Skip to main content

She knows a good alignment when she sees one

Project description

miss-alignment

License PyPI Python Version CI

260_etomo_vs_3x3

Better alignment = cleaner tomograms. GIF shows a denoised reconstruction after patch tracking alignment and miss-alignment.

Installation

Installation is limited at the moment to a specific python, CUDA, and torch version. This might be fixed at some point in the future. For now, its easiest to set everything up in a conda environment.

First create an environment called miss-alignment with cuda-toolkit 12.9 and activate it:

conda create -n miss-alignment -c conda-forge python=3.11 cuda-toolkit=12.9 -y
conda activate miss-alignment

We need to fix some GPU dependencies for accelerated reconstruction:

python -m pip install torch==2.8.0 numpy
python -m pip install torch-projectors --index-url https://warpem.github.io/torch-projectors/cu129/simple/

[!IMPORTANT] If your GPU's have the Blackwell-architecture make sure to install at least v0.11 of torch-projectors.

Finally install miss-alignment with this command:

python -m pip install miss-alignment

Check that the CLI shows up with:

miss-alignment --help

How to run?

See the docs/ folder for some barebones instructions. This will be improved soon.

Changelog

A full list of changes per release is available on the GitHub Releases page.

Citation

Chaillet, M.L., van Loenhout, J., Leung, M.R., Burt, A., and Tegunov, D. (2026) MissAlignment Teaches Itself Better Cryo-ET Tilt-Series Alignment by Making It Worse. bioRxiv. https://doi.org/10.64898/2026.04.29.721716

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

miss_alignment-0.1.8.tar.gz (27.6 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

miss_alignment-0.1.8-py3-none-any.whl (65.7 kB view details)

Uploaded Python 3

File details

Details for the file miss_alignment-0.1.8.tar.gz.

File metadata

  • Download URL: miss_alignment-0.1.8.tar.gz
  • Upload date:
  • Size: 27.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for miss_alignment-0.1.8.tar.gz
Algorithm Hash digest
SHA256 d337ddc43d4161b41332299124266ca3f0e2d951d9e39ee3c1e1bb74f883ae3c
MD5 2cae9fa3aa998698e2a4c24c477c2ae0
BLAKE2b-256 2940576f8b7e0df232eef04035b02ea358889a5a4aeaa9a83ab1a1d8aae64b52

See more details on using hashes here.

Provenance

The following attestation bundles were made for miss_alignment-0.1.8.tar.gz:

Publisher: ci.yml on warpem/miss-alignment

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file miss_alignment-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: miss_alignment-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 65.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for miss_alignment-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 d06c07d7bddb33b76ae4a98b4b26a53b8532d3226d78ab77933243ec60d32b61
MD5 67291a1abfbd3a0a1ae932a0a242460d
BLAKE2b-256 8428d7d5abec566ff23ad410da3884db96a3c5d5858739e6a5cc381e84e3566b

See more details on using hashes here.

Provenance

The following attestation bundles were made for miss_alignment-0.1.8-py3-none-any.whl:

Publisher: ci.yml on warpem/miss-alignment

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page