57 lines
2.7 KiB
Markdown
57 lines
2.7 KiB
Markdown
# Numeric Calculus
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This is a Repository of Python Packages for Numeric Calculus. It contains two packages: Seals and Otter.
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## Seals
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The package Seals is made for Linear Algebra. It's able to:
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* Scan *csv* files to make a numpy matrix.
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* Write a matrix into a *csv* file
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* Insert user input into a matrix or a vector.
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* Use methods to proccess the matrices.
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* Identity Matrix
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* Gauss Elimination
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* Inverse Matrix
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* Cholesky Decomposition
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* LU Decomposition
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* Cramer
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### Syntax
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The function *scan* has the following syntax `scan(path)`, where `path` is the path to your directory.
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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.
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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*.
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### Processes
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The python class *process* has all the methods described in the first session.
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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)`.
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* 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.
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* 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.
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* 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.
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* 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.
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* 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.
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* 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.
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## Installation
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To install the package from source `cd` into the directory and run:
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`pip install .`
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or run
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`pip install Numeric-Calculus_HideyoshiNakazone` |