Numeric Calculus python module in the topic of Linear Algebra
Project description
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)
, whichA
is the coefficient matrix andb
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)
, whichmatrix
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)
, whichA
is the coefficient matrix andb
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)
, whichA
is the coefficient matrix andb
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)
, whichA
is the coefficient matrix andb
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
Project details
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