def test_quad_gauss_degree():
"""
check gaussian quadrature degree
"""
x0, x1 = 0, 1
max_degree = 8
for deg in range(2, max_degree):
p = Monome(deg)
y0 = p[x0, x1]
max_node_count = range(2, 6)
Y = [
quad_gauss(p, x0, x1, node_count) for node_count in max_node_count
]
accuracy = get_log_error(Y, y0 * np.ones_like(Y))
accuracy[np.isinf(accuracy)] = 17
# check accuracy is good enough
for node_count, acc in zip(max_node_count, accuracy):
if 2 * node_count >= deg + 1:
assert acc > 6
plt.plot(max_node_count, accuracy, '.:', label=f'x^{deg}')
plt.legend()
plt.ylabel('accuracy')
plt.xlabel('node count')
plt.suptitle(f'test quad gauss')
plt.show()
def test_quad_degree():
"""
check quadrature degree
Q: why in some cases x^n integrated perfectly with only n nodes?
"""
x0, x1 = 0, 1
max_degree = 7
for deg in range(1, max_degree):
p = Monome(deg)
y0 = p[x0, x1]
max_node_count = range(1, max_degree + 1)
Y = [
quad(p, x0, x1, np.linspace(x0, x1, node_count))
for node_count in max_node_count
]
# Y = [quad(p, x0, x1, x0 + (x1-x0) * np.random.random(node_count)) for node_count in max_node_count]
accuracy = get_log_error(Y, y0 * np.ones_like(Y))
accuracy[np.isinf(accuracy)] = 17
# check accuracy is good enough
for node_count, acc in zip(max_node_count, accuracy):
if node_count >= deg + 1:
assert acc > 6
plt.plot(max_node_count, accuracy, '.:', label=f'x^{deg}')
plt.legend()
plt.ylabel('accuracy')
plt.xlabel('node_count')
plt.suptitle(f'test quad')
plt.show()
def test_composite_quad_degree(v):
"""
Q: convergence maybe somewhat between 3 and 4, why?
"""
from variants import params
plt.figure()
a, b, alpha, beta, f = params(v)
x0, x1 = a, b
# a, b = -10, 10
exact = sp_quad(lambda x: f(x) / (x - a)**alpha / (b - x)**beta, x0, x1)[0]
# plot weights
xs = np.linspace(x0, x1, 101)[1:-1]
ys = 1 / ((xs - a)**alpha * (b - xs)**beta)
#my addition
# for x in xs:
# if (x-a)**alpha * (b-x)**beta:
# print("HERE")
# print(ys)
#
plt.subplot(1, 2, 1)
plt.plot(xs, ys, label='weights')
ax = list(plt.axis())
ax[2] = 0.
plt.axis(ax)
plt.xlabel('x')
plt.ylabel('p(x)')
plt.legend()
L = 2
n_intervals = [L**q for q in range(2, 10)]
n_nodes = 3
Y = [
composite_quad(f,
x0,
x1,
n_intervals=n,
n_nodes=n_nodes,
a=a,
b=b,
alpha=alpha,
beta=beta) for n in n_intervals
]
accuracy = get_log_error(Y, exact * np.ones_like(Y))
x = np.log10(n_intervals)
aitken_degree = aitken(*Y[5:8], L)
# plot acc
plt.subplot(1, 2, 2)
plt.plot(x, accuracy, 'kh')
plt.xlabel('log10(node count)')
plt.ylabel('accuracy')
plt.suptitle(f'variant #{v} (alpha={alpha:4.2f}, beta={beta:4.2f})\n'
f'aitken estimation: {aitken_degree:.2f}')
plt.show()
def test_multi_step():
"""
test Adams method
Q: compare the right plot for both cases and explain the difference
"""
y0 = np.array([0., 1.])
t0 = 0
t1 = 1.
dt = 0.1
f = Harmonic(y0, 1, 1)
ts = np.arange(t0, t1 + dt, dt)
exact = f[ts].T
#print('exact')
#print(exact)
for one_step_method in [
RungeKuttaMethod(collection.rk4_coeffs),
ExplicitEulerMethod(),
]:
plt.figure()
plt.subplot(1, 2, 1), plt.plot(ts, [e[0] for e in exact],
'k',
label='Exact')
for p, c in adams_coeffs.items():
t_adams, y_adams = adams(f,
y0,
ts,
c,
one_step_method=one_step_method)
#print(y_adams)
print(f'Function calls: {f.get_call_counter()}')
err = get_log_error(exact, y_adams)
label = f"Adams's order {p}"
plt.subplot(1, 2, 1), plt.plot(t_adams, [y[0] for y in y_adams],
'.--',
label=label)
plt.subplot(1, 2, 2), plt.plot(t_adams, err, '.--', label=label)
plt.subplot(1, 2, 1), plt.xlabel('t'), plt.ylabel('y'), plt.legend()
plt.subplot(1, 2,
2), plt.xlabel('t'), plt.ylabel('accuracy'), plt.legend()
plt.suptitle(
f'test_multi_step\none step method: {one_step_method.name}')
plt.show()
#test_multi_step()
def test_composite_quad(n_nodes):
"""
test composite 2-, 3-, 5-node quads
Q: explain converge speed for each case
"""
plt.figure()
x0, x1 = 0, 1
L = 2
n_intervals = [L**q for q in range(0, 8)]
for i, degree in enumerate((5, 6)):
p = Monome(degree)
Y = [
composite_quad(p, x0, x1, n_intervals=n, n_nodes=n_nodes)
for n in n_intervals
]
accuracy = get_log_error(Y, p[x0, x1] * np.ones_like(Y))
x = np.log10(n_intervals)
# check convergence
ind = np.isfinite(x) & np.isfinite(accuracy)
k, b = np.polyfit(x[ind], accuracy[ind], 1)
aitken_degree = aitken(*Y[0:6:2], L**2)
plt.subplot(1, 2, i + 1)
plt.title(f'{n_nodes}-node CQ for x^{degree}')
plt.plot(x, k * x + b, 'b:', label=f'{k:.2f}*x+{b:.2f}')
plt.plot(x,
aitken_degree * x + b,
'm:',
label=f'aitken ({aitken_degree:.2f})')
plt.plot(x, accuracy, 'kh', label=f'accuracy for x^{degree}')
plt.xlabel('log10(node count)')
plt.ylabel('accuracy')
plt.legend()
if n_nodes < degree:
assert np.abs(aitken_degree - k) < 0.5, \
f'Aitken estimation {aitken_degree:.2f} is too far from actual {k:.2f}'
plt.show()
def test_one_step():
"""
test Euler and RK methods
"""
y0 = np.array([0., 1.])
t0 = 0
t1 = np.pi / 2
dt = 0.1
f = Harmonic(y0, 1, 1)
ts = np.arange(t0, t1 + dt, dt)
exact = f[ts].T
plt.figure()
plt.subplot(1, 2, 1)
plt.plot(ts, [e[0] for e in exact], 'k', label='Exact')
colors = 'rgbcmyk'
for i, method in enumerate([
ExplicitEulerMethod(),
RungeKuttaMethod(collection.rk4_coeffs),
RungeKuttaMethod(collection.dopri_coeffs),
]):
_, y = fix_step_integration(method, f, y0, ts)
print(f'len(Y): {len(y)}')
print(f'Function calls: {f.get_call_counter()}')
plt.subplot(1, 2, 1), plt.plot(ts, [_y[0] for _y in y],
f'{colors[i]}.--',
label=method.name)
plt.subplot(1, 2, 2), plt.plot(ts,
get_log_error(exact, y),
f'{colors[i]}.--',
label=method.name)
plt.subplot(1, 2, 1), plt.xlabel('t'), plt.ylabel('y'), plt.legend()
plt.subplot(1, 2, 2), plt.xlabel('t'), plt.ylabel('accuracy'), plt.legend()
plt.suptitle('test_one_step')
plt.show()
def test_adaptive(f, y0):
"""
test adaptive step algorithms
"""
t0, t1 = 0, 4 * np.pi
atol = 1e-6
rtol = 1e-3
tss = []
yss = []
methods = (
(ExplicitEulerMethod(), AdaptType.RUNGE),
(RungeKuttaMethod(coeffs=collection.rk4_coeffs), AdaptType.RUNGE),
(EmbeddedRungeKuttaMethod(coeffs=collection.dopri_coeffs),
AdaptType.EMBEDDED),
)
for method, adapt_type in methods:
f.clear_call_counter()
ts, ys = adaptive_step_integration(method=method,
func=f,
y_start=y0,
t_span=(t0, t1),
adapt_type=adapt_type,
atol=atol,
rtol=rtol)
print(f'{method.name} took {f.get_call_counter()} function calls')
tss.append(np.array(ts))
yss.append(ys)
ts = np.array(sorted([t for ts in tss for t in ts]))
exact = f[ts].T
y0 = np.array([y[0] for y in exact])
# plots
plt.figure('y(t)'), plt.suptitle('test_adaptive: y(t)'), plt.xlabel(
't'), plt.ylabel('y')
plt.plot(ts, y0, 'ko-', label='exact')
plt.figure('dt(t)'), plt.suptitle('test_adaptive: step sizes'), plt.xlabel(
't'), plt.ylabel('dt')
plt.figure('dy(t)'), plt.suptitle('test_adaptive: accuracies'), plt.xlabel(
't'), plt.ylabel('accuracy')
for (m, _), ts, ys in zip(methods, tss, yss):
plt.figure('y(t)'), plt.plot(ts, [y[0] for y in ys], '.', label=m.name)
plt.figure('dt(t)'), plt.plot(ts[:-1],
ts[1:] - ts[:-1],
'.-',
label=m.name)
plt.figure('dy(t)'), plt.plot(ts,
get_log_error(f[ts].T, ys),
'.-',
label=m.name)
plt.figure('y(t)'), plt.legend()
plt.figure('dt(t)'), plt.legend()
plt.figure('dy(t)'), plt.legend()
plt.show()