lasp/test/test_cppslm.py

#!/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()