Metadata-Version: 2.1
Name: Ramtrix
Version: 0.1.1
Summary: Ripoff Numpy
Author: Ram
Keywords: python,matrices,row reduction,linear algebra
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown

 Ramtrix is a Python library for manipulating matrices while boiling your RAM. It offers a wide range of functions to perform matrix operations like addition, subtraction, multiplication, row reduction, transposition, and more. With this package, you can efficiently work with matrices for linear algebra tasks and more, all without the need for external dependencies like Numpy.

### Key Functions:
- **row_reduce**: Perform row reduction to echelon form.
- **inverse**: Calculate the inverse of a matrix.
- **determinant**: Calculate the determinant of a matrix.
- **LU_factorize**: Perform LU factorization on a matrix.
- **matrix_multiply**: Multiply two matrices.
- **dot**: Perform dot product on matrices or vectors.
- **add**: Add two matrices together.
- **subtract**: Subtract one matrix from another.
- **scale**: Scale a matrix by a constant.
- **cofactor**: Calculate the cofactor matrix.
- **transpose**: Get the transpose of a matrix.
- **flatten**: Flatten a matrix into a 1D list.
- **create_identity**: Create an identity matrix of a given size.
- **print_matrix**: Display a matrix in a readable format.
- **precise_row_reduce**: Perform row reduction with higher precision.
- **inverse_by_rows**: Calculate the inverse using row operations.
- **brute_inverse**: Calculate the inverse using an Adjoint method(Cramer method).
- **laplace_determinant**: Calculate the determinant using Laplace expansion.
- **check_matrix**: Validate if the input is a proper matrix.
- **tell_version**: Get the current version of the package.

Ramtrix is perfect for educational purposes, matrix operations, and small to medium-scale linear algebra tasks. It is designed to be a lightweight alternative to larger libraries like Numpy, with a focus on simplicity and performance.
