[change] docstrings in compare.py
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@ -109,10 +109,9 @@ class Comparison(object):
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Comparison is carried out with the help of pdf representation of the picks
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and a probabilistic approach to the time difference of two onset
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measurements.
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:param a: filename for pickset A
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:type a: str
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:param b: filename for pickset B
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:type b: str
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:param type: type of the returned `~pylot.core.util.pdf.ProbabilityDensityFunction` object.
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Possible values: 'exp' and 'gauss', representing the type of branches of the PDF
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:type type: str
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:return: dictionary containing the resulting comparison pdfs for all picks
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:rtype: dict
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"""
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@ -142,8 +141,7 @@ class Comparison(object):
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istations = range(nstations)
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fig, axarr = plt.subplots(nstations, 2, sharex='col', sharey='row')
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for n in istations:
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station = stations[n]
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for n, station in enumerate(stations):
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if station not in self.comparison.keys():
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continue
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compare_pdf = self.comparison[station]
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@ -190,6 +188,20 @@ class Comparison(object):
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return self.get_array(phase, 'standard_deviation')
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def hist_expectation(self, phases='all', bins=20, normed=False):
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"""
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Plot a histogram of the expectation values of the PDFs.
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Expectation represents the time difference between two most likely arrival times
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:param phases: type of phases to compare
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:type phases: str
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:param bins: number of bins in histogram
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:type bins: int
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:param normed: Normalize histogram
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:type normed: bool
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:return: None
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:rtype: None
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"""
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phases.strip()
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if phases.find('all') is 0:
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phases = 'ps'
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@ -210,6 +222,20 @@ class Comparison(object):
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plt.show()
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def hist_standard_deviation(self, phases='all', bins=20, normed=False):
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"""
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Plot a histogram of the compared standard deviation values of two arrivals.
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Standard deviation of two compared picks represents the combined uncertainties/pick errors
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(earliest possible pick, latest possible pick)
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:param phases: type of phases to compare
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:type phases: str
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:param bins: number of bins in histogram
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:type bins: int
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:param normed: Normalize histogram
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:type normed: bool
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:return: None
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:rtype: None
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"""
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phases.strip()
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if phases.find('all') == 0:
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phases = 'ps'
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@ -370,7 +396,7 @@ class PDFDictionary(object):
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class PDFstatistics(object):
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"""
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This object can be used to get various statistic values from probabillity density functions.
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This object can be used to get various statistic values from probability density functions.
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Takes a path as argument.
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"""
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