I have a class and some functions. In the 'check_reflexive()' function, there is a variable called reflexive_list. I want to use this variable also in the 'antisymmetric' function.
I checked some examples about class but didn't find a specific example to solve this problem.
I'll be waiting for your advice. Hope you have a nice day
class MyClass():
def __init__(self):
def checkif_pair(k):
for a in k:
if a%2 == 0:
None
else:
return False
return True
def check_reflexive(k):
j = 0
z = 0
reflexive_list = []
while j < len(k):
i = 0
while i < len(k):
if k[i] == k[j]:
tup = k[j],k[i]
reflexive_list.append(tup)
i += 1
else:
None
j = j + 1
else:
None
print(reflexive_list)
if len(reflexive_list) == len(self.list1):
return True
else:
return False
def antisymmetric(k):
antisymettric_list = []
for b in k:
swap1 = b[0]
swap2 = b[1]
newtuple = (swap2, swap1)
antisymettric_list.append(newtuple)
for ü in reflexive_list:
if ü in antisymettric_list:
antisymettric_list.remove(ü)
else:
None
print(antisymettric_list)
for q in antisymettric_list:
if q in k:
print("The system is not Anti-Symmetric.")
break
print("The system is Anti-Symmetric.")
def transitive(k):
result = {}
for first, second in k:
result.setdefault(first, []).append(second)
print(result)
for a, b in k:
for x in result[b]:
if x in result[a]:
None
else:
print("There is no {} in the {}".format(x, result[a]))
return False
return True
You can just use reflexive_list as an instance variable. Just add a constructor where the variable is defined:
class MyClass():
def __init__(self):
self.reflexive_list = []
And everytime you want to use it inside the function, you use self.reflexive_list
Related
I was trying to solve a codewars problem here, and I got a bit stuck. I believe I should be using nested currying in Python.
Let us just take the case of add. Let us constrain the problem even more, and just get nested add working on the right hand side, i.e. write an add function such that
print((add)(3)(add)(5)(4))
prints 12.
It should be possible to nest it as deep as required, for e.g. I want
print((add)(add)(3)(4)(add)(5)(6))
should give me 18.
What I have done so far -
My initial attempt is to use the following nested function -
def add_helper():
current_sum = 0
def inner(inp):
if isinstance(inp, int):
nonlocal current_sum
current_sum += inp
print(f"current_sum = {current_sum}")
return inner
return inner
add = add_helper()
However, this does not do the trick. Instead, I get the following output, for when I do something like print((add)(add)(3)(4)(add)(5)(6))
current_sum = 3
current_sum = 7
current_sum = 12
current_sum = 18
<function add_helper.<locals>.inner at 0x...>
Does anyone know how I have to change my function so that I just return 18, because the function will know it is "done"?
Any help will be appreciated!
UPDATE
After looking at Bharel's comments, I have the following so far -
def add_helper():
val = 0
ops_so_far = []
def inner(inp):
if isinstance(inp, int):
nonlocal val
val += inp
return inner
else:
ops_so_far.append(("+", val))
inp.set_ops_so_far(ops_so_far)
return inp
def set_ops_so_far(inp_list):
nonlocal ops_so_far
ops_so_far = inp_list
def get_val():
nonlocal val
return val
def get_ops_so_far():
nonlocal ops_so_far
return ops_so_far
inner.get_ops_so_far = get_ops_so_far
inner.set_ops_so_far = set_ops_so_far
inner.get_val = get_val
return inner
def mul_helper():
val = 1
ops_so_far = []
def inner(inp):
if isinstance(inp, int):
nonlocal val
val *= inp
return inner
else:
ops_so_far.append(("*", val))
inp.set_ops_so_far(ops_so_far)
return inp
def get_ops_so_far():
nonlocal ops_so_far
return ops_so_far
def set_ops_so_far(inp_list):
nonlocal ops_so_far
ops_so_far = inp_list
def get_val():
nonlocal val
return val
inner.get_ops_so_far = get_ops_so_far
inner.get_val = get_val
inner.set_ops_so_far = set_ops_so_far
return inner
add = add_helper()
mul = mul_helper()
and now when I do
res = (add)(add)(3)(4)(mul)(5)(6)
print(res.get_ops_so_far())
print(res.get_val())
I get
[('+', 0), ('+', 7)]
30
Still not sure if this is the correct direction to be following?
This is how I solved it for anyone still looking in the future -
from copy import deepcopy
def start(arg):
def start_evalutaion(_arg, eval_stack, variables):
new_eval_stack = deepcopy(eval_stack)
new_variables = deepcopy(variables)
to_ret = evaluate_stack(_arg, new_eval_stack, new_variables)
if to_ret is not None:
return to_ret
def inner(inner_arg):
return start_evalutaion(
inner_arg, new_eval_stack, new_variables
)
return inner
return start_evalutaion(arg, [], dict())
add = lambda a, b, variables: variables.get(a, a) + variables.get(b, b)
sub = lambda a, b, variables: variables.get(a, a) - variables.get(b, b)
mul = lambda a, b, variables: variables.get(a, a) * variables.get(b, b)
div = lambda a, b, variables: variables.get(a, a) // variables.get(b, b)
def let(name, val, variables):
variables[name] = val
return
def return_(val, variables):
return variables.get(val, val)
def evaluate_stack(_arg, eval_stack, variables):
if callable(_arg):
if _arg.__name__ == "return_":
req_args = 1
else:
req_args = 2
eval_stack.append((_arg, req_args, []))
else:
while True:
func_to_eval, req_args, args_so_far = eval_stack[-1]
args_so_far.append(_arg)
if len(args_so_far) == req_args:
eval_stack.pop()
_arg = func_to_eval(*args_so_far, variables)
if func_to_eval.__name__ == "return_":
return _arg
elif _arg is None:
break
else:
break
Passes all testcases
The Question
Is there a straightforward algorithm for figuring out if a variable is "used" within a given scope?
In a Python AST, I want to remove all assignments to variables that are not otherwise used anywhere, within a given scope.
Details
Motivating example
In the following code, it is obvious to me (a human), that _hy_anon_var_1 is unused, and therefore the _hy_anon_var_1 = None statements can be removed without changing the result:
# Before
def hailstone_sequence(n: int) -> Iterable[int]:
while n != 1:
if 0 == n % 2:
n //= 2
_hy_anon_var_1 = None
else:
n = 3 * n + 1
_hy_anon_var_1 = None
yield n
# After
def hailstone_sequence(n: int) -> Iterable[int]:
while n != 1:
if 0 == n % 2:
n //= 2
else:
n = 3 * n + 1
yield n
Bonus version
Extend this to []-lookups with string literals as keys.
In this example, I would expect _hyx_letXUffffX25['x'] to be eliminated as unused, because _hyx_letXUffffX25 is local to h, so _hyx_letXUffffX25['x'] is essentially the same thing as a local variable. I would then expect _hyx_letXUffffX25 itself to be eliminated once there are no more references to it.
# Before
def h():
_hyx_letXUffffX25 = {}
_hyx_letXUffffX25['x'] = 5
return 3
# After
def h():
return 3
From what I can tell, this is somewhat of an edge case, and I think the basic algorithmic problem is the same.
Definition of "used"
Assume that no dynamic name lookups are used in the code.
A name is used if any of these are true in a given scope:
It is referenced anywhere in an expression. Examples include: an expression in a return statement, an expression on the right-hand side of an assignment statement, a default argument in a function definition, being referenced inside a local function definition, etc.
It is referenced on the left-hand side of an "augmented assignment" statement, i.e. it is an augtarget therein. This might represent "useless work" in a lot of programs, but for the purpose of this task that's OK and distinct from being an entirely unused name.
It is nonlocal or global. These might be useless nonlocals or globals, but because they reach beyond the given scope, it is OK for my purposes to assume that they are "used".
Please let me know in the comments if this seems incorrect, or if you think I am missing something.
Examples of "used" and "unused"
Example 1: unused
Variable i in f is unused:
def f():
i = 0
return 5
Example 2: unused
Variable x in f is unused:
def f():
def g(x):
return x/5
x = 10
return g(100)
The name x does appear in g, but the variable x in g is local to g. It shadows the variable x created in f, but the two x names are not the same variable.
Variation
If g has no parameter x, then x is in fact used:
def f():
x = 10
def g():
return x/5
return g(100)
Example 3: used
Variable i in f is used:
def f():
i = 0
return i
Example 4: used
Variable accum in silly_map and silly_sum is used in both examples:
def silly_map(func, data):
data = iter(data)
accum = []
def _impl():
try:
value = next(data)
except StopIteration:
return accum
else:
accum.append(value)
return _impl()
return _impl()
def silly_any(func, data):
data = iter(data)
accum = False
def _impl():
nonlocal accum, data
try:
value = next(data)
except StopIteration:
return accum
else:
if value:
data = []
accum = True
else:
return _impl()
return _impl()
The solution below works in two parts. First, the syntax tree of the source is traversed and all unused target assignment statements are discovered. Second, the tree is traversed again via a custom ast.NodeTransformer class, which removes these offending assignment statements. The process is repeated until all unused assignment statements are removed. Once this is finished, the final source is written out.
The ast traverser class:
import ast, itertools, collections as cl
class AssgnCheck:
def __init__(self, scopes = None):
self.scopes = scopes or cl.defaultdict(list)
#classmethod
def eq_ast(cls, a1, a2):
#check that two `ast`s are the same
if type(a1) != type(a2):
return False
if isinstance(a1, list):
return all(cls.eq_ast(*i) for i in itertools.zip_longest(a1, a2))
if not isinstance(a1, ast.AST):
return a1 == a2
return all(cls.eq_ast(getattr(a1, i, None), getattr(a2, i, None))
for i in set(a1._fields)|set(a2._fields) if i != 'ctx')
def check_exist(self, t_ast, s_path):
#traverse the scope stack and remove scope assignments that are discovered in the `ast`
s_scopes = []
for _ast in t_ast:
for sid in s_path[::-1]:
s_scopes.extend(found:=[b for _, b in self.scopes[sid] if AssgnCheck.eq_ast(_ast, b) and \
all(not AssgnCheck.eq_ast(j, b) for j in s_scopes)])
self.scopes[sid] = [(a, b) for a, b in self.scopes[sid] if b not in found]
def traverse(self, _ast, s_path = [1]):
#walk the ast object itself
_t_ast = None
if isinstance(_ast, ast.Assign): #if assignment statement, add ast object to current scope
self.traverse(_ast.targets[0], s_path)
self.scopes[s_path[-1]].append((True, _ast.targets[0]))
_ast = _ast.value
if isinstance(_ast, (ast.ClassDef, ast.FunctionDef, ast.AsyncFunctionDef)):
s_path = [*s_path, (nid:=(1 if not self.scopes else max(self.scopes)+1))]
if isinstance(_ast, (ast.FunctionDef, ast.AsyncFunctionDef)):
self.scopes[nid].extend([(False, ast.Name(i.arg)) for i in _ast.args.args])
_t_ast = [*_ast.args.defaults, *_ast.body]
self.check_exist(_t_ast if _t_ast is not None else [_ast], s_path) #determine if any assignment statement targets have previously defined names
if _t_ast is None:
for _b in _ast._fields:
if isinstance((b:=getattr(_ast, _b)), list):
for i in b:
self.traverse(i, s_path)
elif isinstance(b, ast.AST):
self.traverse(b, s_path)
else:
for _ast in _t_ast:
self.traverse(_ast, s_path)
Putting it all together:
class Visit(ast.NodeTransformer):
def __init__(self, asgn):
super().__init__()
self.asgn = asgn
def visit_Assign(self, node):
#remove assignment nodes marked as unused
if any(node.targets[0] == i for i in self.asgn):
return None
return node
def remove_assgn(f_name):
tree = ast.parse(open(f_name).read())
while True:
r = AssgnCheck()
r.traverse(tree)
if not (k:=[j for b in r.scopes.values() for k, j in b if k]):
break
v = Visit(k)
tree = v.visit(tree)
return ast.unparse(tree)
print(remove_assgn('test_name_assign.py'))
Output Samples
Contents of test_name_assign.py:
def hailstone_sequence(n: int) -> Iterable[int]:
while n != 1:
if 0 == n % 2:
n //= 2
_hy_anon_var_1 = None
else:
n = 3 * n + 1
_hy_anon_var_1 = None
yield n
Output:
def hailstone_sequence(n: int) -> Iterable[int]:
while n != 1:
if 0 == n % 2:
n //= 2
else:
n = 3 * n + 1
yield n
Contents of test_name_assign.py:
def h():
_hyx_letXUffffX25 = {}
_hyx_letXUffffX25['x'] = 5
return 3
Output:
def h():
return 3
Contents of test_name_assign.py:
def f():
i = 0
return 5
Output:
def f():
return 5
Contents of test_name_assign.py:
def f():
x = 10
def g():
return x/5
return g(100)
Ouptut:
def f():
x = 10
def g():
return x / 5
return g(100)
Code first so you'll understand what I'm talking about :
goal = False
count = 0
def function():
if goal==True:
return True
else:
return False
def func():
if dict1["A"]==True:
return True
else:
return False
dict1 = {"A":function()}
dict2 = {"B":func()}
list = [dict1,dict2]
goal = True
for i in list:
count = 0
for x,y in i.items():
if y==True:
count+=1
if count==len(i):
print("Works")
else:
print(i)
>>>{"A":False}
>>>{"B":False}
This is not my current code, but it is the actual issue. This is where I'm asking, how can I update the values in the dicts. Should I do something like :
for i in list:
for x,y in i.items():
y()
?
My current project is used in Ren'Py (.rpy) but as I'm using python blocks, the code works exactly as normal Python.
Within a class named Event, my elements are exactly as it follows:
def ev_check(self):
if self.done==False:
self.count = 0
for x,y in self.conditions.items():
if y==True:
self.count+=1
else:
pass
if self.count==len(self.conditions):
self.valid = True
else:
self.valid = False
else:
self.valid = False
def own_office():
if Promotion_1.done==True: #Once the event is played, .done gets True
return True
else:
return False
def times_worked(x):
if You.worked < x:
return False
else:
return True
Promotion_1.conditions = {"Work 2 times" : times_worked(2)}
Meet_Tigerr.conditions = {"Own office" : own_office()}
#External event that adds a value to the data named You.worked to get it to 2
print(Promotion_1.conditions["Work 2 times"])
>>> False
Expected result : True
Result : False
You can create your custom dict and have this feature. You may try something like this:
class MyDict(dict):
def __getitem__(self, item):
val = super().__getitem__(item)
if callable(val):
return val()
return val
It will work exactly like a dict, except that it will call the callable values for you every time.
d = MyDict()
d['m'] = 1
d['m']
Out[28]: 1
task
Out[33]: <function __main__.task()>
task()
Out[34]: True
d['t'] = task
d['t']
Out[36]: True
EDITED : Modified the code a bit to show how you can even have argument values passed for your parameterized functions:
def func():
return True
def param_func(i):
return 2*i
def param_func2(i, j):
return i*j
class MyDict(dict):
def __getitem__(self, key):
if isinstance(key, tuple):
super_key = key[0]
else:
super_key = key
super_val = super().__getitem__(super_key)
if callable(super_val):
if isinstance(key, tuple):
args = key[1:]
return super_val.__call__(*args)
else:
return super_val.__call__()
return super_val
if __name__ == "__main__":
d = MyDict()
d['num'] = 1
print(d['num'])
d['func'] = func
print(d['func'])
d['param_func'] = param_func
print(d['param_func', 2])
d['param_func2'] = param_func2
print(d['param_func2', 2, 6])
Output :
1
True
4
12
Pylint doesn't like this code:
class sequence(list):
def __init__(self, n):
list.__init__(self)
self += self._generate_collatz_seq(n)
self.pivots = self._generate_pivots()
self.data = self._make_data()
def _collatz_function(self, n):
if n % 2 == 0:
return(int(n/2))
else:
return(3*n + 1)
def _generate_collatz_seq(self, x):
int(x)
sequence_holder = []
while x != 1:
sequence_holder.append(x)
x = self._collatz_function(x)
sequence_holder.append(1)
return sequence_holder
def _generate_pivots(self):
pivots_holder = []
for element in self:
if element % 2 != 0:
pivots_holder.append(element)
return pivots_holder
def _make_data(self):
data_holder = []
data_holder.append(len(self))
data_holder.append(len(self.pivots))
return data_holder
It says
E1101: Instance of 'sequence' has no 'pivots' member(56,36)
This is before I have made any instances of sequence. I'm sure I haven't gone about my task in the most efficient way, but I can't see that I've done anything wrong.
I tried to a leetcode problem. I find one of the following code throws a time limit exceeded error. I created the following testing code. I found the first one pass dictionary as a parameter takes more time the the other one. 0.94s vs 0.84s.
Can anyone explain this ?
class Solution(object):
def longestPalindromeSubseq(self, x):
"""
:type s: str
:rtype: int
"""
#dic = {}
def helper(s, dic):
if len(s) == 0:
return 0
if len(s) == 1:
return 1
if s in dic:
return dic[s]
if s[0] == s[-1]:
res = helper(s[1:-1], dic)+2
else:
l1 = helper(s[:-1], dic)
l2 = helper(s[1:], dic)
res = max(l1,l2)
dic[s] = res
#print (id(dic), dic)
return res
d = {}
ans = helper(x, d)
#print (id(d), d)
return ans
class Solution1(object):
def longestPalindromeSubseq(self, x):
"""
:type s: str
:rtype: int
"""
dic = {}
def helper(s):
if len(s) == 0:
return 0
if len(s) == 1:
return 1
if s in dic:
return dic[s]
if s[0] == s[-1]:
res = helper(s[1:-1])+2
else:
l1 = helper(s[:-1])
l2 = helper(s[1:])
res = max(l1,l2)
dic[s] = res
#print (id(dic), dic)
return res
ans = helper(x)
#print (id(dic), dic)
return ans
import time
if __name__ == "__main__":
x = "gphyvqruxjmwhonjjrgumxjhfyupajxbjgthzdvrdqmdouuukeaxhjumkmmhdglqrrohydrmbvtuwstgkobyzjjtdtjroqpyusfsbjlusekghtfbdctvgmqzeybnwzlhdnhwzptgkzmujfldoiejmvxnorvbiubfflygrkedyirienybosqzrkbpcfidvkkafftgzwrcitqizelhfsruwmtrgaocjcyxdkovtdennrkmxwpdsxpxuarhgusizmwakrmhdwcgvfljhzcskclgrvvbrkesojyhofwqiwhiupujmkcvlywjtmbncurxxmpdskupyvvweuhbsnanzfioirecfxvmgcpwrpmbhmkdtckhvbxnsbcifhqwjjczfokovpqyjmbywtpaqcfjowxnmtirdsfeujyogbzjnjcmqyzciwjqxxgrxblvqbutqittroqadqlsdzihngpfpjovbkpeveidjpfjktavvwurqrgqdomiibfgqxwybcyovysydxyyymmiuwovnevzsjisdwgkcbsookbarezbhnwyqthcvzyodbcwjptvigcphawzxouixhbpezzirbhvomqhxkfdbokblqmrhhioyqubpyqhjrnwhjxsrodtblqxkhezubprqftrqcyrzwywqrgockioqdmzuqjkpmsyohtlcnesbgzqhkalwixfcgyeqdzhnnlzawrdgskurcxfbekbspupbduxqxjeczpmdvssikbivjhinaopbabrmvscthvoqqbkgekcgyrelxkwoawpbrcbszelnxlyikbulgmlwyffurimlfxurjsbzgddxbgqpcdsuutfiivjbyqzhprdqhahpgenjkbiukurvdwapuewrbehczrtswubthodv"
print (x)
t0 = time.time()
sol = Solution()
print (sol.longestPalindromeSubseq(x))
t1 = time.time()
print(t1- t0)
sol1 = Solution1()
print (sol1.longestPalindromeSubseq(x))
t2 = time.time()
print(t2-t1)
Python uses something that is called call by sharing. The function gets only the alias on the parameter. With that in mind, it does't matter what you pass to the function.
But the script may take different time to execute. It is not constant. Using recursion makes it even harder to predict