semvec/pypi-packages huggingface dataset:
torch3dr 0.0.1
A PyTorch-based library for 3D Vision Research
Requires Python: >=3.10

# Torch3DR
`Torch3DR` stands for __Torch 3D Research__. This library is aimed to provide you with a set of classes, functions and visualizers (all compatible with `PyTorch`) to help you with your 3D research projects. 

[!NOTE]
The library is still under development and is not yet ready for production. However, you can already use it for your research projects.


## Installation

### From PyPI
```bash
# First install PyTorch3D
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"

# Then install Torch3DR
pip install torch3dr
```

### From Source
```bash
git clone https://github.com/amirhossein-razlighi/Torch3DR.git
cd Torch3DR
pip install -e .
```
cursor-tts-mcp 0.1.0
A Text-to-Speech MCP server for Cursor IDE

# Cursor TTS MCP Server

A Text-to-Speech MCP server for Cursor IDE that converts all AI responses to speech automatically.

## Installation

```bash
pip install cursor-tts-mcp
```

## Usage

1. Start the server:
```bash
cursor-tts-mcp
```

2. Add to your Cursor settings:
```json
{
  "mcp": {
    "tts": {
      "enabled": true,
      "server": "http://localhost:5000"
    }
  }
}
```

The server will automatically convert all AI responses to speech.

## Features

- High-quality Google Text-to-Speech
- Automatic conversion of AI responses
- Background audio processing
- Cross-platform support
- Easy integration with Cursor IDE

## Configuration

You can configure the server using environment variables:

- `TTS_PORT`: Port number (default: 5000)
- `TTS_LANG`: Language code (default: en)
- `TTS_VOICE_SPEED`: Speech rate (default: 1.0)

## Development

To contribute:

1. Clone the repository
2. Install dependencies: `pip install -e .`
3. Run the server: `python -m cursor_tts_mcp.server`

## License

MIT
mangomango 0.1.3
A simple MongoDB client that works with Pydantic models.
Requires Python: >=3.12

# MangoMango


## Basics

### Usage

#### Connecting to MongoDB

To connect to a MongoDB instance, create a `MangoMango` client:

```python
from mangomango import MangoMango

client = MangoMango(host="localhost", port=27017)
```

#### Creating a Database

To create or get a database, use the `get_database` method:

```python
db = client.get_database("mydatabase")
```

#### Defining a Table

To define a table, use the `table` decorator on a Pydantic model. The type will not change, it will just register a table with this Model.
To get the table, use get_table:

```python
@db.table(collection="users", primary_key="id")
class User(BaseModel):
    id: int
    username: str
    email: str

UserTable = db.get_table("users")
```

#### Inserting Data

To insert data into a table:

```python
user = User(id=0, username="john_doe", email="john@example.com")
UserTable.insert(user)
```

#### Querying Data

To query data from a table:

```python
user = UserTable.find({"username": "john_doe"})
print(user.username, user.email)
```

#### Updating Data

To update data in a table, use the update() method. The first argument is the primary key of the object, it can also be the object itself.

```python
UserTable.update(john.id, {"$set": {"email": "john_new@example.com"}})
```

You can also edit the object and re-push it with the same primary key. use push() method that insert if there is no object with the same primary key, else it remove and then insert

```python
john.email = "john_new@example.com"
UserTable.push(john)
```

#### Deleting Data

To delete one data from a table, use remove() method.
Can take either primary key or object as first argument:

```python
UserTable.remove(john.id)
```

#### Bulk Insert

To insert multiple objects:

```python
users = [
    User(id=1, username="alice", email="alice@example.com"),
    User(id=2, username="bob", email="bob@example.com")
]
UserTable.insert_many_iter(users)
```

#### Bulk Update

To update multiple objects:

```python
UserTable.update_many({"username": {"$in": ["alice", "bob"]}}, {"$set": {"active": True}})
```

#### Bulk Delete

To delete multiple objects:

```python
UserTable.remove_many({"username": {"$in": ["alice", "bob"]}})
```

the doc is not finished
icoc 2.1.0
Control and test software for sensory tool holders (STH), sensor milling heads (SMH) and stationary transceiver units (STU)
Requires Python: >=3.10
Keywords: smart-tool, smh, stu, sth, tool-holder

[![GitHub Workflow Documentation](https://img.shields.io/github/actions/workflow/status/mytoolit/ICOc/documentation.yaml?branch=main&label=Documentation)](https://mytoolit.github.io/ICOc/) [![API Documentation](https://img.shields.io/readthedocs/icoc?label=API%20Documentation)](https://icoc.readthedocs.io/en/stable/) [![GitHub Workflow Tests](https://img.shields.io/github/actions/workflow/status/mytoolit/ICOc/tests.yaml?branch=main&label=Tests)](https://github.com/MyTooliT/ICOc/actions/workflows/tests.yaml)

# ICOc

This repository contains data collection and test software for the [ICOtronic system](https://www.mytoolit.com/ICOtronic/). For more information, please take a look [at the online documentation of ICOc](https://mytoolit.github.io/ICOc/).

## Documentation

While you should be able to read the [various Markdown files of the documentation](Documentation) separately, we recommend you read the [bookdown](https://bookdown.org) manual instead. We provide a prebuilt version of the documentation [**here**](https://mytoolit.github.io/ICOc/).

You can also download the documentation [under the GitHub Actions tab](https://github.com/MyTooliT/ICOc/actions/workflows/documentation.yaml) (just select the latest run and click on the link “ICOc Manual”).

### Build

If you want to build the documentation yourself, you need the following software:

- [R](https://www.r-project.org),
- [bookdown](https://bookdown.org),
- [make](<https://en.wikipedia.org/wiki/Make_(software)>), and
- (optionally for the PDF version of the manual) the [TinyTeX R package](https://yihui.org/tinytex/).

After you installed the required software you can build the

- HTML (`make html`),
- EPUB (`make epub`), and
- PDF (`make pdf`)

version of the documentation. The output will be stored in the folder `Bookdown` in the root of the repository. If you want to build all versions of the documentation, just use the command

```sh
make doc
```

in the repo root.
ingestables 0.1.0
IngesTables: A Recipe for Tabular Foundation Models
Requires Python: >=3.8

# ingestables

[![Unittests](https://github.com/google-research/ingestables/actions/workflows/pytest_and_autopublish.yml/badge.svg)](https://github.com/google-research/ingestables/actions/workflows/pytest_and_autopublish.yml)
[![PyPI version](https://badge.fury.io/py/ingestables.svg)](https://badge.fury.io/py/ingestables)

This repository contains the implementation of the IngesTables paper (TBD).

It contains library code that defines the data preprocessing, training, and
evaluation. It also contains scripts for running it locally or on Google Cloud
Platform (GCP). Do note that using the GCP scripts may incur costs and would
transmit data to GCP and be accessible to those who can access your GCP project.

*This is not an officially supported Google product.*