update knee tests

neurodsp/tests/settings.py

@@ -25,7 +25,7 @@
 FREQS_ARR = np.array([5, 10, 15])
 EXP1 = -1
 EXP2 = -2
-KNEE = 10**2
+KNEE = 10
 
 # Define settings for testing analyses
 F_RANGE = (FREQ1-2, FREQ1+2)

neurodsp/tests/sim/test_aperiodic.py

@@ -30,34 +30,37 @@ def test_sim_knee():
 
     # Build the signal and run a smoke test
     sig = sim_knee(N_SECONDS, FS, EXP1, EXP2, KNEE)
-    check_sim_output(sig)
+    check_sim_output(sig, N_SECONDS, FS)
 
     # Check against the power spectrum when you take the Fourier transform
     sig_len = int(FS*N_SECONDS)
     freqs = np.linspace(0, FS/2, num=sig_len//2, endpoint=True)
 
     # Ignore the DC component to avoid division by zero in the Lorentzian
     freqs = freqs[1:]
-    true_psd = np.array([1/(freq**-EXP1*(freq**(-EXP2-EXP1) + KNEE)) for freq in freqs])
+    true_psd = 1 / ((freqs ** -EXP1 * (freqs ** (-EXP2 - EXP1)+ KNEE**(-2*EXP1 - EXP2))))
 
     # Only look at the frequencies (ignoring DC component) up to the nyquist rate
     sig_hat = np.fft.fft(sig)[1:sig_len//2]
     numerical_psd = np.abs(sig_hat)**2
 
-    np.allclose(true_psd, numerical_psd, atol=EPS)
+    scale = numerical_psd / true_psd
+
+    np.allclose(true_psd*scale, numerical_psd, atol=EPS)
 
     # Accuracy test for a single exponent
     sig = sim_knee(N_SECONDS, FS, 0, EXP2, KNEE)
 
     freqs, powers = compute_spectrum(sig, FS, f_range=(1, 200))
 
     def _estimate_single_knee(xs, offset, knee, exponent):
-        return np.zeros_like(xs) + offset - np.log10(xs**exponent + knee)
+        return np.zeros_like(xs) + offset - np.log10(xs**exponent + knee**exponent)
 
     ap_params, _ = curve_fit(_estimate_single_knee, freqs, np.log10(powers))
-    _, _, EXP2_hat = ap_params[:]
+    _, KNEE_hat, EXP2_hat = ap_params[:]
 
-    assert -round(EXP2_hat) == EXP2
+    np.testing.assert_approx_equal(-EXP2_hat, EXP2, significant=1)
+    np.testing.assert_approx_equal(KNEE_hat, KNEE, significant=1)
 
 def test_sim_random_walk():