pyTMat is a blazing-fast, user-friendly Python package for simulating optical multilayer stacks using the Transfer Matrix Method (TMM). Powered by a Rust core for maximum performance, pyTMat makes it easy to compute reflection and transmission spectra for complex photonic structures — perfect for research, engineering, and education.
- Ultra-fast: Rust-powered core for high-performance calculations
- Easy-to-use: Simple Python API, no need to write Rust
- Parallelized: Automatically uses all your CPU cores for large wavelength arrays
- Polarization Support: TE, TM, and arbitrary polarization angles
- Complex Refractive Indices: Supports lossy materials with complex indices
- Flexible Layer Configurations: Any number of layers with arbitrary thicknesses
- Incident Angles: Specify any angle of incidence
- Wavelength Range: Define custom wavelength ranges for your simulations
- Numpy Integration: Works seamlessly with NumPy arrays
- Perfect for DBRs, filters, sensors, and more!
Install the latest release directly from PyPI:
pip install pytmatimport numpy as np
import pytmat
import matplotlib.pyplot as plt
# Define layer thicknesses (nm)
d = np.array([200.0]) # Example: 3 layers, with two seminfinite layers on either side of a 200 nm layer.
# Define complex refractive indices for each layer at each wavelength
# Shape: (num_layers, num_wavelengths)
# Define number of wavelengths (300 for example)
N = 300
# Define refractive indices for 3 layers (air, glass, air) at 300 wavelengths
# Here, we assume air (n=1.0) and a glass layer (n=1.5)
# The first and last layers are seminfinite air layers, so their refractive index is 1.0
# The middle layer is a glass layer with a refractive index of 1.5
n_air = np.full(N, 1.0, dtype=np.complex128)
n_glass = np.full(N, 1.5, dtype=np.complex128)
n = np.array([n_air,n_glass,n_air])
# Wavelengths (nm)
wl = np.linspace(400, 700, N)
# Angle of incidence (radians) and polarization angle (radians)
theta = 0.0 # normal incidence
phi = 0.0 # TE polarization
# Create the TMM data object
data = pytmat.DataPy(d, n, wl, theta, phi)
# Simulate the multilayer stack
simulation = data.simulate()
# Compute reflection and transmission spectra
R, T = simulation.r, simulation.t
fig, ax = plt.subplots()
ax.plot(wl, R, label='Reflection',color='blue')
ax.plot(wl, T, label='Transmission', color='orange')
ax.set_xlabel('Wavelength (nm)')
ax.set_ylabel('Reflectance / Transmittance')
plt.title('Multilayer Stack Reflection and Transmission')
ax.legend()
plt.show()DataPy(d, n, wl, theta, phi): Main class for defining your multilayer stack.d: 1D array of layer thicknesses (float, nm or μm)n: 2D array of complex refractive indices (layers × wavelengths)wl: 1D array of wavelengthstheta: Angle of incidence (radians)phi: Polarization angle (radians, 0=TE, π/2=TM)
simulate(): DataPy method to run the simulation.Simulation: Result object containing:r: Reflection spectrum (array)t: Transmission spectrum (array)
- Arbitrary polarization: Set
phibetween 0 (TE) and π/2 (TM) for mixed polarization. - Large arrays: For >100 wavelengths, pyTMat automatically parallelizes computations.
- Complex indices: Supports lossy materials (complex n).
pytmat/
├── tmatrix/ # Rust core library
├── pytmat/ # Python bindings (PyO3/maturin)
├── tests/ # Python tests
├── README.md
└── ...
Contributions, bug reports, and feature requests are welcome!
Please open an issue or submit a pull request.
This project is licensed under the MIT License.
pyTMat — Fast, flexible, and fun multilayer optics for Python