def main():
n_samples = 1024
# Ruido vermelho
S3 = powernoise(2, n_samples)
# Caos, usado para gerar o sinal S7
rho = 3.85
a0 = 0.001
S4 = logistic(rho, a0, n_samples)
# Soma os sinais e normalizae modo que <A>=0 e std=1
S7 = pre.standardize(S3 + S4)
# Sinal gerado pelo pmodel
S8 = pmodel(noValues=n_samples, p=0.52, slope=-1.66)
data = S8
z = np.linspace(0, 1024, 1024)
data_norm = waipy.normalize(data)
result = waipy.cwt(data_norm,
1,
1,
0.125,
2,
4 / 0.125,
0.72,
6,
mother='h',
name='S8')
waipy.wavelet_plot('S8', z, data_norm, 0.03125, result)
def main():
'''
d = np.genfromtxt(INFILE).T
time, data = d
'''
fs, data = wavfile.read(INFILE)
plt.plot(data)
# plt.show()
data = data[10000:11000]
# data = scipy.signal.resample(data, len(data)//4)
# time = np.arange(0, len(data), 1./fs)
time = np.arange(len(data))
data = waipy.normalize(data)
result = waipy.cwt(data,
1,
1,
0.25,
2,
4 / 0.25,
alpha,
6,
mother='Morlet')
waipy.wavelet_plot()
wavelet(time, data, True)
raw_input('')
from scipy import signal
import numpy as np
import matplotlib.pyplot as plt
import waipy
datasets = (['data1.dat', 'SAR', 1800], ['data2.dat', 'BIK',
1864], ['data3.dat', 'VUS', 1609])
dt = 1
units = 'mm'
for d, name, t0 in datasets:
data = np.loadtxt(d)
N = data.size
time = np.arange(0, N) * dt + t0
label = 'Precipitation data {}'.format(name)
data_norm = waipy.normalize(data)
alpha = abs(np.corrcoef(data_norm[0:-1], data_norm[1:])[0, 1])
result = waipy.cwt(data_norm,
1,
1,
0.25,
2,
7 / 0.25,
alpha,
6,
mother='Morlet',
name=name)
waipy.wavelet_plot(label, time, data_norm, 1.0e-6, result)
import waipy
import numpy as np
z = np.linspace(0,2048,2048)
x = np.sin(50*np.pi*z)
y = np.cos(50*np.pi*z)
data_norm = waipy.normalize(x)
result = waipy.cwt(data_norm, 1, 1, 0.125, 2, 4/0.125, 0.72, 6,
mother='Morlet',name='x')
waipy.wavelet_plot('Sine', z, data_norm, 0.03125, result)
data_norm1 = waipy.normalize(y)
result1 = waipy.cwt(data_norm1, 1, 1, 0.125, 2, 4/0.125, 0.72, 6,
mother='Morlet',name='y')
waipy.wavelet_plot('Cosine', z, data_norm1, 0.03125, result1)
cross_power, coherence, phase_angle = waipy.cross_wavelet(result['wave'],
result1['wave'])
waipy.plot_cross('Crosspower sine and cosine', cross_power, phase_angle,
z, result, result1)
import waipy
import numpy as np
x = np.linspace(0, 100, 100)
y1 = np.random.rand(100) # Generation of the Random Signal 1
y2 = np.random.rand(100) # Generation of the Random Signal 2
data_norm = waipy.normalize(y1)
data_norm1 = waipy.normalize(y2)
result = waipy.cwt(data_norm, 1, 1, 0.25, 2, 4/0.25, 0.72, 6,
mother='Morlet', name='x')
result1 = waipy.cwt(data_norm1, 1, 1, 0.25, 2, 4/0.25, 0.72, 6,
mother='Morlet', name='y')
waipy.wavelet_plot('y1-random-signal', x, data_norm, 0.03125, result)
waipy.wavelet_plot('y2-random-signal', x, data_norm1, 0.03125, result1)
cross_power, coherence, phase_angle = waipy.cross_wavelet(result['wave'],
result1['wave'])
waipy.plot_cross('Crosspower of y1 and y2', cross_power, phase_angle, x,
result, result1)
import waipy
from waipy import wavelet_plot
tick_spacing = 4
data = np.genfromtxt("data/batch_4.txt", delimiter=",")[20000:30000]
time = (data[:, 0])#/1000
x = 9.8*(data[:, 1])/2048
y = 9.8*(data[:, 2])/2048
z = 9.8*((data[:, 3]))/2048 # -- factory offset of this particular accelerometer!
var = z
dt = 5
data_norm = waipy.normalize(var)
alpha = np.corrcoef(data_norm[0:-1], data_norm[1:])[0,1];
result = waipy.cwt(data_norm, dt, 0, 0.0625, 1, 36, alpha, 6, mother='Morlet', name="name")
waipy.wavelet_plot("img/batch_4_z_start_osc", time, data_norm, 0.03125, result);
"""
CONTINUOUS WAVELET TRANSFORM
pad = 1 # pad the time series with zeroes (recommended)
dj = 0.25 # this will do 4 sub-octaves per octave
s0 = 2*dt # this says start at a scale of 6 months
j1 = 7/dj # this says do 7 powers-of-two with dj sub-octaves each
lag1 = 0.72 # lag-1 autocorrelation for red noise background
param = 6
mother = 'Morlet'
"""
ax.set_ylabel('Period (dekads)')
ax.set_xlabel('Power')
Yticks = 2 ** np.arange(np.ceil(np.log2(period.min())),
np.ceil(np.log2(period.max())))
ax.set_yticks(np.log2(Yticks))
ax.set_yticklabels(Yticks)
ax.invert_yaxis()
ylim = ax.get_ylim()
ax.set_ylim(ylim[0], -1)
cbar = plt.colorbar(cf)
plt.show()
#%%
data_norm = waipy.normalize(arsi.NDVI)
result = waipy.cwt(data_norm, 1, 1, 0.125, 2, 4/0.125, 0.72, 6,mother='Morlet',name='X')
waipy.wavelet_plot('Wavelet Signal', arsi.index.values, data_norm, 0.03125, result)
#%%
#Augmented Dickey-Fuller test
#conclusion: all data are stationary
result = adfuller(arsi.NDVI)
print('ADF Statistic: %f' % result[0])
print('p-value: %f' % result[1])
print('Critical Values:')
for key, value in result[4].items():
print('\t%s: %.3f' % (key, value))
import waipy
import numpy as np
x = np.linspace(0, 100, 100)
y1 = np.random.rand(100) # Generation of the Random Signal 1
y2 = np.random.rand(100) # Generation of the Random Signal 2
data_norm = waipy.normalize(y1)
data_norm1 = waipy.normalize(y2)
result = waipy.cwt(data_norm,
1,
1,
0.25,
2,
4 / 0.25,
0.72,
6,
mother='Morlet',
name='x')
result1 = waipy.cwt(data_norm1,
1,
1,
0.25,
2,
4 / 0.25,
0.72,
6,
mother='Morlet',
name='y')
