def test():
sim = Simulation()
# Geometry and Model Equations
sim.geomy = 'periodic'
sim.stepper = Step.AB3
sim.method = 'Spectral'
sim.dynamics = 'Linear'
sim.flux_method = Flux.spectral_sw
# Specify paramters
sim.Ly = 4000e3
sim.Ny = 256
sim.f0 = 0.
sim.Hs = [100.]
sim.rho = [1025.]
sim.end_time = sim.Ly/(np.sqrt(sim.Hs[0]*sim.g))
# Plotting parameters
sim.animate = 'None'
sim.output = False
sim.diagnose = False
# Initialize the grid and zero solutions
sim.initialize()
for ii in range(sim.Nz):
sim.soln.h[:,:,ii] = sim.Hs[ii]
# Gaussian initial conditions
x0 = 1.*sim.Lx/2.
W = 200.e3
amp = 1.
sim.soln.h[:,:,0] += amp*np.exp(-(sim.Y)**2/(W**2))
IC = sim.soln.h[:,:,0].copy()
sim.run()
# Compare final state to initial conditions
# error_h is normalized using the triangle inequality
error_h = np.linalg.norm(IC - sim.soln.h[:,:,0])/(np.linalg.norm(IC) + np.linalg.norm(sim.soln.h[:,:,0]))
error_v = np.linalg.norm(sim.soln.v[:,:,0])
assert (error_h < 1e-6) and (error_v < 5e-5)
def test():
sim = Simulation() # Create a simulation object
# Geometry and Model Equations
sim.geomx = 'walls'
sim.geomy = 'walls'
sim.stepper = Step.AB3
sim.method = 'Spectral'
sim.dynamics = 'Nonlinear'
sim.flux_method = Flux.spectral_sw
# Specify paramters
sim.Lx = 4000e3
sim.Ly = 4000e3
sim.Nx = 128
sim.Ny = 128
sim.Nz = 1
sim.g = 9.81
sim.f0 = 1.e-4
sim.beta = 0e-11
sim.cfl = 0.1
sim.Hs = [100.]
sim.rho = [1025.]
sim.end_time = 5. * minute
sim.animate = 'None'
sim.output = False
sim.diagnose = False
# Initialize the grid and zero solutions
sim.initialize()
for ii in range(sim.Nz): # Set mean depths
sim.soln.h[:, :, ii] = sim.Hs[ii]
# Gaussian initial conditions
W = 200.e3 # Width
amp = 1. # Amplitude
sim.soln.h[:, :, 0] += amp * np.exp(-(sim.X / W)**2 - (sim.Y / W)**2)
# Run the simulation
sim.run()
def test():
sim = Simulation()
# Geometry and Model Equations
sim.geomy = 'periodic'
sim.stepper = Step.AB2
sim.method = 'Spectral'
sim.dynamics = 'Nonlinear'
sim.flux_method = Flux.spectral_sw
# Specify paramters
sim.Ly = 4000e3
sim.Ny = 128
sim.cfl = 0.5
sim.Hs = [100.]
sim.rho = [1025.]
sim.end_time = 5. * minute
# Plotting parameters
sim.animate = 'None'
sim.output = False
sim.diagnose = False
# Initialize the grid and zero solutions
sim.initialize()
for ii in range(sim.Nz):
sim.soln.h[:, :, ii] = sim.Hs[ii]
# Gaussian initial conditions
x0 = 1. * sim.Lx / 2.
W = 200.e3
amp = 1.
sim.soln.h[:, :, 0] += amp * np.exp(-(sim.Y)**2 / (W**2))
sim.run()
import numpy as np
import matplotlib.pyplot as plt
import sys
# Add the PyRsw tools to the path
# At the moment it is given explicitely.
# In the future, it could also be added to the
# pythonpath environment variable
sys.path.append('../src')
import Steppers as Step
import Fluxes as Flux
from PyRsw import Simulation
from constants import minute, hour, day
sim = Simulation() # Create a simulation object
sim.run_name = '2D_Bickley_Jet_H500'
# Geometry and Model Equations
sim.geomx = 'periodic' # Geometry Types: 'periodic' or 'walls'
sim.geomy = 'walls'
sim.stepper = Step.AB3 # Time-stepping algorithm: Euler, AB2, RK4
sim.method = 'Spectral' # Numerical method: 'Spectral'
sim.dynamics = 'Nonlinear' # Dynamics: 'Nonlinear' or 'Linear'
sim.flux_method = Flux.spectral_sw # Flux method: spectral_sw is only option currently
# Specify paramters
sim.Lx = 200e3 # Domain extent (m)
sim.Ly = 200e3 # Domain extent (m)
sim.Nx = 128 # Grid points in x
sim.Ny = 128 # Grid points in y