semvec/pypi-packages huggingface dataset:
warnet 1.1.19
Monitor and analyze the emergent behaviours of bitcoin networks
License: MIT
Requires Python: >=3.9
Keywords: bitcoin, warnet

![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https://raw.githubusercontent.com/bitcoin-dev-project/warnet/main/pyproject.toml)
# Warnet

Monitor and analyze the emergent behaviors of Bitcoin networks.

## Major Features

* Launch a bitcoin network with a specified number of nodes connected to each other according to a network topology.
* Run scenarios of network behavior across the network which can be programmed using the Bitcoin Core functional [test_framework language](https://github.com/bitcoin/bitcoin/tree/master/test/functional).
* Collect and search data from nodes including log files and p2p messages.
* Monitor and visualize performance data from Bitcoin nodes.
* Connect to a large network running in a remote cluster, or a smaller network running locally.
* Add a Lightning Network with its own channel topology and payment activity.

## Documentation

- [Design](/DESIGN.md)
- [Installation](/docs/install.md)
- [CLI Commands](/docs/warnet.md)
- [Network configuration with yaml files](/docs/config.md)
- [Plugins](/docs/plugins.md)
- [Scenarios](/docs/scenarios.md)
- [Monitoring](/docs/logging_monitoring.md)
- [Snapshots](/docs/snapshots.md)
- [Connecting to local nodes outside the cluster](/docs/connecting-local-nodes.md)
- [Scaling](/docs/scaling.md)
- [Contributing](/docs/developer-notes.md)


## Quick Start

### 1. Create a python virtual environment

```sh
python3 -m venv .venv
source .venv/bin/activate
```

### 2. Install Warnet

```sh
pip install warnet
```

### 3. Set up dependencies

Warnet will ask which back end you want to use, check that it is working,
and install additional client tools into the virtual environment.

```sh
warnet setup
```

### 4. Create a project and network

Warnet will create a new folder structure containing standard scenario and plugin
files, and prompt for details about a network topology to create. Topology details
include number of Bitcoin nodes, which release versions or custom images to deploy
and how many random graph connections to start each node with.

```sh
warnet new /my/work/stuff/projectname
```

### 5. Deploy the network

```sh
warnet deploy /my/work/stuff/projectname/networks/networkname
```

### 6. Run experiments

For example, you can start mining blocks...

```sh
warnet run /my/work/stuff/projectname/scenarios/miner_std.py
```

... and then observe network connectivity and statistics in your browser:

```sh
warnet dashboard
```

### 7. Shut down the network

```sh
warnet down
```

### 8. Customize

Read the docs and learn how to write your own [scenarios](docs/scenarios.md)
or add [plugins](docs/plugins.md) to your network. [Configure](docs/config.md) individual nodes
in the network by editing the `network.yaml` file or configure
defaults for all nodes in the network by editing `node-defaults.yaml`. Once
your network is running use Warnet [CLI](docs/warnet.md) commands to interact with it.


![warnet-art](https://raw.githubusercontent.com/bitcoin-dev-project/warnet/main/docs/machines.webp)
geomeffibem 0.1.10
A small library to facilitate some building energy modeling geometry operations with OpenStudio and EnergyPlus in mind.
License: MIT
Requires Python: <4.0,>=3.9

# GeomEffiBEM


[![pypi](https://img.shields.io/pypi/v/geomeffibem.svg)](https://pypi.org/project/geomeffibem/)
[![python](https://img.shields.io/pypi/pyversions/geomeffibem.svg)](https://pypi.org/project/geomeffibem/)
[![Build Status](https://github.com/jmarrec/geomeffibem/actions/workflows/dev.yml/badge.svg)](https://github.com/jmarrec/geomeffibem/actions/workflows/dev.yml)
[![codecov](https://codecov.io/gh/jmarrec/geomeffibem/branch/main/graphs/badge.svg)](https://codecov.io/github/jmarrec/geomeffibem)


A small library to facilitate some building energy modeling geometry operations with [OpenStudio](https://github.com/NREL/OpenStudio) and [EnergyPlus](https://github.com/NREL/EnergyPlus) in mind. This started as a test bed while I was implementing source code changes to both aforementioned projects and I realized this had value in creating geometry in an efficient manner as well as being able to visualize simple geometry in 2D via matplotlib.

A Jupyter Notebook [GeomEffiBEM_Demonstration.ipynb](https://github.com/jmarrec/geomeffibem/blob/main/GeomEffiBEM_Demonstration.ipynb) can serve as a small demonstration of the features.


* Documentation: <https://jmarrec.github.io/geomeffibem>
* GitHub: <https://github.com/jmarrec/geomeffibem>
* PyPI: <https://pypi.org/project/geomeffibem/>


This is free software (MIT License) contributed by [EffiBEM](https://effibem.com).

Leveraging software, [EffiBEM](https://effibem.com) specializes in providing new ways to streamline your workflows and create new tools that work with limited inputs for your specific applications. We also offer support and training services on BEM simulation engines (OpenStudio and EnergyPlus).
cognihub-pygotemplate 0.0.2
A Python wrapper for Go's text/template engine via CGo
License: MIT
Requires Python: >=3.10
Keywords: template, go, cgo, text-template, golang

# cognihub_pygotemplate

A simple, high-performance Python wrapper for Go's native `text/template` engine.

*[中文文档](README_CN.md)*

This library allows you to use Go templates (like those from Ollama) directly in your Python projects with 100% compatibility, by calling a compiled Go shared library via CGo and Python's `ctypes`.

## Prerequisites

- Python 3.10+
- Go compiler (1.21+ recommended) installed and available in your system's PATH

## Installation

### From PyPI (Recommended)

```bash
pip install cognihub_pygotemplate
```

### From Source

```bash
# Clone the repository
git clone https://github.com/hsz1273327/cognihub_pygotemplate.git
cd cognihub_pygotemplate

# Install the package
pip install .
```

The library will automatically compile the Go source code during installation.

## Usage

```python
from cognihub_pygotemplate import GoTemplateEngine

# Your Go template string
template_str = """
Hello, {{.Name}}!
{{if .Items}}You have {{len .Items}} items:
{{range .Items}}- {{.}}
{{end}}{{else}}You have no items.{{end}}
"""

# Data to render
data = {
    "Name": "World",
    "Items": ["apple", "banana", "cherry"]
}

try:
    # Create an engine instance
    engine = GoTemplateEngine(template_str)

    # Render the template
    output = engine.render(data)

    print(output)

except (RuntimeError, ValueError) as e:
    print(f"An error occurred: {e}")

```

## Development Workflow

Full development cycle: Clean -> Build -> Type Check -> Test. Iterate until requirements are met, then package.

### Clean

```bash
python setup.py clean
```

### Build

```bash
python setup.py build_py
```

### Type Check

```bash
# Use mypy for type checking
python setup.py type_check [--strict]
```

### Test

```bash
# 运行所有测试
python setup.py test

# 或者执行单条测试
python -m unittest tests.test_engine.TestGoTemplateEngine.test_render
# 或者使用自定义测试运行器
```

### Build Wheels

```bash
# For current platform
python setup.py bdist_wheel
```

## Features

- **100% Go Template Compatibility**: Uses Go's native `text/template` engine
- **High Performance**: Direct CGo bindings with minimal overhead
- **Cross-Platform**: Supports Windows, macOS (Intel & ARM64), and Linux
- **Memory Safe**: Proper memory management to prevent leaks
- **Easy Integration**: Simple Python API that feels native

## Error Handling

The library provides clear error messages for common issues:

- `JSON_ERROR`: Invalid data format
- `TEMPLATE_PARSE_ERROR`: Template syntax errors
- `TEMPLATE_EXECUTE_ERROR`: Runtime template execution errors

## License

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

## Contributing

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