Simplified _nlogps

wafo/stats/estimation.py

@@ -1121,6 +1121,42 @@ class FitDistribution(rv_frozen):
     def _nnlf(self, theta, x):
         return self.dist._penalized_nnlf(theta, x, self._penalty)
 
+    def _log_dprb(self, x, args, scale, dist):
+        lowertail = True
+        if lowertail:
+            prb = np.hstack((0.0, dist.cdf(x, *args), 1.0))
+            dprb = np.diff(prb)
+        else:
+            prb = np.hstack((1.0, dist.sf(x, *args), 0.0))
+            dprb = -np.diff(prb)
+
+        log_dprb = log(dprb + _XMIN)
+
+        dx = np.diff(x, axis=0)
+        tie = dx == 0
+        if np.any(tie):
+            # TODO : implement this method for treating ties in data:
+            # Assume measuring error is delta. Then compute
+            # yL = F(xi - delta, theta)
+            # yU = F(xi + delta, theta)
+            # and replace
+            # logDj = log((yU-yL)/(r-1)) for j = i+1,i+2,...i+r-1
+            # The following is OK when only minimization of T is wanted
+            i_tie, = np.nonzero(tie)
+            tiedata = x[i_tie]
+            log_dprb[i_tie + 1] = log(dist._pdf(tiedata, *args)) - log(scale)
+        return log_dprb
+
+    def _compute_penalty(self, Nbad, finiteD):
+        nonfiniteD = 1 - finiteD
+        Nbad += np.sum(nonfiniteD, axis=0)
+        if Nbad > 0:
+            if self._penalty is None:
+                penalty = 100.0 * log(_XMAX) * Nbad
+            else:
+                penalty = 0.0
+        return penalty
+
     def _nlogps(self, theta, x):
         """ Moran's negative log Product Spacings statistic
 
@@ -1146,58 +1182,33 @@ class FitDistribution(rv_frozen):
         product of spacings.",
         IMS Lecture Notes Monograph Series 2006, Vol. 52, pp. 272-283
         """
-        n = 2 if isinstance(self.dist, rv_continuous) else 1
+        def parse_theta(theta, dist):
-        try:
+            n = 2 if isinstance(dist, rv_continuous) else 1
-            loc = theta[-n]
+            try:
-            scale = theta[-1]
+                loc = theta[-n]
-            args = tuple(theta[:-n])
+                scale = theta[-1]
-        except IndexError:
+                args = tuple(theta[:-n])
-            raise ValueError("Not enough input arguments.")
+            except IndexError:
-        if not isinstance(self.dist, rv_continuous):
+                raise ValueError("Not enough input arguments.")
-            scale = 1
+            if not isinstance(dist, rv_continuous):
-        if not self.dist._argcheck(*args) or scale <= 0:
+                scale = 1
-            return np.inf
+            return args, loc, scale
+
         dist = self.dist
+        args, loc, scale = parse_theta(theta, dist)
+        if not dist._argcheck(*args) or scale <= 0:
+            return np.inf
         x = asarray((x - loc) / scale)
         cond0 = (x < dist.a) | (dist.b < x)
         Nbad = np.sum(cond0)
         if Nbad > 0:
             x = argsreduce(~cond0, x)[0]
 
-        lowertail = True
+        log_dprb = self._log_dprb(x, args, scale, dist)
-        if lowertail:
-            prb = np.hstack((0.0, dist.cdf(x, *args), 1.0))
-            dprb = np.diff(prb)
-        else:
-            prb = np.hstack((1.0, dist.sf(x, *args), 0.0))
-            dprb = -np.diff(prb)
 
-        logD = log(dprb + _XMIN)
+        finiteD = np.isfinite(log_dprb)
-        dx = np.diff(x, axis=0)
+        penalty = self._compute_penalty(Nbad, finiteD)
-        tie = (dx == 0)
+        return -np.sum(log_dprb[finiteD], axis=0) + penalty
-        if np.any(tie):
-            # TODO : implement this method for treating ties in data:
-            # Assume measuring error is delta. Then compute
-            # yL = F(xi - delta, theta)
-            # yU = F(xi + delta, theta)
-            # and replace
-            # logDj = log((yU-yL)/(r-1)) for j = i+1,i+2,...i+r-1
-
-            # The following is OK when only minimization of T is wanted
-            i_tie, = np.nonzero(tie)
-            tiedata = x[i_tie]
-            logD[i_tie + 1] = log(dist._pdf(tiedata, *args)) - log(scale)
-
-        finiteD = np.isfinite(logD)
-        nonfiniteD = 1 - finiteD
-        Nbad += np.sum(nonfiniteD, axis=0)
-        if Nbad > 0:
-            if self._penalty is None:
-                penalty = 100.0 * log(_XMAX) * Nbad
-            else:
-                penalty = 0.0
-            return -np.sum(logD[finiteD], axis=0) + penalty
-        return -np.sum(logD, axis=0)
 
     @staticmethod
     def _get_optimizer(kwds):