semvec/pypi-packages huggingface dataset:
Mathmodule590011276 0.1.0
Maths Functions
Requires Python: >=3.6


Math_module.py
Created by Shivangi Singh, Batch 2, B.Tech CSE, 1st Year, UPES.

This Python module provides a collection of fundamental mathematical operations, including:

add: Perform addition of two numbers.
subtract: Compute the difference between two numbers.
multiply: Multiply two numbers.
divide: Divide one number by another.
factorial: Calculate the factorial of a given number.
is_prime: Check if a number is prime.
gcd: Find the greatest common divisor of two numbers.

Thank you for using and sharing this module! 😊 Let me know if you'd like any further enhancements!
nto218145 1.1.25
Read the latest Real Python tutorials
Requires Python: >=3.9
Keywords: feed, reader, tutorial

# This is Python Reader 1.1.25

```markdown
realpython-reader/
│
├── src/
│   └── reader/
│       ├── __init__.py
│       ├── __main__.py
│       ├── config.toml
│       ├── feed.py
│       └── viewer.py
│
├── tests/
│   ├── test_feed.py
│   └── test_viewer.py
│
├── LICENSE
├── MANIFEST.in
├── README.md
└── pyproject.toml
```
xnat-browser 0.0.9a1
Browse XNAT in the terminal
Requires Python: >=3.10
Keywords: TUI, XNAT

![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fgitlab.com%2Flkeb%2Fxnat_browser%2F-%2Fraw%2Fmain%2Fpyproject.toml)
![Gitlab Pipeline Status](https://img.shields.io/gitlab/pipeline-status/lkeb%2Fxnat_browser)
![PyPI - License](https://img.shields.io/pypi/l/xnat-browser)
![PyPI - Status](https://img.shields.io/pypi/status/xnat-browser)
![PyPI - Version](https://img.shields.io/pypi/v/xnat-browser)
![PyPI - Format](https://img.shields.io/pypi/format/xnat-browser)
![Pepy Total Downloads](https://img.shields.io/pepy/dt/xnat-browser)

Browse or query a XNAT server from the terminal using [Textual](https://github.com/Textualize/textual) and 
[xnatpy](https://gitlab.com/radiology/infrastructure/xnatpy).

## browse
`xnat-browser browse http://localhost`
- ![XNAT info](https://gitlab.com/lkeb/xnat_browser/-/raw/main/assets/images/XnatBrowser_XNAT_info.svg)
- ![DICOM info](https://gitlab.com/lkeb/xnat_browser/-/raw/main/assets/images/XnatBrowser_DICOM_info.svg)
- ![image](https://gitlab.com/lkeb/xnat_browser/-/raw/main/assets/images/XnatBrowser_image.svg)

## query
`xnat-browser query http://localhost`
![](https://gitlab.com/lkeb/xnat_browser/-/raw/main/assests/images/xnatquery.svg)
anchorpy 0.21.0
The Python Anchor client.
Requires Python: ~=3.9

# AnchorPy
<div align="center">
    <img src="https://raw.githubusercontent.com/kevinheavey/anchorpy/main/docs/img/logo.png" width="40%" height="40%">
</div>

---

[![Discord Chat](https://img.shields.io/discord/889577356681945098?color=blueviolet)](https://discord.gg/sxy4zxBckh)  

AnchorPy is the gateway to interacting with [Anchor](https://github.com/project-serum/anchor) programs in Python.
It provides:

- A static client generator
- A dynamic client similar to `anchor-ts`
- A Pytest plugin
- A CLI with various utilities for Anchor Python development.

Read the [Documentation](https://kevinheavey.github.io/anchorpy/).



## Installation (requires Python >=3.9)

```sh
pip install anchorpy[cli, pytest]

```
Or, if you're not using the CLI or Pytest plugin features of AnchorPy you can just run `pip install anchorpy`.

### Development Setup

If you want to contribute to AnchorPy, follow these steps to get set up:

1. Install [uv](https://docs.astral.sh/uv/getting-started/installation/)
2. Run the unit tests:
```sh
uv run --all-extras pytest tests/unit

```
atomictoolkit-mcp 0.1.1
MCP server for ASE and more.
Requires Python: >=3.13

# Atomistic Toolkit MCP Server

> [!NOTE]
> This project is under active development. Not everything is working yet.

An MCP-compatible server providing atomistic simulation capabilities through ASE, pymatgen, and machine learning interatomic potentials (MLIPs).

## Features
### ASE Tools
- Structure creation and manipulation
- Geometry optimization
- File I/O operations (read/write structures)
sbcli-max 14.0.57
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
value-iteration 1.0.1
Implementation of value iteration for Markov Decision Processes
Requires Python: >=3.12

# A Value Iteration Algorithm for Solving Markov decision Processes
In this package, we provide an implementation of a value iteration solving algorithm for Markov Decision Processes (MDP). Whilst this package works well for any MDP, it has been particularly optimised for 'Gridworld' problems, in which an agent navigates a discretised world, seeking rewards and avoiding penalty cells. 

## Installation

#### installing from PyPI

    pip install value-iteration

#### installing from github with pip

    python -m pip install git+https://github.com/Harry-Ell/601-assessment-2.git#subdirectory=package


## Example 1: Solving problem 9.27 from  [Artificial Intelligence: Foundations and Computational Agents 2nd edition](https://artint.info/2e/html2e/ArtInt2e.html)
```python
from value_iteration import Value_Iteration
Value_Iteration()
```

This will then open an interactive Command Line Interface (CLI). This is far more usable when ran in a .py file than in a notebook environment. An example CLI for this problem is given below

![CLI](https://raw.githubusercontent.com/Harry-Ell/601-assessment-2/master/figures/9_27_cli.PNG)

Where we can see the optimal policy is returned. In the case of there being more than 2 states, there will be no automatic defaults for other probabilities, they must all be specified and then their sum to 1 will be checked

## Example 2: Gridworld Type Problems

```python
from value_iteration import Value_Iteration
Value_Iteration()
```
![CLI_grid](https://raw.githubusercontent.com/Harry-Ell/601-assessment-2/master/figures/gridworld_cli.PNG)

Following a successful solving, it returns a plot of the policy obtained.

![CLI_grid_pol](https://raw.githubusercontent.com/Harry-Ell/601-assessment-2/master/figures/gridworld.png)
oracledb-mcp-server 0.1.1
MCP Server For Oracle Database
Requires Python: >=3.11

# OracleDB MCP Server

[![smithery badge](https://smithery.ai/badge/@rahgadda/oracledb_mcp_server)](https://smithery.ai/server/@rahgadda/oracledb_mcp_server)

## Overview
- This project will install `MCP - Model Context Protocol Server`, that provides configured Oracle Database Table/Columns as context to LLM's.
- Using this we can enable LLMs to interact with Oracle Database, Generate SQL Statements and Return Results using LLM prompts.

## Installation
- Install package
  ```bash
  pip install oracledb_mcp_server
  ```
- Create .env in a folder with minimum value of `Oracle DB Connection String`. Sample file available [here](https://raw.githubusercontent.com/rahgadda/oracledb_mcp_server/refs/heads/main/.env)
- Test `oracledb_mcp_server` server using `uv run oracledb_mcp_server` from the above folder.

## Claud Desktop
- Configuration details for Claud Desktop
  ```json
  {
    "mcpServers": {
      "oracledb_mcp_server":{
        "command": "uv",
        "args": ["run","oracledb_mcp_server"],
        "env": {
            "DEBUG":"True",
            "COMMENT_DB_CONNECTION_STRING":"oracle+oracledb://USERNAME:PASSWORD@IP:PORT/?service_name=SERVICENAME",
            "DB_CONNECTION_STRING":"oracle+oracledb://USERNAME:PASSWORD@IP:PORT/?service_name=SERVICENAME",
            "TABLE_WHITE_LIST":"ACCOUNTS,CUS_ACC_RELATIONS,CUSTOMERS",
            "COLUMN_WHITE_LIST":"ACCOUNTS.ACC_AAD_ID,CUS_ACC_RELATIONS.CAR_CUS_ID,CUS_ACC_RELATIONS.CAR_AAD_ID,CUSTOMERS.CUS_ID"
        }
      }
    }
  }
  ```

### Configuration
- List of available environment variables
  - `DEBUG`: Enable debug logging (optional default is False)
  - `COMMENT_DB_CONNECTION_STRING`: Oracle DB connection String for comments. (required)
  - `DB_CONNECTION_STRING`: Oracle DB connection String for execution of queries. (required)
  - `TABLE_WHITE_LIST`: White Listed table names in list format ["table1", "table2"] (required)
  - `COLUMN_WHITE_LIST`: White Listed table-column names in list format ["table.column1", "table.column2"] (required)
  - `QUERY_LIMIT_SIZE`: Default value is 10 records if not provided(optional default is 10)

## Interceptor
```bash
npx @modelcontextprotocol/inspector uv --directory "D:\\MyDev\\mcp\\oracledb_mcp_server" run -m oracledb_mcp_server
```

## Contributing
Contributions are welcome.    
Please feel free to submit a Pull Request.

## License
This project is licensed under the terms of the MIT license.

## Demo
![](./images/demo.png)

## Github Stars
[![Star History Chart](https://api.star-history.com/svg?repos=rahgadda/oracledb_mcp_server=Date)](https://star-history.com/#rahgadda/oracledb_mcp_server&Date)
sbcli-zcp 1.0.0
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
python-package-14721 0.0.2
A test python package for class assignment

# Test Package 13630

### Note: This package is a test package uploaded for class assignment.

```diff
- SAP: 590013630
```

_**This package contains 4 functions:**_

```python
def  add(x: int, y: int) -> int
```

```python
def  subtract(x: int, y: int) -> int
```

```python
def  multiply(x: int, y: int) -> int
```

```python
def  divide(x: float, y: float) -> int
```
birra 0.1.1
Python implementation of Bayesian iterative robust rank aggregation (BIRRA), based on Badgeley et al (2014) 'Hybrid Bayesian-rank integration approach improves the predictive power of genomic dataset aggregation' (https://academic.oup.com/bioinformatics/article/31/2/209/2364920).
Requires Python: <4.0,>=3.11
Keywords: python

Python implementation of Bayesian iterative robust rank aggregation (BIRRA), based on Badgeley et al (2014) 'Hybrid Bayesian-rank integration approach improves the predictive power of genomic dataset aggregation' (https://academic.oup.com/bioinformatics/article/31/2/209/2364920).
rich-todo 0.1.6
A CLI tool for managing todos directly from the terminal
Requires Python: >=3.6
Keywords: todo, cli, productivity

# Todo Manager 🚀

[![Python 3.6+](https://img.shields.io/badge/Python-3.6%2B-blue.svg)](https://www.python.org/)
[![PyPI Version](https://img.shields.io/pypi/v/rich-todo.svg)](https://pypi.org/project/rich-todo/)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/Mehranmv/todo-cli)
[![PyPI Downloads](https://static.pepy.tech/badge/rich-todo)](https://pepy.tech/projects/rich-todo)

A modern cross-platform task manager with **dual interface** - Beautiful GUI ✨ and powerful CLI 💻

![UI Screenshot](https://raw.githubusercontent.com/Mehranmv/todo-cli/refs/heads/master/screenshots/gui.png)

## Features 🌟

### 🖥️ Modern GUI Interface
- **Windows 11 Style** with fluent design elements
- **Dark/Light Themes** with system auto-detection
- **Responsive Layout** that adapts to screen size
- **Task Preview Panel** with rich markdown support
- **Drag-and-Drop** task prioritization
- **Visual Category Tags** with custom colors
- **Bulk Operations** (multi-select delete/update)
- **Real-time Search** with instant filtering

### 📊 Interactive Dashboard
- Priority distribution pie chart
- Completion rate timeline graph
- Category usage heatmap
- Quick stats overview

### ⚙️ Advanced UI Features
- Customizable column layouts
- Sortable task lists (click headers)
- Progress percentage indicators
- Due date calendar picker
- Notification system with reminders
- Export/Import UI (JSON/CSV)
- Category color picker dialog

## Installation 💻

```bash
# Install with GUI support
pip install rich-todo

# Launch GUI version
todoui

# Or use CLI version
todo --help
```

## UI Usage 🖱️

### Main Interface

1. **Quick Add Bar** - Type and press Enter
2. **Smart Filters** - Click to toggle views
3. **Priority Badges** - Drag to reorder
4. **Context Menu** - Right-click for actions

### Key Shortcuts
| Shortcut | Action                |
|----------|-----------------------|
| Ctrl+N   | New Task              |
| Ctrl+F   | Search                |
| Ctrl+S   | Save Changes          |
| Ctrl+E   | Export Tasks          |
| Alt+C    | Category Manager      |
| F5       | Refresh List          |

## Theme Customization 🎨
Choose from 6 built-in themes or create your own:
```bash
# Set theme (dark/light/system)
todoui --theme dark

# Custom theme colors
todoui --primary-color #2ecc71 --accent-color #3498db
```

## Documentation 📚
- Custom view configurations
- Notification preferences
- Data visualization tips
- Keyboard shortcut cheatsheet

---

Developed with ❤️ by Mehran Mirzaei  
📧 Contact: [mehranmirzaeiv@gmail.com](mailto:mehranmirzaeiv@gmail.com)  
```
goat-sdk-adapter-ag2 0.1.0
Goat adapter for AG2
Requires Python: <3.14,>=3.10
Keywords: goat, sdk, web3, agents, ai

<div align="center">
<a href="https://github.com/goat-sdk/goat">

<img src="https://github.com/user-attachments/assets/5fc7f121-259c-492c-8bca-f15fe7eb830c" alt="GOAT" width="100px" height="auto" style="object-fit: contain;">
</a>
</div>

# AG2 Adapter for GOAT

Integrate the more than +200 onchain tools of GOAT with [AG2](https://github.com/ag2ai/ag2), the open-source AgentOS (formerly AutoGen).

## Installation
```
pip install goat-sdk-adapter-ag2
poetry add goat-sdk-adapter-ag2
```

## Usage

See a full working example [here](https://github.com/goat-sdk/goat/tree/main/python/examples/by-framework/ag2).

```python
from goat_adapters.ag2.adapter import register_tools
from autogen import ConversableAgent

crypto_agent = ConversableAgent(
    name="crypto_agent",
    system_message="You are a crypto expert...",
)

executor_agent = ConversableAgent(
    name="executor_agent",
    human_input_mode="NEVER",
)

register_tools(
    caller=crypto_agent,
    executor=executor_agent,
    wallet=# Your wallet,
    plugins=[
        # Your plugins
    ],
)
```

<footer>
<br/>
<br/>
<div>
<a href="https://github.com/goat-sdk/goat">
  <img src="https://github.com/user-attachments/assets/59fa5ddc-9d47-4d41-a51a-64f6798f94bd" alt="GOAT" width="100%" height="auto" style="object-fit: contain; max-width: 800px;">
</a>
<div>
</footer>
goat-sdk-plugin-erc20 0.1.2
Goat plugin for ERC20
Requires Python: <4.0,>=3.10
Keywords: goat, sdk, web3, agents, ai

# ERC20 Plugin for GOAT SDK

A plugin for the GOAT SDK that provides ERC20 token interaction functionality.

## Installation

```bash
# Install the plugin
poetry add goat-sdk-plugin-erc20

# Install required wallet dependency
poetry add goat-sdk-wallet-evm
```

## Usage

```python
from goat_plugins.erc20 import erc20, ERC20PluginOptions

# Initialize the plugin
options = ERC20PluginOptions(
    rpc_url="${RPC_PROVIDER_URL}"  # Your EVM RPC provider URL
)
plugin = erc20(options)

# Get token balance
balance = await plugin.balance_of(
    token_address="0x6B175474E89094C44Da98b954EedeAC495271d0F",  # DAI token
    wallet_address="0x742d35Cc6634C0532925a3b844Bc454e4438f44e"
)

# Check allowance
allowance = await plugin.allowance(
    token_address="0x6B175474E89094C44Da98b954EedeAC495271d0F",  # DAI token
    owner="0x742d35Cc6634C0532925a3b844Bc454e4438f44e",
    spender="0x1111111254EEB25477B68fb85Ed929f73A960582"  # 1inch router
)
```

## Features

- Token balance checking
- Allowance management
- Token transfers
- Token approvals
- Supported chains:
  - Ethereum
  - Polygon
  - BSC
  - Arbitrum
  - Optimism
  - Avalanche
  - Base
  - Any EVM-compatible chain

## License

This project is licensed under the terms of the MIT license.
virtualpdu 2.2.0
VirtualPDU is a service for simulating virtual power distribution outlets (PDUs).
Requires Python: >=3.9

===============================
virtualpdu
===============================

VirtualPDU is a service for simulating virtual power distribution outlets
(PDUs).

This software is intended for CI and development use only. Please do not run
VirtualPDU in a production environment for any reason.

* Free software: Apache license, Version 2.0
* Source: http://opendev.org/openstack/virtualpdu
* Bugs: http://bugs.launchpad.net/virtualpdu
virtualbmc 3.2.0
Create virtual BMCs for controlling virtual instances via IPMI
Requires Python: >=3.9

==========
VirtualBMC
==========

Team and repository tags
------------------------

.. image:: https://governance.openstack.org/tc/badges/virtualbmc.svg
    :target: https://governance.openstack.org/tc/reference/tags/index.html

Overview
--------

A virtual BMC for controlling virtual machines using IPMI commands.

This software is intended for CI and development use only. Please do not run
VirtualBMC in a production environment for any reason.

Installation
~~~~~~~~~~~~

.. code-block:: bash

  pip install virtualbmc


Supported IPMI commands
~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: bash

  # Power the virtual machine on, off, graceful off, NMI and reset
  ipmitool -I lanplus -U admin -P password -H 127.0.0.1 power on|off|soft|diag|reset

  # Check the power status
  ipmitool -I lanplus -U admin -P password -H 127.0.0.1 power status

  # Set the boot device to network, hd or cdrom
  ipmitool -I lanplus -U admin -P password -H 127.0.0.1 chassis bootdev pxe|disk|cdrom

  # Get the current boot device
  ipmitool -I lanplus -U admin -P password -H 127.0.0.1 chassis bootparam get 5

Project resources
~~~~~~~~~~~~~~~~~

* Documentation: https://docs.openstack.org/virtualbmc/latest
* Source: https://opendev.org/openstack/virtualbmc
* Bugs: https://bugs.launchpad.net/virtualbmc
* Release Notes: https://docs.openstack.org/releasenotes/virtualbmc/

For information on how to contribute to VirtualBMC, see
https://docs.openstack.org/virtualbmc/latest/contributor
qdeepsdk 1.0.1
Our hybrid solver for QUBO problems merges classical and quantum computing, efficiently solving complex optimization tasks. It offers fast, accurate results for applications in logistics, finance, and more.
Requires Python: >=3.7

# QDeep Hybrid Solver - Quadratic Unconstrained Binary Optimization Solver

**QDeep Hybrid Solver** is a Python library specifically developed for efficiently solving **Quadratic Unconstrained Binary Optimization (QUBO)** problems through advanced hybrid quantum-classical computing methods. It provides a user-friendly interface to submit QUBO matrices and retrieve structured, optimized solutions.

## Mathematical Formulation

A QUBO problem is mathematically expressed as:

```math
\min_{x \in \{0,1\}^n} \; x^T Q x
```

where:

- \(x\) is a binary vector.
- \(Q\) is a symmetric matrix representing the problem's coefficients.

## Features

- Type-hinted implementation using Python's `TypedDict`
- Comprehensive input validation for matrices
- Secure authentication token management
- Structured and detailed response handling

## Installation

Install the required dependencies along with the solver:

```bash
pip install numpy requests qdeepsdk
```

### Dependencies

- `numpy`
- `requests`
- `qdeepsdk`

### Quantum Optimization Parameters

#### `m_budget`

- **Type:** Integer
- **Default Value:** `1000`
- **Description:** Represents the measurement budget, which determines the maximum number of quantum measurement samples executed during the optimization process. This parameter is critical for managing quantum resource allocation while balancing solution quality and computational efficiency.

#### `num_reads`

- **Type:** Integer
- **Default Value:** `10000`
- **Description:** Specifies the number of iterations (reads) performed by the solver. This parameter plays an integral role in the convergence of the algorithm by defining the number of independent solution attempts during the optimization process.

## Usage

```python
import numpy as np
from qdeepsdk import QDeepHybridSolver

# Initialize the solver
solver = QDeepHybridSolver()

# Set the authentication token
solver.token = "your-auth-token-here"

# Configure parameters (if different from defaults)
solver.m_budget = 50000      # Measurement budget
solver.num_reads = 10000    # Number of reads

# Define a QUBO matrix
matrix = np.array([
    [1, 0],
    [0, -1]
])

# Solve the QUBO problem
try:
    response = solver.solve(matrix)
    results = response['QdeepHybridSolver']
    print("Hybrid Solver Results:", results)
except ValueError as e:
    print(f"Error: {e}")
except requests.RequestException as e:
    print(f"API Error: {e}")
```

## Sample Matrix and Response

### Sample Matrix

```python
[[1, 0], [0, -1]]
```

### Sample Response

```json
{
        "QdeepHybridSolver": {
            "configuration": [0.0, 1.0],
            "energy": -1.0,
            "time": 3.6245157718658447
        }

}
```

## Requirements

- Python 3.7+
- NumPy
- Requests

The library includes comprehensive error handling for:

- Invalid or missing authentication tokens
- Non-square matrices
- Non-2D matrices
- Non-numpy array inputs
- API connection issues