data-transformation-wizzard 0.1.4

Transpose.Windowing.Convolution

Data Wizzard Library

Overview

The Data Wizzard Library is a Python package designed to simplify and streamline data transformation tasks. It provides a set of functions that perform common operations such as 2D transposition, 1D windowing, and 2D convolution. These functions are optimized for performance and ease of use, making it easier to manipulate and analyze data in Python.

Features

transpose2d

This function transposes a 2D matrix, effectively flipping it over its diagonal, switching the matrix's row and column indices. This is particularly useful in numerical and matrix computations.

window1d

The window1d function applies windowing operations to 1-dimensional arrays. This is useful in signal processing where segmenting data into overlapping or non-overlapping windows is required.

convolution2d

Performs 2D convolution on an input matrix with a specified kernel and stride. This function is essential for image processing, feature extraction, and machine learning applications involving convolutional neural networks.

Installation

You can install the library using pip:

pip install data-transformation-wizzard

Usage

To use the functions in the Data Wizzard Library, you first need to import the necessary functions. Below are some examples of how to use each feature:

Using transpose2d

from data_transformation_wizzard import transpose2d
matrix = [[1, 2], [3, 4]]
print(transpose2d(matrix))

Using window1d

from data_transformation_wizzard import window1d
array = [1, 2, 3, 4, 5, 6]
size = 3
shift = 1
stride = 1
print(window1d(array, size, shift, stride))

Using convolution2d

from data_transformation_wizzard import convolution2d
input_matrix = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
kernel = [[0, 1], [2, 3]]
stride = 1
print(convolution2d(input_matrix, kernel, stride))