semvec/pypi-packages huggingface dataset:
python-nso-client 0.1.3
Add your description here
Requires Python: >=3.11

# Python NSO Library

Thin wrapper around RestConf designed to interact with NSO.

**Key Features:**

- Detect errors raising meaningful exceptions
- Parameter support in URL to avoid URL Encoding mistakes
- Support for generating and executing YANG Patches
- Handling of dry-run responses

## Usage

Installing

```sh
uv add python-nso-client
```

Writing code with nso_client

```py
from nso_client import NSOClient
from httpx import BasicAuth

nso = NSOClient(
    "https://localhost",
    auth=BasicAuth("acct", "secret")
)

# Fetching data
resp = nso.get("/tailf-ncs:services/bb:backbone", content="config")
for bb in resp["bb:backbone"]:
    print(bb)


# Create objects
resp = nso.put(
    "/tailf-ncs:services/bb:backbone={}",
    "my-bb-1",
    payload={
        "bb:backbone": [
            {
                "name": "my-bb-1",
                "links": [
                    {"device": "xr0", "interface": "TenGigE0/0/0"},
                    {"device": "xr1", "interface": "TenGigE0/0/1"},
                ],
                "metric": 500,
                "admin-state": "in-service",
            }
        ]
    },
)

# Using yang-patch to modify multiple areas in the same transaction
patch = nso.yang_patch("/tailf-ncs:services")
patch.merge("/bb:backbone={}", "my-bb-1", value=...)
patch.merge("/bb:backbone={}", "my-bb-2", value=...)
patch.delete("/bb:backbone={}", "my-bb-3")
resp = patch.commit(dry_run="cli")
print(resp.changes)
patch.commit()

# Error handling
try:
    nso.delete("/tailf-ncs:services/bb:backbone={}", "does-not-exist")
except NotFoundError as exc:
    print("Backbone already deleted", exc)

# Fetching results in a None, not an exception
resp = nso.get("/tailf-ncs:services/bb:backbone={}", "does-not-exist")
assert resp is None
```

## Developing

```sh
# Build
uv build
```
bodosql 2026.2
Bodo's Vectorized SQL execution engine for clusters
Requires Python: >=3.10
Keywords: data, analytics, cluster

# BodoSQL

## BodoSQL: Bodo's Vectorized SQL execution engine for clusters

BodoSQL is Bodo's vectorized SQL execution engine, designed to run on both a single laptop
and across a cluster of machines. BodoSQL integrates with Bodo's Python JIT compiler to
enable high performance analytics split across Python and SQL boundaries.

BodoSQL Documentation: https://docs.bodo.ai/latest/api_docs/sql/

## Additional Requirements

BodoSQL depends on the Bodo package with the same version. If you are already using Bodo you will
need to update your Bodo package to the same version as BodoSQL.

BodoSQL also depends on having Java installed on your system. You will need to download either Java 11
or Java 17 from one of the main available distribution and ensure that the `JAVA_HOME` environment
variable is properly set. If you can run `java -version` in your terminal and see the correct Java version, you are good to go.
bodo-iceberg-connector 2026.2
Bodo Connector for Iceberg
Requires Python: >=3.10
Keywords: data, analytics, cluster

# Bodo Iceberg Connector

## Bodo Iceberg Connector: Bodo's Connector for the Apache Iceberg Data Format

Bodo Iceberg Connector is a Bodo connector for used to access data that is stored in Apache Iceberg tables. It allows users to read and write Iceberg tables using Bodo's and BodoSQL's IO APIs.

Bodo's Iceberg Documentation: https://docs.bodo.ai/latest/iceberg/intro/

## Requirements

The Bodo Iceberg Connector depends on having Java installed on your system. You will need to download
either Java 11 or Java 17 from one of the main available distribution and ensure that the `JAVA_HOME`
environment variable is properly set. If you can run `java -version` in your terminal and see the correct Java version, you are good to go.
hedit 0.7.4a4
Multi-agent system for HED annotation generation and validation
Requires Python: >=3.12
Keywords: hed, annotation, neuroscience, bids, cli

# HEDit

[![PyPI version](https://badge.fury.io/py/hedit.svg)](https://pypi.org/project/hedit/)
[![Tests](https://github.com/Annotation-Garden/hedit/actions/workflows/test.yml/badge.svg)](https://github.com/Annotation-Garden/hedit/actions/workflows/test.yml)

Convert natural language event descriptions into valid [HED](https://hedtags.org) (Hierarchical Event Descriptors) annotations.

Part of the [Annotation Garden Initiative](https://annotation.garden).

## Installation

```bash
# Default (lightweight API client, ~100MB)
pip install hedit

# Standalone mode (run locally without backend, ~2GB)
pip install hedit[standalone]
```

## Quick Start

```bash
# Configure your OpenRouter API key (https://openrouter.ai)
hedit init --api-key sk-or-v1-xxx

# Generate HED annotation from text
hedit annotate "participant pressed the left button"

# Generate HED from an image
hedit annotate-image stimulus.png

# Validate a HED string
hedit validate "Sensory-event, Visual-presentation"
```

## Commands

| Command | Description |
|---------|-------------|
| `hedit init` | Configure API key and preferences |
| `hedit annotate "text"` | Convert natural language to HED |
| `hedit annotate-image <file>` | Generate HED from image |
| `hedit validate "HED-string"` | Validate HED annotation |
| `hedit health` | Check service status |
| `hedit config show` | Display configuration |

## Options

```bash
hedit annotate "text" -o json          # JSON output for scripting
hedit annotate "text" --schema 8.3.0   # Specific HED schema version
hedit annotate "text" --standalone     # Run locally (requires hedit[standalone])
```

## How It Works

HEDit uses a multi-agent system (LangGraph) with feedback loops:

1. **Annotation Agent** - Generates initial HED tags
2. **Validation Agent** - Checks syntax and tag validity
3. **Evaluation Agent** - Assesses faithfulness to input
4. **Assessment Agent** - Identifies missing elements

Annotations are automatically refined until validation passes.

## Links

- [Documentation](https://docs.annotation.garden/hedit)
- [GitHub Repository](https://github.com/Annotation-Garden/HEDit)
- [HED Standard](https://hedtags.org)
- [OpenRouter](https://openrouter.ai) - Get an API key

## License

MIT
ai-24sea 1.1.1
A package containing general functionality for 24sea ai/ml related topics
Requires Python: <3.14,>=3.9.0

# ai-24sea

A Python package for building, training, and managing AI/ML models for 24sea windfarm data. This repository provides a modular framework for data ingestion, preprocessing, splitting, training, and experiment tracking using MLflow.

## Features

- **Configurable ML Pipelines:** Easily define preprocessing, splitting, and training steps via configuration files.
- **Data Ingestion:** Fetch and aggregate windfarm data using the 24sea API.
- **Preprocessing:** Modular, reusable preprocessing functions for feature engineering and cleaning.
- **Train/Test Split:** Flexible splitting with support for custom strategies.
- **Model Training:** Standardized training flow with MLflow experiment tracking and signature enforcement.
- **Testing:** Comprehensive unit tests using `pytest` and `hypothesis`.

## Project Structure
```sh
├── LICENSE
├── README.md
├── VERSION
├── ai_24sea
│   ├── __init__.py
│   ├── modeler
│   │   ├── __init__.py
│   │   ├── config.py
│   │   ├── constants.py
│   │   ├── misc
│   │   │   ├── __init__.py
│   │   │   ├── preprocessors.py
│   │   │   └── utils.py
│   │   └── tasks
│   │       ├── __init__.py
│   │       ├── ingest.py
│   │       ├── optimize.py
│   │       ├── split.py
│   │       ├── train.py
│   │       └── transform.py
│   └── version.py
├── bitbucket-pipelines.yml
├── bumpversion.py
├── notebooks
│   ├── tests.ipynb
│   └── tests.py
├── pyproject.toml
├── tests
│   ├── __init__.py
│   ├── conftest.py
│   ├── misc
│   │   ├── test_preprocessors.py
│   │   └── test_utils.py
│   ├── tasks
│   │   ├── test_ingest.py
│   │   ├── test_optimize.py
│   │   ├── test_split.py
│   │   ├── test_train.py
│   │   ├── test_transform.py
│   │   └── test_validate.py
│   ├── test_config.py
│   └── test_version.py
└── uv.lock
```

## Installation

This project uses `uv` for package management

To install:

```sh
uv sync
```

To install for development:

```sh
uv sync --all-groups
```