2.4.7 Custom noise

This example solves a diffusion equation with a custom noise.

custom noise
  0%|          | 0/10.0 [00:00<?, ?it/s]
Initializing:   0%|          | 0/10.0 [00:00<?, ?it/s]
  0%|          | 0/10.0 [00:04<?, ?it/s]
  0%|          | 0.01/10.0 [00:05<1:31:41, 550.68s/it]
  0%|          | 0.02/10.0 [00:05<45:48, 275.36s/it]
  3%|▎         | 0.32/10.0 [00:05<02:46, 17.22s/it]
 65%|██████▍   | 6.48/10.0 [00:05<00:03,  1.17it/s]
 65%|██████▍   | 6.48/10.0 [00:05<00:03,  1.16it/s]
100%|██████████| 10.0/10.0 [00:05<00:00,  1.79it/s]
100%|██████████| 10.0/10.0 [00:05<00:00,  1.79it/s]

import numpy as np

from pde import DiffusionPDE, ScalarField, UnitGrid
from pde.tools.numba import jit


class DiffusionCustomNoisePDE(DiffusionPDE):
    """Diffusion PDE with custom noise implementations."""

    def noise_realization(self, state, t):
        """Numpy implementation of spatially-dependent noise."""
        noise_field = ScalarField.random_uniform(state.grid, -self.noise, self.noise)
        return state.grid.cell_coords[..., 0] * noise_field

    def _make_noise_realization_numba(self, state):
        """Numba implementation of spatially-dependent noise."""
        noise = float(self.noise)
        x_values = state.grid.cell_coords[..., 0]

        @jit
        def noise_realization(state_data, t):
            return x_values * np.random.uniform(-noise, noise, size=state_data.shape)

        return noise_realization


eq = DiffusionCustomNoisePDE(diffusivity=0.1, noise=0.1)  # define the pde
state = ScalarField.random_uniform(UnitGrid([64, 64]))  # generate initial condition
result = eq.solve(state, t_range=10, dt=0.01)
result.plot()

Total running time of the script: (0 minutes 5.638 seconds)