/
test_diffusion.py
148 lines (111 loc) · 5.86 KB
/
test_diffusion.py
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from parcels import (FieldSet, Field, RectilinearZGrid, ParticleSet, JITParticle,
DiffusionUniformKh, AdvectionDiffusionM1, AdvectionRK4DiffusionM1,
AdvectionDiffusionEM, AdvectionRK4DiffusionEM, ScipyParticle,
Variable)
from parcels import rng as random
from datetime import timedelta as delta
import numpy as np
import pytest
from scipy import stats
ptype = {'scipy': ScipyParticle, 'jit': JITParticle}
def zeros_fieldset(mesh='spherical', xdim=200, ydim=100, mesh_conversion=1):
"""Generates a zero velocity field"""
lon = np.linspace(-1e5*mesh_conversion, 1e5*mesh_conversion, xdim, dtype=np.float32)
lat = np.linspace(-1e5*mesh_conversion, 1e5*mesh_conversion, ydim, dtype=np.float32)
dimensions = {'lon': lon, 'lat': lat}
data = {'U': np.zeros((ydim, xdim), dtype=np.float32),
'V': np.zeros((ydim, xdim), dtype=np.float32)}
return FieldSet.from_data(data, dimensions, mesh=mesh)
@pytest.mark.parametrize('mesh', ['spherical', 'flat'])
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
def test_fieldKh_Brownian(mesh, mode, xdim=200, ydim=100, kh_zonal=100, kh_meridional=50):
mesh_conversion = 1/1852./60 if mesh == 'spherical' else 1
fieldset = zeros_fieldset(mesh=mesh, xdim=xdim, ydim=ydim, mesh_conversion=mesh_conversion)
vec = np.linspace(-1e5*mesh_conversion, 1e5*mesh_conversion, 2)
grid = RectilinearZGrid(lon=vec, lat=vec, mesh=mesh)
fieldset.add_field(Field('Kh_zonal', kh_zonal*np.ones((2, 2)), grid=grid))
fieldset.add_field(Field('Kh_meridional', kh_meridional*np.ones((2, 2)), grid=grid))
npart = 1000
runtime = delta(days=1)
random.seed(1234)
pset = ParticleSet(fieldset=fieldset, pclass=ptype[mode],
lon=np.zeros(npart), lat=np.zeros(npart))
pset.execute(pset.Kernel(DiffusionUniformKh),
runtime=runtime, dt=delta(hours=1))
expected_std_lon = np.sqrt(2*kh_zonal*mesh_conversion**2*runtime.total_seconds())
expected_std_lat = np.sqrt(2*kh_meridional*mesh_conversion**2*runtime.total_seconds())
lats = pset.lat
lons = pset.lon
tol = 200*mesh_conversion # effectively 200 m errors
assert np.allclose(np.std(lats), expected_std_lat, atol=tol)
assert np.allclose(np.std(lons), expected_std_lon, atol=tol)
assert np.allclose(np.mean(lons), 0, atol=tol)
assert np.allclose(np.mean(lats), 0, atol=tol)
@pytest.mark.parametrize('mesh', ['spherical', 'flat'])
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('kernel', [AdvectionDiffusionM1,
AdvectionRK4DiffusionM1,
AdvectionDiffusionEM,
AdvectionRK4DiffusionEM])
def test_fieldKh_SpatiallyVaryingDiffusion(mesh, mode, kernel, xdim=200, ydim=100):
"""Test advection-diffusion kernels on a non-uniform diffusivity field
with a linear gradient in one direction"""
mesh_conversion = 1/1852./60 if mesh == 'spherical' else 1
fieldset = zeros_fieldset(mesh=mesh, xdim=xdim, ydim=ydim, mesh_conversion=mesh_conversion)
Kh = np.zeros((ydim, xdim), dtype=np.float32)
for x in range(xdim):
Kh[:, x] = np.tanh(fieldset.U.lon[x]/fieldset.U.lon[-1]*10.)*xdim/2.+xdim/2. + 100.
grid = RectilinearZGrid(lon=fieldset.U.lon, lat=fieldset.U.lat, mesh=mesh)
fieldset.add_field(Field('Kh_zonal', Kh, grid=grid))
fieldset.add_field(Field('Kh_meridional', Kh, grid=grid))
fieldset.add_constant('dres', 0.0005)
npart = 100
runtime = delta(days=1)
random.seed(1636)
pset = ParticleSet(fieldset=fieldset, pclass=ptype[mode],
lon=np.zeros(npart), lat=np.zeros(npart))
pset.execute(pset.Kernel(kernel),
runtime=runtime, dt=delta(hours=1))
lats = pset.lat
lons = pset.lon
tol = 2000*mesh_conversion # effectively 2000 m errors (because of low numbers of particles)
assert np.allclose(np.mean(lons), 0, atol=tol)
assert np.allclose(np.mean(lats), 0, atol=tol)
assert(stats.skew(lons) > stats.skew(lats))
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('lambd', [1, 5])
def test_randomexponential(mode, lambd, npart=1000):
fieldset = zeros_fieldset()
# Rate parameter for random.expovariate
fieldset.lambd = lambd
# Set random seed
random.seed(1234)
pset = ParticleSet(fieldset=fieldset, pclass=ptype[mode], lon=np.zeros(npart), lat=np.zeros(npart), depth=np.zeros(npart))
def vertical_randomexponential(particle, fieldset, time):
# Kernel for random exponential variable in depth direction
particle.depth = random.expovariate(fieldset.lambd)
pset.execute(vertical_randomexponential, runtime=1, dt=1)
depth = pset.depth
expected_mean = 1./fieldset.lambd
assert np.allclose(np.mean(depth), expected_mean, rtol=.1)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('mu', [0.8*np.pi, np.pi])
@pytest.mark.parametrize('kappa', [2, 4])
def test_randomvonmises(mode, mu, kappa, npart=10000):
fieldset = zeros_fieldset()
# Parameters for random.vonmisesvariate
fieldset.mu = mu
fieldset.kappa = kappa
# Set random seed
random.seed(1234)
class AngleParticle(ptype[mode]):
angle = Variable('angle')
pset = ParticleSet(fieldset=fieldset, pclass=AngleParticle, lon=np.zeros(npart), lat=np.zeros(npart), depth=np.zeros(npart))
def vonmises(particle, fieldset, time):
particle.angle = random.vonmisesvariate(fieldset.mu, fieldset.kappa)
pset.execute(vonmises, runtime=1, dt=1)
angles = np.array([p.angle for p in pset])
assert np.allclose(np.mean(angles), mu, atol=.1)
scipy_mises = stats.vonmises.rvs(kappa, loc=mu, size=10000)
assert np.allclose(np.mean(angles), np.mean(scipy_mises), atol=.1)
assert np.allclose(np.std(angles), np.std(scipy_mises), atol=.1)