54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
import numpy as np
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import pytest
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import scgenerator as sc
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def test_normalisation():
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rng = np.random.default_rng(56)
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t = np.linspace(-10, 10, 512)
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s = np.exp(-((t / 2.568) ** 2)) + rng.random(len(t)) / 15
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target = np.sum(sc.abs2(np.fft.fft(s))) / 512
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noise = sc.noise.NoiseMeasurement.from_time_series(s, 1, "boxcar", detrend=False)
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assert np.sum(noise.psd) == pytest.approx(target)
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def test_time_and_back():
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"""
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sampling a time series from a spectrum and transforming
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it back yields the same spectrum
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"""
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rng = np.random.default_rng(57)
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t = np.linspace(-10, 10, 512)
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signal = np.exp(-((t / 2.568) ** 2)) + rng.random(len(t)) / 15
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noise = sc.noise.NoiseMeasurement.from_time_series(signal, 1, "boxcar", detrend=False)
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_, new_signal = noise.time_series(len(noise.freq))
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new_noise = sc.noise.NoiseMeasurement.from_time_series(new_signal, 1, "boxcar", detrend=False)
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assert new_noise.psd == pytest.approx(noise.psd)
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def test_nyquist():
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"""
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generating a time series and tranforming it back yields the same spectrum.
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Using segements, the nyquist frequency is exactly spread out over the frequency bin width
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"""
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signal = np.cos(np.arange(1024) * np.pi)
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n1 = sc.noise.NoiseMeasurement.from_time_series(signal, 1, None)
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n3 = sc.noise.NoiseMeasurement.from_time_series(signal, 1, None, 512)
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n15 = sc.noise.NoiseMeasurement.from_time_series(signal, 1, None, 128)
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assert n1.psd[-1] == n3.psd[-1] * 2 == n15.psd[-1] * 8
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def test_sampling():
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f = np.geomspace(10, 2e6, 138)
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spec = 1 / (f + 1)
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noise = sc.noise.NoiseMeasurement(f, spec)
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assert noise.sample_spectrum(257)[0][0] == 0
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assert noise.sample_spectrum(257, log_mode=True)[0][0] == 0
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