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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 distribution input variables used for stocastic modeling """
from enthought.traits.api import HasTraits, Property, Float, Int
from enthought.traits.ui.api import View, Item
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
class Constant(Distribution):
""" A constant distribution where all values are the same """
value = Float
traits_view = View(Item('value'))
def _get_value(self, n):
return numpy.repeat(self.value, n)
class Gaussian(Distribution):
""" A gaussian distribution """
mean = Float(50.0)
std = Float(2.0)
traits_view = View(Item('mean'), Item('std'))
def _get_value(self, n):
return self._state.normal(self.mean, self.std, n)
class Triangular(Distribution):
""" A triangular distribution """
mode = Float
low = Float
high = Float
traits_view = View(Item('mode'), Item('low'), Item('high'))
def _get_value(self, n):
return self._state.triangular(self.low, self.mode, self.high, n)
class Uniform(Distribution):
""" A uniform distribution """
low = Float
high = Float
view = View(Item('low'), Item('high'))
def _get_value(self, n):
return self._state.uniform(self.low, self.high, n)
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