(05-08-2021 01:49 PM)Eddie W. Shore Wrote: Matrix Format [ [ row ], [ row ], … [ row ] ]

linspace(start, stop, number of points desired + 1)

arange(start, stop, step size); default step size: 1; returns a 1 row array from start to stop using step size

identity(n): returns an identity matrix as n x n

transpose(matrix): transpose of a matrix

inv(matrix): inverse of a matrix

shape(matrix): returns the dimensions of the matrix in an ordered pair (row, columns)

rref(matrix): row reduced echelon form of a matrix

det(matrix): determinant of a square matrix

peval(array of coefficients, x): polynomial evaluation (order is from high to low), can take complex arguments

horner(array of coefficients, x): polynomial evaluation using Horner’s method

pceoff(array of roots): returns an array representing a polynomial’s coefficients, can take complex arguments

proot(array of coefficients): returns an array of roots, can take complex arguments

add(array, array) or add(matrix, matrix): addition element by element

sub(array, array) or sub(matrix, matrix): subtraction element by element

dot(array, array): dot product

cross(array, array): cross product

imag(complex number): imaginary part – works on arrays and matrices

real(complex number): real part – works on arrays and matrices

I believe that fft and ifft have to do with fast fourier transforms.

Hi, I found it difficult to transpose a matrix with PYTHON. For instance:

transpose ([[1,2,3], [4,5,6]]) gives me this result: [[1,4], [3,2], [5,6]]

The correct result is: [[1,4], [2,5], [3,6]].

Is there a bag perhaps?