Package csb :: Package statistics :: Package samplers |
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# Source Code for Package csb.statistics.samplers

      1  """
      2  Defines abstract samplers.
      3  """
      4
      5  import numpy as np
      6  import csb.core
      7
      8  from abc import ABCMeta, abstractmethod, abstractproperty
      9
     10
     11 -class DimensionError(TypeError):
     12      pass
     13
     14 -class AbstractSampler(object):
     15      """
     16      Abstract interface for sampling algorithms.
     17      """
     18
     19      __metaclass__ = ABCMeta
     20
     21      @abstractmethod
     22 -    def sample(self):
     23          """
     24          Draw a sample.
     25          @rtype: L{AbstractState}
     26          """
     27          pass
     28
     29 -class AbstractState(object):
     30      """
     31      Represents a point in phase-space.
     32      """
     33
     34      __metaclass__ = ABCMeta
     35
     36      @abstractproperty
     37 -    def position(self):
     38          pass
     39
     40      @abstractproperty
     41 -    def momentum(self):
     42          pass
     43
     44 -class State(AbstractState):
     45      """
     46      Represents a point in phase-space.
     47      """
     48
     49      @staticmethod
     50 -    def check_flat_array(*args):
     51          """
     52          Check whether arguments are flat, one-dimensional numpy arrays.
     53          """
     54
     55          for q in args:
     56              if not isinstance(q, np.ndarray):
     57                  raise TypeError(q, 'numpy.ndarray expected!')
     58
     59              if not len(q.squeeze().shape) <= 1:
     60                  raise DimensionError(q, '1d numpy.ndarray expected!')
     61
     62      @staticmethod
     63 -    def check_equal_length(q, p):
     64          """
     65          Check whether arguments have equal length.
     66          """
     67
     68          if len(q) != len(p):
     69              raise DimensionError(p, 'momentum needs to have the same dimension as coordinates!')
     70
     71 -    def __init__(self, position, momentum=None):
     72
     73          self._position = None
     74          self._momentum = None
     75
     76          self.position = position
     77          self.momentum = momentum
     78
     79 -    def __eq__(self, other):
     80
     81          return self.position == other.position and self.momentum == other.momentum
     82
     83      @property
     84 -    def position(self):
     85          return self._position.copy()
     86      @position.setter
     87 -    def position(self, value):
     88          State.check_flat_array(value)
     89          self._position = np.array(value)
     90
     91      @property
     92 -    def momentum(self):
     93          if self._momentum is None:
     94              return None
     95          else:
     96              return self._momentum.copy()
     97      @momentum.setter
     98 -    def momentum(self, value):
     99          if not value is None:
    100              State.check_flat_array(value)
    101              State.check_equal_length(value, self.position)
    102              self._momentum = np.array(value)
    103          else:
    104              self._momentum = None
    105
    106 -    def clone(self):
    107          if self.momentum is not None:
    108              return self.__class__(self.position.copy(), self.momentum.copy())
    109          else:
    110              return self.__class__(self.position.copy())
    111
    112
    113 -class EnsembleState(csb.core.BaseCollectionContainer, AbstractState):
    114      """
    115      Defines an Ensemble Monte Carlo state; it is a read-only collection
    116      of State objects.
    117
    118      @param items: initialization list of states
    119      @type items: list of L{States}
    120      """
    121
    122 -    def __init__(self, items):
    123          super(EnsembleState, self).__init__(items, type=State)
    124
    125      @property
    126 -    def position(self):
    127          return np.array([s.position for s in self])
    128
    129      @property
    130 -    def momentum(self):
    131          return np.array([s.momentum for s in self])
    132