semvec/pypi-packages huggingface dataset:
Homie4 0.4.0
Homie 4.0.0 Implementation
Keywords: HOMIE,MQTT

<p align="center">
  <a href="https://github.com/mjcumming/homie4">
    <img width=100% src="assets/Works%20with%20Homie.png">
  </a>
</p>


# Homie

Python 3 implementation of Homie 4.0.0

Class based system to easily add Homie 4.0.0 support to devices.

Supports the Meta, Statistics, and Firmware extensions

Includes ready to use common devices such as switch, dimmer, contact, thermostat, boolean, etc. 

**Install**

~~~~
pip install Homie4
~~~~

Note: On Raspbian use 

~~~~
pip3 install Homie4
~~~~

**Temperature Probe Example**

Creates a temperature device using a AM2302 sensor. 

~~~~python
import Adafruit_DHT
import time

from homie.device_temperature import Device_Temperature

mqtt_settings = {
    'MQTT_BROKER' : 'OpenHAB',
    'MQTT_PORT' : 1883,
}

try:

    temperature_device = Device_Temperature(device_id="temperature-sensor-1",name = "Temperature_Sensor 1",mqtt_settings=mqtt_settings)
    sensor = Adafruit_DHT.AM2302
    pin = 4

    
    while True:
        humidity, temperature = Adafruit_DHT.read_retry(sensor, pin)
        print(temperature)
        temperature_device.update_temperature(temperature)
        time.sleep(5)

except (KeyboardInterrupt, SystemExit):
    print("Quitting.")   
~~~~


**Dimmer Example** 

To create a dimmer device requires that a set_dimmer method be provided. When creating a device, all that is required is to provide the MQTT settings. All other requirements of the Homie specification are automatically handled.

```python
import time

from homie.device_dimmer import Device_Dimmer

mqtt_settings = {
    'MQTT_BROKER' : 'QueenMQTT',
    'MQTT_PORT' : 1883,
}

class My_Dimmer(Device_Dimmer):

    def set_dimmer(self,percent):
        print('Received MQTT message to set the dimmer to {}. Must replace this method'.format(percent))
        super().set_dimmer(percent)        

try:

    dimmer = My_Dimmer(name = 'Test Dimmer',mqtt_settings=mqtt_settings)
    
    while True:
        dimmer.update_dimmer(0)
        time.sleep(5)
        dimmer.update_dimmer(50)
        time.sleep(5)
        dimmer.update_dimmer(100)
        time.sleep(5)

except (KeyboardInterrupt, SystemExit):
    print("Quitting.")      


````



If creating multiple homie devices, you can specify Homie to only use one MQTT connection. This can be an issue on devices with limited resources. For MQTT_SETTINGS add MQTT_SHARE_CLIENT: True.
ml-tooling 0.12.1
A library for machine learning utilities
Requires Python: >=3.7
Keywords: framework,ml,tooling

# Model Tooling library
[![Build Status](https://github.com/andersbogsnes/ml_tooling/workflows/Integration/badge.svg)](https://github.com/andersbogsnes/ml_tooling/actions?workflow=Tests)
[![codecov](https://codecov.io/gh/andersbogsnes/ml_tooling/branch/main/graph/badge.svg)](https://codecov.io/gh/andersbogsnes/ml_tooling)
[![Python 3](https://img.shields.io/pypi/pyversions/ml_tooling.svg)](https://pyup.io/repos/github/andersbogsnes/ml_tooling/)
[![CodeFactor](https://www.codefactor.io/repository/github/andersbogsnes/ml_tooling/badge)](https://www.codefactor.io/repository/github/andersbogsnes/ml_tooling)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

## Installation
Use pip to install:
`pip install ml-tooling`
Or use conda
`conda install -c conda-forge ml_tooling`

## Test
We use `tox` for managing build and test environments, to install `tox` run:
`pip install tox`
And to run tests:
`tox -e py`

## Example usage
Define a class using ModelData and implement the two required methods.
Here we simply implement a linear regression on the Boston dataset using sklearn.datasets

```python
from sklearn.datasets import fetch_california_housing
from sklearn.linear_model import LinearRegression

from ml_tooling import Model
from ml_tooling.data import Dataset

# Define a new data class
class CaliforniaData(Dataset):
    def load_prediction_data(self, idx):
        x, _ = fetch_california_housing(return_X_y=True)
        return x[idx] # Return given observation

    def load_training_data(self):
        return fetch_california_housing(return_X_y=True)

# Instantiate a model with an estimator
linear_california = Model(LinearRegression())

# Instantiate the data
data = CaliforniaData()

# Split training and test data
data.create_train_test()

# Score the estimator yielding a Result object
result = linear_california.score_estimator(data)

# Visualize the result
result.plot.prediction_error()

print(result)
<Result LinearRegression: {'r2': 0.68}>
```


## Links
* Documentation: https://ml-tooling.readthedocs.io
* Releases: https://pypi.org/project/ml_tooling/
* Code: https://github.com/andersbogsnes/ml_tooling
* Issue Tracker: https://github.com/andersbogsnes/ml_tooling/issues
omoidasu 0.4.10
CLI flashcards tool.
Requires Python: >=3.8,<4.0

![PyPI](https://img.shields.io/pypi/v/omoidasu)
![GitHub release (latest SemVer including pre-releases)](https://img.shields.io/github/v/release/0djentd/omoidasu?include_prereleases)
![GitHub all releases](https://img.shields.io/github/downloads/0djentd/omoidasu/total)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/omoidasu)

![GitHub issues](https://img.shields.io/github/issues/0djentd/omoidasu)
![GitHub commit activity](https://img.shields.io/github/commit-activity/m/0djentd/omoidasu)
![GitHub Repo stars](https://img.shields.io/github/stars/0djentd/omoidasu?style=social)

[![Python package](https://github.com/0djentd/omoidasu/actions/workflows/python-package.yml/badge.svg)](https://github.com/0djentd/omoidasu/actions/workflows/python-package.yml)
[![Pylint](https://github.com/0djentd/omoidasu/actions/workflows/pylint.yml/badge.svg)](https://github.com/0djentd/omoidasu/actions/workflows/pylint.yml)

# omoidasu

### Description

CLI flashcards tool.

### Installation

```
pip install omoidasu
```

### How to use
```
Usage: omoidasu [OPTIONS] COMMAND [ARGS]...

  CLI for Omoidasu.

Options:
  --data-dir TEXT                 Data directory.
  --config-dir TEXT               Config directory.
  --cache-dir TEXT                Cache directory.
  --state-dir TEXT                State directory.
  --log-dir TEXT                  Log directory.
  --flashcards-dir TEXT           Flashcards directory.
  --verbose / --no-verbose        Show additional information.
  --interactive / --no-interactive
                                  Use interactive features.
  --debug / --no-debug            Show debug information.
  --help                          Show this message and exit.

Commands:
  add     Add cards interactively using text editor.
  list    Writes all cards to stdout.
  new     Add card.
  review  Review all cards.
```

```
Usage: omoidasu list [OPTIONS] REGULAR_EXPRESSION

  Writes all cards to stdout.

Options:
  --max-cards INTEGER  Max number of cards to list.
  --help               Show this message and exit.
```

```
Usage: omoidasu review [OPTIONS] REGULAR_EXPRESSION

  Review all cards.

Options:
  --max-cards INTEGER  Max number of cards to review.
  --help               Show this message and exit.
```

```
Usage: omoidasu add [OPTIONS]

  Add cards interactively using text editor. Save empty file to finish adding
  cards.

Options:
  --editor TEXT
  --help         Show this message and exit.
```

```
Usage: omoidasu new [OPTIONS] [SIDES]...

  Add card.

Options:
  --help  Show this message and exit.
```
df-runner 0.2.1
**Dialog Flow Runner** is python module add-on for [Dialog Flow Framework](https://github.com/deepmipt/dialog_flow_framework), a free and open-source software stack for creating chatbots, released under the terms of Apache License 2.0.
Requires Python: >=3.6, <4
Keywords: chatbots,Dialog Flow Runner


# Dialog Flow Runner

**Dialog Flow Runner** is python module add-on for [Dialog Flow Framework](https://github.com/deepmipt/dialog_flow_framework), a free and open-source software stack for creating chatbots, released under the terms of Apache License 2.0.


[Dialog Flow Runner](../..) allows you to ...
[![Codestyle](../../../workflows/codestyle/badge.svg)](../../../actions)
[![Tests](../../../workflows/test_coverage/badge.svg)](../../../actions)
[![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
![Python 3.6, 3.7, 3.8, 3.9](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-green.svg)

<!-- TODO: uncomment one of these to add badges to your project description -->
<!-- [![Documentation Status](https://df_runner.readthedocs.io/en/stable/?badge=stable)]() See readthedocs.io -->
<!-- [![Coverage Status]()]() See coveralls.io -->
<!-- [![PyPI](https://img.shields.io/pypi/v/df_runner)](https://pypi.org/project/df_runner/) -->
<!-- [![Downloads](https://pepy.tech/badge/df_runner)](https://pepy.tech/project/df_runner) -->

# Quick Start
## Installation
```bash
pip install df_runner
```

## Basic example
```python

```

To get more advanced examples, take a look at [examples](examples) on GitHub.

# Contributing to the Dialog Flow Runner

Please refer to [CONTRIBUTING.md](CONTRIBUTING.md).