[Python-checkins] r61739 - in python/trunk/Doc: howto/functional.rst library/decimal.rst
georg.brandl
python-checkins at python.org
Sat Mar 22 12:47:11 CET 2008
Author: georg.brandl
Date: Sat Mar 22 12:47:10 2008
New Revision: 61739
Modified:
python/trunk/Doc/howto/functional.rst
python/trunk/Doc/library/decimal.rst
Log:
Test decimal.rst doctests as far as possible with sphinx doctest.
Modified: python/trunk/Doc/howto/functional.rst
==============================================================================
--- python/trunk/Doc/howto/functional.rst (original)
+++ python/trunk/Doc/howto/functional.rst Sat Mar 22 12:47:10 2008
@@ -271,6 +271,10 @@
Calling :func:`iter` on a dictionary returns an iterator that will loop over the
dictionary's keys:
+.. not a doctest since dict ordering varies across Pythons
+
+::
+
>>> m = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6,
... 'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}
>>> for key in m:
Modified: python/trunk/Doc/library/decimal.rst
==============================================================================
--- python/trunk/Doc/library/decimal.rst (original)
+++ python/trunk/Doc/library/decimal.rst Sat Mar 22 12:47:10 2008
@@ -17,9 +17,14 @@
.. versionadded:: 2.4
+.. import modules for testing inline doctests with the Sphinx doctest builder
.. testsetup:: *
+ import decimal
+ import math
from decimal import *
+ # make sure each group gets a fresh context
+ setcontext(Context())
The :mod:`decimal` module provides support for decimal floating point
arithmetic. It offers several advantages over the :class:`float` datatype:
@@ -118,15 +123,13 @@
The usual start to using decimals is importing the module, viewing the current
context with :func:`getcontext` and, if necessary, setting new values for
-precision, rounding, or enabled traps:
-
-.. doctest:: newcontext
+precision, rounding, or enabled traps::
>>> from decimal import *
>>> getcontext()
Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999999, Emax=999999999,
- capitals=1, flags=[], traps=[Overflow, InvalidOperation,
- DivisionByZero])
+ capitals=1, flags=[], traps=[Overflow, DivisionByZero,
+ InvalidOperation])
>>> getcontext().prec = 7 # Set a new precision
@@ -170,14 +173,14 @@
Decimal('5.85988')
Decimals interact well with much of the rest of Python. Here is a small decimal
-floating point flying circus::
+floating point flying circus:
>>> data = map(Decimal, '1.34 1.87 3.45 2.35 1.00 0.03 9.25'.split())
>>> max(data)
Decimal('9.25')
>>> min(data)
Decimal('0.03')
- >>> sorted(data)
+ >>> sorted(data) # doctest: +NORMALIZE_WHITESPACE
[Decimal('0.03'), Decimal('1.00'), Decimal('1.34'), Decimal('1.87'),
Decimal('2.35'), Decimal('3.45'), Decimal('9.25')]
>>> sum(data)
@@ -198,7 +201,7 @@
>>> c % a
Decimal('0.77')
-And some mathematical functions are also available to Decimal::
+And some mathematical functions are also available to Decimal:
>>> Decimal(2).sqrt()
Decimal('1.414213562373095048801688724')
@@ -211,7 +214,7 @@
The :meth:`quantize` method rounds a number to a fixed exponent. This method is
useful for monetary applications that often round results to a fixed number of
-places::
+places:
>>> Decimal('7.325').quantize(Decimal('.01'), rounding=ROUND_DOWN)
Decimal('7.32')
@@ -229,7 +232,10 @@
In accordance with the standard, the :mod:`Decimal` module provides two ready to
use standard contexts, :const:`BasicContext` and :const:`ExtendedContext`. The
former is especially useful for debugging because many of the traps are
-enabled::
+enabled:
+
+.. doctest:: newcontext
+ :options: +NORMALIZE_WHITESPACE
>>> myothercontext = Context(prec=60, rounding=ROUND_HALF_DOWN)
>>> setcontext(myothercontext)
@@ -263,15 +269,18 @@
Decimal('3.14159292')
>>> getcontext()
Context(prec=9, rounding=ROUND_HALF_EVEN, Emin=-999999999, Emax=999999999,
- capitals=1, flags=[Inexact, Rounded], traps=[])
+ capitals=1, flags=[Rounded, Inexact], traps=[])
The *flags* entry shows that the rational approximation to :const:`Pi` was
rounded (digits beyond the context precision were thrown away) and that the
result is inexact (some of the discarded digits were non-zero).
Individual traps are set using the dictionary in the :attr:`traps` field of a
-context::
+context:
+.. doctest:: newcontext
+
+ >>> setcontext(ExtendedContext)
>>> Decimal(1) / Decimal(0)
Decimal('Infinity')
>>> getcontext().traps[DivisionByZero] = 1
@@ -401,7 +410,7 @@
but the result gives a total ordering on :class:`Decimal`
instances. Two :class:`Decimal` instances with the same numeric
value but different representations compare unequal in this
- ordering::
+ ordering:
>>> Decimal('12.0').compare_total(Decimal('12'))
Decimal('-1')
@@ -444,7 +453,7 @@
.. method:: Decimal.copy_sign(other)
Return a copy of the first operand with the sign set to be the
- same as the sign of the second operand. For example::
+ same as the sign of the second operand. For example:
>>> Decimal('2.3').copy_sign(Decimal('-1.5'))
Decimal('-2.3')
@@ -989,7 +998,9 @@
needed by the application. Another benefit is that rounding immediately
eliminates unintended effects from digits beyond the current precision. In the
following example, using unrounded inputs means that adding zero to a sum can
- change the result::
+ change the result:
+
+ .. doctest:: newcontext
>>> getcontext().prec = 3
>>> Decimal('3.4445') + Decimal('1.0023')
@@ -1246,7 +1257,9 @@
of nearly offsetting quantities resulting in loss of significance. Knuth
provides two instructive examples where rounded floating point arithmetic with
insufficient precision causes the breakdown of the associative and distributive
-properties of addition::
+properties of addition:
+
+.. doctest:: newcontext
# Examples from Seminumerical Algorithms, Section 4.2.2.
>>> from decimal import Decimal, getcontext
@@ -1265,7 +1278,9 @@
Decimal('0.0060000')
The :mod:`decimal` module makes it possible to restore the identities by
-expanding the precision sufficiently to avoid loss of significance::
+expanding the precision sufficiently to avoid loss of significance:
+
+.. doctest:: newcontext
>>> getcontext().prec = 20
>>> u, v, w = Decimal(11111113), Decimal(-11111111), Decimal('7.51111111')
@@ -1331,7 +1346,7 @@
various representations of zero with differing precisions yet equivalent in
value. This takes a bit of getting used to. For an eye accustomed to
normalized floating point representations, it is not immediately obvious that
-the following calculation returns a value equal to zero::
+the following calculation returns a value equal to zero:
>>> 1 / Decimal('Infinity')
Decimal('0E-1000000026')
@@ -1533,7 +1548,7 @@
Q. It is cumbersome to type ``decimal.Decimal('1234.5')``. Is there a way to
minimize typing when using the interactive interpreter?
-A. Some users abbreviate the constructor to just a single letter::
+A. Some users abbreviate the constructor to just a single letter:
>>> D = decimal.Decimal
>>> D('1.23') + D('3.45')
@@ -1544,7 +1559,7 @@
and need to be validated. What methods should be used?
A. The :meth:`quantize` method rounds to a fixed number of decimal places. If
-the :const:`Inexact` trap is set, it is also useful for validation::
+the :const:`Inexact` trap is set, it is also useful for validation:
>>> TWOPLACES = Decimal(10) ** -2 # same as Decimal('0.01')
@@ -1559,7 +1574,7 @@
>>> Decimal('3.214').quantize(TWOPLACES, context=Context(traps=[Inexact]))
Traceback (most recent call last):
...
- Inexact: Changed in rounding
+ Inexact
Q. Once I have valid two place inputs, how do I maintain that invariant
throughout an application?
@@ -1567,7 +1582,7 @@
A. Some operations like addition, subtraction, and multiplication by an integer
will automatically preserve fixed point. Others operations, like division and
non-integer multiplication, will change the number of decimal places and need to
-be followed-up with a :meth:`quantize` step::
+be followed-up with a :meth:`quantize` step:
>>> a = Decimal('102.72') # Initial fixed-point values
>>> b = Decimal('3.17')
@@ -1583,7 +1598,7 @@
Decimal('0.03')
In developing fixed-point applications, it is convenient to define functions
-to handle the :meth:`quantize` step::
+to handle the :meth:`quantize` step:
>>> def mul(x, y, fp=TWOPLACES):
... return (x * y).quantize(fp)
@@ -1601,7 +1616,7 @@
canonical value?
A. The :meth:`normalize` method maps all equivalent values to a single
-representative::
+representative:
>>> values = map(Decimal, '200 200.000 2E2 .02E+4'.split())
>>> [v.normalize() for v in values]
@@ -1617,7 +1632,7 @@
If an application does not care about tracking significance, it is easy to
remove the exponent and trailing zeroes, losing significance, but keeping the
-value unchanged::
+value unchanged:
>>> def remove_exponent(d):
... return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()
@@ -1629,7 +1644,9 @@
A. Yes, all binary floating point numbers can be exactly expressed as a
Decimal. An exact conversion may take more precision than intuition would
-suggest, so we trap :const:`Inexact` to signal a need for more precision::
+suggest, so we trap :const:`Inexact` to signal a need for more precision:
+
+.. testcode:: doctest_block
def float_to_decimal(f):
"Convert a floating point number to a Decimal with no loss of information"
@@ -1642,6 +1659,8 @@
except Inexact:
ctx.prec += 1
+.. doctest:: doctest_block
+
>>> float_to_decimal(math.pi)
Decimal('3.141592653589793115997963468544185161590576171875')
@@ -1649,7 +1668,7 @@
A. There is some question about whether it is advisable to mix binary and
decimal floating point. Also, its use requires some care to avoid the
-representation issues associated with binary floating point::
+representation issues associated with binary floating point:
>>> float_to_decimal(1.1)
Decimal('1.100000000000000088817841970012523233890533447265625')
@@ -1669,23 +1688,27 @@
A. Yes. The principle is that all values are considered to be exact and so is
the arithmetic on those values. Only the results are rounded. The advantage
for inputs is that "what you type is what you get". A disadvantage is that the
-results can look odd if you forget that the inputs haven't been rounded::
+results can look odd if you forget that the inputs haven't been rounded:
+
+.. doctest:: newcontext
>>> getcontext().prec = 3
- >>> Decimal('3.104') + D('2.104')
+ >>> Decimal('3.104') + Decimal('2.104')
Decimal('5.21')
- >>> Decimal('3.104') + D('0.000') + D('2.104')
+ >>> Decimal('3.104') + Decimal('0.000') + Decimal('2.104')
Decimal('5.20')
The solution is either to increase precision or to force rounding of inputs
-using the unary plus operation::
+using the unary plus operation:
+
+.. doctest:: newcontext
>>> getcontext().prec = 3
>>> +Decimal('1.23456789') # unary plus triggers rounding
Decimal('1.23')
Alternatively, inputs can be rounded upon creation using the
-:meth:`Context.create_decimal` method::
+:meth:`Context.create_decimal` method:
>>> Context(prec=5, rounding=ROUND_DOWN).create_decimal('1.2345678')
Decimal('1.2345')
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