コード例 #1
0
def wpi(dataset, n_channels, seq_length):
    """
    function to calculate a wavelet packet energy image
    # Ding.2017: Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis
    :param dataset: the raw data read from .csv or .npy file
    :type dataset: ndarray
    :param n_channels: number of channels
    :type n_channels: int
    :param seq_length: length of one data stream of a single sensor
    :type seq_length: int
    :return: the flattened images, tupel holding the image size
    """
    level = 10  # choose an even number, paper uses 10
    wavelet = 'db8'  # Daubechies 8 used in paper
    # wavelet = 'coif3'  # Daubechies 8 used in paper
    order = "natural"  # other option is "freq"
    clip_energy = 1  # threshold to clip the calculated energy features (negative and positive)

    num_samples = dataset.shape[0]
    img_size = np.power(2, level // 2)
    size_flat = img_size * img_size
    pics = np.zeros([num_samples, n_channels * size_flat])  # initialize array
    wp_image = np.zeros([img_size, img_size])
    for sample in range(num_samples):  # loop over all samples
        for ch in range(n_channels):  # loop over all channels
            # Construct wavelet packet tree for signal of one channel
            wp = WaveletPacket(dataset[sample][ch * seq_length:(ch + 1) *
                                               seq_length],
                               wavelet,
                               'symmetric',
                               maxlevel=level)
            nodes = wp.get_level(
                level, order=order
            )  # !required! access the tree to populate it (might be a bug of the library?)
            i = 0
            # loop through the tree (ordered from aaa..a to ddd..d)
            for node in wp.get_leaf_nodes():
                # use only the coefficients from node (i, p) with p = 0..(2^i-1), i.e. set all other coefficients to zero
                new_wp = WaveletPacket(None, wavelet, 'symmetric', level)
                new_wp[node.path] = wp[node.path].data
                # get the reconstruction coefficients (length 2^i)
                reconst = np.asarray(new_wp.reconstruct(update=False))
                # phase shift --> arrange energy features calculated as the squared sum over the reconstruction coefficients
                wp_image[i % img_size,
                         i // img_size] = np.sum(np.multiply(reconst, reconst),
                                                 axis=-1)
                # remove node from wp tree
                del new_wp[node.path]
                i += 1
            # (!) THP modification (!), clip the wpi to fixed range: especially the approximation coefficients hold a lot of energy which
            # scales very differently
            wp_image = np.clip(wp_image, -clip_energy, clip_energy)
            # collect all pictures, shape (samples, ch1+ch2..)
            pics[sample][ch * size_flat:(ch + 1) * size_flat] = np.reshape(
                wp_image, [1, size_flat])
    return pics, [img_size, img_size]
コード例 #2
0
ファイル: wp_tree.py プロジェクト: 000Nelson000/pywt
#!/usr/bin/env python
# -*- coding: utf-8 -*-

from pywt import WaveletPacket

wp = WaveletPacket(range(16), 'db2', maxlevel=3)
print [node.path for node in wp.get_leaf_nodes(decompose=False)]
print [node.path for node in wp.get_leaf_nodes(decompose=True)]
coeffs = [(node.path, node.data) for node in wp.get_leaf_nodes(decompose=True)]
print coeffs

wp2 = WaveletPacket(None, 'db2', maxlevel=3)
for path, data in coeffs:
    wp2[path] = data
#print wp["a"]
print [node.path for node in wp2.get_leaf_nodes(decompose=False)]
print wp2.reconstruct()
コード例 #3
0
ファイル: wp_tree.py プロジェクト: zhengjing8628/pywt
#!/usr/bin/env python
# -*- coding: utf-8 -*-

from pywt import WaveletPacket

wp = WaveletPacket(range(16), 'db2', maxlevel=3)
print[node.path for node in wp.get_leaf_nodes(decompose=False)]
print[node.path for node in wp.get_leaf_nodes(decompose=True)]
coeffs = [(node.path, node.data) for node in wp.get_leaf_nodes(decompose=True)]
print coeffs

wp2 = WaveletPacket(None, 'db2', maxlevel=3)
for path, data in coeffs:
    wp2[path] = data
#print wp["a"]
print[node.path for node in wp2.get_leaf_nodes(decompose=False)]
print wp2.reconstruct()