pymueller 0.1.0

Tools and functions for 4x4 Mueller matrices

Tools for Mueller Matrices

A collection of functions to manipulate and process Mueller matrix data.

1. Physical Realizability Test

This package provides functions to test the physical realizability of 4×4 Mueller matrices. The original code, written in C++ and Julia, can be found here.

The prtest functions offer several options for physical realizability tests. These functions accept a [4,4] matrix representing a Mueller matrix. If the test is passed, the function returns True.

Available Test Methods:

1. charpoly: Characteristic Polynomial Test
2. choletsky: Cholesky Decomposition Test

Performance benchmarks and detailed implementations can be found in the original repository.

2. Decomposition

This package also allows decomposition of a Mueller matrix into optical parameters, including:

• MMD_D: Diattenuation
• MMD_Delta: Depolarization
• MMD_LR: Linear Retardance
• MMD_CR: Circular Retardance
• MMD_psi: Orientation

Currently, the only available decomposition method is the Lu-Chipman decomposition.

How to Run

First, install and import the necessary modules:

pip install pymueller

import pymueller
import muellerphys
### Example Usage
```python
import numpy as np
import muellerphys

M = np.array([
    [1, 0, 0, 0],
    [0, 1, 0, 0],
    [0, 0, 1, 0],
    [0, 0, 0, 1]
])

# Using the physical realizability test functions
H = py.build_eigen_matrix(M)

if py.choletsky(M):
    print("Matrix passed the Cholesky test.")

if py.charpoly(M):
    print("Matrix is physically realizable.")

# Using the decomposition functions
MMD_D, MMD_Delta, MMD_LR, MMD_CR, MMD_psi = pymueller.lu_chipman(M)
print("Diattenuation:", MMD_D)
print("Depolarissation:", MMD_Delta)
print("Linear Retardance:", MMD_LR)
print("Circular Retardance:", MMD_CR)
print("Orientation:", MMD_psi)