yoshi-seals 2.0.3692068298

Numeric Calculus python module in the topic of Linear Algebra

Seals - Numeric Calculus

This python namespace 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 Eigenvalues and his Eigenvectors.

• Use methods to proccess the matrices.

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

Syntax

To call the package scan use the syntax: from yoshi_seals import scan. The package also has a function for Numpy arrays and Pandas dataframes, and used the following syntax scan.np(path) for Numpy and scan.pd(path) for Pandas, where path is the path to your directory.

To call the package write use the syntax: from yoshi_seals import write. The package also has a function for Numpy arrays and Pandas dataframes, and uses the following syntax 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 write.pd(df,path) for Pandas, where df is the matrix that you desire to output and path is the path to your directory.

To call the package insert use the syntax: from yoshi_seals import insert. The package also has a function for matrix and another for vector, and it has the following syntax 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 eigenvalues and all real eigenvectors, this function uses the power method to find the eigenvalues and inverse power method for the eigenvector.

Processes

To call the module process use the syntax: from yoshi_seals import process as sl, where sl is an alias and will be used to call functions: sl.inverse(array).

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

• 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.decomposition(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.cramer(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