update 48

lib/bs.py

@@ -79,7 +79,7 @@ def BS_plot(ys, g10=None, g70=None, threshold=None, fig=None, ax=None):
 
 
 def normalize(s, srate):
-    """performs BS.1770-3 normalization and returns inverted gain."""
+    """performs BS.1770-3 normalization and returns reciprocal gain."""
     db = BS1770_3(s, srate)
     rms = 10**(db/20)
     return s/rms, rms

lib/fft.py

@@ -1,5 +1,3 @@
-from . import rfft
-
 import numpy as np
 import scipy.signal as sig
 
@@ -30,7 +28,7 @@ def magnitudes(s, size=8192):
     count = 0
     for i in range(0, L - 1, int(step)):
         windowed = s[i:i+win_size]*win
-        power = np.abs(rfft(windowed, size))**2
+        power = np.abs(np.fft.rfft(windowed, 2 * size))**2
         # this scraps the nyquist value to get exactly 'size' outputs
         yield power[0:size]
         count += 1

lib/util.py

@@ -36,19 +36,17 @@ def warp(w):
 
 
 def ceil2(x):
-    return np.power(2, np.ceil(np.log2(x)).astype(int))
+    x = int(x)
+    assert x > 0
+    return 2**(x - 1).bit_length()
 
 
 def pad2(x):
-    return np.r_[x, np.zeros(ceil2(len(x)) - len(x))]
+    return np.r_[x, np.zeros(ceil2(len(x)) - len(x), x.dtype)]
-
-
-def rfft(src, size):
-    return np.fft.rfft(src, size*2)
 
 
 def magnitude(src, size):
-    return 10*np.log10(np.abs(rfft(src, size))**2)[0:size]
+    return 10*np.log10(np.abs(np.fft.rfft(src, 2 * size))**2)[0:size]
 
 
 # x axis for plotting above magnitude
@@ -70,7 +68,7 @@ def xsp(precision=4096):
 
 
 def blocks(a, step, size=None):
-    """break an iterable into chunks"""
+    """break an array into chunks"""
     if size is None:
         size = step
     for start in range(0, len(a), step):

lib/windowing.py

@@ -97,7 +97,3 @@ def welch(N):
 @_deco_win
 def sinc(N):
     return np.sinc((np.arange(N) - (N - 1)/2)/2)
-
-
-def winmod(f):
-    return lambda N: f(N + 2)[1:-1]