# Recursive structures

Discussion in 'Python' started by bearophileHUGS@lycos.com, Dec 20, 2004.

1. ### Guest

(This is a repost from another python newsgroup).
While using some nested data structures, I've seen that I'd like to
have a function that tells me if a given data structure contains one or
more cyclic references (a way to recognise a cycle in a graph is to do
a depth-first search, marking vertices along the way. An already marked
vertex means a cycle.)
Do you know where I can find a function like this?
that call an hypothetical "isrecursive" function (I've added some
examples of big structures because I'd like such function to be fast
for them too):

l = [0]
m = [l, l]
assert isrecursive(m) == False

assert not isrecursive([[[[1]]]])

h = [0]
h[0] = h
print h
assert isrecursive(h)

n = 2000
v = [0] * n
for i in range(n):
v = [0]
for i in range(n):
v[0] = v
assert not (False in map(isrecursive, v) )

n = 10**5
h = range(n)
assert not isrecursive(h)
h[-1] = h
assert isrecursive(h)
h[0] = h
assert isrecursive(h)

h = [0]
h[0] = h
h = tuple(h)
print h
assert isrecursive(h)

d1 = {"a": 1}
d1["a"] = d1
print d1
assert isrecursive(d1)

# I presume that dict keys cannot be recursive
Thank you,
hugs,
Bearophile

, Dec 20, 2004

2. ### Fredrik LundhGuest

<> wrote:

> (This is a repost from another python newsgroup).
> While using some nested data structures, I've seen that I'd like to
> have a function that tells me if a given data structure contains one or
> more cyclic references (a way to recognise a cycle in a graph is to do
> a depth-first search, marking vertices along the way. An already marked
> vertex means a cycle.)
> Do you know where I can find a function like this?
> that call an hypothetical "isrecursive" function (I've added some
> examples of big structures because I'd like such function to be fast
> for them too):
>
> l = [0]
> m = [l, l]
> assert isrecursive(m) == False
>
> assert not isrecursive([[[[1]]]])
>
> h = [0]
> h[0] = h
> print h
> assert isrecursive(h)
>
> n = 2000
> v = [0] * n
> for i in range(n):
> v = [0]
> for i in range(n):
> v[0] = v
> assert not (False in map(isrecursive, v) )
>
> n = 10**5
> h = range(n)
> assert not isrecursive(h)
> h[-1] = h
> assert isrecursive(h)
> h[0] = h
> assert isrecursive(h)
>
> h = [0]
> h[0] = h
> h = tuple(h)
> print h
> assert isrecursive(h)
>
> d1 = {"a": 1}
> d1["a"] = d1
> print d1
> assert isrecursive(d1)
>
> # I presume that dict keys cannot be recursive

def isrecursive(x):
return "..." in repr(x)

(won't work if the structures can contain arbitrary strings, though...)

</F>

Fredrik Lundh, Dec 20, 2004

3. ### Thomas GuettlerGuest

Am Mon, 20 Dec 2004 04:22:24 -0800 schrieb bearophileHUGS:

> I've seen that I'd like to
> have a function that tells me if a given data structure contains one or
> more cyclic references

Hi,

from types import *

def isrecursive(obj, dict=None):
if dict==None:
dict={}
mytype=type(obj)
if mytype in [GeneratorType, SliceType]:
obj=list(obj)
mytype=ListType

l=[]
MethodWrapperType=type(l.__delattr__)

if type(obj) in [NoneType, TypeType, BooleanType, IntType, LongType, FloatType, ComplexType,
StringType, UnicodeType, FunctionType, CodeType, ClassType, MethodType,
UnboundMethodType, BuiltinMethodType, BuiltinFunctionType,
ModuleType, FileType, XRangeType,
EllipsisType, TracebackType, FrameType, BufferType, StringType, MethodWrapperType,
MethodWrapperType]:
return 0
myid=id(obj)
if dict.has_key(myid):
return 1
dict[myid]=1
for attr in dir(obj):
value=getattr(obj, attr)
if isrecursive(value, dict):
return 1
try:
for item in obj:
if isrecursive(item, dict):
return 1
except TypeError:
pass

try:
for key, value in obj.items():
if isrecursive(value, dict):
return 1
except AttributeError:
pass

return 0

def test_isrecursive():
s="sdf"
assert(not isrecursive(s))

l=[]
assert(not isrecursive(l))

l.append(l)
assert(isrecursive(l))
print "OK"

d={}
assert(not isrecursive(d))
d["foo"]=()
assert(not isrecursive(d))
d["foo2"]="foo2"
assert(not isrecursive(d))
d["foo3"]=d
assert(isrecursive(d))

if __name__=="__main__":
test_isrecursive()

--
Thomas Güttler, http://www.thomas-guettler.de/

Thomas Guettler, Dec 20, 2004
4. ### Scott David DanielsGuest

Thomas Guettler wrote:
> code to do the test

The following transformation of his code shows how exceptions
can be used to make code read more clearly. There are a few
issues (such as AVOIDITER) to decide on, and usually you are
more about). Recursive data structures, like deepcopy, are
matters of interpretation of data, and cannot really be answered
without knowing what you mean by your data (and possibly why you
are inquiring). I have also done a few things to speed up the
code (my premature optimization sin) such as lifting constant

from types import *
MethodWrapperType = type([].__delattr__)

LEAVES = set([NoneType, TypeType, BooleanType, IntType, LongType,
FloatType, ComplexType, StringType, UnicodeType, FunctionType,
CodeType, ClassType, MethodType, UnboundMethodType,
BuiltinMethodType, BuiltinFunctionType, ModuleType, FileType,
XRangeType, EllipsisType, TracebackType, FrameType, BufferType,
StringType, MethodWrapperType, MethodWrapperType])

FORCELIST = set([GeneratorType, SliceType])
# these are dicey: can cause lots of side-effects
AVOIDITER = set()
# Here go things like itertools.count -- infinite generators

class RecursionFoundError(Exception): pass

def walks(obj, seen):
identity = id(obj)
if identity in seen:
raise RecursionFoundError, obj
mytype = type(obj)
if mytype in LEAVES:
return
seen = seen.copy() # (obj, obj) is not recursive, so new copy
if mytype in FORCELIST:
obj = list(obj) # this is a new obj, so not in the structure
mytype = ListType
for attr in dir(obj):
walks(getattr(obj, attr), seen)

if mytype not in AVOIDITER:
try:
for item in obj:
walks(item, seen)
except TypeError:
pass
try:
for key, value in obj.items():
walks(key, seen) # Key might be object w/ hash method
walks(value, seen)
except AttributeError:
pass

def isrecursive(obj):
try:
walks(obj, set())
except RecursionFoundError, err:
return True # err.args[0] is the looping object
return False

--Scott David Daniels

Scott David Daniels, Dec 20, 2004
5. ### Guest

Thank you very much Thomas Güttler for you quick answer, but I think
your program doesn't contain an algorithm to spot cycles (like the
usual cyclic graph algorithm). In my first post there was an assert to
spot this problem:

l = [0]
m = [l, l]
print m
print isrecursive(m)

Gives:
[[0], [0]]
1

m contains a shared reference, but not a recursive one.
Thank you Fredrik Lundh too,
bear hugs,
Bearophile

, Dec 20, 2004
6. ### Steven BethardGuest

Scott David Daniels wrote:
> if mytype not in AVOIDITER:
> try:
> for item in obj:
> walks(item, seen)
> except TypeError:
> pass
> try:
> for key, value in obj.items():
> walks(key, seen) # Key might be object w/ hash method
> walks(value, seen)
> except AttributeError:
> pass

You might also write this section something like:

if mytype not in AVOIDITER:
try:
itr = iter(obj)
except TypeError:
pass
else:
for item in itr:
walks(item, seen)
try:
walks(obj[item], seen)
except TypeError:
pass

This should allow you to support any mapping type that supports iter()
over the keys and the __getitem__ protocol.

Steve

Steven Bethard, Dec 20, 2004
7. ### Leif K-BrooksGuest

wrote:
> While using some nested data structures, I've seen that I'd like to
> have a function that tells me if a given data structure contains one or
> more cyclic references (a way to recognise a cycle in a graph is to do
> a depth-first search, marking vertices along the way. An already marked
> vertex means a cycle.)
> Do you know where I can find a function like this?

http://python.org/doc/current/lib/module-pprint.html#l2h-749

Leif K-Brooks, Dec 20, 2004
8. ### Guest

Leif K-Brooks:

Thank you very much, I see that the function is already there, even
with the same name

I've seen that it doesn't work for all my tests, like this one with n =
3000:

from pprint import isrecursive
from time import clock
n = 950
print "n =", n
l = []
for i in xrange(n): l = [n-i, l]
if n < 30: print l
t = clock()
assert not isrecursive(l)
print round(clock()-t, 3), "s"

In the function _safe_repr of the pprint.py module there is a recursive
call:
orepr, oreadable, orecur = _safe_repr(o, context, maxlevels, level)

Probably this _safe_repr function can be modified (with a lot of work?)
to be iterative (using a list as a stack) to avoid such stack overflows
(the list too can overflow, but its capacity is enough for most
purposes). Python doesn't have C-like unsafe buffer overrun, so using a
list as stack probably improves security a little, and probably makes
_safe_repr faster.

See you,
Bearophile

, Dec 20, 2004