[merge] feature/refactor into develop
This commit is contained in:
commit
6b9231abd3
2
PyLoT.py
2
PyLoT.py
@ -62,7 +62,7 @@ from pylot.core.io.data import Data
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from pylot.core.io.inputs import FilterOptions, PylotParameter
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from autoPyLoT import autoPyLoT
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from pylot.core.pick.compare import Comparison
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from pylot.core.pick.utils import getQualityFromUncertainty
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from pylot.core.pick.utils import symmetrize_error, getQualityFromUncertainty, getPickQuality, get_quality_class
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from pylot.core.io.phases import picksdict_from_picks
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import pylot.core.loc.nll as nll
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from pylot.core.util.errors import DatastructureError, \
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@ -127,9 +127,7 @@ class PylotParameter(object):
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:return:
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:rtype: bool
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"""
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if parameter in self.__parameter.keys():
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return True
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return False
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return parameter in self.__parameter.keys()
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def get(self, *args):
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"""
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@ -944,7 +944,7 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
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:rtype:
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"""
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from pylot.core.pick.utils import getQualityFromUncertainty
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from pylot.core.pick.utils import get_quality_class
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from pylot.core.util.utils import loopIdentifyPhase, identifyPhase
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# read all onset weights
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@ -992,7 +992,7 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
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for Pick in arrivals_copy:
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phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
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if phase == 'P':
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Pqual = getQualityFromUncertainty(Pick.time_errors.uncertainty, ErrorsP)
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Pqual = get_quality_class(Pick.time_errors.uncertainty, ErrorsP)
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if Pqual == 0:
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Pw0.append(Pick.time_errors.uncertainty)
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elif Pqual == 1:
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@ -1004,7 +1004,7 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
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elif Pqual == 4:
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Pw4.append(Pick.time_errors.uncertainty)
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elif phase == 'S':
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Squal = getQualityFromUncertainty(Pick.time_errors.uncertainty, ErrorsS)
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Squal = get_quality_class(Pick.time_errors.uncertainty, ErrorsS)
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if Squal == 0:
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Sw0.append(Pick.time_errors.uncertainty)
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elif Squal == 1:
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File diff suppressed because it is too large
Load Diff
@ -243,6 +243,13 @@ class AICcf(CharacteristicFunction):
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class HOScf(CharacteristicFunction):
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def __init__(self, data, cut, pickparams):
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"""
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Call parent constructor while extracting the right parameters:
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:param pickparams: PylotParameters instance
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"""
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super(HOScf, self).__init__(data, cut, pickparams["tlta"], pickparams["hosorder"])
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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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@ -299,6 +306,9 @@ class HOScf(CharacteristicFunction):
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class ARZcf(CharacteristicFunction):
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def __init__(self, data, cut, t1, t2, pickparams):
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super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"], fnoise=pickparams["addnoise"])
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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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@ -431,6 +441,9 @@ class ARZcf(CharacteristicFunction):
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class ARHcf(CharacteristicFunction):
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def __init__(self, data, cut, t1, t2, pickparams):
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super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
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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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@ -580,6 +593,9 @@ class ARHcf(CharacteristicFunction):
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class AR3Ccf(CharacteristicFunction):
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def __init__(self, data, cut, t1, t2, pickparams):
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super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
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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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@ -10,7 +10,7 @@
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import matplotlib.pyplot as plt
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import numpy as np
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import warnings
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from scipy.signal import argrelmax
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from obspy.core import Stream, UTCDateTime
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from pylot.core.util.utils import real_Bool, real_None, SetChannelComponents
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@ -417,9 +417,9 @@ def getSNR(X, TSNR, t1, tracenum=0):
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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SNR = None
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SNRdB = None
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noiselevel = None
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SNR = -1
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SNRdB = -1
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noiselevel = -1
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x = X[tracenum].data
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npts = X[tracenum].stats.npts
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@ -489,13 +489,13 @@ def getsignalwin(t, t1, tsignal):
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Function to extract data out of time series for signal level 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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: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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:return: indices of signal window in t
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:rtype: `~numpy.ndarray`
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"""
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# get signal window
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@ -507,6 +507,26 @@ def getsignalwin(t, t1, tsignal):
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return isignal
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def getslopewin(Tcf, Pick, tslope):
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"""
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Function to extract slope window out of time series
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>>> (np.arange(15., 85.), 30.0, 10.0)
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array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
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:param Tcf:
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:type Tcf:
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:param Pick:
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:type Pick:
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:param tslope:a
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:type tslope:
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:return:
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:rtype: `numpy.ndarray`
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"""
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# TODO: fill out docstring
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slope = np.where( (Tcf <= min(Pick + tslope, Tcf[-1])) & (Tcf >= Pick) )
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return slope[0]
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def getResolutionWindow(snr, extent):
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"""
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Produce the half of the time resolution window width from given SNR value
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@ -736,7 +756,7 @@ def RMS(X):
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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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def checksignallength(X, pick, minsiglength, pickparams, iplot=0, fig=None, linecolor='k'):
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"""
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Function to detect spuriously picked noise peaks.
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@ -747,14 +767,10 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
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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 pickparams: PylotParameter instance that holds the current picker settings loaded from a .in file
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:type pickparams: PylotParameter
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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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@ -766,6 +782,19 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
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:rtype: int
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"""
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"""
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Extract additional parameters from pickparams
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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 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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"""
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TSNR = pickparams["tsnrz"]
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nfac = pickparams["noisefactor"]
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minpercent = pickparams["minpercent"]
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plt_flag = 0
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try:
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iplot = int(iplot)
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@ -1034,7 +1063,7 @@ def jackknife(X, phi, h=1):
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return PHI_jack, PHI_pseudo, PHI_sub
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def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
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def checkZ4S(X, pick, pickparams, iplot, fig=None, linecolor='k'):
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"""
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Function to compare energy content of vertical trace with
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energy content of horizontal traces to detect spuriously
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@ -1051,11 +1080,8 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
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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 zfac: factor for threshold determination, vertical energy must
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exceed coda level times zfac to declare a pick as P onset
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:type zfac: float
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:param checkwin: window length [s] for calculating P-coda engergy content
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:type checkwin: float
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:param pickparams: PylotParameter instance that holds the current picker settings loaded from a .in file
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:type pickparams: PylotParameter
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:param iplot: if iplot > 1, energy content and threshold are shown
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:type iplot: int
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:param fig: Matplotlib figure to plot results in
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@ -1066,6 +1092,17 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
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:rtype: int
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"""
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"""
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Extract required parameters from pickparams
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:param zfac: factor for threshold determination, vertical energy must
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exceed coda level times zfac to declare a pick as P onset
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:type zfac: float
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:param checkwin: window length [s] for calculating P-coda engergy content
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:type checkwin: float
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"""
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zfac = pickparams["zfac"]
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checkwin = pickparams["tsnrz"][2]
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plt_flag = 0
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try:
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iplot = int(iplot)
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@ -1250,7 +1287,7 @@ def getQualityFromSNR(snrdb):
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return quality_modifier
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def getQualityFromUncertainty(uncertainty, Errors):
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def get_quality_class(uncertainty, weight_classes):
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"""
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Script to transform uncertainty into quality classes 0-4 regarding adjusted time errors
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:param uncertainty: symmetric picking error of picks
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@ -1260,7 +1297,182 @@ def getQualityFromUncertainty(uncertainty, Errors):
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:return: quality of pick (0-4)
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:rtype: int
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"""
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try:
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# create generator expression containing all indices of values in weight classes that are >= than uncertainty.
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# call next on it once to receive first value
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quality = next(i for i, v in enumerate(weight_classes) if v >= uncertainty)
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except StopIteration:
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# raised when uncertainty is larger than all values in weight_classes
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# set quality to max possible value
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quality = len(weight_classes)
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return quality
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def set_NaNs_to(data, nan_value):
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"""
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Replace all NaNs in data with nan_value
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:param data: array holding data
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:type data: `~numpy.ndarray`
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:param nan_value: value which all NaNs are set to
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:type nan_value: float, int
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:return: data array with all NaNs replaced with nan_value
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:rtype: `~numpy.ndarray`
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"""
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nn = np.isnan(data)
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if np.any(nn):
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data[nn] = nan_value
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return data
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def taper_cf(cf):
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"""
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Taper cf data to get rid off of side maximas
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:param cf: characteristic function data
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:type cf: `~numpy.ndarray`
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:return: tapered cf
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:rtype: `~numpy.ndarray`
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"""
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tap = np.hanning(len(cf))
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return tap * cf
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def cf_positive(cf):
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"""
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Shifts cf so that all values are positive
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:param cf:
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:type cf: `~numpy.ndarray`
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:return:
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:rtype: `~numpy.ndarray`
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"""
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return cf + max(abs(cf))
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def smooth_cf(cf, t_smooth, delta):
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"""
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Smooth cf by taking samples over t_smooth length
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:param cf: characteristic function data
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:type cf: `~numpy.ndarray`
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:param t_smooth: Time from which samples for smoothing will be taken (s)
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:type t_smooth: float
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:param delta: Sample rate of cf
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:type delta: float
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:return: smoothed cf data
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:rtype: `~numpy.ndarray`
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"""
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ismooth = int(round(t_smooth / delta)) # smooth values this many indexes apart
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cf_smooth = np.zeros(len(cf))
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if len(cf) < ismooth:
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raise ValueError
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for i in range(1, len(cf)):
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if i > ismooth:
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ii1 = i - ismooth
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cf_smooth[i] = cf_smooth[i - 1] + (cf[i] - cf[ii1]) / ismooth
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else:
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cf_smooth[i] = np.mean(cf[1: i])
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offset = abs(min(cf) - min(cf_smooth))
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cf_smooth -= offset # remove offset from smoothed function
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return cf_smooth
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def check_counts_ms(data):
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"""
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check if data is in counts or m/s
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:param data: data array
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:type data: `~numpy.ndarray`
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:return:
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:rtype: `~numpy.ndarray`
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"""
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# this is quick and dirty, better solution?
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if max(data < 1e-3) and max(data >= 1e-6):
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data = data * 1000000.
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elif max(data < 1e-6):
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data = data * 1e13
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return data
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def calcSlope(Data, datasmooth, Tcf, Pick, TSNR):
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"""
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Calculate Slope for Data around a given time Pick.
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:param Data: trace containing data for which a slope will be calculated
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:type Data: `~obspy.core.trace.Trace`
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:param datasmooth: smoothed data array
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:type datasmooth: ~numpy.ndarray`
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:param Tcf: array of time indices for Data array
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:type Tcf: ~numpy.ndarray`
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:param Pick: onset time around which the slope should be calculated
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:type Pick: float
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:param TSNR: tuple containing (tnoise, tsafety, tsignal, tslope). Slope will be calculated in time
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window tslope around the onset
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:type TSNR: (float, float, float, float)
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:return: tuple containing (slope of onset, slope index array, data fit information)
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:rtype: (float, `~numpy.ndarray`, `~numpy.ndarray`
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"""
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islope = getslopewin(Tcf, Pick, TSNR[3])
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try:
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dataslope = Data[0].data[islope]
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except IndexError as e:
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print("Slope Calculation: empty array islope, check signal window")
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raise e
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if len(dataslope) <= 1:
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print('Slope window outside data. No or not enough data in slope window found!')
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raise ValueError
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# find maximum within slope determination window
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# 'cause slope should be calculated up to first local minimum only!
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imaxs, = argrelmax(dataslope)
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if imaxs.size:
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imax = imaxs[0]
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else:
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imax = np.argmax(dataslope)
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iislope = islope[0:imax + 1] # cut index so it contains only the first maximum
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if len(iislope) < 2:
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# calculate slope from initial onset to maximum of AIC function
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print("AICPicker: Not enough data samples left for slope calculation!")
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print("Calculating slope from initial onset to maximum of AIC function ...")
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imax = np.argmax(datasmooth[islope])
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if imax == 0:
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print("AICPicker: Maximum for slope determination right at the beginning of the window!")
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print("Choose longer slope determination window!")
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raise IndexError
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iislope = islope[0][0:imax + 1] # cut index so it contains only the first maximum
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dataslope = Data[0].data[iislope] # slope will only be calculated to the first maximum
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# calculate slope as polynomal fit of order 1
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xslope = np.arange(0, len(dataslope))
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P = np.polyfit(xslope, dataslope, 1)
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datafit = np.polyval(P, xslope)
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if datafit[0] >= datafit[-1]:
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print('AICPicker: Negative slope, bad onset skipped!')
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raise ValueError
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slope = 1 / (len(dataslope) * Data[0].stats.delta) * (datafit[-1] - datafit[0])
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return slope, iislope, datafit
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def get_pickparams(pickparam):
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"""
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Get parameter names out of pickparam into dictionaries and return them
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:return: dictionaries containing 1. p pick parameters, 2. s pick parameters, 3. first motion determinatiion
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parameters, 4. signal length parameters
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:rtype: (dict, dict, dict, dict)
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"""
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# Define names of all parameters in different groups
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p_parameter_names = 'algoP pstart pstop use_taup taup_model tlta tsnrz hosorder bpz1 bpz2 pickwinP aictsmooth tsmoothP ausP nfacP tpred1z tdet1z Parorder addnoise Precalcwin minAICPslope minAICPSNR timeerrorsP checkwindowP minfactorP'.split(' ')
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s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac checkwindowS minfactorS'.split(' ')
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first_motion_names = 'minFMSNR fmpickwin minfmweight'.split(' ')
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signal_length_names = 'minsiglength minpercent noisefactor'.split(' ')
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# Get list of values from pickparam by name
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p_parameter_values = map(pickparam.get, p_parameter_names)
|
||||
s_parameter_values = map(pickparam.get, s_parameter_names)
|
||||
fm_parameter_values = map(pickparam.get, first_motion_names)
|
||||
sl_parameter_values = map(pickparam.get, signal_length_names)
|
||||
# construct dicts from names and values
|
||||
p_params = dict(zip(p_parameter_names, p_parameter_values))
|
||||
s_params = dict(zip(s_parameter_names, s_parameter_values))
|
||||
first_motion_params = dict(zip(first_motion_names, fm_parameter_values))
|
||||
signal_length_params = dict(zip(signal_length_names, sl_parameter_values))
|
||||
|
||||
p_params['use_taup'] = real_Bool(p_params['use_taup'])
|
||||
|
||||
return p_params, s_params, first_motion_params, signal_length_params
|
||||
|
||||
def getQualityFromUncertainty(uncertainty, Errors):
|
||||
# set initial quality to 4 (worst) and change only if one condition is hit
|
||||
quality = 4
|
||||
|
||||
@ -1283,7 +1495,6 @@ def getQualityFromUncertainty(uncertainty, Errors):
|
||||
|
||||
return quality
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import doctest
|
||||
|
||||
|
||||
@ -139,7 +139,7 @@ def excludeQualityClasses(picks, qClasses, timeerrorsP, timeerrorsS):
|
||||
:return: dictionary containing only picks above the excluded quality class(es)
|
||||
:rtype: dict
|
||||
"""
|
||||
from pylot.core.pick.utils import getQualityFromUncertainty
|
||||
from pylot.core.pick.utils import get_quality_class
|
||||
|
||||
if type(qClasses) in [int, float]:
|
||||
qClasses = [qClasses]
|
||||
@ -154,7 +154,7 @@ def excludeQualityClasses(picks, qClasses, timeerrorsP, timeerrorsS):
|
||||
if not type(pick) in [AttribDict, dict]:
|
||||
continue
|
||||
pickerror = phaseError[identifyPhaseID(phase)]
|
||||
quality = getQualityFromUncertainty(pick['spe'], pickerror)
|
||||
quality = get_quality_class(pick['spe'], pickerror)
|
||||
if not quality in qClasses:
|
||||
if not station in picksdict_new:
|
||||
picksdict_new[station] = {}
|
||||
@ -1219,6 +1219,25 @@ def check_event_folder(path):
|
||||
return ev_type
|
||||
|
||||
|
||||
def correct_iplot(iplot):
|
||||
"""
|
||||
iplot should be in range 0...2, but it can be given as True or 'True' as well, which should be converted
|
||||
to an integer. Both will be converted to 2.
|
||||
:type iplot: Bool or int
|
||||
:return: iplot as an integer
|
||||
:rtype: int
|
||||
"""
|
||||
# TODO this is a hack, there should never be the ability to pass anything else but an int
|
||||
try:
|
||||
iplot = int(iplot)
|
||||
except ValueError:
|
||||
if real_Bool(iplot):
|
||||
iplot = 2
|
||||
else:
|
||||
iplot = 0
|
||||
return iplot
|
||||
|
||||
|
||||
def station_id_remove_channel(station_id):
|
||||
"""
|
||||
Remove the channel from a SEED station id and return Network.Station.Location.
|
||||
|
||||
@ -44,7 +44,7 @@ from obspy.taup.utils import get_phase_names
|
||||
from pylot.core.io.data import Data
|
||||
from pylot.core.io.inputs import FilterOptions, PylotParameter
|
||||
from pylot.core.pick.utils import getSNR, earllatepicker, getnoisewin, \
|
||||
getResolutionWindow, getQualityFromUncertainty
|
||||
getResolutionWindow, get_quality_class
|
||||
from pylot.core.pick.compare import Comparison
|
||||
from pylot.core.util.defaults import OUTPUTFORMATS, FILTERDEFAULTS
|
||||
from pylot.core.util.utils import prepTimeAxis, full_range, demeanTrace, isSorted, findComboBoxIndex, clims, \
|
||||
@ -2604,10 +2604,10 @@ class PickDlg(QDialog):
|
||||
|
||||
# get quality classes
|
||||
if self.getPhaseID(phase) == 'P':
|
||||
quality = getQualityFromUncertainty(picks['spe'], self.parameter['timeerrorsP'])
|
||||
quality = get_quality_class(picks['spe'], self.parameter['timeerrorsP'])
|
||||
phaseID = 'P'
|
||||
elif self.getPhaseID(phase) == 'S':
|
||||
quality = getQualityFromUncertainty(picks['spe'], self.parameter['timeerrorsS'])
|
||||
quality = get_quality_class(picks['spe'], self.parameter['timeerrorsS'])
|
||||
phaseID = 'S'
|
||||
|
||||
mpp = picks['mpp'] - self.getStartTime()
|
||||
@ -3619,8 +3619,8 @@ class TuneAutopicker(QWidget):
|
||||
('refSpick', 0),
|
||||
('el_S1pick', 0),
|
||||
('el_S2pick', 0)]
|
||||
qualityPpick = getQualityFromUncertainty(picks['P']['spe'], self.parameter['timeerrorsP'])
|
||||
qualitySpick = getQualityFromUncertainty(picks['S']['spe'], self.parameter['timeerrorsS'])
|
||||
qualityPpick = get_quality_class(picks['P']['spe'], self.parameter['timeerrorsP'])
|
||||
qualitySpick = get_quality_class(picks['S']['spe'], self.parameter['timeerrorsS'])
|
||||
for p_ax in p_axes:
|
||||
axes = self.parent().fig_dict[p_ax[0]].axes
|
||||
if not axes:
|
||||
|
||||
78
tests/testPickingResults.py
Normal file
78
tests/testPickingResults.py
Normal file
@ -0,0 +1,78 @@
|
||||
import unittest
|
||||
from pylot.core.pick.autopick import PickingResults
|
||||
|
||||
class TestPickingResults(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.pr = PickingResults()
|
||||
|
||||
def test_non_existing_key_dot_access(self):
|
||||
"""Accessing an attribute in the class that wasnt added to the dict should give a AttributeError"""
|
||||
with self.assertRaises(AttributeError):
|
||||
self.pr.doesntexist
|
||||
|
||||
def test_non_existing_key_dict_access(self):
|
||||
"""Accessing a missing attribute in a dictionary throws a KeyError"""
|
||||
with self.assertRaises(KeyError):
|
||||
self.pr['keydoesnotexist']
|
||||
|
||||
def test_dot_member_creation(self):
|
||||
self.pr.x = 0
|
||||
self.assertEqual(self.pr.x, 0)
|
||||
self.pr.x += 42
|
||||
self.assertEqual(self.pr.x, 42)
|
||||
|
||||
def test_dot_builtin_member(self):
|
||||
self.assertEqual(self.pr.weight, 4)
|
||||
self.pr.weight = 99
|
||||
self.assertEqual(self.pr.weight, 99)
|
||||
|
||||
def test_key_access(self):
|
||||
self.pr['y'] = 11
|
||||
self.assertEqual(self.pr['y'], 11)
|
||||
|
||||
def test_builtin_fields(self):
|
||||
self.assertEqual(self.pr['weight'], 4)
|
||||
|
||||
def test_in(self):
|
||||
self.assertFalse('keydoesnotexist' in self.pr)
|
||||
self.pr['k'] = 0
|
||||
self.assertTrue('k' in self.pr)
|
||||
|
||||
def test_keys_function(self):
|
||||
a = 99
|
||||
self.pr.newkey = a
|
||||
self.assertIn(a, self.pr.values())
|
||||
self.assertIn('newkey', self.pr.keys())
|
||||
|
||||
def test_len_and_clear(self):
|
||||
self.pr.clear()
|
||||
self.assertEqual(len(self.pr), 0)
|
||||
self.pr.a = 6
|
||||
self.pr['b'] = 9
|
||||
self.assertEqual(len(self.pr), 2)
|
||||
|
||||
def test_get_default(self):
|
||||
self.assertEqual(self.pr.get('keynotexisting', 42), 42)
|
||||
weight = self.pr.get('weight', -1)
|
||||
self.assertEqual(weight, 4)
|
||||
self.assertNotEqual(weight, -1)
|
||||
|
||||
def test_dunder_attributes(self):
|
||||
"""Storing Pythons special dunder method in a dictionary is valid and should not override the instances dunder
|
||||
methods"""
|
||||
prev_len = len(self.pr)
|
||||
try:
|
||||
self.pr['__len__'] = None
|
||||
except Exception:
|
||||
self.fail("test_dunder_attributes failed to add a dunder attribute to the dictionary keys")
|
||||
try:
|
||||
curr_len = len(self.pr)
|
||||
except Exception:
|
||||
self.fail("test_dunder_attributes overwrote an instance internal dunder method")
|
||||
self.assertEqual(prev_len+1, curr_len) # +1 for the added __len__ key/value-pair
|
||||
|
||||
self.pr.__len__ = 42
|
||||
|
||||
self.assertEqual(42, self.pr['__len__'])
|
||||
self.assertEqual(prev_len+1, curr_len, msg="__len__ was overwritten")
|
||||
35
tests/test_PickingParameters.py
Normal file
35
tests/test_PickingParameters.py
Normal file
@ -0,0 +1,35 @@
|
||||
import unittest
|
||||
from pylot.core.pick.autopick import PickingParameters
|
||||
|
||||
class TestPickingParameters(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.simple_dict = {'a': 3, 'b': 14}
|
||||
self.nested_dict = {'a': self.simple_dict, 'b': self.simple_dict}
|
||||
|
||||
def assertParameterEquality(self, dic, instance):
|
||||
"""Test wether all parameters given in dic are found in instance"""
|
||||
for key, value in dic.items():
|
||||
self.assertEqual(value, getattr(instance, key))
|
||||
|
||||
def test_add_params_from_dict_simple(self):
|
||||
pickparam = PickingParameters()
|
||||
pickparam.add_params_from_dict(self.simple_dict)
|
||||
self.assertParameterEquality(self.simple_dict, pickparam)
|
||||
|
||||
def test_add_params_from_dict_nested(self):
|
||||
pickparam = PickingParameters()
|
||||
pickparam.add_params_from_dict(self.nested_dict)
|
||||
self.assertParameterEquality(self.nested_dict, pickparam)
|
||||
|
||||
def test_init(self):
|
||||
pickparam = PickingParameters(self.simple_dict)
|
||||
self.assertParameterEquality(self.simple_dict, pickparam)
|
||||
|
||||
def test_dot_access(self):
|
||||
pickparam = PickingParameters(self.simple_dict)
|
||||
self.assertEqual(pickparam.a, self.simple_dict['a'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
104
tests/test_autopickstation/autoPyLoT_global_taupy_false.in
Normal file
104
tests/test_autopickstation/autoPyLoT_global_taupy_false.in
Normal file
@ -0,0 +1,104 @@
|
||||
%This is a parameter input file for PyLoT/autoPyLoT.
|
||||
%All main and special settings regarding data handling
|
||||
%and picking are to be set here!
|
||||
%Parameters are optimized for %extent data sets!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#main settings#
|
||||
/home/darius #rootpath# %project path
|
||||
alparray #datapath# %data path
|
||||
waveforms_used #database# %name of data base
|
||||
e0093.173.16 #eventID# %event ID for single event processing (* for all events found in database)
|
||||
/home/darius/alparray/metadata #invdir# %full path to inventory or dataless-seed file
|
||||
PILOT #datastructure# %choose data structure
|
||||
True #apverbose# %choose 'True' or 'False' for terminal output
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#NLLoc settings#
|
||||
None #nllocbin# %path to NLLoc executable
|
||||
/home/darius/alparray/auto #nllocroot# %root of NLLoc-processing directory
|
||||
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
|
||||
Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
|
||||
ttime #ttpatter# %pattern of NLLoc ttimes from grid
|
||||
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#parameters for seismic moment estimation#
|
||||
3530.0 #vp# %average P-wave velocity
|
||||
2500.0 #rho# %average rock density [kg/m^3]
|
||||
300.0 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#settings local magnitude#
|
||||
1.0 1.0 1.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
|
||||
1.0 1.0 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#filter settings#
|
||||
0.01 0.01 #minfreq# %Lower filter frequency [P, S]
|
||||
0.5 0.5 #maxfreq# %Upper filter frequency [P, S]
|
||||
3 3 #filter_order# %filter order [P, S]
|
||||
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#common settings picker#
|
||||
global #extent# %extent of array ("local", "regional" or "global")
|
||||
-100.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
350.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
200.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
|
||||
875.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
|
||||
False #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
|
||||
IASP91 #taup_model# %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
|
||||
0.01 0.1 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
|
||||
0.001 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
|
||||
0.01 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
|
||||
0.001 0.5 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
|
||||
#special settings for calculating CF#
|
||||
%!!Edit the following only if you know what you are doing!!%
|
||||
#Z-component#
|
||||
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
|
||||
100.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
|
||||
4 #hosorder# %for HOS-picker, order of Higher Order Statistics
|
||||
2 #Parorder# %for AR-picker, order of AR process of Z-component
|
||||
24.0 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
|
||||
20.0 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
|
||||
16.0 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
|
||||
8.0 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
|
||||
0.5 #addnoise# %add noise to seismogram for stable AR prediction
|
||||
30.0 5.0 20.0 10.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
55.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
|
||||
20.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
|
||||
6.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
|
||||
4.0 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
|
||||
0.5 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
|
||||
1.1 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
|
||||
50.0 #checkwindowP# %time window before HOS/AR-maximum to check for smaller maxima [s]
|
||||
0.7 #minfactorP# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#H-components#
|
||||
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
|
||||
30.0 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
|
||||
18.0 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
|
||||
16.0 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
|
||||
8.0 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
|
||||
4 #Sarorder# %for AR-picker, order of AR process of H-components
|
||||
30.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
|
||||
195.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
|
||||
30.0 10.0 15.0 10.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
22.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
|
||||
10.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
|
||||
0.001 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
|
||||
1.2 #nfacS# %for AR-picker, noise factor for noise level determination (S)
|
||||
250.0 #checkwindowS# %time window before AR-maximum to check for smaller maxima [s]
|
||||
0.4 #minfactorS# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#first-motion picker#
|
||||
1 #minfmweight# %minimum required P weight for first-motion determination
|
||||
3.0 #minFMSNR# %miniumum required SNR for first-motion determination
|
||||
10.0 #fmpickwin# %pick window around P onset for calculating zero crossings
|
||||
#quality assessment#
|
||||
4.0 8.0 12.0 16.0 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
|
||||
4.0 8.0 12.0 16.0 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
|
||||
0.01 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
|
||||
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
|
||||
0.01 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
|
||||
1.1 #minAICSSNR# %below this SNR the initial S pick is rejected
|
||||
12.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
|
||||
1.1 #noisefactor# %noiselevel*noisefactor=threshold
|
||||
20.0 #minpercent# %required percentage of amplitudes exceeding threshold
|
||||
1.25 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
|
||||
60.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
|
||||
60.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
|
||||
5.0 #jackfactor# %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor
|
||||
104
tests/test_autopickstation/autoPyLoT_global_taupy_true.in
Normal file
104
tests/test_autopickstation/autoPyLoT_global_taupy_true.in
Normal file
@ -0,0 +1,104 @@
|
||||
%This is a parameter input file for PyLoT/autoPyLoT.
|
||||
%All main and special settings regarding data handling
|
||||
%and picking are to be set here!
|
||||
%Parameters are optimized for %extent data sets!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#main settings#
|
||||
/home/darius #rootpath# %project path
|
||||
alparray #datapath# %data path
|
||||
waveforms_used #database# %name of data base
|
||||
e0093.173.16 #eventID# %event ID for single event processing (* for all events found in database)
|
||||
/home/darius/alparray/metadata #invdir# %full path to inventory or dataless-seed file
|
||||
PILOT #datastructure# %choose data structure
|
||||
True #apverbose# %choose 'True' or 'False' for terminal output
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#NLLoc settings#
|
||||
None #nllocbin# %path to NLLoc executable
|
||||
/home/darius/alparray/auto #nllocroot# %root of NLLoc-processing directory
|
||||
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
|
||||
Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
|
||||
ttime #ttpatter# %pattern of NLLoc ttimes from grid
|
||||
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#parameters for seismic moment estimation#
|
||||
3530.0 #vp# %average P-wave velocity
|
||||
2500.0 #rho# %average rock density [kg/m^3]
|
||||
300.0 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#settings local magnitude#
|
||||
1.0 1.0 1.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
|
||||
1.0 1.0 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#filter settings#
|
||||
0.01 0.01 #minfreq# %Lower filter frequency [P, S]
|
||||
0.5 0.5 #maxfreq# %Upper filter frequency [P, S]
|
||||
3 3 #filter_order# %filter order [P, S]
|
||||
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#common settings picker#
|
||||
global #extent# %extent of array ("local", "regional" or "global")
|
||||
-100.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
350.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
200.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
|
||||
875.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
|
||||
True #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
|
||||
IASP91 #taup_model# %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
|
||||
0.01 0.1 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
|
||||
0.001 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
|
||||
0.01 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
|
||||
0.001 0.5 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
|
||||
#special settings for calculating CF#
|
||||
%!!Edit the following only if you know what you are doing!!%
|
||||
#Z-component#
|
||||
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
|
||||
100.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
|
||||
4 #hosorder# %for HOS-picker, order of Higher Order Statistics
|
||||
2 #Parorder# %for AR-picker, order of AR process of Z-component
|
||||
24.0 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
|
||||
20.0 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
|
||||
16.0 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
|
||||
8.0 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
|
||||
0.5 #addnoise# %add noise to seismogram for stable AR prediction
|
||||
30.0 5.0 20.0 10.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
55.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
|
||||
20.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
|
||||
6.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
|
||||
4.0 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
|
||||
0.5 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
|
||||
1.1 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
|
||||
50.0 #checkwindowP# %time window before HOS/AR-maximum to check for smaller maxima [s]
|
||||
0.7 #minfactorP# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#H-components#
|
||||
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
|
||||
30.0 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
|
||||
18.0 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
|
||||
16.0 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
|
||||
8.0 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
|
||||
4 #Sarorder# %for AR-picker, order of AR process of H-components
|
||||
30.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
|
||||
195.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
|
||||
30.0 10.0 15.0 10.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
22.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
|
||||
10.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
|
||||
0.001 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
|
||||
1.2 #nfacS# %for AR-picker, noise factor for noise level determination (S)
|
||||
250.0 #checkwindowS# %time window before AR-maximum to check for smaller maxima [s]
|
||||
0.4 #minfactorS# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#first-motion picker#
|
||||
1 #minfmweight# %minimum required P weight for first-motion determination
|
||||
3.0 #minFMSNR# %miniumum required SNR for first-motion determination
|
||||
10.0 #fmpickwin# %pick window around P onset for calculating zero crossings
|
||||
#quality assessment#
|
||||
4.0 8.0 12.0 16.0 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
|
||||
4.0 8.0 12.0 16.0 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
|
||||
0.01 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
|
||||
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
|
||||
0.01 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
|
||||
1.1 #minAICSSNR# %below this SNR the initial S pick is rejected
|
||||
12.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
|
||||
1.1 #noisefactor# %noiselevel*noisefactor=threshold
|
||||
20.0 #minpercent# %required percentage of amplitudes exceeding threshold
|
||||
1.25 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
|
||||
60.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
|
||||
60.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
|
||||
5.0 #jackfactor# %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -0,0 +1,21 @@
|
||||
<?xml version='1.0' encoding='utf-8'?>
|
||||
<q:quakeml xmlns:q="http://quakeml.org/xmlns/quakeml/1.2" xmlns="http://quakeml.org/xmlns/bed/1.2">
|
||||
<eventParameters publicID="smi:local/53a38563-739a-48b2-9f34-bf40ee7b656a">
|
||||
<event publicID="smi:local/e0001.024.16">
|
||||
<origin publicID="smi:local/e0001.024.16">
|
||||
<time>
|
||||
<value>2016-01-24T10:30:30.000000Z</value>
|
||||
</time>
|
||||
<latitude>
|
||||
<value>59.66</value>
|
||||
</latitude>
|
||||
<longitude>
|
||||
<value>-153.45</value>
|
||||
</longitude>
|
||||
<depth>
|
||||
<value>128.0</value>
|
||||
</depth>
|
||||
</origin>
|
||||
</event>
|
||||
</eventParameters>
|
||||
</q:quakeml>
|
||||
1
tests/test_autopickstation/e0001.024.16/notes.txt
Normal file
1
tests/test_autopickstation/e0001.024.16/notes.txt
Normal file
@ -0,0 +1 @@
|
||||
/data/AlpArray/mini_SEED_LH/2016-01-24T10:30:30
|
||||
211
tests/test_autopickstation/test_autopickstation.py
Normal file
211
tests/test_autopickstation/test_autopickstation.py
Normal file
@ -0,0 +1,211 @@
|
||||
import unittest
|
||||
from unittest import skip
|
||||
import obspy
|
||||
from obspy import UTCDateTime
|
||||
import os
|
||||
import sys
|
||||
from pylot.core.pick.autopick import autopickstation
|
||||
from pylot.core.io.inputs import PylotParameter
|
||||
from pylot.core.io.data import Data
|
||||
from pylot.core.util.utils import trim_station_components
|
||||
|
||||
|
||||
class HidePrints:
|
||||
"""
|
||||
Used to hide all standard output the Function to be tested have, since it clutters the test results.
|
||||
"""
|
||||
|
||||
def __init__(self, hide_prints=True):
|
||||
"""Create object with hide_prints=False to disable print hiding"""
|
||||
self.hide = hide_prints
|
||||
|
||||
def __enter__(self):
|
||||
if self.hide:
|
||||
self._original_stdout = sys.stdout
|
||||
devnull = open(os.devnull, "w")
|
||||
sys.stdout = devnull
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
if self.hide:
|
||||
sys.stdout = self._original_stdout
|
||||
|
||||
|
||||
class MockMetadata:
|
||||
"""Mock metadata object used for taupy to avoid reading large dless file from disk.
|
||||
get_coordinates must take the same arguments as pylot.core.utils.dataprocssing.py/class Metadata."""
|
||||
|
||||
def __init__(self):
|
||||
self.station_names = ['GR.GRA1', 'GR.GRA2', 'G.ECH', 'CH.FIESA', 'Z3.A106A']
|
||||
gra1 = {u'azimuth': 0.0, u'dip': -90.0, u'elevation': 499.5, u'latitude': 49.691888, u'local_depth': 0.0,
|
||||
u'longitude': 11.22172}
|
||||
gra2 = {u'azimuth': 0.0, u'dip': -90.0, u'elevation': 512.0, u'latitude': 49.655208, u'local_depth': 0.0,
|
||||
u'longitude': 11.359444}
|
||||
ech = {u'azimuth': 90.0, u'dip': 0.0, u'elevation': 580.0, u'latitude': 48.216313, u'local_depth': 250.0,
|
||||
u'longitude': 7.158961}
|
||||
fiesa = {'azimuth': 0.0, 'dip': -90.0, 'elevation': 2340.5, 'latitude': 46.43521, 'local_depth': 0.0,
|
||||
'longitude': 8.11051}
|
||||
a106 = {'azimuth': 90.0, 'dip': 0.0, 'elevation': 468.0, 'latitude': 48.753388, 'local_depth': 0.0,
|
||||
'longitude': 9.721937}
|
||||
|
||||
self.coordinates = [gra1, gra2, ech, fiesa, a106]
|
||||
|
||||
def get_coordinates(self, station_id, time=None):
|
||||
"""
|
||||
Mocks the method get_coordinates from obspy.io.xseed.parser.Parser object
|
||||
to avoid building a parser for the unit tests
|
||||
:param station_id: 'GR.GRA1..LHZ' or similar
|
||||
:type station_id: str
|
||||
:return: dictionary containing azimuth, dip, elevation, latitude, longitude,
|
||||
local depth as keys
|
||||
:rtype: dict
|
||||
|
||||
>>>m = MockMetadata(); m.get_coordinates('GR.GRA2..LHZ')
|
||||
{u'azimuth': 0.0, u'dip': -90.0, u'elevation': 512.0, u'latitude': 49.655208, u'local_depth': 0.0, u'longitude': 11.359444}
|
||||
"""
|
||||
|
||||
for index, name in enumerate(self.station_names):
|
||||
if station_id.startswith(name):
|
||||
return self.coordinates[index]
|
||||
|
||||
|
||||
class TestAutopickStation(unittest.TestCase):
|
||||
"""
|
||||
Test the autopickstation function as if it were called from GUI.
|
||||
Three stations (GR.GRA1, GR.GRA2, G.ECH) are tested with and without TauPy respectively
|
||||
"""
|
||||
|
||||
def setUp(self):
|
||||
self.event_id = 'e0001.024.16'
|
||||
# Create wfstream for picking
|
||||
mseed_relative_path = os.path.join(os.path.dirname(__file__), self.event_id, '*.mseed')
|
||||
self.wfstream = obspy.read(mseed_relative_path)
|
||||
# trim waveform to get the same results as the GUI call
|
||||
with HidePrints():
|
||||
self.wfstream = trim_station_components(self.wfstream, trim_start=True, trim_end=False)
|
||||
self.gra1 = self.wfstream.select(station='GRA1')
|
||||
self.gra2 = self.wfstream.select(station='GRA2')
|
||||
self.ech = self.wfstream.select(station='ECH')
|
||||
self.fiesa = self.wfstream.select(station='FIESA')
|
||||
self.a106 = self.wfstream.select(station='A106A')
|
||||
self.a005a = self.wfstream.select(station='A005A')
|
||||
# Create input parameter container
|
||||
self.inputfile_taupy_enabled = os.path.join(os.path.dirname(__file__), 'autoPyLoT_global_taupy_true.in')
|
||||
self.inputfile_taupy_disabled = os.path.join(os.path.dirname(__file__), 'autoPyLoT_global_taupy_false.in')
|
||||
self.pickparam_taupy_enabled = PylotParameter(fnin=self.inputfile_taupy_enabled)
|
||||
self.pickparam_taupy_disabled = PylotParameter(fnin=self.inputfile_taupy_disabled)
|
||||
self.xml_file = os.path.join(os.path.dirname(__file__),self.event_id, 'PyLoT_'+self.event_id+'.xml')
|
||||
self.data = Data(evtdata=self.xml_file)
|
||||
# create origin for taupy testing
|
||||
self.origin = [obspy.core.event.origin.Origin(magnitude=7.1, latitude=59.66, longitude=-153.45, depth=128.0, time=UTCDateTime("2016-01-24T10:30:30.0"))]
|
||||
# mocking metadata since reading it takes a long time to read from file
|
||||
self.metadata = MockMetadata()
|
||||
|
||||
# show complete diff when difference in results dictionaries are found
|
||||
self.maxDiff = None
|
||||
|
||||
#@skip("Works")
|
||||
def test_autopickstation_taupy_disabled_gra1(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
def test_autopickstation_taupy_enabled_gra1(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 15.599905299126778, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 36.307013769185403, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 27, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 24, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 28, 690000), 'fm': 'U', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
def test_autopickstation_taupy_disabled_gra2(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'shortsignallength', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'w0': None, 'spe': None, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'GR', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'fm': None, 'spe': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA2', station)
|
||||
|
||||
def test_autopickstation_taupy_enabled_gra2(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 13.957959025719253, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.876879503607871, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 29, 150000), 'w0': None, 'spe': 1.0, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 26, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 30, 150000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.573236990555648, 'network': u'GR', 'weight': 1, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 34, 150000), 'snr': 11.410999834108294, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 33, 150000), 'fm': None, 'spe': 4.666666666666667, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin = self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA2', station)
|
||||
|
||||
def test_autopickstation_taupy_disabled_ech(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'w0': None, 'spe': None, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'G', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'fm': None, 'spe': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_disabled)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('ECH', station)
|
||||
|
||||
def test_autopickstation_taupy_enabled_ech(self):
|
||||
# this station has a long time of before the first onset, so taupy will help during picking
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 9.9753586609166316, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 9.9434218804137107, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'w0': None, 'spe': 1.6666666666666667, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 29), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 35), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 12.698999454169567, 'network': u'G', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 44), 'snr': 18.616581906366577, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 33), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 43), 'fm': None, 'spe': 3.3333333333333335, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('ECH', station)
|
||||
|
||||
def test_autopickstation_taupy_disabled_fiesa(self):
|
||||
# this station has a long time of before the first onset, so taupy will help during picking
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'w0': None, 'spe': None, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'CH', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'fm': None, 'spe': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_disabled)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('FIESA', station)
|
||||
|
||||
def test_autopickstation_taupy_enabled_fiesa(self):
|
||||
# this station has a long time of before the first onset, so taupy will help during picking
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 13.921049277904373, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.666352170589487, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 47), 'w0': None, 'spe': 1.2222222222222285, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 43, 333333), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 48), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.893086316477728, 'network': u'CH', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 51, 5), 'snr': 12.283118216397849, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 59, 333333), 'mpp': UTCDateTime(2016, 1, 24, 10, 51, 2), 'fm': None, 'spe': 2.8888888888888764, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('FIESA', station)
|
||||
|
||||
def test_autopickstation_gra1_z_comp_missing(self):
|
||||
"""Picking on a stream without a vertical trace should return None"""
|
||||
wfstream = self.gra1.copy()
|
||||
wfstream = wfstream.select(channel='*E') + wfstream.select(channel='*N')
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertIsNone(result)
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
def test_autopickstation_gra1_horizontal_comps_missing(self):
|
||||
"""Picking on a stream without horizontal traces should still pick the P phase on the vertical component"""
|
||||
wfstream = self.gra1.copy()
|
||||
wfstream = wfstream.select(channel='*Z')
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'network': u'GR', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.9333333333333323, 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': None, 'weight': 4, 'Mo': None, 'Ao': None, 'lpp': None, 'Mw': None, 'fc': None, 'snr': None, 'marked': [], 'mpp': None, 'w0': None, 'spe': None, 'epp': None, 'fm': 'N', 'channel': None}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertEqual(expected, result)
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
def test_autopickstation_a106_taupy_enabled(self):
|
||||
"""This station has invalid values recorded on both N and E component, but a pick can still be found on Z"""
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 12.862128789922826, 'network': u'Z3', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'Mw': None, 'fc': None, 'snr': 19.329155459132608, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 30), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 33), 'w0': None, 'spe': 1.6666666666666667, 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'Z3', 'weight': 4, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 28, 56), 'Mw': None, 'fc': None, 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 28, 24), 'mpp': UTCDateTime(2016, 1, 24, 10, 28, 40), 'w0': None, 'spe': None, 'fm': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertEqual(expected, result)
|
||||
|
||||
def test_autopickstation_station_missing_in_metadata(self):
|
||||
"""This station is not in the metadata, but Taupy is enabled. Taupy should exit cleanly and modify the starttime
|
||||
relative to the theoretical onset to one relative to the traces starttime, eg never negative.
|
||||
"""
|
||||
self.pickparam_taupy_enabled.setParamKV('pstart', -100) # modify starttime to be relative to theoretical onset
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 14.464757855513506, 'network': u'Z3', 'weight': 0, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 39, 605000), 'Mw': None, 'fc': None, 'snr': 27.956048519707181, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 38, 605000), 'w0': None, 'spe': 1.6666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 35, 605000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.112844176301248, 'network': u'Z3', 'weight': 1, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 51, 605000), 'Mw': None, 'fc': None, 'snr': 10.263238413785425, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 48, 605000), 'w0': None, 'spe': 4.666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 40, 605000), 'fm': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream = self.a005a, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertEqual(expected, result)
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
56
tests/test_get_quality_class.py
Normal file
56
tests/test_get_quality_class.py
Normal file
@ -0,0 +1,56 @@
|
||||
import unittest
|
||||
from pylot.core.pick.utils import get_quality_class
|
||||
|
||||
|
||||
class TestQualityClassFromUncertainty(unittest.TestCase):
|
||||
"""
|
||||
Test function that assigns a quality value [0...4] to a pick uncertainty.
|
||||
The pick uncertainty is compared to the error classes.
|
||||
A pick uncertainty that is below the first error class is assigned the best quality, quality 0.
|
||||
A pick uncertainty that is above the first error class but below the second is assigned quality 1 and so on.
|
||||
A pick uncertainty that is larger than the biggest error class is assigned quality 4.
|
||||
The upper border of a quality class is inclusive, the lower border exclusive. Meaning if a value is exactly on the
|
||||
border between two classes, it is assigned into the higher quality class (represented by the lower number).
|
||||
"""
|
||||
|
||||
def setUp(self):
|
||||
# entries hold upper/lower bound of error classes
|
||||
self.error_classes = [float(x) for x in range(1, 9, 2)]
|
||||
# [1.0, 3.0, 5.0, 7.0]
|
||||
|
||||
def test_out_of_lower_bound(self):
|
||||
# Error out of lower bound of classes
|
||||
self.assertEqual(0, get_quality_class(0.5, self.error_classes))
|
||||
|
||||
def test_out_of_upper_bound(self):
|
||||
# Error out of upper bound of error classes
|
||||
self.assertEqual(4, get_quality_class(14.7, self.error_classes))
|
||||
|
||||
def test_on_lower_border(self):
|
||||
# Error exactly on lower bound
|
||||
self.assertEqual(0, get_quality_class(1., self.error_classes))
|
||||
|
||||
def test_on_upper_border(self):
|
||||
# Error exactly on upper bound
|
||||
self.assertEqual(3, get_quality_class(7., self.error_classes))
|
||||
|
||||
def test_on_middle_border_inclusive(self):
|
||||
# Error exactly between two classes, since lower bound is exclusive and upper bound is inclusive it should
|
||||
# fall into the class with better quality
|
||||
self.assertEqual(1, get_quality_class(3., self.error_classes))
|
||||
self.assertNotEqual(2, get_quality_class(3., self.error_classes))
|
||||
|
||||
def test_in_class1(self):
|
||||
# Error exactly in class 1
|
||||
self.assertEqual(1, get_quality_class(1.5, self.error_classes))
|
||||
|
||||
def test_in_class2(self):
|
||||
# Error exactly in class 2
|
||||
self.assertEqual(2, get_quality_class(3.5, self.error_classes))
|
||||
|
||||
def test_in_class3(self):
|
||||
# Error exactly in class 3
|
||||
self.assertEqual(3, get_quality_class(5.6, self.error_classes))
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in New Issue
Block a user