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() # Create a simulation object
# Geometry and Model Equations
sim.geomx = 'periodic'
sim.geomy = 'periodic'
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()
def test():
sim = Simulation()
# Geometry and Model Equations
sim.geomy = 'periodic'
sim.stepper = Step.Euler
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()
sim.plott = 15.*minute # Period of plots
sim.animate = 'Anim' # 'Save' to create video frames,
# 'Anim' to animate,
# 'None' otherwise
sim.plot_vars = ['h']
#sim.plot_vars = ['vort','div']
#sim.clims = [[-0.015, 0.015],[-0.001, 0.001]]
# Output parameters
sim.output = False # True or False
sim.savet = 1.*hour # Time between saves
# Diagnostics parameters
sim.diagt = 2.*minute # Time for output
sim.diagnose = False # True or 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()
sim.clims = [[-0.8, 0.8], [-0.5, 0.5], [], []]
# Output parameters
sim.output = True # True or False
sim.savet = 10. * day # Time between saves
# Diagnostics parameters
sim.diagt = 2. * minute # Time for output
sim.diagnose = False # True or 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]
# Bickley Jet initial conditions
# First we define the jet
Ljet = 10e3 # Jet width
amp = 0.1 # Elevation of free-surface in basic state
sim.soln.h[:, :, 0] += -amp * np.tanh(sim.Y / Ljet)
sim.soln.u[:, :,
0] = sim.g * amp / (sim.f0 * Ljet) / (np.cosh(sim.Y / Ljet)**2)
# Then we add on a random perturbation
np.random.seed(seed=100)
sim.soln.u[:, :, 0] += 1e-3 * np.exp(-(sim.Y / Ljet)**2) * np.random.randn(
sim.Nx, sim.Ny)
sim.run() # Run the simulation
sim.diagt = 2.*minute # Time for output
sim.diagnose = False # True or 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
x0 = 1.*sim.Lx/2. # Centre
W = 200.e3 # Width
amp = 1. # Amplitude
sim.soln.h[:,:,0] += amp*np.exp(-(sim.Y)**2/(W**2))
sim.run() # Run the simulation
# Hovmuller plot
plt.figure()
t = np.arange(0,sim.end_time+sim.plott,sim.plott)/86400.
if sim.Ny==1:
x = sim.x/1e3
elif sim.Nx == 1:
x = sim.y/1e3
for L in range(sim.Nz):
field = sim.hov_h[:,0,:].T - np.sum(sim.Hs[L:])
cv = np.max(np.abs(field.ravel()))
plt.subplot(sim.Nz,1,L+1)
# 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
x0 = 1. * sim.Lx / 2. # Centre
W = 200.e3 # Width
amp = 1. # Amplitude
sim.soln.h[:, :, 0] += amp * np.exp(-(sim.grid_y.h + sim.Ly / 4.)**2 / (W**2))
#sim.soln.h[:,:,0] += amp*np.exp(-(sim.grid_x.h + sim.Lx/4.)**2/(W**2))
# Run the simulation
sim.run()
# Hovmuller plot
#plt.figure()
#t = np.arange(0,sim.end_time+sim.plott,sim.plott)/86400.
#if sim.Ny==1:
# x = sim.x/1e3
#elif sim.Nx == 1:
# x = sim.y/1e3
#for L in range(sim.Nz):
# field = sim.hov_h[:,0,:].T - np.sum(sim.Hs[L:])
# cv = np.max(np.abs(field.ravel()))
# plt.subplot(sim.Nz,1,L+1)
# plt.pcolormesh(x,t, field,
