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[change] pdf for difference of picks estimated via curve_fit (to be tested)

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This commit is contained in:
Sebastian Wehling-Benatelli 2016-07-11 14:15:41 +02:00
parent 08fc5d554b
commit ef613e48a4
2 changed files with 78 additions and 33 deletions
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104
pylot/core/util/pdf.py
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@ -3,6 +3,7 @@
import warnings
import numpy as np
import scipy.optimize
from obspy import UTCDateTime
from pylot.core.util.utils import find_nearest, clims
from pylot.core.util.version import get_git_version as _getVersionString
@ -34,7 +35,7 @@ def gauss_parameter(te, tm, tl, eta):
a1 = 2 / (1 + sig2 / sig1)
a2 = 2 / (1 + sig1 / sig2)
return sig1, sig2, a1, a2
return tm, sig1, sig2, a1, a2
def exp_parameter(te, tm, tl, eta):
@ -53,7 +54,7 @@ def exp_parameter(te, tm, tl, eta):
sig2 = np.log(eta) / (tm - tl)
a = 1 / (1 / sig1 + 1 / sig2)
return sig1, sig2, a
return tm, sig1, sig2, a
def gauss_branches(k, mu, sig1, sig2, a1, a2):
@ -76,12 +77,18 @@ def gauss_branches(k, mu, sig1, sig2, a1, a2):
:returns fun_vals: list with function values along axes x
'''
if k < mu:
rval = a1 * 1 / (np.sqrt(2 * np.pi) * sig1) * np.exp(-((k - mu) / sig1) ** 2 / 2)
else:
rval = a2 * 1 / (np.sqrt(2 * np.pi) * sig2) * np.exp(-((k - mu) /
sig2) ** 2 / 2)
return rval
def _func(k, mu, sig1, sig2, a1, a2):
if k < mu:
rval = a1 * 1 / (np.sqrt(2 * np.pi) * sig1) * np.exp(-((k - mu) / sig1) ** 2 / 2)
else:
rval = a2 * 1 / (np.sqrt(2 * np.pi) * sig2) * np.exp(-((k - mu) /
sig2) ** 2 / 2)
return rval
try:
return [_func(x, mu, sig1, sig2, a1, a2) for x in iter(k)]
except TypeError:
return _func(k, mu, sig1, sig2, a1, a2)
def exp_branches(k, mu, sig1, sig2, a):
@ -97,11 +104,19 @@ def exp_branches(k, mu, sig1, sig2, a):
:param a:
:returns fun_vals: list with function values along axes x:
'''
if k < mu:
rval = a * np.exp(sig1 * (k - mu))
else:
rval = a * np.exp(-sig2 * (k - mu))
return rval
def _func(k, mu, sig1, sig2, a):
if k < mu:
rval = a * np.exp(sig1 * (k - mu))
else:
rval = a * np.exp(-sig2 * (k - mu))
return rval
try:
return [_func(x, mu, sig1, sig2, a) for x in iter(k)]
except TypeError:
return _func(k, mu, sig1, sig2, a)
# define container dictionaries for different types of pdfs
parameter = dict(gauss=gauss_parameter, exp=exp_parameter)
@ -125,42 +140,52 @@ class ProbabilityDensityFunction(object):
self.params = params
def __add__(self, other):
assert isinstance(other, ProbabilityDensityFunction), \
'both operands must be of type ProbabilityDensityFunction'
if not self.incr == other.incr:
raise NotImplementedError('Upsampling of the lower sampled PDF not implemented yet!')
else:
incr = self.incr
x0, npts = self.commonlimits(incr, other)
x0, incr, npts = self.commonparameter(other)
axis = create_axis(x0, incr, npts)
pdf_self = np.array([self.data(x) for x in axis])
pdf_other = np.array([other.data(x) for x in axis])
pdf = lambda x: np.convolve(pdf_self, pdf_other, 'full') * incr
pdf = np.convolve(pdf_self, pdf_other, 'full') * incr
# shift axis values for correct plotting
npts = pdf.size
x0 *= 2
return ProbabilityDensityFunction(x0, incr, npts, pdf)
axis = create_axis(x0, incr, npts)
params, pcov = scipy.optimize.curve_fit(branches['gauss'], axis, pdf)
mu = axis[np.where(pdf == max(pdf))]
return ProbabilityDensityFunction(x0, incr, npts, branches['gauss'], mu,
params)
def __sub__(self, other):
assert isinstance(other, ProbabilityDensityFunction), \
'both operands must be of type ProbabilityDensityFunction'
x0, incr, npts, pdf_self, pdf_other = self.rearrange(other)
x0, incr, npts = self.commonparameter(other)
axis = create_axis(x0, incr, npts)
pdf_self = np.array([self.data(x) for x in axis])
pdf_other = np.array([other.data(x) for x in axis])
pdf = np.correlate(pdf_self, pdf_other, 'full') * incr
npts = len(pdf)
# shift axis values for correct plotting
npts = len(pdf)
midpoint = npts / 2
x0 = -incr * midpoint
axis = create_axis(x0, incr, npts)
return ProbabilityDensityFunction(x0, incr, npts, pdf)
mu = axis[np.where(pdf == max(pdf))][0]
bounds = ([mu, 0., 0., 0., 0.],[mu, np.inf, np.inf, np.inf, np.inf])
params, pcov = scipy.optimize.curve_fit(branches['gauss'], axis, pdf,
bounds=bounds)
return ProbabilityDensityFunction(x0, incr, npts, branches['gauss'], mu,
params)
def __nonzero__(self):
prec = self.precision(self.incr)
@ -179,10 +204,7 @@ class ProbabilityDensityFunction(object):
return prec if prec >= 0 else 0
def data(self, value):
try:
return [self._pdf(k, self.mu, *self.params) for k in iter(value)]
except TypeError:
return self._pdf(value, self.mu, *self.params)
return self._pdf(value, *self.params)
@property
def mu(self):
@ -359,6 +381,13 @@ class ProbabilityDensityFunction(object):
return l1, r1
def cincr(self, other):
if not self.incr == other.incr:
raise NotImplementedError(
'Upsampling of the lower sampled PDF not implemented yet!')
else:
return self.incr
def commonlimits(self, incr, other, max_npts=1e5):
'''
Takes an increment incr and two left and two right limits and returns
@ -392,6 +421,15 @@ class ProbabilityDensityFunction(object):
return x0, npts
def commonparameter(self, other):
assert isinstance(other, ProbabilityDensityFunction), \
'both operands must be of type ProbabilityDensityFunction'
incr = self.cincr(other)
x0, npts = self.commonlimits(incr, other)
return x0, incr, npts
def rearrange(self, other):
'''

7
pylot/testing/test_pdf.py Normal file
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@ -0,0 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pylot.core.util.pdf import ProbabilityDensityFunction
pdf = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
pdf2 = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
diff = pdf - pdf2