Reverted the addition of a NORMALIZE_NUMBERS option, per Tim Peter's

request.  Tim says that "correct 'fuzzy' comparison of floats cannot
be automated."  (The motivation behind adding the new option
was verifying interactive examples in Python's latex documentation;
several such examples use numbers that don't print consistently on
different platforms.)
This commit is contained in:
Edward Loper 2004-09-28 05:50:57 +00:00
parent 4cda01e260
commit 7d88a58e85
3 changed files with 2 additions and 199 deletions

View File

@ -581,17 +581,6 @@ TypeError: object doesn't support item assignment
\end{datadesc}
\begin{datadesc}{NORMALIZE_NUMBERS}
When specified, number literals in the expected output will match
corresponding number literals in the actual output if their values
are equal (to ten digits of precision). For example, \code{1.1}
will match \code{1.1000000000000001}; and \code{1L} will match
\code{1} and \code{1.0}. Currently, \constant{NORMALIZE_NUMBERS}
can fail to normalize numbers when used in conjunction with
ellipsis. In particular, if an ellipsis marker matches one or
more numbers, then number normalization is not supported.
\end{datadesc}
\begin{datadesc}{COMPARISON_FLAGS}
A bitmask or'ing together all the comparison flags above.
\end{datadesc}
@ -713,7 +702,7 @@ can be useful.
\versionchanged[Constants \constant{DONT_ACCEPT_BLANKLINE},
\constant{NORMALIZE_WHITESPACE}, \constant{ELLIPSIS},
\constant{IGNORE_EXCEPTION_DETAIL}, \constant{NORMALIZE_NUMBERS},
\constant{IGNORE_EXCEPTION_DETAIL},
\constant{REPORT_UDIFF}, \constant{REPORT_CDIFF},
\constant{REPORT_NDIFF}, \constant{REPORT_ONLY_FIRST_FAILURE},
\constant{COMPARISON_FLAGS} and \constant{REPORTING_FLAGS}
@ -751,7 +740,6 @@ in any particular order, so a test like
% Hey! What happened to Monty Python examples?
% Tim: ask Guido -- it's his example!
% doctest: ignore
\begin{verbatim}
>>> foo()
{"Hermione": "hippogryph", "Harry": "broomstick"}
@ -759,7 +747,6 @@ in any particular order, so a test like
is vulnerable! One workaround is to do
% doctest: ignore
\begin{verbatim}
>>> foo() == {"Hermione": "hippogryph", "Harry": "broomstick"}
True
@ -767,7 +754,6 @@ True
instead. Another is to do
% doctest: ignore
\begin{verbatim}
>>> d = foo().items()
>>> d.sort()
@ -779,7 +765,6 @@ There are others, but you get the idea.
Another bad idea is to print things that embed an object address, like
% doctest: ignore
\begin{verbatim}
>>> id(1.0) # certain to fail some of the time
7948648
@ -791,7 +776,6 @@ Another bad idea is to print things that embed an object address, like
The \constant{ELLIPSIS} directive gives a nice approach for the last
example:
% doctest: ignore
\begin{verbatim}
>>> C() #doctest: +ELLIPSIS
<__main__.C instance at 0x...>
@ -801,7 +785,6 @@ Floating-point numbers are also subject to small output variations across
platforms, because Python defers to the platform C library for float
formatting, and C libraries vary widely in quality here.
% doctest: ignore
\begin{verbatim}
>>> 1./7 # risky
0.14285714285714285
@ -1635,7 +1618,6 @@ Doctest provides several mechanisms for debugging doctest examples:
Then an interactive Python session may look like this:
% doctest: ignore
\begin{verbatim}
>>> import a, doctest
>>> doctest.testmod(a)

View File

@ -55,7 +55,6 @@ __all__ = [
'NORMALIZE_WHITESPACE',
'ELLIPSIS',
'IGNORE_EXCEPTION_DETAIL',
'NORMALIZE_NUMBERS',
'COMPARISON_FLAGS',
'REPORT_UDIFF',
'REPORT_CDIFF',
@ -140,14 +139,12 @@ DONT_ACCEPT_BLANKLINE = register_optionflag('DONT_ACCEPT_BLANKLINE')
NORMALIZE_WHITESPACE = register_optionflag('NORMALIZE_WHITESPACE')
ELLIPSIS = register_optionflag('ELLIPSIS')
IGNORE_EXCEPTION_DETAIL = register_optionflag('IGNORE_EXCEPTION_DETAIL')
NORMALIZE_NUMBERS = register_optionflag('NORMALIZE_NUMBERS')
COMPARISON_FLAGS = (DONT_ACCEPT_TRUE_FOR_1 |
DONT_ACCEPT_BLANKLINE |
NORMALIZE_WHITESPACE |
ELLIPSIS |
IGNORE_EXCEPTION_DETAIL |
NORMALIZE_NUMBERS)
IGNORE_EXCEPTION_DETAIL)
REPORT_UDIFF = register_optionflag('REPORT_UDIFF')
REPORT_CDIFF = register_optionflag('REPORT_CDIFF')
@ -280,72 +277,6 @@ class _SpoofOut(StringIO):
if hasattr(self, "softspace"):
del self.softspace
# The number of digits of precision that must be equal for
# NORMALIZE_NUMBERS to consider two numbers equal.
_NORMALIZE_NUMBERS_PRECISION_THRESHOLD = 10
# A regular expression that matches Python number literals. This is
# used by _normalize_numbers to look for numbers that should be
# normalized.
_NUMBER_LITERAL = re.compile(r'''
(\d+[.]\d*(?:[eE][-+]?\d+)?[jJ]? | # float (w/ digits left of ".")
[.]\d+(?:[eE][-+]?\d+)?[jJ]? | # float (no digits left of ".")
\d+ (?:[eE][-+]?\d+) [jJ]? | # float (no ".", exponent only)
\d [jJ] | # float (no ".", imaginary only)
0[xX]\d+[lL]? | # hexint
0[0-7]*[lL]? | # octint or zero
\d+[lL]? ) # decint
''', re.VERBOSE)
def _normalize_numbers(want, got):
"""
If all the numbers in `want` and `got` match (one-for-one), then
return a new version of `got` with the exact number strings from
`want` spliced in. Two numbers match if `str` of their float
values are equal. (I.e., `x` matches `y` if
`str(float(x))==str(float(y))`).
"""
want_pieces = _NUMBER_LITERAL.split(want)
got_pieces = _NUMBER_LITERAL.split(got)
# If they don't have the same number of numbers, fail immediately.
if len(want_pieces) != len(got_pieces):
return got
# If any individual numbers don't match, then fail.
for i in range(1, len(got_pieces), 2):
w, g = eval(want_pieces[i]), eval(got_pieces[i])
if not _numbers_match(w, g):
return got
# Success; replace numbers in got w/ numbers from want.
for i in range(1, len(got_pieces), 2):
got_pieces[i] = want_pieces[i]
return ''.join(got_pieces)
def _numbers_match(x, y):
"""
A helper function for _normalize_numbers, that returns true if the
numbers `x` and `y` are close enough to match for NORMALIZE_NUMBERS.
"""
# Equal numbers match.
if x == y:
return True
# Split up complex numbers into real & imag.
if isinstance(x, complex):
return (isinstance(y, complex) and
_numbers_match(x.real, y.real) and
_numbers_match(x.imag, y.imag))
# If the signs are different, they don't match.
if x*y < 0:
return False
# If one is zero and the other isn't, they don't match.
if x==0 or y==0:
return False
# They're not exactly equal, but are they close enough?
threshold = 10**-_NORMALIZE_NUMBERS_PRECISION_THRESHOLD
return (abs(x-y) / min(abs(x), abs(y))) < threshold
# Worst-case linear-time ellipsis matching.
def _ellipsis_match(want, got):
"""
@ -1572,13 +1503,6 @@ class OutputChecker:
if got == want:
return True
# This flag causes doctest to treat numbers that are within a
# small threshold as if they are equal.
if optionflags & NORMALIZE_NUMBERS:
got = _normalize_numbers(want, got)
if got == want:
return True
# The ELLIPSIS flag says to let the sequence "..." in `want`
# match any substring in `got`.
if optionflags & ELLIPSIS:
@ -1859,7 +1783,6 @@ def testmod(m=None, name=None, globs=None, verbose=None, isprivate=None,
NORMALIZE_WHITESPACE
ELLIPSIS
IGNORE_EXCEPTION_DETAIL
NORMALIZE_NUMBERS
REPORT_UDIFF
REPORT_CDIFF
REPORT_NDIFF
@ -1982,7 +1905,6 @@ def testfile(filename, module_relative=True, name=None, package=None,
NORMALIZE_WHITESPACE
ELLIPSIS
IGNORE_EXCEPTION_DETAIL
NORMALIZE_NUMBERS
REPORT_UDIFF
REPORT_CDIFF
REPORT_NDIFF

View File

@ -1032,107 +1032,6 @@ treated as equal:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
The NORMALIZE_NUMBERS flag causes numbers that are equal (to
approximately 10 decimal places) but formatted differently to match.
>>> def f(x): '''
... Numbers will match if they are exactly equal:
...
... >>> print 1.1, 'intervening text', 1L # should match
... 1.1 intervening text 1L
... >>> print 1.0j, 22, 22.0, 1, 1e1 # should match
... 1j 22.0 22 1 10.0
...
... Numbers will match if they are equal to 14 digits of
... precision:
...
... >>> 2.00000000001 # should match
... 1.99999999999
... >>> 2.000000001 # should not match
... 1.999999999
... >>> 2.00000000001e10 # should match
... 1.99999999999e10
... >>> 2.000000001e10 # should not match
... 1.999999999e10
... '''
>>> # Without the flag:
>>> test = doctest.DocTestFinder().find(f)[0]
>>> doctest.DocTestRunner(verbose=False).run(test)
... # doctest: +ELLIPSIS
**********************************************************************
File ..., line 4, in f
Failed example:
print 1.1, 'intervening text', 1L # should match
Expected:
1.1 intervening text 1L
Got:
1.1 intervening text 1
**********************************************************************
File ..., line 6, in f
Failed example:
print 1.0j, 22, 22.0, 1, 1e1 # should match
Expected:
1j 22.0 22 1 10.0
Got:
1j 22 22.0 1 10.0
**********************************************************************
File ..., line 12, in f
Failed example:
2.00000000001 # should match
Expected:
1.99999999999
Got:
2.00000000001
**********************************************************************
File ..., line 14, in f
Failed example:
2.000000001 # should not match
Expected:
1.999999999
Got:
2.0000000010000001
**********************************************************************
File ..., line 16, in f
Failed example:
2.00000000001e10 # should match
Expected:
1.99999999999e10
Got:
20000000000.099998
**********************************************************************
File ..., line 18, in f
Failed example:
2.000000001e10 # should not match
Expected:
1.999999999e10
Got:
20000000010.0
(6, 6)
>>> # With the flag:
>>> test = doctest.DocTestFinder().find(f)[0]
>>> flags = doctest.NORMALIZE_NUMBERS
>>> doctest.DocTestRunner(verbose=False, optionflags=flags).run(test)
... # doctest: +ELLIPSIS
**********************************************************************
File ..., line 14, in f
Failed example:
2.000000001 # should not match
Expected:
1.999999999
Got:
2.0000000010000001
**********************************************************************
File ..., line 18, in f
Failed example:
2.000000001e10 # should not match
Expected:
1.999999999e10
Got:
20000000010.0
(2, 6)
The ELLIPSIS flag causes ellipsis marker ("...") in the expected
output to match any substring in the actual output: