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('')
def plot_wavelet(self):
d_obs = self.d_obs
d_mod = self.d_mod
d_t_obs = self.d_t_obs
""" plot data wavelet """
scores = []
for j, site in enumerate(self.sitename):
print('Process on Wavelet_' + ''.join(site) + '_No.' + str(j) +
'!')
fig3 = plt.figure(figsize=(8, 8))
# ax3 = fig6.add_subplot(1, 2, 1)
# fig3, ax3 = plt.subplots()
data = d_obs[j, :].compressed()
time_data = d_t_obs[~d_obs[j, :].mask]
# time_data = d_t_obs
result = waipy.cwt(data,
1,
1,
0.125,
2,
4 / 0.125,
0.72,
6,
mother='Morlet',
name='Data')
waipy.wavelet_plot('Data', time_data, data, 0.03125, result, fig3)
fig3.savefig(self.filedir + self.variable + '/' + ''.join(site) +
'_Wavelet_' + self.variable + '.png')
for m in range(len(d_mod)):
fig4 = plt.figure(figsize=(8, 8))
data = d_mod[m][j, :][~d_obs[j, :].mask] - d_obs[
j, :].compressed()
# time_data = d_mod[m][j, :]
result = waipy.cwt(data,
1,
1,
0.125,
2,
4 / 0.125,
0.72,
6,
mother='Morlet',
name='Data')
waipy.wavelet_plot('Data', time_data, data, 0.03125, result,
fig4)
fig4.savefig(self.filedir + self.variable + '/' +
''.join(site) + 'model' + str(m) + '_wavelet_' +
self.variable + '.png')
plt.close('all')
return scores
def Plot_Wavelet(obs, mod, site_id):
print('Process on Wavelet ' + 'No.' + str(site_id) + '!')
time_data = mod.time
data = (obs.extractDatasites(lat=obs.lat, lon=obs.lat)).data[:, site_id]
y = (mod.extractDatasites(lat=obs.lat, lon=obs.lat)).data[:, site_id]
fig3 = plt.figure(figsize=(8, 8))
result = waipy.cwt(data,
1,
1,
0.125,
2,
4 / 0.125,
0.72,
6,
mother='Morlet',
name='Obs')
ax1, ax2, ax3, ax5 = waipy.wavelet_plot('Obs',
time_data,
data,
0.03125,
result,
fig3,
unit=obs.unit)
change_x_tick(mod, site_id, ax2)
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)
def plot_wavelet(self):
d_obs = self.d_obs
d_mod = self.d_mod
d_t_obs = self.d_t_obs
""" plot data wavelet """
scores = []
for j, site in enumerate(self.sitename):
if self.sitename.mask[j]:
continue
print('Process on Wavelet_' + site + '_No.' + str(j) + '!')
data = d_obs[j, :].compressed()
fig3 = plt.figure(figsize=(8, 8))
if len(data) > 0:
time_data = d_t_obs[~d_obs[j, :].mask]
# time_data = d_t_obs
result = waipy.cwt(data,
1,
1,
0.125,
2,
4 / 0.125,
0.72,
6,
mother='Morlet',
name='Obs')
waipy.wavelet_plot('Obs',
time_data,
data,
0.03125,
result,
fig3,
unit=self.d_unit_obs)
# plt.tight_layout()
for m in range(len(d_mod)):
fig4 = plt.figure(figsize=(8, 8))
data2 = d_mod[m][j, :][~d_obs[j, :].mask]
data = data2.compressed() - d_obs[
j, :].compressed()[~data2.mask]
if len(data) > 0:
result = waipy.cwt(data,
1,
1,
0.125,
2,
4 / 0.125,
0.72,
6,
mother='Morlet',
name='Obs - Mod' + str(m + 1))
waipy.wavelet_plot('Obs - Mod' + str(m + 1),
time_data[~data2.mask],
data,
0.03125,
result,
fig4,
unit=self.d_unit_obs,
m=m)
# plt.tight_layout()
fig4.savefig(self.filedir + self.variable + '/' + site +
'model' + str(m) + '_wavelet_' + self.variable +
'.png',
bbox_inches='tight')
fig3.savefig(self.filedir + self.variable + '/' + site +
'_Wavelet_' + self.variable + '.png',
bbox_inches='tight')
plt.close('all')
return scores
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)
##############################################################################
################################### MAIN #####################################
##############################################################################
namefile = "daily-cases-covid-19.csv"
l = pd.read_csv(namefile)
codes = list(set(l["Entity"]))
codes = codes[1:]
l = l.set_index("Entity")
values = []
countries = ["Brazil", "India", "Iran", "South Africa", "Egypt"]
for i in codes:
y = list(l.filter(like=i, axis=0)["Daily confirmed cases (cases)"])
if i in countries:
result = waipy.cwt(y, 1, 1, 0.125, 2, 4 / 0.125, 0.72, 6, 'DOG', "x")
waipy.wavelet_plot(i, range(len(y)), y, 0.03125, result, savefig=True)
if len(y) > 50:
alfa, xdfa, ydfa, reta = funcs.dfa1d(y, 1)
freqs, power, xdata, ydata, amp, index, powerlaw, INICIO, FIM = funcs.psd(
y)
values.append([
funcs.variance(y),
funcs.skewness(y),
funcs.kurtosis(y), alfa, index,
mfdfa.makemfdfa(y), i
])
skew2 = []
alfa = []
kurt = []
index = []
p = p[np.nonzero(p)]
c = counts[np.nonzero(counts)]
log_p = np.log10(p)
a = (log_p[np.argmax(c)] - log_p[np.argmin(c)]) / (np.max(c) - np.min(c))
b = log_Prob[0]
y = b * np.power(10, (a*counts))
""" Plotagem """
plt.clf()
plt.scatter(np.log10(counts), y, marker=".", color="blue")
plt.title('SOC', fontsize = 16)
plt.xlabel('log(ni)')
plt.ylabel('log(Yi)')
plt.grid()
plt.show()
for i in range(1):
x,p=pmodel("Endogenous")
SOC(p)
result=waipy.cwt(p, 1, 1, 0.125, 2, 4/0.125, 0.72, 6, 'DOG', "x")
waipy.wavelet_plot("Endogenous series", x, p, 0.03125, result, True)
for i in range(1):
x,p=pmodel("Exogenous")
SOC(p)
result=waipy.cwt(p, 1, 1, 0.125, 2, 4/0.125, 0.72, 6, 'DOG', "x")
waipy.wavelet_plot("Exogenous series", x, p, 0.03125, result, True)
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'
"""
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))
result = adfuller(arsi.ET)
print('ADF Statistic: %f' % result[0])
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)