undid earlier changes in PDFStatistics
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@ -350,13 +350,15 @@ class PDFstatistics(object):
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self.stations = {}
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self.p_std = {}
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self.s_std = {}
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self.makeDirlist()
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self.getData()
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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.theta015 = []
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self.theta1 = []
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self.theta2 = []
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self.dirlist = list()
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self.evtdict = {}
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#self.makeDirlist()
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#self.getData()
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#self.getStatistics()
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#self.showData()
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@ -367,8 +369,10 @@ class PDFstatistics(object):
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self.evtdict[rd] = glob.glob1((os.path.join(self.directory, rd)), '*.xml')
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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 evt in self.evtdict[dir]:
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print evt
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self.stations[evt] = []
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self.p_std[evt] = []
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self.s_std[evt] = []
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@ -377,16 +381,24 @@ class PDFstatistics(object):
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# print station, pdfs
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try:
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p_std = pdfs['P'].standard_deviation()
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self.theta015.append(pdfs['P'].qtile_dist_quot(0.015))
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self.theta1.append(pdfs['P'].qtile_dist_quot(0.1))
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self.theta2.append(pdfs['P'].qtile_dist_quot(0.2))
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except KeyError:
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p_std = np.nan
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try:
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s_std = pdfs['S'].standard_deviation()
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self.theta015.append(pdfs['S'].qtile_dist_quot(0.015))
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self.theta1.append(pdfs['S'].qtile_dist_quot(0.1))
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self.theta2.append(pdfs['S'].qtile_dist_quot(0.2))
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except KeyError:
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s_std = np.nan
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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.s_std[evt].append(s_std)
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self.arraylen += 1
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arraylen += 1
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self.arraylen = arraylen
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self.makeArray()
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def makeArray(self):
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@ -426,5 +438,11 @@ class PDFstatistics(object):
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if __name__ == "__main__":
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rootdir = '/home/sebastianp/Data/Reassessment/Insheim/'
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rootdir = '/home/sebastianp/Data/Reassessment/Insheim/2012.10/'
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Insheim = PDFstatistics(rootdir)
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Insheim.dirlist = [rootdir]
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Insheim.evtdict[rootdir] = ['e0002.294.12.xml']
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Insheim.getData()
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print Insheim.theta015
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print Insheim.theta1
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print Insheim.theta2
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@ -325,14 +325,15 @@ class ProbabilityDensityFunction(object):
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raise ValueError('value out of bounds: {0}'.format(value))
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return self.prob_limits((value, self.axis[-1]))
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def prob_limits(self, limits):
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lim = np.arange(limits[0], limits[1], self.incr)
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def prob_limits(self, limits, oversampling=1.):
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sampling = self.incr / oversampling
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lim = np.arange(limits[0], limits[1], sampling)
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data = [self.data(t) for t in lim]
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min_est, max_est = 0., 0.
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for n in range(len(data) - 1):
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min_est += min(data[n], data[n + 1])
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max_est += max(data[n], data[n + 1])
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return (min_est + max_est) / 2. * self.incr
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return (min_est + max_est) / 2. * sampling
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def prob_val(self, value):
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if not (self.axis[0] <= value <= self.axis[-1]):
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@ -352,7 +353,7 @@ class ProbabilityDensityFunction(object):
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m = (r + l) / 2
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diff = prob_value - self.prob_lt_val(m)
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while abs(diff) > eps:
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while abs(diff) > eps and ((r - l) > self.incr):
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if diff > 0:
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l = m
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else:
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@ -367,6 +368,13 @@ class ProbabilityDensityFunction(object):
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return qu - ql
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def qtile_dist_quot(self,x):
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if x <= 0 or x >= 0.5:
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raise ValueError('Value out of range.')
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return self.quantile_distance(0.5-x)/self.quantile_distance(x)
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def plot(self, label=None):
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import matplotlib.pyplot as plt
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