Biased droplet dynamics

Demonstrates how droplet dynamics can be biased using an external field, which is here read from an image.

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from numba import jit

from droplets import SphericalDroplet
from pde import CartesianGrid, ScalarField, UnitGrid, get_backend

import emulsim

# set up state
grid = UnitGrid([32, 32], periodic=True)
background = emulsim.ScalarFieldElement.from_field(ScalarField(grid, 0.1))
droplet_data = [SphericalDroplet(grid.get_random_point(), 1) for _ in range(3)]
droplets = emulsim.SphericalDropletsElement.from_droplets(droplet_data)
state = emulsim.State({"background": background, "droplets": droplets})


# define how the equilibrium concentration is calculated
class cEqOutField:
    """Class calculating equilibrium concentrations based on a field."""

    def __init__(self, image: ScalarField):
        self.image = image

    def __call__(self, position, radius, droplet_id):
        """Return equilibrium concentration for a position."""
        return 1e-2 * self.image.interpolate(position)

    def get_function(self, backend):
        """Return compiled function for calculating c_eq for a position."""
        interpolate = get_backend("numba").make_interpolator(self.image)
        image_data = self.image.data

        @jit
        def c_eq(position, radius, droplet_id):
            return 1e-2 * interpolate(position, image_data)

        return c_eq


# set up random field used as the defining image
image_grid = CartesianGrid(grid.axes_bounds, 16, periodic=grid.periodic)
image = ScalarField.random_uniform(image_grid)

# set up simulation
simulation = emulsim.Simulation(state)
simulation.add_actor("background", emulsim.DiffusionActor())
parameters = {"equilibrium_concentration": cEqOutField(image)}
simulation.add_actor(
    ("droplets", "background"), emulsim.SphericalDropletActor(parameters)
)

# run simulation
result = simulation.run(t_range=10)

result.plot()