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renamed array_core to observable_array

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mzwiessele 2014-04-01 10:20:46 +01:00
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commit c71ee8949c
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GPy/core/parameterization/array_core.py
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@ -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