matlab wavefun怎么用,Python pywt.Wavelet方法代碼示例

小鱼儿 2023-01-16 09:37 79阅读 0赞

本文整理匯總了Python中pywt.Wavelet方法的典型用法代碼示例。如果您正苦於以下問題：Python pywt.Wavelet方法的具體用法？Python pywt.Wavelet怎麽用？Python pywt.Wavelet使用的例子？那麽恭喜您, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在模塊pywt的用法示例。

在下文中一共展示了pywt.Wavelet方法的30個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於我們的係統推薦出更棒的Python代碼示例。

示例1: \_wavelet\_coefs

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def \_wavelet\_coefs(data, wavelet\_name='db4'):

"""Compute Discrete Wavelet Transform coefficients.

Parameters

\----------

data : ndarray, shape (n\_channels, n\_times)

wavelet\_name : str (default: db4)

Wavelet name (to be used with \`\`pywt.Wavelet\`\`). The full list of

Wavelet names are given by: \`\`\[name for family in pywt.families() for

name in pywt.wavelist(family)\]\`\`.

Returns

\-------

coefs : list of ndarray

Coefficients of a DWT (Discrete Wavelet Transform). \`\`coefs\[0\]\`\` is

the array of approximation coefficient and \`\`coefs\[1:\]\`\` is the list

of detail coefficients.

"""

wavelet = pywt.Wavelet(wavelet\_name)

levdec = min(pywt.dwt\_max\_level(data.shape\[-1\], wavelet.dec\_len), 6)

coefs = pywt.wavedec(data, wavelet=wavelet, level=levdec)

return coefs

開發者ID:mne-tools，項目名稱:mne-features，代碼行數:25，

示例2: compute\_wavelet\_feature\_vector

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def compute\_wavelet\_feature\_vector(image, wavelet='db6'):

image\_np = np.array(image)

rgb = \[image\_np\[:, :, i\] for i in (0, 1, 2)\]

if isinstance(wavelet, basestring):

wavelet = pywt.Wavelet(wavelet)

feature\_vector = \[\]

for c in rgb:

level = pywt.dwt\_max\_level(min(c.shape\[0\], c.shape\[1\]), wavelet.dec\_len)

levels = pywt.wavedec2(c, wavelet, mode='sym', level=level)

for coeffs in levels:

if not isinstance(coeffs, tuple):

coeffs = (coeffs,)

for w in coeffs:

w\_flat = w.flatten()

feature\_vector += \[float(np.mean(w\_flat)), float(np.std(w\_flat))\]

return feature\_vector

開發者ID:seanbell，項目名稱:opensurfaces，代碼行數:21，

示例3: check\_coefficients

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def check\_coefficients(wavelet):

epsilon = 5e-11

level = 10

w = pywt.Wavelet(wavelet)

\# Lowpass filter coefficients sum to sqrt2

res = np.sum(w.dec\_lo)-np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# sum even coef = sum odd coef = 1 / sqrt(2)

res = np.sum(w.dec\_lo\[::2\])-1./np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

res = np.sum(w.dec\_lo\[1::2\])-1./np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# Highpass filter coefficients sum to zero

res = np.sum(w.dec\_hi)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:22，

示例4: test\_custom\_wavelet

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_custom\_wavelet():

haar\_custom1 = pywt.Wavelet('Custom Haar Wavelet',

filter\_bank=\_CustomHaarFilterBank())

haar\_custom1.orthogonal = True

haar\_custom1.biorthogonal = True

val = np.sqrt(2) / 2

filter\_bank = (\[val\]\*2, \[-val, val\], \[val\]\*2, \[val, -val\])

haar\_custom2 = pywt.Wavelet('Custom Haar Wavelet',

filter\_bank=filter\_bank)

\# check expected default wavelet properties

assert\_(~haar\_custom2.orthogonal)

assert\_(~haar\_custom2.biorthogonal)

assert\_(haar\_custom2.symmetry == 'unknown')

assert\_(haar\_custom2.family\_name == '')

assert\_(haar\_custom2.short\_family\_name == '')

assert\_(haar\_custom2.vanishing\_moments\_phi == 0)

assert\_(haar\_custom2.vanishing\_moments\_psi == 0)

\# Some properties can be set by the user

haar\_custom2.orthogonal = True

haar\_custom2.biorthogonal = True

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:25，

示例5: test\_wavedecn\_coeff\_reshape\_even

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_wavedecn\_coeff\_reshape\_even():

\# verify round trip is correct:

\# wavedecn - >coeffs\_to\_array-> array\_to\_coeffs -> waverecn

\# This is done for wavedec\{1, 2, n\}

rng = np.random.RandomState(1234)

params = \{'wavedec': \{'d': 1, 'dec': pywt.wavedec, 'rec': pywt.waverec\},

'wavedec2': \{'d': 2, 'dec': pywt.wavedec2, 'rec': pywt.waverec2\},

'wavedecn': \{'d': 3, 'dec': pywt.wavedecn, 'rec': pywt.waverecn\}\}

N = 28

for f in params:

x1 = rng.randn(\*(\[N\] \* params\[f\]\['d'\]))

for mode in pywt.Modes.modes:

for wave in wavelist:

w = pywt.Wavelet(wave)

maxlevel = pywt.dwt\_max\_level(np.min(x1.shape), w.dec\_len)

if maxlevel == 0:

continue

coeffs = params\[f\]\['dec'\](x1, w, mode=mode)

coeff\_arr, coeff\_slices = pywt.coeffs\_to\_array(coeffs)

coeffs2 = pywt.array\_to\_coeffs(coeff\_arr, coeff\_slices,

output\_format=f)

x1r = params\[f\]\['rec'\](coeffs2, w, mode=mode)

assert\_allclose(x1, x1r, rtol=1e-4, atol=1e-4)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:27，

示例6: test\_waverecn\_coeff\_reshape\_odd

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_waverecn\_coeff\_reshape\_odd():

\# verify round trip is correct:

\# wavedecn - >coeffs\_to\_array-> array\_to\_coeffs -> waverecn

rng = np.random.RandomState(1234)

x1 = rng.randn(35, 33)

for mode in pywt.Modes.modes:

for wave in \['haar', \]:

w = pywt.Wavelet(wave)

maxlevel = pywt.dwt\_max\_level(np.min(x1.shape), w.dec\_len)

if maxlevel == 0:

continue

coeffs = pywt.wavedecn(x1, w, mode=mode)

coeff\_arr, coeff\_slices = pywt.coeffs\_to\_array(coeffs)

coeffs2 = pywt.array\_to\_coeffs(coeff\_arr, coeff\_slices)

x1r = pywt.waverecn(coeffs2, w, mode=mode)

\# truncate reconstructed values to original shape

x1r = x1r\[\[slice(s) for s in x1.shape\]\]

assert\_allclose(x1, x1r, rtol=1e-4, atol=1e-4)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:20，

示例7: test\_swt\_dtypes

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_swt\_dtypes():

wavelet = pywt.Wavelet('haar')

for dt\_in, dt\_out in zip(dtypes\_in, dtypes\_out):

errmsg = "wrong dtype returned for \{0\} input".format(dt\_in)

\# swt

x = np.ones(8, dtype=dt\_in)

(cA2, cD2), (cA1, cD1) = pywt.swt(x, wavelet, level=2)

assert\_(cA2.dtype == cD2.dtype == cA1.dtype == cD1.dtype == dt\_out,

"swt: " + errmsg)

\# swt2

with warnings.catch\_warnings():

warnings.simplefilter('ignore', FutureWarning)

x = np.ones((8, 8), dtype=dt\_in)

cA, (cH, cV, cD) = pywt.swt2(x, wavelet, level=1)\[0\]

assert\_(cA.dtype == cH.dtype == cV.dtype == cD.dtype == dt\_out,

"swt2: " + errmsg)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:20，

示例8: test\_swt2\_axes

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_swt2\_axes():

atol = 1e-14

current\_wavelet = pywt.Wavelet('db2')

input\_length\_power = int(np.ceil(np.log2(max(

current\_wavelet.dec\_len,

current\_wavelet.rec\_len))))

input\_length = 2\*\*(input\_length\_power)

X = np.arange(input\_length\*\*2).reshape(input\_length, input\_length)

with warnings.catch\_warnings():

warnings.simplefilter('ignore', FutureWarning)

(cA1, (cH1, cV1, cD1)) = pywt.swt2(X, current\_wavelet, level=1)\[0\]

\# opposite order

(cA2, (cH2, cV2, cD2)) = pywt.swt2(X, current\_wavelet, level=1,

axes=(1, 0))\[0\]

assert\_allclose(cA1, cA2, atol=atol)

assert\_allclose(cH1, cV2, atol=atol)

assert\_allclose(cV1, cH2, atol=atol)

assert\_allclose(cD1, cD2, atol=atol)

\# duplicate axes not allowed

assert\_raises(ValueError, pywt.swt2, X, current\_wavelet, 1,

axes=(0, 0))

\# too few axes

assert\_raises(ValueError, pywt.swt2, X, current\_wavelet, 1, axes=(0, ))

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:26，

示例9: test\_accuracy\_pymatbridge

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_accuracy\_pymatbridge():

rstate = np.random.RandomState(1234)

\# max RMSE (was 1.0e-10, is reduced to 5.0e-5 due to different coefficents)

epsilon = 5.0e-5

epsilon\_pywt\_coeffs = 1.0e-10

mlab.start()

try:

for wavelet in wavelets:

w = pywt.Wavelet(wavelet)

mlab.set\_variable('wavelet', wavelet)

for N in \_get\_data\_sizes(w):

data = rstate.randn(N)

mlab.set\_variable('data', data)

for pmode, mmode in modes:

ma, md = \_compute\_matlab\_result(data, wavelet, mmode)

yield \_check\_accuracy, data, w, pmode, ma, md, wavelet, epsilon

ma, md = \_load\_matlab\_result\_pywt\_coeffs(data, wavelet, mmode)

yield \_check\_accuracy, data, w, pmode, ma, md, wavelet, epsilon\_pywt\_coeffs

finally:

mlab.stop()

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:23，

示例10: \_compute\_matlab\_result

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def \_compute\_matlab\_result(data, wavelet, mmode):

""" Compute the result using MATLAB.

This function assumes that the Matlab variables \`wavelet\` and \`data\` have

already been set externally.

"""

if np.any((wavelet == np.array(\['coif6', 'coif7', 'coif8', 'coif9', 'coif10', 'coif11', 'coif12', 'coif13', 'coif14', 'coif15', 'coif16', 'coif17'\])),axis=0):

w = pywt.Wavelet(wavelet)

mlab.set\_variable('Lo\_D', w.dec\_lo)

mlab.set\_variable('Hi\_D', w.dec\_hi)

mlab\_code = ("\[ma, md\] = dwt(data, Lo\_D, Hi\_D, 'mode', '%s');" % mmode)

else:

mlab\_code = "\[ma, md\] = dwt(data, wavelet, 'mode', '%s');" % mmode

res = mlab.run\_code(mlab\_code)

if not res\['success'\]:

raise RuntimeError("Matlab failed to execute the provided code. "

"Check that the wavelet toolbox is installed.")

\# need np.asarray because sometimes the output is a single float64

ma = np.asarray(mlab.get\_variable('ma'))

md = np.asarray(mlab.get\_variable('md'))

return ma, md

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:23，

示例11: test\_3D\_reconstruct

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_3D\_reconstruct():

data = np.array(\[

\[\[0, 4, 1, 5, 1, 4\],

\[0, 5, 26, 3, 2, 1\],

\[5, 8, 2, 33, 4, 9\],

\[2, 5, 19, 4, 19, 1\]\],

\[\[1, 5, 1, 2, 3, 4\],

\[7, 12, 6, 52, 7, 8\],

\[2, 12, 3, 52, 6, 8\],

\[5, 2, 6, 78, 12, 2\]\]\])

wavelet = pywt.Wavelet('haar')

for mode in pywt.Modes.modes:

d = pywt.dwtn(data, wavelet, mode=mode)

assert\_allclose(data, pywt.idwtn(d, wavelet, mode=mode),

rtol=1e-13, atol=1e-13)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:18，

示例12: test\_stride

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_stride():

wavelet = pywt.Wavelet('haar')

for dtype in ('float32', 'float64'):

data = np.array(\[\[0, 4, 1, 5, 1, 4\],

\[0, 5, 6, 3, 2, 1\],

\[2, 5, 19, 4, 19, 1\]\],

dtype=dtype)

for mode in pywt.Modes.modes:

expected = pywt.dwtn(data, wavelet)

strided = np.ones((3, 12), dtype=data.dtype)

strided\[::-1, ::2\] = data

strided\_dwtn = pywt.dwtn(strided\[::-1, ::2\], wavelet)

for key in expected.keys():

assert\_allclose(strided\_dwtn\[key\], expected\[key\])

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:18，

示例13: test\_3D\_reconstruct\_complex

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_3D\_reconstruct\_complex():

\# All dimensions even length so \`take\` does not need to be specified

data = np.array(\[

\[\[0, 4, 1, 5, 1, 4\],

\[0, 5, 26, 3, 2, 1\],

\[5, 8, 2, 33, 4, 9\],

\[2, 5, 19, 4, 19, 1\]\],

\[\[1, 5, 1, 2, 3, 4\],

\[7, 12, 6, 52, 7, 8\],

\[2, 12, 3, 52, 6, 8\],

\[5, 2, 6, 78, 12, 2\]\]\])

data = data + 1j

wavelet = pywt.Wavelet('haar')

d = pywt.dwtn(data, wavelet)

\# idwtn creates even-length shapes (2x dwtn size)

original\_shape = \[slice(None, s) for s in data.shape\]

assert\_allclose(data, pywt.idwtn(d, wavelet)\[original\_shape\],

rtol=1e-13, atol=1e-13)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:21，

示例14: test\_idwtn\_missing

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_idwtn\_missing():

\# Test to confirm missing data behave as zeroes

data = np.array(\[

\[0, 4, 1, 5, 1, 4\],

\[0, 5, 6, 3, 2, 1\],

\[2, 5, 19, 4, 19, 1\]\])

wavelet = pywt.Wavelet('haar')

coefs = pywt.dwtn(data, wavelet)

\# No point removing zero, or all

for num\_missing in range(1, len(coefs)):

for missing in combinations(coefs.keys(), num\_missing):

missing\_coefs = coefs.copy()

for key in missing:

del missing\_coefs\[key\]

LL = missing\_coefs.get('aa', None)

HL = missing\_coefs.get('da', None)

LH = missing\_coefs.get('ad', None)

HH = missing\_coefs.get('dd', None)

assert\_allclose(pywt.idwt2((LL, (HL, LH, HH)), wavelet),

pywt.idwtn(missing\_coefs, 'haar'), atol=1e-15)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:26，

示例15: test\_error\_on\_invalid\_keys

點讚 6

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_error\_on\_invalid\_keys():

data = np.array(\[

\[0, 4, 1, 5, 1, 4\],

\[0, 5, 6, 3, 2, 1\],

\[2, 5, 19, 4, 19, 1\]\])

wavelet = pywt.Wavelet('haar')

LL, (HL, LH, HH) = pywt.dwt2(data, wavelet)

\# unexpected key

d = \{'aa': LL, 'da': HL, 'ad': LH, 'dd': HH, 'ff': LH\}

assert\_raises(ValueError, pywt.idwtn, d, wavelet)

\# mismatched key lengths

d = \{'a': LL, 'da': HL, 'ad': LH, 'dd': HH\}

assert\_raises(ValueError, pywt.idwtn, d, wavelet)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:19，

示例16: \_\_init\_\_

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def \_\_init\_\_(self,nrow=256,ncol=256,wavelet='db4',level=3,fwd\_mode='recon',\\

dtype=np.float64,name=None):

\# Save parameters

self.wavelet = wavelet

self.level = level

shape0 = (nrow,ncol)

shape1 = (nrow,ncol)

dtype0 = dtype

dtype1 = dtype

if pywt.Wavelet(wavelet).orthogonal:

svd\_avail = True \#SVD calculation assumes an orthogonal wavelet

else:

svd\_avail = False

BaseLinTrans.\_\_init\_\_(self, shape0, shape1, dtype0, dtype1,\\

svd\_avail=svd\_avail,name=name)

\# Set the mode to periodic to make the wavelet orthogonal

self.mode = 'periodization'

\# Send a zero image to get the coefficient slices

im = np.zeros((nrow,ncol))

coeffs = pywt.wavedec2(im, wavelet=self.wavelet, level=self.level, \\

mode=self.mode)

\_, self.coeff\_slices = pywt.coeffs\_to\_array(coeffs)

\# Confirm that fwd\_mode is valid

if (fwd\_mode != 'recon') and (fwd\_mode != 'analysis'):

raise common.VpException('fwd\_mode must be recon or analysis')

self.fwd\_mode = fwd\_mode

開發者ID:GAMPTeam，項目名稱:vampyre，代碼行數:35，

示例17: recon

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def recon(self,z1):

"""

Wavelet reconstruction: coefficients -> image

"""

coeffs = pywt.array\_to\_coeffs(z1, self.coeff\_slices, \\

output\_format='wavedec2')

z0 = pywt.waverec2(coeffs, wavelet=self.wavelet, mode=self.mode)

return z0

開發者ID:GAMPTeam，項目名稱:vampyre，代碼行數:10，

示例18: compute\_wavelet\_descriptor

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def compute\_wavelet\_descriptor(beat, family, level):

wave\_family = pywt.Wavelet(family)

coeffs = pywt.wavedec(beat, wave\_family, level=level)

return coeffs\[0\]

\# Compute my descriptor based on amplitudes of several intervals

開發者ID:mondejar，項目名稱:ecg-classification，代碼行數:8，

示例19: plot\_signal\_decomp

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def plot\_signal\_decomp(data, w, title):

"""Decompose and plot a signal S.

S = An + Dn + Dn-1 + ... + D1

"""

w = pywt.Wavelet(w)

a = data

ca = \[\]

cd = \[\]

for i in range(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))

fig = plt.figure()

ax\_main = fig.add\_subplot(len(rec\_a) + 1, 1, 1)

ax\_main.set\_title(title)

ax\_main.plot(data)

ax\_main.set\_xlim(0, len(data) - 1)

for i, y in enumerate(rec\_a):

ax = fig.add\_subplot(len(rec\_a) + 1, 2, 3 + i \* 2)

ax.plot(y, 'r')

ax.set\_xlim(0, len(y) - 1)

ax.set\_ylabel("A%d" % (i + 1))

for i, y in enumerate(rec\_d):

ax = fig.add\_subplot(len(rec\_d) + 1, 2, 4 + i \* 2)

ax.plot(y, 'g')

ax.set\_xlim(0, len(y) - 1)

ax.set\_ylabel("D%d" % (i + 1))

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:43，

示例20: test\_intwave\_deprecation

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_intwave\_deprecation():

wavelet = pywt.Wavelet('db3')

assert\_warns(DeprecationWarning, pywt.intwave, wavelet)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:5，

示例21: test\_centrfrq\_deprecation

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_centrfrq\_deprecation():

wavelet = pywt.Wavelet('db3')

assert\_warns(DeprecationWarning, pywt.centrfrq, wavelet)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:5，

示例22: test\_scal2frq\_deprecation

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_scal2frq\_deprecation():

wavelet = pywt.Wavelet('db3')

assert\_warns(DeprecationWarning, pywt.scal2frq, wavelet, 1)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:5，

示例23: test\_wavelet\_properties

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_wavelet\_properties():

w = pywt.Wavelet('db3')

\# Name

assert\_(w.name == 'db3')

assert\_(w.short\_family\_name == 'db')

assert\_(w.family\_name, 'Daubechies')

\# String representation

fields = ('Family name', 'Short name', 'Filters length', 'Orthogonal',

'Biorthogonal', 'Symmetry')

for field in fields:

assert\_(field in str(w))

\# Filter coefficients

dec\_lo = \[0.03522629188210, -0.08544127388224, -0.13501102001039,

0.45987750211933, 0.80689150931334, 0.33267055295096\]

dec\_hi = \[-0.33267055295096, 0.80689150931334, -0.45987750211933,

\-0.13501102001039, 0.08544127388224, 0.03522629188210\]

rec\_lo = \[0.33267055295096, 0.80689150931334, 0.45987750211933,

\-0.13501102001039, -0.08544127388224, 0.03522629188210\]

rec\_hi = \[0.03522629188210, 0.08544127388224, -0.13501102001039,

\-0.45987750211933, 0.80689150931334, -0.33267055295096\]

assert\_allclose(w.dec\_lo, dec\_lo)

assert\_allclose(w.dec\_hi, dec\_hi)

assert\_allclose(w.rec\_lo, rec\_lo)

assert\_allclose(w.rec\_hi, rec\_hi)

assert\_(len(w.filter\_bank) == 4)

\# Orthogonality

assert\_(w.orthogonal)

assert\_(w.biorthogonal)

\# Symmetry

assert\_(w.symmetry)

\# Vanishing moments

assert\_(w.vanishing\_moments\_phi == 0)

assert\_(w.vanishing\_moments\_psi == 3)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:42，

示例24: check\_coefficients\_orthogonal

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def check\_coefficients\_orthogonal(wavelet):

epsilon = 5e-11

level = 5

w = pywt.Wavelet(wavelet)

phi, psi, x = w.wavefun(level=level)

\# Lowpass filter coefficients sum to sqrt2

res = np.sum(w.dec\_lo)-np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# sum even coef = sum odd coef = 1 / sqrt(2)

res = np.sum(w.dec\_lo\[::2\])-1./np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

res = np.sum(w.dec\_lo\[1::2\])-1./np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# Highpass filter coefficients sum to zero

res = np.sum(w.dec\_hi)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# Scaling function integrates to unity

res = np.sum(phi) - 2\*\*level

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# Wavelet function is orthogonal to the scaling function at the same scale

res = np.sum(phi\*psi)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# The lowpass and highpass filter coefficients are orthogonal

res = np.sum(np.array(w.dec\_lo)\*np.array(w.dec\_hi))

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:38，

示例25: check\_coefficients\_biorthogonal

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def check\_coefficients\_biorthogonal(wavelet):

epsilon = 5e-11

level = 5

w = pywt.Wavelet(wavelet)

phi\_d, psi\_d, phi\_r, psi\_r, x = w.wavefun(level=level)

\# Lowpass filter coefficients sum to sqrt2

res = np.sum(w.dec\_lo)-np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# sum even coef = sum odd coef = 1 / sqrt(2)

res = np.sum(w.dec\_lo\[::2\])-1./np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

res = np.sum(w.dec\_lo\[1::2\])-1./np.sqrt(2)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# Highpass filter coefficients sum to zero

res = np.sum(w.dec\_hi)

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

\# Scaling function integrates to unity

res = np.sum(phi\_d) - 2\*\*level

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

res = np.sum(phi\_r) - 2\*\*level

msg = ('\[RMS\_REC > EPSILON\] for Wavelet: %s, rms=%.3g' % (wavelet, res))

assert\_(res < epsilon, msg=msg)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:31，

示例26: test\_wavefun\_sym3

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_wavefun\_sym3():

w = pywt.Wavelet('sym3')

\# sym3 is an orthogonal wavelet, so 3 outputs from wavefun

phi, psi, x = w.wavefun(level=3)

assert\_(phi.size == 41)

assert\_(psi.size == 41)

assert\_(x.size == 41)

assert\_allclose(x, np.linspace(0, 5, num=x.size))

phi\_expect = np.array(\[0.00000000e+00, 1.04132926e-01, 2.52574126e-01,

3.96525521e-01, 5.70356539e-01, 7.18934305e-01,

8.70293448e-01, 1.05363620e+00, 1.24921722e+00,

1.15296888e+00, 9.41669683e-01, 7.55875887e-01,

4.96118565e-01, 3.28293151e-01, 1.67624969e-01,

\-7.33690312e-02, -3.35452855e-01, -3.31221131e-01,

\-2.32061503e-01, -1.66854239e-01, -4.34091324e-02,

\-2.86152390e-02, -3.63563035e-02, 2.06034491e-02,

8.30280254e-02, 7.17779073e-02, 3.85914311e-02,

1.47527100e-02, -2.31896077e-02, -1.86122172e-02,

\-1.56211329e-03, -8.70615088e-04, 3.20760857e-03,

2.34142153e-03, -7.73737194e-04, -2.99879354e-04,

1.23636238e-04, 0.00000000e+00, 0.00000000e+00,

0.00000000e+00, 0.00000000e+00\])

psi\_expect = np.array(\[0.00000000e+00, 1.10265752e-02, 2.67449277e-02,

4.19878574e-02, 6.03947231e-02, 7.61275365e-02,

9.21548684e-02, 1.11568926e-01, 1.32278887e-01,

6.45829680e-02, -3.97635130e-02, -1.38929884e-01,

\-2.62428322e-01, -3.62246804e-01, -4.62843343e-01,

\-5.89607507e-01, -7.25363076e-01, -3.36865858e-01,

2.67715108e-01, 8.40176767e-01, 1.55574430e+00,

1.18688954e+00, 4.20276324e-01, -1.51697311e-01,

\-9.42076108e-01, -7.93172332e-01, -3.26343710e-01,

\-1.24552779e-01, 2.12909254e-01, 1.75770320e-01,

1.47523075e-02, 8.22192707e-03, -3.02920592e-02,

\-2.21119497e-02, 7.30703025e-03, 2.83200488e-03,

\-1.16759765e-03, 0.00000000e+00, 0.00000000e+00,

0.00000000e+00, 0.00000000e+00\])

assert\_allclose(phi, phi\_expect)

assert\_allclose(psi, psi\_expect)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:43，

示例27: test\_wavedec

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_wavedec():

x = \[3, 7, 1, 1, -2, 5, 4, 6\]

db1 = pywt.Wavelet('db1')

cA3, cD3, cD2, cD1 = pywt.wavedec(x, db1)

assert\_almost\_equal(cA3, \[8.83883476\])

assert\_almost\_equal(cD3, \[-0.35355339\])

assert\_allclose(cD2, \[4., -3.5\])

assert\_allclose(cD1, \[-2.82842712, 0, -4.94974747, -1.41421356\])

assert\_(pywt.dwt\_max\_level(len(x), db1) == 3)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:11，

示例28: test\_multilevel\_dtypes\_1d

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_multilevel\_dtypes\_1d():

\# only checks that the result is of the expected type

wavelet = pywt.Wavelet('haar')

for dt\_in, dt\_out in zip(dtypes\_in, dtypes\_out):

\# wavedec, waverec

x = np.ones(8, dtype=dt\_in)

errmsg = "wrong dtype returned for \{0\} input".format(dt\_in)

coeffs = pywt.wavedec(x, wavelet, level=2)

for c in coeffs:

assert\_(c.dtype == dt\_out, "wavedec: " + errmsg)

x\_roundtrip = pywt.waverec(coeffs, wavelet)

assert\_(x\_roundtrip.dtype == dt\_out, "waverec: " + errmsg)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:15，

示例29: test\_multilevel\_dtypes\_2d

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_multilevel\_dtypes\_2d():

wavelet = pywt.Wavelet('haar')

for dt\_in, dt\_out in zip(dtypes\_in, dtypes\_out):

\# wavedec2, waverec2

x = np.ones((8, 8), dtype=dt\_in)

errmsg = "wrong dtype returned for \{0\} input".format(dt\_in)

cA, coeffsD2, coeffsD1 = pywt.wavedec2(x, wavelet, level=2)

assert\_(cA.dtype == dt\_out, "wavedec2: " + errmsg)

for c in coeffsD1:

assert\_(c.dtype == dt\_out, "wavedec2: " + errmsg)

for c in coeffsD2:

assert\_(c.dtype == dt\_out, "wavedec2: " + errmsg)

x\_roundtrip = pywt.waverec2(\[cA, coeffsD2, coeffsD1\], wavelet)

assert\_(x\_roundtrip.dtype == dt\_out, "waverec2: " + errmsg)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:16，

示例30: test\_multilevel\_dtypes\_nd

點讚 5

\# 需要導入模塊: import pywt \[as 別名\]

\# 或者: from pywt import Wavelet \[as 別名\]

def test\_multilevel\_dtypes\_nd():

wavelet = pywt.Wavelet('haar')

for dt\_in, dt\_out in zip(dtypes\_in, dtypes\_out):

\# wavedecn, waverecn

x = np.ones((8, 8), dtype=dt\_in)

errmsg = "wrong dtype returned for \{0\} input".format(dt\_in)

cA, coeffsD2, coeffsD1 = pywt.wavedecn(x, wavelet, level=2)

assert\_(cA.dtype == dt\_out, "wavedecn: " + errmsg)

for key, c in coeffsD1.items():

assert\_(c.dtype == dt\_out, "wavedecn: " + errmsg)

for key, c in coeffsD2.items():

assert\_(c.dtype == dt\_out, "wavedecn: " + errmsg)

x\_roundtrip = pywt.waverecn(\[cA, coeffsD2, coeffsD1\], wavelet)

assert\_(x\_roundtrip.dtype == dt\_out, "waverecn: " + errmsg)

開發者ID:hello-sea，項目名稱:DeepLearning\_Wavelet-LSTM，代碼行數:16，

注：本文中的pywt.Wavelet方法示例整理自Github/MSDocs等源碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。

matlab wavefun怎么用,Python pywt.Wavelet方法代碼示例

发表评论取消回复

还没有评论，来说两句吧...

相关阅读

相关 matlab wavefun怎么用,Python pywt.Wavelet方法代碼示例

相关 java scanner getline,Java PublicScanner類代碼示例

相关 Tiled Map算法 java_Java ArcGISTiledElevationSource類代碼示例

相关 java byte set方法_Java ByteChunk.setBytes方法代碼示例

相关 Java的match方法_Java Scanner.match方法代碼示例

相关 java simplescalar_Java TemplateDirectiveBody.render方法代碼示例

相关 common java socket_Java SocketConnection.connect方法代碼示例

相关 Java NPOIFSFileSystem.getRoot方法代碼示例

相关用C#代碼封裝JavaScript常用代碼的一個類

相关 WinCE 截屏 - C++ 代碼