semvec/pypi-packages huggingface dataset:
calibsunapi 0.0.33
Lightweight client to interact with Calibsun's API
Requires Python: >=3.8

# Welcome to calibsunapi's documentation!

Calibsunapi is a Python client for interacting with the CalibSun API. This package allows developers to easily integrate and communicate with the CalibSun services, providing functionalities such as data retrieval, data submission, and various API interactions. 

It is designed to be simple, efficient, and easy to use, and provides different abstractions making it an ideal choice for developers looking to leverage CalibSun's capabilities in their Python applications.


## Getting help

Having troubles installing or using Calibsunapi ? Feel free to ask questions at any of the contact methods or file an issue on [Calibsunapi's GitHub page](https://github.com/SOLAIS-FORECAST/api-python-client).


Documentation : https://solais-forecast.github.io/api-python-client/
promptl-ai 1.3.0
PromptL bindings for Python
Requires Python: <3.15,>=3.9

# PromptL

```sh
pip install promptl-ai
```

Requires Python `3.9` through `3.14`.

## What is PromptL?

[PromptL](https://promptl.ai/) offers a common, easy-to-use syntax for defining dynamic prompts for LLMs. It is a simple, yet powerful language that allows you to define prompts in a human-readable format, while still being able to leverage the full power of LLMs.

> This repository contains the Python bindings for PromptL. Go to the [main repository](https://github.com/latitude-dev/promptl) to learn more.

## Usage

```python
from promptl_ai import Promptl

promptl = Promptl()

prompt = """
<step>
    First, think step by step about how to answer the user's question.
    <user>
        Taking into account this context: {{context}}
        I have the following question: {{question}}
    </user>
</step>
<step>
    Finally, answer the user's question succinctly yet complete.
</step>
"""

chain = promptl.chains.create(
    prompt=prompt,
    parameters={
        "context": "PromptL is a templating language specifically designed for LLM prompting.",
        "question": "What is PromptL?",
    },
)
conversation = chain.step()
conversation = chain.step("Reasoning...")  # LLM response
conversation = chain.step("Answer...")  # LLM response

print(conversation.messages)
```

Find more [examples](examples).

## Development

Requires uv `0.8.17` or higher.

- Install dependencies: `uv venv && uv sync --all-extras --all-groups`
- Add [dev] dependencies: `uv add [--dev] <package>`
- Run linter: `uv run scripts/lint.py`
- Run formatter: `uv run scripts/format.py`
- Run tests: `uv run scripts/test.py`
- Build package: `uv build`
- Publish package: `uv publish`

## License

The PromptL bindings are licensed under the [MIT License](https://opensource.org/licenses/MIT) - read the [LICENSE](LICENSE) file for details.
lofar-vlbi-plot 2026.1.2
Repository containing the cli tool for LOFAR-VLBI plot_field
Requires Python: >=3.11
Keywords: radio, astronomy, VLBI, LOFAR

# LOFAR-VLBI/plot_field

This repository now contains only the preparatory script for generating the required catalogues for running PILOT [https://github.com/LOFAR-VLBI/pilot](https://github.com/LOFAR-VLBI/pilot).

The script can be installed by:

```bash
pip install lofar-vlbi-plot
lofar-vlbi-plot --RA 0.234 --DEC 32.234 # optional --vlass --fit_spec 

# Run directly with uv in virtual env
uvx lofar-vlbi-plot --RA 0.234 --DEC 32.234
```
lumaCLI 0.2.16
A CLI tool for managing the data catalog platform.
Requires Python: >=3.10
Keywords: catalog, cli, data, dbt, luma

# luma

## Prerequisites

Make sure your system is running Python version 3.10 or newer.

## Installation

For a smooth installation of `luma`, simply execute the following command:

```bash
pip install luma
```

## Next Steps

Make sure to explore the [Manual](./docs/manual/introduction.md) to familiarize yourself with the extensive API functionalities and commands that `luma` offers.