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 plot(data, w, title):
w = pywt.Wavelet(w)
a = data
ca = []
cd = []
if DWT:
for i in xrange(5):
(a, d) = pywt.dwt(a, w, mode)
ca.append(a)
cd.append(d)
else:
for a, d in pywt.swt(data, w, 5):
ca.append(a)
cd.append(d)
pylab.figure()
ax_main = pylab.subplot(len(ca) + 1, 1, 1)
pylab.title(title)
ax_main.plot(data)
pylab.xlim(0, len(data) - 1)
for i, x in enumerate(ca):
#print len(data), len(x), len(data) / (2**(i+1))
lims = -(len(data) / (2.**(i + 1)) - len(x)) / 2.
ax = pylab.subplot(len(ca) + 1, 2, 3 + i * 2)
ax.plot(x, 'r')
if DWT:
pylab.xlim(0, len(x) - 1)
else:
pylab.xlim(w.dec_len * i, len(x) - 1 - w.dec_len * i)
pylab.ylabel("A%d" % (i + 1))
for i, x in enumerate(cd):
ax = pylab.subplot(len(cd) + 1, 2, 4 + i * 2)
ax.plot(x, 'g')
pylab.xlim(0, len(x) - 1)
if DWT:
pylab.ylim(min(0, 1.4 * min(x)), max(0, 1.4 * max(x)))
else: #SWT
pylab.ylim(
min(
0, 2 * min(x[w.dec_len * (1 + i):len(x) - w.dec_len *
(1 + i)])),
max(
0, 2 * max(x[w.dec_len * (1 + i):len(x) - w.dec_len *
(1 + i)])))
pylab.ylabel("D%d" % (i + 1))
def plot(data, w, title):
print title
w = pywt.Wavelet(w)
a = data
ca = []
cd = []
for i in xrange(5):
(a, d) = pywt.dwt(a, w, mode)
ca.append(a)
cd.append(d)
rec_a = []
rec_d = []
for i, coeff in enumerate(ca):
coeff_list = [coeff, None] + [None]*i
rec_a.append(pywt.waverec(coeff_list, w))
for i, coeff in enumerate(cd):
coeff_list = [None, coeff] + [None]*i
rec_d.append(pywt.waverec(coeff_list, w))
pylab.figure()
ax_main = pylab.subplot(len(rec_a)+1,1,1)
pylab.title(title)
ax_main.plot(data)
pylab.xlim(0, len(data)-1)
for i, y in enumerate(rec_a):
#print len(data), len(x), len(data) / (2**(i+1))
ax = pylab.subplot(len(rec_a)+1, 2, 3+i*2)
ax.plot(y, 'r')
pylab.xlim(0, len(y)-1)
pylab.ylabel("A%d" % (i+1))
for i, y in enumerate(rec_d):
ax = pylab.subplot(len(rec_d)+1, 2, 4+i*2)
ax.plot(y, 'g')
pylab.xlim(0, len(y)-1)
#pylab.ylim(min(0,1.4*min(x)), max(0,1.4*max(x)))
pylab.ylabel("D%d" % (i+1))
def plot(data, w, title):
print title
w = pywt.Wavelet(w)
a = data
ca = []
cd = []
for i in xrange(5):
(a, d) = pywt.dwt(a, w, mode)
ca.append(a)
cd.append(d)
rec_a = []
rec_d = []
for i, coeff in enumerate(ca):
coeff_list = [coeff, None] + [None] * i
rec_a.append(pywt.waverec(coeff_list, w))
for i, coeff in enumerate(cd):
coeff_list = [None, coeff] + [None] * i
rec_d.append(pywt.waverec(coeff_list, w))
pylab.figure()
ax_main = pylab.subplot(len(rec_a) + 1, 1, 1)
pylab.title(title)
ax_main.plot(data)
pylab.xlim(0, len(data) - 1)
for i, y in enumerate(rec_a):
#print len(data), len(x), len(data) / (2**(i+1))
ax = pylab.subplot(len(rec_a) + 1, 2, 3 + i * 2)
ax.plot(y, 'r')
pylab.xlim(0, len(y) - 1)
pylab.ylabel("A%d" % (i + 1))
for i, y in enumerate(rec_d):
ax = pylab.subplot(len(rec_d) + 1, 2, 4 + i * 2)
ax.plot(y, 'g')
pylab.xlim(0, len(y) - 1)
#pylab.ylim(min(0,1.4*min(x)), max(0,1.4*max(x)))
pylab.ylabel("D%d" % (i + 1))
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)
times_idwt = [[] for i in range(len(wavelets))]
repeat = 3
for j, size in enumerate(sizes):
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')]):
def __init__(self):
self.filter_bank = self.dec_lo, self.dec_hi, self.rec_lo, self.rec_hi
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
times_idwt = [[] for i in range(len(wavelets))]
repeat = 3
for j, size in enumerate(sizes):
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
