semvec/pypi-packages huggingface dataset:
ipfs-embeddings-py 0.0.28
A wrapper around huggingface datasets, invoking an IPFS model manager.
Requires Python: >=3.8

# IPFS Huggingface Datasets

This is a model manager and wrapper for huggingface, looks up a index of models from an collection of models, and will download a model from either https/s3/ipfs, depending on which source is the fastest.

# How to use
~~~shell
pip install .
~~~

look run ``python3 example.py`` for examples of usage.

this is designed to be a drop in replacement, which requires only 2 lines to be changed

In your python script
~~~shell
from datasets import load_dataset
from ipfs_datasets import load_dataset
dataset = load_dataset.from_auto_download("bge-small-en-v1.5")  
~~~

or 

~~~shell
from datasets import load_dataset
from ipfs_datasets import load_dataset
dataset = load_dataset.from_ipfs("QmccfbkWLYs9K3yucc6b3eSt8s8fKcyRRt24e3CDaeRhM1")
~~~

or to use with with s3 caching 
~~~shell
from datasets import load_dataset
from ipfs_datasets import load_dataset
dataset = load_dataset.from_auto_download(
    dataset_name="common-crawl",
    s3cfg={
        "bucket": "cloud",
        "endpoint": "https://storage.googleapis.com",
        "secret_key": "",
        "access_key": ""
    }
)
~~~

# IPFS Huggingface Bridge:

for transformers python library visit:
https://github.com/endomorphosis/ipfs_transformers/

for transformers js client visit:                          
https://github.com/endomorphosis/ipfs_transformers_js/

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for fireproof_kit nodejs library visit:
https://github.com/endomorphosis/fireproof_kit

for Faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss/

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/


Author - Benjamin Barber
QA - Kevin De Haan
cqd 0.1.1
cqd - A colorful object attribute inspector
Requires Python: >=3.6

# cqd

A lightweight Python utility that provides colored visualization of object attributes, making it easier to inspect objects during development and debugging.

## Features

- Color-coded attribute display:
  - 🔵 Blue: Dunder methods
  - 🟡 Yellow: Protected attributes (starting with `_`)
  - 🟢 Green: Public attributes and methods


For example - here is a snippet of the tokenizer from huggingface. 

![alt text](assets/example.png)


## Installation

You can install the package using pip:

```
pip install cqd
```

## Usage

To use the `cqd` function, import it from the package:

```python
from cqd import cqd

# Example usage
cqd(your_object)
```

## Contributing

Feel free to submit issues or pull requests for improvements or bug fixes.

## License

This project is licensed under the MIT License.
linear-python 0.2.2
A Python client for the Linear API
Requires Python: >=3.7

# Linear Python

A Python client for the [Linear](https://linear.app/) API.

## Requirements

- Python 3.7+
- Required packages (automatically installed):
  - requests
  - python-dotenv
  - strawberry-graphql
  - pydantic
  - typing-extensions

## Installation

The package and all its dependencies can be installed via pip:

```bash
pip install linear-python
```

## Usage

### Configuration

Retrieve your personal Linear API Key, and then initialize the python Linear client:

```python
from linear_python import LinearClient
client = LinearClient("<Linear API Key Here>")
```

You're now ready to use linear-python! Below are a few sample functions you can call.

#### Get Current User (Viewer)

```python
viewer = client.get_viewer()
```

#### Create an Issue

```python
issue_data = {
    "teamId": "your-team-id",
    "title": "New bug report",
    "description": "Description of the issue"
}
new_issue = client.create_issue(issue_data)
```

## Resources

- [Linear Docs](https://developers.linear.app/docs)

## License

MIT License
trajectory-processing 0.4.6
A package for processing trajectory data

# Trajectory Processing Package

## 安装

使用 pip 安装：

```bash
pip install trajectory_processing
```

## 必要属性名
["user","经度","纬度","gps时间戳"]
user为车牌

## 测试代码

``` 
import os
from trajectory_processing import to_all
if __name__ == '__main__':
    import multiprocessing
    multiprocessing.freeze_support()  # 如果你打算冻结程序生成可执行文件，需要这一行
    input_filepath = r"C:\Users\zhang\Desktop\8个车辆的8~11月数据\待处理的轨迹数据.xlsx"  # 替换为实际文件路径
    output_filepath = r"C:\Users\zhang\Desktop\8个车辆的8~11月数据\处理完的轨迹数据.csv"
    to_all(input_filepath, output_filepath)
```