openmedialibrary_platform/Darwin/lib/python2.7/unittest/util.py
2014-05-16 01:20:41 +02:00

156 lines
4.5 KiB
Python

"""Various utility functions."""
from collections import namedtuple, OrderedDict
__unittest = True
_MAX_LENGTH = 80
def safe_repr(obj, short=False):
try:
result = repr(obj)
except Exception:
result = object.__repr__(obj)
if not short or len(result) < _MAX_LENGTH:
return result
return result[:_MAX_LENGTH] + ' [truncated]...'
def strclass(cls):
return "%s.%s" % (cls.__module__, cls.__name__)
def sorted_list_difference(expected, actual):
"""Finds elements in only one or the other of two, sorted input lists.
Returns a two-element tuple of lists. The first list contains those
elements in the "expected" list but not in the "actual" list, and the
second contains those elements in the "actual" list but not in the
"expected" list. Duplicate elements in either input list are ignored.
"""
i = j = 0
missing = []
unexpected = []
while True:
try:
e = expected[i]
a = actual[j]
if e < a:
missing.append(e)
i += 1
while expected[i] == e:
i += 1
elif e > a:
unexpected.append(a)
j += 1
while actual[j] == a:
j += 1
else:
i += 1
try:
while expected[i] == e:
i += 1
finally:
j += 1
while actual[j] == a:
j += 1
except IndexError:
missing.extend(expected[i:])
unexpected.extend(actual[j:])
break
return missing, unexpected
def unorderable_list_difference(expected, actual, ignore_duplicate=False):
"""Same behavior as sorted_list_difference but
for lists of unorderable items (like dicts).
As it does a linear search per item (remove) it
has O(n*n) performance.
"""
missing = []
unexpected = []
while expected:
item = expected.pop()
try:
actual.remove(item)
except ValueError:
missing.append(item)
if ignore_duplicate:
for lst in expected, actual:
try:
while True:
lst.remove(item)
except ValueError:
pass
if ignore_duplicate:
while actual:
item = actual.pop()
unexpected.append(item)
try:
while True:
actual.remove(item)
except ValueError:
pass
return missing, unexpected
# anything left in actual is unexpected
return missing, actual
_Mismatch = namedtuple('Mismatch', 'actual expected value')
def _count_diff_all_purpose(actual, expected):
'Returns list of (cnt_act, cnt_exp, elem) triples where the counts differ'
# elements need not be hashable
s, t = list(actual), list(expected)
m, n = len(s), len(t)
NULL = object()
result = []
for i, elem in enumerate(s):
if elem is NULL:
continue
cnt_s = cnt_t = 0
for j in range(i, m):
if s[j] == elem:
cnt_s += 1
s[j] = NULL
for j, other_elem in enumerate(t):
if other_elem == elem:
cnt_t += 1
t[j] = NULL
if cnt_s != cnt_t:
diff = _Mismatch(cnt_s, cnt_t, elem)
result.append(diff)
for i, elem in enumerate(t):
if elem is NULL:
continue
cnt_t = 0
for j in range(i, n):
if t[j] == elem:
cnt_t += 1
t[j] = NULL
diff = _Mismatch(0, cnt_t, elem)
result.append(diff)
return result
def _ordered_count(iterable):
'Return dict of element counts, in the order they were first seen'
c = OrderedDict()
for elem in iterable:
c[elem] = c.get(elem, 0) + 1
return c
def _count_diff_hashable(actual, expected):
'Returns list of (cnt_act, cnt_exp, elem) triples where the counts differ'
# elements must be hashable
s, t = _ordered_count(actual), _ordered_count(expected)
result = []
for elem, cnt_s in s.items():
cnt_t = t.get(elem, 0)
if cnt_s != cnt_t:
diff = _Mismatch(cnt_s, cnt_t, elem)
result.append(diff)
for elem, cnt_t in t.items():
if elem not in s:
diff = _Mismatch(0, cnt_t, elem)
result.append(diff)
return result