Cylinder: Coupling vs Wavelength

This example demonstrates how to compute and visualize the coupling efficiency as a function of wavelength for cylindrical scatterers using PyMieSim.

Importing the package dependencies: numpy, PyMieSim

import numpy as np
from PyMieSim.experiment.detector import CoherentMode
from PyMieSim.experiment.scatterer import Cylinder
from PyMieSim.experiment.source import Gaussian
from PyMieSim.experiment import Setup
from PyMieSim import measure
from PyOptik import UsualMaterial

Defining the source

source = Gaussian(
    wavelength=np.linspace(950e-9, 1050e-9, 300),  # Wavelengths ranging from 950 nm to 1050 nm
    polarization_value=0,  # Linear polarization angle in radians
    polarization_type='linear',
    optical_power=1e-3,  # 1 milliwatt
    NA=0.2  # Numerical Aperture
)

Defining the scatterer distribution Here we look at cylinders with a set diameter, refractive index, and medium.

scatterer = Cylinder(
    diameter=np.linspace(100e-9, 8000e-9, 5),  # Diameters ranging from 100 nm to 8000 nm
    material=UsualMaterial.BK7,  # Material of the cylinder
    medium_index=1,  # Refractive index of the surrounding medium
    source=source
)

Defining the detector

detector = CoherentMode(
    mode_number="LP11",  # Specifying the LP11 mode
    NA=[0.05, 0.01],  # Array of Numerical Apertures for the detector
    phi_offset=-180,  # Phi offset in degrees
    gamma_offset=0,  # Gamma offset in degrees
    polarization_filter=None,  # No polarization filter
    sampling=300,  # Number of sampling points
    rotation=0,  # Rotation of the mode field
)

Setting up the experiment

experiment = Setup(
    scatterer=scatterer,
    source=source,
    detector=detector
)

Measuring the coupling efficiency

data = experiment.get(measure.coupling)

Plotting the results Visualizing how the coupling efficiency varies with the wavelength.

figure = data.plot(
    x=experiment.wavelength,  # Wavelength as the x-axis
    std=experiment.diameter  # Standard deviation with respect to cylinder diameter
)

Displaying the plot

_ = figure.show()