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Older Versions/yoshi-seals1.3.3/README.md

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+# 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 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 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 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 Seals import process as sl`, 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`