semvec/pypi-packages huggingface dataset:
csslib 1.0.1
CSSlib is an open-source code for building configuration search space (CSS) of disordered crystals.
Requires Python: >=3.9

<p align="center">
  <img src="./logo.jpg" width="20%" title="CSSlib" alt="CSSlib"/>
</p>

# CSSlib

CSSlib is an open-source code for building configuration search space (CSS) of disordered crystals.

Installation
-----
**CSSlib** requires **Supercell** program. Details on **Supercell** installation can be found at the corresponding [website](https://orex.github.io/supercell/download/).

Tutorial
-----
The best way to learn how to use **CSSlib** is through the [tutorial notebook](tests/csslib_example.ipynb).

References & Citing
-----
If you use this code, please consider citing works that actively used the CSS approach, which resulted in the creation of this library:

1. A.V. Krautsou, I.S. Humonen, V.D. Lazarev, R.A. Eremin, S.A. Budennyy<br/>
   "Impact of crystal structure symmetry in training datasets on GNN-based energy assessments for chemically disordered CsPbI<sub>3</sub>"<br/>
   https://doi.org/10.1038/s41598-025-92669-3
2. N.A. Matsokin, R.A. Eremin, A.A. Kuznetsova, I.S. Humonen, A.V. Krautsou, V.D. Lazarev, Y.Z. Vassilyeva, A.Y. Pak, S.A. Budennyy, A.G. Kvashnin, A.A. Osiptsov<br/>
   "Discovery of chemically modified higher tungsten boride by means of hybrid GNN/DFT approach"<br/>
   https://doi.org/10.1038/s41524-025-01628-z
3. R.A. Zaripov, R.A. Eremin, I.S. Humonen, A.V. Krautsou, V.V. Kuznetsov, K.E. GermanS, S.A. Budennyy, S.V. Levchenko</br>
   "First-principles data-driven approach for assessment of stability of Tc-C systems"</br>
   https://doi.org/10.1016/j.actamat.2025.121704
psychopy 2026.1.0
PsychoPy provides easy, precise, flexible experiments in behavioural sciences
Requires Python: <3.12,>=3.9

# PsychoPy

[![PyPI version](https://img.shields.io/pypi/v/psychopy.svg)](https://pypi.python.org/pypi/PsychoPy)
[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg)](code-of-conduct.md)  

---

PsychoPy is an open-source package for creating experiments in behavioral science. It aims to provide a single package that is:

* precise enough for psychophysics
* easy enough for teaching
* flexible enough for everything else
* able to run experiments in a local Python script or online in JavaScript

To meet these goals PsychoPy provides a choice of interface - you can use a
simple graphical user interface called Builder, or write your experiments in
Python code. The entire application and library are written in Python and is
platform independent.

There is a range of documentation at:

* [PsychoPy Homepage](https://www.psychopy.org)
* [Youtube](https://www.youtube.com/playlist?list=PLFB5A1BE51964D587)
* The textbook, [Building Experiments in PsychoPy](https://uk.sagepub.com/en-gb/eur/building-experiments-in-psychopy/book253480)
* [The discourse user forum](https://discourse.psychopy.org)

## Contributions

To contribute, please fork the repository, hack in a feature branch, and send a
pull request.  For more, see [CONTRIBUTING.md](CONTRIBUTING.md)
and the developers documentation at [https://www.psychopy.org/developers](https://psychopy.org/developers)

## Code Status

Dev branch: [![GH tests dev](https://github.com/psychopy/psychopy/actions/workflows/pytests.yaml/badge.svg?branch=dev)](https://github.com/psychopy/psychopy/actions/workflows/pytests.yaml?query=branch%3Adev)  [![CodeQL (dev)](https://github.com/psychopy/psychopy/actions/workflows/CodeQL.yaml/badge.svg?branch=dev)](https://github.com/psychopy/psychopy/actions/workflows/CodeQL.yaml?query=branch%3Adev) 

Release branch: [![GH tests release](https://github.com/psychopy/psychopy/actions/workflows/pytests.yaml/badge.svg?branch=release)](https://github.com/psychopy/psychopy/actions/workflows/pytests.yaml?query=branch%3Arelease) [![CodeQL (release)](https://github.com/psychopy/psychopy/actions/workflows/CodeQL.yaml/badge.svg?branch=release)](https://github.com/psychopy/psychopy/actions/workflows/CodeQL.yaml?query=branch%3Arelease)

## More information

* Homepage: https://www.psychopy.org
* Forum: https://discourse.psychopy.org
* Issue tracker: https://github.com/psychopy/psychopy/issues
* Changelog: https://www.psychopy.org/changelog.html
slickpy 0.4.8
A lightweight ASGI toolkit, optimized for great performance, flexibility and productivity.
Requires Python: >=3.10
Keywords: ASGI, http, web, toolkit

# SlickPy

![tests](https://github.com/akornatskyy/slickpy/workflows/tests/badge.svg)
[![Coverage Status](https://coveralls.io/repos/github/akornatskyy/slickpy/badge.svg?branch=master)](https://coveralls.io/github/akornatskyy/slickpy?branch=master)
[![pypi version](https://badge.fury.io/py/slickpy.svg)](https://badge.fury.io/py/slickpy)

A lightweight [ASGI](https://asgi.readthedocs.io/en/latest/index.html)
Python toolkit, optimized for great performance, flexibility and productivity.

## Install

```sh
pip install slickpy
```

## Overview

*example.py*:

```python
from slickpy import App, Writer

app = App()


@app.route("/")
async def welcome(w: Writer) -> None:
    await w.end(b"Hello, world!")


main = app.asgi()
```

Then run the example with [uvicorn](https://github.com/encode/uvicorn):

```sh
uvicorn example:main
```

See [examples](https://github.com/akornatskyy/slickpy/tree/master/examples) for more.
fmu-sim2seis 0.1.2
sim2seis
Requires Python: >=3.11
Keywords: energy, subsurface, seismic, scientific, engineering

> [!WARNING]
> `fmu-sim2seis` is not yet qualified technology, and as of today only applicable for selected pilot test fields.

**[📚 User documentation](https://equinor.github.io/fmu-sim2seis/)**

## What is fmu-sim2seis?

Calculates synthetic seismic from reservoir simulation model, together with a petro-elastic model.

`fmu-sim2seis` can be run either from command line, or from ERT, the latter being the
preferred option.

### Installation

To install `fmu-sim2seis`, run

```bash
pip install fmu-sim2seis
```

### ERT jobs

`fmu-sim2seis` is divided into a series of separate jobs:

* sim2seis_seismic_forward
* sim2seis_relative_ai
* sim2seis_observed_data
* sim2seis_map_attributes
* sim2seis_cleanup

There exists also a forward model for running petro-elastic modelling, defined in [**fmu-pem**](https://github.com/equinor/fmu-pem).

In the ERT-file for running fmu-sim2seis, necessary arguments are shown in [this example ERT setup](./ERT/run_sim2seis.ert).

Beware of hardcoded directory names - they should be replaced by **your** project path/name.

```bash
cd ./ERT
ert gui run_sim2seis.ert
```

`fmu-sim2seis` (and `fmu-pem`) are controlled by a series of `.yaml`-files which you find in examples of [here](./tests/data/sim2seis/model/pem).

The files `modelled_data_intervals` and `observed_data_intervals.yml` are used to define zones/intervals
for estimating seismic attributes. `sim2seis_config` contains all configuration parameters for all parts
of `fmu-sim2seis` workflow, except for observed seismic data, which has its own configuration file -
`obs_data_config.yml`.

### Tests

`fmu-sim2seis` has tests that use the `tests/data` structure, which contains the necessary input data
files.

```bash
pytest tests
```

### User interface

Users can visit <https://equinor.github.io/fmu-sim2seis/> in order to get help configuring the `fmu-sim2seis` input data.

Developing the user interface can be done by:

```bash
cd ./documentation
npm ci  # Install dependencies
npm run create-json-schema  # Extract JSON schema from Python code
npm run docs:dev  # Start local development server
```

The JSON schema itself (type, title, description etc.) comes from the corresponding Pydantic models in the Python code.
big-talk-ai 1.2.0
The lightweight, middleware-first LLM framework for Python.
Requires Python: >=3.12
Keywords: llm, middleware, framework, ai, language-models, chatbots, conversational-ai

# 🦜 BigTalk

> The lightweight, middleware-first LLM framework for Python.

BigTalk is designed for developers who want the power of agents (tool calling, loops, memory) without the bloat of
heavy frameworks. It provides a clean, type-safe abstraction over LLMs with a robust middleware pipeline inspired by
Starlette/FastAPI.

## Installation

```bash
pip install big-talk-ai[anthropic]

uv add big-talk-ai[anthropic]
```

## Documentation

Visit the GitHub repository for detailed documentation and examples:
[BigTalk on GitHub](https://github.com/DavidVollmers/big-talk/blob/main/README.md)
pg-bulk-ingest 0.0.58
A collection of Python utility functions for ingesting data into SQLAlchemy-defined PostgreSQL tables, automatically migrating them as needed, and minimising locking
Requires Python: >=3.7.7

# pg-bulk-ingest

[![PyPI package](https://img.shields.io/pypi/v/pg-bulk-ingest?label=PyPI%20package&color=%234c1)](https://pypi.org/project/pg-bulk-ingest/) [![Test suite](https://img.shields.io/github/actions/workflow/status/uktrade/pg-bulk-ingest/test.yml?label=Test%20suite)](https://github.com/uktrade/pg-bulk-ingest/actions/workflows/test.yml) [![Code coverage](https://img.shields.io/codecov/c/github/uktrade/pg-bulk-ingest?label=Code%20coverage)](https://app.codecov.io/gh/uktrade/pg-bulk-ingest)

A Python utility function for ingesting data into SQLAlchemy-defined PostgreSQL tables, automatically migrating them as needed, allowing concurrent reads as much as possible.

Allowing concurrent writes is not an aim of pg-bulk-ingest. It is designed for use in ETL pipelines where PostgreSQL is used as a data warehouse, and the only writes to the table are from pg-bulk-ingest. It is assumed that there is only one pg-bulk-ingest running against a given table at any one time.


## Features

pg-bulk-ingest exposes a single function as its API that:

- Creates the tables if necessary
- Migrates any existing tables if necessary, minimising locking
- Ingests data in batches, where each batch is ingested in its own transaction
- Handles "high-watermarking" to carry on from where a previous ingest finished or errored
- Optionally performs an "upsert", matching rows on primary key
- Optionally deletes all existing rows before ingestion
- Optionally calls a callback just before each batch is visible to other database clients


---

Visit the [pg-bulk-ingest documentation](https://pg-bulk-ingest.docs.trade.gov.uk/) for usage instructions.
quantify 26.8.0
A framework for controlling quantum computing and solid-state physics experiments.
Requires Python: >=3.10
Keywords: quantum, quantify

# Quantify

![Quantify logo](https://gitlab.com/quantify-os/quantify/-/raw/main/docs/source/images/quantify-landscape.svg)

[![try live tutorials using binder](https://img.shields.io/badge/try%20live-Tutorials-5D8DEE?style=flat&logo=jupyter&logoColor=white)](https://mybinder.org/v2/gl/quantify-os%2Fquantify/main?urlpath=lab/tree/docs/source/tutorials)
[![PyPI](https://img.shields.io/pypi/v/quantify.svg)](https://pypi.org/project/quantify/)
[![pipeline status](https://gitlab.com/quantify-os/quantify/badges/main/pipeline.svg)](https://gitlab.com/quantify-os/quantify/-/pipelines)

[![documentation](https://img.shields.io/badge/docs-stable-informational.svg)](https://quantify-os.gitlab.io/quantify/)
[![code quality](https://app.codacy.com/project/badge/Grade/bd24bae47c5d4c66855d78ad3b217f51)](https://app.codacy.com/gl/quantify-os/quantify/dashboard?utm_source=gl&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[![code style: ruff](https://img.shields.io/badge/code%20style-ruff-informational.svg)](https://docs.astral.sh/ruff/)

[![license: BSD-3-Clause](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)](./LICENSE)
[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](./CONTRIBUTING.md)
[![Slack](https://img.shields.io/badge/chat-Slack-purple.svg)](https://join.slack.com/t/quantify-hq/shared_invite/zt-1nd78r4e9-rbWdna53cW4DO_YbtMhVuA)

Quantify is a Python-based data acquisition framework focused on Quantum Computing and
solid-state physics experiments.
Currently it is a metapackage for [quantify-core](https://pypi.org/project/quantify-core/)
([git repo](https://gitlab.com/quantify-os/quantify-core/))
and [quantify-scheduler](https://pypi.org/project/quantify-scheduler/)
([git repo](https://gitlab.com/quantify-os/quantify-scheduler/)).
They are built on top of [QCoDeS](https://qcodes.github.io/Qcodes/) and are a spiritual
successor of [PycQED](https://github.com/DiCarloLab-Delft/PycQED_py3).

Versioning: CalVer `vYY.WW.MICRO` (see [calver.org](https://calver.org/)).

Take a look at the latest documentation for
[quantify-core](https://quantify-os.org/docs/quantify-core/v0.9.1/) and
[quantify-scheduler](https://quantify-os.org/docs/quantify-scheduler/v0.27.0/)
for the usage instructions.

## Overview and Community

For a general overview of Quantify and connecting to its open-source community,
see [quantify-os.org](https://quantify-os.org/).
Quantify is maintained by the Orange Quantum Systems.

[![Orange Quantum Systems logo](https://gitlab.com/quantify-os/quantify/-/raw/main/docs/source/images/orangeqs-logo-tagline-2022.svg)](https://orangeqs.com)

The software is licensed under a [BSD 3-clause license](https://gitlab.com/quantify-os/quantify/-/raw/main/LICENSE).
astrolabe-sdk 1.2.0
Python SDK for the OKAPI:Astrolabe API
Requires Python: >=3.10
Keywords: space, space debris, astrolabe, space traffic coordination, okapi orbits, sdk, REST API

# Python SDK for OKAPI:Astrolabe

This is a lightweight Python SDK to access the public OKAPI:Astrolabe API.

## Installation

The SDK is easy to install using `pip`. We recommend creating a virtual environment for the project.

```
python3 -m venv .venv
source .venv/bin/activate
pip install astrolabe-sdk
```

## Functionality

The SDK allows for easy access to CDMs, fleet management, upload of ephemerides and maneuver plans, etc.

For more details, please consult the respective section in the OKAPI:Astrolabe user manual.