Ongoing changes to symbolic.

GPy/core/parameterization/observable_array.py

@@ -1,137 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-__updated__ = '2014-03-31'
-
-import numpy as np
-from parameter_core import Observable, Pickleable
-
-class ObsAr(np.ndarray, Pickleable, Observable):
-    """
-    An ndarray which reports changes to its observers.
-    The observers can add themselves with a callable, which
-    will be called every time this array changes. The callable
-    takes exactly one argument, which is this array itself.
-    """
-    __array_priority__ = -1 # Never give back ObsAr
-    def __new__(cls, input_array, *a, **kw):
-        if not isinstance(input_array, ObsAr):
-            obj = np.atleast_1d(np.require(input_array, dtype=np.float64, requirements=['W', 'C'])).view(cls)
-        else: obj = input_array
-        #cls.__name__ = "ObsAr" # because of fixed printing of `array` in np printing
-        super(ObsAr, obj).__init__(*a, **kw)
-        return obj
-
-    def __array_finalize__(self, obj):
-        # see InfoArray.__array_finalize__ for comments
-        if obj is None: return
-        self._observer_callables_ = getattr(obj, '_observer_callables_', None)
-
-    def __array_wrap__(self, out_arr, context=None):
-        return out_arr.view(np.ndarray)
-
-    def copy(self):
-        memo = {}
-        memo[id(self)] = self
-        return self.__deepcopy__(memo)
-
-    def __deepcopy__(self, memo):
-        s = self.__new__(self.__class__, input_array=self.view(np.ndarray).copy())
-        memo[id(self)] = s
-        import copy
-        s.__dict__.update(copy.deepcopy(self.__dict__, memo))
-        return s
-
-    def __reduce__(self):
-        func, args, state = super(ObsAr, self).__reduce__()
-        return func, args, (state, Pickleable.__getstate__(self))
-
-    def __setstate__(self, state):
-        np.ndarray.__setstate__(self, state[0])
-        Pickleable.__setstate__(self, state[1])
-
-    def __setitem__(self, s, val):
-        super(ObsAr, self).__setitem__(s, val)
-        self.notify_observers()
-
-    def __getslice__(self, start, stop):
-        return self.__getitem__(slice(start, stop))
-
-    def __setslice__(self, start, stop, val):
-        return self.__setitem__(slice(start, stop), val)
-
-    def __ilshift__(self, *args, **kwargs):
-        r = np.ndarray.__ilshift__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-    def __irshift__(self, *args, **kwargs):
-        r = np.ndarray.__irshift__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __ixor__(self, *args, **kwargs):
-        r = np.ndarray.__ixor__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __ipow__(self, *args, **kwargs):
-        r = np.ndarray.__ipow__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __ifloordiv__(self, *args, **kwargs):
-        r = np.ndarray.__ifloordiv__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __isub__(self, *args, **kwargs):
-        r = np.ndarray.__isub__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __ior__(self, *args, **kwargs):
-        r = np.ndarray.__ior__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __itruediv__(self, *args, **kwargs):
-        r = np.ndarray.__itruediv__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __idiv__(self, *args, **kwargs):
-        r = np.ndarray.__idiv__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __iand__(self, *args, **kwargs):
-        r = np.ndarray.__iand__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __imod__(self, *args, **kwargs):
-        r = np.ndarray.__imod__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __iadd__(self, *args, **kwargs):
-        r = np.ndarray.__iadd__(self, *args, **kwargs)
-        self.notify_observers()
-        return r
-
-
-    def __imul__(self, *args, **kwargs):
-        r = np.ndarray.__imul__(self, *args, **kwargs)
-        self.notify_observers()
-        return r