[refs #195] realized an object oriented implementation of comparison

comparing pdf represented picks should be easy, thus objects returning everything needed are implemented; histograms and other plots are planned next
This commit is contained in:
Sebastian Wehling-Benatelli 2016-04-05 22:19:55 +02:00
parent a475b366d4
commit 5f9a9242d1

View File

@ -1,6 +1,8 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import copy
from obspy import read_events
from pylot.core.read.io import picks_from_evt
@ -11,27 +13,46 @@ __version__ = _getVersionString()
__author__ = 'sebastianw'
def read_data(fn, type='exp'):
class Comparison(object):
"""
Reads pick data from QuakeML files named FN and returns a dictionary
containing a ProbabilityDensityFunction object for each pick.
:param fn: name of the QuakeML file which contains the picks
:type fn: str
:return: a dictionary containing the picks represented as pdfs
A Comparison object contains information on the evaluated picks' probability
density function and compares these in terms of building the difference of
compared pick sets. The results can be displayed as histograms showing its
properties.
"""
pdf_picks = picks_from_evt(read_events(fn)[0])
def __init__(self, **kwargs):
names = list()
self._pdfs = dict()
for name, fn in kwargs:
self._pdfs[name] = PDFDictionary.from_quakeml(fn)
names.append(name)
if len(names) > 2:
raise ValueError('Comparison is only defined for two '
'arguments!')
self._names = names
for station, phases in pdf_picks.items():
for phase, values in phases.items():
phases[phase] = ProbabilityDensityFunction.fromPick(values['epp'],
values['mpp'],
values['lpp'],
type=type)
def __nonzero__(self):
if not len(self.names) == 2 or not self._pdfs:
return False
return True
return pdf_picks
def get(self, name):
return self._pdfs[name]
@property
def names(self):
return self._names
def compare_picksets(a, b):
@names.setter
def names(self, names):
assert isinstance(names, list) and len(names) == 2, 'variable "names"' \
' is either not a' \
' list or its ' \
'length is not 2:' \
'names : {names}'.format(names=names)
self._names = names
def compare_picksets(self):
"""
Compare two picksets A and B and return a dictionary compiling the results.
Comparison is carried out with the help of pdf representation of the picks
@ -44,18 +65,72 @@ def compare_picksets(a, b):
:return: dictionary containing the resulting comparison pdfs for all picks
:rtype: dict
"""
pdf_a = read_data(a)
pdf_b = read_data(b)
compare_pdfs = dict()
pdf_a = self.get(self.names[0])
pdf_b = self.get(self.names[1])
for station, phases in pdf_a.items():
if station in pdf_b.keys():
compare_pdf = dict()
for phase in phases:
if phase in pdf_b[station].keys():
compare_pdf[phase] = phases[phase] - pdf_b[station][phase]
compare_pdf[phase] = phases[phase] - pdf_b[station][
phase]
if compare_pdf is not None:
compare_pdfs[station] = compare_pdf
return compare_pdfs
class PDFDictionary(object):
"""
A PDFDictionary is a dictionary like object containing structured data on
the probability density function of seismic phase onsets.
"""
def __init__(self, data):
self._pickdata = data
def __nonzero__(self):
if len(self.pick_data) < 1:
return False
else:
return True
@property
def pick_data(self):
return self._pickdata
@pick_data.setter
def pick_data(self, data):
self._pickdata = data
@classmethod
def from_quakeml(self, fn):
cat = read_events(fn)
if len(cat) > 1:
raise NotImplementedError('reading more than one event at the same '
'time is not implemented yet! Sorry!')
self.pick_data = picks_from_evt(cat[0])
def pdf_data(self, type='exp'):
"""
Returns probabiliy density function dictionary containing the
representation of the actual pick_data.
:param type: type of the returned
`~pylot.core.util.pdf.ProbabilityDensityFunction` object
:type type: str
:return: a dictionary containing the picks represented as pdfs
"""
pdf_picks = copy.deepcopy(self.pick_data)
for station, phases in pdf_picks.items():
for phase, values in phases.items():
phases[phase] = ProbabilityDensityFunction.fromPick(values['epp'],
values['mpp'],
values['lpp'],
type=type)
return pdf_picks