undid earlier changes in PDFStatistics
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@ -339,6 +339,12 @@ class PDFDictionary(object):
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class PDFstatistics(object):
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class PDFstatistics(object):
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'''
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To do:
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plots for std, quantiles,
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calc: quantiles
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encountering error when trying to get quantile... float.. utcdatetime error
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'''
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def __init__(self, directory):
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def __init__(self, directory):
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self.directory = directory
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self.directory = directory
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self.stations = {}
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self.stations = {}
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@ -347,19 +353,20 @@ class PDFstatistics(object):
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self.makeDirlist()
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self.makeDirlist()
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self.getData()
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self.getData()
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self.getStatistics()
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self.getStatistics()
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self.arraylen = 0
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self.Theta015 = []
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self.Theta25 = []
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self.Theta485 = []
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#self.showData()
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#self.showData()
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def makeDirlist(self, fn_pattern='*'):
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def makeDirlist(self, fn_pattern='*'):
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self.dirlist = glob.glob1(self.directory, fn_pattern)
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self.dirlist = glob.glob1(self.directory, fn_pattern)
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#print self.dirlist
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self.evtdict = {}
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self.evtdict = {}
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for rd in self.dirlist:
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for rd in self.dirlist:
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#print rd
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self.evtdict[rd] = glob.glob1((os.path.join(self.directory, rd)), '*.xml')
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self.evtdict[rd] = glob.glob1((os.path.join(self.directory, rd)), '*.xml')
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#print rd, self.evtdict[rd]
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def getData(self):
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def getData(self):
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arraylen = 0
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for dir in self.dirlist:
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for dir in self.dirlist:
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for evt in self.evtdict[dir]:
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for evt in self.evtdict[dir]:
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self.stations[evt] = []
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self.stations[evt] = []
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@ -367,31 +374,34 @@ class PDFstatistics(object):
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self.s_std[evt] = []
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self.s_std[evt] = []
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self.getPDFDict(dir, evt)
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self.getPDFDict(dir, evt)
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for station, pdfs in self.pdfdict.pdf_data.items():
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for station, pdfs in self.pdfdict.pdf_data.items():
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#print station, pdfs
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# print station, pdfs
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try:
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try:
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p_std = pdfs['P'].standard_deviation()
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p_std = pdfs['P'].standard_deviation()
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except KeyError:
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except KeyError:
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p_std = 0
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p_std = np.nan
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try:
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try:
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s_std = pdfs['S'].standard_deviation()
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s_std = pdfs['S'].standard_deviation()
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except KeyError:
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except KeyError:
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s_std = 0
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s_std = np.nan
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self.stations[evt].append(station)
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self.stations[evt].append(station)
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self.p_std[evt].append(p_std)
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self.p_std[evt].append(p_std)
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self.s_std[evt].append(s_std)
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self.s_std[evt].append(s_std)
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arraylen += 1
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self.arraylen += 1
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self.makeArray(arraylen)
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def makeArray(self, arraylen):
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def makeArray(self):
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index = 0
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index = 0
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self.p_stdarray = np.zeros(arraylen)
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self.p_stdarray = np.zeros(self.arraylen)
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self.s_stdarray = np.zeros(arraylen)
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self.s_stdarray = np.zeros(self.arraylen)
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for evt in self.p_std.keys():
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for evt in self.p_std.keys():
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for n in range(len(self.p_std[evt])):
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for n in range(len(self.p_std[evt])):
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self.p_stdarray[index] = self.p_std[evt][n]
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self.p_stdarray[index] = self.p_std[evt][n]
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self.s_stdarray[index] = self.s_std[evt][n]
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self.s_stdarray[index] = self.s_std[evt][n]
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index += 1
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index += 1
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def showData(self):
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def showData(self):
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#figure(p, s) = plt.pyplot.subplots(2)
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#figure(p, s) = plt.pyplot.subplots(2)
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#p.hist(self.p_stdarray, Bins=100, range=[min(self.p_stdarray),max(self.p_stdarray)/256])
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#p.hist(self.p_stdarray, Bins=100, range=[min(self.p_stdarray),max(self.p_stdarray)/256])
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@ -405,6 +415,7 @@ class PDFstatistics(object):
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def getPDFDict(self, month, evt):
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def getPDFDict(self, month, evt):
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self.pdfdict = PDFDictionary.from_quakeml(os.path.join(self.directory,month,evt))
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self.pdfdict = PDFDictionary.from_quakeml(os.path.join(self.directory,month,evt))
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def getStatistics(self):
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def getStatistics(self):
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self.p_mean = self.p_stdarray.mean()
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self.p_mean = self.p_stdarray.mean()
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self.p_std_std = self.p_stdarray.std()
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self.p_std_std = self.p_stdarray.std()
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@ -412,3 +423,8 @@ class PDFstatistics(object):
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self.s_mean = self.s_stdarray.mean()
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self.s_mean = self.s_stdarray.mean()
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self.s_std_std = self.s_stdarray.std()
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self.s_std_std = self.s_stdarray.std()
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self.s_median = np.median(self.s_stdarray)
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self.s_median = np.median(self.s_stdarray)
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if __name__ == "__main__":
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rootdir = '/home/sebastianp/Data/Reassessment/Insheim/'
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Insheim = PDFstatistics(rootdir)
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@ -105,6 +105,7 @@ def exp_branches(k, (mu, sig1, sig2, a)):
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'''
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'''
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def _func(k, mu, sig1, sig2, a):
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def _func(k, mu, sig1, sig2, a):
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mu = float(mu)
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if k < mu:
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if k < mu:
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rval = a * np.exp(sig1 * (k - mu))
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rval = a * np.exp(sig1 * (k - mu))
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else:
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else:
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@ -311,7 +312,7 @@ class ProbabilityDensityFunction(object):
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mu = self.mu
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mu = self.mu
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rval = 0
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rval = 0
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for x in self.axis:
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for x in self.axis:
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rval += (x - mu) ** 2 * self.data(x)
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rval += (x - float(mu)) ** 2 * self.data(x)
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return rval * self.incr
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return rval * self.incr
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def prob_lt_val(self, value):
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def prob_lt_val(self, value):
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