Rename beta.

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