semvec/pypi-packages huggingface dataset:
package-name-1 0.0.1
Desafio criação de pacotes
Requires Python: >=3.8

# package_name

Description. 
The package image_processing_01 is used to:
	Processing:
		-Histogram matching
		-Structural similarity
		-Resize image
	Utils
		-Read image
		-Save image
		-Plot image
		-Plot result
		-Plot histogram
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing_01
```

## Author
Rafael

## License
[MIT](https://choosealicense.com/licenses/mit/)
Macal 3.1.0
Specialized scripting language
Requires Python: >=3.7

# Macal Package

This is the installation package for Macal, a Python script Language by Marco Caspers.

Macal was developed for a limited and specific use.

Creating a human and machine readable configuration file that loads data, transforms the data and emits the data.
Loading happens by calling functions from libraries specific to a particular (Rest/XML) API.

Emitting data is currently limited to the console and SysLog.

In the tests folder find the test.py utility that contains several tests.

13-12-2021:
Please note that this package was previously called pyql, however there already existed a pyql project so i renamed it.

Whats new in 2.0:

- Fully revised lexer, parser and interpreter.
- Script language now support defining functions.
- The interface for libraries was changed to make it much easier to write libraries.
- Published on github in a private repository.
- Extension for examples changed from .pyql into .mcl
- Examples in example subfolder
- Removed http library, may be re-added later
- Removed external dependancies due to removal of http library
- Changed interface of syslog commands

Version 2.0.1:
- Minor bugfix, validatefunction throws an exception which needed scope to be passed, but the function didn't have that arg.
- Function Parameters/Arguments for library RegisterFunction now created by FuncArg() instead of ExpressionItem() to 
  make the name more obvious and easier to read.

Version 3.0.0:
- Lexer token is now a class.
- Rewrite of Lexer to greatly simplify it.
- Changes to parser to take into account changes in the Lexer/Lexer token.

Version 3.0.1:
- MANY bugfixes in the interpreter.
- Several updates to libraries.

Version 3.0.3 - 13-01-2022:
- Updated this document.
- Trying to fix issue with pip where it doesn't go to the normal branch but to legacy?

Version 3.0.5 - 14-01-2022:
- Fixed parser issue with comments in blocks (parse_instruction).
  When a block contained a comment just before the closing bracket the skipping of the comment 
  caused the closing bracket itself to be considered an illegal character.

Version 3.0.6 - 26-01-2022:
- Fixed dependancy on Python 3.8.4 and later by removing type decorators for lists.

Version 3.0.7 - 26-01-2022:
- Fixed interpreter version # 3.0.1 > 3.0.6
- Updated this readme.

Version 3.1.0 - 04-05-2022:
- Added "include" command to the language.
  Usage:
    include name[, name]...;

    "name" is the filename of the to be included "library" without the .mcl extension.

    Any function defined therein can be called. Any variable defined can also be used.

    Macal will try to prevent including files that are already included to prevent having to run an interpreter on the same file multiple times.
atribucion 2.3
Libreria para correr modelos de atribucion para marketing digital

# MODELOS DE ATRIBUCION
Esta es una librería creada para el trabajo con modelos de atribución sobre datos de marketing digital.
Con esta librería se podrán crear modelos heurísticos (First click, Last click, Linear) o modelos data-driven (Markov, Shapley).
Funciona tanto con datos previamente formateados como con datos sin formatear.

## INSTALACIÓN
```
pip install atribucion
```
## CÓMO SE USA
Primero, es necesario crear un modelo pasando la data a utilizar. Se deberá indicar si la información fue previamente formateada o no.

La data formateada debe ser un dataframe con 2 columnas:
1. lista o array de recorridos (string)
2. conversión (boolean o int)

La data sin formatear debe ser un dataframe con 4 columnas:
1. tiempo (date o timestamp)
2. usuario (string)
3. canal (string)
4. conversion (boolean o int)

```
modelo = atribucion.Modelo(data, formateada=True)
```
Si la data no está formateada, se puede formatear para Markov o para Shapley. Los modelos heurísticos usan indistintamente cualquiera de las dos.
Para formatear la data, hay que indicar 3 parámetros:
1. ventana (int): cuántos días para atrás de la conversión se van a tener en cuenta para armar los recorridos.
2. touchpoints (int): la mayor cantidad de touchpoints que se admiten en cada recorrido.
3. conversión (bool): si se corta cada recorrido al encontrar una conversión o no.

```
modelo.formatear_markov(ventana=30, touchpoints=8, conversion=True)
modelo.formatear_shapley(ventana=30, touchpoints=8, conversion=True)
```
Luego, se piden los modelos deseados llamando a la función correspondiente.
La única que admite un parámetro es markov (el orden).

````
resultado_markov = modelo.markov(orden=1)
resultado_shapley = modelo.shapley()
resultado_first = modelo.first()
resultado_last = modelo.last()
resultado_linear = modelo.linear()
````
KahootSpam 1.2.0
A kahoot api to spam bots
Requires Python: >=3.6

# Kahoot Api

A simple kahoot api to spam kahoot servers.

## Install

you can install the ```KahootSpam``` module by running this code:

```
$ pip3 install KahootSpam
```

## Usage

you can spam kahoot servers by using the ```spam()``` function:

```python
# import module
import KahootSpam

# spam
KahootSpam.spam(name, code, amount)
```
theory-game 1.9.1
A package for EGE 19-21 tasks in computer science dedicated to game theory
Requires Python: >=3.6

This package is designed to simplify the tasks of 19-21 EGE in computer science, which are dedicated to game theory.

Provided four classes (Game1, Game2, Game1Ceil, Game2Ceil) for one and two heaps, and the same, but only with a winning ceiling, respectively. Class Game is abstract and parent to others.
entityrender 0.2.0
Basic entity span highlighting in text for visualization in notebooks

# Simple entity span visualization in text

This is a simple widget, adapted from the code in Spacy visualizers, that can be used for simple visualization of text spans in Jupyter notebooks.

To install:

```
$ pip install entityrender
```

Example code:

```
from IPython.core.display import HTML
from entityrender.entityrender import EntityRenderer

text = """When Sebastian Thrun started working on self-driving cars at Google in 2007,
few people outside of the company took him seriously."""

spans = [{"start": 5, "end": 20, "label": "Person"},
         {"start": 61, "end": 67, "label": "Company"},
         {"start": 71, "end": 75, "label": "Date"}]

er = EntityRenderer(labels=["Person", "Company", "Date"])
output = er.render_ents(text, spans)

HTML(output)

```

Result:

![Example result](images/usage.png)