semvec/pypi-packages huggingface dataset:
riscv-emulator 0.1.1
A Python-based RISC-V assembler, emulator, disassembler, and debugger
Requires Python: >=3.7

# 🖥️ RISC-V Emulator

A simple RISC-V **assembler**, **emulator**, and **disassembler** written in Python.  
It can be used both as a **command-line tool** and as a **Python library**.

---

## 👋 Features
- Assemble `.s` RISC-V assembly files into binary machine code
- Run binary files on a simple RISC-V CPU emulator
- Disassemble binary files back into readable assembly
- One-step assemble-and-run mode
- Works as both CLI tool and importable Python package

---

## 📦 Installation

From PyPI:
```bash
pip install riscv-emulator
```

From source:
```bash
git clone https://github.com/samalneedi/riscv_emulator.git
cd riscv_emulator
pip install .
```

---

## 🚀 Usage

### CLI Commands
```bash
# Assemble a file
riscv assemble program.s

# Run a binary
riscv run program.bin

# Assemble and run in one step
riscv full program.s

# Disassemble a hexcode
riscv disassemble [hexcode]
```

---

### Python API
```python
from riscv_emulator import emulator

emu = emulator()
emu.assemble("program.s")
emu.load_program("program.bin")
emu.run()
```

---

## 📂 Project Structure
```
riscv_emulator/
├── riscv_emulator/
│   ├── __init__.py
│   ├── assembler.py
│   ├── debugger.py
│   ├── disassembler.py
│   ├── emulator.py
│   ├── main.py
├── README.md
├── pyproject.toml
├── LICENSE
```

---

## 📝 License
This project is licensed under the terms of the **MIT License**.  
See the [LICENSE](LICENSE) file for details.

---

## 💡 Example
Example assembly file (`program.s`):
```asm
addi x1, zero, 3
addi x2, zero, 4
add  x3, x1, x2
```
Run it:
```bash
riscv full program.s
```

---

## 🤝 Contributing
Pull requests are welcome!  
If you find a bug or have a feature request, please open an issue on GitHub.

---

## 🛠 Requirements
- Python 3.8+
- No external dependencies
ncdgen 3.0.0
Plane geometry diagram generation using Newclid
Requires Python: >=3.11

# Data generation based on Newclid JGEX constructions

## Installation

From root:
```bash
uv sync --package ncdgen
```


## Usage

### Run localy

Local CLI is using [hydra](https://hydra.cc/docs/advanced/override_grammar/basic/) for specifying configurations overrides if needed for quick experiments.

```bash
uv run --no-sync ncdgen --config-name baseline
```

To see all available configuration fields for data generation see [DiagramGenerationConfig](src/ncdgen/generation_configuration.py#L19).
aprilgrid 0.5.0
Pure python version of aprilgrid.
Requires Python: >=3.8

# Aprilgrid
[![Ubuntu Build Status](https://github.com/powei-lin/aprilgrid/workflows/ubuntu/badge.svg)](https://github.com/powei-lin/aprilgrid/actions/workflows/ubuntu.yml)

### Pure python version of aprilgrid
Inspired by [Apriltag](https://github.com/AprilRobotics/apriltag) and [Kalibr](https://github.com/ethz-asl/kalibr). There are lots of existing python binding of Apriltag. But as everyone knows, python binding causes a lot of issues. So I rewrite a pure python version of Apriltag, and it's also able to detect Aprilgrid.

<img src="example/showcase.avif" width="800" alt="Slow down for show case.">

## Install from PyPI
```
pip install aprilgrid
```

## Usage
Some examples of usage can be seen in the example/main.py file.

```py
from aprilgrid import Detector

detector = Detector("t36h11")

detector.detect(img)
```

Kalibr uses 2 bit black border while apriltag3 uses 1 bit black border for each tag. The default of aprilgrid follows Kalibr. Please use `t36h11b1` if you're using tags with 1 bit black border.

<img src="docs/tag_border_issue.png" width="400" alt="Slow down for show case.">

## Development
```sh
git clone https://github.com/powei-lin/aprilgrid.git
cd aprilgrid
pip install -e .
```

## TODO
- [ ] Add output for opencv calibrate camera.
- [ ] Provide pregenerated pdf and easy way to customize.
- [ ] Support all tag families.
- [ ] Accelerate and multi-threading.
- [ ] Robustness.

## Reference
- https://github.com/AprilRobotics/apriltag
- https://github.com/ethz-asl/kalibr
tempest-anonymous 1.4.4
A library to sample temporal random walks from in-memory temporal graphs
Requires Python: >=3.8

# 🚀 Tempest

[![PyPI Latest Release](https://img.shields.io/pypi/v/tempest-anonymous.svg)](https://pypi.org/project/tempest-anonymous/)
[![PyPI Downloads](https://img.shields.io/pypi/dm/tempest-anonymous.svg)](https://pypi.org/project/tempest-anonymous/)

**A high-performance temporal random walk sampler for dynamic networks with GPU acceleration. Built for scale.**

---

## 🔥 Why Tempest?
✅ **Performance First** – GPU-accelerated sampling for massive networks<br>
✅ **Memory Efficient** – Smart memory management for large graphs  
✅ **Flexible Integration** – Easy Python bindings with **NumPy/NetworkX** support  
✅ **Production Ready** – Tested with hundreds of extensive unit tests.<br>
✅ **Multi Platform** Builds and runs seamlessly on devices with or without CUDA.

---

## ⚡ Quick Start

```python
from tempest import Tempest

# Create a directed temporal graph
walker = Tempest(is_directed=True, use_gpu=True, max_time_capacity=-1)

# Add edges - can be numpy arrays or python lists
sources = [3, 2, 0, 3, 3, 1]
targets = [4, 4, 2, 1, 2, 4]
timestamps = [71, 82, 19, 34, 79, 19]

walker.add_multiple_edges(sources, targets, timestamps)

# Sample walks with exponential time bias
walk_nodes, walk_timestamps, walk_lens = walker.get_random_walks_and_times_for_all_nodes(
    max_walk_len=5,
    walk_bias="ExponentialIndex",
    num_walks_per_node=10,
    initial_edge_bias="Uniform"
)
```

## ✨ Key Features
- ⚡ **GPU acceleration** for large graphs   
- 🎯 **Multiple sampling strategies** – Uniform, Linear, Exponential
- 🔄 **Forward & backward** temporal walks
- 📡 **Rolling window support** for streaming data
- 🔗 **NetworkX integration**
- 🛠️ **Efficient memory management**
- ⚙️ Uses **C++ std libraries** or **Thrust API** selectively based on hardware availability and configuration. 

---

## 📦 Dependencies

| Dependency     | Purpose                                     |
|---------------|---------------------------------------------|
| **pybind11**  | Python-C++ bindings                         |
| **python3**   | Required for building the python interfaces |
| **gtest**     | Unit testing framework                      |


> 💡 **Tip:** Use **vcpkg** to easily install and link the C++ dependencies.

---

## 📦 Installation

```sh
pip install tempest-anonymous
```

## 📖 Documentation

📌 **[C++ Documentation →](https://htmlpreview.github.io/?https://github.com/anonymousresearcher1701/tempest-anonymous/blob/master/docs/html/classTempest.html)**<br>
📌 **[Python Interface Documentation →](docs/_tempest.md)**

---

## 📚 Inspired By

**Nguyen, Giang Hoang, et al.**  
*"Continuous-Time Dynamic Network Embeddings."*  
*Companion Proceedings of The Web Conference 2018.*
sam-url-cache 0.7
A simple URL caching library for Python

# sam_url_cache

A simple Python package for caching the contents of URLs using SQLite. This helps avoid repeated network requests for the same URL.

## PYPI
https://pypi.org/project/sam-url-cache/

## Features

- Caches URL responses in a local SQLite database.
- Automatically fetches and stores content on cache miss.
- Simple API for checking if a URL has been seen before.

## Installation

Install via pip

```sh
pip install sam-url-cache
```

## Usage

```python
from sam_url_cache import URLCache

cache = URLCache()  # Uses default database path 'url_cache.db'

# Fetch and cache a URL
content = cache.get_url('https://example.com')

# Check if a URL has been seen before
if cache.seen_before('https://example.com'):
    print("URL is cached!")
```

## Object

### `URLCache(databse_path: str = 'url_cache.db')`

Create a new URL cache instance. Optionally specify a custom database path.

### `get_url(url: str) -> str`

Fetch the content of the URL. Returns cached content if available, otherwise fetches from the web and caches it.

### `seen_before(url: str) -> bool`

Check if the URL is already cached.