本文整理汇总了Python中pywt.wavedecn函数的典型用法代码示例。如果您正苦于以下问题:Python wavedecn函数的具体用法?Python wavedecn怎么用?Python wavedecn使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了wavedecn函数的20个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: test_wavedecn_shapes_and_size
def test_wavedecn_shapes_and_size():
wav = pywt.Wavelet('db2')
for data_shape in [(33, ), (64, 32), (1, 15, 30)]:
for axes in [None, 0, -1]:
for mode in pywt.Modes.modes:
coeffs = pywt.wavedecn(np.ones(data_shape), wav,
mode=mode, axes=axes)
# verify that the shapes match the coefficient shapes
shapes = pywt.wavedecn_shapes(data_shape, wav,
mode=mode, axes=axes)
assert_equal(coeffs[0].shape, shapes[0])
expected_size = coeffs[0].size
for level in range(1, len(coeffs)):
for k, v in coeffs[level].items():
expected_size += v.size
assert_equal(shapes[level][k], v.shape)
# size can be determined from either the shapes or coeffs
size = pywt.wavedecn_size(shapes)
assert_equal(size, expected_size)
size = pywt.wavedecn_size(coeffs)
assert_equal(size, expected_size)
开发者ID:PyWavelets,项目名称:pywt,代码行数:25,代码来源:test_multilevel.py
示例2: test_wavedecn_coeff_reshape_axes_subset
def test_wavedecn_coeff_reshape_axes_subset():
# verify round trip is correct when only a subset of axes are transformed:
# wavedecn - >coeffs_to_array-> array_to_coeffs -> waverecn
# This is done for wavedec{1, 2, n}
rng = np.random.RandomState(1234)
mode = 'symmetric'
w = pywt.Wavelet('db2')
N = 16
ndim = 3
for axes in [(-1, ), (0, ), (1, ), (0, 1), (1, 2), (0, 2), None]:
x1 = rng.randn(*([N] * ndim))
coeffs = pywt.wavedecn(x1, w, mode=mode, axes=axes)
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs, axes=axes)
if axes is not None:
# if axes is not None, it must be provided to coeffs_to_array
assert_raises(ValueError, pywt.coeffs_to_array, coeffs)
# mismatched axes size
assert_raises(ValueError, pywt.coeffs_to_array, coeffs,
axes=(0, 1, 2, 3))
assert_raises(ValueError, pywt.coeffs_to_array, coeffs,
axes=())
coeffs2 = pywt.array_to_coeffs(coeff_arr, coeff_slices)
x1r = pywt.waverecn(coeffs2, w, mode=mode, axes=axes)
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
开发者ID:rgommers,项目名称:pywt,代码行数:27,代码来源:test_multilevel.py
示例3: test_waverecn_axes_errors
def test_waverecn_axes_errors():
data = np.ones((8, 8, 8))
c = pywt.wavedecn(data, 'haar')
# repeated axes not allowed
assert_raises(ValueError, pywt.waverecn, c, 'haar', axes=(1, 1))
# out of range axis not allowed
assert_raises(ValueError, pywt.waverecn, c, 'haar', axes=(0, 1, 3))
开发者ID:rgommers,项目名称:pywt,代码行数:7,代码来源:test_multilevel.py
示例4: setup
def setup(self, D, n, wavelet, dtype):
try:
from pywt import waverecn
except ImportError:
raise NotImplementedError("waverecn not available")
super(WaverecnTimeSuite, self).setup(D, n, wavelet, dtype)
self.data = pywt.wavedecn(self.data, wavelet)
开发者ID:HenryZhou1002,项目名称:pywt,代码行数:7,代码来源:dwt_benchmarks.py
示例5: test_waverecn
def test_waverecn():
rstate = np.random.RandomState(1234)
# test 1D through 4D cases
for nd in range(1, 5):
x = rstate.randn(*(4, )*nd)
coeffs = pywt.wavedecn(x, 'db1')
assert_(len(coeffs) == 3)
assert_allclose(pywt.waverecn(coeffs, 'db1'), x, rtol=tol_double)
开发者ID:rgommers,项目名称:pywt,代码行数:8,代码来源:test_multilevel.py
示例6: test_waverecn_axes_subsets
def test_waverecn_axes_subsets():
rstate = np.random.RandomState(0)
data = rstate.standard_normal((8, 8, 8, 8))
# test all combinations of 3 out of 4 axes transformed
for axes in combinations((0, 1, 2, 3), 3):
coefs = pywt.wavedecn(data, 'haar', axes=axes)
rec = pywt.waverecn(coefs, 'haar', axes=axes)
assert_allclose(rec, data, atol=1e-14)
开发者ID:rgommers,项目名称:pywt,代码行数:8,代码来源:test_multilevel.py
示例7: test_waverecn_int_axis
def test_waverecn_int_axis():
# waverecn should also work for axes as an integer
rstate = np.random.RandomState(0)
data = rstate.standard_normal((8, 8))
for axis in [0, 1]:
coefs = pywt.wavedecn(data, 'haar', axes=axis)
rec = pywt.waverecn(coefs, 'haar', axes=axis)
assert_allclose(rec, data, atol=1e-14)
开发者ID:rgommers,项目名称:pywt,代码行数:8,代码来源:test_multilevel.py
示例8: test_waverecn_all_wavelets_modes
def test_waverecn_all_wavelets_modes():
# test 2D case using all wavelets and modes
rstate = np.random.RandomState(1234)
r = rstate.randn(80, 96)
for wavelet in wavelist:
for mode in pywt.Modes.modes:
coeffs = pywt.wavedecn(r, wavelet, mode=mode)
assert_allclose(pywt.waverecn(coeffs, wavelet, mode=mode),
r, rtol=tol_single, atol=tol_single)
开发者ID:rgommers,项目名称:pywt,代码行数:9,代码来源:test_multilevel.py
示例9: test_array_to_coeffs_invalid_inputs
def test_array_to_coeffs_invalid_inputs():
coeffs = pywt.wavedecn(np.ones(2), 'haar')
arr, arr_slices = pywt.coeffs_to_array(coeffs)
# empty list of array slices
assert_raises(ValueError, pywt.array_to_coeffs, arr, [])
# invalid format name
assert_raises(ValueError, pywt.array_to_coeffs, arr, arr_slices, 'foo')
开发者ID:rgommers,项目名称:pywt,代码行数:9,代码来源:test_multilevel.py
示例10: _wavelet_threshold
def _wavelet_threshold(img, wavelet, threshold=None, sigma=None, mode='soft'):
"""Performs wavelet denoising.
Parameters
----------
img : ndarray (2d or 3d) of ints, uints or floats
Input data to be denoised. `img` can be of any numeric type,
but it is cast into an ndarray of floats for the computation
of the denoised image.
wavelet : string
The type of wavelet to perform. Can be any of the options
pywt.wavelist outputs. For example, this may be any of ``{db1, db2,
db3, db4, haar}``.
sigma : float, optional
The standard deviation of the noise. The noise is estimated when sigma
is None (the default) by the method in [2]_.
threshold : float, optional
The thresholding value. All wavelet coefficients less than this value
are set to 0. The default value (None) uses the SureShrink method found
in [1]_ to remove noise.
mode : {'soft', 'hard'}, optional
An optional argument to choose the type of denoising performed. It
noted that choosing soft thresholding given additive noise finds the
best approximation of the original image.
Returns
-------
out : ndarray
Denoised image.
References
----------
.. [1] Chang, S. Grace, Bin Yu, and Martin Vetterli. "Adaptive wavelet
thresholding for image denoising and compression." Image Processing,
IEEE Transactions on 9.9 (2000): 1532-1546.
DOI: 10.1109/83.862633
.. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
DOI: 10.1093/biomet/81.3.425
"""
coeffs = pywt.wavedecn(img, wavelet=wavelet)
detail_coeffs = coeffs[-1]['d' * img.ndim]
if sigma is None:
# Estimates via the noise via method in [2]
sigma = np.median(np.abs(detail_coeffs)) / 0.67448975019608171
if threshold is None:
# The BayesShrink threshold from [1]_ in docstring
threshold = sigma**2 / np.sqrt(max(img.var() - sigma**2, 0))
denoised_detail = [{key: pywt.threshold(level[key], value=threshold,
mode=mode) for key in level} for level in coeffs[1:]]
denoised_root = pywt.threshold(coeffs[0], value=threshold, mode=mode)
denoised_coeffs = [denoised_root] + [d for d in denoised_detail]
return pywt.waverecn(denoised_coeffs, wavelet)
开发者ID:dfcollin,项目名称:scikit-image,代码行数:57,代码来源:_denoise.py
示例11: test_dwtn_max_level
def test_dwtn_max_level():
# predicted and empirical dwtn_max_level match
for wav in [pywt.Wavelet('db2'), 'sym8']:
for data_shape in [(33, ), (64, 32), (1, 15, 30)]:
for axes in [None, 0, -1]:
for mode in pywt.Modes.modes:
coeffs = pywt.wavedecn(np.ones(data_shape), wav,
mode=mode, axes=axes)
max_lev = pywt.dwtn_max_level(data_shape, wav, axes)
assert_equal(len(coeffs[1:]), max_lev)
开发者ID:PyWavelets,项目名称:pywt,代码行数:10,代码来源:test_multilevel.py
示例12: test_waverecn_accuracies
def test_waverecn_accuracies():
# testing 3D only here
rstate = np.random.RandomState(1234)
x0 = rstate.randn(4, 4, 4)
for dt, tol in dtypes_and_tolerances:
x = x0.astype(dt)
if np.iscomplexobj(x):
x += 1j*rstate.randn(4, 4, 4).astype(x.real.dtype)
coeffs = pywt.wavedecn(x.astype(dt), 'db1')
assert_allclose(pywt.waverecn(coeffs, 'db1'), x, atol=tol, rtol=tol)
开发者ID:rgommers,项目名称:pywt,代码行数:10,代码来源:test_multilevel.py
示例13: test_per_axis_wavelets_and_modes
def test_per_axis_wavelets_and_modes():
# tests seperate wavelet and edge mode for each axis.
rstate = np.random.RandomState(1234)
data = rstate.randn(24, 24, 16)
# wavelet can be a string or wavelet object
wavelets = (pywt.Wavelet('haar'), 'sym2', 'db2')
# The default number of levels should be the minimum over this list
max_levels = [pywt._dwt.dwt_max_level(nd, nf) for nd, nf in
zip(data.shape, wavelets)]
# mode can be a string or a Modes enum
modes = ('symmetric', 'periodization',
pywt._extensions._pywt.Modes.reflect)
coefs = pywt.wavedecn(data, wavelets, modes)
assert_allclose(pywt.waverecn(coefs, wavelets, modes), data, atol=1e-14)
assert_equal(min(max_levels), len(coefs[1:]))
coefs = pywt.wavedecn(data, wavelets[:1], modes)
assert_allclose(pywt.waverecn(coefs, wavelets[:1], modes), data,
atol=1e-14)
coefs = pywt.wavedecn(data, wavelets, modes[:1])
assert_allclose(pywt.waverecn(coefs, wavelets, modes[:1]), data,
atol=1e-14)
# length of wavelets or modes doesn't match the length of axes
assert_raises(ValueError, pywt.wavedecn, data, wavelets[:2])
assert_raises(ValueError, pywt.wavedecn, data, wavelets, mode=modes[:2])
assert_raises(ValueError, pywt.waverecn, coefs, wavelets[:2])
assert_raises(ValueError, pywt.waverecn, coefs, wavelets, mode=modes[:2])
# dwt2/idwt2 also support per-axis wavelets/modes
data2 = data[..., 0]
coefs2 = pywt.wavedec2(data2, wavelets[:2], modes[:2])
assert_allclose(pywt.waverec2(coefs2, wavelets[:2], modes[:2]), data2,
atol=1e-14)
assert_equal(min(max_levels[:2]), len(coefs2[1:]))
开发者ID:HenryZhou1002,项目名称:pywt,代码行数:40,代码来源:test_multilevel.py
示例14: test_unravel_invalid_inputs
def test_unravel_invalid_inputs():
coeffs = pywt.wavedecn(np.ones(2), 'haar')
arr, slices, shapes = pywt.ravel_coeffs(coeffs)
# empty list for slices or shapes
assert_raises(ValueError, pywt.unravel_coeffs, arr, slices, [])
assert_raises(ValueError, pywt.unravel_coeffs, arr, [], shapes)
# unequal length for slices/shapes
assert_raises(ValueError, pywt.unravel_coeffs, arr, slices[:-1], shapes)
# invalid format name
assert_raises(ValueError, pywt.unravel_coeffs, arr, slices, shapes, 'foo')
开发者ID:PyWavelets,项目名称:pywt,代码行数:13,代码来源:test_multilevel.py
示例15: test_multilevel_dtypes_nd
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)
开发者ID:rgommers,项目名称:pywt,代码行数:14,代码来源:test_multilevel.py
示例16: test_default_level
def test_default_level():
# default level is the maximum permissible for the transformed axes
data = np.ones((128, 32, 4))
wavelet = ('db8', 'db1')
for dec_func in [pywt.wavedec2, pywt.wavedecn]:
for axes in [(0, 1), (2, 1), (0, 2)]:
c = dec_func(data, wavelet, axes=axes)
max_lev = np.min([pywt.dwt_max_level(data.shape[ax], wav)
for ax, wav in zip(axes, wavelet)])
assert_equal(len(c[1:]), max_lev)
for ax in [0, 1]:
c = pywt.wavedecn(data, wavelet[ax], axes=(ax, ))
assert_equal(len(c[1:]),
pywt.dwt_max_level(data.shape[ax], wavelet[ax]))
开发者ID:PyWavelets,项目名称:pywt,代码行数:15,代码来源:test_multilevel.py
示例17: test_coeffs_to_array_padding
def test_coeffs_to_array_padding():
rng = np.random.RandomState(1234)
x1 = rng.randn(32, 32)
mode = 'symmetric'
coeffs = pywt.wavedecn(x1, 'db2', mode=mode)
# padding=None raises a ValueError when tight packing is not possible
assert_raises(ValueError, pywt.coeffs_to_array, coeffs, padding=None)
# set padded values to nan
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs, padding=np.nan)
npad = np.sum(np.isnan(coeff_arr))
assert_(npad > 0)
# pad with zeros
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs, padding=0)
assert_(np.sum(np.isnan(coeff_arr)) == 0)
assert_(np.sum(coeff_arr == 0) == npad)
# Haar case with N as a power of 2 can be tightly packed
coeffs_haar = pywt.wavedecn(x1, 'haar', mode=mode)
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs_haar, padding=None)
# shape of coeff_arr will match in this case, but not in general
assert_equal(coeff_arr.shape, x1.shape)
开发者ID:rgommers,项目名称:pywt,代码行数:24,代码来源:test_multilevel.py
示例18: test_waverecn_coeff_reshape_odd
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)
开发者ID:rgommers,项目名称:pywt,代码行数:18,代码来源:test_multilevel.py
示例19: test_ravel_invalid_input
def test_ravel_invalid_input():
# wavedec ravel does not support any coefficient arrays being set to None
coeffs = pywt.wavedec(np.ones(8), 'haar')
coeffs[1] = None
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
# wavedec2 ravel cannot have None or a tuple/list of None
coeffs = pywt.wavedec2(np.ones((8, 8)), 'haar')
coeffs[1] = (None, None, None)
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
coeffs[1] = [None, None, None]
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
coeffs[1] = None
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
# wavedecn ravel cannot have any dictionary elements as None
coeffs = pywt.wavedecn(np.ones((8, 8, 8)), 'haar')
coeffs[1]['ddd'] = None
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
开发者ID:PyWavelets,项目名称:pywt,代码行数:19,代码来源:test_multilevel.py
示例20: range
plt.figure()
plt.title('Reconstructed with level %i of details' %(n-1))
plt.imshow(dCat,cmap=colormap)
return
colormap=plt.get_cmap('gray')
cat = imageio.imread('/home/az/Desktop/Wavelets/Imagem/Im.jpg')
cat = cat[:,:,0]
plt.figure()
plt.title('Original Image')
plt.imshow(cat,cmap=colormap)
wavelet = 'db2'
lv = 7
coeffs = pywt.wavedecn(cat, wavelet, level=lv)
arr, coeff_slices = pywt.coeffs_to_array(coeffs)
for n in range(1,len(coeff_slices)):
PrintReconstructions(coeffs,n)
plt.figure()
vec = [np.linalg.norm(arr[coeff_slices[0]])]
for i in range(1,7):
vec.append(np.linalg.norm(arr[coeff_slices[i]['dd']]))
vec = vec/np.linalg.norm(vec)
plt.plot([0,1,2,3,4,5,6], vec, 'o')
plt.grid()
开发者ID:MatheusNali,项目名称:WavePySeminar,代码行数:31,代码来源:PyWavelets.py
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