semvec/pypi-packages huggingface dataset:
cumd 1.6.0
Church Slavonic extensions to Markdown
License: MIT
Requires Python: >=3.8
Keywords: church slavonic, markdown

# cumd

Church Slavonic dialect of Markdown

## Markup

1. Use `=` symbol to mark red spans, for example: `=Слава и ныне=`
2. Use `+` to mark expanded font: `+Священник глаголет:+`
3. Use leading tilda to mark first letter of a word red: `~Христос рождается`
4. For pagebreak anchors use `<<5: л. 3 об.>>` or simply ``<<5>>`` when label is the same 
   as the page number
5. Use leading curcumflex symbol to mark bukvitsa: `^Вначале было Слово`
6. For verse numbering use `((и))`
7. For footnote use `[[footnote text]]` (Attention: this is likely to change)

This package provides two commands:

* `cumd` - renders markdown into html
* `cuxml` - converts Ponomar XML into markdown

## cumd

```bash
usage: cumd [-h] [--html] [--simplified] [--extensions EXTENSIONS] input output

Converts CU markdown to HTML

positional arguments:
  input                 File name of the input *.md file
  output                File name of the output *.html file

options:
  -h, --help            show this help message and exit
  --html                Set to generate viewable HTML
  --simplified, -s      Set to generate HTML without custom tags
  --extensions EXTENSIONS, -e EXTENSIONS
                        List of comma-separated extensions to enable. For example -e footnotes,math
```

## cuxml

```bash
usage: cuxml [-h] input output

Converts XML to cu-flavored markdown

positional arguments:
  input       input XML file
  output      output Markdown file

optional arguments:
  -h, --help  show this help message and exit
```
odrive-can 2.1.0
Command ODrive over CAN with a simple interface
License: MIT
Requires Python: >=3.11

# Asyncio driver for ODrive



Command ODrive over CAN with a simple interface.


* `v1.x` = Odrive 3.6
* `v2.x` = Odrive Pro


**Source code:** [https://gitlab.com/roxautomation/components/odrive-can](https://gitlab.com/roxautomation/components/odrive-can)

**Documentation:** [https://roxautomation.gitlab.io/components/odrive-can](https://roxautomation.gitlab.io/components/odrive-can)


## Features

* designed to work with `asyncio`
* implements full odrive can protocol.
* includes `OdriveMock` class for device simulation
* cli tools for message inspection, backup and demo's.

## Installation

    pip install odrive-can

## Basic usage

```python
import asyncio
from odrive_can import ODriveCAN, CanMsg
from odrive_can.tools import UDP_Client

AXIS_ID = 0
INTERFACE = "vcan0"
SETPOINT = 50

udp = UDP_Client()  # send data to UDP server for plotting


def feedback_callback_fcn(msg: CanMsg, caller: ODriveCAN):
    """called on position estimate"""
    print(msg)
    udp.send(msg.data)


async def main():
    """connect to odrive"""
    drv = ODriveCAN(axis_id=AXIS_ID, interface=INTERFACE)

    # set up callback (optional)
    drv.feedback_callback = feedback_callback_fcn

    # start
    await drv.start()

    # check errors (raises exception if any)
    drv.check_errors()

    # set controller mode
    drv.set_controller_mode("POSITION_CONTROL", "POS_FILTER")

    # reset encoder
    drv.set_linear_count(0)

    # set axis state
    await drv.set_axis_state("CLOSED_LOOP_CONTROL")

    # set position gain
    drv.set_pos_gain(3.0)

    for _ in range(2):
        # setpoint
        drv.set_input_pos(SETPOINT)
        await asyncio.sleep(5.0)
        drv.set_input_pos(-SETPOINT)
        await asyncio.sleep(5.0)

    drv.set_input_pos(0.0)


asyncio.run(main())


```


## CLI interface

    Usage: odrive_can [OPTIONS] COMMAND [ARGS]...

    Options:
    --help  Show this message and exit.

    Commands:
    backup   Backup config to config folder
    demo     demonstration of control modes
    info     Print package info
    inspect  Inspect and decode ODrive CAN messages
    mock     Mock ODrive CAN interface


## Using virtual devices

1. create a virtual can adapter (see [docs](https://odrive-can-roxautomation-components-9f5f4b809336bc0ecbd5b8cd8e4.gitlab.io/can_tools/#virtual-can))
2. in first terminal run `odrive_can mock`
3. in second terminal run `odrive_can inspect vcan0`



## Position control demo

    odrive_can demo position --interface vcan0

### Live plotting data

Demo scripts send data as `json` to `udp://localhost:5005` .
This data can be visualized with [plotjuggler](https://plotjuggler.io/)

(sender code is found in `tools.UDP_Client`)




## Development

### Virtual environment

create virtual envrionment with   `make venv`

### Devcontainer

* `docker` folder contains devcontainer environment.
* `.devcontainer` is VSCode devcontainer environment



## Support

commercial support is available through [www.roxautomation.com](https://www.roxautomation.com)
nafes 0.0.4
A python project for prototype-based feature selection
License: MIT
Requires Python: >=3.10
Keywords: nafes, feature selection, machine learning, AI

# nafes


[![image](https://img.shields.io/pypi/v/nafes.svg)](https://pypi.python.org/pypi/nafes)
[![python: 3.10](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/release/python-31011/)
[![github](https://img.shields.io/badge/version-0.0.4-yellow.svg)](https://github.com/naotoo1/nafes)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)


**A python package for prototype-based feature selection**

Nafes is a prototype-based feature selection package designed as a wrapper centered on the highly interpretable and powerful Generalized Matrix Learning Vector Quantization (GMLVQ) classification algorithm and its local variant (LGMLVQ). Nafes utilizes the learned relevances evaluated by the mutation validation scheme for Learning Vector quantization (LVQ), which iteratively converges to selected features that relevantly contribute to the prototype-based classifier decisions. 

    

## Installation
nafes can be installed using pip.
```python
pip install nafes
```

If you have installed nafes before and want to upgrade to the latest version, you can run the following command in your terminal:
Prosemble can be installed using pip.
```python
pip install -U nafes
```


To install the development version from GitHub using Git, run the following command in your terminal:
```python
pip install git+https://github.com/naotoo1/nafes
```


## Bibtex
If you would like to cite the package, please use this:
```python
@misc{Otoo_nafes_2023,
author = {Otoo, Nana Abeka},
title = {nafes},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished= {\url{https://github.com/naotoo1/nafes}},
}
```