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+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