diff --git a/lib/__init__.py b/lib/__init__.py
index 94ac186..352aeb5 100644
--- a/lib/__init__.py
+++ b/lib/__init__.py
@@ -14,7 +14,6 @@ from .windowing import *
 from .piir import *
 
 import numpy as np
-from matplotlib.pylab import show
 
 def analog(b, a):
     import sympy as sym
diff --git a/lib/plot.py b/lib/plot.py
index a26fc6a..3b1d889 100644
--- a/lib/plot.py
+++ b/lib/plot.py
@@ -26,20 +26,17 @@ def cleanplot():
     return fig, ax
 
 def new_response(*args, **kwargs):
-    fig = plt.figure()
-    ax = fig.gca()
+    fig, ax = plt.subplots()
     response_setup(ax, *args, **kwargs)
     return fig, ax
 
 def new_phase_response(*args, **kwargs):
-    fig = plt.figure()
-    ax = fig.gca()
+    fig, ax = plt.subplots()
     phase_response_setup(ax, *args, **kwargs)
     return fig, ax
 
 def new_bode(magnitude_offset=0):
-    fig = plt.figure()
-    ax1 = fig.gca()
+    fig, ax1 = plt.subplots()
     ax2 = ax1.twinx()
     ymin = -24 + magnitude_offset
     ymax = 24 + magnitude_offset
diff --git a/lib/util.py b/lib/util.py
index ea6ef56..333f3d4 100644
--- a/lib/util.py
+++ b/lib/util.py
@@ -25,9 +25,7 @@ magnitude_x = lambda srate, size: np.arange(0, srate/2, srate/2/size)
 degrees_clamped = lambda x: ((x*180/np.pi + 180) % 360) - 180
 
 def xsp(precision=4096):
-    """create #precision log-spaced points from 20 to 20480 Hz"""
-    # i opt not to use steps or linspace here,
-    # as the current method is less error-prone for me.
+    """create #precision log-spaced points from 20 Hz (inclusive) to 20480 Hz (exclusive)"""
     xs = np.arange(0,precision)/precision
     return 20*1024**xs
 
@@ -45,7 +43,7 @@ def convolve_each(s, fir, mode='same', axis=0):
     return np.apply_along_axis(lambda s: sig.fftconvolve(s, fir, mode), axis, s)
 
 def count_channels(s):
-    if len(s.shape) < 2:
+    if s.ndim < 2:
         return 1
     return s.shape[1]
 
@@ -55,8 +53,7 @@ def monoize(s):
     existing mono signals are passed through unmodified."""
     channels = count_channels(s)
     if channels != 1:
-        s = np.sum(s, 1)
-        s /= channels
+        s = np.average(s, axis=1)
     return s
 
 def div0(a, b):
diff --git a/lib/wav.py b/lib/wav.py
index 7aecea0..b729f94 100644
--- a/lib/wav.py
+++ b/lib/wav.py
@@ -1,12 +1,9 @@
 import numpy as np
 from .util import lament, count_channels
 
-# TODO: don't use wavfile, it breaks on perfectly good files
-import scipy.io.wavfile as wav
-import ewave
-
 def wav_smart_read(fn):
     lament('wav_smart_read(): DEPRECATED; use wav_read instead.')
+    import scipy.io.wavfile as wav # don't use this, it fails to load good files
     srate, s = wav.read(fn)
     if s.dtype != np.float64:
         bits = s.dtype.itemsize*8
@@ -15,12 +12,14 @@ def wav_smart_read(fn):
 
 def wav_smart_write(fn, srate, s):
     lament('wav_smart_write(): DEPRECATED; use wav_write instead.')
+    import scipy.io.wavfile as wav
     si = np.zeros_like(s, dtype='int16')
     bits = si.dtype.itemsize*8
     si += np.clip(s*2**(bits - 1), -32768, 32767)
     wav.write(fn, srate, si)
 
 def wav_read(fn):
+    import ewave
     with ewave.open(fn) as f:
         s = f.read()
         srate = f.sampling_rate
@@ -32,6 +31,7 @@ def wav_read(fn):
     return s, srate
 
 def wav_write(fn, s, srate, dtype='h'):
+    import ewave
     if dtype in ('b', 'h', 'i', 'l') and np.max(np.abs(s)) > 1:
         lament('wav_write(): WARNING; clipping')
     with ewave.open(fn, 'w', srate, dtype, count_channels(s)) as f:
diff --git a/lib/windowing.py b/lib/windowing.py
index a42c1e6..acd9873 100644
--- a/lib/windowing.py
+++ b/lib/windowing.py
@@ -43,7 +43,7 @@ blackman_nuttall = _h(0.3635819, 0.4891775, 0.1365995, 0.0106411)
 blackman_harris = _h(0.35875, 0.48829, 0.14128, 0.01168)
 nuttall = _h(0.355768, 0.487396, 0.144232, 0.012604)
 flattop        = _h(*_normalize(1, 1.93, 1.29, 0.388, 0.028)) # FTSRS
-#flattop_weird = _h(_normalize(1, 1.93, 1.29, 0.388, 0.032)) # ??? wtf
+#flattop_weird = _h(*_normalize(1, 1.93, 1.29, 0.388, 0.032)) # ??? wtf
 flattop_weird = _h(0.2156, 0.4160, 0.2781, 0.0836, 0.0069) # ??? scipy crap
 hann = _h(0.5, 0.5)
 hamming_inexact = _h(0.54, 0.46)