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[new] started implementation of probability density representation routines

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Sebastian Wehling-Benatelli 2016-02-05 13:41:22 +01:00
parent 0d0b43103b
commit ada9f4e780
2 changed files with 151 additions and 111 deletions
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111
pylot/core/pick/utils.py
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@ -133,117 +133,6 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, stealthMode = False):
return EPick, LPick, PickError return EPick, LPick, PickError
def gauss_parameter(te, tm, tl, eta):
'''
takes three onset times and returns the parameters sig1, sig2, a1 and a2
to represent the pick as a probability density funtion (PDF) with two
Gauss branches
:param te:
:param tm:
:param tl:
:param eta:
:return:
'''
sig1 = (tm - te) / np.sqrt(2 * np.log(1 / eta))
sig2 = (tl - tm) / np.sqrt(2 * np.log(1 / eta))
a1 = 2 / (1 + sig2 / sig1)
a2 = 2 / (1 + sig1 / sig2)
return sig1, sig2, a1, a2
def exp_parameter(te, tm, tl, eta):
'''
takes three onset times te, tm and tl and returns the parameters sig1,
sig2 and a to represent the pick as a probability density function (PDF)
with two exponential decay branches
:param te:
:param tm:
:param tl:
:param eta:
:return:
'''
sig1 = np.log(eta) / (te - tm)
sig2 = np.log(eta) / (tm - tl)
a = 1 / (1 / sig1 + 1 / sig2)
return sig1, sig2, a
def gauss_branches(x, mu, sig1, sig2, a1, a2):
'''
function gauss_branches takes an axes x, a center value mu, two sigma
values sig1 and sig2 and two scaling factors a1 and a2 and return a
list containing the values of a probability density function (PDF)
consisting of gauss branches
:param x:
:type x:
:param mu:
:type mu:
:param sig1:
:type sig1:
:param sig2:
:type sig2:
:param a1:
:type a1:
:param a2:
:returns fun_vals: list with function values along axes x
'''
fun_vals = []
for k in x:
if k < mu:
fun_vals.append(a1 * 1 / (np.sqrt(2 * np.pi) * sig1) * np.exp(-((k - mu) / sig1)**2 / 2 ))
else:
fun_vals.append(a2 * 1 / (np.sqrt(2 * np.pi) * sig2) * np.exp(-((k - mu) / sig2)**2 / 2))
return fun_vals
def exp_branches(x, mu, sig1, sig2, a):
'''
function exp_branches takes an axes x, a center value mu, two sigma
values sig1 and sig2 and a scaling factor a and return a
list containing the values of a probability density function (PDF)
consisting of exponential decay branches
:param x:
:param mu:
:param sig1:
:param sig2:
:param a:
:returns fun_vals: list with function values along axes x:
'''
fun_vals = []
for k in x:
if k < mu:
fun_vals.append(a * np.exp(sig1 * (k - mu)))
else:
fun_vals.append(a * np.exp(-sig2 * (k - mu)))
return fun_vals
def pick_pdf(t, te, tm, tl, type='gauss', eta=0.01):
'''
:param t:
:param te:
:param tm:
:param tl:
:param type:
:param eta:
:param args:
:return:
'''
parameter = dict(gauss=gauss_parameter, exp=exp_parameter)
branches = dict(gauss=gauss_branches, exp=exp_branches)
params = parameter[type](te, tm, tl, eta)
return branches[type](t, tm, *params)
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None): def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
''' '''
Function to derive first motion (polarity) of given phase onset Pick. Function to derive first motion (polarity) of given phase onset Pick.

151
pylot/core/util/pdf.py Normal file
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@ -0,0 +1,151 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
__author__ = 'sebastianw'
def gauss_parameter(te, tm, tl, eta):
'''
takes three onset times and returns the parameters sig1, sig2, a1 and a2
to represent the pick as a probability density funtion (PDF) with two
Gauss branches
:param te:
:param tm:
:param tl:
:param eta:
:return:
'''
sig1 = (tm - te) / np.sqrt(2 * np.log(1 / eta))
sig2 = (tl - tm) / np.sqrt(2 * np.log(1 / eta))
a1 = 2 / (1 + sig2 / sig1)
a2 = 2 / (1 + sig1 / sig2)
return sig1, sig2, a1, a2
def exp_parameter(te, tm, tl, eta):
'''
takes three onset times te, tm and tl and returns the parameters sig1,
sig2 and a to represent the pick as a probability density function (PDF)
with two exponential decay branches
:param te:
:param tm:
:param tl:
:param eta:
:return:
'''
sig1 = np.log(eta) / (te - tm)
sig2 = np.log(eta) / (tm - tl)
a = 1 / (1 / sig1 + 1 / sig2)
return sig1, sig2, a
def gauss_branches(x, mu, sig1, sig2, a1, a2):
'''
function gauss_branches takes an axes x, a center value mu, two sigma
values sig1 and sig2 and two scaling factors a1 and a2 and return a
list containing the values of a probability density function (PDF)
consisting of gauss branches
:param x:
:type x:
:param mu:
:type mu:
:param sig1:
:type sig1:
:param sig2:
:type sig2:
:param a1:
:type a1:
:param a2:
:returns fun_vals: list with function values along axes x
'''
fun_vals = []
for k in x:
if k < mu:
fun_vals.append(a1 * 1 / (np.sqrt(2 * np.pi) * sig1) * np.exp(-((k - mu) / sig1)**2 / 2 ))
else:
fun_vals.append(a2 * 1 / (np.sqrt(2 * np.pi) * sig2) * np.exp(-((k - mu) / sig2)**2 / 2))
return np.array(fun_vals)
def exp_branches(x, mu, sig1, sig2, a):
'''
function exp_branches takes an axes x, a center value mu, two sigma
values sig1 and sig2 and a scaling factor a and return a
list containing the values of a probability density function (PDF)
consisting of exponential decay branches
:param x:
:param mu:
:param sig1:
:param sig2:
:param a:
:returns fun_vals: list with function values along axes x:
'''
fun_vals = []
for k in x:
if k < mu:
fun_vals.append(a * np.exp(sig1 * (k - mu)))
else:
fun_vals.append(a * np.exp(-sig2 * (k - mu)))
return np.array(fun_vals)
# define container dictionaries for different types of pdfs
parameter = dict(gauss=gauss_parameter, exp=exp_parameter)
branches = dict(gauss=gauss_branches, exp=exp_branches)
class ProbabilityDensityFunction(object):
'''
A probability density function toolkit.
'''
version = __version__
def __init__(self, x, lbound, midpoint, rbound, decfact=0.01, type='gauss'):
'''
Initialize a new ProbabilityDensityFunction object. Takes arguments x,
lbound, midpoint and rbound to define a probability density function
defined on the interval of x. Maximum density is given at the midpoint
and on the boundaries the function has declined to decfact times the
maximum value. Integration of the function over a particular interval
gives the probability for the variable value to lie in that interval.
:param x: interval on which the pdf is defined
:param lbound: left boundary
:param midpoint: point of maximum probability density
:param rbound: right boundary
:param decfact: boundary decline factor
:param type: determines the type of the probability density function's
branches
'''
self.axis = np.array(x)
self.nodes = dict(lbound=lbound, midpoint=midpoint, rbound=rbound, eta=decfact)
self.type = type
def __add__(self, other):
pass
def __sub__(self, other):
pass
@property
def type(self):
return self.type
@type.setter
def type(self, type):
self.type = type
def params(self):
return parameter[self.type](**self.nodes)
def data(self):
return branches[self.type](self.axis, self.nodes['midpoint'], *self.params())