[change] docstrings in pick/utils.py
This commit is contained in:
Darius Arnold
parent
fab1f53a41
commit
a5b3c5da1b
@ -16,28 +16,34 @@ from obspy.core import Stream, UTCDateTime
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecolor='k'):
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'''
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"""
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Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
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as reasonable uncertainties. Latest possible pick is based on noise level,
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earliest possible pick is half a signal wavelength in front of most likely
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pick given by PragPicker or manually set by analyst. Most likely pick
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(initial pick Pick1) must be given.
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:param: X, time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
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:type: int
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:param: TSNR, length of time windows around pick used to determine SNR [s]
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:type: tuple (T_noise, T_gap, T_signal)
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:param: Pick1, initial (most likely) onset time, starting point for earllatepicker
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:type: float
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:param: iplot, if given, results are plotted in figure(iplot)
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:type: int
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'''
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:param X: time series (seismogram)
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:type X: `~obspy.core.stream.Stream`
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:param nfac: (noise factor), nfac times noise level to calculate latest possible pick
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:type nfac: int
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:param TSNR: length of time windows around pick used to determine SNR [s]
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:type TSNR: tuple (T_noise, T_gap, T_signal)
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:param Pick1: initial (most likely) onset time, starting point for earllatepicker
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:type Pick1: float
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:param iplot: if given, results are plotted in figure(iplot)
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:type iplot: int
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:param verbosity: amount of displayed information about the process:
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2 = all
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1 = default
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0 = none
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:type verbosity: int
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:param fig: Matplotlib figure ised for plotting
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:type fig: `~matplotlib.figure.Figure`
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:param linecolor: color for plotting results
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:type linecolor: str
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:return: tuple containing earliest possible pick, latest possible pick and pick error
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:rtype: (float, float, float)
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"""
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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@ -162,26 +168,27 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecol
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def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
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'''
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"""
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Function to derive first motion (polarity) of given phase onset Pick.
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Calculation is based on zero crossings determined within time window pickwin
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after given onset time.
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:param: Xraw, unfiltered time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: Xfilt, filtered time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: pickwin, time window after onset Pick within zero crossings are calculated
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:type: float
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:param: Pick, initial (most likely) onset time, starting point for fmpicker
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:type: float
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:param: iplot, if given, results are plotted in figure(iplot)
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:type: int
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'''
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:param Xraw: unfiltered time series (seismogram)
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:type Xraw: `~obspy.core.stream.Stream`
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:param Xfilt: filtered time series (seismogram)
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:type Xfilt: `~obspy.core.stream.Stream`
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:param pickwin: time window after onset Pick within zero crossings are calculated
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:type pickwin: float
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:param Pick: initial (most likely) onset time, starting point for fmpicker
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:type Pick: float
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:param iplot: if given, results are plotted in figure(iplot)
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:type iplot: int
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:param fig: Matplotlib figure ised for plotting
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:type fig: `~matplotlib.figure.Figure`
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:param linecolor: color for plotting results
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:type linecolor: str
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:return: None if first motion detection was skipped, otherwise string indicating the polarity
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:rtype: None, str
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"""
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plt_flag = 0
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try:
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@ -360,8 +367,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
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def crossings_nonzero_all(data):
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"""
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Returns the indices of zero crossings the data array
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:param data: data array (seismic trace)
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:type data: `~numpy.ndarray`
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:return: array containing indices of zero crossings in the data array.
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:rtype: `~numpy.ndarray`
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"""
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pos = data > 0
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npos = ~pos
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npos = ~pos # get positions of negative values
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return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
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@ -372,26 +386,28 @@ def symmetrize_error(dte, dtl):
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:param dte: relative lower uncertainty
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:param dtl: relative upper uncertainty
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:return: symmetrized error
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:rtype: float
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"""
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return (dte + 2 * dtl) / 3
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def getSNR(X, TSNR, t1, tracenum=0):
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'''
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"""
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Function to calculate SNR of certain part of seismogram relative to
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given time (onset) out of given noise and signal windows. A safety gap
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between noise and signal part can be set. Returns SNR and SNR [dB] and
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noiselevel.
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:param: X, time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: TSNR, length of time windows [s] around t1 (onset) used to determine SNR
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:type: tuple (T_noise, T_gap, T_signal)
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:param: t1, initial time (onset) from which noise and signal windows are calculated
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:type: float
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'''
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:param X: time series (seismogram)
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:type X: `~obspy.core.stream.Stream`
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:param TSNR: length of time windows [s] around t1 (onset) used to determine SNR
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:type TSNR: (T_noise, T_gap, T_signal)
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:param t1: initial time (onset) from which noise and signal windows are calculated
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:type t1: float
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:param tracenum: used to select the trace in stream X
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:type tracenum: int
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:return: tuple containing SNR, SNRdB and noise level
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:rtype: (float, float, float)
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"""
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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@ -435,23 +451,21 @@ def getSNR(X, TSNR, t1, tracenum=0):
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def getnoisewin(t, t1, tnoise, tgap):
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'''
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Function to extract indeces of data out of time series for noise calculation.
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Returns an array of indeces.
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:param: t, array of time stamps
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:type: numpy array
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:param: t1, time from which relativ to it noise window is extracted
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:type: float
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:param: tnoise, length of time window [s] for noise part extraction
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:type: float
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:param: tgap, safety gap between t1 (onset) and noise window to
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ensure, that noise window contains no signal
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:type: float
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'''
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"""
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Function to extract indices of data out of time series for noise calculation.
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Returns an array of indices.
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:param t: array of time stamps
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:type t: `numpy.ndarray`
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:param t1: time from which relative to it noise window is extracted
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:type t1: float
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:param tnoise: length of time window [s] for noise part extraction
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:type tnoise: float
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:param tgap: safety gap between t1 (onset) and noise window to ensure, that
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the noise window contains no signal
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:type tgap: float
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:return: indices of noise window in t
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:rtype: `~numpy.ndarray`
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"""
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# get noise window
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inoise, = np.where((t <= max([t1 - tgap, 0])) \
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@ -465,19 +479,18 @@ def getnoisewin(t, t1, tnoise, tgap):
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def getsignalwin(t, t1, tsignal):
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'''
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"""
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Function to extract data out of time series for signal level calculation.
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Returns an array of indeces.
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:param: t, array of time stamps
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:type: numpy array
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:param: t1, time from which relativ to it signal window is extracted
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:type: float
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:param: tsignal, length of time window [s] for signal level calculation
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:type: float
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'''
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Returns an array of indices.
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:param t: array of time stamps
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:type t: `numpy.ndarray`
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:param t1: time from which relative to it signal window is extracted
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:type t1: float
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:param tsignal: length of time window [s] for signal level calculation
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:type tsignal: float
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:return: indices of signal window i t
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:rtype: `numpy.ndarray`
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"""
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# get signal window
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isignal, = np.where((t <= min([t1 + tsignal, t[-1]])) \
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@ -490,27 +503,18 @@ def getsignalwin(t, t1, tsignal):
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def getResolutionWindow(snr, extent):
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"""
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Number -> Float
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produce the half of the time resolution window width from given SNR
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value
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Produce the half of the time resolution window width from given SNR value
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SNR >= 3 -> 2 sec HRW
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3 > SNR >= 2 -> 5 sec MRW
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2 > SNR >= 1.5 -> 10 sec LRW
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1.5 > SNR -> 15 sec VLRW
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see also Diehl et al. 2009
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:parameter: extent, can be 'local', 'regional', 'global'
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>>> getResolutionWindow(0.5)
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7.5
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>>> getResolutionWindow(1.8)
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5.0
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>>> getResolutionWindow(2.3)
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2.5
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>>> getResolutionWindow(4)
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1.0
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>>> getResolutionWindow(2)
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2.5
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:param snr: Signal to noise ration which decides the witdth of the resolution window
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:type snr: float
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:param extent: can be 'local', 'regional', 'global'
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:type extent: str
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:return: half width of the resolution window
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:rtype: float
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"""
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res_wins = {
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@ -535,17 +539,17 @@ def getResolutionWindow(snr, extent):
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def select_for_phase(st, phase):
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'''
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takes a STream object and a phase name and returns that particular component
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"""
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Takes a Stream object and a phase name and returns that particular component
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which presumably shows the chosen PHASE best
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:param st: stream object containing one or more component[s]
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:type st: `~obspy.core.stream.Stream`
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:param phase: label of the phase for which the stream selection is carried
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out; 'P' or 'S'
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:param phase: label of the phase for which the stream selection is carried out; 'P' or 'S'
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:type phase: str
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:return:
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'''
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:return: stream object containing the selected phase
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:rtype: `~obspy.core.stream.Stream`
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"""
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from pylot.core.util.defaults import SetChannelComponents
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sel_st = Stream()
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@ -569,21 +573,22 @@ def select_for_phase(st, phase):
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def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
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'''
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"""
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Function to calculate Wadati-diagram from given P and S onsets in order
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to detect S pick outliers. If a certain S-P time deviates by dttolerance
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from regression of S-P time the S pick is marked and down graded.
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: param: pickdic, dictionary containing picks and quality parameters
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: type: dictionary
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: param: dttolerance, maximum adjusted deviation of S-P time from
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S-P time regression
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: type: float
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: param: iplot, if iplot > 1, Wadati diagram is shown
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: type: int
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'''
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:param pickdic: dictionary containing picks and quality parameters
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:type pickdic: dict
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:param dttolerance: dttolerance, maximum adjusted deviation of S-P time from
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S-P time regression
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:type dttolerance: float
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:param iplot: iplot, if iplot > 1, Wadati diagram is shown
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:type iplot: int
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:param fig_dict: Matplotlib figure used for plotting
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:type fig_dict: `~matplotlib.figure.Figure`
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:return: dictionary containing all onsets that passed the wadati check
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:rtype: dict
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"""
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checkedonsets = pickdic
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@ -714,48 +719,51 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
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def RMS(X):
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'''
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Function returns root mean square of a given array X
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'''
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"""
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Returns root mean square of a given array X
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:param X: Array
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:type X: `~numpy.ndarray`
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:return: root mean square value of given array
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:rtype: float
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"""
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return np.sqrt(np.sum(np.power(X, 2)) / len(X))
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def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None, linecolor='k'):
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'''
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"""
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Function to detect spuriously picked noise peaks.
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Uses RMS trace of all 3 components (if available) to determine,
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how many samples [per cent] after P onset are below certain
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threshold, calculated from noise level times noise factor.
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: param: X, time series (seismogram)
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: type: `~obspy.core.stream.Stream`
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: param: pick, initial (AIC) P onset time
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: type: float
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: param: TSNR, length of time windows around initial pick [s]
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: type: tuple (T_noise, T_gap, T_signal)
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: param: minsiglength, minium required signal length [s] to
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declare pick as P onset
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: type: float
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: param: nfac, noise factor (nfac * noise level = threshold)
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: type: float
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: param: minpercent, minimum required percentage of samples
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above calculated threshold
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: type: float
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: param: iplot, if iplot > 1, results are shown in figure
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: type: int
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'''
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:param X: time series (seismogram)
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:type X: `~obspy.core.stream.Stream`
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:param pick: initial (AIC) P onset time
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:type pick: float
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:param TSNR: length of time windows around initial pick [s]
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:type TSNR: (T_noise, T_gap, T_signal)
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:param minsiglength: minium required signal length [s] to declare pick as P onset
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:type minsiglength: float
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:param nfac: noise factor (nfac * noise level = threshold)
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:type nfac: float
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:param minpercent: minimum required percentage of samples above calculated threshold
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:type minpercent: float
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:param iplot: iplot, if iplot > 1, results are shown in figure
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:type iplot: int
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:param fig: Matplotlib figure to plot results in
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:type fig: `~matplotlib.figure.Figure`
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:param linecolor: color of seismic traces
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:type linecolor: str
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:return: flag, value of 1 if signal reached required length, 0 if signal is shorter than
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required length
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:rtype: int
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"""
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plt_flag = 0
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try:
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iplot = int(iplot)
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except:
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if iplot == True or iplot == 'True':
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if real_Bool(iplot):
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iplot = 2
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else:
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iplot = 0
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@ -828,21 +836,26 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
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def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
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'''
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"""
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Function to check statistics of P-onset times: Control deviation from
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median (maximum adjusted deviation = dttolerance) and apply pseudo-
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bootstrapping jackknife.
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: param: pickdic, dictionary containing picks and quality parameters
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: type: dictionary
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: param: dttolerance, maximum adjusted deviation of P-onset time from
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median of all P onsets
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: type: float
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: param: iplot, if iplot > 1, Wadati diagram is shown
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: type: int
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'''
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:param pickdic: dictionary containing picks and quality parameters
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:type pickdic: dict
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:param dttolerance: maximum adjusted deviation of P onset time from the
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median of all P onsets
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:type dttolerance: float
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:param jackfactor: if pseudo value is larger than jackfactor * Jackknife estimator,
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the distorts the estimator too much and will be removed
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:type jackfactor: int
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:param iplot: if iplot > 1, Wadati diagram is shown
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:type iplot: int
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:param fig_dict: Matplotlib figure used for plotting
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:type fig_dict: `~matplotlib.figure.Figure`
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:return: dictionary containing all onsets that passed the jackknife and the
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median test
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:rtype: dict
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"""
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checkedonsets = pickdic
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@ -943,24 +956,27 @@ def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
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return checkedonsets
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def jackknife(X, phi, h):
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'''
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def jackknife(X, phi, h=1):
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"""
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Function to calculate the Jackknife Estimator for a given quantity,
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special type of boot strapping. Returns the jackknife estimator PHI_jack
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the pseudo values PHI_pseudo and the subgroup parameters PHI_sub.
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special type of boot strapping.
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: param: X, given quantity
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: type: list
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: param: phi, chosen estimator, choose between:
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Returns the jackknife estimator PHI_jack the pseudo values PHI_pseudo
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and the subgroup parameters PHI_sub.
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:param X: list containing UTCDateTime objcects representing P onsets
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:type X: list (`~obspy.core.utcdatetime.UTCDateTime`)
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:param phi:phi, chosen estimator, choose between:
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"MED" for median
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"MEA" for arithmetic mean
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"VAR" for variance
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: type: string
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: param: h, size of subgroups, optinal, default = 1
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: type: integer
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'''
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||||
:type phi: str
|
||||
:param h: size of subgroups, optional (default = 1)
|
||||
:type h: int
|
||||
:return: Tuple containing the Jackknife estimator PHI_jack, a list of jackknife pseudo
|
||||
values (PHI_pseudo) and the Jackknife estimators (PHI_sub) of the subgroups.
|
||||
Will return (None, None, None) if X cannot be divided in h subgroups of equals size
|
||||
:rtype: (float, list, list)
|
||||
"""
|
||||
|
||||
PHI_jack = None
|
||||
PHI_pseudo = None
|
||||
@ -1008,40 +1024,42 @@ def jackknife(X, phi, h):
|
||||
|
||||
|
||||
def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
|
||||
'''
|
||||
"""
|
||||
Function to compare energy content of vertical trace with
|
||||
energy content of horizontal traces to detect spuriously
|
||||
picked S onsets instead of P onsets. Usually, P coda shows
|
||||
larger longitudal energy on vertical trace than on horizontal
|
||||
traces, where the transversal energy is larger within S coda.
|
||||
Be careful: there are special circumstances, where this is not
|
||||
the case!
|
||||
picked S onsets instead of P onsets.
|
||||
|
||||
: param: X, fitered(!) time series, three traces
|
||||
: type: `~obspy.core.stream.Stream`
|
||||
Usually, P coda shows larger longitudal energy on vertical trace
|
||||
than on horizontal traces, where the transversal energy is larger
|
||||
within S coda. Be careful: there are special circumstances, where
|
||||
this is not the case!
|
||||
To pass the test, vertical P-coda level must exceed horizontal P-coda level
|
||||
zfac times EN-coda level
|
||||
|
||||
: param: pick, initial (AIC) P onset time
|
||||
: type: float
|
||||
|
||||
: param: zfac, factor for threshold determination,
|
||||
vertical energy must exceed coda level times zfac
|
||||
to declare a pick as P onset
|
||||
: type: float
|
||||
|
||||
: param: checkwin, window length [s] for calculating P-coda
|
||||
energy content
|
||||
: type: float
|
||||
|
||||
: param: iplot, if iplot > 1, energy content and threshold
|
||||
are shown
|
||||
: type: int
|
||||
'''
|
||||
:param X: fitered(!) time series, three traces
|
||||
:type X: `~obspy.core.stream.Stream`
|
||||
:param pick: initial (AIC) P onset time
|
||||
:type pick: float
|
||||
:param zfac: factor for threshold determination, vertical energy must
|
||||
exceed coda level times zfac to declare a pick as P onset
|
||||
:type zfac: float
|
||||
:param checkwin: window length [s] for calculating P-coda engergy content
|
||||
:type checkwin: float
|
||||
:param iplot: if iplot > 1, energy content and threshold are shown
|
||||
:type iplot: int
|
||||
:param fig: Matplotlib figure to plot results in
|
||||
:type fig: `~matplotlib.figure.Figure`
|
||||
:param linecolor: color of seismic traces
|
||||
:type linecolor: str
|
||||
:return: returnflag; 0 if onset failed test, 1 if onset passed test
|
||||
:rtype: int
|
||||
"""
|
||||
|
||||
plt_flag = 0
|
||||
try:
|
||||
iplot = int(iplot)
|
||||
except:
|
||||
if iplot == True or iplot == 'True':
|
||||
if real_Bool(iplot):
|
||||
iplot = 2
|
||||
else:
|
||||
iplot = 0
|
||||
@ -1154,9 +1172,15 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
|
||||
|
||||
|
||||
def getQualityFromUncertainty(uncertainty, Errors):
|
||||
'''Script to transform uncertainty into quality classes 0-4
|
||||
regarding adjusted time errors Errors.
|
||||
'''
|
||||
"""
|
||||
Script to transform uncertainty into quality classes 0-4 regarding adjusted time errors
|
||||
:param uncertainty: symmetric picking error of picks
|
||||
:type uncertainty: float
|
||||
:param Errors: Width of uncertainty classes 0-4 in seconds
|
||||
:type Errors: list
|
||||
:return: quality of pick (0-4)
|
||||
:rtype: int
|
||||
"""
|
||||
|
||||
# set initial quality to 4 (worst) and change only if one condition is hit
|
||||
quality = 4
|
||||
|
||||
Loading…
Reference in New Issue
Block a user