reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/tsa/descriptivestats.py

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