semvec/pypi-packages huggingface dataset:
grypereport 0.0.17
Grype vulnerability scanner report builder and CSV exporter.
Requires Python: >=3.11
Keywords: grype, report, builder, csv, csv-export, ci

<p align="left">
  <a href="https://www.python.org" alt="python">
    <img src="https://img.shields.io/badge/3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue?logo=python&logoColor=white&logoSize=auto&label=python&labelColor=grey" alt="Python"></a>
  <a href="https://pypi.org/project/grypereport/" alt="pypi">
    <img src="https://img.shields.io/pypi/v/grypereport?logo=pypi&logoColor=white&color=%2390A1B9" alt="PyPI"></a>
  <a href="https://github.com/amarienko/GrypeReport" alt="github tag">
    <img src="https://img.shields.io/github/v/tag/amarienko/GrypeReport?logo=github&color=orange" alt="GitHub"></a>
  <a href="https://opensource.org/licenses/MIT" alt="License">
    <img src="https://img.shields.io/github/license/amarienko/GrypeReport"/></a>
  <a href="https://github.com/psf/black" alt="black">
    <img src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
</p>

# GrypeReport
`grypereport` is a lightweight CLI tool for generating custom reports from the standard JSON output of the [Grype](https://github.com/anchore/grype) vulnerability scanner. `grypereport` generates reports on detected vulnerabilities, exports them to CSV, and integrates with [TeamCity](https://www.jetbrains.com/teamcity/) by publishing a build tag with the total and critical vulnerability counts via TeamCity Service Messages.

**Disclaimer**: [Grype](https://github.com/anchore/grype) and [TeamCity](https://www.jetbrains.com/teamcity/) are trademarks and copyrights of their respective owners, this project is not affiliated with, endorsed by, or sponsored by them.
cudaq-qec 0.5.0
Accelerated libraries for Quantum Error Correction built on CUDA-Q
Requires Python: >=3.11
Keywords: qec, cudaq, cuda-quantum, cuda, quantum, quantum computing, nvidia, high-performance computing

# CUDA-Q QEC Library

CUDA-Q QEC is a high-performance quantum error correction library that leverages
NVIDIA GPUs to accelerate classical decoding and processing of quantum error
correction codes. The library provides optimized implementations of common QEC
tasks including syndrome extraction, decoding, and logical operation tracking.

This is package is a meta-package that when installed, simply installs the
version of the package that is appropriate for your system, e.g.
[`cudaq-qec-cu12`](https://pypi.org/project/cudaq-qec-cu12/) or
[`cudaq-qec-cu13`](https://pypi.org/project/cudaq-qec-cu13/). Please click those
links for more details about the package.

The optional dependencies that work with the above sub-packages also work with
this meta-package.
cudaq-qec-cu12 0.5.0
Accelerated libraries for Quantum Error Correction built on CUDA-Q
Requires Python: >=3.11

# CUDA-Q QEC Library

CUDA-Q QEC is a high-performance quantum error correction library
that leverages NVIDIA GPUs to accelerate classical decoding and
processing of quantum error correction codes. The library provides optimized
implementations of common QEC tasks including syndrome extraction,
decoding, and logical operation tracking.

**Note**: CUDA-Q QEC is currently only supported on Linux operating systems
using `x86_64` processors or `aarch64`/`arm64` processors. CUDA-Q QEC does
not require a GPU to use, but some components are GPU-accelerated.

## Features

- Fast syndrome extraction and processing on GPUs
- Common decoders for surface codes and other topological codes
- Real-time decoding capabilities for quantum feedback
- Integration with CUDA-Q quantum program execution

## Optional Dependencies

Some decoders require additional dependencies to operate. You can install them with

- `pip install cudaq-qec[tensor-network-decoder]` for the Tensor Network Decoder
- `pip install cudaq-qec[trt-decoder]` for the TensorRT Decoder

## Getting Started

For detailed documentation, tutorials, and API reference, visit the
[CUDA-Q QEC Documentation](https://nvidia.github.io/cudaqx/components/qec/introduction.html).

## License

Most components of CUDA-Q QEC are open source. The source code is available on
[GitHub][github_link] and licensed under [Apache License
2.0](https://github.com/NVIDIA/cudaqx/blob/main/LICENSE).

The `libcudaq-qec-nv-qldpc-decoder.so` library (distributed with CUDA-Q QEC) is
closed source and is subject to the [NVIDIA Software License Agreement][github_qec_license]

[github_link]: https://github.com/NVIDIA/cudaqx/tree/main/libs/qec
[github_qec_license]: https://github.com/NVIDIA/cudaqx/blob/main/libs/qec/LICENSE
cudaq-qec-cu13 0.5.0
Accelerated libraries for Quantum Error Correction built on CUDA-Q
Requires Python: >=3.11

# CUDA-Q QEC Library

CUDA-Q QEC is a high-performance quantum error correction library
that leverages NVIDIA GPUs to accelerate classical decoding and
processing of quantum error correction codes. The library provides optimized
implementations of common QEC tasks including syndrome extraction,
decoding, and logical operation tracking.

**Note**: CUDA-Q QEC is currently only supported on Linux operating systems
using `x86_64` processors or `aarch64`/`arm64` processors. CUDA-Q QEC does
not require a GPU to use, but some components are GPU-accelerated.

## Features

- Fast syndrome extraction and processing on GPUs
- Common decoders for surface codes and other topological codes
- Real-time decoding capabilities for quantum feedback
- Integration with CUDA-Q quantum program execution

## Optional Dependencies

Some decoders require additional dependencies to operate. You can install them with

- `pip install cudaq-qec[tensor-network-decoder]` for the Tensor Network Decoder
- `pip install cudaq-qec[trt-decoder]` for the TensorRT Decoder

## Getting Started

For detailed documentation, tutorials, and API reference, visit the
[CUDA-Q QEC Documentation](https://nvidia.github.io/cudaqx/components/qec/introduction.html).

## License

Most components of CUDA-Q QEC are open source. The source code is available on
[GitHub][github_link] and licensed under [Apache License
2.0](https://github.com/NVIDIA/cudaqx/blob/main/LICENSE).

The `libcudaq-qec-nv-qldpc-decoder.so` library (distributed with CUDA-Q QEC) is
closed source and is subject to the [NVIDIA Software License Agreement][github_qec_license]

[github_link]: https://github.com/NVIDIA/cudaqx/tree/main/libs/qec
[github_qec_license]: https://github.com/NVIDIA/cudaqx/blob/main/libs/qec/LICENSE
cudaq-solvers 0.5.0
Accelerated libraries for Quantum Error Correction built on CUDA-Q
Requires Python: >=3.11
Keywords: cudaq, cuda-quantum, cuda, quantum, quantum computing, nvidia, high-performance computing

# CUDA-Q Solvers Library

CUDA-Q Solvers provides GPU-accelerated implementations of common
quantum-classical hybrid algorithms and numerical routines frequently used in
quantum computing applications. The library is designed to work seamlessly with
CUDA-Q quantum programs.

This is package is a meta-package that when installed, simply installs the
version of the package that is appropriate for your system, e.g.
[`cudaq-solvers-cu12`](https://pypi.org/project/cudaq-solvers-cu12/) or
[`cudaq-solvers-cu13`](https://pypi.org/project/cudaq-solvers-cu13/). Please
click those links for more details about the package.

The optional dependencies that work with the above sub-packages also work with
this meta-package.
cudaq-solvers-cu13 0.5.0
Accelerated libraries for quantum-classical solvers built on CUDA-Q
Requires Python: >=3.11

# CUDA-Q Solvers Library

CUDA-Q Solvers provides GPU-accelerated implementations of common
quantum-classical hybrid algorithms and numerical routines frequently
used in quantum computing applications. The library is designed to
work seamlessly with CUDA-Q quantum programs.

**Note**: CUDA-Q Solvers is currently only supported on Linux operating systems
using `x86_64` processors or `aarch64`/`arm64` processors. CUDA-Q Solvers does
not require a GPU to use, but some components are GPU-accelerated.

**Note**: CUDA-Q Solvers will require the presence of `libgfortran`, which is not distributed with the Python wheel, for provided classical optimizers. If `libgfortran` is not installed, you will need to install it via your distribution's package manager. On debian based systems, you can install this with `apt-get install gfortran`.

## Features

- Variational quantum eigensolvers (VQE)
- ADAPT-VQE
- Quantum approximate optimization algorithm (QAOA)
- Hamiltonian simulation routines

Note: if you would like to use our Generative Quantum Eigensolver API, you will need
additional dependencies installed. You can install them with
`pip install cudaq-solvers[gqe]`.

## Getting Started

For detailed documentation, tutorials, and API reference,
visit the [CUDA-Q Solvers Documentation](https://nvidia.github.io/cudaqx/components/solvers/introduction.html).

## License

CUDA-Q Solvers is an open source project. The source code is available on
[GitHub][github_link] and licensed under [Apache License
2.0](https://github.com/NVIDIA/cudaqx/blob/main/LICENSE).

[github_link]: https://github.com/NVIDIA/cudaqx/tree/main/libs/solvers
cudaq-solvers-cu12 0.5.0
Accelerated libraries for quantum-classical solvers built on CUDA-Q
Requires Python: >=3.11

# CUDA-Q Solvers Library

CUDA-Q Solvers provides GPU-accelerated implementations of common
quantum-classical hybrid algorithms and numerical routines frequently
used in quantum computing applications. The library is designed to
work seamlessly with CUDA-Q quantum programs.

**Note**: CUDA-Q Solvers is currently only supported on Linux operating systems
using `x86_64` processors or `aarch64`/`arm64` processors. CUDA-Q Solvers does
not require a GPU to use, but some components are GPU-accelerated.

**Note**: CUDA-Q Solvers will require the presence of `libgfortran`, which is not distributed with the Python wheel, for provided classical optimizers. If `libgfortran` is not installed, you will need to install it via your distribution's package manager. On debian based systems, you can install this with `apt-get install gfortran`.

## Features

- Variational quantum eigensolvers (VQE)
- ADAPT-VQE
- Quantum approximate optimization algorithm (QAOA)
- Hamiltonian simulation routines

Note: if you would like to use our Generative Quantum Eigensolver API, you will need
additional dependencies installed. You can install them with
`pip install cudaq-solvers[gqe]`.

## Getting Started

For detailed documentation, tutorials, and API reference,
visit the [CUDA-Q Solvers Documentation](https://nvidia.github.io/cudaqx/components/solvers/introduction.html).

## License

CUDA-Q Solvers is an open source project. The source code is available on
[GitHub][github_link] and licensed under [Apache License
2.0](https://github.com/NVIDIA/cudaqx/blob/main/LICENSE).

[github_link]: https://github.com/NVIDIA/cudaqx/tree/main/libs/solvers
celltraj 0.1.79
Toolset for single-cell trajectory modeling.
Requires Python: >=3.5

========
celltraj
========


.. image:: https://img.shields.io/pypi/v/celltraj.svg
        :target: https://pypi.python.org/pypi/celltraj

--------------------------------------------------------------------------------------
A toolset for the modeling and analysis of single-cell trajectories.
--------------------------------------------------------------------------------------

Key Features
------------
- **Single-Cell Trajectory Analysis**: Leverages time-lapse imaging data to construct detailed trajectories of single-cell behavior, capturing changes in morphology and motility.
- **Morphodynamical State Decomposition**: Utilizes data-driven methods to define and analyze cell states based on dynamic cellular features, providing insights into cell state transitions.
- **Dynamical Modeling**: Implements MSMs and Koopman operator-based approaches to kinetically characterize cell state transitions and generate embeddings for visualizing cell dynamics.
- **Integration with Molecular Data**: Maps live-cell imaging data to gene expression profiles, enabling predictions of RNA transcript levels based on cell state dynamics.
- **Tutorials**: Includes jupyter-notebooks with links to Zenodo repositories with downloadable data to guide users through the process of trajectory embedding and MMIST (Molecular and Morphodynamics-Integrated Single-cell Trajectories).

References
----------
- Copperman, Jeremy, Sean M. Gross, Young Hwan Chang, Laura M. Heiser, and Daniel M. Zuckerman. “Morphodynamical cell state description via live-cell imaging trajectory embedding.” Communications Biology 6, no. 1 (2023): 484.
- Copperman, Jeremy, Ian C. Mclean, Sean M. Gross, Young Hwan Chang, Daniel M. Zuckerman, and Laura M. Heiser. “Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.” bioRxiv (2024): 2024-01.

License
-------
Free software: MIT license

Documentation
-------------
- `celltraj documentation <https://jcopperm.github.io/celltraj>`_

Tutorials
---------
- `Morphodynamical Trajectory Embedding Tutorial <https://github.com/jcopperm/celltraj/blob/main/tutorials/trajectory_embedding.ipynb>`_
- `MMIST: Molecular and Morphodynamics-Integrated Single-cell Trajectories Tutorial <https://github.com/jcopperm/celltraj/blob/main/tutorials/mmist.ipynb>`_

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage