#!/usr/bin/python3
import numpy as np
from lasp import SLM
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 = SLM.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 = SLM.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)



if __name__ == '__main__':
    test_cppslm1()
    test_cppslm2()

    # plt.plot(t,out[:,0])    
    # plt.close('all')
    # from scipy.signal import sosfreqz
    # omg, H = sosfreqz(filt)
    # freq = omg / (2*np.pi)*fs
    # plt.figure()
    # plt.semilogx(freq[1:],20*np.log10(np.abs(H[1:])))