Working on SOS implementation of frequency weighting filters

This commit is contained in:
Anne de Jong 2020-01-17 22:10:42 +01:00
parent f10e78ff41
commit 15c8b0d923
2 changed files with 116 additions and 81 deletions

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@ -7,103 +7,137 @@ Description: Filter design for frequency weighting curves (i.e. A and C
weighting) weighting)
""" """
from .fir_design import freqResponse, arbitrary_fir_design from .fir_design import freqResponse, arbitrary_fir_design
from scipy.signal import bilinear_zpk, zpk2sos
import numpy as np import numpy as np
__all__ = ['A', 'C', 'A_fir_design', 'C_fir_design', __all__ = ['SPLFilterDesigner']
'show_Afir', 'show_Cfir']
fr = 1000.
fL = 10**1.5
fH = 10**3.9
fLsq = fL**2
fHsq = fH**2
frsq = fr**2
fA = 10**2.45
D = np.sqrt(.5)
b = (1/(1-D))*(frsq+fLsq*fHsq/frsq-D*(fLsq+fHsq))
c = fLsq*fHsq
f2 = (3-np.sqrt(5.))/2*fA
f3 = (3+np.sqrt(5.))/2*fA
f1 = np.sqrt((-b-np.sqrt(b**2-4*c))/2)
f4 = np.sqrt((-b+np.sqrt(b**2-4*c))/2)
f4sq = f4**2
def A_uncor(f): class SPLFilterDesigner:
""" fr = 1000.
Computes the uncorrected frequency response of the A-filter fL = 10**1.5
""" fH = 10**3.9
fsq = f**2
num = f4sq*fsq**2
denom1 = (fsq+f1**2)
denom2 = np.sqrt((fsq+f2**2)*(fsq+f3**2))*(fsq+f4sq)
return (num/(denom1*denom2))
def A(f): fLsq = fL**2
""" fHsq = fH**2
Computes the linear A-weighting freqency response. Hence, to obtain frsq = fr**2
A-weighted values, the *amplitude* need to be multiplied with this value. fA = 10**2.45
Hence, to correct dB levels, the value of 20*log(A) needs to be added to D = np.sqrt(.5)
the level
Args: b = (1/(1-D))*(frsq+fLsq*fHsq/frsq-D*(fLsq+fHsq))
f: Frequency array to compute values for c = fLsq*fHsq
Returns: f2 = (3-np.sqrt(5.))/2*fA
A(f) for each frequency f3 = (3+np.sqrt(5.))/2*fA
""" f1 = np.sqrt((-b-np.sqrt(b**2-4*c))/2)
Auncor = A_uncor(f) f4 = np.sqrt((-b+np.sqrt(b**2-4*c))/2)
A1000 = A_uncor(1000.) f4sq = f4**2
return Auncor/A1000
def _A_uncor(self, f):
"""
Computes the uncorrected frequency response of the A-filter
Args:
f: Frequency (array, float)
Returns:
Linear filter transfer function
"""
fsq = f**2
num = self.f4sq*fsq**2
denom1 = (fsq+self.f1**2)
denom2 = np.sqrt((fsq+self.f2**2)*(fsq+self.f3**2))*(fsq+self.f4sq)
return (num/(denom1*denom2))
def C_uncor(f): def A(self, f):
""" """
Computes the uncorrected frequency response of the C-filter Computes the linear A-weighting freqency response. Hence, to obtain
""" A-weighted values, the *amplitude* need to be multiplied with this value.
fsq = f**2 Hence, to correct dB levels, the value of 20*log(A) needs to be added to
num = f4sq*fsq the level
denom1 = (fsq+f1**2)
denom2 = (fsq+f4**2) Args:
return num/(denom1*denom2) f: Frequency array to compute values for
Returns:
A(f) for each frequency
"""
Auncor = self._A_uncor(f)
A1000 = self._A_uncor(self.fr)
return Auncor/A1000
def C(f): def _C_uncor(self, f):
""" """
Computes the linear A-weighting freqency response Computes the uncorrected frequency response of the C-filter
""" """
Cuncor = C_uncor(f) fsq = f**2
C1000 = C_uncor(1000.) num = self.f4sq*fsq
return Cuncor/C1000 denom1 = (fsq+self.f1**2)
denom2 = (fsq+self.f4**2)
return num/(denom1*denom2)
def A_fir_design(): def C(self, f):
fs = 48000. """
freq_design = np.linspace(0, 17e3, 3000) Computes the linear A-weighting freqency response
freq_design[-1] = fs/2 """
amp_design = A(freq_design) Cuncor = self._C_uncor(f)
amp_design[-1] = 0. C1000 = self._C_uncor(self.fr)
return Cuncor/C1000
L = 2048 # Filter order
fir = arbitrary_fir_design(fs, L, freq_design, amp_design,
window='rectangular')
return fir
def C_fir_design(): def A_fir_design(self, fs):
fs = 48000.
freq_design = np.linspace(0, 17e3, 3000)
freq_design[-1] = fs/2
amp_design = C(freq_design)
amp_design[-1] = 0.
L = 2048 # Filter order assert int(fs) == 48000
fir = arbitrary_fir_design(fs, L, freq_design, amp_design, freq_design = np.linspace(0, 17e3, 3000)
window='rectangular') freq_design[-1] = fs/2
return fir amp_design = self.A(freq_design)
amp_design[-1] = 0.
L = 2048 # Filter order
fir = arbitrary_fir_design(fs, L, freq_design, amp_design,
window='rectangular')
return fir
def C_fir_design(self, fs):
assert int(fs) == 48000
fs = 48000.
freq_design = np.linspace(0, 17e3, 3000)
freq_design[-1] = fs/2
amp_design = C(freq_design)
amp_design[-1] = 0.
L = 2048 # Filter order
fir = arbitrary_fir_design(fs, L, freq_design, amp_design,
window='rectangular')
return fir
def C_Sos_design(self, fs):
"""
Create filter coefficients of the C-weighting filter. Uses the bilinear
transform to convert the analog filter to a digital one.
Args:
fs: Sampling frequency [Hz]
Returns:
Sos: Second order sections
"""
p1 = 2*np.pi*self.f1
p4 = 2*np.pi*self.f4
zeros_analog = [0,0]
poles_analog = [p1, p1, p4, p4]
k_analog = p4**2/self._C_uncor(self.fr)
z, p, k = bilinear_zpk(zeros_analog, poles_analog, k_analog, fs)
sos = zpk2sos(z, p, k)
return sos
# return z, p, k
# return zeros_analog, poles_analog, k_analog
def show_Afir(): def show_Afir():
from asceefig.plot import Figure from asceefig.plot import Figure
@ -155,3 +189,4 @@ def show_Cfir():
f.plot(freq_check, 20*np.log10(np.abs(H))) f.plot(freq_check, 20*np.log10(np.abs(H)))
f.fig.get_axes()[0].set_ylim(-30, 1) f.fig.get_axes()[0].set_ylim(-30, 1)

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@ -5,7 +5,7 @@ Weighting and calibration filter in one
@author: J.A. de Jong - ASCEE @author: J.A. de Jong - ASCEE
""" """
from .lasp_common import FreqWeighting from .lasp_common import FreqWeighting
from .filter import (A, C, arbitrary_fir_design, freqResponse as frp) from .filter import SPLFilterDesigner
from lasp.lasp_config import ones, empty from lasp.lasp_config import ones, empty
from .wrappers import FilterBank from .wrappers import FilterBank
import numpy as np import numpy as np