FFT_tools: wrap long function definition lines for PEP 8 compliance

diff --git a/FFT_tools.py b/FFT_tools.py
index 8e9bbd7f2deb59e08973ca4fe3a21ae56443c350..352083a0925beac8d3b7a13a000f831b73ccfa82 100644 (file)
--- a/FFT_tools.py
+++ b/FFT_tools.py
@@ -177,7 +177,8 @@ def _rect(t):
     else:
         return 0
 
-def _test_unitary_rfft_rect(a=1.0, time_shift=5.0, samp_freq=25.6, samples=256):
+def _test_unitary_rfft_rect(
+    a=1.0, time_shift=5.0, samp_freq=25.6, samples=256):
     "Show fft(rect(at)) = 1/abs(a) * _numpy.sinc(f/a)"
     samp_freq = _numpy.float(samp_freq)
     a = _numpy.float(a)
@@ -227,7 +228,8 @@ def _test_unitary_rfft_rect_suite():
 def _gaussian(a, t):
     return _numpy.exp(-a * t**2)
 
-def _test_unitary_rfft_gaussian(a=1.0, time_shift=5.0, samp_freq=25.6, samples=256):
+def _test_unitary_rfft_gaussian(
+    a=1.0, time_shift=5.0, samp_freq=25.6, samples=256):
     "Show fft(rect(at)) = 1/abs(a) * sinc(f/a)"
     samp_freq = _numpy.float(samp_freq)
     a = _numpy.float(a)
@@ -439,7 +441,8 @@ def _gaussian2(area, mean, std, t):
     return area/(std*_numpy.sqrt(2.0*_numpy.pi)) * _numpy.exp(
         -0.5*((t-mean)/std)**2)
 
-def _test_unitary_power_spectrum_gaussian(area=2.5, mean=5, std=1, samp_freq=10.24 ,samples=512): #1024
+def _test_unitary_power_spectrum_gaussian(
+    area=2.5, mean=5, std=1, samp_freq=10.24 ,samples=512):
     x = _numpy.zeros((samples,), dtype=_numpy.float)
     mean = _numpy.float(mean)
     for i in range(samples):
@@ -563,9 +566,9 @@ def unitary_avg_power_spectrum(data, freq=1.0, chunk_size=2048,
     # The normalization approaches perfection as chunk_size -> infinity.
     return (freq_axis, power)
 
-def _test_unitary_avg_power_spectrum_sin(sin_freq=10, samp_freq=512, samples=1024,
-                                         chunk_size=512, overlap=True,
-                                         window=window_hann):
+def _test_unitary_avg_power_spectrum_sin(
+    sin_freq=10, samp_freq=512, samples=1024, chunk_size=512, overlap=True,
+    window=window_hann):
     x = _numpy.zeros((samples,), dtype=_numpy.float)
     samp_freq = _numpy.float(samp_freq)
     for i in range(samples):