80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
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"""Descriptive Statistics for Time Series
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Created on Sat Oct 30 14:24:08 2010
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Author: josef-pktd
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License: BSD(3clause)
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"""
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import numpy as np
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from . import stattools as stt
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#todo: check subclassing for descriptive stats classes
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class TsaDescriptive:
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'''collection of descriptive statistical methods for time series
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'''
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def __init__(self, data, label=None, name=''):
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self.data = data
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self.label = label
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self.name = name
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def filter(self, num, den):
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from scipy.signal import lfilter
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xfiltered = lfilter(num, den, self.data)
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return self.__class__(xfiltered, self.label, self.name + '_filtered')
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def detrend(self, order=1):
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from . import tsatools
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xdetrended = tsatools.detrend(self.data, order=order)
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return self.__class__(xdetrended, self.label, self.name + '_detrended')
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def fit(self, order=(1,0,1), **kwds):
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from .arima_model import ARMA
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self.mod = ARMA(self.data)
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self.res = self.mod.fit(order=order, **kwds)
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#self.estimated_process =
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return self.res
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def acf(self, nlags=40):
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return stt.acf(self.data, nlags=nlags)
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def pacf(self, nlags=40):
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return stt.pacf(self.data, nlags=nlags)
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def periodogram(self):
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#does not return frequesncies
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return stt.periodogram(self.data)
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# copied from fftarma.py
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def plot4(self, fig=None, nobs=100, nacf=20, nfreq=100):
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data = self.data
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acf = self.acf(nacf)
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pacf = self.pacf(nacf)
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w = np.linspace(0, np.pi, nfreq, endpoint=False)
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spdr = self.periodogram()[:nfreq] #(w)
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if fig is None:
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax = fig.add_subplot(2,2,1)
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namestr = ' for %s' % self.name if self.name else ''
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ax.plot(data)
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ax.set_title('Time series' + namestr)
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ax = fig.add_subplot(2,2,2)
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ax.plot(acf)
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ax.set_title('Autocorrelation' + namestr)
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ax = fig.add_subplot(2,2,3)
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ax.plot(spdr) # (wr, spdr)
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ax.set_title('Power Spectrum' + namestr)
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ax = fig.add_subplot(2,2,4)
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ax.plot(pacf)
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ax.set_title('Partial Autocorrelation' + namestr)
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return fig
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