improved noise

tests/test_noise.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 
 import scgenerator as sc
 
@@ -17,3 +18,48 @@ def test_segmentation():
     assert np.all(
         sc.noise.segments(t, 7) == np.vstack([r, r + 4, r + 8, r + 12, r + 16, r + 20, r + 24])
     )
+
+
+def test_normalisation():
+    rng = np.random.default_rng(56)
+    t = np.linspace(-10, 10, 512)
+    s = np.exp(-((t / 2.568) ** 2)) + rng.random(len(t)) / 15
+
+    target = np.sum(sc.abs2(np.fft.fft(s))) / 512
+    noise = sc.noise.NoiseMeasurement.from_time_series(s, 1, "Square", force_no_dc=False)
+    assert np.sum(noise.psd) == pytest.approx(target)
+
+
+def test_no_dc():
+    rng = np.random.default_rng(56)
+    t = np.linspace(-10, 10, 512)
+    s = rng.normal(0, 1, len(t)).cumsum()
+    noise = sc.noise.NoiseMeasurement.from_time_series(s, 1)
+    assert noise.psd[0] == pytest.approx(0)
+
+
+def test_time_and_back():
+    """
+    sampling a time series from a spectrum and transforming
+    it back should yield the same spectrum
+    """
+    rng = np.random.default_rng(57)
+    t = np.linspace(-10, 10, 512)
+    signal = np.exp(-((t / 2.568) ** 2)) + rng.random(len(t)) / 15
+
+    noise = sc.noise.NoiseMeasurement.from_time_series(signal, 1, "Square", force_no_dc=False)
+    _, new_signal = noise.time_series(len(t))
+    new_noise = sc.noise.NoiseMeasurement.from_time_series(
+        new_signal, 1, "Square", force_no_dc=False
+    )
+    assert new_noise.psd == pytest.approx(noise.psd)
+
+
+def test_sampling():
+    f = np.geomspace(10, 2e6, 138)
+    spec = 1 / (f + 1)
+
+    noise = sc.noise.NoiseMeasurement(f, spec)
+
+    assert noise.sample_spectrum(257)[0][0] == 0
+    assert noise.sample_spectrum(257, log_mode=True)[0][0] == 0