semvec/pypi-packages huggingface dataset:
ufa 0.2.0
A tiny UFA client
Requires Python: >=3.10

# ufa-client-tiny

Small UFA client

## Downloading files


```python
from pathlib import Path
from ufa.client import UFAClient

client = UFAClient(base_url="https://api.edge.deeporigin.io/files/", token="...", org_key="deeporigin")

# Download to a specific directory; it will be created if missing
out_path = asyncio.run(
    client.download_file(
        file_path="tests/ufa/ligand.sdf",
        download_dir=Path("/tmp/ufa-downloads")
    )
)
print(out_path)  # absolute path to the downloaded file
```
llamacpp-client 0.2.2
Client for LlamaCpp HTTP Server
Requires Python: >=3.9

# llamacpp_client

`llamacpp_client` is a Python client library designed to simplify communication with the [LlamaCpp HTTP server](https://github.com/ggerganov/llama.cpp) (as provided by the official Docker image). It provides both synchronous and asynchronous interfaces for interacting with LlamaCpp's completion and chat endpoints.

## Features

- **Synchronous and Asynchronous API:**
  - `LlamaCppClient.completion`: Synchronous API for blocking calls to `/completion` endpoint on LlamaCpp server.
  - `LlamaCppClient.v1_chat_completions`: Synchronous API for synchronous, blocking calls, to OpenAI `/v1/chat/completions` endpoint on LlamaCpp server (or any other OpenAI compatible server).
  - `LlamaCppClient.async_completion`: Asynchronous API for non-blocking, streaming responses to `/completion` endpoint on LlamaCpp server.
  - `LlamaCppClient.async_v1_chat_completions`: Asynchronous API for non-blocking, streaming responses to OpenAI `/v1/chat/completions` endpoint on LlamaCpp server (or any other OpenAI compatible server).
- **Multiple Endpoints Support:**
  Load-balance requests across multiple LlamaCpp server endpoints.
- **Easy Configuration:**
  Use [`LlamaCppServerAddress`](src/llamacpp_client/config.py) to specify server host and port.

## Installation

```sh
pip install .
```

Or add to your pyproject.toml dependencies:

```toml
llamacpp_client = { path = "path/to/llamacpp_client" }
```

## Usage

Synchronous example:

```python
from llamacpp_client import LlamaCppClient, LlamaCppServerAddress

endpoints = [
    LlamaCppServerAddress(host="localhost", port=8080)
]
client = LlamaCppClient(endpoints)
response = client.completion(prompt="Hello, world!")
print(response)
```

Asynchronous example:

```python
import asyncio
from llamacpp_client import LlamaCppClient, LlamaCppServerAddress

async def main():
    endpoints = [
        LlamaCppServerAddress(host="localhost", port=8080)
    ]
    client = LlamaCppClient(endpoints)
    async for chunk in client.async_completion(prompt="Hello, world!"):
        print(chunk.decode(), end="")

asyncio.run(main())
```


## Requirements

- Python 3.9+
- [httpx](https://www.python-httpx.org/)
- [requests](https://docs.python-requests.org/)


## License

MIT

--
Developed by Luís Gomes <luismsgomes@gmail.com>.
lcheapo 2.2
LCHEAPO data routines
Requires Python: >=3.8
Keywords: oceanography, marine, OBS

# lcheapo

Viewing and modifying LCHEAPO-2000 OBS data

## Overview

### Command-line programs

Type ``{command} -h`` to get a list of parameters and options

#### Programs that don't modify files

| Program     | description                                           |
| ----------- | ----------------------------------------------------- |
| lcdump      | dump raw information from LCHEAPO files               |
| lcinfo      | return basic information about an LCHEAPO file        |
| lcplot      | plot an LCHEAPO file                                  |
| lc_examples | create a directory with examples of lcplot and lctest |

#### Programs that create or modify files

These programs integrate the [sdpchain](https://github.com/WayneCrawford/sdpchainpy)
module, to document the processing workflow.

| Program     | description                                                                   |
| ----------- | ----------------------------------------------------------------------------- |
| lccut       | extract section of an LCHEAPO file                                            |
| lcfix       | fix common bugs in an LCHEAPO file                                            |
| lcheader    | create an LCHEAPO header + directory                                          |
| lc2ms_py    | converts LCHEAPO file to basic miniSEED files                                 |
| lc2SDS_py   | converts LCHEAPO file to SeisComp Data Structure, with basic drift correction |
harm-analysis 1.4.1
A Python library to estimate parameters from a signal containing a tone.
Requires Python: <4.0,>=3.10.0
Keywords: DSP, Signal-Processing, THD, SNR


[![PyPI Version](https://badge.fury.io/py/harm-analysis.svg)](https://badge.fury.io/py/harm-analysis)
[![Python Build](https://github.com/ericsmacedo/harm-analysis/actions/workflows/main.yml/badge.svg)](https://github.com/ericsmacedo/harm-analysis/actions/workflows/main.yml)
[![Documentation](https://readthedocs.org/projects/harm-analysis/badge/?version=latest)](https://harm-analysis.readthedocs.io/en/latest/)
[![Coverage Status](https://coveralls.io/repos/github/ericsmacedo/harm-analysis/badge.svg?branch=main)](https://coveralls.io/github/ericsmacedo/harm-analysis?branch=main)
[![python-versions](https://img.shields.io/pypi/pyversions/harm-analysis.svg)](https://pypi.python.org/pypi/harm-analysis)
[![semantic-versioning](https://img.shields.io/badge/semver-2.0.0-green)](https://semver.org/)

[![Downloads](https://img.shields.io/pypi/dm/harm-analysis.svg?label=pypi%20downloads)](https://pypi.python.org/pypi/harm-analysis)
[![PRs welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)
[![Issues](https://img.shields.io/github/issues/ericsmacedo/harm-analysis)](https://github.com/ericsmacedo/harm-analysis/issues)

# Harmonic Analysis package

* [Documentation](https://harm-analysis.readthedocs.io/en/latest/)
* [PyPI](https://pypi.org/project/harm-analysis/)
* [Sources](https://github.com/ericsmacedo/harm-analysis)
* [Issues](https://github.com/ericsmacedo/harm-analysis/issues)

## Introduction

The harmonic analysis package uses FFT to estimate parameters of an input signal.
The package provides two main functions:

- `harm_analysis`: for simulations with an injected tone, returning SNR, THDN, Noise, etc.
- `spec_analysis`: for cases without an injected tone, that auto-detects DC, tones, and noise from the spectrum.

See [usage](./docs/usage.md) for examples.


## Installation

Installing it is pretty easy:

```bash
pip install harm-analysis
```
doodl 0.8.2
A python module for including beautiful charts in Markdown for conversion to HTML
Requires Python: >=3.7

# Doodl

Doodl is the Swiss Army knife of data visualization. It has several audiences:

- Folks who want to write documents in Markdown that include high quality
  interactive (output format permitting) visualizations.

- Data analysts and data scientists who use any of the several Jupyter notebook
  derivative platforms to do data science.

- Web developers who would like to easily add charts and graphs to their Web
  apps in any of the major Web app platforms (work in progress).

- And soon, chatbot and other gen AI developers who would like their products
  to include high quality graphs and charts.

Under the hood, doodl is based on [d3](http://d3js.org), which means that
anything that you can do in d3, you can do in a PDF, Jupyter notebook, Web
app or a chatbot.

If you'd like to give it a spin, mosey on over to [doodl.ai](https://doodl.ai)
and click on "[Get started](https://doodl.ai/get-started.html)". You'll find
[extensive documentation](http://localhost:5173/docs.html) there as well.

Doodl is a project of [Hypericum](https://hypericum.ai), the computational
intuition company.