Refactored _reduce_func

wafo/stats/estimation.py

@@ -723,6 +723,7 @@ def plot_all_profiles(phats, plot=None):
         profile_phat_k = Profile(phats, i=k)
         m = 0
         while hasattr(profile_phat_k, 'best_par') and m < 7:
+            # iterate to find optimum phat!
             phats.fit(*profile_phat_k.best_par)
             profile_phat_k = Profile(phats, i=k)
             m += 1
@@ -841,10 +842,9 @@ class ProfileQuantile(Profile):
         prb = exp(self.log_sf)
         return self.fit_dist.dist.isf(prb, *mphat)
 
-    def _set_plot_labels(self, method):
+    def _set_plot_labels(self, method, title='', xlabel='x'):
         title = '{:s} quantile'.format(self.fit_dist.dist.name)
-        super(ProfileQuantile, self)._set_plot_labels(method, title,
-                                                      xlabel='x')
+        super(ProfileQuantile, self)._set_plot_labels(method, title, xlabel)
 
 
 class ProfileProbability(Profile):
@@ -939,7 +939,7 @@ class ProfileProbability(Profile):
         logsf = self.fit_dist.dist.logsf(self.x, *mphat)
         return np.where(np.isfinite(logsf), logsf, np.nan)
 
-    def _set_plot_labels(self, method):
+    def _set_plot_labels(self, method, title='', xlabel=''):
         title = '{:s} probability'.format(self.fit_dist.dist.name)
         xlabel = 'log(sf)'
         super(ProfileProbability, self)._set_plot_labels(method, title, xlabel)
@@ -1154,7 +1154,7 @@ class FitDistribution(rv_frozen):
             t.append('%s = %s\n' % (par, str(getattr(self, par))))
         return ''.join(t)
 
-    def _reduce_func(self, args, kwds):
+    def _convert_fshapes2fnum(self, kwds):
         # First of all, convert fshapes params to fnum: eg for stats.beta,
         # shapes='a, b'. To fix `a`, can specify either `f1` or `fa`.
         # Convert the latter into the former.
@@ -1169,10 +1169,13 @@ class FitDistribution(rv_frozen):
                         raise ValueError("Duplicate entry for %s." % key)
                     else:
                         kwds[key] = val
+        return kwds
+
+    def _unpack_args_kwds(self, args, kwds):
+        kwds = self._convert_fshapes2fnum(kwds)
         args = list(args)
-        nargs = len(args)
         fixedn = []
-        names = ['f%d' % n for n in range(nargs - 2)] + ['floc', 'fscale']
+        names = ['f%d' % n for n in range(len(args) - 2)] + ['floc', 'fscale']
         x0 = []
         for n, key in enumerate(names):
             if key in kwds:
@@ -1180,7 +1183,12 @@ class FitDistribution(rv_frozen):
                 args[n] = kwds.pop(key)
             else:
                 x0.append(args[n])
+        return x0, args, fixedn
 
+    def _reduce_func(self, args, kwds):
+        x0, args, fixedn = self._unpack_args_kwds(args, kwds)
+
+        nargs = len(args)
         fitfun = self._fitfun
 
         if len(fixedn) == 0:
@@ -1194,7 +1202,7 @@ class FitDistribution(rv_frozen):
             def restore(args, theta):
                 # Replace with theta for all numbers not in fixedn
                 # This allows the non-fixed values to vary, but
-                #  we still call self.nnlf with all parameters.
+                # we still call self.nnlf with all parameters.
                 i = 0
                 for n in range(nargs):
                     if n not in fixedn:
@@ -1358,7 +1366,7 @@ class FitDistribution(rv_frozen):
 
         H = np.asmatrix(self._hessian(self._fitfun, self.par, self.data))
         # H = -nd.Hessian(lambda par: self._fitfun(par, self.data),
-        #               method='forward')(self.par)
+        #                 method='forward')(self.par)
         self.H = H
         try:
             if somefixed: