diff --git a/applications/lofar2/model/pfb_os/dsp.py b/applications/lofar2/model/pfb_os/dsp.py
index d14f59f46db71123b03bebfa1945225679cbd9f5..017ac10ca2c939f80132cb4ca85b33b95bb5fd1e 100644
--- a/applications/lofar2/model/pfb_os/dsp.py
+++ b/applications/lofar2/model/pfb_os/dsp.py
@@ -121,15 +121,15 @@ def impulse_at_zero_crossing(x):
# Windowed sinc filter design
###############################################################################
-def raised_cosine_response(Ntaps, Nsps, beta):
+def raised_cosine_response(Ntaps, Nsps, roBeta):
"""Generate a raised cosine (RC) FIR filter impulse response.
Input:
. Ntaps : FIR filter length
. Nsps: symbol period Tsymbol in number of samples per symbol
- . beta : Roll off factor in [0, 1.0], BW = (1 + beta) / Tsymbol, so:
- - beta = 0.0: rectangular spectrum with BW = 1 / Tsymbol
- - beta = 1.0: cosine spectrum with BW = 2 / Tsymbol
+ . roBeta : Roll off factor in [0, 1.0], BW = (1 + roBeta) / Tsymbol, so:
+ - roBeta = 0.0: rectangular spectrum with BW = 1 / Tsymbol
+ - roBeta = 1.0: cosine spectrum with BW = 2 / Tsymbol
Return:
. hRc : impulse response of the raised cosine filter.
"""
@@ -145,12 +145,12 @@ def raised_cosine_response(Ntaps, Nsps, beta):
# apply cos term, use for loop instead of array assignment, to detect divide by 0
for tI in tIndices:
t = tI - tCenter
- if np.abs(t) != Tsymbol / (2 * beta):
- hRc[tI] *= np.cos(np.pi * beta * t / Tsymbol) / (1 - (2 * beta * t / Tsymbol)**2)
+ if np.abs(t) != Tsymbol / (2 * roBeta):
+ hRc[tI] *= np.cos(np.pi * roBeta * t / Tsymbol) / (1 - (2 * roBeta * t / Tsymbol)**2)
return hRc
-def square_root_raised_cosine_response(Ntaps, Nsps, beta):
+def square_root_raised_cosine_response(Ntaps, Nsps, roBeta):
"""Generate a square root raised cosine (SRRC) FIR filter impulse response.
Reference:
@@ -160,7 +160,7 @@ def square_root_raised_cosine_response(Ntaps, Nsps, beta):
Input:
. Ntaps : FIR filter length
. Nsps: symbol period Tsymbol in number of samples per symbol
- . beta : Roll off factor in [0, 1.0]
+ . roBeta : Roll off factor in [0, 1.0]
Return:
. hSrRc : impulse response of the square root raised cosine filter.
"""
@@ -171,20 +171,20 @@ def square_root_raised_cosine_response(Ntaps, Nsps, beta):
t = tIndices - tCenter
# numerator term, using array assignment
- hSrRc = 1 / Tsymbol * (np.cos(np.pi * (1 + beta) * t / Tsymbol) * 4 * beta * t / Tsymbol +
- np.sin(np.pi * (1 - beta) * t / Tsymbol))
+ hSrRc = 1 / Tsymbol * (np.cos(np.pi * (1 + roBeta) * t / Tsymbol) * 4 * roBeta * t / Tsymbol +
+ np.sin(np.pi * (1 - roBeta) * t / Tsymbol))
# apply denumerator term, use for loop instead of array assignment, to detect divide by 0
for tI in tIndices:
t = tI - tCenter
if t == 0.0:
- hSrRc[tI] = 1 / Tsymbol * (1 + beta * (4 / np.pi - 1))
- elif np.abs(t) == Tsymbol / (4 * beta):
- hSrRc[tI] = 1 / Tsymbol * beta / np.sqrt(2) * \
- ((1 + 2 / np.pi) * np.sin(np.pi / (4 * beta)) +
- (1 - 2 / np.pi) * np.cos(np.pi / (4 * beta)))
+ hSrRc[tI] = 1 / Tsymbol * (1 + roBeta * (4 / np.pi - 1))
+ elif np.abs(t) == Tsymbol / (4 * roBeta):
+ hSrRc[tI] = 1 / Tsymbol * roBeta / np.sqrt(2) * \
+ ((1 + 2 / np.pi) * np.sin(np.pi / (4 * roBeta)) +
+ (1 - 2 / np.pi) * np.cos(np.pi / (4 * roBeta)))
else:
- hSrRc[tI] /= (1 - (4 * beta * t / Tsymbol)**2) * (np.pi * t / Tsymbol)
+ hSrRc[tI] /= (1 - (4 * roBeta * t / Tsymbol)**2) * (np.pi * t / Tsymbol)
return hSrRc
@@ -195,7 +195,7 @@ def square_root_raised_cosine_response(Ntaps, Nsps, beta):
def nof_taps_kaiser_window(fs, fpass, fstop, atten_db):
"""Number of FIR LPF taps using Kaiser window based design
- Reference: [HARRIS 3.2, Fig. 3.8 for beta]
+ Reference: [HARRIS 3.2, Fig. 3.8 for kaiserBeta]
"""
df = fstop - fpass
return int((fs / df) * (atten_db - 8) / 14)
@@ -242,7 +242,7 @@ def ideal_low_pass_filter(Npoints, Npass, bandEdgeGain=1.0):
def prototype_fir_low_pass_filter(method='firls',
Npoints=1024, Ntaps=16, Ncoefs=1024*16,
- hpFactor=0.9, transitionFactor=0.4, stopRippleFactor=1000000, beta=1, fs=1.0):
+ hpFactor=0.9, transitionFactor=0.4, stopRippleFactor=1000000, kaiserBeta=1, fs=1.0):
"""Derive FIR coefficients for prototype low pass filter
Use method 'firls' or 'remez'.
@@ -274,7 +274,7 @@ def prototype_fir_low_pass_filter(method='firls',
- hpFactor : Half power bandwidth of the filter relative to BWbin
- transitionFactor: transition bandwidth factor relative to fpass
- stopRippleFactor: stopband ripple factor relative to pass band ripple
- - beta: When beta > 0 then additionally apply a Kaiser window on FIR
+ - kaiserBeta: When kaiserBeta > 0 then additionally apply a Kaiser window on FIR
coefficients
- fs: sample frequency, for logging
Return:
@@ -292,12 +292,12 @@ def prototype_fir_low_pass_filter(method='firls',
rippleWeights = [1, stopRippleFactor]
# Design subband filter
- h = design_fir_low_pass_filter(method, Ncoefs, fpass, fstop, fcutoff, cutoffGain, rippleWeights, beta, fs)
+ h = design_fir_low_pass_filter(method, Ncoefs, fpass, fstop, fcutoff, cutoffGain, rippleWeights, kaiserBeta, fs)
return h
def design_fir_low_pass_filter(method,
- Ncoefs, fpass, fstop, fcutoff=0, cutoffGain=0.5, rippleWeights=[1, 1], beta=0, fs=1.0):
+ Ncoefs, fpass, fstop, fcutoff=0, cutoffGain=0.5, rippleWeights=[1, 1], kaiserBeta=0, fs=1.0):
"""Derive FIR coefficients for prototype low pass filter
Use method 'firls' or 'remez', fs = 1.0
@@ -318,7 +318,7 @@ def design_fir_low_pass_filter(method,
- fcutoff: when fcutoff > 0, then define cutoff frequency point in transition band, fpass < fcutoff < fstop
- cutoffGain: normalized LPF gain at fcutoff
- rippleWeights: relative ripple factors for pass band, optional fcutoff, and stop band
- - beta: When beta > 0, then additionally apply a Kaiser window on FIR
+ - kaiserBeta: When kaiserBeta > 0, then additionally apply a Kaiser window on FIR
coefficients
Return:
- h: FIR coefficients for requested Ncoefs
@@ -368,11 +368,11 @@ def design_fir_low_pass_filter(method,
hFir = signal.remez(N, [0, f_pb, f_co, f_co, f_sb, fNyquist],
[1, cutoffGain, 0], rippleWeights, fs=fs)
- # Additionally apply a Kaiser window, with beta = 1 like in pfs_coeff_final.m, this improves
+ # Additionally apply a Kaiser window, with kaiserBeta = 1 like in pfs_coeff_final.m, this improves
# the stopband attenuation near the transition band somewhat
- # . beta: 0 rect, 5 hamming, 6 hanning
- if beta:
- win = signal.windows.kaiser(N, beta)
+ # . kaiserBeta: 0 rect, 5 hamming, 6 hanning
+ if kaiserBeta:
+ win = signal.windows.kaiser(N, kaiserBeta)
hFir *= win
# Normalize DC gain