Recursive structures

[bearophileHUGS]

(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?
To be more explicit about this function purpose, here are some asserts,
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[i] = [0]
for i in range(n):
v[i][0] = v[i]
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

[Fredrik Lundh]

<be************@lycos.com> 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?
To be more explicit about this function purpose, here are some asserts,
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[i] = [0]
for i in range(n):
v[i][0] = v[i]
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

how about:

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

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

</F>

[Thomas Guettler]

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,

does this help you?

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/

[Scott David Daniels]

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
inquiring about your own data structures (which you'll know
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
expressions to module load time.
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
seen.add(identity)
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
Sc***********@Acm.Org

[bearophileHUGS]

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

[Steven Bethard]

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

[Leif K-Brooks]

be************@lycos.com 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/mo...t.html#l2h-749

[bearophileHUGS]

Leif K-Brooks:

http://python.org/doc/current/lib/mo...t.html#l2h-749

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