Tools for ensemble modeling
Project description
enspara
MSMs at Scale
Reference
If you use enspara for published research, please cite us:
Porter, J.R., Zimmerman, M.I. and Bowman, G.R., 2019. Enspara: Modeling molecular ensembles with scalable data structures and parallel computing. The Journal of chemical physics, 150(4), p.044108.
Installation
Installation is documented here.
conda create -n enspara
conda install -c bowmanlab -c conda-forge enspara
Alternatively if you wish to build the latest:
git clone https://github.com/bowman-lab/enspara
mamba create -n enspara -c conda-forge cython numpy mdtraj scipy python=3.12 mpi4py
mamba activate enspara
cd enspara
pip install -e .
Optionally, install pytests to run the tests:
mamba install -c conda-forge pytest
Building the docs
Enspara uses sphinx for documentation. They're a bit of a work in progress, but most of the most important stuff is documented already.
cd docs
make html
Running the tests
Enspara uses pytest as a test discovery and running tool. To run the
tests, you should first make sure you have the development dependencies
installed then, from the enspara directory, run:
pytest
By default this runs without MPI.
If you wish to explicitly skip the MPI tests, you can run:
pytest -m 'not mpi'
If you then want to run the mpi tests (including the MPI ones), you can additionally run:
mpirun -n 2 python -m pytest -m 'mpi'
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file enspara-0.3.1.tar.gz.
File metadata
- Download URL: enspara-0.3.1.tar.gz
- Upload date:
- Size: 641.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ef0d1eb809cdff08e1600f0701468c6a10f9b1c57239413878252da6e3c69047
|
|
| MD5 |
adc620fdcbfd2b35face14e52aeecc86
|
|
| BLAKE2b-256 |
26bc96fde18018f95cba438cd9d0845485a3eaad6e2b139fd9bda0122d33da5c
|
File details
Details for the file enspara-0.3.1-cp313-cp313-macosx_11_0_arm64.whl.
File metadata
- Download URL: enspara-0.3.1-cp313-cp313-macosx_11_0_arm64.whl
- Upload date:
- Size: 922.3 kB
- Tags: CPython 3.13, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
cb186adbba0353a2d37205b80193f10f752425b27f590e11627bf591bc03f510
|
|
| MD5 |
cee1c1f9a6832b871a8993aa84849329
|
|
| BLAKE2b-256 |
d6917d23a8a5bb61d1ce0a043a41dbefd7149ffd56684d6aeb725b05eb92df83
|
File details
Details for the file enspara-0.3.1-cp312-cp312-macosx_11_0_arm64.whl.
File metadata
- Download URL: enspara-0.3.1-cp312-cp312-macosx_11_0_arm64.whl
- Upload date:
- Size: 923.9 kB
- Tags: CPython 3.12, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
763f81cda9b62785cbb5aebebc7c7e87c181bada040055f11062fd5f54b297c0
|
|
| MD5 |
d261528760215398883946cc1842b1fd
|
|
| BLAKE2b-256 |
509d4225d52108a4471e23a7dcbfd9d4ae36485c9e617495019c7cd73944a071
|
File details
Details for the file enspara-0.3.1-cp311-cp311-macosx_11_0_arm64.whl.
File metadata
- Download URL: enspara-0.3.1-cp311-cp311-macosx_11_0_arm64.whl
- Upload date:
- Size: 922.1 kB
- Tags: CPython 3.11, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
862f9015332d3fa19587af101a854f16d7f9adba865c0e00a12cd53917f48792
|
|
| MD5 |
7a92dfb57e554b17cd7c140731009b58
|
|
| BLAKE2b-256 |
d7dae5dbac6051df2d228cfdbdda193343ab4616857f6935e3a04d0a60e0fd4d
|