76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
|
#!/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:])))
|