def show_L0_eigfuns(G, n=12, **kwargs):
'''
Show eigenfunctions of 0-Laplacian
'''
obs = gds.node_gds(G)
# L = np.array(nx.laplacian_matrix(G).todense())
L = -obs.laplacian(np.eye(obs.ndim))
# vals, vecs = sp.sparse.linalg.eigs(-L, k=n, which='SM')
vals, vecs = np.linalg.eigh(L)
vals, vecs = np.real(vals), np.real(vecs)
# pdb.set_trace()
sys = dict()
sys['Surface'] = gds.face_gds(G)
sys['Surface'].set_evolution(nil=True)
canvas = dict()
canvas['Surface'] = [[[sys['Surface']]]]
for i, (ev, vec) in enumerate(sorted(zip(vals, vecs.T), key=lambda x: np.abs(x[0]))):
ev = np.round(ev, 5)
obs = gds.node_gds(G)
obs.set_evolution(nil=True)
obs.set_initial(y0=lambda x: vec[obs.X[x]])
if ev in canvas:
sys[f'eigval_{ev} eigfun_{len(canvas[ev])}'] = obs
canvas[ev].append([[obs]])
else:
sys[f'eigval_{ev} eigfun_{0}'] = obs
canvas[ev] = [[[obs]]]
if i == n:
break
# canvas = sorted(list(canvas.values()), key=len)
canvas = list(canvas.values())
sys = gds.couple(sys)
gds.render(sys, canvas=canvas, n_spring_iters=1200, title=f'L0-eigenfunctions', **kwargs)
def heat_test():
G = gds.grid_graph(10, 10)
temp1 = gds.node_gds(G)
x = (5, 5)
i = temp1.X[x]
B = temp1.incidence.copy().todense()
B[i][B[i] < 0] = 0
temp1.set_evolution(dydt=lambda t, y: -B @ B.T @ y)
temp1.set_initial(y0=lambda n: 1.0 if n == x else 0.0)
temp2 = gds.node_gds(G)
temp2.set_evolution(dydt=lambda t, y: temp2.laplacian(y))
temp2.set_constraints(dirichlet={x: 1.0})
return temp1, temp2
def schrodinger(G: nx.Graph, V: Callable, psi0: Callable, **kwargs):
re, im = gds.node_gds(G), gds.node_gds(G)
V_arr = np.array([V(x) for x in re.X])
def f(t, y):
psi = re.y + 1j*im.y
return 1j * (re.laplacian(psi) - V_arr*psi)
re.set_evolution(dydt=lambda t, y: np.real(f(t, y)))
re.set_initial(y0=lambda x: np.real(psi0(x)))
im.set_evolution(dydt=lambda t, y: np.imag(f(t, y)))
im.set_initial(y0=lambda x: np.imag(psi0(x)))
sys = gds.couple({
'real': re,
'imag': im,
})
gds.render(sys, dynamic_ranges=True, node_palette=cc.bmy, n_spring_iters=1000, **kwargs)
def lagrangian_tracer(velocity: gds.edge_gds, alpha=1.0) -> gds.node_gds:
''' Passive tracer '''
tracer = gds.node_gds(velocity.G)
tracer.set_evolution(dydt=lambda t, y: -alpha * tracer.advect(velocity))
start_x = random.choice(list(velocity.G.nodes()))
tracer.set_initial(y0=lambda x: 1. if x == start_x else 0.)
return tracer
def wave(G: nx.Graph, c: float=5.0): n = 20 G = gds.triangular_lattice(n, n*2, periodic=True) amplitude = gds.node_gds(G) amplitude.set_evolution(dydt=lambda t, y: (c**2)*amplitude.laplacian(), order=2) amplitude.set_initial(y0=lambda x: x[0]*x[1]*(n-x[0])*(n-x[1]) / (n**3)) gds.render(amplitude, dynamic_ranges=True, node_palette=cc.bmy, n_spring_iters=1000, title='Waves on a simplicial torus')
def navier_stokes(G: nx.Graph,
viscosity=1e-3,
density=1.0,
v_free=[],
body_force=None,
advect=None,
integrator=Integrators.lsoda,
**kwargs) -> (gds.node_gds, gds.edge_gds):
if body_force is None:
body_force = lambda t, y: 0
if advect is None:
advect = lambda v: v.advect()
pressure = gds.node_gds(G, **kwargs)
pressure.set_evolution(lhs=lambda t, p: pressure.laplacian(p) / density,
refresh_cvx=False)
v_free = np.array([pressure.X[x] for x in set(v_free)], dtype=np.intp)
velocity = gds.edge_gds(G, v_free=v_free, **kwargs)
velocity.set_evolution(
dydt=lambda t, u: velocity.
leray_project(-advect(velocity) + body_force(t, u) +
(0 if viscosity == 0 else velocity.laplacian() *
viscosity / density)) - pressure.grad() / density,
max_step=1e-3,
integrator=integrator,
)
return velocity, pressure
def grid_tv_boundary():
G = gds.square_lattice(10, 10)
temperature = gds.node_gds(G)
temperature.set_evolution(dydt=lambda t, y: temperature.laplacian())
temperature.set_constraints(dirichlet=gds.combine_bcs(
lambda x: 0 if x[0] == 9 else None,
lambda t, x: np.sin(t + x[1] / 4)**2 if x[0] == 0 else None))
gds.render(temperature, title='Heat equation with time-varying boundary')
def SIR_model(
G,
dS=0.1,
dI=0.5,
dR=0.0, # Diffusive terms
muS=0.1,
muI=0.3,
muR=0.1, # Death rates
Lambda=0.5, # Birth rate
beta=0.2, # Rate of contact
gamma=0.2, # Rate of recovery
initial_population=100,
patient_zero=None,
**kwargs):
'''
Reaction-Diffusion SIR model
Based on Huang et al, https://www.researchgate.net/publication/281739911_The_reaction-diffusion_system_for_an_SIR_epidemic_model_with_a_free_boundary
'''
susceptible = gds.node_gds(G, **kwargs)
infected = gds.node_gds(G, **kwargs)
recovered = gds.node_gds(G, **kwargs)
susceptible.set_evolution(dydt=lambda t, y: dS * susceptible.laplacian(
) - muS * susceptible.y - beta * susceptible.y * infected.y + Lambda)
infected.set_evolution(
dydt=lambda t, y: dI * infected.laplacian() + beta * susceptible.y *
infected.y - muI * infected.y - gamma * infected.y)
recovered.set_evolution(dydt=lambda t, y: dR * recovered.laplacian() +
gamma * infected.y - muR * recovered.y)
if patient_zero is None:
patient_zero = random.choice(list(G.nodes()))
print(patient_zero)
susceptible.set_initial(y0=lambda x: initial_population)
infected.set_initial(y0=lambda x: 1 if x == patient_zero else 0.)
sys = gds.couple({
'Susceptible': susceptible,
'Infected': infected,
'Recovered': recovered
})
return sys
def swift_hohenberg(G: nx.graph, a: float, b: float, c: float, gam0: float, gam2: float) -> gds.node_gds: assert c > 0, 'Unstable' amplitude = gds.node_gds(G) amplitude.set_evolution( dydt=lambda t, y: -a*y - b*(y**2) - c*(y**3) + gam0*amplitude.laplacian(y) - gam2*amplitude.bilaplacian(y) ) # amplitude.set_initial(y0=lambda _: np.random.uniform()) amplitude.set_initial(y0=lambda x: x[0]+x[1]) return amplitude
def heat_grid(n=10, steady_state=False) -> gds.node_gds:
G = gds.square_lattice(n, n)
temp = gds.node_gds(G)
if steady_state:
''' Steady-state version (for comparison) '''
temp.set_evolution(cost=lambda t, y: temp.laplacian(y))
else:
temp.set_evolution(dydt=lambda t, y: temp.laplacian(y))
return temp
def advection(G, v_field, kind=None):
flow_diff = np.zeros(len(G.edges()))
flow = gds.edge_gds(G)
flow.set_evolution(dydt=lambda t, y: flow_diff)
flow.set_initial(y0 = v_field)
conc = gds.node_gds(G)
if kind == None:
conc.set_evolution(dydt=lambda t, y: -conc.advect(flow))
elif kind == 'lie':
conc.set_evolution(dydt=lambda t, y: -conc.lie_advect(flow))
else:
raise Exception('unrecognized kind')
return conc, flow
def test_orientation():
G = gds.hexagonal_lattice(3, 4)
# G = gds.random_planar_graph(100, 0.2)
eq1 = gds.face_gds(G)
eq1.set_evolution(nil=True)
eq1.set_initial(y0=lambda x: 0.5)
eq2 = gds.node_gds(G)
eq2.set_evolution(nil=True)
sys = gds.couple({
'eq1': eq1,
'eq2': eq2,
})
gds.render(sys, face_orientations=True)
def navier_stokes(G: nx.Graph,
viscosity=1e-3,
density=1.0,
v_free=[],
e_free=[],
e_normal=[],
advect=None,
integrator=Integrators.lsoda,
**kwargs) -> (gds.node_gds, gds.edge_gds):
if advect is None:
advect = lambda v: v.advect()
pressure = gds.node_gds(G, **kwargs)
velocity = gds.edge_gds(G, **kwargs)
v_free = np.array(
[pressure.X[x] for x in set(v_free)],
dtype=np.intp) # Inlet/outlet nodes (typically, pressure boundaries)
e_free = np.array([velocity.X[x] for x in set(e_free)],
dtype=np.intp) # Free-slip surface
e_normal = np.array([velocity.X[x] for x in set(e_normal)],
dtype=np.intp) # Inlets/outlet edges normal to surface
min_step = 1e-3
def pressure_f(t, y):
dt = max(min_step, velocity.dt)
lhs = velocity.div(velocity.y / dt - advect(velocity) +
velocity.laplacian(free=e_free, normal=e_normal) *
viscosity / density)
lhs[v_free] = 0.
lhs -= pressure.laplacian(y) / density
return lhs
def velocity_f(t, y):
return -advect(
velocity) - pressure.grad() / density + velocity.laplacian(
free=e_free, normal=e_normal) * viscosity / density
pressure.set_evolution(lhs=pressure_f)
velocity.set_evolution(dydt=velocity_f, integrator=integrator)
return velocity, pressure
