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-0.1.7.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-0.1.7-py3-none-any.whl (31.3 kB view details)

Uploaded Python 3

File details

Details for the file hsr-0.1.7.tar.gz.

File metadata

  • Download URL: hsr-0.1.7.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-0.1.7.tar.gz
Algorithm Hash digest
SHA256 f55c10ebd352478e7ea67b425821ae0fa21a377a79b03b6a24884851030017ea
MD5 5d18feb6e44e90855a03b68806cd907f
BLAKE2b-256 34d451d04526f52c6021e6977bd6ca0e15c7b6ed74bbad7af7251309296b7a9d

See more details on using hashes here.

File details

Details for the file hsr-0.1.7-py3-none-any.whl.

File metadata

  • Download URL: hsr-0.1.7-py3-none-any.whl
  • Upload date:
  • Size: 31.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for hsr-0.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 43c52d279411074c12f9dc40847921791575dff721d7e8025cc1f1aec3c70fb3
MD5 d6f5febe7044114e1c946a6134b0b619
BLAKE2b-256 89bcb74dc4219cb0edb30a5b7e07ec9a63d8412f3e628dae0d0e498f63091200

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