semvec/pypi-packages huggingface dataset:
dft2kp 1.2.0
Calculates kp model and coefficients from DFT ab initio data.
Requires Python: >=3.9

# DFT2kp

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![PyPI](https://img.shields.io/pypi/v/dft2kp)](https://pypi.org/project/dft2kp/)

A numerical framework to explicitly calculate kp matrix elements from *ab-initio* data.

**Documentation** available at [dft2kp.gitlab.io/dft2kp](https://dft2kp.gitlab.io/dft2kp/)

**Code repository** available at [gitlab.com/dft2kp/dft2kp](https://gitlab.com/dft2kp/dft2kp)

## Current features

- Calculates kp matrix elements using the DFT eigenstates from [Quantum Espresso (QE)](https://gitlab.com/QEF/q-e).
- Folding down of the effective Hamiltonian into a set of selected bands.
- Rotates the numeriacal basis into a representation informed by the user.

## See also

- List of authors: [AUTHORS.md](https://gitlab.com/dft2kp/dft2kp/-/blob/main/AUTHORS.md)
- References to cite if we use our code: [CITING.md](https://gitlab.com/dft2kp/dft2kp/-/blob/main/CITING.md)
- Quick install intructions and compatibility requirements: [INSTALL.md](https://gitlab.com/dft2kp/dft2kp/-/blob/main/INSTALL.md)
shoalsoft-pants-mcp-plugin 0.1.0
Pantsbuild Model Context Protocol Plugin from Shoal Software LLC
Requires Python: ==3.11.*

# Pantsbuild Model Context Protocol Plugin

## Installation

From PyPI:

1. In the relevant Pants project, edit `pants.toml` to set the `[GLOBAL].plugins` option to include `shoalsoft-pants-mcp-plugin==VERSION` (replacing `VERSION` with the applicable version) and the `[GLOBAL].backend_packages` option to include `shoalsoft.pants_mcp_plugin`.

2. Confogure your LLM coding agent to invoke `pants shoalsoft-mcp --run-stdio-server` in the repository as follows:

  - [Claude Code setup](https://docs.claude.com/en/docs/claude-code/mcp#option-1%3A-add-a-local-stdio-server)
  - [ChatGPT Codex setup](https://github.com/openai/codex/blob/main/docs/advanced.md#model-context-protocol-mcp)

## Licensing

This project is commercial software subject [to a license](https://github.com/shoalsoft/shoalsoft-pants-mcp-plugin/blob/main/LICENSE).

Permission is granted for personal, non-commercial use and for commercial evaluation, but not regular commercial use (which includes using the project for software development workflows in a commercial setting) without explicit permission.
pytrajlib 1.0.0a6
Simulate the flight of a ballistic missile
Requires Python: >=3.10

L.M. Arthur, Cambridge, MA, 2025

This code is a work in progress, and is not yet ready for use. It is being developed for research purposes in the MIT Laboratory for Nuclear Security and Policy. 

# Overview
This code is a package for simulating the flight of a ballistic missile. It is designed to be modular, with separate modules for geodesy, atmosphere, vehicle, gravity, stepper, integrator, flight, monte carlo, guidance, and control. The code is written in a combination of C, Python, and bash.

# User Guide
This section provides a brief introduction to using pytrajlib, and will be updated as additional features are rolled out. Run all commands from the pytrajlib directory. To ensure that your version of the code is up-to-date, run 

```git pull```

To compile the code and run the test suite, run 

```bash ./scripts/compile.sh```

And, to run the code, use the 

```bash ./scripts/run.sh```

command. The run parameters can be adjusted in the ```.toml``` files in the ```/input``` directory. The results will be placed in the ```/output``` directory. 

To generate trajectory plots from an existing ```trajectory.txt``` file, run 

```python ./src/traj_plot.py```

To generate a new ```trajectory.txt``` file, run the simulation with ```traj_output = 1``` in the relevant ```.toml``` file. 

## TODO: 
- [ ] Determine the optimal values of the navigation gain and the gearing ratio
- [ ] Plots: (Note that all of these should be done with the optimal nav gain and gearing)
    - [ ] Initial position error vs miss distance (or CEP)
    - [ ] Initial velocity error vs miss distance (or CEP)
    - [ ] Pitching mode excitation magnitude vs miss distance (Use a step function to model the pitching mode excitation and note that the expected excitation magnitude and duration is a function of velocity and altitude)
    - [ ] Boundary layer excitation magnitude vs miss distance (Note that this is probably negligible, we may not even want to include a plot)
    - [ ] Anomalous lift coefficient vs miss distance
- [ ] Explore the relationship between reentry speed, reentry angle, and accuracy

## BUG REPORTS: 
None
drcal 0.0.0
DrCal for camera calibrations
Requires Python: >=3.11

# drcal: a sanity-preserving fork of mrcal.

Mrcal is amazing work, and I want to be able to `pip install` it.
I don't want to jump through any hoops to use it as a runtime dependency, and I don't like hacks.

This is my attempt to make it usable in a sane software stack.

# TODO

- Put the generation of the `numpysane` bindings into the build script
- Make the python code pass
  - type checking
  - `ruff check`

* Put on PyPi, using `cibuildwheel` for automatic wheel building.
* Salvage the tests
nxdk-pgraph-test-repacker 0.0.4
Reconfigures nxdk_pgraph_tester ISOs
Requires Python: >=3.9
Keywords: nv2a, pgraph, xbox, xemu

# nxdk-pgraph-test-repacker

[![PyPI - Version](https://img.shields.io/pypi/v/nxdk-pgraph-test-repacker.svg)](https://pypi.org/project/nxdk-pgraph-test-repacker)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nxdk-pgraph-test-repacker.svg)](https://pypi.org/project/nxdk-pgraph-test-repacker)

-----

## Purpose

A simple tool to reconfigure
the [nxdk_pgraph_tests xiso](https://github.com/abaire/nxdk_pgraph_tests),
suitable for use in automated testing.

## Installation

```console
pip install nxdk-pgraph-test-repacker
```

## Usage

```console
python -m nxdk_pgraph_test_repacker -h
```

### Docker

```console
docker pull ghcr.io/abaire/nxdk-pgraph-test-repacker:latest
```

To download the latest nxdk_pgraph_tests image to
output/latest_nxdk_pgraph_tests_xiso.iso:

```console
docker run --rm -it \
    -v "${PWD}/output":/work \
    nxdk-pgraph-test-repacker \
    --download
```

To update a previously retrieved ISO called `data/clean_nxdk_pgraph_tests.iso`
using a new config named `data/config.json` and write to
`data/nxdk_pgraph_tests_xiso-updated.iso`:

```console
docker run --rm -it \
    -v "${PWD}/data":/work \
    nxdk-pgraph-test-repacker \
    --iso clean_nxdk_pgraph_tests.iso \
    --config config.json
```

## License

`nxdk-pgraph-test-repacker` is distributed under the terms of
the [MIT](https://spdx.org/licenses/MIT.html) license.
capnweb 0.1.0
A Python implementation of the Cap'n Web RPC protocol
Requires Python: >=3.13

# Cap'n Web

According to it's own [documentation](https://github.com/cloudflare/capnweb):

> Cap'n Web is a spiritual sibling to Cap'n Proto (and is created by the same author), 
> but designed to play nice in the web stack.

**What is this**
Short version: a python implementation of the same RPC system, it's meant to play nice with the modern
Python ecosystem (async/await, Pydantic...) and offer the same features as the typescript-based implementation.

**This implementation is a work in progress and it's not production ready**