lasp/test/test_cppslm.py

76 lines
1.8 KiB
Python
Raw Normal View History

#!/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:])))