matrix link

The matrix namespace provides operations for creating and manipulating two-dimensional matrices. Matrices store elements of the same type in rows and columns, supporting operations ranging from basic element access to advanced linear algebra transformations like eigenvalue decomposition and matrix inversion.

Quick Example link

from pynecore.lib import matrix, script

@script.indicator(title="Matrix Analysis", overlay=False)
def main():
    # Create a 3x3 matrix
    m = matrix.new(3, 3, 0.0)
    
    # Set diagonal values
    matrix.set(m, 0, 0, 10.0)
    matrix.set(m, 1, 1, 20.0)
    matrix.set(m, 2, 2, 30.0)
    
    # Analyze matrix properties
    is_diag: bool = matrix.is_diagonal(m)
    trace_val: float = matrix.trace(m)  # Sum of diagonal
    avg_val: float = matrix.avg(m)

Matrix Creation and Management link

new() link

Create a new matrix object.

Returns: New matrix object

m = matrix.new(3, 4, 0.0)  # 3×4 matrix filled with 0.0

copy() link

Create an independent copy of an existing matrix.

Returns: New matrix object

m2 = matrix.copy(m1)  # m2 is independent from m1

Dimensions and Properties link

rows() link

Get the number of rows in a matrix.

Returns: int

row_count: int = matrix.rows(m)  # 3

columns() link

Get the number of columns in a matrix.

Returns: int

col_count: int = matrix.columns(m)  # 4

elements_count() link

Get the total number of elements in a matrix (rows × columns).

Returns: int

total: int = matrix.elements_count(m)  # 12

Element Access and Modification link

get() link

Retrieve an element at a specific row and column.

Returns: Element value

val: float = matrix.get(m, 0, 1)  # 15.5

set() link

Set an element at a specific row and column.

Returns: None

matrix.set(m, 2, 3, 42.0)

fill() link

Fill a rectangular area of a matrix with a single value.

Returns: None

matrix.fill(m, 0.0, 1, 3, 0, 2)  # Fill rows 1–2, columns 0–1

Row and Column Operations link

row() link

Extract a row as a one-dimensional array.

Returns: list — Array of row values

row_vals: list = matrix.row(m, 1)

col() link

Extract a column as a one-dimensional array.

Returns: list — Array of column values

col_vals: list = matrix.col(m, 2)

add_row() link

Insert a new row at a specified index.

Returns: None

matrix.add_row(m, 1, [1.0, 2.0, 3.0, 4.0])

add_col() link

Insert a new column at a specified index.

Returns: None

matrix.add_col(m, 0, [5.0, 6.0, 7.0])

remove_row() link

Remove a row and return its values as an array.

Returns: list — Removed row values

removed: list = matrix.remove_row(m, 1)

remove_col() link

Remove a column and return its values as an array.

Returns: list — Removed column values

removed: list = matrix.remove_col(m, 2)

swap_rows() link

Exchange two rows in a matrix.

Returns: None

matrix.swap_rows(m, 0, 2)

swap_columns() link

Exchange two columns in a matrix.

Returns: None

matrix.swap_columns(m, 1, 3)

Matrix Property Tests link

is_square() link

Check if a matrix has equal rows and columns.

Returns: bool

is_sq: bool = matrix.is_square(m)  # True

is_diagonal() link

Check if all elements outside the main diagonal are zero.

Returns: bool

is_diag: bool = matrix.is_diagonal(m)  # True

is_antidiagonal() link

Check if all elements outside the secondary diagonal are zero.

Returns: bool

is_antidiag: bool = matrix.is_antidiagonal(m)  # False

is_identity() link

Check if the matrix is an identity matrix (ones on main diagonal, zeros elsewhere).

Returns: bool

is_id: bool = matrix.is_identity(m)  # False

is_symmetric() link

Check if a matrix equals its own transpose.

Returns: bool

is_sym: bool = matrix.is_symmetric(m)  # True

is_antisymmetric() link

Check if a matrix equals the negative of its transpose.

Returns: bool

is_antisym: bool = matrix.is_antisymmetric(m)  # False

is_binary() link

Check if all elements are either 0 or 1.

Returns: bool

is_bin: bool = matrix.is_binary(m)  # False

is_triangular() link

Check if all elements above or below the main diagonal are zero.

Returns: bool

is_tri: bool = matrix.is_triangular(m)  # True

is_stochastic() link

Check if the matrix is stochastic (rows or columns sum to 1).

Returns: bool

is_stoch: bool = matrix.is_stochastic(m)  # False

is_zero() link

Check if all elements are zero.

Returns: bool

is_z: bool = matrix.is_zero(m)  # False

Arithmetic Operations link

sum() link

Add two matrices or a matrix and a scalar.

Returns: New matrix

m3 = matrix.sum(m1, m2)    # Element-wise addition
m4 = matrix.sum(m1, 5.0)   # Add 5.0 to all elements

diff() link

Subtract two matrices or a scalar from a matrix.

Returns: New matrix

m3 = matrix.diff(m1, m2)   # Element-wise subtraction
m4 = matrix.diff(m1, 3.0)  # Subtract 3.0 from all elements

mult() link

Multiply two matrices or a matrix by a scalar.

Returns: New matrix

m3 = matrix.mult(m1, m2)   # Matrix multiplication
m4 = matrix.mult(m1, 2.5)  # Scalar multiplication

pow() link

Raise a square matrix to a power (multiply by itself n times).

Returns: New matrix

m_squared = matrix.pow(m, 2)  # m × m

Statistical Functions link

avg() link

Calculate the average of all matrix elements.

Returns: float \| int

average: float = matrix.avg(m)  # 42.5

min() link

Find the smallest element in the matrix.

Returns: float \| int

min_val: float = matrix.min(m)  # 0.0

max() link

Find the largest element in the matrix.

Returns: float \| int

max_val: float = matrix.max(m)  # 100.0

median() link

Calculate the median of all matrix elements.

Returns: float \| int

med: float = matrix.median(m)  # 50.0

mode() link

Find the most frequently occurring element in the matrix.

Returns: Most frequent value (or smallest if no unique mode)

most_common: float = matrix.mode(m)  # 25.0

Linear Algebra link

transpose() link

Create a new matrix with rows and columns swapped.

Returns: New matrix

m_t = matrix.transpose(m)

det() link

Calculate the determinant of a square matrix.

Returns: float \| int

determinant: float = matrix.det(m)  # -2.0

inv() link

Calculate the inverse of a square matrix.

Returns: New matrix

m_inv = matrix.inv(m)

pinv() link

Calculate the pseudo-inverse (Moore-Penrose inverse) of a matrix.

Returns: New matrix

m_pinv = matrix.pinv(m)

rank() link

Calculate the rank of a matrix.

Returns: int

r: int = matrix.rank(m)  # 2

trace() link

Calculate the trace (sum of main diagonal elements).

Returns: float \| int

tr: float = matrix.trace(m)  # 30.0

eigenvalues() link

Calculate the eigenvalues of a square matrix.

Returns: list — Array of eigenvalues

evals: list = matrix.eigenvalues(m)

eigenvectors() link

Calculate the eigenvectors of a square matrix.

Returns: New matrix — Each column is an eigenvector

evecs = matrix.eigenvectors(m)

Matrix Transformations link

submatrix() link

Extract a rectangular submatrix.

Returns: New matrix

sub = matrix.submatrix(m, 1, 3, 0, 2)  # Rows 1–2, columns 0–1

reshape() link

Reorganize the matrix to new dimensions (preserves all elements).

Returns: None — Modifies in place

matrix.reshape(m, 4, 3)  # Reorganize to 4×3

reverse() link

Reverse the order of rows and columns.

Returns: None — Modifies in place

matrix.reverse(m)

concat() link

Append one matrix to another.

Returns: Modified first matrix

m3 = matrix.concat(m1, m2)

sort() link

Sort rows based on values in a specific column.

Returns: None — Modifies in place

matrix.sort(m, 2, "ascending")

kron() link

Calculate the Kronecker product of two matrices.

Returns: New matrix

m_kron = matrix.kron(m1, m2)

Compatibility Notes link

• All matrix elements must be the same type
• Matrix indices are zero-based
• NA (not available) values are handled automatically—operations on NA return NA
• Linear algebra functions (inv, eigenvalues, etc.) require square or compatible matrices
• For very large matrices, performance depends on matrix size and operation complexity