def test_l1tf_on_mock(alpha=1.0, beta=0.0, noise=0.0, doplot=doplot):
if doplot:
plt.clf()
mock = make_l1tf_mock(noise=noise)
l1tf_fit = myl1tf.l1tf(mock["y"], alpha=alpha, beta=beta)
resid = l1tf_fit["x"] - mock["y_with_seasonal"]
assert_is_good(resid, mean_abs_resid_max=0.035, max_abs_resid_max=0.1)
if doplot:
plt.plot(mock["x"], l1tf_fit["x"], marker="o", linestyle="-", label="L1TF, alpha=%s" % alpha)
plt.legend(loc="lower center")
plt.show()
def test_first_derivative_on_noisy_data(
num=10, slope=1.0, offset=3.0, noise=1.0, alpha=1.0, beta=0.0, seed=7863, doplot=doplot
):
if doplot:
plt.clf()
np.random.seed(seed)
i = np.arange(num)
x = i * slope + offset
y = x + noise * np.random.randn(num)
l1tf_fit = myl1tf.l1tf(y, alpha=alpha, beta=beta)
resid = l1tf_fit["x"] - y
assert_is_good(resid, mean_abs_resid_max=0.6, max_abs_resid_max=1)
if doplot:
plt.plot(i, y, marker="o", markersize=12, alpha=0.8, linestyle="")
plt.plot(i, l1tf_fit["x"], marker="o", linestyle="-", markersize=4, alpha=0.8)
def test_l1tf_on_mock_with_period_l1p(alpha=1.0, period=6, eta=0.1, sea_amp=0.05, doplot=doplot):
if doplot:
plt.clf()
mock = make_l1tf_mock(period=period, sea_amp=sea_amp)
l1tf_fit = myl1tf.l1tf(mock["y_with_seasonal"], alpha=alpha, period=period, eta=eta, with_l1p=True)
resid = l1tf_fit["x"] - mock["y_with_seasonal"]
assert_is_good(resid, mean_abs_resid_max=0.035, max_abs_resid_max=0.1)
if doplot:
lab = "L1TF, period=%s, alpha=%s, eta=%s" % (period, alpha, eta)
plt.plot(mock["x"], l1tf_fit["x"], marker="o", linestyle="-", markersize=4, alpha=0.8, label=lab)
lab = "L1TF + seasonal, period=%s, alpha=%s, eta=%s" % (period, alpha, eta)
plt.plot(mock["x"], l1tf_fit["x_with_seasonal"], marker="o", markersize=4, linestyle="-", label=lab)
plt.legend(loc="lower left")
plt.ylim(0, 1)
plt.show()
def test_l1tf_on_mock_with_period_l1p_with_spike_and_step(alpha=0.5, period=6, eta=0.1, doplot=doplot, sea_amp=0.05):
# no test for this yet
if doplot:
plt.clf()
mock = make_l1tf_mock2(period=period, sea_amp=sea_amp)
y = mock["y_with_seasonal"]
ymax = max(y)
l1tf_fit = myl1tf.l1tf(y, alpha=alpha, period=period, eta=eta, with_l1p=True)
if doplot:
lab = "L1TF, period=%s, alpha=%s, eta=%s" % (period, alpha, eta)
plt.plot(mock["x"], l1tf_fit["x"], marker="o", linestyle="-", markersize=4, alpha=0.8, label=lab)
lab = "L1TF + seasonal, period=%s, alpha=%s, eta=%s" % (period, alpha, eta)
plt.plot(mock["x"], l1tf_fit["x_with_seasonal"], marker="o", markersize=4, linestyle="-", label=lab)
plt.legend(loc="upper left")
plt.ylim(0, ymax)
plt.plot(mock["x"], y)
plt.show()
return l1tf_fit
