[change] docstrings for functions in charfuns.py
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Darius Arnold
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@ -22,33 +22,27 @@ from obspy.core import Stream
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class CharacteristicFunction(object):
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'''
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"""
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SuperClass for different types of characteristic functions.
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'''
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"""
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def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None):
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'''
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"""
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Initialize data type object with information from the original
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Seismogram.
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:param: data
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:type: `~obspy.core.stream.Stream`
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:param: cut
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:type: tuple
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:param: t2
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:type: float
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:param: order
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:type: int
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:param: t1
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:type: float (optional, only for AR)
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:param: fnoise
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:type: float (optional, only for AR)
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'''
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:param data: stream object containing traces for which the cf should
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be calculated
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:type data: ~obspy.core.stream.Stream
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:param cut: (starttime, endtime) in seconds relative to beginning of trace
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:type cut: tuple
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:param t2:
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:type t2: float
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:param order:
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:type order: int
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:param t1: float (optional, only for AR)
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:param fnoise: (optional, only for AR)
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:type fnoise: float
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"""
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assert isinstance(data, Stream), "%s is not a stream object" % str(data)
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@ -120,6 +114,10 @@ class CharacteristicFunction(object):
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return self.dt
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def getTimeArray(self):
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"""
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:return: array if time indices
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:rtype: np.array
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"""
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incr = self.getIncrement()
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self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0]
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return self.TimeArray
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@ -137,13 +135,15 @@ class CharacteristicFunction(object):
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return self.xcf
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def getDataArray(self, cut=None):
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'''
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"""
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If cut times are given, time series is cut from cut[0] (start time)
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till cut[1] (stop time) in order to calculate CF for certain part
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only where you expect the signal!
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input: cut (tuple) ()
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cutting window
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'''
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:param cut: contains (start time, stop time) for cutting the time series
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:type cut: tuple
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:return: cut data/time series
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:rtype:
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"""
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if cut is not None:
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if len(self.orig_data) == 1:
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if self.cut[0] == 0 and self.cut[1] == 0:
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@ -209,17 +209,15 @@ class CharacteristicFunction(object):
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class AICcf(CharacteristicFunction):
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'''
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Function to calculate the Akaike Information Criterion (AIC) after
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Maeda (1985).
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:param: data, time series (whether seismogram or CF)
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:type: tuple
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Output: AIC function
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'''
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def calcCF(self, data):
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"""
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Function to calculate the Akaike Information Criterion (AIC) after Maeda (1985).
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:param data: data, time series (whether seismogram or CF)
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:type data: tuple
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:return: AIC function
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:rtype:
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"""
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x = self.getDataArray()
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xnp = x[0].data
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ind = np.where(~np.isnan(xnp))[0]
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@ -244,14 +242,17 @@ class AICcf(CharacteristicFunction):
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class HOScf(CharacteristicFunction):
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'''
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def calcCF(self, data):
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"""
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Function to calculate skewness (statistics of order 3) or kurtosis
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(statistics of order 4), using one long moving window, as published
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in Kueperkoch et al. (2010).
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'''
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def calcCF(self, data):
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:param data: data, time series (whether seismogram or CF)
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:type data: tuple
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:return: HOS cf
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:rtype:
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"""
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x = self.getDataArray(self.getCut())
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xnp = x[0].data
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nn = np.isnan(xnp)
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@ -297,8 +298,16 @@ class HOScf(CharacteristicFunction):
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class ARZcf(CharacteristicFunction):
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def calcCF(self, data):
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def calcCF(self, data):
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"""
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function used to calculate the AR prediction error from a single vertical trace. Can be used to pick
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P onsets.
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:param data:
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:type data: ~obspy.core.stream.Stream
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:return: ARZ cf
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:rtype:
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"""
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print('Calculating AR-prediction error from single trace ...')
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x = self.getDataArray(self.getCut())
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xnp = x[0].data
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@ -342,7 +351,7 @@ class ARZcf(CharacteristicFunction):
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self.xcf = x
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def arDetZ(self, data, order, rind, ldet):
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'''
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"""
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Function to calculate AR parameters arpara after Thomas Meier (CAU), published
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in Kueperkoch et al. (2012). This function solves SLE using the Moore-
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Penrose inverse, i.e. the least-squares approach.
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@ -359,7 +368,7 @@ class ARZcf(CharacteristicFunction):
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:type: int
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Output: AR parameters arpara
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'''
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"""
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# recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
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rhs = np.zeros(self.getOrder())
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@ -383,7 +392,7 @@ class ARZcf(CharacteristicFunction):
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self.arpara = np.dot(np.linalg.pinv(A), rhs)
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def arPredZ(self, data, arpara, rind, lpred):
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'''
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"""
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Function to predict waveform, assuming an autoregressive process of order
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p (=size(arpara)), with AR parameters arpara calculated in arDet. After
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Thomas Meier (CAU), published in Kueperkoch et al. (2012).
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@ -400,8 +409,8 @@ class ARZcf(CharacteristicFunction):
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:type: int
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Output: predicted waveform z
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'''
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# be sure of the summation indeces
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"""
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# be sure of the summation indices
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if rind < len(arpara):
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rind = len(arpara)
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if rind > len(data) - lpred:
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@ -421,7 +430,18 @@ class ARZcf(CharacteristicFunction):
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class ARHcf(CharacteristicFunction):
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def calcCF(self, data):
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"""
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Function to calculate a characteristic function using autoregressive modelling of the waveform of
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both horizontal traces.
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The waveform is predicted in a moving time window using the calculated AR parameters. The difference
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between the predicted and the actual waveform servers as a characteristic function.
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:param data: wavefor stream
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:type data: ~obspy.core.stream.Stream
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:return: ARH cf
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:rtype:
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"""
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print('Calculating AR-prediction error from both horizontal traces ...')
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@ -475,7 +495,7 @@ class ARHcf(CharacteristicFunction):
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self.xcf = xnp
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def arDetH(self, data, order, rind, ldet):
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'''
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"""
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Function to calculate AR parameters arpara after Thomas Meier (CAU), published
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in Kueperkoch et al. (2012). This function solves SLE using the Moore-
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Penrose inverse, i.e. the least-squares approach. "data" is a structured array.
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@ -494,7 +514,7 @@ class ARHcf(CharacteristicFunction):
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:type: int
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Output: AR parameters arpara
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'''
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"""
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# recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
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rhs = np.zeros(self.getOrder())
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@ -517,7 +537,7 @@ class ARHcf(CharacteristicFunction):
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self.arpara = np.dot(np.linalg.pinv(A), rhs)
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def arPredH(self, data, arpara, rind, lpred):
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'''
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"""
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Function to predict waveform, assuming an autoregressive process of order
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p (=size(arpara)), with AR parameters arpara calculated in arDet. After
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Thomas Meier (CAU), published in Kueperkoch et al. (2012).
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@ -535,7 +555,7 @@ class ARHcf(CharacteristicFunction):
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Output: predicted waveform z
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:type: structured array
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'''
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"""
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# be sure of the summation indeces
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if rind < len(arpara) + 1:
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rind = len(arpara) + 1
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@ -559,8 +579,18 @@ class ARHcf(CharacteristicFunction):
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class AR3Ccf(CharacteristicFunction):
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def calcCF(self, data):
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def calcCF(self, data):
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"""
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Function to calculate a characteristic function using autoregressive modelling of the waveform of
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all three traces.
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The waveform is predicted in a moving time window using the calculated AR parameters. The difference
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between the predicted and the actual waveform servers as a characteristic function
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:param data: stream holding all three traces
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:type data: ~obspy.core.stream.Stream
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:return: AR3C cf
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:rtype:
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"""
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print('Calculating AR-prediction error from all 3 components ...')
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xnp = self.getDataArray(self.getCut())
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@ -618,7 +648,7 @@ class AR3Ccf(CharacteristicFunction):
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self.xcf = xnp
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def arDet3C(self, data, order, rind, ldet):
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'''
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"""
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Function to calculate AR parameters arpara after Thomas Meier (CAU), published
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in Kueperkoch et al. (2012). This function solves SLE using the Moore-
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Penrose inverse, i.e. the least-squares approach. "data" is a structured array.
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@ -637,7 +667,7 @@ class AR3Ccf(CharacteristicFunction):
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:type: int
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Output: AR parameters arpara
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'''
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"""
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# recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
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rhs = np.zeros(self.getOrder())
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@ -662,7 +692,7 @@ class AR3Ccf(CharacteristicFunction):
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self.arpara = np.dot(np.linalg.pinv(A), rhs)
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def arPred3C(self, data, arpara, rind, lpred):
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'''
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"""
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Function to predict waveform, assuming an autoregressive process of order
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p (=size(arpara)), with AR parameters arpara calculated in arDet3C. After
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Thomas Meier (CAU), published in Kueperkoch et al. (2012).
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@ -680,7 +710,7 @@ class AR3Ccf(CharacteristicFunction):
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Output: predicted waveform z
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:type: structured array
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'''
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"""
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# be sure of the summation indeces
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if rind < len(arpara) + 1:
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rind = len(arpara) + 1
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