semvec/pypi-packages huggingface dataset:
qcvharmonizer 0.1.9
A package for harmonize oceanographic insitu data.
License: MIT
Requires Python: >=3.10
Keywords: oceanography, harmonization, profiles, Argo, Glider

# QCV Harmonizer

A Python package that harmonize data following FAIREASE qcv nomenclature.
base of this package is cerbere, developped by JF.Piolle - Ifremer. 
Cerbere is available here : 
* Gitlab = https://gitlab.ifremer.fr/cerbere/cerbere 
* pip : https://pypi.org/project/cerbere/]
## Installation
WARNING : To use this package, it is needed to install cerberecontrib-marineinsitu package from git before. 
Hopefully, the command is written below .
Have fun !

To install cerberecontrib-marineinsitu, you can use `pip` directly from GitLab:

```bash
pip install git+https://gitlab.ifremer.fr/cerbere/cerberecontrib-marineinsitu.git@dev_cweber
```

Then it is posible to install qcvharmonizer package thru pip standard usage : 
```bash
pip install qcvharmonizer
```
yapas 0.1.0
Yet another Python async server
License: MIT
Requires Python: <4.0,>=3.12

# Async Server

This project is an asynchronous server built using Python's `asyncio.create_server`,
designed for efficient request handling. It features a simple router-based request dispatcher,
static file serving, and logging configuration.
The server can handle both `GET` and `POST` requests and allows for easy route registration.

## Features

- **Async server** powered by `asyncio.create_server`
- **Customizable logging** via log levels (debug, info, warning, error)
- **Static file serving** (optional)
- **Simple router-based dispatching** for request handling, configured in `locations.ini`
- **Error handling** for 404 (Not Found), 405 (Method Not Allowed), and 500 (Internal Server Error)
- **Flexible configuration** for host, port, and log levels

## How to Run

### Default Run

The server starts on `0.0.0.0:8079` by default. You can run the server with the following command:

```bash
python -m yapas
```

### Custom Parameters

You can customize the server's host, port, static file path, and logging level
with the following command-line options:

```bash
python -m yapas --host <host_ip> --port <port_number> --log_level <log_level>
```

### Example

```bash
python -m yapas --host 127.0.0.1 --port 8080 --log_level info --use_proxy
```

### Parameters:

* `host`: IP address of the server (default: `0.0.0.0`)
* `port`: Port to bind the server to (default: `8079`)
* `log_level`: Logging level (`debug`, `info`, `warning`, `error`) (default: `debug`)
* `use_proxy`: use or not pre-set reverse proxy (see `locations.ini`), 
ensure `localhost:8000` is listened, if this parameter is set

### Error Handling

The server supports custom error handling for common HTTP errors:

* `404 Not Found`: Raised if a route is not found.
* `405 Method Not Allowed`: Raised if the requested method is not supported by the route.
* `500 Internal Server Error`: Raised for any unhandled exceptions.

### Static Content Management

* Basic in-memory caching is implemented for static content.

### Signal Handling

* Server listens signals to gracefully terminate (`SIGTERM`) or restart (`SIGHUP`).

### Proxy support

* Server supports a basic reverse proxy feature to forward requests to another backend server,
  retaining client headers such as `User-Agent` and `Cookies`.

### Backlog

* **SSL/TLS Support**: Add HTTPS support using Python's built-in `ssl` module
  to enable secure communication over SSL/TLS.
* **Round Robin Load Balancing**: Extend reverse proxying to support multiple backend servers
  with a round-robin load balancing algorithm for better distribution of incoming requests.

### License

This project is licensed under the MIT License.
RHEED-Learn 0.1.0
RHEED-Learn is a Python package for analyzing and visualizing Reflection High-Energy Electron Diffraction (RHEED) data, designed to streamline the processing of thin-film growth data and assist with materials characterization.
License: MIT

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|

===========
RHEED-Learn
===========


    RHEED-Learn is a Python package for analyzing and visualizing Reflection High-Energy Electron Diffraction (RHEED) data, designed to streamline the processing of thin-film growth data and assist with materials characterization.


RHEED-Learn is a dedicated Python package for researchers and scientists working with Reflection High-Energy Electron Diffraction (RHEED) data, commonly used in thin-film growth analysis and materials science. The package provides an array of tools to process, visualize, and analyze RHEED data, making it easier to track film growth dynamics and extract meaningful insights from complex diffraction patterns. With capabilities to handle raw RHEED intensity oscillation data, kinematic analysis, and more, RHEED-Learn allows users to better understand growth modes, surface roughness, and lattice structures in various material systems. This package aims to facilitate reproducible research by offering modular functions, supporting customized analyses, and providing integration with other Python data analysis workflows. Ideal for both routine RHEED characterization and advanced studies, RHEED-Learn empowers researchers to push forward in the field of materials synthesis and thin-film research.


.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.6. For details and usage
information on PyScaffold see https://pyscaffold.org/.