semvec/pypi-packages huggingface dataset:
j-auth-client 0.1.0
Auth Client for auth requests
Requires Python: >=3.12

# j_auth_client


## Usage example

```python
from requests import post

from j_auth_client.client import JAuthClient


class MyClient(JAuthClient):
    def __init__(self, username: str, password: str, auth_url: str, routes: dict):
        super().__init__(username, password, auth_url)
        self.routes = routes
    
    def create_todo(self, todo: dict) -> dict:
        return self.request(
            method=post,
            url=self.routes["create_todo"],
            json=todo,
            auth_server_authenticated=True,
        )
```
python-analyst-utility 0.1.8
A utility package for analysts with Pandas, Excel, CSV helpers and more.
Requires Python: >=3.7

# python-analyst-utility

A Python package providing streamlined tools for analysts, including Pandas wrappers, Excel and CSV helpers, and file management utilities. Simplify common tasks and boost productivity with this all-in-one utility package.

## Features

- 📊 Enhanced Pandas functionality with intuitive wrappers
- 📑 Simplified Excel and CSV file handling
- 🗂️ File management utilities for common analyst tasks
- ⚡ Performance-optimized data operations
- 🔧 Easy-to-use API for common data analysis tasks

## Installation

Install using pip:

```bash
pip install python-analyst-utility
```

## Quick Start

```python
from analyst_utility import excel, pandas_helper

# Example: Reading an Excel file with enhanced features
df = excel.read_excel("data.xlsx", sheet_name="Sheet1")

# Example: Using pandas helper functions
cleaned_df = pandas_helper.clean_column_names(df)
```

## Documentation

### Excel Module

```python
# Read Excel files with additional options
excel.read_excel(filename, sheet_name="Sheet1", headers=True)
# Write to Excel with formatting
excel.write_excel(df, filename, sheet_name="Output")
```

### Pandas Helper Module

```python
# Clean column names
pandas_helper.clean_column_names(df)
# Quick data quality check
pandas_helper.check_data_quality(df)
```

## Building The Package

Build the package using:

```bash
python setup.py sdist bdist_wheel
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
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

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Support

For support, please open an issue in the GitHub repository.

## Changelog

### Version 1.0.0
- Initial release
- Basic Excel and CSV functionality
- Pandas helper functions
- File management utilities

---
autojs-py 0.1.1
autojs python version
Requires Python: <4.0,>=3.12

# AutoJS Python Project

## Overview
This project provides a set of Python functions to automate GUI interactions using `pyautogui` and perform OCR (Optical Character Recognition) using `pytesseract`.

## Project Structure
```commandline
autojs_python_project/
├── .gitignore
├── README.md
├── pyproject.toml
├── poetry.lock
├── src/
│   └── autojs_py/
│       ├── __init__.py
│       ├── opearation.py
│       ├── ocr.py
│       └── ...
├── tests/
│   ├── __init__.py
│   ├── test_opearation.py
│   └── ...
└── docs/
    └── ...
```

## Installation 

1. **Install Tesseract OCR**

- **Windows**:
  Download the installer from [Tesseract at UB Mannheim](https://github.com/UB-Mannheim/tesseract/wiki) and follow the installation instructions.

- **Linux**:
    ```sh
    sudo apt-get update
    sudo apt-get install tesseract-ocr
    ```

- **macOS**:
    ```sh
    brew install tesseract
    ```
2 **Install use pip**:
```sh
pip install autojs-py
```
## Development
**Clone the repository**:
```sh
git clone https://github.com/angleyanalbedo/your-repo-name.git
cd your-repo-name
```

**Install dependencies using Poetry:**
    ```sh
    poetry install
    ```


## Usage
### GUI Automation
The `opearation.py` file contains functions to perform various mouse and keyboard actions:
- `Click(x, y)`
- `LClick(x, y)`
- `RClick(x, y)`
- `DClick(x, y)`
- `Send(text)`
- `Press(key)`
- `LongClick(s)`
- `tclick(template)`
- `tlclick(template)`
- `trcick(template)`
- `dclick(template)`
- `Swap(pos, pos2)`
- `tswap(template, direct)`
- `clicktext(text)`

### OCR
The `ocr.py` file contains functions to perform OCR on images:
- `ocr2str(image_path)`
- `ocr2box(img)`

## Example
Here is an example of how to use the `clicktext` function:
```python
from autojs_py.opearation import clicktext

clicktext("example text")
pome7o 0.0.3
pome7o
Requires Python: <3.12,>=3.10

# OmAgent Core

## 🧐 What is OmAgent Core
OmAgent Core is an integral component of the OmAgent project, encompassing the foundational framework for constructing OmAgent. It features a workflow system configurable via configuration files and a recursive general task-solving mechanism, referred to as the DnC Loop, among other elements. This core framework empowers you to create the intelligent agent of your design.

## How to use
### Install
```bash
pip install for_test
```

### Project
**[OmAgent: A Multi-modal Agent Framework for Complex Video Understanding](https://github.com/om-ai-lab/OmAgent)**
botmailroom 0.0.14
A python client for the BotMailRoom API
Requires Python: >=3.9

# BotMailroom Python Client

The BotMailroom Python client allows you to interact with the [BotMailroom API](https://docs.botmailroom.com/). It provides both synchronous and asynchronous methods for interacting with the API.

## Quickstart

### 1. Get an API Key

You can get an API key by signing up for a BotMailroom account and creating an API key at https://auth.botmailroom.com/account/api_keys

### 2. Install the Client

```bash
pip install botmailroom
```

### 3. Initialize the Client

```python
from botmailroom import BotMailRoom

client = BotMailRoom(api_key="your_api_key") # or set the BOTMAILROOM_API_KEY environment variable
```

### 4. Create an Inbox

```python
inbox = client.create_inbox(name="My Inbox", email_address="CHANGE_THIS@inbox.botmailroom.com")
```

### 5. Check Emails

Unless you have specific allow and block rules that prevent it, you will receive an email from `support@inbox.botmailroom.com` after you create an inbox **for the first time**. If you’d like to send a test email to your inbox, you can do so by:

1. Using a mail client of your choice.
2. Sending a request with the `send_email` method.

You can then check for new emails using the `get_emails` method.

```python
emails = client.get_emails(inbox_id=inbox.id)
print(emails)
```
base-dag 0.1.10
A simple base implementation of a Directed Acyclic Graph, intended to be subclassed for more specific functionality.
Keywords: DAG, data structures, directed acyclic graph, graph

# base-dag
A simple base implementation of a DAG. Users should feel free to subclass this DAG class to provide their desired functionality. The API is intended to be similar to the NetworkX API, though not an exact replica. Note that multiple edges between the same two nodes are not supported by this package.

## Creating a DAG.
 ```python
from base_dag import DAG

# Initialize the DAG
dag = DAG()

# Add nodes and edges
dag.add_nodes_from([1, 2])
dag.add_node(3)
dag.add_edge((1, 2))
dag.add_edge((2, 3))

# Remove nodes and edges
dag.remove_edge((2, 3))
dag.remove_node(3)
```

## Subgraph
```python
# Re-add a third node.
dag.add_node(3)
# Extract the subgraph of nodes 1 and 2.
sub_dag = dag.subgraph([1, 2])
```

## Other Operations
### All nodes & edges
```python
all_nodes = dag.nodes() # (1, 2, 3)
all_edges = dag.edges() # ( (1, 2), (2, 3) )
```

### Successors and Predecessors
```python
successors = dag.successors(1) # [2]
predecessors = dag.predecessors(2) # [1]
```

### Descendants and Ancestors
```python
descendants = dag.descendants(1) # [2, 3]
ancestors = dag.ancestors(2) # [1]
```

### Indegree and Outdegree
```python
in_degree = dag.indegree(2) # 1
out_degree = dag.outdegree(1) # 1
```

### Reverse
```python
# Reverse the direction of the edges in the graph in place, without affecting the nodes at all.
dag.reverse()
```

### Topological sort
```python
sorted_nodes = dag.topological_sort()
```

### Topological generations
```python
generations = dag.topological_generations()
```

### Sorted topological generations
```python
sorted_generations = dag.sorted_topological_generations()
```

### has_path
```python
has_path = dag.has_path(1, 3) # True
```

### is_acyclic
```python
is_acyclic = dag.is_acyclic() # True
```