Scatterer Collection

This script simulates a flow cytometer using the FlowCyPy library. It models light scattering from particles detected by two detectors (Forward Scatter and Side Scatter) and visualizes the scattering intensities in a 2D hexbin plot.

Flow cytometry is used to analyze the physical and chemical properties of particles as they flow through a laser beam.

Steps in the Workflow:

1. Define the flow parameters (e.g., speed, area, and total simulation time).

2. Create particle size and refractive index distributions.

3. Set up a laser source and detectors.

4. Simulate the flow cytometry experiment.

5. Visualize the scattering intensity in a 2D hexbin plot.

Import necessary libraries and modules

from FlowCyPy import ScattererCollection
from FlowCyPy import distribution, population
from FlowCyPy.units import nanometer, RIU, particle, milliliter
import numpy as np

# Set random seed for reproducibility
np.random.seed(20)


# Step 1: Define Particle Size and Refractive Index Distributions
# ---------------------------------------------------------------
# Two particle populations are defined with different sizes and refractive indices.
scatterer_collection = ScattererCollection(medium_refractive_index=1.33 * RIU)

population_0 = population.Sphere(
    name='EV',
    particle_count=2e+9 * particle / milliliter,
    diameter=distribution.RosinRammler(characteristic_property=50 * nanometer, spread=4.5),
    refractive_index=distribution.Normal(mean=1.39 * RIU, std_dev=0.05 * RIU)
)

population_1 = population.Sphere(
    name='LP',
    particle_count=1e+10 * particle / milliliter,
    diameter=distribution.RosinRammler(characteristic_property=200 * nanometer, spread=4.5),
    refractive_index=distribution.Normal(mean=1.45 * RIU, std_dev=0.05 * RIU)
)

scatterer_collection.add_population(population_0, population_1)

# Initialize the scatterer with the flow cell
df = scatterer_collection.get_population_dataframe(total_sampling=600, use_ratio=False)  # Visualize the particle population

df.plot(x='Diameter', bins='auto')

<Figure size 700x500 with 1 Axes>