def indices(self, states):
"""
indices(states) -> index_array
returns an array of the enumeration indices for the
states stored in the array 'states'.
"""
if self.stoc_vec == None:
states = numpy.asarray(states)
else:
states = numpy.asarray(states[self.stoc_vec,:])
# assume states is a two dimensional array with
# potentially non unique rows
# due to non-uniqueness, there is a bit of messing
# about in order to reduce the states to
# a unique set, find the indices for those states, then
# invert the unique operation
#pdb.set_trace()
unique_states, unique_inverse = lexarrayset.unique(states,return_inverse=True)
# subtlety : we need the boolean array members to correspond
# to the ordered states and thus also to the current index,
# hence we test to see which elements of ordered_states
# are contained in the unique states
#
# note that this differs from the members query in the
# contains method
members = lexarrayset.member(self.ordered_states, unique_states)
#member_index = numpy.array(self.index[members] + self.offset)
member_index = numpy.arange(len(self.index))[members]
return member_index[unique_inverse]
def indices(self, states):
"""
indices(states) -> index_array
returns an array of the enumeration indices for the
states stored in the array 'states'.
"""
if self.stoc_vec is None:
states = numpy.asarray(states)
else:
states = numpy.asarray(states[self.stoc_vec, :])
# assume states is a two dimensional array with
# potentially non unique rows
# due to non-uniqueness, there is a bit of messing
# about in order to reduce the states to
# a unique set, find the indices for those states, then
# invert the unique operation
#pdb.set_trace()
unique_states, unique_inverse = lexarrayset.unique(states,
return_inverse=True)
# subtlety : we need the boolean array members to correspond
# to the ordered states and thus also to the current index,
# hence we test to see which elements of ordered_states
# are contained in the unique states
#
# note that this differs from the members query in the
# contains method
members = lexarrayset.member(self.ordered_states, unique_states)
#member_index = numpy.array(self.index[members] + self.offset)
member_index = numpy.arange(len(self.index))[members]
return member_index[unique_inverse]
def contains(self, states):
"""
contains(states) -> bool_array
returns a boolean array of flags indicates which of the
states stored in the array 'states' are contained in the
state enumeration.
"""
if self.stoc_vec == None:
states = numpy.asarray(states)
else:
states = numpy.asarray(states[self.stoc_vec,:])
unique_states, unique_inverse = lexarrayset.unique(states,return_inverse=True)
# subtlety : we need the boolean array members to correspond to the
# unique states, hence we test to see which elements of the unique
# states are contained in the ordered states
#
# note that this differs from the members query in the indices
# method
members = lexarrayset.member(unique_states,self.ordered_states)
return members[unique_inverse]
def contains(self, states):
"""
contains(states) -> bool_array
returns a boolean array of flags indicates which of the
states stored in the array 'states' are contained in the
state enumeration.
"""
if self.stoc_vec is None:
states = numpy.asarray(states)
else:
states = numpy.asarray(states[self.stoc_vec, :])
unique_states, unique_inverse = lexarrayset.unique(states,
return_inverse=True)
# subtlety : we need the boolean array members to correspond to the
# unique states, hence we test to see which elements of the unique
# states are contained in the ordered states
#
# note that this differs from the members query in the indices
# method
members = lexarrayset.member(unique_states, self.ordered_states)
return members[unique_inverse]
