93 lines
2.3 KiB
Python
93 lines
2.3 KiB
Python
#!/usr/bin/python3
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import numpy as np
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from lasp import cppSLM
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from lasp.filter import SPLFilterDesigner
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import matplotlib.pyplot as plt
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def test_cppslm1():
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"""
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Generate a sine wave
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"""
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fs = 48000
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omg = 2*np.pi*1000
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slm = cppSLM.fromBiquads(fs, 2e-5, 1, 0.125, [1.,0,0,1,0,0])
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t = np.linspace(0, 10, 10*fs, endpoint=False)
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# Input signal with an rms of 1 Pa
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in_ = np.sin(omg*t)*np.sqrt(2)
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# Compute overall RMS
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rms = np.sqrt(np.sum(in_**2)/in_.size)
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# Compute overall level
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level = 20*np.log10(rms/2e-5)
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# Output of SLM
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out = slm.run(in_)
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# Output of SLM should be close to theoretical
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# level, at least for reasonable time constants
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# (Fast, Slow etc)
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assert(np.isclose(out[-1,0], level))
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def test_cppslm2():
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"""
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Generate a sine wave, now A-weighted
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"""
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fs = 48000
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omg = 2*np.pi*1000
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filt = SPLFilterDesigner(fs).A_Sos_design()
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slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125, filt.flatten(), [1.,0,0,1,0,0])
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t = np.linspace(0, 10, 10*fs, endpoint=False)
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# Input signal with an rms of 1 Pa
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in_ = np.sin(omg*t) *np.sqrt(2)
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# Compute overall RMS
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rms = np.sqrt(np.sum(in_**2)/in_.size)
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# Compute overall level
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level = 20*np.log10(rms/2e-5)
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# Output of SLM
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out = slm.run(in_)
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# Output of SLM should be close to theoretical
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# level, at least for reasonable time constants
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# (Fast, Slow etc)
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assert np.isclose(out[-1,0], level, atol=1e-2)
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def test_cppslm3():
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fs = 48000
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omg = 2*np.pi*1000
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filt = SPLFilterDesigner(fs).A_Sos_design()
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slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125, filt.flatten(), [1.,0,0,1,0,0])
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t = np.linspace(0, 10, 10*fs, endpoint=False)
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in_ = 10*np.sin(omg*t) * np.sqrt(2)+np.random.randn()
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# Compute overall RMS
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rms = np.sqrt(np.sum(in_**2)/in_.size)
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# Compute overall level
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level = 20*np.log10(rms/2e-5)
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# Output of SLM
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out = slm.run(in_)
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Lpeak = 20*np.log10(np.max(np.abs(in_)/2e-5))
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Lpeak
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slm.Lpeak()
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assert np.isclose(out[-1,0], slm.Leq()[0][0], atol=1e-2)
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assert np.isclose(Lpeak, slm.Lpeak()[0][0], atol=1e0)
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if __name__ == '__main__':
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test_cppslm1()
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test_cppslm2()
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test_cppslm3()
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