from cases.wave_equation.dirichlet.derivative.derivative import WaveEquationDerivative
from utils import State
c = 2.0
num_grid_points = 1000
dt = 1 / (16 * num_grid_points * c)
params = {
'num_grid_points': num_grid_points,
'domain_size': 1.0,
'dt': dt,
'sampling_rate': 100
}
time_derivative_input = [c]
# case_sol_input = [c, [(1, 1.0), (2, 2.0)]]
axes = np.tile(
np.linspace(0, params['domain_size'], num_grid_points + 1)[:-1],
(2, 1)) # setup the axes
state = State(2, num_grid_points, axes, [("x", "u"), ("x", "v")])
state_vars = state.get_state_vars()
starting_cond = starting_conditions.GaussianBump(params['domain_size'] * 0.5,
50)
state_vars[0] = starting_cond.get_start_condition(axes[0])
#state_vars[0] = np.sin(axes[0] * 2 * np.pi / params['domain_size'])
run_utils.run_visual_without_solution(params, Explicit, WaveEquationDerivative,
time_derivative_input, state)
num_grid_points = 1000
dt = 1 / (16 * num_grid_points * c)
params = {
'num_grid_points': num_grid_points,
'domain_size': 1.0,
'dt': dt,
'sampling_rate': 1000
}
time_derivative_input = [c]
axes = np.tile(
np.linspace(0, params['domain_size'], num_grid_points + 1)[:-1],
(2, 1)) # setup axes
state = State(2, num_grid_points, axes, [("u", "x"),
("v", "x")]) # create state
state_vars = state.get_state_vars() # get underlying numpy-array
starting_cond = GaussianBump(params['domain_size'] * 0.5,
200.0) # choose a starting-condition
state_vars[0] = starting_cond.get_start_condition(
axes[0]) # apply the starting condition variables
# state_vars[1] = starting_cond.derivative(axes[1])
run_utils.run_visual_without_solution(params, Explicit,
PeriodicWaveTimeDerivative,
time_derivative_input, state)