[new] implementation of a probability density function representation of the pick (untested)

This commit is contained in:
Sebastian Wehling-Benatelli 2016-02-01 06:13:17 +01:00
parent 70f2761003
commit 5170a0b1c6

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@ -133,6 +133,117 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, stealthMode = False):
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):
'''
Function to derive first motion (polarity) of given phase onset Pick.