations...", param_val + ) + for i in range(max_tries): + param_val = normal(param_val_init, perturb_std) + logger.diagnostic("Attempt #%d: param_val=%s", i + 1, param_val) + try: + theta = get_summary(param_val, param_id, omegas_gt, k_s, kmax=k_max) + logger.diagnostic( + "Success with param_val=%s on attempt #%d", param_val, i + 1 + ) + break + except Exception: + if i == max_tries - 1: + logger.critical( + "All attempts to get_summary failed for param_val=%s", + param_val_init, + ) + raise RuntimeError("get_summary repeatedly failed.") + continue + + from io import BytesIO + from selfisys.utils.low_level import stderr_redirector, stdout_redirector + + cosmo_vect = omegas_gt + cosmo_vect[param_id] = param_val + + logger.debug("Evaluating model with HPC redirection, setup_only=%s", setup_only) + f = BytesIO() + g = BytesIO() + try: + with stderr_redirector(f): + with stdout_redirector(g): + if setup_only: + BB_selfi.switch_setup() + else: + BB_selfi.switch_recompute_pool(prefix_mocks=prefix_mocks) + + Phi = BB_selfi.evaluate( + theta, + spectrum_name, + seedphase_init + index, + seednoise_init + index, + i=batch_idx + index, + thetaIsP=True, + remove_sbmy=True, + force_powerspectrum=dbg, + force_parfiles=dbg, + check_output=dbg, + abc=poolname_abc, + cosmo_vect=cosmo_vect, + ) + if setup_only: + BB_selfi.switch_setup() + else: + BB_selfi.switch_recompute_pool(prefix_mocks=prefix_mocks) + + except Exception as e: + logger.critical("Error while evaluating model: %s", str(e)) + raise RuntimeError("Simbelmyne_worker model evaluation failed.") from e + finally: + g.close() + f.close() + + logger.debug("Returning param_val=%s with resulting Phi of shape %s", param_val, Phi.shape) + return param_val, Phi + + except OSError as e: + logger.error("File I/O error in Simbelmyne_worker: %s", str(e)) + raise + except Exception as e: + logger.critical("Unexpected error in Simbelmyne_worker: %s", str(e)) + raise RuntimeError("Simbelmyne_worker HPC run failed.") from e + finally: + gc.collect() + + +def worker_gradient_Symbelmyne( + coeff: float, + delta_x: float, + omega, + param_index: int, + k_s, + delta: float, + kmax: float, +): + """ + Worker function for evaluating the gradient of the power spectrum + using finite differences. + + Parameters + ---------- + coeff : float + Coefficient for the finite difference. + delta_x : float + Step size in the parameter space. + omega : ndarray + Base cosmological parameter vector. + param_index : int + Index of the parameter being varied. + k_s : ndarray + Array of wavenumbers. + delta : float + Denominator for finite differences (scaled). + kmax : float + Maximum wavenumber for power spectrum. + + Returns + ------- + ndarray + The gradient of the power spectrum wrt the specified parameter. + + Raises + ------ + RuntimeError + If the gradient evaluation fails. + """ + import numpy as np + from pysbmy.power import get_Pk + from selfisys.utils.tools import cosmo_vector_to_Simbelmyne_dict + + omega_new = omega.copy() + try: + omega_new[param_index] += delta_x + ps = get_Pk(k_s, cosmo_vector_to_Simbelmyne_dict(omega_new, kmax=kmax)) + contrib_to_grad = (coeff * ps) / delta + return np.array(contrib_to_grad) + except Exception as e: + logger.critical("Error in worker_gradient_Symbelmyne: %s", str(e)) + raise RuntimeError("worker_gradient_Symbelmyne failed.") from e + finally: + gc.collect() + + +def evaluate_gradient_of_Symbelmyne( + omega, + param_index: int, + k_s, + coeffs: List[float] = [2 / 3.0, -1 / 12.0], + deltas_x: List[float] = [0.01, 0.02], + delta: float = 1e-2, + kmax: float = 1.4, +): + """ + Estimate the gradient of CLASS with respect to the cosmological + parameters using central finite differences of arbitrary order. + + Parameters + ---------- + omega : ndarray + Base cosmological parameter vector. + param_index : int + Index of the parameter to differentiate against. + k_s : ndarray + Wavenumbers for the power spectrum. + coeffs : list of float, optional + Coefficients for the finite-difference scheme, typically + [2/3, -1/12] etc. Default is [2/3.0, -1/12.0]. + deltas_x : list of float, optional + Step sizes. The corresponding negative steps are generated + automatically. Default is [0.01, 0.02]. + delta : float, optional + Scale for the finite difference in the denominator. Default is + 1e-2. + kmax : float, optional + Maximum wavenumber for the power spectrum. Default is 1.4. + + Returns + ------- + ndarray + The gradient of the power spectrum wrt the specified parameter. + + Raises + ------ + RuntimeError + If the gradient evaluation fails. + """ + import numpy as np + from multiprocessing import Pool + + try: + grad = np.zeros(len(k_s)) + full_coeffs = np.concatenate((-np.array(coeffs)[::-1], coeffs)) + deltas_x_full = np.concatenate((-np.array(deltas_x)[::-1], deltas_x)) + + tasks = [ + (c, dx, omega, param_index, k_s, delta, kmax) + for c, dx in zip(full_coeffs, deltas_x_full) + ] + logger.diagnostic("Starting parallel HPC for gradient, tasks=%d", len(tasks)) + + with Pool() as mp_pool: + results = mp_pool.starmap(worker_gradient_Symbelmyne, tasks) + for contrib in results: + grad += contrib + + logger.diagnostic("Gradient evaluation completed. Shape=%s", grad.shape) + return grad + except Exception as e: + logger.critical("Unexpected error in evaluate_gradient_of_Symbelmyne: %s", str(e)) + raise RuntimeError("evaluate_gradient_of_Symbelmyne failed.") from e + finally: + gc.collect() diff --git a/src/setup.py b/src/setup.py new file mode 100644 index 0000000..b168726 --- /dev/null +++ b/src/setup.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +__author__ = "Tristan Hoellinger" +__version__ = "0.1.0" +__date__ = "2024" +__license__ = "GPLv3" + +""" +Setup script for the SelfiSys package. + +SelfiSys enables thorough diagnosis of systematic effects in +field-based, implicit likelihood inference (ILI) of cosmological +parameters from large-scale spectroscopic galaxy surveys. +""" + +from setuptools import setup, find_packages +import os + +# Read the long description from README.md +here = os.path.abspath(os.path.dirname(__file__)) +with open(os.path.join(here, "../README.md"), encoding="utf-8") as f: + long_description = f.read() + +setup( + name="selfisys", + version="0.1.0", + author="Tristan Hoellinger", + author_email="tristan.hoellinger@iap.fr", + description="Diagnosing systematic effects in implicit likelihood cosmological inferences.", + long_description=long_description, + long_description_content_type="text/markdown", + packages=find_packages(), + include_package_data=True, + url="https://github.com/hoellin/selfisys_public", + package_data={"selfisys": ["preamble.tex"]}, + classifiers=[ + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", + "Programming Language :: Python :: 3", + "Topic :: Scientific/Engineering :: Astronomy", + ], + python_requires=">=3.7", + license="GPLv3", + keywords="cosmology systematic-effects large-scale-structure systematics implicit-likelihood-inference misspecification robust-inference galaxy-surveys", +) diff --git a/submit/step0a.sh b/submit/step0a.sh new file mode 100644 index 0000000..0952387 --- /dev/null +++ b/submit/step0a.sh @@ -0,0 +1,44 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + + +export OMP_NUM_THREADS=8 +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step0a.py" \ + --wd_ext dev/ \ + --name run1 \ + --size 64 \ + --Np0 64 \ + --Npm0 64 \ + --L 3600 \ + --S 32 \ + --Pinit 50 \ + --Nnorm 2 \ + --total_steps 10 \ + --sim_params "custom19COLA20" \ + --aa 0.05 0.1 0.4 1 \ + --Ne 150 \ + --Ns 3 \ + --OUTDIR $SELFISYS_OUTPUT_PATH \ + --radial_selection "multiple_lognormal" \ + --selection_params 0.1150 0.1492 0.1818 0.1500 0.4925 0.8182 1 1 1 \ + --survey_mask_path $SELFISYS_OUTPUT_PATH"expl_notebooks/surveymask/raw_mask_N64.npy" \ + --lin_bias 1.47 1.99 2.32 \ + --prior "planck2018" \ + --nsamples_prior 10000 \ + --noise 0.1 \ + --force True + +exit 0 diff --git a/submit/step0b.sh b/submit/step0b.sh new file mode 100644 index 0000000..602cc9c --- /dev/null +++ b/submit/step0b.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + +ii_array=-1 +# ii_array=( $(seq 0 1 ) ) +# ii_array=( $(seq 0 9 ) ) + +export OMP_NUM_THREADS=8 +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step0b.py" \ + --pool_path $SELFISYS_OUTPUT_PATH"dev/643600502/run1/data/" \ + --ii ${ii_array[@]} \ + --npar 1 \ + --force False + +exit 0 \ No newline at end of file diff --git a/submit/step0c.sh b/submit/step0c.sh new file mode 100644 index 0000000..747baab --- /dev/null +++ b/submit/step0c.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + + +export OMP_NUM_THREADS=8 +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step0c.py" \ + --wd $SELFISYS_OUTPUT_PATH"dev/643600502/run1/" \ + --npar_norm 1 \ + --survey_mask_path $SELFISYS_OUTPUT_PATH"expl_notebooks/surveymask/raw_mask_N64.npy" \ + --force False + +exit 0 diff --git a/submit/step0d.sh b/submit/step0d.sh new file mode 100644 index 0000000..8b6b1d6 --- /dev/null +++ b/submit/step0d.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + +export OMP_NUM_THREADS=8 +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step0d.py" \ + --wd $SELFISYS_OUTPUT_PATH"dev/643600502/run1/" \ + --survey_mask_path $SELFISYS_OUTPUT_PATH"expl_notebooks/surveymask/raw_mask_N64.npy" \ + --name_obs obs \ + --reset_window_function True \ + --force_obs True + +exit 0 diff --git a/submit/step0e.sh b/submit/step0e.sh new file mode 100644 index 0000000..1db3442 --- /dev/null +++ b/submit/step0e.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + +export OMP_NUM_THREADS=1 +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step0e.py" \ + --wd $SELFISYS_OUTPUT_PATH"dev/643600502/run1/" \ + --N_THREADS 8 \ + --prior "planck2018" \ + --nsamples_prior 100 \ + --survey_mask_path $SELFISYS_OUTPUT_PATH"expl_notebooks/surveymask/raw_mask_N64.npy" \ + --name_obs obs \ + --force_recompute_prior False + +exit 0 diff --git a/submit/step1.sh b/submit/step1.sh new file mode 100644 index 0000000..1ba9e24 --- /dev/null +++ b/submit/step1.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + +# Run the Simbelmynë simulations at expansion point (all sims if +# pp_array=-1, selected sims otherwise). + +dir_array=0 + +pp_array=-1 +# pp_array=( $(seq 0 50 ) ) +# pp_array=( $(seq 495 499 ) ) + +export OMP_NUM_THREADS=1 # number of threads per process e.g. number of + # threads per Simbelmynë simulation +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step1_2.py" \ + --pool_path $SELFISYS_OUTPUT_PATH"dev/643600502/run1/pool/" \ + --directions ${dir_array[@]} \ + --pp ${pp_array[@]} \ + --Npop 3 \ + --npar 8 \ + --force True + +exit 0 diff --git a/submit/step2.sh b/submit/step2.sh new file mode 100644 index 0000000..f7803a4 --- /dev/null +++ b/submit/step2.sh @@ -0,0 +1,37 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + +# Run the Simbelmynë simulations in the directions dir_array of the +# parameter space, (all sims if pp_array=-1, selected sims otherwise). + +dir_array=( $(seq 1 32 ) ) +# dir_array=( $(seq 62 64 ) ) +# dir_array=( $(seq 1 64 ) ) + +pp_array=-1 +# pp_array=( $(seq 0 1 ) ) +# pp_array=( $(seq 0 9 ) ) + +export OMP_NUM_THREADS=1 +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step1_2.py" \ + --pool_path $SELFISYS_OUTPUT_PATH"dev/643600502/run1/pool/" \ + --directions ${dir_array[@]} \ + --pp ${pp_array[@]} \ + --Npop 3 \ + --npar 8 \ + --force True + +exit 0 \ No newline at end of file diff --git a/submit/step3.sh b/submit/step3.sh new file mode 100644 index 0000000..a183dff --- /dev/null +++ b/submit/step3.sh @@ -0,0 +1,51 @@ +#!/bin/bash +# ---------------------------------------------------------------------- +# Copyright (C) 2024 Tristan Hoellinger +# Distributed under the GNU General Public License v3.0 (GPLv3). +# See the LICENSE file in the root directory for details. +# SPDX-License-Identifier: GPL-3.0-or-later +# ---------------------------------------------------------------------- + +# Author: Tristan Hoellinger +# Version: 0.1 +# Date: 2024 +# License: GPLv3 + +eval "$(conda shell.bash hook)" +conda activate simbel + +# Notes: +# - One shall use "recompute_mocks True" when running step3 for the +# first time within a given run. +# - Afterwards, one can, again, use "recompute_mocks True" for +# recomputing the mocks with a misspecified model. + +export OMP_NUM_THREADS=1 +python $SELFISYS_ROOT_PATH"src/selfisys/pipeline/step3.py" \ + --wd $SELFISYS_OUTPUT_PATH"dev/643600502/run1/" \ + --N_THREADS 8 \ + --N_THREADS_PRIOR 8 \ + --prior "planck2018" \ + --nsamples_prior 1000 \ + --survey_mask_path $SELFISYS_OUTPUT_PATH"expl_notebooks/surveymask/raw_mask_N64.npy" \ + --params_obs None \ + --name_obs obs \ + --force_obs False \ + --recompute_obs_mock False \ + --reset_window_function True \ + --selection_params 0.1150 0.1492 0.1818 0.1500 0.4925 0.8182 1 1 1 \ + --Ne 150 \ + --Ns 3 \ + --prefix_mocks None \ + --force_recompute_prior False \ + --lin_bias 1.47 1.99 2.32 \ + --recompute_mocks True \ + --perform_score_compression True \ + --force_score_compression False + + # --lin_bias 1.47 1.99 2.32 \ + # --recompute_mocks True \ + + # --noise_dbg 0.1 \ + +exit 0