semvec/pypi-packages huggingface dataset:
ez-context 1.0.2
An ez generator-based context manager
Requires Python: >=3.12

# EZ Context Managers

Allows you to create generator-based context managers without try/finally.
Fully static-typed and thread-safe.

Installing
```
pip install ez_context
```

Example:
```python
@context_mgr
def example():
    print("entering")
    yield
    print("exiting")

with example as ex:
    print("Hello World!")
    
```
```
entering
Hello World!
exiting
```

Detailed Example:

```python
@context_mgr
def random_value(a=0, b=100):
    rand_num: int = random.randint(a, b)
    print(f"entering {rand_num}")

    # `yield` runs the body of the `with` statement.
    # The yielded value is stored in VAR by `with ... as VAR`
    error = yield rand_num
    # If the body raises an Exception, it's returned here.
    # Otherwise, None is returned

    print(f"exiting {rand_num}, {error=}")
    if isinstance(error, ValueError):
        # Return True to suppress the Exception
        return True


with random_value(-5, -1) as n:
    assert_type(n, int)  # statically typed!
    print(n)

# Parentheses are optional if there are no arguments.
with random_value as n1:

    # Reusable and re-entrant
    with random_value as n2:
        raise ValueError("CoolBeans")

```



Created for a comment on [this mCoding video](https://www.youtube.com/watch?v=LBJlGwJ899Y&t=36s).
STFD 0.0.1
STFD: Series of deep learning-based foundation models for spatial transcriptomic data analysis
Requires Python: >=3.9

# Spatial Transcriptomics **F**oun**d**ation Models

**S**patial **T**ranscriptomics **F**oun**d**ation Models is a Series of deep-learning based models for the analysis of spatial transcriptomics data. 


## Foundation Model 1: 
![img1](./images/model1.png)



## Installation

## Usage

## License

## Citation
iqtools 0.7.3
Utility for working with IQ waveform data
Requires Python: <4.0,>=3.9

# IQ Tools

# Build Instructions

1. Clone the repo: `git clone git@github.com:loganrf/IQtools.git`
2. Install dependencies: `poetry install`
3. Build: `poetry build`

# Example Run Instructions
1. Follow steps 1/2 from **Build Instructions** above
2. Run `poetry run python main.py`
html-fu 0.1.0
Generate HTML with Python code (no templating)
Requires Python: >=3.8
Keywords: html, generate, templating

# html-fu
Generate HTML with Python code (not templating)

# image-shrinker

Go directly to the [DOCUMENTATION](https://image-shrinker.readthedocs.io/).

## Description
Generate HTML with Python code (not templating)

## Documentation
For instructions on how to install and use *image-shrinker*,
see [html-fu.readthedocs.io](https://image-shrinker.readthedocs.io/).

## Support
Open an issue [HERE](https://github.com/weaddvalue/html-fu/issues).

## Contributing
Contributions welcome.

## Acknowledgment
Inspired by these libraries: (coming soon)

## License
MIT Licensed. See [LICENSE](LICENSE).

## Project status
Actively but lightly maintained by Don Parakin.
hzh 1.1
Get the high-frequency words in the paper to the local txt file, which is convenient to pass the word book to recite
Requires Python: >=3

# Get the high-frequency words in the paper to the local txt file, which is convenient to pass the word book to recite
'''
sample:
pip install hzh
hzh.get_words_from_pdf(pdf_path, output_path)
'''
UCMEC 2.0.0
Reinforcement Learning Environment for User-centric Mobile Edge Computing

# User-Centric Mobile Edge Computing Environments
## A single/multi-agent environment for Deep Reinforcement Learning


<!-- TABLE OF CONTENTS -->
## Table of Contents

* [About the Project](#about-the-project)
* [Getting Started](#getting-started)
  * [Installation](#installation)
* [Usage](#usage)
* [Please Cite](#please-cite)
* [Contributing](#contributing)
* [Contact](#contact)



<!-- ABOUT THE PROJECT -->
## About The Project

This environment is a mixed cooperative-competitive game, which focuses on the coordination of the agents involved. Agents navigate a world and offload computing tasks to the edge computing-enabled CPU via user-centric wireless transmission.







<!-- GETTING STARTED -->
## Getting Started

### Installation

Install using pip
```sh
pip install UCMEC
```
Or to ensure that you have the latest version:
```sh
git clone https://github.com/qlt315/UCMEC_env.git
cd UCMEC_env
pip install -e .
```


<!-- USAGE EXAMPLES -->
## Usage

Create environments with the gym framework.
First import
```python
import UCMEC
```

Then create an environment:
```python
env = gym.make("MA_UCMEC_env") # for multi-agent
```
or 
```python
env = gym.make("SA_UCMEC_env") # for single-agent
```


Similarly to Gym, step() function in multi-agent environment is defined as
```python
obs, reward, done, info = env.step(actions)
```

Where obs, rewards, done and info are LISTS of N items (where N is the number of agents). The i'th element of each list should be assigned to the i'th agent. 
action space is a LIST of N*M numbers that should be executed in that step.(M is the number of actions for each agent, like offloading decision, power allocation, etc.) 



<!-- CITATION -->
# Please Cite
1. The paper that first uses this implementation of single-agent UCMEC environment:
```
@ARTICLE{9952196,
  author={Qin, Langtian and Lu, Hancheng and Wu, Feng},
  journal={IEEE Communications Magazine}, 
  title={When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization}, 
  year={2023},
  volume={61},
  number={1},
  pages={114-120},
  doi={10.1109/MCOM.006.2200283}}

```





<!-- CONTRIBUTING -->
## Contributing

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request


<!-- CONTACT -->
## Contact

Langtian Qin - qlt315@mail.ustc.edu.cn

Project Link: [https://github.com/qlt315/UCMEC_env](https://github.com/qlt315/UCMEC_env)