semvec/pypi-packages huggingface dataset:
hypermanager 0.1.12
Query orchestrator for Hypersync
Requires Python: >=3.8

# hypermanager-py
`hypermanager-py` is an orchestration manager built ontop of the [hypersync python client](https://github.com/enviodev/hypersync-client-python). It makes cross-chain log queries easy to manage and works with blocks and transactions data as well.


## Installation
Install with `pip install hypermanager`. The repository is built with Python 3.12.5. You can easily make a virtual environment for this python version by using [rye](https://rye.astral.sh/guide/installation/) for virtual environment management.

## Example
There is an example that retrieves all log events from the [Across Protocol](https://github.com/across-protocol/contracts) bridging. The example is located in the `examples` directory. Run the example with `python examples/across/spoke_pool_v3.py` to see how to execute a multi-chain events query.

## Adding Events:
The EventConfig class is a key component of the HyperManager framework, designed to facilitate efficient querying and processing of blockchain events across various protocols. It encapsulates event details, enables dynamic querying, and specifies data mapping. Contributions are welcome to enhance the framework's capabilities and ensure comprehensive data coverage for cross chain events:

```python
@dataclass
class EventConfig:
    name: str
    signature: str
    contract: Optional[str] = None
    column_mapping: Optional[hypersync.ColumnMapping] = None
```

* name: Human-readable event name
* signature: Unique event signature
* contract: (Optional) Associated contract address
* column_mapping: (Optional) Defines transaction and block data processing1

### Add a new event configuration:
1. Understand the event structure (name, signature, contract address, decoded log fields)
2. Create an EventConfig instance
3. Ensure consistency in data types
4. Add the event to the configuration dictionary and submit a pull request (optional)
pytoshop-fix-packbits 1.1.7
A Python-based library to write Photoshop PSD files

===============================
pytoshop
===============================


.. image:: https://img.shields.io/pypi/v/pytoshop.svg
        :target: https://pypi.python.org/pypi/pytoshop

.. image:: https://img.shields.io/travis/mdboom/pytoshop.svg
        :target: https://travis-ci.org/mdboom/pytoshop

.. image:: https://readthedocs.org/projects/pytoshop/badge/?version=latest
        :target: https://pytoshop.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status

.. image:: https://img.shields.io/codecov/c/github/mdboom/pytoshop.svg
        :target: https://codecov.io/gh/mdboom/pytoshop
        :alt: Coverage status


A Python-based library to read and write Photoshop PSD and PSB files.

Based on the specification `from Adobe
<https://www.adobe.com/devnet-apps/photoshop/fileformatashtml/>`__,
but also with the help of the `psd-tools
<https://github.com/psd-tools/psd-tools/>`__ source code.


* Free software: BSD license
* Documentation: https://pytoshop.readthedocs.io.


Features
--------

- Parsing of the most important tags.  This is not complete, but the
  infrastructure is in place to add support for more quite easily.

- Loading of complex nested layer structures, and the ability to edit
  them and write them back out.
vws 0.2
Consists of three main classes: File, Directory, and Workspace. Together, these classes allow users to create, manage, and organize files and directories in a virtual workspace.
Requires Python: >=3.6

# VirtualWorkspace

**VirtualWorkspace** is a Python package that allows users to create, manage, and organize files and directories in a virtual workspace. It consists of three main classes: `File`, `Directory`, and `Workspace`, which work together to provide a structured environment for file management.

## Features

- Create and manage files with customizable content and emoji icons.
- Organize files within directories, allowing for nested structures.
- Easily list contents of directories and files.
- Search for files by name within directories.

## Installation

To install the VirtualWorkspace package, you can use pip:

```bash
pip install vws
```

## Usage
Hereâ€™s a quick example of how to use the VirtualWorkspace package:

```python
from vws import Workspace

# Create a new workspace
my_workspace = Workspace("My Workspace")

# Create directories
my_workspace.create_directory("Documents")
my_workspace.create_directory("Images")

# Create files
my_workspace.create_file("resume.pdf", "This is my resume content.")
my_workspace.create_file("photo.jpg", "This is a photo content.")

# List contents of the root directory
print(my_workspace.list_contents())

# List contents of a specific directory
print(my_workspace.list_directory_contents("Documents"))

# Read file content
print(my_workspace.read_file_content("resume.pdf"))
```
## Classes
File
Represents a file with a name, content, and an optional emoji icon.

## Directory
Represents a directory that can contain files and/or subdirectories. It provides methods to add files and directories, find files by name, and list its contents.

## Workspace
Represents a virtual workspace with a root directory. It allows users to create files and directories and provides methods to list and read contents.

## Contributing
If you would like to contribute to the development of VirtualWorkspace, feel free to fork the repository and submit a pull request. Any contributions, whether bug fixes, enhancements, or documentation improvements, are welcome!


## Author
Lyan
Email: admin@blackcoffee.lol
GitHub: Akicuo
ihunt 0.1.8
Information gathering tool.
Requires Python: >=3.12

# ihunt

Information gathering tool that collects information using OSINT and AI, while remaining undetected by the target.

## Installation

### Option 1. Using Pip

The most easiest way is to install the Python package with `pip`.

```sh
pipx install ihunt
# or
pip install ihunt
```

After installing, run the following command to show the usage:

```sh
ihunt --help
```

### Option 2. Using `uv`

If you want to use the latest features (but unstable), clone the repository and run the following command.  
Assuming that you've already installed the [uv](https://github.com/astral-sh/uv) package manager.

```sh
git clone https://github.com/hideckies/ihunt.git
cd ihunt
uv run ihunt --help
```

<br />

## Usage

It's easy to use. Simply run one of the following commands:

```sh
# Research domain
ihunt example.com

# Research email address
ihunt user@example.com

# Research hash
ihunt ed076287532e86365e841e92bfc50d8c

# Research IP address
ihunt 8.8.8.8

# Research organization
ihunt Google

# Research person
ihunt "Elon Musk"

# Research phone number
ihunt "+1 202-555-0123"

# Research URL
ihunt https://example.com
```

### Using API Keys

Some API need api keys, so if you want to collect information more, I recommend to set the api keys.  
To set them, copy the [.config.template](/.config.template) and set each api key, then execute with the `-c/--config` option:

```sh
ihunt -c .config example.com
```

Alternatively, we can directly set api keys to environment variables via command-line:

```sh
export IHUNT_APIKEY_ABUSEIPDB=xxxxxxxxxxxxxxx...
ihunt 8.8.8.8
```

### Output Format

Using `-o/--output` option, the result will be saved to a specified file. The ihunt detects the output format from the extension of the file path as below:

```sh
# JSON
ihunt -o output.json example.com

# YAML
ihunt -o output.yaml example.com

# Others (pretty print)
ihunt -o output.txt example.com
```
llm-structured-output-Detect-repairs 0.1.4
一个简单的HTTP客户端库，用于与结构化输出API交互
Requires Python: >=3.6

# llm_structured_output_Detect_repairs

A simple HTTP client library for interacting with a structured output API.

## 安装

```bash
pip install llm_structured_output_Detect_repairs

以下是一个完整的示例代码，展示了如何使用 llm_structured_output_Detect_repairs 发送请求并解析响应：



import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from llm_structured_output_Detect_repairs.llm_output_parser import LLMOutputParser
import json

# 创建 LLMOutputParser 实例
client = LLMOutputParser()
model = 'openai'
output_format = [
    {"name": "name", "description": "name of people", "type": "str"},
    {"name": "age", "description": "age of person", "type": "int"},
    {"name": "stories", "description": "all stories in the life, list of story details", "type": "list"}
]
content = "李白简介"
response_data = client.parse(model, content, output_format)

if response_data:
    print("服务端返回数据:")
    print(json.dumps(response_data, indent=4, ensure_ascii=False))
else:
    print("请求失败或解析错误")
ardupilot_log_reader 0.3.5
module to read ardupilot binary logs to memory
Requires Python: >=3.12

# Ardupilot Log Reader

This package is a bit like mavlogdump in pymavlink. It reads an ardupilot log to a pandas dataframe.

## usage

set up the parser
```
from ArdupilotLogReader import Ardupilot
parser = Ardupilot(
    log_file, # the log file, .bin
    types = ['ARSP', 'ATT', 'BARO', 'GPS', 'IMU', 'RCIN', 'RCOU', 'BAT', 'MODE', 'NKF1', 'STAT', 'XKF1'],  # fields to read from the log
    )

print(parser.dfs) # a dict containing a dataframes of log data for each field requested.
print(parser.join_logs(['ARSP', 'ATT'])) #returns a pandas dataframe containing the ARSP and ATT data joined on time
print(parser.parms) # returns the parameters read from the top of the log

```