semvec/pypi-packages huggingface dataset:
PyOcclusion-Package 1.0.0
A package for video editing with occlusion noise
Requires Python: >=3.6

# PyOcclusion
![PyPI](https://img.shields.io/pypi/v/PyOcclusion)
![License](https://img.shields.io/github/license/BohdanSynytskyi/PyOcclusion)
[![Downloads](https://static.pepy.tech/badge/pyocclusion)](https://pepy.tech/project/pyocclusion)

PyOcclusion is a Python package for adding noise of occlusionary type to video files. There is a broad range of parameters for altering in order to achieve the desirded occlusion density, coverage percent, etc. The process is automated and requires no additional prescribing.

Here is an example of what can be done with PyOcclusion:

![Description of the image](grid_image.png)

# Statement Of Need

While most of the software tends to reduce noise in the file, there are many cases when people need to add noise in order to test the new visual model, recognition software, etc. However, most of software tends to generate Gaussian noise, Salt-and-Pepper Noise and so on. PyOcclusion gives an opportunity to overlay an occlusion on both image and video files. There is a broad variety of parameters for customizing occlusionary noise to achieve desired result. 

# Installation

PyOcclusion can be installed from PyPI:

```pip install PyOcclusion```

Once installed, package can be loaded as:

```import PyOcclusion```

# Example

``` 
import PyOcclusion

# instantiate an editor

row_velocity = 20
column_velocity = 0
height_of_shape = 17
width_of_shape = 17
num = 500
height_of_frame = 1080
width_of_frame = 1920

editor = PyOcclusion.VideoEditor(row_velocity, column_velocity, height_of_shape, width_of_shape, num, height_of_frame, width_of_frame)

# generate new video

path_to_file = "./video.mp4"

path_to_new_file = "./newVideo.mp4"

editor.editVideo(path_to_file, path_to_new_file)
```

# Documentation

General:

[API](PyOcclusion-Package/docs/API.md)

[Usage Examples](PyOcclusion-Package/examples/Example1.ipynb)

# Citation

The package can be cited as follows:

```
@software{Synytskyi_PyOcclusion_2024,
  author = {Synytskyi, Bohdan and Livingstone, Steven R},
  month = {08},
  title = {{PyOcclusion}},
  url = {https://github.com/BohdanSynytskyi/PyOcclusion},
  version = {1.0.0},
  year = {2024},
}
```
kye 0.1.2
A Data Validation Framework
Requires Python: >=3.7

# Kye Validation Tool
Kye is a validation tool in progress that allows you to define models to validate your data against.

# Getting Started

### Install
```bash
pip install kye
```

### Run
Pass the location of the kye file, the data (`.csv`, `.json` or `.jsonl`) file, and the name of the Model to evaluate the data against. If the data does not pass the model's assertions then the errors and errant data will be displayed.
```
kye user.kye --data users.csv --model User
```

Models are defined in a `.kye` file using the Kye language. 

The Kye language can optionally be compiled into a json or yaml file. Using the `-c` flag followed by a path to a `.json` or `.yaml` file. Run the compiled file like you would a normal `.kye` file
```
kye user.kye -c user.kye.yaml
kye user.kye.yaml --data users.csv --model User
```

### Kye Models
```kye
User(id)(username) {
  id: Number
  username: String
  name: String
  age?: Number

  assert age > 0 & age <= 120
}
```

#### Models

The above Kye script defines a `User` table.
Table names must be upper-cased.

#### Indexes

The name of the table is followed by its index definition.
The `(id)(username)` means that we expect
both the `id` or `username` columns to be able
to uniquely identify a record.

Composite indexes are defined by listing multiple
column names within a single set of parenthesis ex. `(id, username)`

#### Columns

Column names must start with a lowercase and not contain 
spaces or other special characters. If the source data has
column names that don't follow these rules, then you can specify
the full column name in quotes after the column name.
```
  id "User Id": Number
```

The column definitions specify the value type as `Number`, `String` or `Boolean`. More data types like date/time and user defined types are coming soon.

You can specify whether the column allows null values by prefixing the colon with a `?`
```
age?: Number
```

You can also specify if the column allows multiple values (like an array of values) by using `+` if you expect at least one value, or `*` if it is okay to have no values.
```
# Expect at least one version
versions+: String

# It's okay for a post to have no tags
tags*: String 
```

#### Assertions
You can specify extra assertions through the `assert` keyword. Just write an expression that evaluates to true or false, and the rows that evaluate to false will be flagged. You can reference columns by their names.

Expressions support the basic operations:
- `+ - * / %` math
- `== != >= > < <=` comparison
- `! & | ^` logical (not, and, or, xor)
- `()` parenthesis