semvec/pypi-packages huggingface dataset:
earthkit-workflows-anemoi 0.3.12
An earthkit-workflows interface to anemoi inference
Requires Python: >=3.10

# earthkit-workflows-anemoi

<p align="center">
  <a href="https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity">
    <img src="https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity/sandbox_badge.svg" alt="Static Badge">
  </a>

<a href="https://codecov.io/gh/ecmwf/earthkit-workflows-anemoi">
    <img src="https://codecov.io/gh/ecmwf/earthkit-workflows-anemoi/branch/develop/graph/badge.svg" alt="Code Coverage">
  </a>

<a href="https://opensource.org/licenses/apache-2-0">
    <img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License: Apache 2.0">
  </a>

<a href="https://github.com/ecmwf/earthkit-workflows-anemoi/releases">
    <img src="https://img.shields.io/github/v/release/ecmwf/earthkit-workflows-anemoi?color=blue&label=Release&style=flat-square" alt="Latest Release">
  </a>
</p>

> \[!IMPORTANT\]
> This software is **Sandbox** and subject to ECMWF's guidelines on [Software Maturity](https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity).

Earthkit-Workflows-Anemoi is a Python library for connecting [anemoi-inference](https://github.com/ecmwf/anemoi-inference) to [earthkit-workflows](https://github.com/ecmwf/earthkit-workflows). Allowing for the inference tasks to be run as part of a larger DAG. It provides an API to directly create a graph consisting of initial condition retrieval and model execution, or to run inference off other source nodes which themselves are the initial conditions.

## Installation

Install via `pip` with:

```bash
pip install 'earthkit-workflows-anemoi[all]'
```

For development, you can use `pip install -e .` Additionally you may want to install pre-commit hooks via

```bash
pip install pre-commit
pre-commit install
```

## Quick Start

To create a graph relying on anemoi-inference to get the initial conditions the following can be used:

```python

from earthkit.workflows.plugins import anemoi as anemoi_workflows

CKPT = {'huggingface': 'ecmwf/aifs-single-1.0'}

model_action = anemoi_workflows.fluent.from_input(CKPT, 'mars', '2022-01-01T00:00', lead_time = '7D', ensemble_members=51)
model_action

```

Given other nodes as the initial conditions:

```python

from earthkit.workflows.plugins import anemoi as anemoi_workflows
from earthkit.workflows import fluent

SOURCE_NODES: fluent.Action
CKPT = {'huggingface': 'ecmwf/aifs-single-1.0'}

SOURCE_NODES.anemoi.infer(CKPT, lead_time = '7D', ensemble_members = 51)

```
group-terminal 0.0.2
Group Terminal
Requires Python: <3.15,>=3.11

# Introduction

`group-terminal` is a minimalistic, terminal-based group chat system designed for testing and prototyping AI service integrations. Most group chat systems require authentication, databases, and user management before you can test a single message handler or prototype collaboration of user groups with AI. `group-terminal` eliminates this complexity by providing a minimal WebSocket-based chat server and [Rich](https://github.com/Textualize/rich)-powered terminal clients that let you focus on testing your integration logic rather than infrastructure setup.

The project delivers four capabilities to accelerate development workflows. A zero-configuration design means no authentication or database setup is required. Server-side message handlers let you build and test services that respond to group chat messages. The Rich-based terminal interface provides immediate visual feedback with configurable colors for different message types. Finally, the explicit testing-focused design ensures you spend time building features rather than maintaining production concerns like security or persistence.

> [!NOTE]
> `group-terminal` uses [termios](https://docs.python.org/3/library/termios.html) for terminal control and is Unix-specific. It will not work on Windows systems.

## Documentation

- [User Guide](https://gradion-ai.github.io/group-terminal/): Overview and getting started guide
- [API Documentation](https://gradion-ai.github.io/group-terminal/api/server/): Complete API reference

## LLM-Readable Documentation

For AI assistants and LLM-based tools, optimized documentation formats are available:

- [llms.txt](https://gradion-ai.github.io/group-terminal/llms.txt): Concise documentation suitable for LLM context windows
- [llms-full.txt](https://gradion-ai.github.io/group-terminal/llms-full.txt): Complete documentation with full API details

## Development

For development setup, see [DEVELOPMENT.md](DEVELOPMENT.md)
IservMultiTabReportsLib 0.0.9
Для формирования xlsx отчета из нескольких csv файлов.
Requires Python: >=3.9

# **Библиотека для создания файлов отчетов** # 

Для формирования xlsx отчета из нескольких csv файлов.

## Разработка ##

    source venv/bin/activate

## Установка ##

    pip install MultiTabReportsLib

## Использование ##

python3 index.py OUTPUT_NAME="test/res.xlsx" HEADERS_PATH="" META_NAME="test/meta.csv" SEP=";"

<!-- python3 OUTPUT_NAME="res.xlsx" HEADERS_PATH="" META_NAME="excel.meta" SEP=;  "Опросный лист ТУ_2025-07-28_14-28-44.csv" "Опросный лист УСПД_2025-07-28_14-33-04.csv" -->

## Сборка ##

python3 -m build
python3 -m twine upload --repository pypi dist/*