A Python package for all things linear algebra, built in vanilla python3
Built during MAT-1001 / Linear Algebra at Ashoka University.
The package is available on PyPI:
pip install nullityTo start using, simply import the package in your Python file:
from nullity import MatrixInstantiation
m = Matrix(m, n, *args)The matrix must be instantiated by first passing in the number of rows (m), then the number of columns (n), and finally the m*n values of the matrix separated by commas.
These values are arranged first in rows and then in columns.
For example:
>>> m = Matrix(2, 3, 1, 2, 3, 4, 5, 6)
>>> print(m)
1.00 2.00 3.00
4.00 5.00 6.00Methods
| Function | Output |
|---|---|
nrows |
Number of rows |
ncolumns |
Number of columns |
is_square |
Whether the matrix is square |
rref |
RREF of the matrix |
rank |
Rank of the matrix |
nullity |
Nullity (dimension of the null space) of the matrix |
is_invertible |
Whether the matrix is invertible |
inverse |
Inverse of the invertible matrix |
rank_factorization |
The two rank factorized matrices r and c
|
row_basis |
Basis of the row space |
col_basis |
Basis of the column space |
null_basis |
Basis of the null space |
transpose |
Transpose of the matrix |
plu |
PLU decomposed matrices p, l, and u
|
det |
Determinant of a square matrix |
qr |
QR decomposed matrices q and r
|
charpol |
Characteristic polynomial of the matrix |
eigenvals |
Eigenvalues of the matrix (from numpy) |
eigenvecs |
Eigenvectors corresponding to the respective eigenvalues |
svd |
Singular value decomposed matrices u, sigma, and v_transpose
|
Along with these, addition and multiplication of 2 matrices is also supprted using the inbuilt operators + and * respectively.
Python's decimal arithmetic often results in incorrect answers. In some cases, approximated output might be sufficient, however in others, errors may be raised.
