[enhancement] improved functionality and flexibility of PDFstatistics class
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@ -12,6 +12,7 @@ from obspy import read_events
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from pylot.core.io.phases import picksdict_from_picks
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from pylot.core.util.pdf import ProbabilityDensityFunction
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from pylot.core.util.utils import find_in_list
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from pylot.core.util.version import get_git_version as _getVersionString
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__version__ = _getVersionString()
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@ -356,9 +357,9 @@ class PDFstatistics(object):
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def __init__(self, directory):
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"""Initiates some values needed when dealing with pdfs later"""
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self.directory = directory
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self.evtlist = list()
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self.return_phase = None
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self._rootdir = directory
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self._evtlist = list()
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self._rphase = None
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self.make_fnlist()
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def make_fnlist(self, fn_pattern='*.xml'):
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@ -368,136 +369,97 @@ class PDFstatistics(object):
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:type fn_pattern: string
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:return: creates a list of events saved in the PDFstatistics object.
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"""
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evtlist = glob.glob1((os.path.join(self.directory)), fn_pattern)
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if not evtlist:
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for root, _, files in os.walk(self.directory):
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evtlist = list()
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for root, _, files in os.walk(self.root):
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for file in files:
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if file.endswith(fn_pattern[1:]):
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evtlist.append(os.path.join(root, file))
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self.evtlist = evtlist
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self._evtlist = evtlist
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def __iter__(self):
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"""Iterating over the PDFstatistics object yields every single pdf from the list of events"""
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assert isinstance(self.return_phase, str), 'phase has to be set before being able to iterate over items...'
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for evt in self.evtlist:
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self.getPDFDict(self.directory, evt)
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for station, pdfs in self.pdfdict.pdf_data.items():
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try:
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yield pdfs[self.return_phase]
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except KeyError:
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continue
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for evt in self._evtlist:
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yield PDFDictionary.from_quakeml(evt)
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def set_return_phase(self, type):
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def __getitem__(self, item):
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evt = find_in_list(self._evtlist, item)
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if evt:
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return PDFDictionary.from_quakeml(evt)
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return None
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@property
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def root(self):
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return self._rootdir
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@root.setter
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def root(self, value):
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if os.path.exists(value):
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self._rootdir = value
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else:
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raise ValueError("path doesn't exist: %s" % value)
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@property
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def curphase(self):
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"""
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Sets the phase typ of event data that is returned on iteration over the object.
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:param type: can be either p (p-phase) or s (s-phase).
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:type type: string
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return the current phase type of interest
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:return: current phase
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"""
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return self._rphase
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@curphase.setter
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def curphase(self, type):
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"""
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setter method for property curphase
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:param type: specify the phase type of interest
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:type type: string ('p' or 's')
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:return: -
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"""
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if type.upper() not in 'PS':
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raise ValueError("phase type must be either 'P' or 'S'!")
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else:
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self.return_phase = type.upper()
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self._rphase = type.upper()
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def quantile_distances(self, value):
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def get(self, property='std', value=None):
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"""
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takes a probability value and and returns the distance
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between two complementary quantiles
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.. math::
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QA_\alpha = Q(1 - \alpha) - Q(\alpha)
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takes a property str and a probability value and returns all
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property's values for the current phase of interest
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:func:`self.curphase`
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:param property: property name (default: 'std')
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:type property: str
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:param value: probability value :math:\alpha
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:type value: float
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:return: list of all quantile distances for all pdfs in
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the list of events.
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:return: list containing all property's values
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"""
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assert isinstance(self.curphase,
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str), 'phase has to be set before being ' \
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'able to iterate over items...'
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rlist = []
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for pdf in self:
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rval = pdf.quantile_distance(value)
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rlist.append(rval)
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return rlist
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method_options = dict(STD='standard_deviation',
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Q='quantile',
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QD='quantile_distance',
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QDF='quantile_dist_frac')
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def quantile_distance_fractions(self, value):
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"""
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takes a probability value and returns the fraction of two
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corresponding quantile distances
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.. math::
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Q\Theta_\alpha = \frac{QA(0.5 - \alpha)}{QA(\alpha)}
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:param value: probability value :math:\alpha
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:return: returns a list of all quantile fractions for all pdfs in
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the list of events.
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"""
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rlist = list()
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for pdf in self:
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rval = pdf.quantile_dist_frac(value)
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rlist.append(rval)
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return rlist
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def getSTD(self):
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"""
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Iterates over PDFstatistics object and returns the standard
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deviation of all pdfs in the list of events.
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:return: saves an instance of self.p_stdarray or
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self.s_stdarray, depending on set phase.
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"""
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std = []
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for pdf in self:
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# create method caller for easy mapping
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if property.upper() == 'STD':
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method = operator.methodcaller(method_options[property.upper()])
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elif value is not None:
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try:
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std.append(pdf.standard_deviation())
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method = operator.methodcaller(method_options[property.upper()],
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value)
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except KeyError:
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continue
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std = np.array(std)
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self.set_stdarray(std)
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def set_stdarray(self, array):
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"""
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Helper function for self.getSTD(). This function
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should not be called directly.
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"""
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if self.return_phase == 'P':
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self.p_stdarray = array
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elif self.return_phase == 'S':
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self.s_stdarray = array
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raise KeyError('unknwon property: {0}'.format(property.upper()))
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else:
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raise ValueError('phase type not set properly...\n'
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'Actual phase type: {0}'.format(self.return_phase))
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raise ValueError("for call to method {0} value has to be "
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"defined but is 'None' ".format(method_options[
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property.upper()]))
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for pdf_dict in self:
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# create worklist
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wlist = pdf_dict.get_all(self.curphase)
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# map method calls to object in worklist
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rlist += map(method, wlist)
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def getPDFDict(self, month, evt):
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"""
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Helper function for __iter__(). Should not be called directly.
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"""
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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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"""
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On call function will get mean, median and standard deviation values
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from self.p_stdarray and self.s_stdarray. Both must be
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instances before calling this function.
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:return: Creates instances of self.p_mean, self.p_std_std and self.p_median
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for both phases (simultaneously) for the PDFstatistics object.
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"""
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if not self.p_stdarray or not self.s_stdarray:
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raise NotImplementedError('Arrays are not properly set yet!')
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elif type(self.p_stdarray) != type(np.zeros(1)) or type(self.s_stdarray) != type(np.zeros(1)):
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raise TypeError('Array is not a proper numpy array.')
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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_median = np.median(self.p_stdarray)
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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_median = np.median(self.s_stdarray)
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return rlist
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def writeThetaToFile(self,array,out_dir):
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"""
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@ -518,10 +480,8 @@ class PDFstatistics(object):
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def main():
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root_dir ='/home/sebastianp/Codetesting/xmls/'
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Insheim = PDFstatistics(root_dir)
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Insheim.make_fnlist()
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Insheim.set_return_phase('p')
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Insheim.getSTD()
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qdlist = Insheim.quantile_distance_fractions(0.2)
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Insheim.curphase = 'p'
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qdlist = Insheim.get('qdf', 0.2)
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print qdlist
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