semvec/pypi-packages huggingface dataset:
iotconnect-lib 2.0.0
Avnet IoTConnect SDK Library
Requires Python: >=3.9
Keywords: IoTconnect, AWS, IoTCore, Azure, IoTHub, Greengrass


> This document is reformatted to better viewing as a standalone document.
> We recommend visiting this [GitHub v2.0.0 link](https://github.com/avnet-iotconnect/iotc-python-lib/blob/v2.0.0/) for best experience.

# Introduction
This project is the library that abstracts the /IOTCONNECT protocols for the SDKs like the
[/IOTCONNECT Python Lite SDK](https://github.com/avnet-iotconnect/iotc-python-lite-sdk)
and the [/IOTCONNECT Greengrass SDK](https://github.com/avnet-iotconnect/iotc-python-greengrass-sdk).

This library should generally not be used in other projects as the SDKs should be able 
to provide all the functionality that you would need in your python applications.

# Features

The library provides common code for interacting with /IOTCONNECT MQTT and HTTP device connectivity services:
* Format Telemetry (Reporting) messages
* Provide events for OTA and Command processing
* Streamline OTA and Command acknowledgements
* Obtain Discovery and Identity information

# Using The Library

To use this library in your project, ensure that your pyton project depends on iotconnect-lib.
Use a fixed major version dependency (E.g. "iotconnect-lib<2.0.0".) to 
avoid potential major version breaking your application calls.

The best way to learn how to use this library is to examine the unit test usage examples
in the [tests](https://github.com/avnet-iotconnect/iotc-python-lib/blob/v2.0.0/tests) directory or use the SDK implementations listed above in this document
for reference. 

# Testing

Regression tests should be run with every release using pytest.   

See [tests/TESTS_CONFIGURATION.md](https://github.com/avnet-iotconnect/iotc-python-lib/blob/v2.0.0/tests/TESTS_CONFIGURATION.md) for more details.

# Licensing

This python package is distributed under the [MIT License](https://github.com/avnet-iotconnect/iotc-python-lib/blob/v2.0.0/LICENSE.md).
lens-lang 0.1.0
Language for Exploratory & Narrative Statistics - A declarative analytics language
Requires Python: >=3.10

# LENS - Language for Exploratory & Narrative Statistics

**LENS** is an open-source analytics programming language that bridges the gap between business questions and data execution using natural language.

## Features
- **Declarative Syntax**: English-like queries (`show net_sales by region`)
- **Time Intelligence**: Built-in FY, YTD, rolling dates
- **Auto-Visualization**: Generates best-fit charts (Vega-Lite)
- **Story Engine**: Produces narratives from data trends
- **No-Code Studio**: Streamlit-based visual interface

## Installation

```bash
pip install lens-lang
```

Or install from source:
```bash
git clone https://github.com/Anay007-git/lens.git
cd lens
pip install -e .
```

## Quick Start

### CLI
```bash
python cli/lens_cli.py -q "show net_sales by region for FY2025"
```

### Studio (No-Code UI)
```bash
python -m streamlit run studio/app.py
```

### Python API
```python
from lens.compiler.parser import LensParser

parser = LensParser()
ast = parser.parse("show net_sales by region for FY2025")
print(ast)
```

## Example Query

```lens
show net_sales by region
for FY2025
compare with previous year
```

**Output:**
- SQL query
- Data table
- Auto-generated chart
- Business narrative

## Architecture

```
lens/
├── compiler/        # Lark parser + AST
├── semantics/       # Metric/Dimension registry
├── time_intelligence/  # FY, YTD, relative dates
├── planner/         # Logical plan builder
├── sql_gen/         # DuckDB SQL generator
├── runtime/         # Query execution
├── visualization/   # Altair/Vega-Lite charts
└── story/           # Trend analysis + narratives
```

## License

Apache 2.0

## Author

**Anay Biswas**

---

*Built with ❤️ for the analytics community*
remotivelabs-cli 0.8.0
CLI for operating RemotiveCloud and RemotiveBroker
Requires Python: <4.0,>=3.10
Keywords: automotive, autotech, networking, CAN

# RemotiveLabs - CLI
[![PyPI - Version](https://img.shields.io/pypi/v/remotivelabs-cli.svg)](https://pypi.org/project/remotivelabs-cli)

Use this CLI with our cloud and broker as a compliment to code and web tools.

Read more at https://docs.remotivelabs.com/docs/remotive-cli
datasette-youtube-embed 0.2
Turn YouTube URLs into embedded players in Datasette
Requires Python: >=3.10

# datasette-youtube-embed

[![PyPI](https://img.shields.io/pypi/v/datasette-youtube-embed.svg)](https://pypi.org/project/datasette-youtube-embed/)
[![Changelog](https://img.shields.io/github/v/release/simonw/datasette-youtube-embed?include_prereleases&label=changelog)](https://github.com/simonw/datasette-youtube-embed/releases)
[![Tests](https://github.com/simonw/datasette-youtube-embed/workflows/Test/badge.svg)](https://github.com/simonw/datasette-youtube-embed/actions?query=workflow%3ATest)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-youtube-embed/blob/main/LICENSE)

Turn YouTube URLs into embedded players in Datasette

## Installation

Install this plugin in the same environment as Datasette.

    datasette install datasette-youtube-embed

## Usage

Once installed, any columns containing URLs that look like one of these:

- `https://www.youtube.com/watch?v=xyz`
- `https://www.youtube.com/watch?v=xyz&start=30`
- `https://www.youtube.com/watch?v=xyz&start=30&end=50`

Will be turned into YouTube embeds like this:

```html
<lite-youtube videoid="xyz" params="start=30" style="min-width: 200px"></lite-youtube>
```
These will then be rendered using [Lite YouTube Embed](https://github.com/paulirish/lite-youtube-embed), which avoids loading the full YouTube embed until someone interacts with it.

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

    cd datasette-youtube-embed
    python3 -m venv venv
    source venv/bin/activate

Now install the dependencies and test dependencies:

    pip install -e '.[test]'

To run the tests:

    pytest