def basic_example():
print('Family =', pywt.families())
for family in pywt.families():
print('\t%s family: ' % family + ', '.join(pywt.wavelist(family)))
# Discrete wavelet object:
# haar family:
# 'haar'.
# db family:
# 'db1', 'db2', 'db3', 'db4', 'db5', 'db6', 'db7', 'db8', 'db9', 'db10', 'db11', 'db12', 'db13', 'db14', 'db15', 'db16', 'db17', 'db18', 'db19', 'db20', 'db21', 'db22', 'db23', 'db24', 'db25', 'db26', 'db27', 'db28', 'db29', 'db30', 'db31', 'db32', 'db33', 'db34', 'db35', 'db36', 'db37', 'db38'.
# sym family:
# 'sym2', 'sym3', 'sym4', 'sym5', 'sym6', 'sym7', 'sym8', 'sym9', 'sym10', 'sym11', 'sym12', 'sym13', 'sym14', 'sym15', 'sym16', 'sym17', 'sym18', 'sym19', 'sym20'.
# coif family:
# 'coif1', 'coif2', 'coif3', 'coif4', 'coif5', 'coif6', 'coif7', 'coif8', 'coif9', 'coif10', 'coif11', 'coif12', 'coif13', 'coif14', 'coif15', 'coif16', 'coif17'.
# bior family:
# 'bior1.1', 'bior1.3', 'bior1.5', 'bior2.2', 'bior2.4', 'bior2.6', 'bior2.8', 'bior3.1', 'bior3.3', 'bior3.5', 'bior3.7', 'bior3.9', 'bior4.4', 'bior5.5', 'bior6.8'.
# rbio family:
# 'rbio1.1', 'rbio1.3', 'rbio1.5', 'rbio2.2', 'rbio2.4', 'rbio2.6', 'rbio2.8', 'rbio3.1', 'rbio3.3', 'rbio3.5', 'rbio3.7', 'rbio3.9', 'rbio4.4', 'rbio5.5', 'rbio6.8'.
# dmey family:
# 'dmey'.
# Continuous wavelet object:
# gaus family:
# 'gaus1', 'gaus2', 'gaus3', 'gaus4', 'gaus5', 'gaus6', 'gaus7', 'gaus8'.
# mexh family:
# 'mexh'.
# morl family:
# 'morl'.
# cgau family:
# 'cgau1', 'cgau2', 'cgau3', 'cgau4', 'cgau5', 'cgau6', 'cgau7', 'cgau8'.
# Discrete continuous wavelet object:
# shan family:
# 'shan'.
# fbsp family:
# 'fbsp'.
# cmor family:
# 'cmor'.
#--------------------
w = pywt.Wavelet('db3')
print('Summary =', w)
print('Name = {}, short family name = {}, family name = {}.'.format(
w.name, w.short_family_name, w.family_name))
# Decomposition (dec_len) and reconstruction (rec_len) filter lengths.
print('dec_len = {}, rec_len = {}.'.format(int(w.dec_len), int(w.rec_len)))
# Orthogonality and biorthogonality.
print('Orthogonal = {}, biorthogonal = {}.'.format(w.orthogonal,
w.biorthogonal))
# Symmetry.
print('Symmetry = {}.'.format(w.symmetry))
# Number of vanishing moments for the scaling function phi (vanishing_moments_phi) and the wavelet function psi (vanishing_moments_psi) associated with the filters.
print('vanishing_moments_phi = {}, vanishing_moments_psi = {}.'.format(
w.vanishing_moments_phi, w.vanishing_moments_psi))
# Lowpass and highpass decomposition filters and lowpass and highpass reconstruction filters.
print('Filter bank? =',
w.filter_bank == (w.dec_lo, w.dec_hi, w.rec_lo, w.rec_hi))
def setupContent(self, content_widget: QWidget):
self.ui = Ui_Wavelet()
self.ui.setupUi(content_widget)
self.ui.level_spin.setMinimum(1)
families = pywt.families(False)
self.ui.wavelet_family_combo.addItems(families)
self.ui.wavelet_combo.addItems(pywt.wavelist(pywt.families()[self.ui.wavelet_family_combo.currentIndex()]))
self.ui.wavelet_family_combo.currentIndexChanged.connect(self._onFamilyChanged)
def dwt_library(cls, img_arr):
'''Display all the availbale DWT (single level) for the input array
'''
t1 = time.time()
count = 0
for fam in pywt.families():
for mothwv in pywt.wavelist(fam):
for mod in pywt.Modes.modes:
print '\tWavelet: {0} / Mode: {1}'.format(mothwv, mod)
(c_A, (c_H, c_V, c_D)) = pywt.dwt2(img_arr,
pywt.Wavelet(mothwv),
mod)
count += 1
fig = plt.figure(figsize=(6, 11))
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
ax1.imshow(c_A, cmap='seismic') #'flag'
ax2.imshow(c_V, cmap='seismic')
ax3.imshow(c_H, cmap='seismic')
ax4.imshow(c_D, cmap='seismic')
plt.title('Wavelet: {0} / Mode: {1}'.format(mothwv, mod))
plt.subplots_adjust(left=.06,
bottom=0.05,
right=0.99,
top=0.99,
wspace=0.,
hspace=0.)
plt.show()
t2 = time.time()
print(
'\nTotal time in all {1} modes+mother wavelets: {0:.2f}\''.format(
(t2 - t1) / 60., count))
def test_pywt():
import numpy as np
import matplotlib.pyplot as plt
import pywt
import pywt.data
# Load image
original = pywt.data.camera()
# Wavelet transform of image, and plot approximation and details
titles = [
'Approximation', ' Horizontal detail', 'Vertical detail',
'Diagonal detail'
]
coeffs2 = pywt.dwt2(original, 'bior1.3')
LL, (LH, HL, HH) = coeffs2
plt.imshow(original)
plt.colorbar(shrink=0.8)
fig = plt.figure(figsize=(12, 3))
for i, a in enumerate([LL, LH, HL, HH]):
ax = fig.add_subplot(1, 4, i + 1)
ax.imshow(a, interpolation="nearest", cmap=plt.cm.gray)
ax.set_title(titles[i], fontsize=10)
ax.set_xticks([])
ax.set_yticks([])
fig.tight_layout()
plt.show()
print(pywt.families())
def _plot_wavelet_families(all=False):
'''Plotting waveletes approximations'''
lvl = 4 # because!
for name in pywt.families():
if not all:
_plot_wavelet(pywt.wavelist(name)[0], lvl)
else:
for wv in pywt.wavelist(name):
_plot_wavelet(wv, lvl, True)
def wrapper(*args, **kwargs):
wavelet = args[2]
families = pywt.families()
wavelets = list()
[wavelets.extend(pywt.wavelist(family)) for family in families]
if not wavelet in wavelets:
raise TypeError("{} is not a valid wavelet".format(wavelet))
return func(*args, **kwargs)
def startup():
global w, N, L0, mode, level
print pywt.families()
print pywt.wavelist('db')
w = pywt.Wavelet('db6')
mode = pywt.MODES.per
print w
print "vanishing_moments_psi:", w.vanishing_moments_psi
print "vanishing_moments_phi:", w.vanishing_moments_phi
N = 2**9
print "max level = ", pywt.dwt_max_level(N, w.dec_len)
L0 = numpy.zeros((N,N), 'double')
if True:
for i in xrange(0,N):
L0[i][i-1], L0[i][i], L0[i-1][i] = (1., -2., 1.)
else:
for i in xrange(1,N):
L0[i][i-1], L0[i][i], L0[i-1][i] = (1., -2., 1.)
L0[0][0] = -2.
#L0[0][N-1], L0[0][0], L0[N-1][0] = (1, -2, 1)
#L0 = numpy.eye(N)
#for i in xrange(0,N):
#L0[i] = [1,2,3,4,5,6,7,8]
numpy.core.arrayprint.set_printoptions(threshold=N*N+1, linewidth=100000)
#print L0
#coeffs = pywt.wavedec2(L0, w) #, level=pywt.dwt_max_level(N, w.dec_len))
#print coeffs
#print pywt.waverec2(coeffs, w)
#
#coeffs = pywt.wavedec2(L0, w) #, level=pywt.dwt_max_level(N, w.dec_len))
#print coeffs
print "max level = ", pywt.dwt_max_level(N, w.dec_len)
def __init__(self, wavelet="db8"):
# create list of supported wavelets
supported = []
for family in pywt.families():
supported += pywt.wavelist(family)
# check if wavelet is supported
if wavelet not in supported:
raise ValueError("DWT supports only " + str(supported) +
" as input wavelet")
self.m_wavelet = wavelet
def wavelet_descomposition_experiment():
dataset = '../datasets/grabado como el libro super bien'
experiment_rsult = './descomposition experiment con entonema 6'
if not os.path.exists(experiment_rsult):
os.mkdir(experiment_rsult)
name = os.path.basename(dataset)
for family in pywt.families():
wavelet = pywt.wavelist(family=family, kind='discrete')[0]
if wavelet in pywt.wavelist(kind='discrete'):
directory = '{}/{}'.format(experiment_rsult, wavelet)
if not os.path.exists(directory):
os.mkdir(directory)
for wav in os.listdir('{}/6'.format(dataset)):
plot_descomposition(dataset, 6, wav, wavelet, directory)
print('Finish')
def select_wavelet(data, r, method='median'):
comparison = {}
level = 5
r.initial = data
for family in pywt.families():
for wv in pywt.wavelist(family):
values = pywt.Wavelet(wv).wavefun(level=level)
r('coef <- ccf(initial, %s)$acf' % Str4R(values[-2])) #psi
if method == 'mean':
r('med <- mean(coef)')
elif method == 'sum':
r('med <- sum(coef)')
elif method == 'median':
r('med <- coef[length(coef)/2]')
else:
r('med <- coef[length(coef)/2]')
comparison[r.med] = (family, wv) #it's okay to rewrite keys
return comparison
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 4:
image = piio.read(sys.argv[1])
prefix = sys.argv[2]
levels = int(sys.argv[3])
suffix = sys.argv[4]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " input prefix levels suffix [-t type]")
print(" multiscale decomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal"%wtype
decompose(image, prefix, levels, suffix, wtype)
def main():
levels = [4]
img_type = ['ad', 'da', 'dd']
families = []
for family in pywt.families():
families = families + pywt.wavelist(family)
pathToDataset = "E://clg/Graduation project/Data set/DS"
pathToFolder = "E://clg/Dr waleed work/testing"
images, states = Read_data(pathToDataset)
counter = 0
for level in levels:
for mode in families:
for img in img_type:
Wavelet_Features(images, states, mode, level, img,
pathToFolder)
counter = counter + 1
print(counter)
pass
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 4:
image = piio.read(sys.argv[1])
prefix = sys.argv[2]
levels = int(sys.argv[3])
suffix = sys.argv[4]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " input prefix levels suffix [-t type]")
print(" multiscale decomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal" % wtype
decompose(image, prefix, levels, suffix, wtype)
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 3:
image = piio.read(sys.argv[1])
coarse = piio.read(sys.argv[2])
oname = sys.argv[3]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " image coarse result [-t type]")
print(" 2 scale recomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal"%wtype
res = recompose(image, coarse, wtype)
piio.write(oname, res);
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 3:
image = piio.read(sys.argv[1])
coarse = piio.read(sys.argv[2])
oname = sys.argv[3]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " image coarse result [-t type]")
print(" 2 scale recomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal"%wtype
res = recompose(image, coarse, wtype)
piio.write(oname, res);
def __init__(self, wavelet: str = 'db8') -> None:
"""
Parameters
----------
wavelet : str
Wavelet.
Returns
-------
NoneType
None
"""
# create list of supported wavelets
supported = []
for item in pywt.families():
supported += pywt.wavelist(item)
# check if wavelet is supported
if wavelet not in supported:
raise ValueError(
f'DWT supports only {supported} as input wavelet.')
self.m_wavelet = wavelet
def __init__(self, wavelet="db8"):
"""
Parameters
----------
wavelet : str
Wavelet.
Returns
-------
NoneType
None
"""
# create list of supported wavelets
supported = []
for family in pywt.families():
supported += pywt.wavelist(family)
# check if wavelet is supported
if wavelet not in supported:
raise ValueError("DWT supports only " + str(supported) +
" as input wavelet.")
self.m_wavelet = wavelet
import pywt
import pandas as pd
from os.path import join
from os import listdir
from os.path import isfile, join
import os
def get_folders(path):
return [join(path, f) for f in listdir(path) if not isfile(join(path, f))]
print '{\\bf \\#} & {\\bf Raw}',
for family in pywt.families():
for wavelet_name in pywt.wavelist(family)[-1:]:
print '& {\\bf', wavelet_name, '}',
print '\\\\'
print '\\midrule'
rows = []
i = 0
for folder in get_folders('NewlyAddedDatasets/'):
print i + 1,
print '&',
row = {"dataset": i + 1}
try:
df = pd.DataFrame.from_csv(join(folder, 'fastdtw_prediction_db1.csv'), index_col=False)
raw = 100.0 * df[df['label'] == df['original_data']].shape[0] / df.shape[0]
print "{0:.1f}".format(raw),
except:
def init_window(self):
self.master.title("Thermal Fusion GUI")
menu = Menu(self)
self.master.config(menu=menu)
filemenu = Menu(menu)
menu.add_cascade(label="File", menu=filemenu)
filemenu.add_command(label="Open RGB Image",
command=lambda: self.loadFile(True))
filemenu.add_command(label="Open IR Image",
command=lambda: self.loadFile(False))
filemenu.add_command(label="Open Example Images",
command=self.loadDefault)
filemenu.add_separator()
filemenu.add_command(label="Exit", command=self.client_exit)
self.pack(fill=BOTH, expand=1)
canvasRGB = Canvas(self, width=382, height=288, bg='white')
canvasRGB.grid(column=0, row=0)
RGB_id = canvasRGB.create_text(191, 142, fill="blue", tag="RGB")
canvasRGB.insert(RGB_id, 12, "RGB")
self.canvasRGB = canvasRGB
canvasIR = Canvas(self, width=382, height=288, bg='white')
canvasIR.grid(column=1, row=0)
IR_id = canvasIR.create_text(191, 142, fill="blue", tag="IR")
canvasIR.insert(IR_id, 12, "IR")
self.canvasIR = canvasIR
button = Button(self,
text="Load example images",
command=self.loadDefault)
button.grid(column=2, row=0)
self.button = button
buttonRGB = Button(self,
text="Select RGB Image",
command=lambda: self.loadFile(True))
buttonRGB.grid(column=0, row=1)
self.buttonRGB = buttonRGB
buttonIR = Button(self,
text="Select IR Image",
command=lambda: self.loadFile(False))
buttonIR.grid(column=1, row=1)
self.buttonIR = buttonIR
fusion = Button(self,
text="Start fusion process",
command=self.startFusion)
fusion.grid(column=0, row=2)
self.fusion = fusion
text = Text(self, width=70, height=15)
text.grid(column=0, row=3)
self.text = text
self.progressbar = Progressbar(self,
orient=HORIZONTAL,
mode='indeterminate',
length=100)
self.progressbar.grid(column=1, row=3)
options = [
"All", "Min", "Max", "Mean", "Entropy", "MACD", "Edge", "Deviation"
]
self.variable = StringVar(self)
self.variable.set(options[0])
self.dropdown = OptionMenu(self, self.variable, options[0], *options)
self.dropdown.grid(column=3, row=1)
# Only the first wavelets are functionnal
wavelets = families(short=False)[:7]
shortWavelets = families()[:7]
self.dictWavelets = dict(zip(wavelets, shortWavelets))
self.waveletVar = StringVar(self)
self.waveletVar.set(wavelets[1])
self.waveletDropdown = OptionMenu(self, self.waveletVar, wavelets[1],
*wavelets)
self.waveletDropdown.grid(column=4, row=1)
self.queue = Queue()
self.rgb_path = ""
self.ir_path = ""
def printDetails():
print("Family of wavelets available:")
print(pywt.families())
print("\n{0}".format(pywt.Wavelet('haar')))
frequency = np.arange(n / 2) / (n * interval)
nfft = abs(ft[range(int(n / 2))] / n )
plt.plot(frequency, nfft, 'maroon')
#plt.scatter(frequency[0],nfft[0],marker='o',c='maroon',edgecolors='red',linewidths=2)
plt.xlabel('Freq'), plt.ylabel('RH')
plt.show()
def fft(a):
yy=np.fft.fft(y)#快速傅里叶变换
return yy
#show(y,yy)
#plt.show()
#小波变换
print pywt.families()
print pywt.wavelist(kind='discrete')#选择离散还是连续
print pywt.ContinuousWavelet('morl')#连续小波morl的系列
mode = pywt.Modes.smooth
#离散小波变换
def plot_signal_decomp(data, w, title,order):
"""Decompose and plot a signal S.
S = An + Dn + Dn-1 + ... + D1
"""
w = pywt.Wavelet(w)#选取小波函数
a = data
ca = []#近似分量/低频
cd = []#细节分量/高频
for i in range(order):
(a, d) = pywt.dwt(a, w, mode)#进行order阶离散小波变换
def populateWindow(self):
self.cbbWaveletFamily.addItems(pywt.families())
self.cbbWavelet.addItems(pywt.wavelist('haar'))
import sys, os
import numpy
import matplotlib.pyplot
from PIL import Image
import pywt
def rgb2gray(rgb):
return numpy.dot(rgb[..., :3], [0.299, 0.587, 0.114])
image_path = sys.argv[1]
dwt_method = sys.argv[2] # dwt/dwt2
print pywt.families(short=False)
#######################################################################3
# read img from file
print '#######################################################################'
img_file = Image.open(image_path)
img = numpy.array(img_file)
print 'rgb matrix'
print img
# turn rbg to gray
gray = rgb2gray(img)
print 'gray matrix', gray.shape
print gray
# create a figure
figure, ax = matplotlib.pyplot.subplots(5, 2)
def all_wavelets():
for family in pywt.families():
for n in pywt.wavelist(family):
if n == 'db1': # Same as Haar
continue
yield n
Pw = []
for i,n in enumerate(xrange(0,log(N,2))):
pylab.figure()
Pw.append(deepcopy(P))
print len(Pw)
level = n
print "n=",n
Pw[i] = xfrm2d(Pw[i]).T
Pw[i] = xfrm2d(Pw[i]).T
#Pw[Pw==0.0] = 255
pylab.matshow(Pw[i])
def two():
global P
eps = 1
Pw = deepcopy(P)
for i,n in enumerate(xrange(1,4)):
X = pywt.wavedec2(Pw, 'sym5', level=n)
Pw = X[1][0]
#Pw[abs(Pw) < eps] = 0
#Pw[abs(Pw) >= eps] = 1
pylab.matshow(Pw)
print pywt.families()
pylab.gray()
P = load_image()
two()
pylab.show()
def initComponents(self):
self.setWindowFlags(Qt.Tool)
self.setWindowTitle('Custom settings')
self.waveletFamily.addItems(pywt.families())
self.signalEx.addItems(pywt.MODES.modes)
self.periodic.clicked.connect(self.showFrequency)
self.options['enable'] = False
self.setStyleSheet('QPushButton {\
color: #333;\
border: 1px solid #555;\
border-radius: 11px;\
padding: 2px;\
background: qradialgradient(cx: 0.3, cy: -0.4,\
fx: 0.3, fy: -0.4,\
radius: 1.35, stop: 0 #fff, stop: 1 #888);\
min-width: 80px;}\
QPushButton:hover {\
color: #fff;\
background: qradialgradient(cx: 0.3, cy: -0.4,\
fx: 0.3, fy: -0.4,\
radius: 1.35, stop: 0 #fff, stop: 1 #bbb);}\
QPushButton:pressed {\
background: qradialgradient(cx: 0.4, cy: -0.1,\
fx: 0.4, fy: -0.1,\
radius: 1.35, stop: 0 #fff, stop: 1 #ddd);}\
QPushButton:checked {\
background: qradialgradient(cx: 0.4, cy: -0.1,\
fx: 0.4, fy: -0.1,\
radius: 1.35, stop: 0 #fff, stop: 1 #ddd);}\
QComboBox {\
color: #333;\
border: 1px solid #555;\
border-radius: 11px;\
padding: 1px 18px 1px 3px;\
background: qradialgradient(cx: 0.3, cy: -0.4,\
fx: 0.3, fy: -0.4,\
radius: 1.35, stop: 0 #fff, stop: 1 #888);\
min-width: 20px;}\
QComboBox:hover {\
color: #fff;\
background: qradialgradient(cx: 0.3, cy: -0.4,\
fx: 0.3, fy: -0.4,\
radius: 1.35, stop: 0 #fff, stop: 1 #bbb);}\
QComboBox::down-arrow {\
image: url(' + RES + ICONS + ARROW_DOWN + ');}\
QComboBox::down-arrow:on {\
top: 1px;\
left: 1px;}\
QComboBox::drop-down {\
subcontrol-origin: padding;\
subcontrol-position: top right;\
width: 15px;\
border-left-width: 1px;\
border-left-color: darkgray;\
border-left-style: solid;\
border-top-right-radius: 3px;\
border-bottom-right-radius: 3px;}\
QToolButton {\
color: #333;\
border: 1px solid #555;\
border-radius: 11px;\
padding: 2px;\
background: qradialgradient(cx: 0.3, cy: -0.4,\
fx: 0.3, fy: -0.4,\
radius: 1.35, stop: 0 #fff, stop: 1 #888);\
min-width: 20px;}\
QToolButton:hover {\
color: #fff;\
background: qradialgradient(cx: 0.3, cy: -0.4,\
fx: 0.3, fy: -0.4,\
radius: 1.35, stop: 0 #fff, stop: 1 #bbb);}\
QToolButton:pressed {\
background: qradialgradient(cx: 0.4, cy: -0.1,\
fx: 0.4, fy: -0.1,\
radius: 1.35, stop: 0 #fff, stop: 1 #ddd);}\
QToolButton:checked {\
background: qradialgradient(cx: 0.4, cy: -0.1,\
fx: 0.4, fy: -0.1,\
radius: 1.35, stop: 0 #fff, stop: 1 #ddd);}')
def _onFamilyChanged(self, index):
self.ui.wavelet_combo.clear()
self.ui.wavelet_combo.addItems(pywt.wavelist(pywt.families()[index]))
import pywt
from matplotlib import pyplot
import numpy
from PIL import Image
import urllib.request
import io
import torch
from torch.autograd import Variable
URL = 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/bc/Zuse-Z4-Totale_deutsches-museum.jpg/315px-Zuse-Z4-Totale_deutsches-museum.jpg'
print(pywt.families())
w=pywt.Wavelet('bior2.2')
pyplot.plot(w.dec_hi[::-1], label="dec hi")
pyplot.plot(w.dec_lo[::-1], label="dec lo")
pyplot.plot(w.rec_hi, label="rec hi")
pyplot.plot(w.rec_lo, label="rec lo")
pyplot.title("Bior 2.2 Wavelets")
pyplot.legend()
dec_hi = torch.Tensor(w.dec_hi[::-1])
dec_lo = torch.Tensor(w.dec_lo[::-1])
rec_hi = torch.Tensor(w.rec_hi)
rec_lo = torch.Tensor(w.rec_lo)
imgraw = Image.open(io.BytesIO(urllib.request.urlopen(URL).read())).resize((256,256))
img = numpy.array(imgraw).mean(2)/255
img = torch.from_numpy(img).float()
pyplot.figure()
import numpy as np
import matplotlib.pyplot as plt
import os
import pywt
import datetime
elliot_waves = []
wavelets = pywt.families()
def showPlot(date, data, file_name):
fig, ax = plt.subplots()
ax.plot(date, data)
fig.savefig(file_name)
plt.close(fig)
# def generate_elliot_waves(folder):
common_folder = 'static/results/'
folder_name = 'elliot/'
def trend(data):
return 0 * np.arange(len(data))
def print_wave(data, file_name):
date = np.arange(len(data))
return showPlot(date, data, file_name)
import pywt
import pandas as pd
import matplotlib.pyplot as plt
print(pywt.families()) # 打印出小波族
# ['haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey', 'gaus', 'mexh', 'morl', 'cgau', 'shan', 'fbsp', 'cmor']
for family in pywt.families(): # 打印出每个小波族的每个小波函数
print('%s family: ' % (family) + ','.join(pywt.wavelist(family)))
# haar family: haar
# db family: db1,db2,db3,db4,db5,db6,db7,db8,db9,db10,db11,db12,db13,db14,db15,db16,db17,db18,db19,db20,db21,db22,db23,db24,db25,db26,db27,db28,db29,db30,db31,db32,db33,db34,db35,db36,db37,db38
# sym family: sym2,sym3,sym4,sym5,sym6,sym7,sym8,sym9,sym10,sym11,sym12,sym13,sym14,sym15,sym16,sym17,sym18,sym19,sym20
# coif family: coif1,coif2,coif3,coif4,coif5,coif6,coif7,coif8,coif9,coif10,coif11,coif12,coif13,coif14,coif15,coif16,coif17
# bior family: bior1.1,bior1.3,bior1.5,bior2.2,bior2.4,bior2.6,bior2.8,bior3.1,bior3.3,bior3.5,bior3.7,bior3.9,bior4.4,bior5.5,bior6.8
# rbio family: rbio1.1,rbio1.3,rbio1.5,rbio2.2,rbio2.4,rbio2.6,rbio2.8,rbio3.1,rbio3.3,rbio3.5,rbio3.7,rbio3.9,rbio4.4,rbio5.5,rbio6.8
# dmey family: dmey
# gaus family: gaus1,gaus2,gaus3,gaus4,gaus5,gaus6,gaus7,gaus8
# mexh family: mexh
# morl family: morl
# cgau family: cgau1,cgau2,cgau3,cgau4,cgau5,cgau6,cgau7,cgau8
# shan family: shan
# fbsp family: fbsp
# cmor family: cmor
db3 = pywt.Wavelet('db3') # 创建一个小波对象
print(db3)
# Filters length: 6 #滤波器长度
# Orthogonal: True #正交
# Biorthogonal: True #双正交
# Symmetry: asymmetric #对称性，不对称
* Haar (``haar``)
* Daubechies (``db``)
* Symlets (``sym``)
* Coiflets (``coif``)
* Biorthogonal (``bior``)
* Reverse biorthogonal (``rbio``)
* `"Discrete"` FIR approximation of Meyer wavelet (``dmey``)
* Gaussian wavelets (``gaus``)
* Mexican hat wavelet (``mexh``)
* Morlet wavelet (``morl``)
* Complex Gaussian wavelets (``cgau``)
* Shannon wavelets (``shan``)
* Frequency B-Spline wavelets (``fbsp``)
* Complex Morlet wavelets (``cmor``)
"""
# 得到支持的小波
wavelet_list = pywt.families()
wavelet_list_long = pywt.families(short=False)
print(wavelet_list)
print(wavelet_list_long)
# 得到所有可利用的小波
wave_haar = pywt.wavelist(family="haar")
print(wave_haar)
wave_coif = pywt.wavelist(family="coif")
print(wave_coif)
wave_continuous = pywt.wavelist(kind="continuous")
print(wave_continuous)
def __modelling_cycle():
initial_data = test_data
fig_init = plt.figure()
fig_init.canvas.manager.set_window_title('Initial data')
plt.plot(initial_data, color='g')
#--------------- wavelet decomposition -------------------#
decomposition_level = 2
wavelet_families = pywt.families()
wavelet_family = wavelet_families[0]
selected_wavelet = pywt.wavelist(wavelet_family)[0]
wavelet = pywt.Wavelet(selected_wavelet) #NB: taking first variant of wavelet (e.g. haar1)
# discrete (non stationary) multilevel decomposition
wCoefficients_Discrete = pywt.wavedec(initial_data, wavelet, level=decomposition_level) #NB: output length also depends on wavelet type
# stationary (Algorithme à trous ~ does not decimate coefficients at every transformation level) multilevel decomposition
wCoefficients_Stationary = pywt.swt(initial_data, wavelet, level=decomposition_level)
fig_discrete = plt.figure(); n_coeff = 1
fig_discrete.canvas.manager.set_window_title('Discrete decomposition [ ' + str(decomposition_level) + ' level(s) ]')
for coeff in wCoefficients_Discrete:
# print coeff
fig_discrete.add_subplot(len(wCoefficients_Discrete), 1, n_coeff); n_coeff += 1
plt.plot(coeff)
fig_stationary = plt.figure(); n_coeff = 1; rows = 0
fig_stationary.canvas.manager.set_window_title('Stationary decomposition [ ' + str(decomposition_level) + ' level(s) ]')
for item in wCoefficients_Stationary: rows += len(item)
i = 0; j = 0 # tree coeffs
for coeff in wCoefficients_Stationary:
for subcoeff in coeff:
print i, j
# print subcoeff
fig_stationary.add_subplot(rows, 1, n_coeff); n_coeff += 1
plt.plot(subcoeff)
j += 1
i += 1
plt.show()
fig_stat_sum = plt.figure(); n_coeff = 1
fig_stat_sum.canvas.manager.set_window_title('SWT sum by levels [ ' + str(decomposition_level) + ' level(s) ]')
for coeff in wCoefficients_Stationary:
sum = coeff[0] + coeff[1]
fig_stat_sum.add_subplot(len(wCoefficients_Discrete), 1, n_coeff); n_coeff += 1
plt.plot(sum)
# plt.show()
#------------------ modelling by level -------------------#
r = R()
r.i_data = initial_data # or r['i_data'] = initial_data
### Holt-Winters ###
# non-seasonal Holt-Winters
print r('hw <- HoltWinters( i_data, gamma = FALSE )')
# seasonal Holt-Winters
r.freq = 4 #series sampling (month, days, years, etc)
# print r( 'hw <- HoltWinters( ts ( %s, frequency = %s ) )' % ( Str4R(r.i_data), Str4R(r.freq) ) )
# print r( 'hw <- HoltWinters( ts ( %s, frequency = %s, start = c(1,1) ) )' % ( Str4R(r.i_data), Str4R(r.freq) ) )
# resulting Square Estimation Sum
print r.hw['SSE']
# bruteforce frequency search
# print 'test ahead:'
# sse_dict = {}
# for i in xrange(2, 50):
# r.freq = i
## r( 'hw <- HoltWinters( ts ( %s, frequency = %s, start = c(1,1) ) )' % ( Str4R(r.i_data), Str4R(r.freq) ) )
# r( 'hw <- HoltWinters( ts ( %s, frequency = %s ) )' % ( Str4R(r.i_data), Str4R(r.freq) ) )
# print r.hw['SSE']
# sse_dict[r.hw['SSE']] = i; i += 1
# print 'Resulting:'
# m = min(sse_dict.keys())
# print sse_dict[m], m
fig = plt.figure()
fig.canvas.manager.set_window_title('Holt-winters model')
ax = fig.add_subplot(111)
# ax.plot(r.hw['fitted'][:,0]) # the colums are: xhat, level, trend
# plt.show()
# forecast length
r.steps_ahead = 50
# print r('pred <- predict(%s, %s, prediction.interval = TRUE)' % ( Str4R(r.hw), Str4R(r.steps_ahead)) )
# print r( 'pred <- predict(hw, %s, prediction.interval = TRUE)', Str4R(r.steps_ahead) )
print r( 'pred <- predict(hw, 50, prediction.interval = TRUE)')
# plt.plot(r.pred)
ax.plot(initial_data)
ax.plot(append(r.hw['fitted'][:,0], r.pred[:,0])) # concatenating reconstructed model and resulting forecast
# plt.show()
#------------------ reconstruction -------------------#
# multilevel idwt
reconstructed_Discrete = pywt.waverec(wCoefficients_Discrete, selected_wavelet)
fig_dis_r = plt.figure()
fig_dis_r.canvas.manager.set_window_title('DWT reconstruction')
plt.plot(reconstructed_Discrete)
# plt.show()
# multilevel stationary
reconstructed_Stationary = iswt(wCoefficients_Stationary, selected_wavelet)
fig_sta_r = plt.figure()
fig_sta_r.canvas.manager.set_window_title('SWT reconstruction')
plt.plot(reconstructed_Stationary)
plt.show()
print 'end'
def denoise(st, wavelet="coif4", MODE="zero", remove_bg=True,
threshold='soft', zero_coarse_levels=1, zero_fine_levels=1,
preevent_window=10.0, preevent_threshold_reduction=2.0,
store_orig=False, store_noise=False):
"""Remove noise from waveforms using wavelets in a two-step
process. In the first step, noise is identified via a Kurtosis
analysis of the wavelet coefficients. In the second step, the
noise level in a pre-event window is determined for each wavelet
level and then removed from the waveform using a soft threshold.
:type wavelet: str
:param wavelet: Name of wavelet to use in denoising.
:type remove_bg: bool
:param remove_bg: If True, perform the first step in the denoising process.
:type preevent_window: float
:param preevent_window: Size of pre-event window to use in second step. Skip second step if <= 0.
:type preevent_threshold_reduction: float
:param preevent_threshold_reduction: Factor to reduce threshold of noise level in second step.
:type store_orig: bool
:param store_orig: Return a copy of the original waveforms.
:type store_noise: bool
:param store_noise: Return the noise waveforms removed.
:returns: Dictionary containing the denoised waveforms and, if
requested, original waveforms and noise waveforms.
"""
# Use of "__name__" is to grab the module's name in this python package namespace
logger = logging.getLogger(__name__)
try:
import pywt
except ImportError:
raise ImportError("dwt_denoise() requires PyWavelets (pywt) Python module.")
# Incorporate use of other wavelets, check that they are valid
try:
wt_fams = pywt.families()
wt_fams_lists = []
for i, fam in enumerate(wt_fams):
wt_fams_lists.append(pywt.wavelist(fam))
if ~(wavelet in wt_fams_lists):
logger.info("")
except LookupError:
logger.info("The wavelet selected by the user is not supported by PyWavelets")
# Incorporate other options for padding, while default is still zero
# Do at some point
# Keep a copy of the input data
dataOut = {}
if store_orig:
dataOut["orig"] = st.copy()
# Prep in case user wants to also keep noise
tracesNoise = []
for tr in st:
channelLabel = "%s.%s.%s" % (tr.stats.network,
tr.stats.station,
tr.stats.channel)
coefsNoise = []
coefs = pywt.wavedec(tr.data, wavelet, mode=MODE)
# Do kurtosis analysis to determine noise
if remove_bg:
coefsNoise = utils.kurtosis(channelLabel, coefs, logger)
# Identify pre-event noise at all wavelet levels and remove
coefs, coefsNoise = utils.remove_pre_event_noise(tr,coefs, preevent_window, preevent_threshold_reduction)
for ilevel in range(1+zero_coarse_levels):
coefsNoise[ilevel] += coefs[ilevel].copy()
coefs[ilevel] *= 0.0
for ilevel in range(zero_fine_levels):
index = -(1+ilevel)
coefsNoise[index] += coefs[index].copy()
coefs[index] *= 0.0
# Reconstruct a reduced noise signal from processed wavelet coefficients
tr.data = pywt.waverec(coefs, wavelet, mode=MODE)
if store_noise:
trNoise = tr.copy()
trNoise.data = pywt.waverec(coefsNoise, wavelet, mode=MODE)
tracesNoise.append(trNoise)
#Signal to noise ratio
if threshold == 'soft':
tr = utils.soft_threshold(tr, channelLabel, coefs, coefsNoise, logger)
elif threshold == 'hard':
tr = utils.soft_threshold(tr, channelLabel, coefs, coefsNoise, logger)
elif threshold == 'block':
logger.info("Block thresholding currenlty under development")
else:
logger.info("Unsupported thresholding option")
dataOut["data"] = st
if store_noise:
import obspy.core
dataOut["noise"] = obspy.core.Stream(traces=tracesNoise)
return dataOut
def show(self):
print("Available wavelets:")
for family in pywt.families():
print("* %s family: " % family + ', '.join(pywt.wavelist(family)))
def modelling_cycle():
#--------------- initialization -------------------#
# initial_data = test_data
initial_data = test_data_one
# fig_init = plt.figure()
# fig_init.canvas.manager.set_window_title('Initial data')
# plt.plot(initial_data, color='g')
wavelet_families = pywt.families()
print 'Wavelet families:', ', '.join(wavelet_families)
wavelet_family = wavelet_families[4]
selected_wavelet = pywt.wavelist(wavelet_family)[0]
wavelet = pywt.Wavelet(selected_wavelet)
print 'Selected wavelet:', selected_wavelet
max_level = pywt.swt_max_level(len(initial_data))
# decomposition_level = max_level / 2
decomposition_level = 3
print 'Max level:', max_level, '\t Decomposition level:', decomposition_level
#--------------- decomposition -------------------#
w_initial_coefficients = pywt.swt(initial_data, wavelet, level=decomposition_level)
w_selected_coefficiets = select_levels_from_swt(w_initial_coefficients)
w_node_coefficients = select_node_levels_from_swt(w_initial_coefficients) #something terribly wrong here, yet the rest works!
#------------------ threshold --------------------#
threshold = measure_threshold(w_initial_coefficients)
w_threshold_coeff = w_initial_coefficients[:]
apply_threshold(w_threshold_coeff)
plot_initial_updated(w_initial_coefficients, w_threshold_coeff)
# plt.figure()
# for coeff in w_selected_coefficiets:
# plt.plot(coeff)
# plt.figure()
# for coeff in w_node_coefficients:
# plt.plot(coeff)
# plt.show()
#--------------- modification -------------------#
r = R()
w_new_coefficients = [0] * len(w_selected_coefficiets)
for index in range(0, len(w_selected_coefficiets)):
r.i_data = w_selected_coefficiets[index]
r('hw <- HoltWinters( ts(i_data, frequency = 12), gamma = TRUE )')
r('pred <- predict(hw, 50, prediction.interval = TRUE)')
w_new_coefficients[index] = append(w_selected_coefficiets[index], r.pred[:,0])
index += 1
w_new_node_coefficients = [0] * len(w_node_coefficients)
for index in range(0, len(w_node_coefficients)):
r.i_data = w_node_coefficients[index]
r('hw <- HoltWinters( ts(i_data, frequency = 12), gamma = TRUE )')
r('pred <- predict(hw, 50, prediction.interval = TRUE)')
w_new_node_coefficients[index] = append(w_node_coefficients[index], r.pred[:,0])
index += 1
#----
# plt.figure()
# for coeff in w_new_coefficients:
# plt.plot(coeff)
# plt.figure()
# for coeff in w_new_node_coefficients:
# plt.plot(coeff)
# plt.show()
#--------------- reconstruction -------------------#
# wInitialwithUpdated_Nodes = update_node_levels_swt(w_initial_coefficients, w_new_node_coefficients)
# plot_initial_updated(w_initial_coefficients, w_new_node_coefficients, True)
# plot_initial_updated(w_initial_coefficients, wInitialwithUpdated_Nodes) (!)
# plt.figure()
# for dyad in wInitialwithUpdated_Nodes:
# plt.plot(dyad[0])
# plt.plot(dyad[1])
#
# plt.figure()
# for dyad in w_initial_coefficients:
# plt.plot(dyad[0])
# plt.plot(dyad[1])
#
# plt.show()
# w_updated_coefficients = update_selected_levels_swt(w_initial_coefficients, w_selected_coefficiets)
# w_updated_coefficients = update_selected_levels_swt(w_initial_coefficients, w_new_coefficients)
#----
# w_updated_coefficients = update_swt(w_initial_coefficients, w_selected_coefficiets, w_node_coefficients)
w_updated_coefficients_nodes = update_swt(w_initial_coefficients, w_new_coefficients, w_new_node_coefficients)
w_updated_coefficients = update_selected_levels_swt(w_initial_coefficients, w_new_coefficients)
plot_initial_updated(w_initial_coefficients, w_updated_coefficients_nodes)
plot_initial_updated(w_initial_coefficients, w_updated_coefficients)
reconstructed_Stationary_nodes = iswt(w_updated_coefficients_nodes, selected_wavelet)
reconstructed_Stationary = iswt(w_updated_coefficients, selected_wavelet)
fig_sta_r = plt.figure()
fig_sta_r.canvas.manager.set_window_title('SWT reconstruction')
plt.plot(reconstructed_Stationary)
fig_sta_r_n = plt.figure()
fig_sta_r_n.canvas.manager.set_window_title('SWT reconstruction (nodes)')
plt.plot(reconstructed_Stationary_nodes)
plt.show()
coeffs2 = pywt.dwt2(original, 'bior1.3')
LL, (LH, HL, HH) = coeffs2
fig = plt.figure(figsize=(12, 3))
for i, a in enumerate([LL, LH, HL, HH]):
ax = fig.add_subplot(1, 4, i + 1)
ax.imshow(a, interpolation="nearest", cmap=plt.cm.gray)
ax.set_title(titles[i], fontsize=10)
ax.set_xticks([])
ax.set_yticks([])
fig.tight_layout()
plt.show()
# In[5]:
pywt.families()
# In[6]:
pywt.families(short=False)
# In[7]:
pywt.wavelist(family='coif', kind='discrete')
# In[8]:
pywt.wavelist(family='haar', kind='continuous')
# In[9]:
import numpy as np
import matplotlib.pyplot as plt
import os
import pywt
import datetime
elliot_waves = []
wavelets = pywt.families()
def showPlot(date, data, file_name):
fig, ax = plt.subplots()
ax.plot(date, data)
fig.savefig(file_name)
plt.close(fig)
# def generate_elliot_waves(folder):
common_folder = 'static/results/'
folder_name = 'elliot/'
def trend(data):
return 0 * np.arange(len(data))
def print_wave(data, file_name):
date = np.arange(len(data))
return showPlot(date, data, file_name)
#PLOT FFT COEFFICIENTS
fig, axarr = plt.subplots(nrows=2, figsize=(12, 8))
axarr[0].title.set_text('FFT of Periodic Signal')
axarr[0].plot(f_values1, fft_values1)
axarr[1].title.set_text('FFT of Chirp Signal')
axarr[1].plot(f_values2, fft_values2)
for ax in axarr.flat:
ax.set(xlabel='Frequency [Hz]', ylabel='Amplitude')
ax.set_xlim([0, 100])
plt.subplots_adjust(top=1, hspace=0.3)
plt.show()
#%% PRINT MOTHER WAVELETS & WAVELET FAMILY INFO
wavelet_families = pywt.families(short=False)
discrete_mother_wavelets = pywt.wavelist(kind='discrete')
continuous_mother_wavelets = pywt.wavelist(kind='continuous')
print("PyWavelets contains the following families: ")
print(wavelet_families)
print()
print("PyWavelets contains the following Continuous families: ")
print(continuous_mother_wavelets)
print()
print("PyWavelets contains the following Discrete families: ")
print(discrete_mother_wavelets)
print()
for family in pywt.families():
print(" * The {} family contains: {}".format(family,
pywt.wavelist(family)))
def wavelist():
for w in pywt.families():
print pywt.wavelist(w)
def wavelist():
for w in pywt.families():
print pywt.wavelist(w)
def createCompressions(hist, cumHist, buckets, plot, log, coord, plotBreakFraction, plotBreakMaxVal):
solutions = queue.PriorityQueue()
classify = classifyHist(cumHist, plotBreakMaxVal)
if classify > plotBreakFraction:
return classify
# Now, let's create all built-in wavelet compressions
# Sort them by quality of compression and space usage.
for family in pywt.families():
for wave in pywt.wavelist(family):
for compression in range(0, 8):
restoredCoeffs, recCumHist, stored, traildel, coeffs = generateCompression(cumHist, wave, compression)
if stored > 40:
continue
# restore approximate histogram from cumulative
recHist = np.diff(recCumHist)
# calculate quality as sum of cumulative differences
# note: lower is better for quality
diff = np.absolute(np.subtract(cumHist, recCumHist))
quality = calculateQuality(diff)
# push the solution to the solutions-queue
thisSol = Comp(hist, recHist, diff, quality, "wave ("+wave+")", "compression ("+str(compression)+"), traildel ("+str(traildel)+")", "size: "+str(stored)+", max 10: {:.2%}".format(classifyHist(cumHist, 10)), coord)
solutions.put((quality, thisSol))
if log:
classify = classifyHist(cumHist, 10)
print "Region "+coord+" Classify (max 10): "+str(classify)+" Quality: "+str(quality)
# Also create a compression just based on reducing the number of buckets (if flag set)
if buckets:
res = 4
bucCumHist = np.zeros(len(cumHist)/res)
bucCumHist[0] = cumHist[0]
for i in range(1, len(bucCumHist)):
bucCumHist[i] = cumHist[i*res]
recBucCumHist = np.zeros(len(cumHist))
for i in range(0, len(recBucCumHist)):
ind = math.floor(i/res)
recBucCumHist[i] = bucCumHist[ind]
# Add linear approximation ..
if ind < len(bucCumHist)-1:
recBucCumHist[i] += (i % res) * ((bucCumHist[ind+1] - bucCumHist[ind])/res)
recHist = np.diff(recBucCumHist)
diff = np.absolute(np.subtract(cumHist, recBucCumHist))
quality = calculateQuality(diff)
# plot the bucket solution
bucketSol = Comp(hist, recHist, diff, quality, "buckets", "equal-spaced, under-approximation", "size: "+str(stored)+", max 10: "+classifyHist(len(bucCumHist), 10), coord)
bucketSol.plot(plt)
# plot the best solution seen
solutions.get()[1].plot(plt)
# return classification
return classify