X-Git-Url: http://git.tremily.us/?p=hooke.git;a=blobdiff_plain;f=FFT_tools.py;h=91670785172b1072479d6b7b88f7622b28aa8f28;hp=b6b7b353ecf12dd25c9ed1da8be136ce50d223d8;hb=22367d5bba0e723b7a578070241686f165d66310;hpb=2229b7919df93e1b29af4e7617e95a195acf64c8

diff --git a/FFT_tools.py b/FFT_tools.py
index b6b7b35..9167078 100644
--- a/FFT_tools.py
+++ b/FFT_tools.py
@@ -1,6 +1,7 @@
-#!/usr/bin/python
+# Copyright
+
+"""Wrap :mod:`numpy.fft` to produce 1D unitary transforms and power spectra.
 
-"""
 Define some FFT wrappers to reduce clutter.
 Provides a unitary discrete FFT and a windowed version.
 Based on numpy.fft.rfft.
@@ -45,7 +46,6 @@ def ceil_pow_of_two(num) :
     return num
 
 def _test_rfft(xs, Xs) :
-    print "Test numpy rfft definition"
     # Numpy's FFT algoritm returns
     #          n-1
     #   X[k] = SUM x[m] exp (-j 2pi km /n)
@@ -70,6 +70,7 @@ def _test_rfft(xs, Xs) :
         "Mismatch on Parseval's, %g != 1/%d * %g" % (timeSum, n, freqSum)
 
 def _test_rfft_suite() :
+    print "Test numpy rfft definition"
     xs = [1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1]
     _test_rfft(xs, rfft(xs))
 
@@ -120,7 +121,6 @@ def unitary_rfft(data, freq=1.0) :
     return (freq_axis, trans)
 
 def _test_unitary_rfft_parsevals(xs, freq, freqs, Xs):
-    print "Test unitary rfft on Parseval's theorem"
     # Which should satisfy the discretized integral form of Parseval's theorem
     #   n-1              n-1
     #   SUM |x_m|^2 dt = SUM |X_k|^2 df
@@ -139,6 +139,7 @@ def _test_unitary_rfft_parsevals(xs, freq, freqs, Xs):
         % (lhs, rhs)
 
 def _test_unitary_rfft_parsevals_suite():
+    print "Test unitary rfft on Parseval's theorem"
     xs = [1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1]
     dt = pi
     freqs,Xs = unitary_rfft(xs, 1.0/dt)
@@ -161,6 +162,7 @@ def _test_unitary_rfft_rect(a=1.0, time_shift=5.0, samp_freq=25.6, samples=256)
         t = i*dt
         x[i] = _rect(a*(t-time_shift))
     freq_axis, X = unitary_rfft(x, samp_freq)
+    #_test_unitary_rfft_parsevals(x, samp_freq, freq_axis, X)
 
     # remove the phase due to our time shift
     j = complex(0.0,1.0) # sqrt(-1)
@@ -209,6 +211,7 @@ def _test_unitary_rfft_gaussian(a=1.0, time_shift=5.0, samp_freq=25.6, samples=2
         t = i*dt
         x[i] = _gaussian(a, (t-time_shift))
     freq_axis, X = unitary_rfft(x, samp_freq)
+    #_test_unitary_rfft_parsevals(x, samp_freq, freq_axis, X)
 
     # remove the phase due to our time shift
     j = complex(0.0,1.0) # sqrt(-1)