conda-forge / pysr-feedstock

A conda-smithy repository for pysr.

About pysr-feedstock

Feedstock license: BSD-3-Clause

Home: https://github.com/MilesCranmer/PySR

Package license: Apache-2.0

Summary: Simple, fast, and parallelized symbolic regression in Python

Development: https://github.com/MilesCranmer/PySR

Documentation: https://astroautomata.com/PySR

PySR is a simple, fast, and parallelized symbolic regression package for Python and Julia, which makes use of an algorithm based on regularized evolution and simulated annealing.

Current build status

All platforms:

Current release info

Name	Downloads	Version	Platforms

Installing pysr

Installing pysr from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge
conda config --set channel_priority strict

Once the conda-forge channel has been enabled, pysr can be installed with conda:

conda install pysr

or with mamba:

mamba install pysr

It is possible to list all of the versions of pysr available on your platform with conda:

conda search pysr --channel conda-forge

or with mamba:

mamba search pysr --channel conda-forge

Alternatively, mamba repoquery may provide more information:

# Search all versions available on your platform:
mamba repoquery search pysr --channel conda-forge

# List packages depending on `pysr`:
mamba repoquery whoneeds pysr --channel conda-forge

# List dependencies of `pysr`:
mamba repoquery depends pysr --channel conda-forge

About conda-forge

conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by Azure, GitHub, CircleCI, AppVeyor, Drone, and TravisCI it is possible to build and upload installable packages to the conda-forge anaconda.org channel for Linux, Windows and OSX respectively.

To manage the continuous integration and simplify feedstock maintenance conda-smithy has been developed. Using the conda-forge.yml within this repository, it is possible to re-render all of this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.

For more information please check the conda-forge documentation.

Terminology

feedstock - the conda recipe (raw material), supporting scripts and CI configuration.

conda-smithy - the tool which helps orchestrate the feedstock. Its primary use is in the construction of the CI .yml files and simplify the management of many feedstocks.

conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)

Updating pysr-feedstock

If you would like to improve the pysr recipe or build a new package version, please fork this repository and submit a PR. Upon submission, your changes will be run on the appropriate platforms to give the reviewer an opportunity to confirm that the changes result in a successful build. Once merged, the recipe will be re-built and uploaded automatically to the conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge channel. Note that all branches in the conda-forge/pysr-feedstock are immediately built and any created packages are uploaded, so PRs should be based on branches in forks and branches in the main repository should only be used to build distinct package versions.

In order to produce a uniquely identifiable distribution:

• If the version of a package is not being increased, please add or increase the build/number.
• If the version of a package is being increased, please remember to return the build/number back to 0.

Feedstock Maintainers

• @MilesCranmer

pysr-feedstock's Issues

Set `CONDA_JL_HOME`?

@ngam @mkitti do we need to set CONDA_JL_HOME anywhere? I noticed this part of Conda.jl's README:

Using a pre-existing Conda installation

To use a pre-existing Conda installation, first create an environment for
Conda.jl and then set the CONDA_JL_HOME environment variable to the full
path of the environment.
(You have to rebuild Conda.jl and many of the packages that use it after this.)
In Julia, run:

julia> run(`conda create -n conda_jl python conda`)

julia> ENV["CONDA_JL_HOME"] = "/path/to/miniconda/envs/conda_jl"  # change this to your path

pkg> build Conda

Perhaps we don't, but I was just curious.

Do not ever store build artefacts on `main`

This pollutes our azure accounts (and storage allowance) for 2 years with useless stuff. It's fine for debugging a PR, but 925d132 should have never been merged to main.

CC @ngam

Unpin numpy as a host-dependency

By having >= 1.14 in the host section, this disables our mechanism for building against the oldest supported numpy. This means the recipe will always pull in the newest numpy (currently 1.24), and by the way the numpy C API works, this means the all pysr builds need the newest numpy to run¹.

Effectively, this turns >=1.14 into >=1.24 (soon 1.25), which is clearly not the intent. Please have a look at the relevant section in the knowledge base.

Footnotes

1. numpy doesn't bump the relevant marker for every version, but almost. ↩

let's improve this recipe (and set a standard for python packages calling julia packages)

Solution to issue cannot be found in the documentation.

• I checked the documentation.

Issue

This line shouldn't end like that and it useless; the package on anaconda.org doesn't have any of these crazy julia modules being installed: https://anaconda.org/conda-forge/pysr/files

pysr-feedstock/recipe/meta.yaml

Line 14 in cbf0bb3

script: {{ PYTHON }} -m pip install . -vv && {{ PYTHON }} -c 'import pysr; pysr.install()'

If you really want && {{ PYTHON }} -c 'import pysr; pysr.install() you should add it in an activation script.

@ocefpaf, sorry I just saw your question on gitter. I am never there, and usually not very good at instant messaging 😅 it is probably easier to get my attention to something via tag in an open pull request or issue.

Also tagging @isuruf if you have time to look into this with us.

The original message on gitter:

Filipe @ocefpaf Aug 06 10:55
Folks, what are the best practices for packaging a Python module that depends on a Julia pkg? @ngam I recall you packaging some Julia/Python stuff, do you have any advice for me?

Do you have a specific recipe/package? I am happy to work on it together in staged-recipes! Just tag me or respond here :)

Installed packages

n/a

Environment info

n/a

@conda-forge-admin, rerender feedstock

Additional comment:

No response

`@conda-forge-admin, please add bot automerge`

Additional comment:

No response