Skip to main content

No project description provided

Project description

Hyper Shape recognition (HSR): a general framework for moment-based similarity measures

HSR is a versatile, moment-based similarity measure tailored for three-dimensional (3D) chemical representations annotated with atomic features. It enhances the robustness and versatility of the Ultrafast Shape Recognition (USR) method by incorporating multidimensional features for each atom, such as protons, neutrons, and formal charges.

Getting Started

Installing HSR

You can install HSR using either pip or conda:

pip install hsr

or

conda install hsr -c conda-forge

Build from source

Clone this repository on your machine. Move inside it and create the conda environment:

conda env create -f environment.yml
conda activate HSR_devel

Verify the correct creation of the environment by running:

pytest

To use HSR from CLI you can run:

python -m hsr.hsr_cli 

If HSR is installed with pip or conda, the above command is replace by the simple use of hsr

Basic Usage

Run the folowing command to get help in using HSR from CLI:

hsr -h

For a detailed overview of HSR's methodology check our documentation.

Licensing

HSR is licensed under the GNU Affero General Public License Version 3. For more details, see the LICENSE file.

Citing HSR

If you use HSR in your research, please cite it as follows:

[TODO: Add citation]

Contributing

Contributions to HSR are welcome! Please read our Contributing Guidelines for information on how to get started.

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

hsr-0.1.9.tar.gz (28.0 kB view details)

Uploaded Source

Built Distribution

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

hsr-0.1.9-py3-none-any.whl (31.5 kB view details)

Uploaded Python 3

File details

Details for the file hsr-0.1.9.tar.gz.

File metadata

  • Download URL: hsr-0.1.9.tar.gz
  • Upload date:
  • Size: 28.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for hsr-0.1.9.tar.gz
Algorithm Hash digest
SHA256 dfeeb5e37c3c229f44afc98cad05bdf246fe401454560828f4940d907ab9a675
MD5 b557010d0ee79983c6d6922c4e813a01
BLAKE2b-256 41890be03f1fcb431276a28e508526884d7d777a6668d8a361dd156c95776a04

See more details on using hashes here.

File details

Details for the file hsr-0.1.9-py3-none-any.whl.

File metadata

  • Download URL: hsr-0.1.9-py3-none-any.whl
  • Upload date:
  • Size: 31.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for hsr-0.1.9-py3-none-any.whl
Algorithm Hash digest
SHA256 6a60b908c54d492db6a317371c37952ce3ac9890e701f21b4c70c24d258cd37b
MD5 14bb710cf6467bad3b61314471dbe4f6
BLAKE2b-256 ed92179ba1e72a5b14ec03b1360a25e1639756f1726564b3e523931ecbafcd0c

See more details on using hashes here.

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