semvec/pypi-packages huggingface dataset:
pytamp 0.5.1
Robotics Task and Motion Planning Library
Requires Python: >=3

# pytamp

[![PyPI version](https://badge.fury.io/py/pytamp.svg)](https://badge.fury.io/py/pytamp)  [![MIT License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](LICENSE)

*Our paper named "Perturbation-Based Best Arm Identification for Efficient Task Planning with Monte-Carlo Tree Search", accepted to ICRA 2023.*

Python Interface for the robot task and motion planning(TAMP) library.
In the future, we are going to update it so that it can be operated in mujoco simulator.
We want to provide an environment for applying reinforcement learning in the robot domain.

## Features

- Pure python library
- Support only kinematic world, so not consider dynamics in this repo
- Support simple motion planning (RRT* , PRM* , Cartesian planning)
- Render scene using matplotlib or trimesh.Scene
- Support Robot Task and Motion Planning examples with MCTS in 4 benchmarks

## Installation

### Requirements

You need [pygraphviz](https://github.com/pygraphviz/pygraphviz) and python3-tk package to see a MCTS Tree.

- On Ubuntu 18.04 or 20.04, Download graphviz and graphviz-dev and python3-tk using  `apt`

  `sudo apt install graphviz graphviz-dev python3-tk`

- On Mac, Download graphviz using `brew`

  `brew install graphviz`

### Install pytamp

~~~
pip install pytamp
~~~

## Usage

~~~shell
# Benchmark 1 with MCTS
$ sh scripts/run_benchmark1.sh
# Benchmark 2 with MCTS
$ sh scripts/run_benchmark2.sh
# Benchmark 3 with MCTS
$ sh scripts/run_benchmark3.sh
# Benchmark 4 with MCTS
$ sh scripts/run_benchmark4.sh
~~~

If you want to see the result of the output,  Run the benchmark#_result_level_2.ipynb file for that benchmark in the **results** directory.


### Benchmark

We provide 4 benchmarks.

We also show the results when using our algorithm.

|                          Benchmark1                          |                          Benchmark2                          |                          Benchmark3                          |                          Benchmark4                          |
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|                                                              |                                                              |                                                              |                                                              |
pesn-sdk 1.2.3
Quantori Python SDK for PerkinElmer Signals Notebook
Requires Python: >=3.9

# Quantori Python SDK for PerkinElmer Signals Notebook
Copyright (c) 2022 Quantori.

`pesn-sdk` is a Python package that provides an interface between your Python application and PerkinElmer's external API of Signals Notebook.

## Pre-requisites

We assume, that you have already contacted with PerkinElmer and have a working instance of Signals Notebook.

### Obtain an API key

Visit the page `https://<your signals notebook instance>/snconfig/settings/apikey` and generate an API Key

## Installation

```shell
pip install pesn-sdk
```

## Usage

Import and initialize the API instance with your Signals Notebook host and API-token
```python
from signals_notebook.api import SignalsNotebookApi

SignalsNotebookApi.init('https://signalsnotebook.perkinelmer.cloud', '<your api key>')
```
Then you can perform CRUD operations with such entities as Notebook, Experiment, etc.

### Examples
Create a new Notebook
```python
from signals_notebook.entities.notebook import Notebook

notebook = Notebook.create(name='Test creation by SDK', description='Created by me')
```
change its fields
```python
notebook.name = 'Changed name'
notebook.save()
```
finally delete it
```python
notebook.delete()
```
Also, you can retrieve all existing notebooks
```python
notebooks = Notebook.get_list()
```
or one specified by id
```python
from signals_notebook.entities.entity_store import EntityStore
notebook = EntityStore.get("journal:111a8a0d-2772-47b0-b5b8-2e4faf04119e")
```
Deletion can be performed without retrieving the whole object if you know an id
```python
EntityStore.delete("journal:111a8a0d-2772-47b0-b5b8-2e4faf04119e")
```
Jupyter Notebooks with examples see in examples folder

## Additional information
 - [Examples of usage](examples)
 - [API Reference](https://quantori.github.io/quantori-pesn-python-sdk/)
 - [Developers notes](DEVNOTES.md)

## License
Quantori Python SDK for PerkinElmer Signals Notebook is released under [Apache License, Version 2.0](LICENSE)