"""
Using simulation trackers
=========================
This example illustrates how trackers can be used to analyze simulations.
"""
import pde
grid = pde.UnitGrid([32, 32]) # generate grid
state = pde.ScalarField.random_uniform(grid) # generate initial condition
storage = pde.MemoryStorage()
trackers = [
"progress", # show progress bar during simulation
"steady_state", # abort when steady state is reached
storage.tracker(interval=1), # store data every simulation time unit
pde.PlotTracker(show=True), # show images during simulation
# print some output every 5 real seconds:
pde.PrintTracker(interval=pde.RealtimeInterrupts(duration=5)),
]
eq = pde.DiffusionPDE(0.1) # define the PDE
eq.solve(state, 3, dt=0.1, tracker=trackers)
for field in storage:
print(field.integral)
"""
Solver comparison
=================
This example shows how to set up solvers explicitly and how to extract
diagnostic information.
"""
import pde
# initialize the grid, an initial condition, and the PDE
grid = pde.UnitGrid([32, 32])
field = pde.ScalarField.random_uniform(grid, -1, 1)
eq = pde.DiffusionPDE()
# try the explicit solver
solver1 = pde.ExplicitSolver(eq)
controller1 = pde.Controller(solver1, t_range=1, tracker=None)
sol1 = controller1.run(field, dt=1e-3)
sol1.label = "explicit solver"
print("Diagnostic information from first run:")
print(controller1.diagnostics)
print()
# try the standard scipy solver
solver2 = pde.ScipySolver(eq)
controller2 = pde.Controller(solver2, t_range=1, tracker=None)
sol2 = controller2.run(field)
sol2.label = "scipy solver"
print("Diagnostic information from second run:")
print(controller2.diagnostics)
def eval(self, z):
eq = pde.DiffusionPDE(diffusivity=z)
result = eq.solve(self.init_state, t_range=self.t)
return result