The ASPIRE-Python project supersedes Matlab ASPIRE.
ASPIRE is an open-source software package for processing single-particle cryo-EM data to determine three-dimensional structures of biological macromolecules. The package includes advanced algorithms based on rigorous mathematics and recent developments in statistics and machine learning. It provides unique and improved solutions to important computational challenges of the cryo-EM processing pipeline, including 3-D ab-initio modeling, 2-D class averaging, automatic particle picking, and 3-D heterogeneity analysis.
For more information about the project, algorithms, and related publications please refer to the ASPIRE Project website.
For full documentation and tutorials see the docs.
Please cite using the following DOI. This DOI represents all versions, and will always resolve to the latest one.
ComputationalCryoEM/ASPIRE-Python: v0.13.0 https://doi.org/10.5281/zenodo.5657281
ASPIRE is a pip-installable package for Linux/Mac/Windows, and
requires Python 3.8-3.11. The recommended method of installation for
getting started is to use Anaconda (64-bit) for your platform to
install Python. Python's package manager pip can then be used to
install aspire safely in that environment.
If you are unfamiliar with conda, the
Miniconda
distribution for x86_64 is recommended.
Assuming you have conda and a compatible system, the following steps
will checkout current code release, create an environment, and install
ASPIRE.
# Clone the code
git clone https://github.com/ComputationalCryoEM/ASPIRE-Python.git
cd ASPIRE-Python
# Create a fresh environment
conda create --name aspire python=3.8 pip
# Enable the environment
conda activate aspire
# Install the `aspire` package from the checked out code
# with the additional `dev` extension.
pip install -e ".[dev]"
If you prefer not to use Anaconda, or have other preferences for managing environments, you should be able to directly use pip with Python >= 3.8 from the local checkout or via PyPI.
Please see the full documentation for details and advanced instructions.
To check the installation, a unit test suite is provided, taking approximate 15 minutes on an average machine.
pytest