semvec/pypi-packages huggingface dataset:
ezmsg-lsl 1.4.4
LabStreamingLayer source and sink for ezmsg
Requires Python: >=3.10

# ezmsg-lsl

Interface to Lab Streaming Layer [LSL](https://labstreaminglayer.readthedocs.io/) in [ezmsg](https://github.com/iscoe/ezmsg).

## Installation

```bash
pip install ezmsg-lsl
```

Or install the latest development version:

```bash
pip install git+https://github.com/ezmsg-org/ezmsg-lsl@dev
```

### Dependencies
* `ezmsg`
* `pylsl`
* `numpy`

## Usage

See the `examples` folder for more details.

## Developers

We use [`uv`](https://docs.astral.sh/uv/getting-started/installation/) for development. It is not strictly required, but if you intend to contribute to ezmsg-lsl then using `uv` will lead to the smoothest collaboration.

1. Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) if not already installed.
2. Fork ezmsg-lsl and clone your fork to your local computer.
3. Open a terminal and `cd` to the cloned folder.
4. `uv sync` to create a .venv and install dependencies.
5. (Optional) Install pre-commit hooks: `uv run pre-commit install`
6. After editing code and making commits, Run the test suite before making a PR: `uv run pytest tests`
   * Currently, there are no substantial tests.
iflow-mcp_ai4curation-owl-mcp 0.1.1
A MCP server for OWL ontology operations
Requires Python: <=3.12,>=3.10

# OWL-MCP

OWL-MCP is a Model-Context-Protocol (MCP) server for working with Web Ontology Language (OWL) ontologies. 

![img](https://avatars.githubusercontent.com/u/182288589?s=200&v=4)
![img](https://avatars.githubusercontent.com/u/4671070?s=200&v=4) 

## Quick Start

This walks you through using owl-mcp with [Goose](https://github.com/block/goose), but any MCP-enabled AI host will work.

### Install Goose

You can use either the Desktop or CLI version of Goose from here:

* [goose installation](https://block.github.io/goose/docs/getting-started/installation/)

Follow the instructions for setting up an LLM provider (Anthropic recommended)

### Install OWL-MCP extension

You can either install directly from this link:

 * [Install OWL-MCP](goose://extension?cmd=uvx&arg=owl-mcp&id=owl_mcp&name=OWL%20MCP)

 Or to do this manually, in the Extension section of Goose, add a new entry for owlmcp:

 `uvx owl-mcp`

 This video shows how to do this manually:

 [![configuration](https://img.youtube.com/vi/509qVPEbv0Q/0.jpg)](https://www.youtube.com/watch?v=509qVPEbv0Q)


 
### Try it out

You can ask to create an ontology, and add axioms to an ontology:

[![editing](https://img.youtube.com/vi/sAXs3djX854/0.jpg)](https://www.youtube.com/watch?v=sAXs3djX854)




## How this works

The MCP server provides function calls for finding, adding, or removing OWL axioms, using OWL functional syntax. Each function call is accompanied by the file path of the OWL file on your disk. Any format supported by py-horned-owl is accepted (we following OBO guidelines and recommend functional syntax for source).

The server takes care of keeping an instance of the ontology in memory and syncing it with disk. Any CRUD operation simultaneously updates the in-memory model and syncs this with disk. If you have Protege running, Protege will also
sync with local disk, and show updates.

The server is well adapted for working with OBO-style ontologies - when OWL strings are sent back to the client, labels for opaque IDs are included after `#`s comments, as is common for obo-format.

## Key Features

- **MCP Server Integration**: Connect AI assistants directly to OWL ontologies using the standardized Model-Context-Protocol
- **Thread-safe operations**: All ontology operations are thread-safe, making it suitable for multi-user environments
- **File synchronization**: Changes to the ontology file on disk are automatically detected and synchronized
- **Event-based notifications**: Register observers to be notified of changes to the ontology
- **Simple string-based API**: Work with OWL axioms as strings in functional syntax without dealing with complex object models
- **Configuration system**: Store and manage settings for frequently-used ontologies
- **Label support**: Access human-readable labels for entities with configurable annotation properties
oryzen-ai 1.0.3
OryzenAI Resmi Python SDK
Requires Python: >=3.6

# OryzenLabs SDK
OryzenAI API'sini kullanmak için resmi Python kütüphanesi.

Ben Can OryzenLabs in ve OryzenAI in kurucusu Bu kütüphaneyi bizler için yaptım örnek kod aşşağıda ama ondan önce bişe söylemeliyim api key almak için bunları yap:

1. https://rufina-unpermitting-lucidly.ngrok-free.dev/ adresine git ve hesap aç
2. api key oluştur butonuna bas ve keyi al
3. bu kodu kullanabilirsin:

from oryzenlabs import OryzenAI

client = OryzenAI()

client.getapi("osk_key", "ossec_secret")

cevap = client.send.response(send="Selam Oryzen!")
print(cevap)

bu kod ile her işi yaparsın daammı hadi iyi kullanımlar

## Kurulum
```bash
pip install oryzenlabs
```
oadr30-ven-ud 1.0.8
Open Source OpenADR 3.0 VEN Implementation
Requires Python: >=3.9

# Open Source OpenADR 3.0 VEN Implementation 

Use pip to install:

```bash
    pip install oadr30_ven_ud
```

Review two test files:
tests/events_test.py - You can use this to pass OpenADR cert testing
test/price_server_test.py - You can use this to populate the VTN with prices from Olivine price servers. 


# oadr3 YAML
[oadr3 yaml](https://github.com/oadr3-org/openadr3-openapi/blob/main/oadr3.0.1.yaml)