def ImplementBooleanFn(variables,Ones,dont_care ):
''' An interactive function which takes in templates/maxterms and
prints the Boolean Function and implementable form.
Don't Care Conditions can also be provided (optional)
Eg:
Enter the list of variables
A,B,C
Do you want to enter templates (m) or maxterms(M)?
m
Enter list of templates
1,4,7
Enter list of Don't Care terms
Enter X if there are none
2,5
The logical expression is (((NOT B) AND C) OR (A AND C) OR (A AND (NOT B)))
Can be implemented as OR(AND(NOT(B), C), AND(A, C), AND(A, NOT(B)))
'''
for i in range(len(Ones)):
if Ones[i] >= pow(2, len(variables)):
raise Exception("Error: Invalid Minterm")
qm = QM(variables)
if len(dont_care) != 0:
LogicalExpression = qm.get_function(qm.solve(Ones, dont_care)[1])
else:
LogicalExpression = qm.get_function(qm.solve(Ones)[1])
GateForm = convertExpression(LogicalExpression)
# print("The logical expression is " + LogicalExpression)
# print("Can be implemented as " + GateForm)
return [LogicalExpression, GateForm]
class InternalOptimizer:
def __init__(self, state_word_len, variables):
""" Initialize the internal helper class. """
self.state_word_len = state_word_len
self.variables = variables
variable_names = map(lambda i:'S%d'%i, xrange(self.state_word_len))
variable_names += self.variables
self.qm = QM(variable_names)
def solve(self, f_on, f_off = None):
"""
Returns a function that satisfies the conditions given.
f_on: a list of lambda expressions; if one of the lambda expressions
evaluates to True then the requested function should evaluate to True
f_off: a list of lambda expressions; if one of them evaluates to True
then the requested function whould evaluate to False
returns: a tuple a,b; a is the complexity of the function and b is the
function
"""
self.state_env = self.State()
self.variables_env = self.Variables(self.variables)
c = self.state_word_len
d = len(self.variables)
ones = []
dc = set(i for i in xrange(1<<(d+c)))
for variables_word in xrange(1<<d):
self.variables_env.word = variables_word
for state,state_word in self.state_map.iteritems():
self.state_env.state = state
on = self.evaluate(f_on)
if f_off == None:
off = not on
else:
off = self.evaluate(f_off)
assert not (on and off)
if on:
ones.append(variables_word<<c|state_word)
dc.remove(variables_word<<c|state_word)
elif off:
dc.remove(variables_word<<c|state_word)
dc = list(dc)
return self.qm.solve(ones,dc)
def evaluate(self, f_array):
"""
Evaluates a list of lambda expressions in the state and variables
environment. The lambda expressions are terms of an OR expression.
f_array: a list of lambda expressions
returns: the logical OR after evaluate the lambda expression in the setup
environment
"""
for f in f_array:
if f(self.state_env,self.variables_env):
return True
return False
"""
This class provides access to the state word from the lambda expressions.
"""
class State:
def __getitem__(self, item):
return self.state == item
"""
This class provides access to the input variables from the lambda
expressions.
"""
class Variables:
def __init__(self, variables):
self.variables = {}
for i in xrange(len(variables)):
self.variables[variables[i]] = 1<<i
def __getitem__(self, item):
return bool(self.word & self.variables[item])
def get_function(self, minterms):
""" Retrieve a human readable form of the given function. """
return self.qm.get_function(minterms)
class InternalOptimizer:
def __init__(self, state_word_len, variables):
""" Initialize the internal helper class. """
self.state_word_len = state_word_len
self.variables = variables
variable_names = map(
lambda i: 'S%d' %
i, xrange(
self.state_word_len))
variable_names += self.variables
self.qm = QM(variable_names)
def solve(self, f_on, f_off=None):
"""
Returns a function that satisfies the conditions given.
f_on: a list of lambda expressions; if one of the lambda
expressions evaluates to True then the requested function
should evaluate to True
f_off: a list of lambda expressions; if one of them evaluates
to True then the requested function whould evaluate to False
returns: a tuple a,b; a is the complexity of the function and
b is the function
"""
self.state_env = self.State()
self.variables_env = self.Variables(self.variables)
c = self.state_word_len
d = len(self.variables)
ones = []
dc = set(i for i in xrange(1 << (d + c)))
for variables_word in xrange(1 << d):
self.variables_env.word = variables_word
for state, state_word in self.state_map.iteritems():
self.state_env.state = state
on = self.evaluate(f_on)
if f_off is None:
off = not on
else:
off = self.evaluate(f_off)
assert not (on and off)
if on:
ones.append(variables_word << c | state_word)
dc.remove(variables_word << c | state_word)
elif off:
dc.remove(variables_word << c | state_word)
dc = list(dc)
return self.qm.solve(ones, dc)
def evaluate(self, f_array):
"""
Evaluates a list of lambda expressions in the state and variables
environment. The lambda expressions are terms of an OR expression.
f_array: a list of lambda expressions
returns: the logical OR after evaluate the lambda expression in the
setup environment
"""
for f in f_array:
if f(self.state_env, self.variables_env):
return True
return False
class State:
"""
This class provides access to the state word from the lambda
expressions.
"""
def __getitem__(self, item):
return self.state == item
class Variables:
"""
This class provides access to the input variables from the
lambda expressions.
"""
def __init__(self, variables):
self.variables = {}
for i in xrange(len(variables)):
self.variables[variables[i]] = 1 << i
def __getitem__(self, item):
return bool(self.word & self.variables[item])
def get_function(self, minterms):
""" Retrieve a human readable form of the given function. """
return self.qm.get_function(minterms)