[task] read statistics from file
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@ -349,19 +349,33 @@ 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.theta015 = []
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self.theta0 = []
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self.theta1 = []
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self.theta2 = []
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self.makefilelist()
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def readTheta(self, arname, dir, fnpattern):
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exec('self.' + arname +' = []')
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filelist = glob.glob1(dir, fnpattern)
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for file in filelist:
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fid = open(os.path.join(dir,file), 'r')
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list = []
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for line in fid.readlines():
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list.append(eval(line))
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exec('self.' + arname + ' += list')
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fid.close()
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def fromFileList(self):
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self.makeFileList()
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self.getData()
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self.getStatistics()
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#self.showData()
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def makefilelist(self, fn_pattern='*'):
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def makeFileList(self, fn_pattern='*'):
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self.evtlist = glob.glob1((os.path.join(self.directory)), '*.xml')
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def getData(self):
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for evt in self.evtlist:
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print evt
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@ -373,14 +387,14 @@ 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.theta0.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.theta0.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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@ -403,29 +417,24 @@ class PDFstatistics(object):
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index += 1
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def histplot(self, array, label=None):
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# baustelle
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def histplot(self, num, label=None):
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binlist = []
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if array[-1] == '5':
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if num == 0:
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bfactor = 0.001
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badd = 0
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elif array[-1] == '1':
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elif num == 1:
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bfactor = 0.003
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badd = 0
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else:
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bfactor = 0.006
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badd = 0.4
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for i in range(100):
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binlist.append(badd+bfactor*i)
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import matplotlib.pyplot as plt
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plt.hist(eval('self.'+array),bins = binlist)
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plt.hist(self.getTheta(num),bins = binlist)
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plt.xlabel('Values')
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plt.ylabel('Frequency')
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#plt.autoscale(axis='x', tight=True)
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title_str = 'Quantile distance quotient distribution'
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if label:
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title_str += ' (' + label + ')'
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@ -435,14 +444,13 @@ class PDFstatistics(object):
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def getTheta(self,number):
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if number == 0:
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return self.theta015
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return self.theta0
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elif number == 1:
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return self.theta1
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elif number == 2:
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return self.theta2
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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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@ -456,9 +464,12 @@ class PDFstatistics(object):
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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/2012.10/'
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Insheim = PDFstatistics(rootdir)
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Insheim.makefilelist()
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Insheim.getData()
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def writeThetaToFile(self,array,out_dir,filename = None):
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fid = open(os.path.join(out_dir,filename), 'w')
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for val in array:
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fid.write(str(val)+'\n')
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fid.close()
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#if __name__ == "__main__":
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