lasp/test/test_cppslm.py

93 lines
2.3 KiB
Python

#!/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()