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Updated tests
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This commit is contained in:
Anne de Jong 2023-01-12 20:31:55 +01:00
parent 3da6595b0c
commit d1dea1483f
3 changed files with 48 additions and 50 deletions
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7
test/test_biquadbank.py → examples/example_biquadbank.py
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@ -2,15 +2,10 @@
import numpy as np
from lasp import SeriesBiquad, AvPowerSpectra
from lasp.filter import SPLFilterDesigner
import matplotlib.pyplot as plt
from scipy.signal import sosfreqz
# plt.close('all')
plt.close('all')
# def test_cppslm2():
# """
# Generate a sine wave, now A-weighted
# """
fs = 48000
omg = 2*np.pi*1000

2
test/test_aps.py
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@ -8,8 +8,6 @@ Created on Mon Jan 15 19:45:33 2018
import numpy as np
from lasp import AvPowerSpectra, Window
import matplotlib.pyplot as plt
# plt.close('all')
def test_aps1():
nfft = 16384

89
test/test_cppslm.py
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@ -2,34 +2,35 @@
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)
omg = 2 * np.pi * 1000
slm = cppSLM.fromBiquads(fs, 2e-5, 1, 0.125,
np.array([[1., 0, 0, 1, 0, 0]]).T)
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)
# 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)
rms = np.sqrt(np.sum(in_**2) / in_.size)
# Compute overall level
level = 20*np.log10(rms/2e-5)
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))
assert (np.isclose(out[-1, 0], level))
def test_cppslm2():
@ -37,53 +38,57 @@ def test_cppslm2():
Generate a sine wave, now A-weighted
"""
fs = 48000
omg = 2*np.pi*1000
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)
slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125,
filt.flatten(), # Pre-filter coefs
np.array([[1., 0, 0, 1, 0, 0]]).T # Bandpass coefs
)
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)
rms = np.sqrt(np.sum(in_**2) / in_.size)
# Compute overall level
level = 20*np.log10(rms/2e-5)
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)
assert np.isclose(out[-1, 0], level, atol=1e-2)
def test_cppslm3():
fs = 48000
omg = 2*np.pi*1000
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()
slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125,
filt.flatten(),
np.array([[1., 0, 0, 1, 0, 0]]).T)
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)
rms = np.sqrt(np.sum(in_**2) / in_.size)
# Compute overall level
level = 20*np.log10(rms/2e-5)
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 = 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)
assert np.isclose(out[-1, 0], slm.Leq()[0], atol=1e-2)
assert np.isclose(Lpeak, slm.Lpeak()[0], atol=2e0)
if __name__ == '__main__':