semvec/pypi-packages huggingface dataset:
circuitsim 0.0.1
Run gate-level HDL simulations from python.
Requires Python: >=3.6

# CircuitSim

[![Python package](https://github.com/circuitgraph/circuitsim/actions/workflows/python-package.yml/badge.svg)](https://github.com/circuitgraph/circuitsim/actions/workflows/python-package.yml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)

An interafce to perform simulations of gate-level HDL designs from python.

## Installation

CircuitSim requires Python3.6 or greater
The easiest way to install is via PyPi:
```shell
pip install circuitsim
```

Finally, to install in-place with the source, use:
```shell
cd <install location>
git clone https://github.com/circuitgraph/circuitsim.git
cd circuitsim
pip install -e .

In order to perform simulations, you must have at least one of the available simulators installed.
```

## Using the simulator
Simulation can be performed using the `CircuitSimulator` class.

This allows the simulation to be compiled once and then quieried multiple times without having to recompile.

The `CircuitSimulator` accepts circuits in the form of [circuitgraph](https://circuitgraph.github.io/circuitgraph/) `Circuit` objects.

Vectors are accepted and returned as dictionaries mapping input/output names to logical values (`True` or `False`).

## Available simulators

Currently, [iverilog](http://iverilog.icarus.com), [verilator](https://www.veripool.org/verilator/), and VCS are supported. In order to use a given simulator, it must be in your `PATH`.

## Usage Example
```python
import random
import shutil

import circuitgraph as cg

from circuitsim import CircuitSimulator


def main():
    # Exercise 3 random inputs patterns 5 times.
    num_trials = 5
    num_vectors = 3
    c = cg.from_lib("c17")
    simulator = CircuitSimulator(c)
    for sim_num in range(num_trials):
        vectors = []
        for _ in range(num_vectors):
            vectors.append({i: random.choice([True, False])
                           for i in c.inputs()})
        sim_results = simulator.simulate(vectors)
        print(f"Simulation {sim_num}")
        print(f"inputs: {vectors}")
        print(f"outputs: {sim_results}")


if __name__ == "__main__":
    main()
```

## Contributing

If you want to develop an improvement for this library, please consider the information below.

Tests are run using the builtin unittest framework. Some basic linting is performed using flake8.
```shell
pip instsall flake8
make test
```

Documentation is built using pdoc3.
```shell
pip install pdoc3
make doc
```

Code should be formatted using [black](https://black.readthedocs.io/en/stable/).
[Pre-commit](https://pre-commit.com) is used to automatically run black on commit.
```shell
pip install black pre-commit
pre-commit install
```
Pre-commit also runs a few other hooks, including a docstring formatter and linter. Docs follow the `numpy` documentation convention.
frumpy 1.0.4
It's FrumPy time.
Requires Python: >=3.6

# FrumPy

Welcome to FrumPy. Welcome to the Frump.

<p align="center">
  <img width="400" src="https://raw.githubusercontent.com/frumpyfrumpster/frumpy/main/Frumpy.png">
</p>

## Installation

```bash
pip install frumpy
```

## Usage

Use exactly like `numpy` lol

```python
import frumpy
x = frumpy.array((0, 1, 2))
print(x)
>>> [0 1 2]
```
crowdplay-datasets 0.1.0
A collection of crowdsourced human demonstration datasets for offline learning
Requires Python: >=3.7, <4

# The CrowdPlay Atari Dataset

The CrowdPlay Atari dataset is a collection of over 300 hours of human Atari 2600 demonstrations. In addition to vanilla gameplay, it includes substantial amounts of multiagent data, including human-human and human-AI data, as well as multimodal behavioral data, where participants were asked to follow a specific behavior in the game.

## Installing the CrowdPlay Atari Dataset

### 1. Installing the Python Package

Run ```pip install crowdplay_datasets``` to install the dataset package.

### 2. Downloading and Extracting the Dataset

Then, run `python -m crowdplay_datasets.install --dataset=crowdplay_atari-v0` to download and extract the actual dataset. The dataset is about 15GB in size, but during installation will temporarily require about 30GB of disk space.

### 3. Optional: Re-pack into Gzip

Trajectories are compressed using bzip, which is space-efficient but slow. If you will load trajectories many times, you can re-pack the dataset into gzip using the `scripts/convert_to_gzip.sh` script. Note that this requires around 250GB of disk space. There is an additional script to unpack the dataset entirely, but this requires around 10TB of space and is not noticeably faster than gzip. Run either script inside the dataset directory (shown during installation).

## More Information

For more information see [https://mgerstgrasser.github.io/crowdplay/](https://mgerstgrasser.github.io/crowdplay/).
FrumPy 1.0.3
It's Frumpy time.
Requires Python: >=3.6

# FrumPy

Welcome to FrumPy. Welcome to the Frump.

<p align="center">
  <img width="400" src="https://raw.githubusercontent.com/frumpyfrumpster/frumpy/main/Frumpy.png">
</p>

## Installation

```bash
pip install frumpy
```

## Usage

Use exactly like `numpy` lol

```python
import frumpy
x = frumpy.array((0, 1, 2))
print(x)
>>> [0 1 2]
```