[change] new concept for evaluation of the pdf instead of holding a sampled array works now also for difference pdfs using a scipy's spline interpolation capabilities

pylot/core/util/pdf.py

@@ -3,9 +3,8 @@
 
 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.utils import fit_curve, find_nearest, clims
 from pylot.core.util.version import get_git_version as _getVersionString
 
 __version__ = _getVersionString()
@@ -57,7 +56,7 @@ def exp_parameter(te, tm, tl, eta):
     return tm, sig1, sig2, a
 
 
-def gauss_branches(k, mu, sig1, sig2, a1, a2):
+def gauss_branches(k, (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
@@ -91,7 +90,7 @@ def gauss_branches(k, mu, sig1, sig2, a1, a2):
         return _func(k, mu, sig1, sig2, a1, a2)
 
 
-def exp_branches(k, mu, sig1, sig2, a):
+def exp_branches(k, (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
@@ -130,17 +129,22 @@ class ProbabilityDensityFunction(object):
 
     version = __version__
 
-    def __init__(self, x0, incr, npts, pdf, mu, params):
+    def __init__(self, x0, incr, npts, pdf, mu, params, eta=0.01):
         self.x0 = x0
         self.incr = incr
         self.npts = npts
         self.axis = create_axis(x0, incr, npts)
         self.mu = mu
+        self.eta = eta
         self._pdf = pdf
         self.params = params
 
     def __add__(self, other):
 
+        assert self.eta == other.eta, 'decline factors differ please use equally defined pdfs for comparison'
+
+        eta = self.eta
+
         x0, incr, npts = self.commonparameter(other)
 
         axis = create_axis(x0, incr, npts)
@@ -153,16 +157,19 @@ class ProbabilityDensityFunction(object):
         npts = pdf.size
         x0 *= 2
         axis = create_axis(x0, incr, npts)
+        mu = axis[np.where(pdf == max(pdf))][0]
 
-        params, pcov = scipy.optimize.curve_fit(branches['gauss'], axis, pdf)
+        func, params = fit_curve(axis, pdf)
 
-        mu = axis[np.where(pdf == max(pdf))]
+        return ProbabilityDensityFunction(x0, incr, npts, func, mu,
-
+                                          params, eta)
-        return ProbabilityDensityFunction(x0, incr, npts, branches['gauss'], mu,
-                                          params)
 
     def __sub__(self, other):
 
+        assert self.eta == other.eta, 'decline factors differ please use equally defined pdfs for comparison'
+
+        eta = self.eta
+
         x0, incr, npts = self.commonparameter(other)
 
         axis = create_axis(x0, incr, npts)
@@ -176,16 +183,12 @@ class ProbabilityDensityFunction(object):
         midpoint = npts / 2
         x0 = -incr * midpoint
         axis = create_axis(x0, incr, npts)
-
         mu = axis[np.where(pdf == max(pdf))][0]
 
-        bounds = ([mu, 0., 0., 0., 0.],[mu, np.inf, np.inf, np.inf, np.inf])
+        func, params = fit_curve(axis, pdf)
-
-        params, pcov = scipy.optimize.curve_fit(branches['gauss'], axis, pdf,
-                                                bounds=bounds)
 
         return ProbabilityDensityFunction(x0, incr, npts, branches['gauss'], mu,
-                                          params)
+                                          params, eta)
 
     def __nonzero__(self):
         prec = self.precision(self.incr)
@@ -204,7 +207,15 @@ class ProbabilityDensityFunction(object):
         return prec if prec >= 0 else 0
 
     def data(self, value):
-        return self._pdf(value, *self.params)
+        return self._pdf(value, self.params)
+
+    @property
+    def eta(self):
+        return self._eta
+
+    @eta.setter
+    def eta(self, value):
+        self._eta = value
 
     @property
     def mu(self):
@@ -271,7 +282,7 @@ class ProbabilityDensityFunction(object):
 
         # return the object
         return ProbabilityDensityFunction(x0, incr, npts, pdf, barycentre,
-                                          params)
+                                          params, decfact)
 
     def broadcast(self, pdf, si, ei, data):
         try:

pylot/core/util/utils.py

@@ -3,6 +3,7 @@
 
 import hashlib
 import numpy as np
+from scipy.interpolate import splrep, splev
 import os
 import pwd
 import re
@@ -16,6 +17,17 @@ def _pickle_method(m):
     else:
         return getattr, (m.im_self, m.im_func.func_name)
 
+def fit_curve(x, y):
+
+    return splev, splrep(x, y)
+
+def getindexbounds(f, eta):
+    mi = f.argmax()
+    m = max(f)
+    b = m * eta
+    l = find_nearest(f[:mi], b)
+    u = find_nearest(f[mi:], b) + mi
+    return mi, l, u
 
 def worker(func, input, cores='max', async=False):
     import multiprocessing