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#------------------------------------------------------------------------------
# Copyright (c) 2007, Enthought, Inc.
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD
# license included in enthought/LICENSE.txt and may be redistributed only
# under the conditions described in the aforementioned license. The license
# is also available online at http://www.enthought.com/licenses/BSD.txt
# Thanks for using Enthought open source!
#
# Author: Enthought, Inc.
# Description: <Enthought statistical distribution package component>
#------------------------------------------------------------------------------
""" Base class representing ditribution input variables used for stocastic modeling """
from enthought.traits.api import HasTraits, Enum, Property, Float, Int
from enthought.traits.ui.api import View, Item, Handler, InstanceEditor
import numpy
class Distribution(HasTraits):
""" Base Class for input variables representing a variable which
produces a range of values
"""
# the values representing the distribution
values = Property(Int)
_values = None
# how many values should be generated?
samples = Int(10)
_state = None
def _get_values(self):
""" getter for the values property """
if self._state is None:
self._state = numpy.random.RandomState()
if self._values is None:
self._values = self._get_value(self.samples)
return self._values
def _get_value(self, n):
""" returns 'n' values for the distribution """
raise NotImplemented
def get_state(self):
""" returns the random state variable """
if self._state is None:
self.set_state(None)
return self._state.get_state()
def set_state(self, state):
""" sets the random state. If the argument is None the state
will be initialized to a new random state. The method
returns the state that was set
"""
if state is None:
self._state = numpy.random.RandomState()
else:
self._state.set_state(state)
#invalidate the cached values
self._values = None
return self._state.get_state()
def _anytrait_changed(self):
#invalidate the _values so they have to be regenerated
self._values = None
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