semvec/pypi-packages huggingface dataset:
sentine-mock-plan 0.4.0
Create a mock plan for Hashicorp Sentinel from 0.26.2 to 0.40.0
Requires Python: <4.0,>=3.8

# Sentinel Mock Plan

This project creates a mock plan for **HashiCorp Sentinel 0.40.0** using a JSON plan file generated by Terraform. It converts the plan file into a format that can be used in Sentinel policies for testing and validation.

## Table of Contents

- [Overview](#overview)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)

## Overview

This project provides a Python utility that processes a JSON plan file (produced by Terraform) and converts it into a **mock plan** file compatible with HashiCorp Sentinel 0.40.0. The generated mock plan is useful for simulating infrastructure changes and running Sentinel policies without applying actual changes to cloud infrastructure.

### Features:
- Converts a JSON Terraform plan into a mock Sentinel plan.
- Customizable input and output file options.
- Supports overwriting existing output files.

## Prerequisites

- Python 3.8+
- [Terraform](https://www.terraform.io/) (to generate the plan file)
- HashiCorp Sentinel 0.40.0 (for testing with the generated mock plan)

## Installation

1. Install the package via `pip`:

   ```bash
   pip install sentinel-mock-plan
   ```

## Usage

To use this utility, first generate the Terraform plan JSON file and then run the Python script to convert it.

### Generating the JSON Plan

1. Initialize Terraform with the desired backend configuration:

   ```bash
   terraform init -reconfigure -backend-config=backends/dev.tfvars
   ```

2. Generate a Terraform plan:

   ```bash
   terraform plan -out=tf.plan -var-file=environments/dev.tfvars
   ```

3. Export the plan to a JSON format:

   ```bash
   terraform show -no-color -json tf.plan > plan.json
   ```

### Converting the JSON Plan to a Sentinel Mock Plan

Now, run the Python utility to convert the `plan.json` file into a mock Sentinel plan:

```bash
python -m sentinel_mock_plan --infile plan.json --outfile mock-tfplan-v2.sentinel
```

#### Options:

- `--infile`: Input file name (default: `plan.json`)
- `--outfile`: Output file name (default: `mock-tfplan-v2.sentinel`)
- `--overwrite`: Overwrite the output file if it exists

### Example:

```bash
python -m sentinel_mock_plan --infile plan.json --outfile mock-tfplan-v2.sentinel --overwrite
```

This will generate a mock plan file named `mock-tfplan-v2.sentinel` from the `plan.json` input.

## Example Workflow

1. Run Terraform commands:

   ```bash
   terraform init -reconfigure -backend-config=backends/dev.tfvars
   terraform plan -out=tf.plan -var-file=environments/dev.tfvars
   terraform show -no-color -json tf.plan > plan.json
   ```

2. Convert the JSON plan to a mock Sentinel plan:

   ```bash
   python -m sentinel_mock_plan --infile plan.json --outfile mock-tfplan-v2.sentinel
   ```

3. Use the generated mock plan in your Sentinel tests.
sentinel-mock-plan 0.4.0
Create a mock plan for Hashicorp Sentinel from 0.26.2 to 0.40.0
Requires Python: <4.0,>=3.8

# Sentinel Mock Plan

This project creates a mock plan for **HashiCorp Sentinel 0.40.0** using a JSON plan file generated by Terraform. It converts the plan file into a format that can be used in Sentinel policies for testing and validation.

## Table of Contents

- [Overview](#overview)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)

## Overview

This project provides a Python utility that processes a JSON plan file (produced by Terraform) and converts it into a **mock plan** file compatible with HashiCorp Sentinel 0.40.0. The generated mock plan is useful for simulating infrastructure changes and running Sentinel policies without applying actual changes to cloud infrastructure.

### Features:
- Converts a JSON Terraform plan into a mock Sentinel plan.
- Customizable input and output file options.
- Supports overwriting existing output files.

## Prerequisites

- Python 3.8+
- [Terraform](https://www.terraform.io/) (to generate the plan file)
- HashiCorp Sentinel 0.40.0 (for testing with the generated mock plan)

## Installation

1. Install the package via `pip`:

   ```bash
   pip install sentinel-mock-plan
   ```

## Usage

To use this utility, first generate the Terraform plan JSON file and then run the Python script to convert it.

### Generating the JSON Plan

1. Initialize Terraform with the desired backend configuration:

   ```bash
   terraform init -reconfigure -backend-config=backends/dev.tfvars
   ```

2. Generate a Terraform plan:

   ```bash
   terraform plan -out=tf.plan -var-file=environments/dev.tfvars
   ```

3. Export the plan to a JSON format:

   ```bash
   terraform show -no-color -json tf.plan > plan.json
   ```

### Converting the JSON Plan to a Sentinel Mock Plan

Now, run the Python utility to convert the `plan.json` file into a mock Sentinel plan:

```bash
python -m sentinel_mock_plan --infile plan.json --outfile mock-tfplan-v2.sentinel
```

#### Options:

- `--infile`: Input file name (default: `plan.json`)
- `--outfile`: Output file name (default: `mock-tfplan-v2.sentinel`)
- `--overwrite`: Overwrite the output file if it exists

### Example:

```bash
python -m sentinel_mock_plan --infile plan.json --outfile mock-tfplan-v2.sentinel --overwrite
```

This will generate a mock plan file named `mock-tfplan-v2.sentinel` from the `plan.json` input.

## Example Workflow

1. Run Terraform commands:

   ```bash
   terraform init -reconfigure -backend-config=backends/dev.tfvars
   terraform plan -out=tf.plan -var-file=environments/dev.tfvars
   terraform show -no-color -json tf.plan > plan.json
   ```

2. Convert the JSON plan to a mock Sentinel plan:

   ```bash
   python -m sentinel_mock_plan --infile plan.json --outfile mock-tfplan-v2.sentinel
   ```

3. Use the generated mock plan in your Sentinel tests.
musclepy 1.1.0
MusclePy is a Python package for structural analysis that focuses on the design, analysis, and optimization of tensegrity, tension-based, and truss-like structures.
Requires Python: >=3.9

# MusclePy

## Overview
MusclePy is a Python package for structural analysis that focuses on the design, analysis, and optimization of tensegrity, tension-based, and truss-like structures.

## Features
- **Finite Element Modeling**: Create and manipulate structural models with `PyNodes`, `PyElements`, and `PyTruss`
- **Singular Value Decomposition (SVD)**: Analyze equilibrium matrices to identify mechanisms and self-stress modes
- **Self-stress Modes**: Localize and sort self-stress modes in tensegrity structures
- **Displacement Methods**: Solve structural problems using linear and nonlinear displacement methods
- **Dynamic Relaxation**: Form-finding and analysis using dynamic relaxation techniques
- **Dynamic Modal Analysis**: Compute natural frequencies and mode shapes of vibrations of structures 

## Installation
```bash
pip install musclepy
```

## Quick Start
```python
import musclepy as mp

# Create a simple structure
nodes = mp.PyNodes()
elements = mp.PyElements(nodes)
truss = mp.PyTruss(nodes, elements)

# Add nodes and elements
# ...

# Compute SVD
results = mp.main_singular_value_decomposition(truss)

# Localize self-stress modes
localized_modes = mp.localize_self_stress_modes(results.self_stress_modes)
```

## License
Licensed under the Apache License, Version 2.0

## Citation
If you use MusclePy in your research, please cite:
```
Feron J., Payen B., Pacheco De Almeida J., Latteur P. MUSCLE: a new open-source Grasshopper plug-in for the interactive design of tensegrity structures.International Association for Shell and Spatial Structures (IASS) 2024 (Zurich, du 26/08/2024 au 29/08/2024).
```

## Contact
Professor Pierre LATTEUR - pierre.latteur@uclouvain.be - supervisor of the research project.
rlgym-sacd 0.1.1
A package for rlgym SACD algorithms
Requires Python: >=3.7

# RLGym-SACD
A vectorized implementation of SAC with Discrete actions for use with [RLGym](rlgym.org).

## INSTALLATION
1. install [RLGym-sim](https://github.com/AechPro/rocket-league-gym-sim). 
2. If you would like to use a GPU install [PyTorch with CUDA](https://pytorch.org/get-started/locally/)
3. Install this project via `pip install git+https://github.com/Martico2432/rlgym-sacd`

## USAGE
Simply import the learner with `from rlgym_sacd import Learner`, pass it a function that will return an RLGym environment
and run the learning algorithm. A simple example follows:
```
from rlgym_sacd import Learner

def my_rlgym_function():
    import rlgym_sim
    return rlgym_sim.make()

learner = Learner(my_rlgym_env_function)
learner.learn()
```
Note that users must implement a function to configure Rocket League (or RocketSim) in RLGym that returns an 
RLGym environment. See the `example.py` file for an example of writing such a function.


This has been inspired by [rlgym-ppo](https://github.com/AechPro/rlgym-ppo)
cmi-sensor-kaggle 0.1.3
Utility package for the CMI sensor data Kaggle competition
Requires Python: >=3.8

# CMI Sensor Kaggle

My approach to participate in [CMI - Detect Behavior with Sensor Data](https://www.kaggle.com/competitions/cmi-detect-behavior-with-sensor-data).

## Installation note

Install `pybullet` using conda.

```bash
conda install -c conda-forge pybullet
```
ashur 0.1.2
Python client for the Ashur bot API
Requires Python: >=3.7

# AshurBot Python Client Library

A Python client for the [Ashur bot API](https://github.com/Ashur-App/botapi). This library allows you to interact with your Ashur bot using Python.

## Installation

```
pip install ashur
```

## Usage

```python
from ashur.client import AshurBotClient

client = AshurBotClient(
    bot_token="YOUR_BOT_TOKEN",
    bot_secret="YOUR_BOT_SECRET"
    # api_base_url is optional and defaults to https://ashurbots.vercel.app
)

# Send a message
client.send_message(target="user_or_group_id", message="Hello!")

# Read messages
messages = client.read_messages(target="user_or_group_id")

# Webhook example
# You must set your bot's webhook URL to your server's public address + /webhook

def handle_event(event):
    print("Received webhook event:", event)
    # Do stuff with the event dict

client.on_webhook(handle_event)  # Starts a Flask server on http://0.0.0.0:8080/webhook
```

## Supported Methods
- `send_message(target, message)`
- `read_messages(target, limit=20)`
- `edit_message(target, message_id, new_text)`
- `delete_message(target, message_id)`
- `reply_to(target, message, reply_to_id)`
- `get_bot_info()`
- `update_bot_profile(name=None, pic=None, bio=None, webhook_url=None)`
- `list_chats()`
- `get_chat_info(chat_id, is_group=False)`
- `list_group_members(group_id)`
- `add_group_member(group_id, user_id)`
- `remove_group_member(group_id, user_id)`
- `pin_message(target, message_id, is_group=False)`
- `unpin_message(target, message_id, is_group=False)`
- `set_chat_title(group_id, name)`
- `set_group_photo(group_id, pic)`
- `leave_chat(target, is_group=False)`
- `get_message_by_id(target, message_id, is_group=False)`
- `forward_message(from_target, message_id, to_target, from_is_group=False, to_is_group=False)`
- `get_bot_stats()`
- `on_webhook(handler, host="0.0.0.0", port=8080, path="/webhook")` — Start a Flask server to receive webhook events

## Requirements
- Python 3.7+
- `requests` library
- `flask` library (for webhook server)

## License
AGPL-3.0