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