semvec/pypi-packages huggingface dataset:
PathWise 1.2.0
A Python package implementing various search algorithms for maze solving
Requires Python: >=3.6
Keywords: maze search algorithms pathfinding BFS DFS A* UCS

# PathFinder

PathFinder is a Python package that provides classic pathfinding algorithms such as:

- Breadth-First Search (BFS)

- Depth-First Search (DFS)

- Uniform Cost Search (UCS)

- A-Star Search (A*)

These algorithms work on 2D mazes represented as grids, and are designed to help with visualization, teaching, or solving pathfinding problems programmatically.

## 💾 Installation
```bash
pip install PathWise
```

## 📝 Features

- Supports 2D grid mazes with customizable start, goal, and wall positions

- Clear API to run any algorithm and get the path, cost, and visited nodes

- Easily extendable for diagonal movement or custom cost functions

- Suitable for AI projects, teaching, and maze-solving

## ⚖️ Algorithms Included

### BFS

- Explores nodes level by level

- Guarantees shortest path if all moves have equal cost

### DFS

- Explores deep into one branch before backtracking

- May not find the shortest path

### UCS

- Uses a priority queue (cost-based)

- Always finds the lowest-cost path

### A*

- Uses cost + heuristic (e.g. Manhattan distance)

- Highly efficient for large or complex mazes

## 💡 Usage

### 1. Representing the Maze

A maze is a 2D list of characters:

maze = [
  ["S", " ", " ", "#", "G"],
  ["#", "#", " ", "#", " "],
  [" ", " ", " ", " ", " "],
  [" ", "#", "#", "#", " "],
  [" ", " ", " ", " ", " "]
]

S = Start

G = Goal

\# = Wall (Represented by 1)

' '  = Open path (Represented by 0)

### 2. Running an Algorithm
```python
import numpy as np
from PathWise import BFS, DFS, UCS, A_Star

Maze = [[...], [...], ...]
Maze = np.array(Maze).astype('str')

path, nodesExpanded = DFS(Maze, start, end)         #   or BFS
path, cost, nodesExpanded = UCS(Maze, start, end)   #   or A_Star

print("Path:", path)
print("Visited:", visited)
print("Cost: ", cost)                               #   if UCS or A_Star has been used
```
## 📁 Project Structure
```
./
├── PathWise/
│   ├── __init__.py
│   ├── A_Star.py
│   ├── BFS.py
│   ├── DFS.py
│   ├── Helper.py
│   └── UCS.py
├── .gitignore
├── LICENSE
├── README.md
└── setup.py

```
## 📃 License

This project is licensed under the MIT License.

## Author
Developed by Mahdi Jaffery
dewret 0.12.1
DEclarative Workflow REndering Tool
Requires Python: >=3.11

# dewret

DEclarative Workflow REndering Tool

_Pron_: durr-it, like "durable"

### Introduction

Dewret allows certain workflows written in a dynamic
style to be rendered to a static representation.

Advantages of doing so include:

* **git-versionable workflows**: while code can be versioned, the changes of a dynamic workflow
  do not necessarily clearly correspond to changes in the executed workflow. This maintains
  a precise trackable history.
* **plan and play**: the workflow can be rapidly iterated, analysed and optimized before it
  is sent for real execution on expensive or restricted HPC hardware.
* **optimization**: creating the workflow explicitly opens up possibilities for static analysis
  and refactoring before real execution.
* **debugging**: a number of classes of workflow planning bugs will not appear until late
  in a simulation run that might take days or weeks. This catches them before startup.
* **continuous integration and testing**: complex dynamic workflows can be rapidly sense-checked
  in CI without needing all the hardware and internal algorithms present to run them.

### Documentation

For further information, see the [documentation](https://flaxandteal.github.io/dewret).

## Developer Set up

For development with conda, run the relevant script in `.set_up`
ionbench 1.0.1
A benchmarking tool for comparing different parameter optimization algorithms for ion channel models
Requires Python: <4,>=3.9

# ionBench
A benchmarking tool for comparing different parameter optimization algorithms for ion channel models.

## Project Structure
The tree structure of this project is outlined below. 
```
├───docs
├───ionbench
├───scipts
│   ├───text
│   └───figures
└───test
```

The [docs](docs) directory contains information and guides on how to use the benchmarker problems and the optimisation algorithms. 

The [ionbench](ionbench) directory contains the majority of the code, including the benchmarker and problems classes and the different optimisation algorithms. This is what is installed using pip.

The [scripts](scripts) directory contains scripts for generating figures, tables and data for the paper.

The [test](test) directory contains unit tests for ensuring changes do not break previous functionality.

## Installation
### Installing ionBench package
The ***ionBench*** package can be installed from PyPI using pip.

```pip install ionbench```

Note that ***ionBench*** uses ***myokit*** to do its simulations, which relies on CVODES (from Sundials). For Linux and macOS users a working installation of CVODES is required. For Windows users, CVODES should be automatically installed with ***myokit***.

### Getting the scripts and results
The scripts are results from ***ionBench*** are available in this repo. If you also want access to these, you should:

1. Clone the repository.
2. Install ***ionBench*** locally using pip (navigate to the the root directory of the repo and run ```pip install -e .```) or install ***ionBench*** from PyPI (```pip install ionbench```).
3. Navigate to the scripts directory and run the scripts you are interested in.

## Getting Started
If you want to use ***ionBench***, check out the [introduction.md](docs/introduction.md) and [tutorial.ipynb](docs/tutorial.ipynb) in the [docs](docs) directory.

## Workflow
The intended workflow for using the benchmarker is to generate a benchmarker object, set up the optimisers modification and apply it to the benchmarker, and pass the benchmarker into the optimisation algorithm to evaluate. All optimisers should accept a single benchmarker as input with all other inputs being optional. 

```python
import ionbench

bm = ionbench.problems.staircase.HH()
modification = ionbench.optimisers.pints_optimisers.cmaes_pints.get_modification()
modification.apply(bm)
optimisedParameters = ionbench.optimisers.scipy_optimisers.nelderMead_scipy.run(bm)
```
MyLLM 4.19.15
A python package to interact with llm model supported by g4f and langchain.
Requires Python: >=3.10
Keywords: chatgpt, llm, ai, llama, ai, g4f, freegpt

<table style="border: 1px solid transparent">
  <tr>
    <td>
<a href="http://talky.readthedocs.io"><img src="https://img.shields.io/badge/Wiki-%23000000.svg?style=for-the-badge&logo=wikipedia&logoColor=white"></a>
<a href="https://github.com/mraniki/tt/"><img src="https://img.shields.io/badge/github-%23000000.svg?style=for-the-badge&logo=github&logoColor=white"></a><br>
<a href="https://hub.docker.com/r/mraniki/tt"><img src="https://img.shields.io/docker/pulls/mraniki/tt?style=for-the-badge"></a><br>
       </td>
    <td align="center"><img width="200" alt="Logo" src="/docs/_static/logo-full.png"></td>
  </tr>
  <tr>
    <td>
      <a href="https://pypi.org/project/myllm/"><img src="https://img.shields.io/pypi/v/myllm?style=for-the-badge&logo=PyPI&logoColor=white"></a><br>
      <a href="https://pypi.org/project/myllm/"><img src="https://img.shields.io/pypi/dm/myllm?style=for-the-badge&logo=PyPI&logoColor=white"></a><br>
      <a href="https://github.com/mraniki/myllm/"><img src="https://img.shields.io/github/actions/workflow/status/mraniki/myllm/%F0%9F%91%B7Flow.yml?style=for-the-badge&logo=GitHub&logoColor=white"></a><br>
      <a href="https://talky.readthedocs.io/projects/myllm/"><img src="https://readthedocs.org/projects/myllm/badge/?version=latest&style=for-the-badge"></a><br>
      <a href="https://codebeat.co/projects/github-com-mraniki-myllm-main"><img src="https://codebeat.co/badges/0567b9d9-3cbb-4263-80ec-8ac8043332ea"/></a> <br>
      <a href="https://app.codacy.com/gh/mraniki/MyLLM/dashboard"><img src="https://app.codacy.com/project/badge/Grade/75f2650d58044355957fe667aeed50a1"/></a> <br>
      <a href="https://codecov.io/gh/mraniki/myllm"><img src="https://codecov.io/gh/mraniki/myllm/branch/main/graph/badge.svg?token=WAHUEMAJN6"/></a><br>
    </td>
    <td align="left"> 
Interact with LLM in simple way,<br>via  <a href="https://github.com/xtekky/gpt4free">G4F</a>, <a href="https://github.com/openai/openai-python">OPENAI</a>/<a href="https://localai.io/basics/getting_started/">LOCALAI</a><br>
  </td>   
  </tr>
</table>


<h5>How to use it</h5>
<pre>
<code>
    talky = MyLLM()

    chat = await talky.chat("My name is Jack")
    print(chat)
    # 🐻 Hello Jack, this is Bing. I'm happy to help you with general tasks. 😊

    chat = await talky.chat("tell me who is president of the united states?")
    print(chat)
    # The current president of the United States is **Joe Biden**[^1^][1] [^2^][2].
    # He took office on **January 20, 2021**[^1^][1] [^2^][2].
    # He is the **46th** president of the United States[^2^][2] [^3^][5].

    chat = await talky.chat("what is my name")
    print(chat)
    # 🐻 You told me your name is Jack. Is that correct?

    await talky.export_chat_history()
    # talky.clear_chat_history()
</code>
</pre>

<h5>Documentation</h5>
<a href="https://talky.readthedocs.io/projects/myllm/en/latest/"><img src="https://img.shields.io/badge/Documentation-000000?style=for-the-badge&logo=readthedocs&logoColor=white"></a><br>