semvec/pypi-packages huggingface dataset:
lineshape_tools 0.1.2
Software to evaluate the optical lineshape function of a defect.
License: MIT
Requires Python: <3.14,>=3.10
Keywords: defect, luminescence, absorption, electron-phonon coupling, density functional theory

# lineshape_tools
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`lineshape_tools` is a Python software that implements the formulation for evaluating the effects of electron-phonon coupling on the optical properties of defects.
In particular, it implements the approach pioneered by [Alkauskas *et al.*](https://doi.org/10.1088/1367-2630/16/7/073026) to compute the lineshape function within Huang-Rhys theory.
The code interfaces with [`mace`](https://mace-docs.readthedocs.io/en/latest/) and [`phonopy`](https://phonopy.github.io/phonopy/) to evaluate the dynamical matrix and obtain the phonons of a defect-containing supercell.

_Key Features_:
 - Compute the temperature-dependent luminescence and absorption spectrum
 - Compatible with phonons computed directly with Phonopy
 - Accelerate evaluation of phonons with `mace` foundation models
 - Convenience tools to fine-tune a `mace` foundation model to your specific system

### Installation
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To install the latest version of `lineshape_tools`, create a new virtual environment and run
```
pip install lineshape_tools
```
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For more installation information and some performance considerations, see the [Installation page](https://lineshape-tools.readthedocs.io/en/latest/install.html).

### Usage 
`lineshape_tools` provides a command-line interface for interacting with the code. See
```
lineshape_tools --help
```
Detailed usage information can be found in the [Tutorials page](https://lineshape-tools.readthedocs.io/en/latest/tutorials.html).

### How to Cite
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If you use this code, please consider citing
```bibtex
@misc{turiansky_machine_2025,
  title = {Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects}, 
  author = {Mark E. Turiansky and John L. Lyons and Noam Bernstein},
  year = {2025},
  number = {arXiv:2508.09113},
  eprint = {2508.09113},
  archiveprefix = {arXiv},
  primaryclass = {cond-mat.mtrl-sci},
  doi = {10.48550/arXiv.2508.09113},
  url = {https://arxiv.org/abs/2508.09113}, 
}
```
<!-- cite end -->
Please also consider citing the foundational works that made this code possible on the [Citation page](https://lineshape-tools.readthedocs.io/en/latest/cite.html).

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poetry-plugin-ivcap 0.6.1
A custom Poetry command for IVCAP deployments
License: MIT
Requires Python: <4.0,>=3.10

# poetry-plugin-ivcap

A custom Poetry plugin that adds a `poetry ivcap` command for local and Docker-based deployments.

## Example Configuration

Add to your `pyproject.toml`:

```toml
[tool.poetry-plugin-ivcap]
service-file = "service.py"
service-id = "urn:ivcap:service:ac158a1f-dfb4-5dac-bf2e-9bf15e0f2cc7"
port = 8077
# docker
# docker-run-opts = { port = 8079 }```

## Installation

```bash
poetry self add poetry-plugin-ivcap
```

## Usage

```bash
poetry ivcap run
poetry ivcap docker-build
poetry ivcap docker-run
poetry ivcap docker-publish
poetry ivcap service-register
poetry ivcap create-service-idget
poetry ivcap tool-register
```

To get help on the currently installed version
```
% poetry ivcap

IVCAP plugin

Supporting the development of services and tools for the IVCAP platform

Available subcommands:
    run                 Run the service locally
    docker-build        Build the docker image for this service
    docker-run          Run the service's docker image locally
    docker-publish      Publish the service's docker image to IVCAP
    service-register    Register the service with IVCAP
    create-service-id   Create a unique service ID for the service
    tool-register       Register the service as an AI Tool with IVCAP

Example:
  poetry ivcap run

Configurable optiosn in pyproject.toml:

  [tool.poetry-plugin-ivcap]
  service-file = "service.py"  # The Python file that implements the service
  service-file = "service.py"
  service-id = "urn:ivcap:service:ac158a1f-dfb4-5dac-bf2e-9bf15e0f2cc7" # A unique identifier for the service

  docker-build-template = "docker buildx build -t #DOCKER_NAME#  ."
  docker-run-template = "docker run -rm -p #PORT#:#PORT#"
```

## Development

### Test the plugin as part of the "global" installed poetry

```shell
# 1) remove any existing installed copy first
poetry self remove poetry-plugin-ivcap

# 2) install this checkout as an editable plugin
poetry self add -e $PWD

# 3) verify Poetry sees the plugin
poetry self show plugins
```


Switch back:

```
poetry self remove poetry-plugin-ivcap
poetry self add poetry-plugin-ivcap@latest
```

### Build the Plugin Package

```bash
poetry build
```

This creates .whl and .tar.gz files in the dist/ directory.

### Publish to PyPI

Create an account at https://pypi.org/account/register/

Add your credentials:
```bash
poetry config pypi-token.pypi <your-token>
```

Publish:
```bash
poetry publish --build
```

### Optional: Test on TestPyPI First

To verify your setup without publishing to the real PyPI:

```bash
poetry config repositories.test-pypi https://test.pypi.org/legacy/
poetry publish -r test-pypi --build
```

Then test installing via:

```bash
poetry self add --source test-pypi poetry-plugin-deploy
```