Small refactoring FitDistribution

wafo/stats/_distn_infrastructure.py

@@ -561,7 +561,7 @@ def fit2(self, data, *args, **kwds):
     `data` is sorted using this function, so if `copydata`==False the data
     in your namespace will be sorted as well.
     '''
-    return FitDistribution(self, data, *args, **kwds)
+    return FitDistribution(self, data, args, **kwds)
 
 
 rv_generic.freeze = freeze

wafo/stats/estimation.py

@@ -13,7 +13,7 @@ import warnings
 from wafo.plotbackend import plotbackend
 from wafo.misc import ecross, findcross, argsreduce
 from wafo.stats._constants import _EPS, _XMAX
-from wafo.stats._distn_infrastructure import rv_frozen
+from wafo.stats._distn_infrastructure import rv_frozen, rv_continuous
 from scipy._lib.six import string_types
 import numdifftools as nd  # @UnresolvedImport
 from scipy import special
@@ -21,7 +21,7 @@ from scipy.linalg import pinv2
 from scipy import optimize
 
 import numpy as np
-from numpy import (alltrue, arange, ravel, zeros, log, sqrt, exp,
+from numpy import (alltrue, arange, zeros, log, sqrt, exp,
                    atleast_1d, any, asarray, nan, pi, isfinite)
 from numpy import flatnonzero as nonzero
 
@@ -550,7 +550,8 @@ class FitDistribution(rv_frozen):
     >>> sf_ci = Lsf.get_bounds(alpha=0.2)
     '''
 
-    def __init__(self, dist, data, *args, **kwds):
+    def __init__(self, dist, data, args=(), method='ML', alpha=0.05,
+                 par_fix=None, search=True, copydata=True, **kwds):
         extradoc = '''
     plotfitsummary()
          Plot various diagnostic plots to asses quality of fit.
@@ -603,25 +604,23 @@ class FitDistribution(rv_frozen):
         self.dist = dist
         numargs = dist.numargs
 
-        self.method = self.alpha = self.par_fix = self.search = None
-        self.copydata = None
-        m_variables = ['method', 'alpha', 'par_fix', 'search', 'copydata']
-        m_defaults = ['ml', 0.05, None, True, True]
-        for (name, val) in zip(m_variables, m_defaults):
-            setattr(self, name, kwds.pop(name, val))
+        self.method = method
+        self.alpha = alpha
+        self.par_fix = par_fix
+        self.search = search
+        self.copydata = copydata
 
         if self.method.lower()[:].startswith('mps'):
             self._fitfun = self._nlogps
         else:
             self._fitfun = self._nnlf
 
-        self.data = ravel(data)
+        self.data = np.ravel(data)
         if self.copydata:
             self.data = self.data.copy()
         self.data.sort()
-
-        par, fixedn = self._fit(*args, **kwds)
-        # super(FitDistribution, self).__init__(dist, *par)
+        par, fixedn = self._fit(*args, **kwds.copy())
+        super(FitDistribution, self).__init__(dist, *par)
         self.par = arr(par)
         somefixed = len(fixedn) > 0
         if somefixed:
@@ -658,8 +657,9 @@ class FitDistribution(rv_frozen):
         # 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.
-        if self.shapes:
-            shapes = self.shapes.replace(',', ' ').split()
+        shapes = self.dist.shapes
+        if shapes:
+            shapes = shapes.replace(',', ' ').split()
             for j, s in enumerate(shapes):
                 val = kwds.pop('f' + s, None) or kwds.pop('fix_' + s, None)
                 if val is not None:
@@ -784,16 +784,16 @@ class FitDistribution(rv_frozen):
         product of spacings.",
         IMS Lecture Notes Monograph Series 2006, Vol. 52, pp. 272-283
         """
-        n = 2 if self._rv_continous else 1
+        n = 2 if isinstance(self.dist, rv_continuous) else 1
         try:
             loc = theta[-n]
             scale = theta[-1]
             args = tuple(theta[:-n])
         except IndexError:
             raise ValueError("Not enough input arguments.")
-        if not self._rv_continous:
+        if not isinstance(self.dist, rv_continuous):
             scale = 1
-        if not self._argcheck(*args) or scale <= 0:
+        if not self.dist._argcheck(*args) or scale <= 0:
             return np.inf
         dist = self.dist
         x = asarray((x - loc) / scale)
@@ -867,7 +867,7 @@ class FitDistribution(rv_frozen):
             # by now kwds must be empty, since everybody took what they needed
             if kwds:
                 raise TypeError("Unknown arguments: %s." % kwds)
-            vals = optimizer(func, x0, args=(ravel(data),), disp=0)
+            vals = optimizer(func, x0, args=(np.ravel(data),), disp=0)
             vals = tuple(vals)
         else:
             vals = tuple(x0)