Installation#
Dependencies#
Our mandatory dependencies are limited to:
We highly encourage using the Anaconda Python distribution
or its standalone package manager Conda.
Especially for Intel processors, this ensures a higher performance with no configuration (e.g.,
the linking to Intel MKL library, a highly optimized BLAS library created by Intel).
For learning, however, the standard installation is often good enough.
Step-by-step installation for users#
Conda (recommended)#
If using conda, install our conda-forge distribution via:
>> conda install --channel conda-forge pyproximal
Using the conda-forge distribution is recommended as all the dependencies (both required
and optional) will be automatically installed for you.
Pip#
If you are using pip, and simply type the following command in your terminal
to install the PyPI distribution:
>> pip install pyproximal
Note that when installing via pip, only required dependencies are installed.
From Source#
To access the latest source from github:
>> pip install https://github.com/PyLops/pyproximal.git@dev
Step-by-step installation for developers#
Fork PyProximal#
Fork the PyProximal repository and clone it by executing the following in your terminal:
>> git clone https://github.com/YOUR-USERNAME/pyproximal.git
Install dependencies#
We recommend installing dependencies into a separate environment. For that end, we provide a Makefile with useful commands for setting up the environment.
Conda (recommended)#
For a conda environment, run
>> make dev-install_conda # for x86 (Intel or AMD CPUs)
>> make dev-install_conda_arm # for arm (M-series Mac)
This will create and activate an environment called pylops, with all required and optional dependencies.
Pip#
If you prefer a pip installation, we provide the following command
>> make dev-install
Note that, differently from the conda command, the above will not create a virtual environment.
Make sure you create and activate your environment previously.
Run tests#
To ensure that everything has been setup correctly, run tests:
>> make tests
Make sure no tests fail, this guarantees that the installation has been successful.
Add remote (optional)#
To keep up-to-date on the latest changes while you are developing, you may optionally add the PyLops repository as a remote. Run the following command to add the PyLops repo as a remote named upstream:
>> git remote add upstream https://github.com/PyLops/pylops
From then on, you can pull changes (for example, in the dev branch) with:
>> git pull upstream dev
Install pre-commit hooks#
To ensure consistency in the coding style of our developers we rely on
pre-commit to perform a series of checks when you are
ready to commit and push some changes. This is accomplished by means of git hooks
that have been configured in the .pre-commit-config.yaml file.
In order to setup such hooks in your local repository, run:
>> pre-commit install
Once this is set up, when committing changes, pre-commit will reject and “fix” your code by running the proper hooks.
At this point, the user must check the changes and then stage them before trying to commit again.
Final steps#
PyLops does not enforce the use of a linter as a pre-commit hook, but we do highly encourage using one before submitting a Pull Request.
A properly configured linter (flake8) can be run with:
>> make lint
In addition, it is highly encouraged to build the docs prior to submitting a Pull Request. Apart from ensuring that docstrings are properly formatted, they can aid in catching bugs during development. Build (or update) the docs with:
>> make doc
or
>> make docupdate