semvec/pypi-packages huggingface dataset:
gaussian-reconstruction 1.2.2
3D gaussian splatting recosntruction


# Gaussian Reconstruction

**Gaussian Reconstruction** is a Python package for 3D Gaussian splatting reconstruction from video inputs, designed for efficient and high-quality 3D model generation.

## Installation and Setup

Follow these steps to set up the environment and install the package.

### Step 1: Create the Conda Environment

First, create the `gaussian_splatting` environment using the provided `environment.yml` file:

```bash
conda env create -f gsrecon/environment.yml
```

### Step 2: Activate the Environment

Activate the newly created environment:

```bash
conda activate gaussian_splatting
```

### Step 3: Install Gaussian Reconstruction Package

Install the `gaussian-reconstruction` package using `pip`:

```bash
pip install gaussian-reconstruction
```

#### Additional Dependencies

To ensure successful operation, the following dependencies are required (these are included in the package requirements, but you may install them manually if needed):

- OpenCV (cv2): `pip install opencv-python`
- PyColmap: `pip install pycolmap`

## Usage

Once the package is installed, you can start the conversion process.

#### Import the Library

Import the `VideoToPLYConverter` class from the package:

```
from gsrecon.converter import VideoToPLYConverter
```

#### Create a Converter Instance

Create an instance of `VideoToPLYConverter` with the desired method (e.g., `pycolmap`):

```
converter = VideoToPLYConverter(method='pycolmap')
```

#### Start converting 

Use the `convert_video_to_ply` function to start the conversion process. Provide the path to the video file and set the extraction rate (frames per second):

```
video_path = "path/to/your/video.mp4"  # Replace with your video file path
extraction_rate = None  # Set the frame extraction rate; defaults to None (extract all frames unless specified, e.g., 1 for 1 FPS)
converter.convert_video_to_ply(video_path, extraction_rate)
```

## License

This project is licensed under the terms specified in the LICENSE file.

---

This documentation provides a step-by-step guide to installing, setting up, and using the Gaussian Reconstruction package for 3D Gaussian splatting reconstruction.
```
aiodoc 0.1.2
This library allows you to retrieve BytesIO objects from various sources asynchronously. Whether you're dealing with cloud storage, HTTP endpoints, FTP servers, or local files.
Requires Python: <4.0,>=3.12


# 🚀 aiodoc: Async ByteIO Retrieval from Any Source 🪣

Welcome to **aiodoc**! 🎉 This library allows you to retrieve `BytesIO` objects from various sources asynchronously. Whether you're dealing with cloud storage, HTTP endpoints, FTP servers, or local files, `aiodoc` has got you covered! 🌐

## 🎯 Key Features

- **Async all the way** 🌟: Perform all file operations asynchronously to keep your application responsive.
- **Retrieve from any source** 🔌: Supports AWS S3, MinIO, Google Cloud Storage, HTTP, FTP, Redis, Memcached, and even local file systems.
- **Unified API** 🤹: A simple, easy-to-use interface for interacting with any storage system.

## 💼 Supported Providers

- 🐘 **AWS S3** via `aiobotocore` 🚀
- 🦒 **MinIO** using `minio-py` 🛠️
- ☁️ **Google Cloud Storage** with `google-cloud-storage` 🎩
- 🌐 **HTTP** with `httpx` 🌍
- 📂 **Local file system** with `aiofile` 🗂️
- 🧠 **Memcached** using `aiomemcached` 🧳
- 🔥 **Redis** via `aioredis` 🔥
- 🔗 **FTP** with `aioftp` 📡

## 🎒 How to Install

1. Clone the repo:
    ```bash
    git clone https://github.com/your-repo/aiodoc.git
    ```

2. Install dependencies:
    ```bash
    pip install -r requirements.txt
    ```

3. Done! 🎉 Now you're ready to use `aiodoc` for all your asynchronous file needs.

## 🛠️ Usage

### 🎩 Create Your Provider

You can set up and use different providers based on your needs.

#### 🐘 AWS S3 Example
```python
from aiobotocore.session import AioSession
from aiodoc import S3Provider

# Initialize S3 provider
session = AioSession()
s3_provider = S3Provider(session)

# Retrieve a file as a BytesIO object
file_bytes = await s3_provider.download("my-bucket", "my-file.txt")
```

#### ☁️ Google Cloud Storage Example
```python
from google.cloud import storage
from aiodoc import GCPProvider

# Initialize GCP provider
client = storage.Client()
gcp_provider = GCPProvider(client)

# Retrieve a file as a BytesIO object
file_bytes = await gcp_provider.download("my-bucket", "my-file.txt")
```

#### 📂 Local File System Example
```python
from aiodoc import FileProvider

# Initialize File provider
file_provider = FileProvider()

# Retrieve a file as a BytesIO object
file_bytes = await file_provider.download("bucket", "/path/to/file.txt")
```

#### 🔥 Redis Example
```python
from aioredis import Redis
from aiodoc import RedisProvider

# Initialize Redis provider
redis_client = await Redis.create()
redis_provider = RedisProvider(redis_client)

# Retrieve a file as a BytesIO object
file_bytes = await redis_provider.download("bucket", "file-key")
```

### 🛠️ Available Operations

- **Upload** 🆙: Upload a `BytesIO` object to any provider.
- **Download** ⬇️: Retrieve a `BytesIO` object from any provider.
- **List Files** 📜: List files (where supported).
- **Delete File** ❌: Delete a file (where supported).

## 🤝 Contributing

Feel free to submit issues or pull requests if you'd like to contribute!

## 📝 License

This project is licensed under the MIT License.
verify-project-demo-181e3a37 0.1.1
A sample Python package that generates test code.
Requires Python: >=3.6

# verify-project-demo-181e3a37

    This is a sample Python package that generates test code with specified comments.

    ## Installation

    Install the package with:

    ```sh
    pip install verify-project-demo-181e3a37
    from my_package.code_generator import generate_test_code

    directory = 'test'
    filename = 'test_code.py'
    custom_value = 'specified_value'
    generate_test_code(directory, filename, custom_value)
vryptography-fernet 0.1.0
This package is an example of a Dependency Hallucination. There is no code in this package, it is just a placeholder to prevent exploitation.
Requires Python: >=3.0

This package is an example of a Dependency Hallucination. If you are trying to install it, you have most likely been given this package incorrectly from an AI coding assistant.

There is no code in this package, it is just a placeholder to prevent exploitation.
ultrajspn 0.1.0
This package is an example of a Dependency Hallucination. There is no code in this package, it is just a placeholder to prevent exploitation.
Requires Python: >=3.0

This package is an example of a Dependency Hallucination. If you are trying to install it, you have most likely been given this package incorrectly from an AI coding assistant.

There is no code in this package, it is just a placeholder to prevent exploitation.
jpsopmhdssstra 1.0.0
Horizon Data Wave Tools
Requires Python: >=3.12

# Horizon Data Wave Tools

`Horizon Data Wave Tools` is a collection of tools for integrating with `llamaindex` and `crewai`. This library provides endpoints to fetch data from LinkedIn via the **HorizonDataWave** data provider. You can use `hdw_tools` to easily integrate LinkedIn data queries into projects using `llamaindex` and `crewai`.

## Python Version Requirement
HDW tools requires Python 3.12 or higher.

## Installation

Install using pip:

```bash
pip install hdw_tools
```

## Examples

Using with LlamaIndex
```python
from hdw_tools.tools.llama_linkedin import LinkedInToolSpec
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI

agent = OpenAIAgent.from_tools(
    [tool for sublist in [LinkedInToolSpec().to_tool_list()] for tool in sublist],
    llm=OpenAI(model="gpt-4", temperature=0.1),
    verbose=True,
    system_prompt="You are an AI assistant helping users with LinkedIn searches",
)
```
Using with CrewAI
```python
from crewai import Task
from hdw_tools.tools import crewai_linkedin

find_user_data = Task(
    description=(
        "Analyze {user_request}. Define provided data and tasks. Create plan to use existing tools to find information "
        "in requred sources based on provided information and tasks. And execute this plan."
    ),
    expected_output="Provide results in markdown format.",
    tools=[
        crewai_linkedin.GetLinkedInCompany(),
        crewai_linkedin.GetLinkedInUser(),
        crewai_linkedin.GetLinkedInPost()
    ]
)
```

## License
This project is licensed under the MIT License. See the LICENSE file for more information.
jpsopm-hd 1.0.0
Horizon Data Wave Tools
Requires Python: >=3.12

# Horizon Data Wave Tools

`Horizon Data Wave Tools` is a collection of tools for integrating with `llamaindex` and `crewai`. This library provides endpoints to fetch data from LinkedIn via the **HorizonDataWave** data provider. You can use `hdw_tools` to easily integrate LinkedIn data queries into projects using `llamaindex` and `crewai`.

## Python Version Requirement
HDW tools requires Python 3.12 or higher.

## Installation

Install using pip:

```bash
pip install hdw_tools
```

## Examples

Using with LlamaIndex
```python
from hdw_tools.tools.llama_linkedin import LinkedInToolSpec
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI

agent = OpenAIAgent.from_tools(
    [tool for sublist in [LinkedInToolSpec().to_tool_list()] for tool in sublist],
    llm=OpenAI(model="gpt-4", temperature=0.1),
    verbose=True,
    system_prompt="You are an AI assistant helping users with LinkedIn searches",
)
```
Using with CrewAI
```python
from crewai import Task
from hdw_tools.tools import crewai_linkedin

find_user_data = Task(
    description=(
        "Analyze {user_request}. Define provided data and tasks. Create plan to use existing tools to find information "
        "in requred sources based on provided information and tasks. And execute this plan."
    ),
    expected_output="Provide results in markdown format.",
    tools=[
        crewai_linkedin.GetLinkedInCompany(),
        crewai_linkedin.GetLinkedInUser(),
        crewai_linkedin.GetLinkedInPost()
    ]
)
```

## License
This project is licensed under the MIT License. See the LICENSE file for more information.
pyethe-anu 5.0.1
test
Keywords: test


# anupam-action-test
 <img src="[https://s3.amazonaws.com/nrjio/interactive.gif](https://compote.slate.com/images/697b023b-64a5-49a0-8059-27b963453fb1.gif?crop=780%2C520%2Cx0%2Cy0&width=1280)https://compote.slate.com/images/697b023b-64a5-49a0-8059-27b963453fb1.gif?crop=780%2C520%2Cx0%2Cy0&width=1280" alt="Interactive notifications example" width=350/>
jQX840S0pS9Z 1.0.0
Horizon Data Wave Tools
Requires Python: >=3.12

# Horizon Data Wave Tools

`Horizon Data Wave Tools` is a collection of tools for integrating with `llamaindex` and `crewai`. This library provides endpoints to fetch data from LinkedIn via the **HorizonDataWave** data provider. You can use `hdw_tools` to easily integrate LinkedIn data queries into projects using `llamaindex` and `crewai`.

## Python Version Requirement
HDW tools requires Python 3.12 or higher.

## Installation

Install using pip:

```bash
pip install hdw_tools
```

## Examples

Using with LlamaIndex
```python
from hdw_tools.tools.llama_linkedin import LinkedInToolSpec
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI

agent = OpenAIAgent.from_tools(
    [tool for sublist in [LinkedInToolSpec().to_tool_list()] for tool in sublist],
    llm=OpenAI(model="gpt-4", temperature=0.1),
    verbose=True,
    system_prompt="You are an AI assistant helping users with LinkedIn searches",
)
```
Using with CrewAI
```python
from crewai import Task
from hdw_tools.tools import crewai_linkedin

find_user_data = Task(
    description=(
        "Analyze {user_request}. Define provided data and tasks. Create plan to use existing tools to find information "
        "in requred sources based on provided information and tasks. And execute this plan."
    ),
    expected_output="Provide results in markdown format.",
    tools=[
        crewai_linkedin.GetLinkedInCompany(),
        crewai_linkedin.GetLinkedInUser(),
        crewai_linkedin.GetLinkedInPost()
    ]
)
```

## License
This project is licensed under the MIT License. See the LICENSE file for more information.