PyMieSim vs PyMieScatt Comparison

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  plt.tight_layout()

# Standard library imports
import numpy as np
import matplotlib.pyplot as plt

# PyMieSim imports
from PyMieSim.experiment.scatterer import Sphere
from PyMieSim.experiment.source import Gaussian
from PyMieSim.experiment import Setup
import PyMieSim.measure as measure

# PyMieScatt import
import PyMieScatt as pms

# Define parameters
wavelength = 632.8e-9  # Wavelength of the light source in meters
polarization_value = 0
polarization_type = 'linear'
optical_power = 1e-3  # Power in watts
NA = 0.2  # Numerical aperture
medium_index = 1.21
refractive_index = 1.4
diameter_range = np.geomspace(10e-9, 1e-6, 50)  # Geometric space for diameters

# Setup source
source = Gaussian(
    wavelength=wavelength,
    polarization_value=polarization_value,
    polarization_type=polarization_type,
    optical_power=optical_power,
    NA=NA
)

# Setup scatterer
scatterer = Sphere(
    diameter=diameter_range,
    index=refractive_index,
    medium_index=medium_index,
    source=source
)

# Define experiment setup
experiment = Setup(
    scatterer=scatterer,
    source=source,
    detector=None  # No detector set specified
)

# Simulate using PyMieSim
sim_data = experiment.get(measure.Qsca, export_as_numpy=True).squeeze()

# Simulate using PyMieScatt
scatt_data = np.array([
    pms.MieQ(m=refractive_index, diameter=d, wavelength=wavelength, nMedium=medium_index)[1]
    for d in diameter_range
])

# Plot results
plt.figure(figsize=(8, 4))
plt.plot(diameter_range * 1e6, sim_data, 'C1-', linewidth=3, label='PyMieSim')
plt.plot(diameter_range * 1e6, scatt_data, 'k--', linewidth=1, label='PyMieScatt')
plt.xlabel('Diameter (μm)')
plt.ylabel('Scattering Efficiency')
plt.title('Comparison of Scattering Efficiency: PyMieSim vs PyMieScatt')
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()