yoshi-seals 1.3

Numeric Calculus python module in the topic of Linear Algebra

Seals - Numeric Calculus

This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:

• Scan csv files to make a numpy matrix.

• Write a matrix into a csv file

• Insert user input into a matrix or a vector.

• Calculate Eigen Values

• Use methods to proccess the matrices.

  • Identity Matrix
  • Gauss Elimination
  • Inverse Matrix
  • Cholesky Decomposition
  • LU Decomposition
  • Cramer

Syntax

The module scan has a function for Numpy arrays and Pandas dataframes, and used the following syntax Seals.scan.np(path) for Numpy and Seals.scan.pd(path) for Pandas, where path is the path to your directory.

The module write has a function for Numpy arrays and Pandas dataframes, and uses the following syntax Seals.write.np(array,path) for Numpy, where array is the matrix that you desire to output and path is the path to your directory, and Seals.write.pd(df,path) for Pandas, where df is the matrix that you desire to output and path is the path to your directory.

The module insert has a function for matrix and another for vector, and it has the following syntax Seals.insert.function(array), where insert is the Python Module and function is either a matrix or a vector and array is either a matrix or a vector.

There is also a function that given a matrix it return all real eigen values

Processes

To call the module process use the syntax: sl = Seals.process, where sl is an instance and to use a function you have to append the desired function in front of the instance like: sl.identity(array).

• The function identity returns a numpy identity matrix of the order of the matrix passed into to it, and it has the following syntax sl.identity(array), which array is a square matrix.

• The function gauss returns a numpy vector containing the vector of variables from the augmented matrix. sl.gauss(matrix), which matrix is the augmented matrix.

• The function inverse returns a numpy inverse matrix of the matrix passed into to it, and it has the following syntax sl.inverse(matrix), which matrix is a square matrix.

• The function cholesky returns a numpy vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax sl.cholesky(A,b), which A is the coefficient matrix and b is the constants vector.

• The function decomposition returns a numpy vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax sl.cholesky(A,b), which A is the coefficient matrix and b is the constants vector.

• The function cramer returns a numpy vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax sl.cholesky(A,b), which A is the coefficient matrix and b is the constants vector.

Installation

To install the package from source cd into the directory and run:

pip install .

or run

pip install yoshi-seals