semvec/pypi-packages huggingface dataset:
lampctl 0.1.2
CLI utility for controlling smart lamps

# Lampctl

[![PyPI](https://img.shields.io/pypi/v/lampctl)](https://pypi.org/project/lampctl)
[![Test](https://github.com/fwcd/lampctl/actions/workflows/test.yml/badge.svg)](https://github.com/fwcd/lampctl/actions/workflows/test.yml)

A small CLI utility for controlling smart lamps.

![Icon](icon.png)

Currently only Philips Hue lamps are supported, but adding support for other backends is easy.

## Usage

To use, first create the file `~/.config/lampctl/config.json` pointing to your lamp systems:

```json
{
  "systems": [
    {
      "type": "hue",
      "bridge-ip": "your.ip.here"
    }
  ],
  "default-lamp": "My Lamp"
}
```

> If no `default-lamp` is set you can use the environment variable `DEFAULT_LAMP` or `-n` to select a lamp to control.

Now you can use the CLI to control your lamps. For example:

```sh
lampctl on               # turn selected lamp on
lampctl off              # turn selected lamp off
lampctl toggle           # toggle selected lamps
lampctl color blue       # set selected lamps to blue
lampctl dim 50           # dim selected lamps to 50%
lampctl --all on         # turn all lamps on
lampctl list             # list all lamps
lampctl status           # list selected lamps
lampctl -n "My Lamp" on  # turn lamp with name `MyLamp` on
```

## Development

* Optionally setup a virtual environment:
    * Create a venv using `python3 -m venv venv`
    * Activate the venv using `source venv/bin/activate`
* Make sure that Wheel is installed using `pip3 install wheel`
* Finally install the dependencies with `pip3 install -r requirements.txt`
* Now you can run `lampctl` with `python3 -m lampctl ...`
* To run the test suite, invoke `python3 -m unittest` in this directory

## Installation

* First make sure you are not in a virtual environment
* Then run `pip3 install .`
* If your Python packages are available on your `PATH` you should now be able to invoke `lampctl` from anywhere
dsbix 0.1
API für DSBMobile via tesseract.
Requires Python: >=3.5

An updated version of nerrixde's DSBApi.
Install all requirements before installing or just use 'pip install dsapi'

Contact me on Github(https://github.com/zfqtim) or write me a E-Mail at zfq@tuta.io if there are any problems.

This repo works right now so i won't update it unless theres some kind of change.
er_infrastructure 0.0.1
A stub for the EntityRisk PROVEN Platform infrastructure package
Requires Python: >=3.9,<4.0

This is a public stub of a proprietary EntityRisk package uploaded to protect
users of the EntityRisk PROVEN Platform from
[dependency confusion](https://medium.com/@alex.birsan/dependency-confusion-4a5d60fec610).

This package is non-functional and users accidentally installing it will
receive an error message when trying to import it.
ivadomed 2.9.10
Feature conditioning for IVADO medical imaging project.
Requires Python: >=3.7,<3.11

  
![ivadomed Overview](https://raw.githubusercontent.com/ivadomed/doc-figures/main/index/overview_title.png)

[![DOI](https://joss.theoj.org/papers/10.21105/joss.02868/status.svg)](https://doi.org/10.21105/joss.02868)
[![Coverage Status](https://coveralls.io/repos/github/ivadomed/ivadomed/badge.svg?branch=master)](https://coveralls.io/github/ivadomed/ivadomed?branch=master)
[![test status](https://github.com/ivadomed/ivadomed/workflows/Run%20tests/badge.svg)](https://github.com/ivadomed/ivadomed/actions?query=workflow%3A%22Run+tests%22)
[![publish package](https://github.com/ivadomed/ivadomed/workflows/Publish%20Package/badge.svg)](https://github.com/ivadomed/ivadomed/actions?query=workflow%3A%22Publish+Package%22)
[![Documentation Status](https://readthedocs.org/projects/ivado-medical-imaging/badge/?version=latest)](https://ivadomed.org/en/latest/?badge=latest)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.md)
[![Twitter Follow](https://img.shields.io/twitter/follow/ivadomed.svg?style=social&label=Follow)](https://twitter.com/ivadomed)

`ivadomed` is an integrated framework for medical image analysis with deep learning.

The technical documentation is available [here](https://ivadomed.org).  The more detailed installation instruction is available [there](https://ivadomed.org/installation.html)

## Installation

``ivadomed`` requires Python >= 3.7 and < 3.10 as well as PyTorch == 1.8. We recommend working under a virtual environment, which could be set as follows:

```bash
python -m venv ivadomed_env
source ivadomed_env/bin/activate
```

### Install from release (recommended)

Install ``ivadomed`` and its requirements from `Pypi <https://pypi.org/project/ivadomed/>`__:

```bash
pip install --upgrade pip
pip install ivadomed
```

### Install from source

Bleeding-edge developments builds are available on the project's master branch on Github. Installation procedure is the following:

```bash
git clone https://github.com/neuropoly/ivadomed.git
cd ivadomed
pip install -e .
```


## Contributors
<p float="left">
  <img src="https://raw.githubusercontent.com/ivadomed/doc-figures/main/contributors/neuropoly_logo.png" height="80" />
  <img src="https://raw.githubusercontent.com/ivadomed/doc-figures/main/contributors/mila_logo.png" height="80" />
  <img src="https://raw.githubusercontent.com/ivadomed/doc-figures/main/contributors/ivado_logo.png" height="80" />
</p>

This project results from a collaboration between the [NeuroPoly Lab](https://www.neuro.polymtl.ca/)
and the [Mila](https://mila.quebec/en/). The main funding source is [IVADO](https://ivado.ca/en/).

[List of contributors](https://github.com/neuropoly/ivadomed/graphs/contributors)

## Consult our Wiki(https://github.com/ivadomed/ivadomed/wiki) here for more help
Open-STS-Miner 0.2
A package implementing the Spatiotemporal Sequence Miner (STS Miner) algorithm
Requires Python: >=3.8

# Open STS Miner

This package implements the Spatiotemporal Sequence Miner (STS Miner) algorithm, first introduced by Huang et al. (2008): A framework for mining sequential patterns from spatio-temporal event data sets.  Given a database of spatiotemporal events (i.e., records with an event type, location, and time), STS Miner finds significant sequences of events that occur nearby in space and shortly after one another in time.  These sequences are of the form A -> B -> C, where A, B, and C are event types.  The definition of "close" is set by user parameters.  Significance of a sequence is determined by comparing the local event densities to global event densities, again with a user-defined threshold.  See Huang et al.'s 2008 paper for details on the algorithm.

Open STS Miner implements the Naive STS Miner algorithm and the optimized Slicing STS Miner algorithm, which processes overlapping temporal slices to handle large datasets.

This package is entirely free and open source.  It is provided as-is.  You are free to use the package and modify the source code.  Open STS Miner can be downloaded with pip for use in Python: ***.

The src directory contains the core code for Open STS Miner in the sts_miner.py file.  Unit tests are provided in the test.py file.  Utility functions are provided in the util.py file.  The sts_predictor file contains code to turn STS Miner into a predictor.

The examples directory contains some example code for using Open STS Miner on the sample dataset provided in Huang et al.'s original paper; this dataset is located in the sample_data directory.
latexgenerator-Topalidi 0.1
Библиотека для генерации LaTeX кода для таблиц и изображений
Requires Python: >=3.6


```markdown
# LaTeXGenerator

LaTeXGenerator - это Python библиотека, предназначенная для генерации LaTeX документов, включая таблицы и изображения.

## Установка

Чтобы установить последнюю версию LaTeXGenerator, выполните следующую команду:

```bash
pip install LaTeXGenerator
```

## Использование

### Генерация таблицы

Чтобы сгенерировать таблицу в LaTeX:

```python
from latexgenerator import generate_latex_table

data = [
    ["Header 1", "Header 2", "Header 3"],
    ["Data 1", "Data 2", "Data 3"],
    ["Data 4", "Data 5", "Data 6"]
]
latex_code = generate_latex_table(data)
print(latex_code)
```

### Генерация изображений

Чтобы вставить изображение в LaTeX документ:

```python
from latexgenerator import generate_latex_image

latex_code = generate_latex_image('path/to/your/image.png', 'Описание изображения')
print(latex_code)
```

## Генерация PDF файла

Для создания PDF из вашего LaTeX файла, вам понадобится установленный LaTeX дистрибутив. После установки вы можете скомпилировать ваш `.tex` файл следующей командой:

```bash
pdflatex ваш_файл.tex
```