Metadata-Version: 2.1
Name: yoshi-seals
Version: 1.2.1
Summary: Numeric Calculus python module in the topic of Linear Algebra
Home-page: https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git
Author: Vitor Hideyoshi
Author-email: vitor.h.n.batista@gmail.com
License: UNKNOWN
Description: # 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.
        
        * Use methods to proccess the matrices.
          * Identity Matrix
          * Gauss Elimination
          * Inverse Matrix
          * Cholesky Decomposition
          * LU Decomposition
          * Cramer
        
        ## Syntax
        
        The function *scan* has the following syntax `scan(path)`, where `path` is the path to your directory.
        
        The function *solution* has the following syntax `write(array,path)`, where `array` is the matrix that you desire to output and `path` is the path to your directory.
        
        The python class *Insert* has a method for *matrix* and another for *vector*, and it has the following syntax `Insert.method(array)`, where `Insert` is the *Python Class* and `method` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
        
        ### Processes
        
        The python class *process* has all the methods described in the first session.
        
        To call the method use a syntax like `sl = Seals.process()`, where `sl` is an instance and to use a method you have to append the method in front of the instance like: `sl.identity(array)`.
        
        * The method *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 method *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
        
        * The method *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 method *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 method *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 method *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`
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 2 - Pre-Alpha
Requires-Python: >=3.6
Description-Content-Type: text/markdown