def compute_measures(self, var, window=None):
"""
Computing some measures with the wind series
Window is a dictionary with a keyword for the windoe size and a window length
:return:
"""
if self.raw_data is None:
raise NameError("Raw data is not loaded")
if var > self.raw_data.shape[1]:
raise NameError("Invalid variable number")
dvals = {}
dvals['SpecEnt'] = spectral_entropy(self.raw_data[:, var], sf=1)
dvals['SampEnt'] = sample_entropy(self.raw_data[:, var], order=2)
data = self.raw_data[:, var]
for w in window:
lw = window[w]
length = int(data.shape[0] / lw)
size = lw * length
datac = data[:size]
datac = datac.reshape(-1, lw)
means = np.mean(datac, axis=1)
vars = np.std(datac, axis=1)
dvals[f'Stab{w}'] = np.std(means)
dvals[f'Lump{w}'] = np.std(vars)
return dvals
def compute_measures(self, var, window=None):
"""
Computing some measures with the wind series
Window is a dictionary with a keyword for the windoe size and a window length
:return:
"""
if self.raw_data is None:
raise NameError("Raw data is not loaded")
if var > self.raw_data.shape[1]:
raise NameError("Invalid variable number")
dvals = {}
dvals['SpecEnt'] = spectral_entropy(self.raw_data[:, var], sf=1)
dvals['SampEnt'] = sample_entropy(self.raw_data[:, var], order=2)
data = self.raw_data[:, var]
for w in window:
print(w)
lw = window[w]
print(lw)
length = int(data.shape[0] / lw)
size = lw * length
datac = data[:size]
datac = datac.reshape(-1, lw)
means = np.mean(datac, axis=1)
vars = np.std(datac, axis=1)
dvals[f'Stab{w}'] = np.std(means)
dvals[f'Lump{w}'] = np.std(vars)
# decompositions
stl = STL(self.raw_data[:, var], period=lw)
res = stl.fit()
strength_seasonality = 1 - (np.var(
res.resid)) / (np.var(res.observed - res.trend))
strength_trend = 1 - (np.var(
res.resid)) / (np.var(res.observed - res.seasonal))
dvals[f'strength_seasonality{w}'] = strength_seasonality
dvals[f'strength_trend{w}'] = strength_trend
fig = res.plot()
return dvals