semvec/pypi-packages huggingface dataset:
pyrplidar 0.1.2
Full-featured python library for Slamtec RPLIDAR series
License: MIT

# PyRPlidar ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyrplidar) ![PyPI](https://img.shields.io/pypi/v/pyrplidar) [![MIT License](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/Hyun-je/pyrplidar/blob/master/LICENSE)

![61845532-a5413e80-aede-11e9-9eee-db8438055619](https://user-images.githubusercontent.com/7419790/61871806-b14bf100-af1c-11e9-94a6-812b4f10930a.png)

## Introduction
[PyRPlidar](https://github.com/Hyun-je/pyrplidar) is a python library for Slamtec RPLIDAR series.

* Supports all series (A1, A2 and A3)
* Implement all features of the device
* Simple code & Easy to use
* Use generator pattern (for performance)

## Installation
```sh
$ pip install pyrplidar
```

## Example Code
```Python
from pyrplidar import PyRPlidar

lidar = PyRPlidar()
lidar.connect(port="/dev/ttyUSB0", baudrate=256000, timeout=3)
# Linux   : "/dev/ttyUSB0"
# MacOS   : "/dev/cu.SLAB_USBtoUART"
# Windows : "COM5"


info = lidar.get_info()
print("info :", info)

health = lidar.get_health()
print("health :", health)

samplerate = lidar.get_samplerate()
print("samplerate :", samplerate)


scan_modes = lidar.get_scan_modes()
print("scan modes :")
for scan_mode in scan_modes:
    print(scan_mode)


lidar.disconnect()
```

## Documentation
This library implement full specifications on the [protocol documentation](http://bucket.download.slamtec.com/ccb3c2fc1e66bb00bd4370e208b670217c8b55fa/LR001_SLAMTEC_rplidar_protocol_v2.1_en.pdf) of Slamtec.
aimcli 0.1.1
AI Deployment tool
License: MIT
Requires Python: >=3.6.0


# Aim

Aim is an AI deployment and version control system.
It can handle both small and large projects through their whole life cycle with efficiency and speed.
It is built to seamlessly blend in with existing ML stack and become an integral part of the development lifecycle.

## Aim CLI
Aim CLI is a command line tool for building end-to-end AI.
Aim is built to be:
compatible with the existing ecosystem of tools
be familiar
just work
make building AI productive

Aim has three main features: tracking of training, export and deploy.

### Tracking - ML Training
Command: `aim train`
Aim train runs training for the given aim repository. Aim train tracks the gradients and updates in the model with given interval and saves them for visualization and analysis.
Aim Train is paired with UI that visualizes the artifacts tracked.
Aim Tracking is used to debug and have a detailed understanding of the process of training.

### Export - ML Model
Command: `aim export`
Aim export creates the saved model checkpoint file and exports .aim model which could be committed and pushed to the Aimhub and/or deployed to different platforms.
Exported .aim model could also be converted to .onnx, .tf and other checkpoints for other frameworks.
Aim CLI Export is based on aim Intermediate Representation that allows for automatic deployment of the model.
Aim Export can also export pre-processing steps similarly to the model and could be included in the model deployment process.

### Deploy - Aim Model
Command: `aim deploy`
Aim Deploy produces a deployable artifact from .aim (model and preprocessing) files. The produced artifacts can run in cloud, on different hardware and as a hybrid.
Deployments are also reflected on Aimhub to track and version the deployed artifacts.


### Other Commands

```shell
aim fork
aim branch off
aim pause, continue
aim convert
```
subtokenizer 0.0.19
Subwords tokenizer for neural natural language processing
License: MIT
Requires Python: >=2.6, <4.0
Keywords: nlp tokenization

# SubTokenizer
[![Build status](https://travis-ci.org/kovalevfm/SubTokenizer.svg?master)](https://travis-ci.org/kovalevfm)

Subwords tokenizer based on google code from tensor2tensor. It supports tags and combined tokens in addition to google tokenizer.
* Tags are tokens starting from `@`, they are not splited on parts.
* No break symbol `¬` `'\xac'` allows to join several words in one token.

Tokenizer does unicode normalization and controls characters escaping. It's also possible to encode rare symbols so they can be splited on parts by subwords algorithm.

Original google subwords tokenizer: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/text_encoder.py

By default before learning and tokenizing SubTokenizer encodes all control characters and `@` `¬` symbols. To use tags it's needed to run encoding first then add tags and after learn/tokenize with `encode_controls=Flase` or `--no_encode_controls` in command line mode.

Install:
```bash
 pip install subtokenizer
```

Usage:
```bash
cat text_file.txt | subtokenizer learn -o bpe.file -s 1000 -r reserved_tokens.txt
cat text_file.txt | subtokenizer tokenize -s bpe.file > tokenized_file.txt
cat tokenized_file.txt | subtokenizer detokenize -s bpe.file > text_file.txt
```
Or:
```python
from subtokenizer import SubTokenizer

tokenizer = SubTokenizer.learn(words_count)
tokenizer.save(subwords_filename)

tokenizer = SubTokenizer.load(subwords_filename)
tokens = tokenizer.tokenize(line)
line = tokenizer.detokenize(tokens)

```
aimhub 0.1.0
AI Deployment tool
License: MIT
Requires Python: >=3.6.0


# Aim

Aim is an AI deployment and version control system.
It can handle both small and large projects through their whole life cycle with efficiency and speed.
It is built to seamlessly blend in with existing ML stack and become an integral part of the development lifecycle.

## Aim CLI
Aim CLI is a command line tool for building end-to-end AI.
Aim is built to be:
compatible with the existing ecosystem of tools
be familiar
just work
make building AI productive

Aim has three main features: tracking of training, export and deploy.

### Tracking - ML Training
Command: `aim train`
Aim train runs training for the given aim repository. Aim train tracks the gradients and updates in the model with given interval and saves them for visualization and analysis.
Aim Train is paired with UI that visualizes the artifacts tracked.
Aim Tracking is used to debug and have a detailed understanding of the process of training.

### Export - ML Model
Command: `aim export`
Aim export creates the saved model checkpoint file and exports .aim model which could be committed and pushed to the Aimhub and/or deployed to different platforms.
Exported .aim model could also be converted to .onnx, .tf and other checkpoints for other frameworks.
Aim CLI Export is based on aim Intermediate Representation that allows for automatic deployment of the model.
Aim Export can also export pre-processing steps similarly to the model and could be included in the model deployment process.

### Deploy - Aim Model
Command: `aim deploy`
Aim Deploy produces a deployable artifact from .aim (model and preprocessing) files. The produced artifacts can run in cloud, on different hardware and as a hybrid.
Deployments are also reflected on Aimhub to track and version the deployed artifacts.


### Other Commands

```shell
aim fork
aim branch off
aim pause, continue
aim convert
```