semvec/pypi-packages huggingface dataset:
naive-cartographer 3.0.1
A naive Bayes Markov decision process learner
Requires Python: >=3.12

# Fuzzy Naive Cartographer

The "model" part of a model-based reinforcement learning algorithm.

## Installation

Pull down the FNC repository.
At the command line: 

```
uv add naive-cartographer
```

## Usage

The FNC class Naive Cartographer is intended to be used in tandem with
a planner (not included) to build a reinforcement learning agent.
At each time step 

1. The FNC makes a prediction
2. The planner uses that prediction to choose an action
3. The FNC registers the action selection

The outline of an agent class looks like this:

```python
from cartographer import NaiveCartographer as Model 

class Agent
    def __init__(self, n_sensors, n_actions):
        model = Model(n_sensors=n_sensors, n_actions=n_actions)
        planner = ...

    def step(self, sensors, reward):
        predictions, rewards, uncertainties = model.predict(sensors, reward)
        actions = planner.choose_actions(predictions, rewards, uncertainties)
        model.update_actions(actions)
        return actions
```

## Testing

Run some unit tests with

```
uv run pytest
```

## About

A Fuzzy Naive Cartographer (FNC) builds a
Markov Decision Process-like model of
its world in the form of a set of sequences.
Sequences are of the form of state-action-state as in MDPs.
Here they're also referred to as feature-action-outcome
sequences, to disambiguate the before- and after-state.

FNC builds a value function too.
It creates a set of feature-action pairs and associates a reward
with each. This is analogous to the state-action
value functions of Q-learning.

A model and a value function allow for prediction and planning.
Knowing the current active features and recent actions,
both the reward and the resulting features can be anticipated.

Technically FNC is just the "model" part of a model-based
RL algorithm. It needs to paired with a planner, some element that
will choose an action or goal, to be a complete RL algorithm.
There are some rudimentary planners included with this module
to get you up and running.

There is more detail in the white paper at
[https://brandonrohrer.com/cartographer](https://brandonrohrer.com/cartographer)
pyplis 1.5.0
Python library for the analysis UV SO2 camera data
Requires Python: >=3.9

|build-status| |docs|

Pyplis -  Python Plume Imaging Software
---------------------------------------

Pyplis is a Python toolbox originally developed for the analysis of UV SO2 camera data. The software includes a comprehensive and flexible collection of algorithms for the analysis of atmospheric imaging data and is tested for all major operating systems and most recent python versions.

Useful links
============

- **Installation**: `Installation instructions <https://pyplis.readthedocs.io/en/latest/intro.html#installation>`_
- **Getting Started**: `Getting Started guide <https://pyplis.readthedocs.io/en/latest/intro.html#getting-started>`_
- **Main Features**: `Feature overview <https://pyplis.readthedocs.io/en/latest/intro.html#main-features>`_
- **Contributing**: `Contributing guide <https://pyplis.readthedocs.io/en/latest/contributing.html>`_
- **Scientific background**: `Peer reviewed pyplis publication <http://www.mdpi.com/2076-3263/7/4/134>`_

Features
========

- Comprehensive algorithms for all aspects of UV SO2 camera data analysis.
- Flexible tools for atmospheric imaging data processing.
- Cross-platform compatibility (Windows, Linux).
- Tested with recent Python versions.

For more details on the features, please refer to the `documentation <https://pyplis.readthedocs.io/en/latest/intro.html#main-features>`__.

Installation
============

To install Pyplis, simply run:

.. code-block:: bash

   pip install pyplis

If you use conda, you can also install pyplis using conda-forge:

.. code-block:: bash

   conda install -c conda-forge pyplis

For detailed installation instructions, please refer to the `documentation <https://pyplis.readthedocs.io/en/latest/intro.html>`__.

Documentation
=============

The pyplis documentation is hosted `here <https://pyplis.readthedocs.io/>`_. It contains all relevant
information about the software, including installation instructions, a detailed API reference, and a gallery of examples. 
The documentation is continuously updated and improved.

Contributions are welcome!
==========================

The pyplis software is maintained during the author's free time, and the author is no longer actively engaged in the field of volcano remote sensing. Contributions from users or developers with expertise in UV SO2 cameras are highly valued. Interested individuals are encouraged to reach out via email at jonasgliss@gmail.com, submit pull requests through a fork, or create issues on the repository (tagging @jgliss in issues and pull requests is recommended).

.. |build-status| image:: https://github.com/jgliss/pyplis/workflows/CI/badge.svg
   :target: https://github.com/jgliss/pyplis/actions

.. |docs| image:: https://readthedocs.org/projects/pyplis/badge/?version=latest
    :target: https://pyplis.readthedocs.io/en/latest/?badge=latest
wynncraft-waypoint-utils 0.0.3
Wynncraft Waypoint Utils
Requires Python: >=3.8

# wynncraft-waypoint-utils
Utilities for managing waypoints on Wynncraft

## Requirements
- Python 3

## Examples

Radius Based Filtering
```bash
wynncraft-waypoint-utils --input in1.json in2.json --filter-radius=3 --output out.json
```