semvec/pypi-packages huggingface dataset:
atc-gym 0.2
Gym environment for an ATC simulation
License: MIT

# ATC AI
Contains
* A program that helps minimize air crashes by 
using Machine Learning to control air traffic.
* A gym environment for ATC simulation
## Implementing
* `env.render()` is not implemented, running it will raise `NotImplementedError`.
* `env.reset()` opens the GUI.
* `env.fps` contains the fps to run the game at. You can set it using:
    ```python
    env.fps = 60
    ```
## Installation
For the latest installation (may be unstable) 
```bash
git clone https://github.com/vivek3141/atc-ai
pip install -e .
```
Install stable release by
```bash
pip install atc-gym
```

## Creating The Environment
The environment can be created by doing the following:
```python
import gym
import atc_gym
env = gym.make("atc-v0")
```

## Environments
* `atc-v0` Returns a NxN RGB image in the form of a numpy array for the observations
* `atc-tiled-v0` Returns a NxN matrix for the observations.
<br>
N is undecided until implementation
mtwaffle 0.7
Magnetotelluric data analysis
License: MIT
Keywords: science geophysics

# mtwaffle

![](https://img.shields.io/badge/status-alpha-red.svg)
[![Version](http://img.shields.io/pypi/v/mtwaffle.svg)](https://pypi.python.org/pypi/mtwaffle/)
[![License](http://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/kinverarity1/mtwaffle/blob/master/LICENSE)
[![Python versions](https://img.shields.io/pypi/pyversions/mtwaffle.svg)](https://www.python.org/downloads/)

Assorted functions for processing and interpreting magnetotelluric data.

To install:

```bash
$ pip install mtwaffle
```

Requirements: numpy, scipy, matplotlib

See the [quickstart tutorial](https://github.com/kinverarity1/mtwaffle/blob/master/notebooks/Quickstart%20tutorial.ipynb) Jupyter notebook to get started. There are other tutorials in the [notebooks folder](https://github.com/kinverarity1/mtwaffle/blob/master/notebooks).
butterworth 0.0.3
Real-time digital butterworth IIR filter
License: MIT
Keywords: signal processing butterworth filter IIR infinite impulse response forward-backward filtering

butter
======
Python implementation of the digital butterworth IIR filter

Dependencies
============
- **numpy** -- used for its math modules and fast array calculations
- **numba** -- used to increase calculation speed

Basic Usage
===========
Importing the Butter module::

  from butterworth import Butter

Creating the Butter instance for a lowpass butterworth filter with cutoff frequency 1000Hz and rolloff frequency 48Hz for data taken with sampling frequency 44100::

  filter_ = Butter(btype="Lowpass", cutoff=1000, rolloff=48, sampling=44100)

Sending sample data into filter and retrieving the filtered data::

  data=[1.0, -2.0, 3.0, . . . .]
  filtered_data = filter_.send(data)

Retrieving forward-backward filtered data for the accumulated data::

  data1=[1.0, -2.0, 3.0, . . . .]
  filtered_data = filter_.send(data1)
  data2=[-1.0, 2.0, -3.0, . . . .]
  more_filtered_data = filter_.send(data2)
  forward_backwards_filtered_data = filter_.filtfilt()