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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 import numpy as np
from lasp import SeriesBiquad, AvPowerSpectra from lasp import SeriesBiquad, AvPowerSpectra
from lasp.filter import SPLFilterDesigner from lasp.filter import SPLFilterDesigner
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from scipy.signal import sosfreqz from scipy.signal import sosfreqz
# plt.close('all') plt.close('all')
# def test_cppslm2():
# """
# Generate a sine wave, now A-weighted
# """
fs = 48000 fs = 48000
omg = 2*np.pi*1000 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 import numpy as np
from lasp import AvPowerSpectra, Window from lasp import AvPowerSpectra, Window
import matplotlib.pyplot as plt
# plt.close('all')
def test_aps1(): def test_aps1():
nfft = 16384 nfft = 16384

89
test/test_cppslm.py
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@ -2,34 +2,35 @@
import numpy as np import numpy as np
from lasp import cppSLM from lasp import cppSLM
from lasp.filter import SPLFilterDesigner from lasp.filter import SPLFilterDesigner
import matplotlib.pyplot as plt
def test_cppslm1(): def test_cppslm1():
""" """
Generate a sine wave Generate a sine wave
""" """
fs = 48000 fs = 48000
omg = 2*np.pi*1000 omg = 2 * np.pi * 1000
slm = cppSLM.fromBiquads(fs, 2e-5, 1, 0.125, [1.,0,0,1,0,0]) 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)
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 # Compute overall RMS
rms = np.sqrt(np.sum(in_**2)/in_.size) rms = np.sqrt(np.sum(in_**2) / in_.size)
# Compute overall level # Compute overall level
level = 20*np.log10(rms/2e-5) level = 20 * np.log10(rms / 2e-5)
# Output of SLM # Output of SLM
out = slm.run(in_) out = slm.run(in_)
# Output of SLM should be close to theoretical # Output of SLM should be close to theoretical
# level, at least for reasonable time constants # level, at least for reasonable time constants
# (Fast, Slow etc) # (Fast, Slow etc)
assert(np.isclose(out[-1,0], level)) assert (np.isclose(out[-1, 0], level))
def test_cppslm2(): def test_cppslm2():
@ -37,53 +38,57 @@ def test_cppslm2():
Generate a sine wave, now A-weighted Generate a sine wave, now A-weighted
""" """
fs = 48000 fs = 48000
omg = 2*np.pi*1000 omg = 2 * np.pi * 1000
filt = SPLFilterDesigner(fs).A_Sos_design() filt = SPLFilterDesigner(fs).A_Sos_design()
slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125, filt.flatten(), [1.,0,0,1,0,0]) slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125,
filt.flatten(), # Pre-filter coefs
t = np.linspace(0, 10, 10*fs, endpoint=False) np.array([[1., 0, 0, 1, 0, 0]]).T # Bandpass coefs
)
# Input signal with an rms of 1 Pa
in_ = np.sin(omg*t) *np.sqrt(2) 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 # Compute overall RMS
rms = np.sqrt(np.sum(in_**2)/in_.size) rms = np.sqrt(np.sum(in_**2) / in_.size)
# Compute overall level # Compute overall level
level = 20*np.log10(rms/2e-5) level = 20 * np.log10(rms / 2e-5)
# Output of SLM # Output of SLM
out = slm.run(in_) out = slm.run(in_)
# Output of SLM should be close to theoretical # Output of SLM should be close to theoretical
# level, at least for reasonable time constants # level, at least for reasonable time constants
# (Fast, Slow etc) # (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(): def test_cppslm3():
fs = 48000 fs = 48000
omg = 2*np.pi*1000 omg = 2 * np.pi * 1000
filt = SPLFilterDesigner(fs).A_Sos_design() filt = SPLFilterDesigner(fs).A_Sos_design()
slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125, filt.flatten(), [1.,0,0,1,0,0]) slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125,
t = np.linspace(0, 10, 10*fs, endpoint=False) filt.flatten(),
np.array([[1., 0, 0, 1, 0, 0]]).T)
in_ = 10*np.sin(omg*t) * np.sqrt(2)+np.random.randn() t = np.linspace(0, 10, 10 * fs, endpoint=False)
in_ = 10 * np.sin(omg * t) * np.sqrt(2) + np.random.randn()
# Compute overall RMS # Compute overall RMS
rms = np.sqrt(np.sum(in_**2)/in_.size) rms = np.sqrt(np.sum(in_**2) / in_.size)
# Compute overall level # Compute overall level
level = 20*np.log10(rms/2e-5) level = 20 * np.log10(rms / 2e-5)
# Output of SLM # Output of SLM
out = slm.run(in_) 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 Lpeak
slm.Lpeak() slm.Lpeak()
assert np.isclose(out[-1,0], slm.Leq()[0][0], atol=1e-2) assert np.isclose(out[-1, 0], slm.Leq()[0], atol=1e-2)
assert np.isclose(Lpeak, slm.Lpeak()[0][0], atol=2e0) assert np.isclose(Lpeak, slm.Lpeak()[0], atol=2e0)
if __name__ == '__main__': if __name__ == '__main__':