diff --git a/.flake8 b/.flake8 deleted file mode 100644 index 4cc136f4..00000000 --- a/.flake8 +++ /dev/null @@ -1,62 +0,0 @@ -[flake8] -ignore = - # No space before colon - E203 - W503 - # Missing docstring in __init__ (we do it in the class) - D107 - # "First line should be in imperative mood" -- this doesn't work for properties, see https://github.com/PyCQA/pydocstyle/issues/301 - D401 - # allow method names to be the same as python builtins - A003 - # inline strong start-string without end-string. This is OK in the case of **kwargs in parameters. - RST210 - # Ignore public methods without docstrings, because it doesn't check for inheritance :/ - D102 - # TODO: remove. (one-line docstring should fit on one line with quotes) - D200 - # TODO: remove. (first line should end with a period) - D400 - # TODO: remove (line too long) - E501 - # TODO: remove - D105 - # TODO: remove - D204 - # TODO: remove - D202 - # TODO: remove - D205 - # logging statement uses f-string - G004 - # No explicit stacklevel keyword argument in warn calls - B028 -max-line-length = 88 -# Should be 18. -max-complexity = 25 -exclude = - development/* -# Not ready for this yet. -per-file-ignores = - tests/*:D,T001,T201 # print statements allowed in tests - scripts/*: F821, T001 - docs/conf.py:D,A - */__init__.py:F401 - src/hmf/mass_function/fitting_functions.py:D101 -rst-roles = - class - func - mod - data - const - meth - attr - exc - obj -rst-directives = - note - warning - versionadded - versionchanged - deprecated - seealso diff --git a/.gitignore b/.gitignore index 504b2771..c828dd0a 100644 --- a/.gitignore +++ b/.gitignore @@ -18,3 +18,5 @@ docs/_*/* pip-wheel-metadata/ htmlcov/ .qodo + +docs/autoapi/* diff --git a/.readthedocs.yml b/.readthedocs.yml index 461ccc7f..5a3dca17 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -9,12 +9,18 @@ version: 2 sphinx: configuration: docs/conf.py -conda: - environment: docs/environment.yaml -python: - version: 3.13 - install: - - method: pip - path: . - system_packages: true +build: + os: ubuntu-24.04 + tools: + python: latest + + jobs: + pre_create_environment: + - asdf plugin add uv + - asdf install uv latest + - asdf global uv latest + create_environment: + - uv venv "${READTHEDOCS_VIRTUALENV_PATH}" + install: + - UV_PROJECT_ENVIRONMENT="${READTHEDOCS_VIRTUALENV_PATH}" uv sync --frozen --group docs diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst deleted file mode 100644 index 5f554347..00000000 --- a/CONTRIBUTING.rst +++ /dev/null @@ -1,142 +0,0 @@ -Contributing to hmf -=================== -Thank you for considering contributing to hmf! - -hmf is an open source project, and will only get better as the community of contributors -grows. -There are many ways to contribute, including writing tutorials, -improving the documentation, submitting bug reports and feature requests or writing code -which can be incorporated into hmf itself. Following the guidelines and patterns -suggested below helps us maintain a high quality code base and review your -contributions faster. - -How to report a bug -------------------- -First check the issues to see if your bug already exists. Feel free to comment on the -existing issue to provide more context or just to note that it is affecting you as well. -If your bug is not in the issue list, make a new issue. - -When making an issue, try to provide as much context as possible including: - -1. What version of python and hmf are you using? -2. What operating system are you using? -3. What did you do? -4. What did you expect to see? -5. What did you see instead? -6. Any code to reproduce the bug (as minimal an example as possible) - -If you're really inspired, you can make a pull request adding a test that fails because -of the bug. This is likely to lead to the bug being fixed more quickly. - -How to suggest a feature or enhancement ---------------------------------------- -First check the issues to see if your feature request already exists. Feel free to -comment on the existing issue to provide more context or just to note that you would -like to see the feature implemented as well. If your feature request is not in the issue -list, make a new issue. - -When making a feature request, try to provide as much context as possible. -Feel free to include suggestions for implementations. - -Guidelines for contributing to the code ---------------------------------------- -* Create issues for any major changes and enhancements that you wish to make. Discuss - things transparently and get community feedback. -* Keep pull requests as small as possible. Ideally each pull request should implement - ONE feature or bugfix. If you want to add or fix more than one thing, submit more than - one pull request. -* Do not commit changes to files that are irrelevant to your feature or bugfix. -* Be aware that the pull request review process is not immediate, and is generally - proportional to the size of the pull request. - -Your First Contribution -~~~~~~~~~~~~~~~~~~~~~~~ -Contributing for the first time can seem daunting, but we value contributions from our -user community and we will do our best to help you through the process. Here’s some -advice to help make your work on hmf more useful and rewarding. - -* Use issue labels to guide you - - Unsure where to begin contributing to hmf? You can start by looking through issues - labeled ``good first issue`` and ``help wanted`` issues. -* Pick a subject area that you care about, that you are familiar with, or that you want - to learn about - - There are many aspects to hmf, from cosmography, through cosmological initial - conditions, through to filter functions and fitting functions. Choose the one - you're most interested in! -* Start small - - It’s easier to get feedback on a little issue or pull request than on a big one. -* If you’re going to take on a big change, make sure that your idea has support first - - This means getting someone else to confirm that a bug is real before you fix the - issue, and ensuring that there’s consensus on a proposed feature before you work to - implement it. Use the issue log to start conversations about major changes and - enhancements. -* Be bold! Leave feedback! - - Sometimes it can be scary to make new issues or comment on existing issues or pull - requests, but contributions from the wider community are what ensure that hmf serves - the whole community as well as possible. -* Be rigorous - - Our requirements on code style, testing and documentation are important. If you have - questions about them or difficulty meeting them, please ask for help, we will do our - best to support you. Your contributions will be reviewed and integrated much more - quickly if your pull request meets the requirements. - -If you are new to the GitHub or the pull request process you can start by taking a look -at these tutorials: http://makeapullrequest.com/ and http://www.firsttimersonly.com/. -If you have more questions, feel free to ask for help, everyone is a beginner at first -and all of us are still learning! - -Getting started ---------------- -1. Create your own fork or branch of the code. -2. Follow the [Developer Installation](INSTALLATION.rst) instructions to ensure that you - have all the required packages for testing your changes. -3. Run ``pre-commit install`` to enable code-quality checks. -4. Make the changes in your fork or branch. -5. If you like the change and think the project could use it: - - If you're fixing a bug, include a new test that breaks as a result of the bug (if possible). - - Ensure that all your new code is covered by tests and that the existing tests pass. - Tests can be run by running ``pytest`` in the top level ``hmf`` directory. - - Ensure that you fully document any new features via docstrings, and potentially - as a new tutorial in the ``docs/`` directory. -6. Make a Pull Request from your fork/branch. - -Code review process -------------------- -The core team looks at pull requests on a regular basis and tries to provide feedback as -quickly as possible. Larger pull requests generally require more time for review. - -Release Cycle and Versioning ----------------------------- -``hmf`` uses git-flow and GitHub to manage releases. This means it - -In this workflow, ``master`` is protected and commits may *not* be pushed to it directly, -but must first undergo testing and review via a Pull Request. - -From v3.1.0, ``hmf`` will be using strict semantic versioning, such that increases in -the **major** version have potential API breaking changes, **minor** versions introduce -new features, and **patch** versions fix bugs and other non-breaking internal changes. - -Releases will be cut on the following timescales: - -* Major versions: no more than one each six months -- aiming for one per year or less. -* Minor versions: no more than one each two months. -* Patch versions: no restrictions. - -Note that patches will generally *not* be applied to previous major or minor versions. - -To cut a release, maintainers do the following: - -1. Ensure all relevant branches have been merged to master -2. Checkout master -3. Check all commits since last tag: ``git log $(git describe --tags --abbrev=0)..HEAD --oneline`` -4. If a feature commit is present, bump the minor version. If a BREAKING CHANGE is present, - bump the major version. Otherwise, bump the patch version. -5. Create a new branch named vX.Y.Zdev with the proper bump. -6. Update the changelog and check any other things that need to be fixed. -7. Push the branch -8. Merge to master -9. Run ``git tag vX.Y.Z``. - -This is very simple, and doesn't support adding fixes to the current release if a feature -or breaking change has been merged to master in the meantime. This may be updated in -the future. diff --git a/INSTALLATION.rst b/INSTALLATION.rst deleted file mode 100644 index 39e0e630..00000000 --- a/INSTALLATION.rst +++ /dev/null @@ -1,50 +0,0 @@ -Installation -============ - -This page will guide you through installing ``hmf`` -- either as purely a user, or -as a potential developer. - -Dependencies ------------- -``hmf`` has a number of dependencies, all of which should be automatically installed -as you install the package itself. You therefore do not need to worry about installing -them yourself, except in some circumstances. - -If you are using ``conda`` to create your Python environments, you may wish to manually -install the dependencies using ``conda`` (as they will be installed automatically with -``pip`` otherwise). For a user install, this can be done with the following:: - - conda install -c conda-forge numpy scipy astropy camb - -If you do not wish to install ``camb`` using ``conda``, you will need to ensure you -have a fortran compiler on your system path. If using ``gcc``, the version needs to be -greater than v6. - - -User Install ------------- -You may install the latest release of ``hmf`` using ``pip``:: - - pip install hmf - -This will install all uninstalled dependencies (see previous section). -Alternatively, for the very bleeding edge, install from the master branch of the repo:: - - pip install hmf @ git+git://github.com/steven-murray/hmf.git - -Developer Install ------------------ -If you intend to develop ``hmf``, clone the repository (or your fork of it):: - - git clone https://github.com//hmf.git - -Move to the directory and install with:: - - pip install -e ".[dev]" - -This will install all dependencies -- both for using and developing the package (testing, -creating docs, etc.). Again, see above about dependencies with ``conda`` if you are -using a ``conda`` environment (which is recommended). - -.. note:: Once the package is installed, you will need to locally run ``pre-commit install``, - to have constant checks on your code formatting before commits are accepted. diff --git a/README.rst b/README.rst index 5e5ebd43..ed815635 100644 --- a/README.rst +++ b/README.rst @@ -12,23 +12,29 @@ hmf :target: https://codecov.io/gh/halomod/hmf .. image:: https://img.shields.io/pypi/pyversions/hmf.svg :target: https://pypi.org/project/hmf/ -.. image:: https://img.shields.io/badge/code%20style-black-000000.svg - :target: https://github.com/psf/black +.. image:: https://readthedocs.org/projects/hmf/badge/?version=latest + :target: https://hmf.readthedocs.io/en/latest/?badge=latest + :alt: Documentation +.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json + :target: https://github.com/halomod/hmf ``hmf`` is a python application that provides a flexible and simple way to calculate the Halo Mass Function for a range of varying parameters. It is also the backend to -`HMFcalc `_, the online HMF calculator. +`TheHaloMod `_, the online HMF and halo model calculator. + +To get started, see the +`quickstart guide `_ and the +`API docs `_. -Full Documentation ------------------- -`Read the docs. `_ +.. important:: Pleae remember to cite ``hmf`` if you use it in your work. The citation information can be found + in the `attribution page `_. Features -------- * Calculate mass functions and related quantities extremely easily. * Very simple to start using, but wide-ranging flexibility. * Caching system for optimal parameter updates, for efficient iteration over parameter space. -* Support for all LambdaCDM cosmologies. +* Support for all LambdaCDM cosmologies, as well as WDM. * Focus on flexibility in models. Each "Component", such as fitting functions, filter functions, growth factor models and transfer function fits are implemented as generic classes that can easily be altered by the user without touching the source code. @@ -42,74 +48,6 @@ Features * 4 filter functions * 20 hmf fitting functions -* Includes models for Warm Dark Matter * Nonlinear power spectra via HALOFIT * Functions for sampling the mass function. -* CLI scripts for producing any quantity included. -* Python 2 and 3 compatible - -Note -~~~~ -From v3.1, ``hmf`` supports Python 3.6+, and has dropped support for Python 2. - - -Quickstart ----------- -Once you have ``hmf`` installed, you can quickly generate a mass function -by opening an interpreter (e.g. IPython/Jupyter) and doing:: - - >>> from hmf import MassFunction - >>> hmf = MassFunction() - >>> mass_func = hmf.dndlnm - -Note that all parameters have (what I consider reasonable) defaults. In particular, -this will return a Tinker (2008) mass function between -10^10 and 10^15 solar masses, at z=0 for the default PLANCK15 cosmology. -Nevertheless, there are several parameters which can be input, either cosmological -or otherwise. The best way to see these is to do:: - - >>> MassFunction.parameter_info() - -We can also check which parameters have been set in our "default" instance:: - - >>> hmf.parameter_values - -To change the parameters (cosmological or otherwise), one should use the -``update()`` method, if a MassFunction() object already exists. For example:: - - >>> hmf = MassFunction() - >>> hmf.update(cosmo_params={"Ob0": 0.05}, z=10) #update baryon density and redshift - >>> cumulative_mass_func = hmf.ngtm - -For a more involved introduction to ``hmf``, check out the `tutorials `_, -or the `API docs `_. - -Using the CLI -~~~~~~~~~~~~~ -You can also run ``hmf`` from the command-line. For basic usage, do:: - - hmf run --help - -Configuration for the run can be specified on the CLI or via a TOML file (recommended). -An example TOML file can be found in `examples/example_run_config.toml `_. -Any parameter specifiable in the TOML file can alternatively be specified on the commmand -line after an isolated double-dash, eg.:: - - hmf run -- z=1.0 hmf_model='SMT01' - -Versioning ----------- -From v3.1.0, ``hmf`` will be using strict semantic versioning, such that increases in -the **major** version have potential API breaking changes, **minor** versions introduce -new features, and **patch** versions fix bugs and other non-breaking internal changes. - -If your package depends on ``hmf``, set the dependent version like this:: - - hmf>=3.1<4.0 - -Attribution ------------ -Please cite `Murray, Power and Robotham (2013) `_, -`Murray, Diemer, Chen, et al. (2021) `_ -and/or https://ascl.net/1412.006 (whichever is more appropriate) if you find this -code useful in your research. Please also consider starring the GitHub repository. +* CLI commands for producing any quantity included. diff --git a/bump b/bump deleted file mode 100755 index 1d8fa939..00000000 --- a/bump +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/bash -set -e - -PART=$1 - -OLDVERSION=$(cat VERSION) -NEWVERSION=$(bump2version --dry-run --list ${PART} | grep new_version | sed -r s,"^.*=",,) -echo "New Version: ${NEWVERSION}" - -# Actually Run The Update -bump2version $PART - -# Now add in stuff to the changelog -python changethelog.py ${NEWVERSION} - -# Now commit -git add . -git commit -m "Bump Version: ${OLDVERSION} -> ${NEWVERSION}" diff --git a/docs/api.rst b/docs/api.rst index 0f634fe4..79502b6f 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -1,21 +1,54 @@ -API Summary -=========== +hmf +=== + +Frameworks +---------- .. autosummary:: + :caption: Frameworks :toctree: _autosummary :template: modules.rst hmf.cosmology.cosmo - hmf.cosmology.growth_factor hmf.density_field.transfer + hmf.mass_function.hmf + +Model Components +---------------- + +.. autosummary:: + :caption: Model Components + :toctree: _autosummary + :template: component-module.rst + + hmf.cosmology.growth_factor hmf.density_field.transfer_models - hmf.density_field.halofit hmf.density_field.filters hmf.halos.mass_definitions - hmf.mass_function.hmf hmf.mass_function.fitting_functions - hmf.mass_function.integrate_hmf + +Alternative Cosmologies +----------------------- +Alternative cosmology modules contain both new model components and patched Frameworks +to enable consistent modeling of alternative cosmological scenarios. + +.. autosummary:: + :caption: Alternative Cosmologies + :toctree: _autosummary + :template: modules.rst + hmf.alternatives.wdm + +Other Calculations and Utilities +-------------------------------- + +.. autosummary:: + :caption: Functions & Utilities + :toctree: _autosummary + :template: modules.rst + + hmf.density_field.halofit + hmf.mass_function.integrate_hmf hmf.helpers.sample hmf.helpers.functional diff --git a/docs/attribution.rst b/docs/attribution.rst new file mode 100644 index 00000000..424c9c6f --- /dev/null +++ b/docs/attribution.rst @@ -0,0 +1,6 @@ +Attribution +----------- +Please cite `Murray, Power and Robotham (2013) `_, +`Murray, Diemer, Chen, et al. (2021) `_ +and/or https://ascl.net/1412.006 (whichever is more appropriate) if you find this +code useful in your research. Please also consider starring the GitHub repository. diff --git a/docs/conf.py b/docs/conf.py index 1f67d6bb..68147097 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -40,6 +40,7 @@ "sphinx.ext.viewcode", "sphinx.ext.mathjax", "sphinx.ext.autosectionlabel", + #"autoapi.extension", "numpydoc", "nbsphinx", "IPython.sphinxext.ipython_console_highlighting", @@ -47,7 +48,28 @@ ] numpydoc_show_class_members = False + +# # Auto-API settings +# autoapi_options=[ +# 'members', +# 'undoc-members', +# 'show-inheritance', +# 'show-module-summary', +# 'special-members', +# 'imported-members', +# 'inherited-members', +# ] +# autoapi_dirs = ['../src/hmf'] +# autoapi_add_toctree_entry = False # We add it ourselves in index.rst +# autoapi_python_class_content = 'init' +# autoapi_member_order = 'groupwise' +# autoapi_own_page_level = 'class' +# autoapi_keep_files = True +# autodoc_typehints = 'description' + autosummary_generate = True +numpydoc_show_class_members = False + # Add any paths that contain templates here, relative to this directory. templates_path = ["templates"] @@ -63,7 +85,7 @@ # General information about the project. project = "hmf" -copyright = f"{time.localtime()[0]}, Steven Murray" +copyright = f"{time.localtime()[0]}, Steven G. Murray" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the diff --git a/docs/developing.rst b/docs/developing.rst index d17fc38e..84a080fe 100644 --- a/docs/developing.rst +++ b/docs/developing.rst @@ -1,131 +1,118 @@ -Developing hmf -============== - -If you're interested in developing hmf -- welcome! You should first read -the guide to contributing. This page is about more technical details of how -hmf is developed, and its philosophy. - - -Branching and Releasing ------------------------ -The aim is to make hmf's releases as useful, comprehendible, and automatic -as possible. This section lays out explicitly how this works (mostly for the benefit of -the admin(s)). - -Versioning -~~~~~~~~~~ -The first thing to mention is that we use strict `semantic versioning `_ -(since v2.0). Thus the versions are ``MAJOR.MINOR.PATCH``, with ``MAJOR`` including -API-breaking changes, ``MINOR`` including new features, and ``PATCH`` fixing bugs or -documentation etc. If you depend on hmf, you can set your dependency as -``hmf >= X.Y < X+1`` and not worry that we'll break your code with an update. - -To mechanically handle versioning within the package, we use two methods that we make -to work together automatically. The "true" version of the package is set with -`setuptools-scm `_. This stores the version -in the git tag. There are many benefits to this -- one is that the version is unique -for every single change in the code, with commits on top of a release changing the -version. This means that versions accessed via ``hmf.__version__`` are unique and track -the exact code in the package (useful for reproducing results). To get the current -version from command line, simply do ``python setup.py --version`` in the top-level -directory. - -To actually bump the version, we use ``bump2version``. The reason for this is that the -CHANGELOG requires manual intervention -- we need to change the "dev-version" section -at the top of the file to the current version. Since this has to be manual, it requires -a specific commit to make it happen, which thus requires a PR (since commits can't be -pushed to master). To get all this to happen as smoothly as possible, we have a little -bash script ``bump`` that should be used to bump the version, which wraps ``bump2version``. -What it does is: - -1. Runs ``bump2version`` and updates the ``major``, ``minor`` or ``patch`` part (passed like - ``./bump minor``) in the VERSION file. -2. Updates the changelog with the new version heading (with the date), - and adds a new ``dev-version`` heading above that. -3. Makes a commit with the changes. - -.. note:: Using the ``bump`` script is currently necessary, but future versions of - ``bump2version`` may be able to do this automatically, see - https://github.com/c4urself/bump2version/issues/133. - -The VERSION file might seem a bit redundant, and it is NOT recognized as the "official" -version (that is given by the git tag). Notice we didn't make a git tag in the above -script. That's because the tag should be made directly on the merge commit into master. -We do this using a Github Action (``tag-release.yaml``) which runs on every push to master, -reads the VERSION file, and makes a tag based on that version. - - -Branching -~~~~~~~~~ -For branching, we use a very similar model to `git-flow `_. -That is, we have a ``dev`` branch which acts as the current truth against which to develop, -and ``master`` essentially as a deployment branch. -I.e., the ``dev`` branch is where all features are merged (and some -non-urgent bugfixes). ``master`` is always production-ready, and corresponds -to a particular version on PyPI. Features should be branched from ``dev``, -and merged back to ``dev``. Hotfixes can be branched directly from ``master``, -and merged back there directly, *as well as* back into ``dev``. -*Breaking changes* must only be merged to ``dev`` when it has been decided that the next -version will be a major version. We do not do any long-term support of releases -(so can't make hotfixes to ``v2.x`` when the latest version is ``2.(x+1)``, or make a -new minor version in 2.x when the latest version is 3.x). We have set the default -branch to ``dev`` so that by default, branches are merged there. This is deemed best -for other developers (not maintainers/admins) to get involved, so the default thing is -usually right. - -.. note:: Why not a more simple workflow like Github flow? The simple answer is it just - doesn't really make sense for a library with semantic versioning. You get into - trouble straight away if you want to merge a feature but don't want to update - the version number yet (you want to merge multiple features into a nice release). - In practice, this happens quite a lot. - -.. note:: OK then, why not just use ``master`` to accrue features and fixes until such - time we're ready to release? The problem here is that if you've merged a few - features into master, but then realize a patch fix is required, there's no - easy way to release that patch without releasing all the merged features, thus - updating the minor version of the code (which may not be desirable). You could - then just keep all features in their own branches until you're ready to release, - but this is super annoying, and doesn't give you the chance to see how they - interact. - - -Releases -~~~~~~~~ -To make a **patch** release, follow these steps: - -1. Branch off of ``master``. -2. Write the fix. -3. Write a test that would have broken without the fix. -4. Update the changelog with your changes, under the ``**Bugfixes**`` heading. -5. Commit, push, and create a PR. -6. Locally, run ``./bump patch``. -7. Push. -8. Get a PR review and ensure CI passes. -9. Merge the PR - -Note that in the background, Github Actions *should* take care of then tagging master -with the new version, deploying that to PyPI, creating a new PR from master back into -dev, and accepting that PR. If it fails for one of these steps, they can all be done -manually. - -Note that you don't have to merge fixes in this way. You can instead just branch off -``dev``, but then the fix won't be included until the next ``minor`` version. -This is easier (the admins do the adminy work) and useful for non-urgent fixes. - -Any other fix/feature should be branched from ``dev``. Every PR that does anything -noteworthy should have an accompanying edit to the changelog. However, you do not have -to update the version in the changelog -- that is left up to the admin(s). To make a -minor release, they should: - -1. Locally, ``git checkout release`` -2. ``git merge dev`` -3. No new features should be merged into ``dev`` after that branching occurs. -4. Run ``./bump minor`` -5. Make sure everything looks right. -6. ``git push`` -7. Ensure all tests pass and get a CI review. -8. Merge into ``master`` - -The above also works for ``MAJOR`` versions, however getting them *in* to ``dev`` is a little -different, in that they should wait for merging until we're sure that the next version -will be a major version. +Contributing to hmf +=================== +Thank you for considering contributing to hmf! + +hmf is an open source project, and will only get better as the community of contributors +grows. +There are many ways to contribute, including writing tutorials, +improving the documentation, submitting bug reports and feature requests or writing code +which can be incorporated into hmf itself. Following the guidelines and patterns +suggested below helps us maintain a high quality code base and review your +contributions faster. + +How to report a bug +------------------- +First check the issues to see if your bug already exists. Feel free to comment on the +existing issue to provide more context or just to note that it is affecting you as well. +If your bug is not in the issue list, make a new issue. + +When making an issue, try to provide as much context as possible including: + +1. What version of python and hmf are you using? +2. What operating system are you using? +3. What did you do? +4. What did you expect to see? +5. What did you see instead? +6. Any code to reproduce the bug (as minimal an example as possible) + +If you're really inspired, you can make a pull request adding a test that fails because +of the bug. This is likely to lead to the bug being fixed more quickly. + +How to suggest a feature or enhancement +--------------------------------------- +First check the issues to see if your feature request already exists. Feel free to +comment on the existing issue to provide more context or just to note that you would +like to see the feature implemented as well. If your feature request is not in the issue +list, make a new issue. + +When making a feature request, try to provide as much context as possible. +Feel free to include suggestions for implementations. + +Guidelines for contributing to the code +--------------------------------------- +* Create issues for any major changes and enhancements that you wish to make. Discuss + things transparently and get community feedback. +* Keep pull requests as small as possible. Ideally each pull request should implement + ONE feature or bugfix. If you want to add or fix more than one thing, submit more than + one pull request. +* Do not commit changes to files that are irrelevant to your feature or bugfix. +* Be aware that the pull request review process is not immediate, and is generally + proportional to the size of the pull request. + +Your First Contribution +~~~~~~~~~~~~~~~~~~~~~~~ +Contributing for the first time can seem daunting, but we value contributions from our +user community and we will do our best to help you through the process. Here’s some +advice to help make your work on hmf more useful and rewarding. + +* **Use issue labels to guide you.** Unsure where to begin contributing to hmf? You can + start by looking through issues labeled ``good first issue`` and ``help wanted`` issues. +* **Pick a subject area that you care about, that you are familiar with, or that you want + to learn about.** There are many aspects to hmf, from cosmography, through cosmological + initial conditions, through to filter functions and fitting functions. Choose the one + you're most interested in! +* **Start small.** It’s easier to get feedback on a little issue or pull request than on + a big one. +* **If you’re going to take on a big change, make sure that your idea has support first.** + This means getting someone else to confirm that a bug is real before you fix the + issue, and ensuring that there’s consensus on a proposed feature before you work to + implement it. Use the issue log to start conversations about major changes and + enhancements. +* **Be bold! Leave feedback!** Sometimes it can be scary to make new issues or comment + on existing issues or pull requests, but contributions from the wider community are + what ensure that hmf serves the whole community as well as possible. +* **Be rigorous.** Our requirements on code style, testing and documentation are + important. If you have questions about them or difficulty meeting them, please ask for + help, we will do our best to support you. Your contributions will be reviewed and + integrated much more quickly if your pull request meets the requirements. + +If you are new to the GitHub or the pull request process you can start by taking a look +at these tutorials: http://makeapullrequest.com/ and http://www.firsttimersonly.com/. +If you have more questions, feel free to ask for help, everyone is a beginner at first +and all of us are still learning! + +Getting started +--------------- +1. Create your own fork or branch of the code. +2. Follow the [Developer Installation](INSTALLATION.rst) instructions to ensure that you + have all the required packages for testing your changes. +3. Run ``pre-commit install`` to enable code-quality checks. +4. Make the changes in your fork or branch. +5. If you like the change and think the project could use it: + + - If you're fixing a bug, include a new test that breaks as a result of the bug (if possible). + - Ensure that all your new code is covered by tests and that the existing tests pass. + Tests can be run by running ``pytest`` in the top level ``hmf`` directory. + - Ensure that you fully document any new features via docstrings, and potentially + as a new tutorial in the ``docs/`` directory. + +6. Make a Pull Request from your fork/branch. + +Code review process +------------------- +The core team looks at pull requests on a regular basis and tries to provide feedback as +quickly as possible. Larger pull requests generally require more time for review. + +Release Cycle and Versioning +---------------------------- +From v3.1.0, ``hmf`` uses strict semantic versioning, such that increases in +the **major** version have potential API breaking changes, **minor** versions introduce +new features, and **patch** versions fix bugs and other non-breaking internal changes. + +Note that patches will generally *not* be applied to previous major or minor versions. + +To cut a release, maintainers do the following: + +1. Ensure all relevant branches have been merged to main +2. Make a Github release tagged with the new version number (according to semantic versioning). +3. This will trigger a GitHub action to build and upload the new version to PyPI, and update the documentation on ReadTheDocs. diff --git a/docs/index.rst b/docs/index.rst index e0239fa6..13ab0216 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -5,24 +5,28 @@ .. include:: ../README.rst - -Contents --------- .. toctree:: :maxdepth: 1 + :hidden: + quickstart installation tutorials - license - api technical/index + attribution + +.. toctree:: + :caption: API Reference + :hidden: + + api + +.. toctree:: + :hidden: + :caption: Development + + versioning + license changelog contributors developing - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/installation.rst b/docs/installation.rst index 469b9988..1cc35ccf 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -1 +1,47 @@ -.. include:: ../INSTALLATION.rst +Installation +============ + +Dependencies +------------ +``hmf`` has a number of dependencies, all of which should be automatically installed +as you install the package itself. You therefore do not need to worry about installing +them yourself, except in some circumstances. + +If you are using ``conda`` to create your Python environments, you may wish to manually +install the dependencies using ``conda`` (as they will be installed automatically with +``pip`` otherwise). For a user install, this can be done with the following:: + + conda install -c conda-forge numpy scipy astropy camb + +If you do not wish to install ``camb`` using ``conda``, you will need to ensure you +have a fortran compiler on your system path. If using ``gcc``, the version needs to be +greater than v6. + + +User Install +------------ +You may install the latest release of ``hmf`` using ``pip``:: + + pip install hmf + +This will install all uninstalled dependencies (see previous section). +Alternatively, for the very bleeding edge, install from the master branch of the repo:: + + pip install hmf @ git+git://github.com/steven-murray/hmf.git + +Developer Install +----------------- +If you intend to develop ``hmf``, clone the repository (or your fork of it):: + + git clone https://github.com//hmf.git + +Move to the directory and install with:: + + pip install -e ".[dev]" + +This will install all dependencies -- both for using and developing the package (testing, +creating docs, etc.). Again, see above about dependencies with ``conda`` if you are +using a ``conda`` environment (which is recommended). + +.. note:: Once the package is installed, you will need to locally run ``pre-commit install``, + to have constant checks on your code formatting before commits are accepted. diff --git a/docs/quickstart.rst b/docs/quickstart.rst new file mode 100644 index 00000000..cac2eb1e --- /dev/null +++ b/docs/quickstart.rst @@ -0,0 +1,55 @@ +Quickstart +========== + +Installation +------------ +Please see the `installation instructions `_ for details on how to +install ``hmf`` and its dependencies. For most users, the following will be sufficient:: + + pip install hmf[extra] + +Using the Library +----------------- + +Once you have ``hmf`` installed, you can quickly generate a mass function +by opening an interpreter (e.g. IPython/Jupyter) and doing:: + + >>> from hmf import MassFunction + >>> hmf = MassFunction() + >>> mass_func = hmf.dndlnm + +Note that all parameters have (what I consider reasonable) defaults. In particular, +this will return a Tinker (2008) mass function between +10^10 and 10^15 solar masses, at z=0 for the default PLANCK15 cosmology. +Nevertheless, there are several parameters which can be input, either cosmological +or otherwise. The best way to see these is to do:: + + >>> MassFunction.parameter_info() + +We can also check which parameters have been set in our "default" instance:: + + >>> hmf.parameter_values + +To change the parameters (cosmological or otherwise), one should use the +``update()`` method, if a MassFunction() object already exists. For example:: + + >>> hmf = MassFunction() + >>> hmf.update(cosmo_params={"Ob0": 0.05}, z=10) #update baryon density and redshift + >>> cumulative_mass_func = hmf.ngtm + +For a more involved introduction to ``hmf``, check out the `tutorials `_, +or the `API docs `_. + +Using the CLI +------------- +You can also run ``hmf`` from the command-line. For basic usage, do:: + + hmf run --help + +Configuration for the run can be specified on the CLI or via a TOML file (recommended). +An example TOML file can be found in +`examples/example_run_config.toml `_. +Any parameter specifiable in the TOML file can alternatively be specified on the commmand +line after an isolated double-dash, eg.:: + + hmf run -- z=1.0 hmf_model='SMT01' diff --git a/docs/technical/growth_factors.rst b/docs/technical/growth_factors.rst index af7e735e..0371ac6b 100644 --- a/docs/technical/growth_factors.rst +++ b/docs/technical/growth_factors.rst @@ -29,7 +29,7 @@ be denoted by :math:`D_+(a)`. Unless otherwise noted, primes will denote derivat with respect to *scale factor* (e.g. :math:`D'(a)`), while dots will denote derivatives with respect to time (e.g. :math:`\dot{D}`). - +.. label-definitions: Definitions ----------- @@ -234,7 +234,7 @@ and noting that :math:`E(a) = \Omega_m a^{-3/2}` in this case, which gives: .. math:: 0 + \left(\frac{3}{a} - \frac{3}{2a} \right) - \frac{3}{2} \frac{1}{a^5 \Omega_m a^{-3}} a = 0 .. math:: \frac{3}{2a} - \frac{3}{2a} = 0. - +.. label-radiation-dom: Matter-Radiation Domination ~~~~~~~~~~~~~~~~~~~~~~~~~~~ At early times, radiation cannot be neglected, and the growth factor is not given by the @@ -309,6 +309,31 @@ where :math:`w` is the equation of state parameter for the dark energy component Unfortuantely, the case :math:`w=-1` is not a special case of this solution. +Solving the ODE +--------------- +The full ODE (without any assumptions/approximations on :math:`E(a)`) does not admit +closed-form solutions. However, it can be solved numerically. A convenient way to setup +the problem is as an initial value problem for a coupled system of first-order ODEs, +which can be solved with standard ODE solvers. In this case, the relevant system is +(see :ref:label-definitions`): + +.. math:: \begin{cases} x_1' = x_2 \\ x_2' = = -\left(\frac{3}{a} + \frac{E'}{E}\right) x_1' + \frac{3}{2} \frac{\Omega_m}{a^5 E^2} x_1 \end{cases} + +The only question is how to properly set the initial conditions for this system. +It is natural to set the initial conditions in the early universe, but it is common +to find initial conditions actually set in the matter-dominated era, where :math:`D(a) \to a` +and :math:`D'(a) \to 1`. However, choosing such initial conditions restricts the range +of redshifts in the solution to begin in matter-domination---probably enough for most +applications but still a needless restriction. Furthermore, it is also a little arbitrary, +since one must decide on which redshift is best representative of matter domination for +the particular cosmology on hand. A more natural choice is to set the initial conditions +in the radiation-dominated era, where we can start at arbitrarily high redshift and use +the solution for the growth factor in the radiation-dominated era to set the initial +conditions. In this case, we have :math:`D(a) \approx 2 a_{\rm eq}/3` and +:math:`D'(a) \approx 0` as :math:`a \to 0` (see :ref:`label-radiation-dom`), which is +the choice that we make in ``hmf``. + + Approximations -------------- diff --git a/docs/templates/class.rst b/docs/templates/class.rst index 398b40dd..e32c7910 100644 --- a/docs/templates/class.rst +++ b/docs/templates/class.rst @@ -1,31 +1,34 @@ -{{ fullname }} +{{ objname }} {{ underline }} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} - {% block methods %} - - {% if methods %} - .. rubric:: Methods - - .. autosummary:: - :toctree: {{ objname }} - {% for item in methods %} - ~{{ name }}.{{ item }} - {%- endfor %} - {% endif %} - {% endblock %} - {% block attributes %} {% if attributes %} .. rubric:: Attributes .. autosummary:: :toctree: {{ objname }} + :noindex: + {% for item in attributes %} ~{{ name }}.{{ item }} {%- endfor %} {% endif %} {% endblock %} + + + {% block methods %} + + {% if methods %} + .. rubric:: Methods + + {% for item in methods %} + .. automethod:: {{ item }} + :noindex: + {% endfor %} + + {% endif %} + {% endblock %} diff --git a/docs/templates/component-module.rst b/docs/templates/component-module.rst new file mode 100644 index 00000000..8b5d37e8 --- /dev/null +++ b/docs/templates/component-module.rst @@ -0,0 +1,52 @@ +hmf.{{ objname }} +{{ underline }} + +.. automodule:: {{ fullname }} + + {% block classes %} + .. rubric:: Base Component(s) + + .. autosummary:: + :toctree: {{ objname }} + :template: class.rst + {% for item in classes %} + {% if item.startswith("Base") or item.endswith("Component") %} + {{ item }} + {%- endif %} + {%- endfor %} + + .. rubric:: Models + + .. autosummary:: + :toctree: {{ objname }} + :template: class.rst + {% for item in classes %} + {% if not item.startswith("Base") and not item.endswith("Component") %} + {{ item }} + {%- endif %} + {%- endfor %} + + {% endblock %} + + {% block functions %} + {% if functions %} + .. rubric:: Functions + + .. autosummary:: + :toctree: {{ objname }} + {% for item in functions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block exceptions %} + {% if exceptions %} + .. rubric:: Exceptions + + .. autosummary:: + {% for item in exceptions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} diff --git a/docs/templates/modules.rst b/docs/templates/modules.rst index 516127b3..ef82cfb0 100644 --- a/docs/templates/modules.rst +++ b/docs/templates/modules.rst @@ -1,28 +1,28 @@ -{{ fullname }} +hmf.{{ objname }} {{ underline }} .. automodule:: {{ fullname }} - {% block functions %} - {% if functions %} - .. rubric:: Functions + {% block classes %} + {% if classes %} + .. rubric:: Classes .. autosummary:: :toctree: {{ objname }} - {% for item in functions %} + :template: class.rst + {% for item in classes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} - {% block classes %} - {% if classes %} - .. rubric:: Classes + {% block functions %} + {% if functions %} + .. rubric:: Functions .. autosummary:: :toctree: {{ objname }} - :template: class.rst - {% for item in classes %} + {% for item in functions %} {{ item }} {%- endfor %} {% endif %} diff --git a/docs/versioning.rst b/docs/versioning.rst new file mode 100644 index 00000000..86b8461b --- /dev/null +++ b/docs/versioning.rst @@ -0,0 +1,9 @@ +Versioning +---------- +From v3.1.0, ``hmf`` will be using strict semantic versioning, such that increases in +the **major** version have potential API breaking changes, **minor** versions introduce +new features, and **patch** versions fix bugs and other non-breaking internal changes. + +If your package depends on ``hmf``, set the dependent version like this:: + + hmf>=3.1<4.0 diff --git a/pyproject.toml b/pyproject.toml index 329d9781..f3721a9a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,11 +42,22 @@ dependencies = [ ] [project.optional-dependencies] +cosmo = [ + "colossus>=1.2.1" +] +fit = [ + "emcee>=3.0" +] +extra = ["hmf[cosmo,fit]"] + +[dependency-groups] docs = [ "Sphinx>=1.7.5", "numpydoc>=0.8.0", - "nbsphinx", "furo>=2025.12.19", + "sphinx-autoapi", + "nbsphinx>=0.9.8", + "ipython>=9.11.0", ] tests = [ @@ -56,22 +67,14 @@ tests = [ "mpmath>=1.0.0", "colossus>=1.2.1", "halomod>=1.4.6", - "numba", + "numba>=0.64.0", ] dev = [ - "hmf[docs,tests]", - "ruff", - "pre-commit", -] -cosmo = [ - "colossus>=1.2.1" -] -fit = [ - "emcee>=3.0" -] -all = [ - "hmf[dev,cosmo,fit]" + {include-group = "docs"}, + {include-group = "tests"}, + "pre-commit>=4.5.1", + "ruff>=0.15.6", ] [project.scripts] diff --git a/src/hmf/cosmology/__init__.py b/src/hmf/cosmology/__init__.py index d86f36ed..d00026b7 100644 --- a/src/hmf/cosmology/__init__.py +++ b/src/hmf/cosmology/__init__.py @@ -1,12 +1,14 @@ """Cosmographic calculations, and other purely cosmological quantities, such as growth factor.""" -from . import cosmo +from . import cosmo, growth_factor from .cosmo import Cosmology, astropy_to_colossus -from .growth_factor import GrowthFactor +from .growth_factor import BaseGrowthFactor, GrowthFactor __all__ = [ + "BaseGrowthFactor", "Cosmology", "GrowthFactor", "astropy_to_colossus", "cosmo", + "growth_factor", ] diff --git a/src/hmf/cosmology/growth_factor.py b/src/hmf/cosmology/growth_factor.py index 2e3d4ecb..2ccd7836 100644 --- a/src/hmf/cosmology/growth_factor.py +++ b/src/hmf/cosmology/growth_factor.py @@ -1,10 +1,30 @@ """ -Module defining the growth factor. - -The primary class, :class:`GrowthFactor`, executes a full -numerical ODE calculation appropriate for any FLRW cosmology. Simplifications -which may be more efficient, or extensions to alternate cosmologies, -may be implemented. +Module defining calculations of the cosmological growth factor and growth rate. + +The primary class, :class:`GrowthFactor`, intelligently dispatches to a number of +different methods for calculating the growth factor, depending on the cosmology and +the parameters. The most general method, which is applicable for any FLRW cosmology, is +:class:`ODEGrowthFactor`, which solves the full ODE for the growth factor. This is the +most general method, but also the slowest. For cosmologies with negligible radiation +density, the growth factor can be calculated using the integral form defined in +:class:`IntegralGrowthFactor`. If the cosmology is also *flat*, then the growth factor +can be calculated using the analytical formulae implemented in +:class:`Eisenstein97GrowthFactor`, which is the fastest method. Various other +limiting cases and assumptions are also implemented. If using the base :class:`GrowthFactor` class, +the appropriate method will be chosen automatically such that it uses the fastest +available *correct* method. + +In addition, a couple of approximate formulae are included, for example +:class:`Carroll1992` and :class:`GenMFGrowth`, which are not exact for any cosmology +but are very fast to compute and quite accurate across a broad range of cosmologies. + +Full details of the formulae, their assumptions and limitations, and derivations, are +given in the `technical documentation `_. + +The main two functions that the model component provides are `growth_factor` and +`growth_rate`. The growth rate is the logarithmic derivative of the growth factor. In +all classes, the growth rate will be internally consistent with the growth factor (i.e. +any approximations made to one will also be made to the other). """ import warnings @@ -30,8 +50,26 @@ @pluggable -class _GrowthFactor(Cmpt): - r"""General class for a growth factor calculation.""" +class BaseGrowthFactor(Cmpt): + r"""General class for a growth factor calculation. + + Sub-classes must implement :method:`_d_plus_unnormalized`, which should take a + single argument -- the redshift, ``z`` -- and return an array or float (depending + on the input type) of the un-normalized growth factor, :math:`D^+(a)`. Most + typically, the normalization of this function is such that :math:`D^+(a) \approx a` + at early times, but this is not enforced, and does not affect the user-facing + methods of the class. + + Sub-classes *may* also implement :method:`growth_rate`, which takes the same + argument and returns the growth rate: + + .. math:: f(a) = d\ln D^+ / d\ln a. + + If this is not implemented, then the growth rate will be calculated by taking the + derivative of a spline fitted to the growth factor numerically, and in which the + growth factor is evaluated on a logarithmic grid of the scale factor from ``amin`` + to 1 in steps of ``dlna``. + """ _defaults: ClassVar[dict[str, float]] = {"dlna": 0.01, "amin": 1e-8} @@ -50,6 +88,53 @@ def _lna(self): def _zvec(self): return 1.0 / np.exp(self._lna) - 1.0 + def _validate_assumptions(self, z: float | np.ndarray): + """Validate the assumptions of the growth factor calculation. + + This is called in the base class, and can be over-ridden by sub-classes to check + for specific assumptions. By default, it does nothing. + """ + + def radiation_density(self, z): + """The fractional radiation density as a function of redshift.""" + # In astropy, the radiation density (i.e. the thing in front of the + # a^-4 term in the Friedmann equation) is has both neutrinos and photons: + + Or = self.cosmo.Ogamma0 + ( + self.cosmo.Onu0 + if not self.cosmo._nu_info.has_massive_nu + else self.cosmo.Ogamma0 * self.cosmo.nu_relative_density(z) + ) + return Or * (1 + z) ** 4 * self.cosmo.inv_efunc(z) ** 2 + + @cached_property + def Or0(self): + """The fractional radiation density at redshift zero.""" + return self.radiation_density(0) + + def dlne_dlna(self, z): + r"""Compute the derivative of ln(E(a)) with respect to ln(a). + + This is useful for the growth factor, which has terms + :math:`E'(a)/E(a) \equiv (1/a)*dlnE/dlna` in its definition. + + This implementation simply uses the exact definition from astropy of E(a) + and writes down the derivative analytically. + """ + a = 1 / (1 + z) + + Or = self.cosmo.Ogamma0 + ( + self.cosmo.Onu0 + if not self.cosmo._nu_info.has_massive_nu + else self.cosmo.Ogamma0 * self.cosmo.nu_relative_density(z) + ) + + Or = -4 * Or * a**-5 + Om = -3 * self.cosmo.Om0 * a**-4 + Ok = -2 * self.cosmo.Ok0 * a**-3 + + return a * 0.5 * (Or + Om + Ok) * self.cosmo.inv_efunc(z) ** 2 + @abstractmethod def _d_plus_unnormalized(self, z): """Compute the unnormalized growth factor, D^+(a).""" @@ -62,7 +147,7 @@ def _d_plus0(self) -> float: def growth_factor(self, z): r""" - Compute the normalized growth factor, :math:`d(a) = D^+(a)/D^+(a=1)`. + Compute the normalized growth factor, :math:`D(a) = D^+(a)/D^+(a=1)`. Parameters ---------- @@ -74,6 +159,7 @@ def growth_factor(self, z): gf : array_like The growth factor at `z`. """ + self._validate_assumptions(z) return self._d_plus_unnormalized(z) / self._d_plus0 @cached_property @@ -99,11 +185,15 @@ def growth_rate(self, z) -> float | np.ndarray: gr : array_like The growth rate at `z`. """ + self._validate_assumptions(z) + return self._growth_rate(z) + + def _growth_rate(self, z) -> float | np.ndarray: # The bog standard method will just be numerical derivative. return self._growth_rate_spline(np.log(1 / (1 + z))) -class GrowthFactor(_GrowthFactor): +class GrowthFactor(BaseGrowthFactor): r"""Growth factor calculation that intelligently chooses which method to use. This class chooses which growth factor calculation to use based on the cosmology and @@ -122,105 +212,75 @@ class GrowthFactor(_GrowthFactor): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - # Create empty attributes for the different growth factor calculations, - # which will be filled in as needed. - self._ode_growth_factor = None - self._integral_growth_factor = None - self._eisenstein_growth_factor = None + @cached_property + def _ode_gf(self): + return ODEGrowthFactor(self.cosmo, **self.params) - def _d_plus_unnormalized(self, z): - """Calculate the growth factor at redshift z. + @cached_property + def _integral_gf(self): + return IntegralGrowthFactor(self.cosmo, **self.params) - See class documentation for the logic tree used here. - """ - a = 1 / (1 + z) + @cached_property + def _eisenstein_gf(self): + return Eisenstein97GrowthFactor(self.cosmo, **self.params) - if self._ode_growth_factor is not None: - # Might as well use it since it's already been initialized and it's the - # most accurate. - return self._ode_growth_factor._d_plus_unnormalized(z) + @cached_property + def _heath_gf(self): + return Heath77GrowthFactor(self.cosmo, **self.params) + def _choose_solution(self, z): if not isinstance(self.cosmo, cosmology.LambdaCDM): - self._ode_growth_factor = ODEGrowthFactor(self.cosmo, **self.params) - return self._ode_growth_factor._d_plus_unnormalized(z) - - if (np.all(z < 5) or self.cosmo.Ogamma0 == 0) and isinstance( - self.cosmo, cosmology.FlatLambdaCDM - ): - # At low z for a flat cosmology, we can use the formulae in Eisenstein 1997, - # which is very fast. - self._eisenstein_growth_factor = Eisenstein97GrowthFactor(self.cosmo, **self.params) - return self._eisenstein_growth_factor._d_plus_unnormalized(z) + return self._ode_gf - if self.cosmo.Ogamma0 == 0 or np.all(a > 100 * self.cosmo.Ogamma0 / self.cosmo.Om0): - self._integral_growth_factor = IntegralGrowthFactor(self.cosmo, **self.params) - return self._integral_growth_factor._d_plus_unnormalized(z) + # Low radiation component solutions. + if np.all(self.radiation_density(z) < 0.02): + if self.cosmo.is_flat: + return self._eisenstein_gf + if self.cosmo.Ode0 == 0: + return self._heath_gf + return self._integral_gf - self._ode_growth_factor = ODEGrowthFactor(self.cosmo, **self.params) - return self._ode_growth_factor._d_plus_unnormalized(z) + return self._ode_gf - def growth_rate(self, z): - """Calculate the growth rate at redshift z. + def _d_plus_unnormalized(self, z): + """Calculate the growth factor at redshift z. See class documentation for the logic tree used here. """ - a = 1 / (1 + z) + return self._choose_solution(z)._d_plus_unnormalized(z) - if self._ode_growth_factor is not None: - # Might as well use it since it's already been initialized and it's the - # most accurate. - return self._ode_growth_factor.growth_rate(z) + def _growth_rate(self, z): + return self._choose_solution(z)._growth_rate(z) - if not isinstance(self.cosmo, cosmology.LambdaCDM): - self._ode_growth_factor = ODEGrowthFactor(self.cosmo, **self.params) - return self._ode_growth_factor.growth_rate(z) - if (np.all(z < 5) or self.cosmo.Ogamma0 == 0) and isinstance( - self.cosmo, cosmology.FlatLambdaCDM - ): - # At low z for a flat cosmology, we can use the formulae in Eisenstein 1997, - # which is very fast. - self._eisenstein_growth_factor = Eisenstein97GrowthFactor(self.cosmo, **self.params) - return self._eisenstein_growth_factor.growth_rate(z) - - if self.cosmo.Ogamma0 == 0 or np.all(a > 100 * self.cosmo.Ogamma0 / self.cosmo.Om0): - self._integral_growth_factor = IntegralGrowthFactor(self.cosmo, **self.params) - return self._integral_growth_factor.growth_rate(z) - - self._ode_growth_factor = ODEGrowthFactor(self.cosmo, **self.params) - return self._ode_growth_factor.growth_rate(z) - - -class ODEGrowthFactor(_GrowthFactor): +class ODEGrowthFactor(BaseGrowthFactor): r""" Growth factor calculation that solves the full ODE. The ODE solved here is given in many references, e.g. Peebles 1980 "The Large Scale Structure of the Universe". The implementation here does not assume either flatness, a cosmological constant, or negligible radiation density, and so should be - accurate for any FLRW cosmology. The growth factor is normalised such that D+(a) = 1 - at a=1. + accurate for any FLRW cosmology. Notes ----- - The growth factor is calculated by solving the ODE for the growth function - :math:`G(a) = D+(a)/a`, which is given by + The growth factor is calculated by solving the following ODE: - .. math:: G''(a) + \left(\frac{H'(a)}{H(a)} + \frac{5}{a}\right) G'(a) + - \left(\frac{3}{a^2} + \frac{H'(a)}{a H(a)} - - \frac{3}{2} \frac{\Omega_{m,0}}{a^5 H(a)^2}\right) G(a) = 0. + .. math:: D''(a) + \left(\frac{H'(a)}{H(a)} + \frac{3}{a}\right) D'(a) - + \frac{3}{2} \frac{\Omega_{m,0}}{a^5 H(a)^2}\right) D(a) = 0. - The growth factor is then given by :math:`D^+(a) = G(a) \cdot a`. We use - astropy to calculate :math:`H(a)` and its derivative, which ensures that the growth - factor is calculated self-consistently with the cosmology. The growth factor is - normalised such that :math:`D^+(a=1) = 1`. + We use astropy to calculate :math:`H(a)`, and its derivative is computed via a + spline fit, which ensures that the growth factor is calculated self-consistently + with the cosmology. The ODE is solved using scipy's `solve_ivp` function, with initial conditions set in - the matter-dominated era, where :math:`G(a) \approx 1` and :math:`G'(a) \approx 0`. + the radiation-dominated era, where :math:`D(a) \approx 2 a_{\rm eq}/3` and + :math:`D'(a) \approx 0` (see the growth factor + `derivation documentation `_ + for details). Although these initial conditions are used for the ODE solution, the growth factor is ultimately normalised such that :math:`D^+(a=1) = 1`, so the final result is not - sensitive to the exact choice of initial conditions (beyond the condition on - the normalisation). + sensitive to one effective choice of initial condition. The growth rate is calculated by taking the derivative of the growth factor spline, which is equivalent to the expression given in many references, e.g. Carroll et al. @@ -233,7 +293,8 @@ class ODEGrowthFactor(_GrowthFactor): factor. Since the ODE solution is numerical, it must be evaluated on a grid, and since it - is solved as an initial value problem, the grid must start at some early time. + is solved as an initial value problem, the grid must start at some early time + (well into radiation-domination). The parameters `dlna` and `amin` control the grid on which the ODE is solved, and so the accuracy of the solution. The grid is in log-space for the scale factor, and so `dlna` controls the step-size in log-space, while `amin` controls how far back in @@ -241,15 +302,6 @@ class ODEGrowthFactor(_GrowthFactor): cosmologies. """ - @cached_property - def _dlnefunc_dlna(self): - """Compute the derivative of ln(H) with respect to ln(a). - - This is used later for computing H'(a)/H(a) == (1/a)*dlnH/dlna for the growth - rate calculation. - """ - return Spline(self._lna, np.log(self.cosmo.efunc(self._zvec))).derivative() - @cached_property def _ode_solution(self): # This function implementation borrowed heavily from the Colossus code @@ -259,9 +311,8 @@ def ode(a, y): D, Dp = y z = 1 / a - 1 - lna = np.log(a) - dlnhdlna = self._dlnefunc_dlna(lna) + dlnhdlna = self.dlne_dlna(z) return [ Dp, -(3 / a + dlnhdlna / a) * Dp @@ -275,7 +326,7 @@ def ode(a, y): # q is the radiation to matter ratio. If there is no radiation, then we are in # the matter dominated era and the solution is just a (i.e. with a derivative # of unity) - aeq = self.cosmo.Ogamma0 / self.cosmo.Om0 + aeq = self.Or0 / self.cosmo.Om0 sol = solve_ivp( ode, (a.min(), a.max()), @@ -319,17 +370,9 @@ def growth_factor_inverse(self, d): @cached_property def _growth_rate_spline(self): - r"""Calculate the growth rate, dln(d)/dln(a). - - Given the integral form of the growth factor, we can write out the growth - rate analytically as - - .. math:: \frac{d\ln D^+}{d\ln a} = dlnH/dln(a) - + 2.5 \frac{\Omega_m(a)}{a^2 H^2(a) D^+(a)}. - """ return self._ode_solution[0].derivative() - def growth_rate(self, z: float | np.ndarray) -> float | np.ndarray: + def _growth_rate(self, z: float | np.ndarray) -> float | np.ndarray: """ Growth rate, dln(D+)/dln(a). @@ -342,25 +385,50 @@ def growth_rate(self, z: float | np.ndarray) -> float | np.ndarray: return self._growth_rate_spline(lna) -class IntegralGrowthFactor(_GrowthFactor): +class IntegralGrowthFactor(BaseGrowthFactor): r"""Growth factor computed using the integral of Heath 1977. This growth factor is only applicable when the radiation density is negligible, and - so is only accurate at low redshifts in cosmologies with radiation. + so is only accurate at "low" redshifts in cosmologies with radiation. + + The integral formula is: + + .. math:: D^+(a) = \frac{5}{2} \Omega_{m,0} E(a) \int_0^a \frac{da'}{(a'^3 E(a')^3)}. + + In this class, if a cosmology is given that has non-zero radiation density, + the radiation density will be included in the calculation of E(a), even though + the assumptions of the formula are not valid in this case. If the growth factor is + being computed at high redshifts, a warning will be raised due to the inaccuracy + of the method, but otherwise the calculation will continue. The ability to run + the calculation in this case is provided to allow for comparison. + + The growth rate has a particularly simple form under the assumptions of the integral + formula, and so is calculated using this form, which is consistent with the growth + factor calculation, and is also faster to compute than taking the derivative of the + growth factor spline. It is: + + .. math:: \frac{d\ln D^+}{d\ln a} = dlnH/dln(a) + 2.5 \frac{\Omega_m(a)}{a^2 H^2(a) D^+(a)}. """ - _defaults: ClassVar[dict[str, float]] = {"dlna": 0.01, "amin": 1e-8} + def _validate_assumptions(self, z: float | np.ndarray): - @cached_property - def _lna(self): - lna = np.arange(np.log(self.params["amin"]), 0, self.params["dlna"]) - if lna[-1] != 0: - lna = np.concatenate((lna, [0])) - return lna + if np.any(self.radiation_density(z) > 0.02): + warnings.warn( + f"The {self.__class__.__name__} is not accurate when the radiation " + f"density is significant. Consider using the " + "ODEGrowthFactor, or GrowthFactor (which switches between models " + "automatically) instead. You requested growth factor where the " + f"radiation density is {np.max(self.cosmo.Ogamma(z))}, which is not negligible.", + stacklevel=2, + ) - @cached_property - def _zvec(self): - return 1.0 / np.exp(self._lna) - 1.0 + if not isinstance(self.cosmo, cosmology.LambdaCDM): + warnings.warn( + f"The {self.__class__.__name__} is only accurate for cosmologies with a " + "constant dark energy equation of state. Consider using the " + "ODEGrowthFactor instead.", + stacklevel=2, + ) @cached_property def _integral(self): @@ -400,21 +468,6 @@ def _d_plus_unnormalized(self, z: float | np.ndarray) -> float | np.ndarray: a = 1 / (1 + z) lna = np.log(a) - if np.any(a < self.cosmo.Ogamma0 / self.cosmo.Om0 * 50): - warnings.warn( - f"The IntegralGrowthFactor is not accurate at high redshifts " - f"({np.max(z):.2f}) in cosmologies with radiation. Consider using the " - "ODEGrowthFactor instead.", - stacklevel=2, - ) - if not isinstance(self.cosmo, cosmology.LambdaCDM): - warnings.warn( - "The IntegralGrowthFactor is only accurate for cosmologies with a " - "constant dark energy equation of state. Consider using the " - "ODEGrowthFactor instead.", - stacklevel=2, - ) - if np.any(z < 0): raise ValueError("Redshifts <0 not supported") if np.any(a < a_min): @@ -434,11 +487,7 @@ def _efunc(self, z): """ return self.cosmo.efunc(z) - @cached_property - def _dlnhdlna(self): - return Spline(self._lna, np.log(self._efunc(self._zvec))).derivative() - - def growth_rate(self, z): + def _growth_rate(self, z): """Calculate the growth rate, using the integral form of the growth factor.""" a = 1 / (1 + z) @@ -446,7 +495,12 @@ def growth_rate(self, z): # This is because the coefficients in front of the second term are chosen # based on the normalization specified in _d_plus_unnormalized (i.e. 5/2 Om0). # This is a convenient choice so that D(a) -> a for a -> 0. - return self._dlnhdlna(np.log(a)) + 2.5 * self.cosmo.Om0 / ( + if np.any(self.radiation_density(z) > 0.02): + # Need to use the basic spline because that is always consistent + # with the growth factor. The analytic equation is only consistent with + # the growth factor under the assumption of negligible radiation. + return super()._growth_rate(z) + return self.dlne_dlna(z) + 2.5 * self.cosmo.Om0 / ( a**2 * self._efunc(z) ** 2 * self._d_plus_unnormalized(z) ) @@ -454,44 +508,45 @@ def growth_rate(self, z): class Eisenstein97GrowthFactor(IntegralGrowthFactor): r"""Growth factor calculated using the formulae in Eisenstein 1997. - This is the result from Eqs 8-10. + This is the result from their Eqs 8-10, or equivalently in the + `technical docs `_. This growth factor is only applicable for flat cosmology with a cosmological constant and negligible radiation (i.e. low redshifts). """ - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) + def _validate_assumptions(self, z: float | np.ndarray): + super()._validate_assumptions(z) + + if not self.cosmo.is_flat: + raise ValueError(f"The {self.__class__.__name__} only supports flat cosmologies") def _efunc(self, z): """Calculate E(z) = H(z)/H0 under assumptions of the Eisenstein+97 formulae.""" a = 1 / (1 + z) return np.sqrt(self.cosmo.Om0 * a**-3 + (1 - self.cosmo.Om0)) - def _dlnhdlna(self, lna): - # We want the growth rate to be consistent with the growth factor, so we - # calculate a*H'(a)/H(a) under the same assumptions as directly assumed in the - # analytic formula for the growth factor, rather than - # using the cosmology given. This ensures that the growth rate is consistent - # with the growth factor, even if the growth factor is not exact. - # The formula for D+ in Eisenstein 1997 assumes both flatness and negligible - # radiation, so we calculate a*H'(a)/H(a) under the same assumptions. - a = np.exp(lna) - Hsq = self._efunc(1 / a - 1) ** 2 - return -1.5 * self.cosmo.Om0 * a**-3 / Hsq + def dlne_dlna(self, z): + r""" + Compute the derivative of ln(E(a)) with respect to ln(a). + + This is useful for the growth factor, which has terms + :math:`E'(a)/E(a) \equiv (1/a)*dlnE/dlna` in its definition. + + We want the growth rate to be consistent with the growth factor, so we + calculate a*H'(a)/H(a) under the same assumptions as directly assumed in the + analytic formula for the growth factor, rather than + using the cosmology given. This ensures that the growth rate is consistent + with the growth factor, even if neither are consistent with the cosmology. + The formula for D+ in Eisenstein 1997 assumes both flatness and negligible + radiation, so we calculate a*H'(a)/H(a) under the same assumptions. + """ + esq = self.cosmo.Om0 * (1 + z) ** 3 + (1 - self.cosmo.Om0) + return -1.5 * self.cosmo.Om0 * (1 + z) ** 3 / esq def _d_plus_unnormalized(self, z: float | np.ndarray) -> float | np.ndarray: from scipy.special import ellipeinc, ellipkinc - if np.any(z > 100) and self.cosmo.Ogamma0 > 0: - warnings.warn( - "The Eisenstein97GrowthFactor is not accurate at high redshifts (z > 100)." - " Consider using the ODEGrowthFactor instead.", - stacklevel=2, - ) - if not isinstance(self.cosmo, cosmology.FlatLambdaCDM): - raise ValueError("Eisenstein97GrowthFactor only supports flat LambdaCDM cosmologies") - a = 1 / (1 + z) v = 1 / a * (self.cosmo.Om0 / self.cosmo.Ode0) ** (1 / 3) @@ -516,32 +571,23 @@ def _d_plus_unnormalized(self, z: float | np.ndarray) -> float | np.ndarray: class Heath77GrowthFactor(IntegralGrowthFactor): - r"""Growth factor calculated using the formulae in Heath 1977. + r"""Growth factor calculated using the analytic formula in Heath 1977. These results apply when Lambda = 0 and the radiation density is negligible, and is given in Eq 13 of Heath 1977. """ - def _d_plus_unnormalized(self, z: float | np.ndarray) -> float | np.ndarray: - if self.cosmo.Ogamma0 > 0 and np.any( - z > 1 / (100 * self.cosmo.Ogamma0 / self.cosmo.Om0) - 1 - ): - warnings.warn( - "The Heath77GrowthFactor is not accurate at high redshifts (z > " - "100*Ogamma0/Om0 - 1) in cosmologies with radiation. Consider using the" - " ODEGrowthFactor instead.", - stacklevel=2, - ) - if (hasattr(self.cosmo, "w0") and self.cosmo.w0 != -1) or ( - hasattr(self.cosmo, "wa") and self.cosmo.wa != 0 - ): + def _validate_assumptions(self, z: float | np.ndarray): + super()._validate_assumptions(z) + + if self.cosmo.Ode0 != 0: warnings.warn( - "The Heath77GrowthFactor is only accurate for cosmologies with a " - "constant dark energy equation of state. Consider using the " - "ODEGrowthFactor instead.", + "The Heath77GrowthFactor is only accurate for cosmologies with Lambda=0. " + "Consider using the ODEGrowthFactor or IntegralGrowthFactor instead.", stacklevel=2, ) + def _d_plus_unnormalized(self, z: float | np.ndarray) -> float | np.ndarray: sigma0 = self.cosmo.Om0 / 2 p = (2 * sigma0 * z + 1) * (1 + z) ** 2 @@ -549,10 +595,8 @@ def _d_plus_unnormalized(self, z: float | np.ndarray) -> float | np.ndarray: theta = np.arccos((sigma0 * z - sigma0 + 1) / (sigma0 * (1 + z))) elif sigma0 < 0.5: theta = np.arccosh((sigma0 * z - sigma0 + 1) / (sigma0 * (1 + z))) - elif sigma0 == 0.5 and self.cosmo.Ode0 == 0: + elif sigma0 == 0.5: return 1 / (1 + z) # Einstein-de Sitter case - else: - raise ValueError("Heath77GrowthFactor cannot compute OmegaM = 1 case with Lambda!=0.") term1 = (6 * sigma0 * z + 4 * sigma0 + 1) / np.abs(2 * sigma0 - 1) term2 = 3 * theta * sigma0 * np.sqrt(p) / np.abs(2 * sigma0 - 1) ** (3 / 2) @@ -561,7 +605,7 @@ def _d_plus_unnormalized(self, z: float | np.ndarray) -> float | np.ndarray: @_inherit -class FromFile(_GrowthFactor): +class FromFile(BaseGrowthFactor): r""" Import a growth factor from file. @@ -579,7 +623,6 @@ class FromFile(_GrowthFactor): Location of the file to import. """ - supported_cosmos = (cosmology.LambdaCDM, cosmology.w0waCDM, cosmology.wCDM) _defaults: ClassVar[dict[str, Any]] = {"dlna": 0.01, "amin": 1e-8, "fname": ""} def _d_plus_unnormalized(self, z): @@ -609,7 +652,6 @@ class FromArray(FromFile): The growth factor at `z`. """ - supported_cosmos = (cosmology.LambdaCDM, cosmology.w0waCDM, cosmology.wCDM) _defaults: ClassVar[dict[str, Any]] = {"dlna": 0.01, "amin": 1e-8, "z": None, "d": None} def _d_plus_unnormalized(self, z): @@ -625,7 +667,7 @@ def _d_plus_unnormalized(self, z): @_inherit -class GenMFGrowth(_GrowthFactor): +class GenMFGrowth(BaseGrowthFactor): r""" Port of growth factor routines found in the ``genmf`` code. @@ -642,6 +684,16 @@ class GenMFGrowth(_GrowthFactor): :zmax: Maximum redshift to integrate to. Only used for :meth:`growth_factor_fn`. """ + def _validate_assumptions(self, z): + if not isinstance(self.cosmo, cosmology.LambdaCDM): + raise ValueError( + "The GenMFGrowth factor is only accurate with a cosmological constant. " + "Consider using the ODEGrowthFactor instead." + ) + + if not self.cosmo.Ok0 >= 0: + raise ValueError("GenMFGrowth only supports flat or open LambdaCDM cosmologies") + def _general_case(self, w, x): x = np.atleast_1d(x) xn_vec = np.linspace(0, x.max(), 1000) @@ -671,15 +723,6 @@ def _d_plus_unnormalized(self, z): a = 1 / (1 + z) w = 1 / self.cosmo.Om0 - 1.0 - if not isinstance(self.cosmo, cosmology.LambdaCDM): - raise ValueError( - "The GenMFGrowth factor is only accurate for LambdaCDM cosmologies. " - "Consider using the ODEGrowthFactor instead." - ) - - if not isinstance(self.cosmo, cosmology.FlatLambdaCDM) and not self.cosmo.Ok0 >= 0: - raise ValueError("GenMFGrowth only supports flat or open LambdaCDM cosmologies") - if self.cosmo.Om0 == 1: return a if self.cosmo.Ode0 > 0: @@ -716,7 +759,7 @@ def _d_plus_unnormalized(self, z): term3 = (1.0 + 0.5 * Omega_m) * (1.0 + Omega_L / 70.0) return coeff / (term1 - Omega_L + term3) - def growth_rate(self, z): + def _growth_rate(self, z): """ Growth rate, dln(d)/dln(a). @@ -744,8 +787,6 @@ class CambGrowth(GrowthFactor): """ - supported_cosmos = (cosmology.LambdaCDM, cosmology.w0waCDM, cosmology.wCDM) - def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) diff --git a/src/hmf/density_field/__init__.py b/src/hmf/density_field/__init__.py index a3fa9f7e..a74e240e 100644 --- a/src/hmf/density_field/__init__.py +++ b/src/hmf/density_field/__init__.py @@ -6,16 +6,18 @@ """ from . import filters, transfer, transfer_models -from .filters import Filter +from .filters import BaseFilter, Filter from .halofit import halofit from .transfer import Transfer -from .transfer_models import CAMB, EH +from .transfer_models import CAMB, EH, TransferComponent __all__ = [ "CAMB", "EH", - "Filter", + "BaseFilter", + "Filter", # the old name for BaseFilter "Transfer", + "TransferComponent", "filters", "halofit", "transfer", diff --git a/src/hmf/density_field/filters.py b/src/hmf/density_field/filters.py index b54ce5f0..5cc58b40 100644 --- a/src/hmf/density_field/filters.py +++ b/src/hmf/density_field/filters.py @@ -1,4 +1,26 @@ -"""A module containing various smoothing filter Component models.""" +r"""A module containing various smoothing filter models. + +Filters handle the calculation of the mass variance from the power +spectrum, via a window function. Subclasses of :class:`~BaseFilter` implement +specific window functions. + +Besides the raw filter function itself, two quantities are of primary interest: +firstly the mass variance (see :meth:`sigma`), which appears in many cosmological +applications, and secondly its logarithmic derivative with mass, which appears in +the Press-Schechter formalism for the halo mass function. + +To remain extensible and general, the methodology in these classes is to calculate the +latter quantity as + +.. math:: \frac{d\ln\sigma}{d\ln m} = \frac{1}{2} \frac{d \ln \sigma^2}{d\ln R} + \frac{d\ln R}{d\ln m}. + +Each of the quantities on the right can be separately calculated, improving +extensibility. + +The factor :math:`\frac{d\ln R}{d\ln m}` is typically 1/3, but this is not +necessarily the case for window functions of arbitrary shape. +""" import collections import warnings @@ -12,12 +34,12 @@ @_framework.pluggable -class Filter(_framework.Component): +class BaseFilter(_framework.Component): r""" Base class for Filter components. Filters handle the calculation of the mass variance from the power - spectrum, via a window function. Subclasses of :class:`~Filer` implement + spectrum, via a window function. Subclasses of :class:`~BaseFilter` implement specific window functions. The general design is to specify all quantities in terms of length scales, @@ -267,8 +289,12 @@ def nu(self, r, delta_c=1.686): return (delta_c / self.sigma(r)) ** 2 +# For backwards compatibility, alias Filter to BaseFilter. +Filter = BaseFilter + + @_utils.inherit_docstrings -class TopHat(Filter): +class TopHat(BaseFilter): r""" Real-space top-hat window function. @@ -323,7 +349,7 @@ def dw_dlnkr(self, kr): @_utils.inherit_docstrings -class Gaussian(Filter): +class Gaussian(BaseFilter): r""" Gaussian window function. @@ -373,7 +399,7 @@ def dw_dlnkr(self, kr): @_utils.inherit_docstrings -class SharpK(Filter): +class SharpK(BaseFilter): r""" Fourier-space top-hat window function. diff --git a/src/hmf/density_field/transfer.py b/src/hmf/density_field/transfer.py index 703124f8..63e8cced 100644 --- a/src/hmf/density_field/transfer.py +++ b/src/hmf/density_field/transfer.py @@ -99,9 +99,9 @@ def growth_model(self, val): """ The model to use to calculate the growth function/growth rate. - :type: `hmf.growth_factor._GrowthFactor` subclass + :type: `hmf.growth_factor.BaseGrowthFactor` subclass """ - return get_mdl(val, "_GrowthFactor") + return get_mdl(val, "BaseGrowthFactor") @parameter("param") def growth_params(self, val): diff --git a/src/hmf/density_field/transfer_models.py b/src/hmf/density_field/transfer_models.py index c2e1442e..03c900d4 100644 --- a/src/hmf/density_field/transfer_models.py +++ b/src/hmf/density_field/transfer_models.py @@ -1,8 +1,8 @@ """ -Various models for computing the transfer function. +Models for computing the transfer function. Note that these are not transfer function "frameworks". The framework is found -in :mod:`hmf.transfer`. +in :mod:`~hmf.density_field.transfer`. """ import pickle diff --git a/src/hmf/halos/__init__.py b/src/hmf/halos/__init__.py index ef06550e..f1534593 100644 --- a/src/hmf/halos/__init__.py +++ b/src/hmf/halos/__init__.py @@ -5,9 +5,10 @@ """ from . import mass_definitions -from .mass_definitions import MassDefinition +from .mass_definitions import BaseMassDefinition, MassDefinition __all__ = [ - "MassDefinition", + "BaseMassDefinition", + "MassDefinition", # the old name for BaseMassDefinition "mass_definitions", ] diff --git a/src/hmf/halos/mass_definitions.py b/src/hmf/halos/mass_definitions.py index a98c4372..146158c9 100644 --- a/src/hmf/halos/mass_definitions.py +++ b/src/hmf/halos/mass_definitions.py @@ -1,7 +1,11 @@ -"""A model for mass definitions. +"""Models defining various definitions of halo mass. This is primarily inspired by Benedikt Diemer's COLOSSUS code: -https://bdiemer.bitbucket.io/colossus/halo_mass_defs.html +https://bdiemer.bitbucket.io/colossus/halo_mass_defs.html. + +Definitions include both spherical overdensity (with various overdensities) and +friends-of-friends (with various linking lengths). The main class is +:class:`BaseMassDefinition`, which defines the interface for all mass definitions. """ import warnings @@ -17,6 +21,7 @@ __all__ = [ "FOF", + "BaseMassDefinition", "MassDefinition", "OptimizationError", "SOCritical", @@ -27,7 +32,7 @@ @_framework.pluggable -class MassDefinition(_framework.Component): +class BaseMassDefinition(_framework.Component): """A base class for a Mass Definition.""" @staticmethod @@ -195,7 +200,11 @@ def __eq__(self, other): return self.__class__.__name__ == other.__class__.__name__ and self.params == other.params -class SphericalOverdensity(MassDefinition): +# For backwards compatibility, alias MassDefinition to BaseMassDefinition. +MassDefinition = BaseMassDefinition + + +class SphericalOverdensity(BaseMassDefinition): """An abstract base class for all spherical overdensity mass definitions.""" def __str__(self): @@ -270,7 +279,7 @@ def __str__(self): return "SOVirial" -class FOF(MassDefinition): +class FOF(BaseMassDefinition): """A mass definition based on FroF networks with given linking length.""" _defaults: ClassVar[dict[str, float]] = {"linking_length": 0.2} diff --git a/src/hmf/mass_function/__init__.py b/src/hmf/mass_function/__init__.py index 2c8935d1..68a13125 100644 --- a/src/hmf/mass_function/__init__.py +++ b/src/hmf/mass_function/__init__.py @@ -1,12 +1,13 @@ """Subpackage for determining the halo mass function in Spherical Collapse.""" from . import fitting_functions, hmf, integrate_hmf -from .fitting_functions import PS, SMT, FittingFunction, Tinker08 +from .fitting_functions import PS, SMT, BaseFittingFunction, FittingFunction, Tinker08 from .hmf import MassFunction __all__ = [ "PS", "SMT", + "BaseFittingFunction", "FittingFunction", "MassFunction", "Tinker08", diff --git a/src/hmf/mass_function/fitting_functions.py b/src/hmf/mass_function/fitting_functions.py index 9dd61426..e7be9754 100644 --- a/src/hmf/mass_function/fitting_functions.py +++ b/src/hmf/mass_function/fitting_functions.py @@ -1,8 +1,9 @@ """ -A module defining several mass function fits. +A module defining many several halo mass function fits. -Each fit is taken from the literature. If there are others out there that are not -listed here, please advise via GitHub. +The :class:`~BaseFittingFunction` defines the interface for all fitting functions, and +the included fitting functions are subclasses of this. See the documentation of +:class:`~BaseFittingFunction` for details on how to define a new fitting function. """ import warnings @@ -135,7 +136,7 @@ def _makedoc(pdocs, lname, sname, eq, ref): @_framework.pluggable -class FittingFunction(_framework.Component): +class BaseFittingFunction(_framework.Component): r""" Base-class for a halo mass function fit. @@ -221,7 +222,7 @@ def __init__( m: None | np.ndarray = None, z: float = 0.0, n_eff: None | np.ndarray = None, - mass_definition: None | md.MassDefinition = None, + mass_definition: None | md.BaseMassDefinition = None, cosmo: csm.FLRW = csm.Planck15, delta_c: float = 1.686, **model_parameters, @@ -325,7 +326,11 @@ def fsigma(self): r"""The function :math:`f(\sigma)\equiv\nu f(\nu)`.""" -class PS(FittingFunction): +# For backwards compatibility, alias FittingFunction to BaseFittingFunction. +FittingFunction = BaseFittingFunction + + +class PS(BaseFittingFunction): """Press-Schechter mass function fit.""" # Subclass requirements @@ -338,7 +343,7 @@ class PS(FittingFunction): "http://adsabs.harvard.edu/full/1974ApJ...187..425P" ) - __doc__ = _makedoc(FittingFunction._pdocs, "Press-Schechter", "PS", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Press-Schechter", "PS", _eq, _ref) normalized = True @override @@ -347,7 +352,7 @@ def fsigma(self): return np.sqrt(2.0 / np.pi) * self.nu * np.exp(-0.5 * self.nu2) -class SMT(FittingFunction): +class SMT(BaseFittingFunction): """Sheth-Mo-Tormen mass function fit.""" # Subclass requirements @@ -359,7 +364,7 @@ class SMT(FittingFunction): r"Sheth, R. K., Mo, H. J., Tormen, G., May 2001. MNRAS 323 (1), 1-12. " r"http://doi.wiley.com/10.1046/j.1365-8711.2001.04006.x" ) - __doc__ = _makedoc(FittingFunction._pdocs, "Sheth-Mo-Tormen", "SMT", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Sheth-Mo-Tormen", "SMT", _eq, _ref) _defaults: ClassVar[dict[str, Any]] = {"a": 0.707, "p": 0.3, "A": None} normalized = True @@ -418,7 +423,7 @@ class ST(SMT): """Alias of :class:`SMT`.""" -class Jenkins(FittingFunction): +class Jenkins(BaseFittingFunction): """Jenkins mass function fit.""" # Subclass requirements @@ -429,7 +434,7 @@ class Jenkins(FittingFunction): r"Jenkins, A. R., et al., Feb. 2001. MNRAS 321 (2), 372-384. " r"http://doi.wiley.com/10.1046/j.1365-8711.2001.04029.x" ) - __doc__ = _makedoc(FittingFunction._pdocs, "Jenkins", "Jenkins", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Jenkins", "Jenkins", _eq, _ref) _defaults: ClassVar[dict[str, float]] = {"A": 0.315, "b": 0.61, "c": 3.8} normalized = False @@ -467,7 +472,7 @@ def fsigma(self): return A * np.exp(-(np.abs(self.lnsigma + b) ** c)) -class Warren(FittingFunction): +class Warren(BaseFittingFunction): """Warren mass function fit.""" # Subclass requirements @@ -482,7 +487,7 @@ class Warren(FittingFunction): r"Warren, M. S., et al., Aug. 2006. ApJ 646 (2), 881-885." r"http://adsabs.harvard.edu/abs/2006ApJ...646..881W" ) - __doc__ = _makedoc(FittingFunction._pdocs, "Warren", "Warren", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Warren", "Warren", _eq, _ref) _defaults: ClassVar[dict[str, float]] = { "A": 0.7234, @@ -555,7 +560,7 @@ class Reed03(SMT): _eq = r"f_{\rm SMT}(\sigma)\exp\left(-\frac{c}{\sigma \cosh^5(2\sigma)}\right)" _ref = r"""Reed, D., et al., Dec. 2003. MNRAS 346 (2), 565-572. http://adsabs.harvard.edu/abs/2003MNRAS.346..565R""" - __doc__ = _makedoc(FittingFunction._pdocs, "Reed03", "R03", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Reed03", "R03", _eq, _ref) _defaults: ClassVar[dict[str, float]] = {"a": 0.707, "p": 0.3, "A": 0.3222, "c": 0.7} normalized = False @@ -590,7 +595,7 @@ def cutmask(self): return np.logical_and(self.lnsigma > -1.7, self.lnsigma < 0.9) -class Reed07(FittingFunction): +class Reed07(BaseFittingFunction): """Reed 2007 mass function fit.""" req_neff = True @@ -604,7 +609,7 @@ class Reed07(FittingFunction): """Reed, D. S., et al., Jan. 2007. MNRAS 374 (1), 2-15. """ """http://adsabs.harvard.edu/abs/2007MNRAS.374....2R""" ) - __doc__ = _makedoc(FittingFunction._pdocs, "Reed07", "R07", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Reed07", "R07", _eq, _ref) _defaults: ClassVar[dict[str, float]] = {"A": 0.3222, "p": 0.3, "c": 1.08, "a": 0.764} @@ -679,7 +684,7 @@ def cutmask(self): return np.logical_and(self.lnsigma > -0.5, self.lnsigma < 1.2) -class Peacock(FittingFunction): +class Peacock(BaseFittingFunction): """Peacock mass function fit.""" req_z = False @@ -687,7 +692,7 @@ class Peacock(FittingFunction): _eq = r"\nu\exp(-c\nu^2)(2cd\nu+ba\nu^{b-1})/d^2" _ref = """Peacock, J. A., Aug. 2007. MNRAS 379 (3), 1067-1074. http://adsabs.harvard.edu/abs/2007MNRAS.379.1067P""" - __doc__ = _makedoc(FittingFunction._pdocs, "Peacock", "Pck", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Peacock", "Pck", _eq, _ref) _defaults: ClassVar[dict[str, float]] = {"a": 1.529, "b": 0.704, "c": 0.412} sim_definition = copy(Warren.sim_definition) @@ -715,13 +720,13 @@ def cutmask(self): return np.logical_and(self.m < 1e10, self.m > 1e15) -class Angulo(FittingFunction): +class Angulo(BaseFittingFunction): """Angulo mass function fit.""" req_mass = True _ref = """Angulo, R. E., et al., 2012. arXiv:1203.3216v1""" _eq = r"$A \left[\left(\frac{d}{\sigma}\right)^b + 1 \right] \exp(-c/\sigma^2)$" - __doc__ = _makedoc(FittingFunction._pdocs, "Angulo", "Ang", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Angulo", "Ang", _eq, _ref) _defaults: ClassVar[dict[str, float]] = {"A": 0.201, "b": 1.7, "c": 1.172, "d": 2.08} sim_definition = SimDetails( @@ -774,7 +779,7 @@ class Watson_FoF(Warren): """Watson, W. A., et al., MNRAS, 2013. """ """http://adsabs.harvard.edu/abs/2013MNRAS.433.1230W """ ) - __doc__ = _makedoc(FittingFunction._pdocs, "Watson FoF", "WatF", Warren._eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Watson FoF", "WatF", Warren._eq, _ref) _defaults: ClassVar[dict[str, float]] = { "A": 0.282, "b": 2.163, @@ -807,7 +812,7 @@ def cutmask(self): return np.logical_and(self.lnsigma > -0.55, self.lnsigma < 1.31) -class Watson(FittingFunction): +class Watson(BaseFittingFunction): """Watson mass function fit.""" req_cosmo = True @@ -819,7 +824,7 @@ class Watson(FittingFunction): """http://adsabs.harvard.edu/abs/2013MNRAS.433.1230W """ ) _eq = r"\Gamma A \left((\frac{\beta}{\sigma}^\alpha+1\right)\exp(-\gamma/\sigma^2)" - __doc__ = _makedoc(FittingFunction._pdocs, "Watson", "WatS", _eq, Watson_FoF._ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Watson", "WatS", _eq, Watson_FoF._ref) sim_definition = copy(Watson_FoF.sim_definition) sim_definition.halo_finder_type = "SO" @@ -913,7 +918,7 @@ class Crocce(Warren): req_z = True _ref = """Crocce, M., et al. MNRAS 403 (3), 1353-1367. http://doi.wiley.com/10.1111/j.1365-2966.2009.16194.x""" - __doc__ = _makedoc(FittingFunction._pdocs, "Crocce", "Cro", Warren._eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Crocce", "Cro", Warren._eq, _ref) _defaults: ClassVar[dict[str, Any]] = { "A_a": 0.58, "A_b": 0.13, @@ -963,7 +968,7 @@ class Courtin(SMT): req_sigma = True _ref = """Courtin, J., et al., Oct. 2010. MNRAS 1931. http://doi.wiley.com/10.1111/j.1365-2966.2010.17573.x""" - __doc__ = _makedoc(FittingFunction._pdocs, "Courtin", "Ctn", SMT._eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Courtin", "Ctn", SMT._eq, _ref) _defaults: ClassVar[dict[str, float]] = {"A": 0.348, "a": 0.695, "p": 0.1} normalized = False @@ -1000,7 +1005,7 @@ class Bhattacharya(SMT): _eq = r"f_{\rm SMT}(\sigma) (\nu\sqrt{a})^{q-1}" _ref = """Bhattacharya, S., et al., May 2011. ApJ 732 (2), 122. http://labs.adsabs.harvard.edu/ui/abs/2011ApJ...732..122B""" - __doc__ = _makedoc(FittingFunction._pdocs, "Bhattacharya", "Btc", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Bhattacharya", "Btc", _eq, _ref) _defaults: ClassVar[dict[str, Any]] = { "A_a": 0.333, "A_b": 0.11, @@ -1088,7 +1093,7 @@ def _norm(self): ) -class Tinker08(FittingFunction): +class Tinker08(BaseFittingFunction): """Tinker 2008 mass function fit.""" req_z = True @@ -1096,7 +1101,7 @@ class Tinker08(FittingFunction): _eq = r"A\left(\frac{\sigma}{b}^{-a}+1\right)\exp(-c/\sigma^2)" _ref = r"""Tinker, J., et al., 2008. ApJ 688, 709-728. http://iopscience.iop.org/0004-637X/688/2/709""" - __doc__ = _makedoc(FittingFunction._pdocs, "Tinker08", "Tkr", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Tinker08", "Tkr", _eq, _ref) sim_definition = SimDetails( L=[ @@ -1406,7 +1411,7 @@ def cutmask(self): return np.logical_and(self.lnsigma / np.log(10) > -0.2, self.lnsigma / np.log(10) < 0.4) -class Tinker10(FittingFunction): +class Tinker10(BaseFittingFunction): """Tinker 2010 mass function fit.""" req_z = True @@ -1414,7 +1419,7 @@ class Tinker10(FittingFunction): _eq = r"(1+(\beta\nu)^{-2\phi})\nu^{2\eta+1}\exp(-\gamma\nu^2/2)" _ref = """Tinker, J., et al., 2010. ApJ 724, 878. http://iopscience.iop.org/0004-637X/724/2/878/pdf/apj_724_2_878.pdf""" - __doc__ = _makedoc(FittingFunction._pdocs, "Tinker10", "Tkr", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Tinker10", "Tkr", _eq, _ref) sim_definition = copy(Tinker08.sim_definition) @@ -1584,7 +1589,7 @@ class Behroozi(Tinker08): This is an empirical modification to the :class:`Tinker08` fit, to improve accuracy at high redshift. - {FittingFunction._pdocs} + {BaseFittingFunction._pdocs} References ---------- @@ -1645,7 +1650,7 @@ class Pillepich(Warren): """Pillepich mass function fit.""" _ref = r"""Pillepich, A., et al., 2010, arxiv:0811.4176""" - __doc__ = _makedoc(FittingFunction._pdocs, "Pillepich", "Pillepich", Warren._eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Pillepich", "Pillepich", Warren._eq, _ref) _defaults: ClassVar[dict[str, float]] = { "A": 0.6853, "b": 1.868, @@ -1683,7 +1688,7 @@ class Manera(SMT): """Manera mass function fit.""" _ref = r"""Manera, M., et al., 2010, arxiv:0906.1314""" - __doc__ = _makedoc(FittingFunction._pdocs, "Manera", "Man", SMT._eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Manera", "Man", SMT._eq, _ref) # These are for z=0, new ML method, l_linnk = 0.2 _defaults: ClassVar[dict[str, Any]] = {"A": None, "a": 0.709, "p": 0.289} @@ -1716,7 +1721,7 @@ class Ishiyama(Warren): _eq = r"A\left[\left(\frac{e}{\sigma}\right)^b + 1\right]\exp(\frac{d}{\sigma^2})" _ref = r"""Ishiyama, T., et al., 2015, arxiv:1412.2860""" - __doc__ = _makedoc(FittingFunction._pdocs, "Ishiyama", "Ishiyama", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Ishiyama", "Ishiyama", _eq, _ref) _defaults: ClassVar[dict[str, float]] = { "A": 0.193, @@ -1760,7 +1765,7 @@ class Bocquet200mDMOnly(Warren): _eq = r"A\left[\left(\frac{e}{\sigma}\right)^b + 1\right]\exp(-\frac{d}{\sigma^2})" _ref = r"""Bocuet, S., et al., 2016, MNRAS 456 2361""" - __doc__ = _makedoc(FittingFunction._pdocs, "Bocquet", "Bocquet", _eq, _ref) + __doc__ = _makedoc(BaseFittingFunction._pdocs, "Bocquet", "Bocquet", _eq, _ref) _defaults: ClassVar[dict[str, Any]] = { "A": 0.216, "b": 1.87, @@ -1826,7 +1831,7 @@ class Bocquet200mHydro(Bocquet200mDMOnly): """Bocquet mass function fit for 200m definition with hydrodynamics.""" __doc__ = _makedoc( - FittingFunction._pdocs, + BaseFittingFunction._pdocs, "Bocquet", "Bocquet", Bocquet200mDMOnly._eq, @@ -1849,7 +1854,7 @@ class Bocquet200cDMOnly(Bocquet200mDMOnly): """Bocquet mass function fit for 200c definition with dark matter only.""" __doc__ = _makedoc( - FittingFunction._pdocs, + BaseFittingFunction._pdocs, "Bocquet", "Bocquet", Bocquet200mDMOnly._eq, @@ -1888,7 +1893,7 @@ class Bocquet200cHydro(Bocquet200cDMOnly): """Bocquet mass function fit for 200c definition with hydrodynamics.""" __doc__ = _makedoc( - FittingFunction._pdocs, + BaseFittingFunction._pdocs, "Bocquet", "Bocquet", Bocquet200mDMOnly._eq, @@ -1912,7 +1917,7 @@ class Bocquet500cDMOnly(Bocquet200cDMOnly): """Bocquet mass function fit for 500c definition with dark matter only.""" __doc__ = _makedoc( - FittingFunction._pdocs, + BaseFittingFunction._pdocs, "Bocquet", "Bocquet", Bocquet200mDMOnly._eq, @@ -1947,7 +1952,7 @@ class Bocquet500cHydro(Bocquet500cDMOnly): """Bocquet mass function fit for 500c definition with hydrodynamics.""" __doc__ = _makedoc( - FittingFunction._pdocs, + BaseFittingFunction._pdocs, "Bocquet", "Bocquet", Bocquet200mDMOnly._eq, diff --git a/src/hmf/mass_function/hmf.py b/src/hmf/mass_function/hmf.py index a3be3c1e..e10e4a49 100644 --- a/src/hmf/mass_function/hmf.py +++ b/src/hmf/mass_function/hmf.py @@ -16,8 +16,8 @@ from .._internals._cache import cached_quantity, parameter from .._internals._framework import get_mdl from ..density_field import transfer -from ..density_field.filters import Filter, TopHat -from ..halos.mass_definitions import MassDefinition as MassDef +from ..density_field.filters import BaseFilter, TopHat +from ..halos.mass_definitions import BaseMassDefinition as MassDef from ..halos.mass_definitions import SOGeneric, SOMean from . import fitting_functions as ff from .integrate_hmf import hmf_integral_gtm as int_gtm @@ -100,12 +100,12 @@ def __init__( Mmin: float = 10.0, Mmax: float = 15.0, dlog10m: float = 0.01, - hmf_model: str | ff.FittingFunction = ff.Tinker08, + hmf_model: str | ff.BaseFittingFunction = ff.Tinker08, hmf_params: dict[str, Any] | None = None, mdef_model: None | str | MassDef = None, mdef_params: dict | None = None, delta_c: float = 1.686, - filter_model: str | Filter = TopHat, + filter_model: str | BaseFilter = TopHat, filter_params: dict | None = None, disable_mass_conversion: bool = True, **transfer_kwargs, @@ -180,7 +180,7 @@ def filter_model(self, val): :type: :class:`hmf.filters.Filter` subclass """ - return get_mdl(val, "Filter") + return get_mdl(val, "BaseFilter") @parameter("param") def filter_params(self, val): @@ -219,7 +219,7 @@ def hmf_model(self, val): """ if val is None: return val - return get_mdl(val, "FittingFunction") + return get_mdl(val, "BaseFittingFunction") @parameter("param") def hmf_params(self, val): @@ -239,7 +239,7 @@ def mdef_model(self, val): """ if val is None or (isinstance(val, str) and val.lower() == "none"): return None - return get_mdl(val, "MassDefinition") + return get_mdl(val, "BaseMassDefinition") @parameter("param") def mdef_params(self, val): diff --git a/tests/test_fits.py b/tests/test_fits.py index 7ef7635f..9629f40c 100644 --- a/tests/test_fits.py +++ b/tests/test_fits.py @@ -13,8 +13,8 @@ ff, lambda member: ( inspect.isclass(member) - and issubclass(member, ff.FittingFunction) - and member is not ff.FittingFunction + and issubclass(member, ff.BaseFittingFunction) + and member is not ff.BaseFittingFunction and member is not ff.PS ), ) diff --git a/tests/test_fitting_functions_extra.py b/tests/test_fitting_functions_extra.py index d559bbe7..84875860 100644 --- a/tests/test_fitting_functions_extra.py +++ b/tests/test_fitting_functions_extra.py @@ -22,7 +22,7 @@ def test_fittingfunction_sets_measured_mdef(): def test_get_measured_mdef_unrecognized_overdensity_warns(): - class _BadSO(ff.FittingFunction): + class _BadSO(ff.BaseFittingFunction): sim_definition = ff.SimDetails( L=1, N=1, @@ -37,7 +37,7 @@ class _BadSO(ff.FittingFunction): def test_get_measured_mdef_unknown_halo_finder_warns(): - class _BadFinder(ff.FittingFunction): + class _BadFinder(ff.BaseFittingFunction): sim_definition = ff.SimDetails( L=1, N=1, diff --git a/tests/test_growth.py b/tests/test_growth.py index 539ac960..1e268715 100644 --- a/tests/test_growth.py +++ b/tests/test_growth.py @@ -26,10 +26,8 @@ def test_growth_rate_ode_high_z_with_rad(): gf = growth_factor.ODEGrowthFactor(cosmo, amin=a / 10) z = 1 / a - 1 - assert np.isclose(gf.growth_rate(z), 0, atol=0.01) - np.testing.assert_allclose( - gf._d_plus_unnormalized(z), 2 * cosmo.Ogamma0 / 3 / cosmo.Om0, rtol=1e-2 - ) + assert np.isclose(gf._growth_rate(z), 0, atol=0.01) + np.testing.assert_allclose(gf._d_plus_unnormalized(z), 2 * gf.Or0 / 3 / cosmo.Om0, rtol=1e-2) @pytest.mark.parametrize( @@ -81,7 +79,12 @@ def test_heath_vs_ode_no_omegal(omegam): np.testing.assert_allclose(d_ode, d_heath, rtol=1e-3) -@pytest.mark.filterwarnings("ignore:not accurate at high redshifts") +@pytest.mark.filterwarnings( + "ignore:The IntegralGrowthFactor is not accurate when the radiation density" +) +@pytest.mark.filterwarnings( + "ignore:The Eisenstein97GrowthFactor is not accurate when the radiation density" +) @pytest.mark.parametrize( "model", [ @@ -154,11 +157,11 @@ def test_genmf_good_approximation(): def test_unsupported_cosmo(): cosmo = w0waCDM(H0=70.0, Om0=0.3, Ode0=0.7, w0=-0.9, Ob0=0.05, Tcmb0=2.7) - with pytest.raises(ValueError, match="only accurate for LambdaCDM cosmologies"): + with pytest.raises(ValueError, match="only accurate with a cosmological constant"): growth_factor.GenMFGrowth(cosmo=cosmo).growth_factor(0) # But shouldn't raise error for CAMBGrowth - growth_factor.CambGrowth(cosmo=cosmo) + growth_factor.CambGrowth(cosmo=cosmo).growth_factor(0) def test_pickleability_of_cambgrowth(): @@ -210,12 +213,12 @@ def test_using_ode_when_it_is_already_computed(): gf = growth_factor.GrowthFactor(cosmo) gf.growth_factor(100) # This will trigger using the ODE solver - gf.growth_rate(100) + gf._growth_rate(100) # This normally wouldn't need the ODE solver, but since it's already # instantiated, it might as well use it. gf.growth_factor(1) - gf.growth_rate(1) + gf._growth_rate(1) def test_growth_rate_uses_integral(): @@ -225,7 +228,7 @@ def test_growth_rate_uses_integral(): # At a low redshift, this will trigger using the integral method, which should work # even when Ode0 is not 0. - gf.growth_rate(0.1) + gf._growth_rate(0.1) def test_growth_rate_uses_ode_at_highz(): @@ -234,13 +237,13 @@ def test_growth_rate_uses_ode_at_highz(): gf = growth_factor.GrowthFactor(cosmo) # At a high redshift, this will trigger using the ODE method. - gf.growth_rate(1000) + gf._growth_rate(1000) def test_expected_warnings(): """Test that we get expected warnings for unsupported cosmologies.""" cosmo = Planck13 - with pytest.warns(UserWarning, match="not accurate at high redshifts"): + with pytest.warns(UserWarning, match="not accurate when the radiation density is significant"): growth_factor.IntegralGrowthFactor(cosmo=cosmo).growth_factor(10000) with pytest.warns(UserWarning, match="only accurate for cosmologies with a constant"): @@ -253,30 +256,29 @@ def test_expected_warnings(): with ( pytest.raises(ValueError, match="Cannot compute integral"), - pytest.warns(UserWarning, match="not accurate at high redshifts"), + pytest.warns(UserWarning, match="not accurate when the radiation density"), ): growth_factor.IntegralGrowthFactor(cosmo, amin=1e-3).growth_factor(1e5) with pytest.raises(ValueError, match="Eisenstein97GrowthFactor only supports flat"): growth_factor.Eisenstein97GrowthFactor( - cosmo=Planck13.clone(Tcmb0=0.0, Om0=0.3, Ode0=0.7, to_nonflat=True) + cosmo=Planck13.clone(Tcmb0=0.0, Om0=0.3, Ode0=0.8, to_nonflat=True) ).growth_factor(0) - with pytest.warns( - UserWarning, - match=( - "The Heath77GrowthFactor is only accurate for cosmologies with a constant dark energy" + with ( + pytest.warns( + UserWarning, + match=("only accurate for cosmologies with a constant dark energy"), + ), + pytest.warns( + UserWarning, + match=("The Heath77GrowthFactor is only accurate for cosmologies with Lambda=0"), ), ): growth_factor.Heath77GrowthFactor( cosmo=cosmology.w0waCDM(H0=70.0, Tcmb0=0.0, Om0=0.3, Ode0=0.7, w0=-0.9, wa=0.3) ).growth_factor(0) - with pytest.raises(ValueError, match=r"Heath77GrowthFactor cannot compute OmegaM = 1 case"): - growth_factor.Heath77GrowthFactor( - Planck13.clone(Tcmb0=0.0, Om0=1.0, Ode0=0.3, to_nonflat=True) - ).growth_factor(0) - with pytest.raises(ValueError, match=r"You must supply an array for both z and d"): growth_factor.FromArray( z=np.array([0, 1, 2]), @@ -292,7 +294,7 @@ def 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