def test_perfect_reconstruction(families, wavelets, modes, epsilon, dtype):
for wavelet in wavelets:
for pmode, mmode in modes:
print "Wavelet: %-8s Mode: %s" % (wavelet, pmode),
w = pywt.Wavelet(wavelet)
data_size = range(2, 40) + [100, 200, 500, 1000, 2000, 10000, 50000, 100000]
ok, over = 0, 0
for N in data_size:
data = numpy.asarray(numpy.random.random(N), dtype)
# compute dwt coefficients
pa, pd = pywt.dwt(data, wavelet, pmode)
# compute reconstruction
rec = pywt.idwt(pa, pd, wavelet, pmode)
if len(data) % 2:
rec = rec[:len(data)]
rms_rec = rms(data, rec)
if rms_rec > epsilon:
if not over:
print
print '[RMS_REC > EPSILON] for Mode: %s, Wavelet: %s, Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_rec, )
over += 1
else:
ok += 1
if not over:
print "- RMSE for all %d cases was under %s" % (len(data_size), epsilon)
def test_perfect_reconstruction(families, wavelets, modes, epsilon, dtype):
for wavelet in wavelets:
for pmode, mmode in modes:
print "Wavelet: %-8s Mode: %s" % (wavelet, pmode),
w = pywt.Wavelet(wavelet)
data_size = range(
2, 40) + [100, 200, 500, 1000, 2000, 10000, 50000, 100000]
ok, over = 0, 0
for N in data_size:
data = numpy.asarray(numpy.random.random(N), dtype)
# compute dwt coefficients
pa, pd = pywt.dwt(data, wavelet, pmode)
# compute reconstruction
rec = pywt.idwt(pa, pd, wavelet, pmode)
if len(data) % 2:
rec = rec[:len(data)]
rms_rec = rms(data, rec)
if rms_rec > epsilon:
if not over:
print
print '[RMS_REC > EPSILON] for Mode: %s, Wavelet: %s, Length: %d, rms=%.3g' % (
pmode,
wavelet,
len(data),
rms_rec,
)
over += 1
else:
ok += 1
if not over:
print "- RMSE for all %d cases was under %s" % (len(data_size),
epsilon)
data = [1,2,3,4,5,6]
############################################################################
print "Case 1 (custom filter bank - Haar wavelet)"
myBank = FilterBank()
# pass the user supplied filter bank as argument
myWavelet = pywt.Wavelet(name="UserSuppliedWavelet", filter_bank=myBank)
#print myWavelet.get_filters_coeffs()
print "data:", data
a, d = pywt.dwt(data, myWavelet)
print "a:", a
print "d:", d
print "rec:", pywt.idwt(a, d, myWavelet)
############################################################################
print "-" * 75
print "Case 2 (Wavelet object as filter bank - db2 wavelet)"
# builtin wavelets can also be treated as filter banks with theirs
# filter_bank attribute
builtinWavelet = pywt.Wavelet('db2')
myWavelet = pywt.Wavelet(name="UserSuppliedWavelet", filter_bank=builtinWavelet)
print "data:", data
a, d = pywt.dwt(data, myWavelet)
print "a:", a
print "d:", d
if size > 500000:
print "Warning, too big data size may cause page swapping if computer does not have enough memory."
data = numpy.ones((size, ), numpy.float64)
print("%d/%d" % (j + 1, len(sizes))).rjust(6), str(size).rjust(9),
for i, w in enumerate(wavelets):
min_t1, min_t2 = 9999., 9999.
for _ in xrange(repeat):
t1 = clock()
(a, d) = pywt.dwt(data, w, mode)
t1 = clock() - t1
min_t1 = min(t1, min_t1)
t2 = clock()
a0 = pywt.idwt(a, d, w, mode)
t2 = clock() - t2
min_t2 = min(t2, min_t2)
times_dwt[i].append(min_t1)
times_idwt[i].append(min_t2)
print '.',
gc.collect()
print
for j, (times, name) in enumerate([(times_dwt, 'dwt'), (times_idwt, 'idwt')]):
pylab.figure(j)
pylab.title(name)
for i, n in enumerate(wavelet_names):
pylab.loglog(sizes, times[i], label=n)
if size > 500000:
print "Warning, too big data size may cause page swapping if computer does not have enough memory."
data = numpy.ones((size,), numpy.float64)
print ("%d/%d" % (j + 1, len(sizes))).rjust(6), str(size).rjust(9),
for i, w in enumerate(wavelets):
min_t1, min_t2 = 9999.0, 9999.0
for _ in xrange(repeat):
t1 = clock()
(a, d) = pywt.dwt(data, w, mode)
t1 = clock() - t1
min_t1 = min(t1, min_t1)
t2 = clock()
a0 = pywt.idwt(a, d, w, mode)
t2 = clock() - t2
min_t2 = min(t2, min_t2)
times_dwt[i].append(min_t1)
times_idwt[i].append(min_t2)
print ".",
gc.collect()
print
for j, (times, name) in enumerate([(times_dwt, "dwt"), (times_idwt, "idwt")]):
pylab.figure(j)
pylab.title(name)
for i, n in enumerate(wavelet_names):
pylab.loglog(sizes, times[i], label=n)
