oca/oca-ocb-core
2
0
Fork 0
mirror of https://github.com/bringout/oca-ocb-core.git synced 2026-04-19 02:52:03 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Initial commit: Core packages

Browse source
This commit is contained in:
Ernad Husremovic 2025-08-29 15:20:45 +02:00
commit 12c29a983b
9512 changed files with 8379910 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

282
odoo-bringout-oca-ocb-base/odoo/tools/float_utils.py Normal file
View file

@ -0,0 +1,282 @@
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from __future__ import print_function
import builtins
import math
def round(f):
# P3's builtin round differs from P2 in the following manner:
# * it rounds half to even rather than up (away from 0)
# * round(-0.) loses the sign (it returns -0 rather than 0)
# * round(x) returns an int rather than a float
#
# this compatibility shim implements Python 2's round in terms of
# Python 3's so that important rounding error under P3 can be
# trivially fixed, assuming the P2 behaviour to be debugged and
# correct.
roundf = builtins.round(f)
if builtins.round(f + 1) - roundf != 1:
return f + math.copysign(0.5, f)
# copysign ensures round(-0.) -> -0 *and* result is a float
return math.copysign(roundf, f)
def _float_check_precision(precision_digits=None, precision_rounding=None):
assert (precision_digits is not None or precision_rounding is not None) and \
not (precision_digits and precision_rounding),\
"exactly one of precision_digits and precision_rounding must be specified"
assert precision_rounding is None or precision_rounding > 0,\
"precision_rounding must be positive, got %s" % precision_rounding
if precision_digits is not None:
return 10 ** -precision_digits
return precision_rounding
def float_round(value, precision_digits=None, precision_rounding=None, rounding_method='HALF-UP'):
"""Return ``value`` rounded to ``precision_digits`` decimal digits,
minimizing IEEE-754 floating point representation errors, and applying
the tie-breaking rule selected with ``rounding_method``, by default
HALF-UP (away from zero).
Precision must be given by ``precision_digits`` or ``precision_rounding``,
not both!
:param float value: the value to round
:param int precision_digits: number of fractional digits to round to.
:param float precision_rounding: decimal number representing the minimum
non-zero value at the desired precision (for example, 0.01 for a
2-digit precision).
:param rounding_method: the rounding method used: 'HALF-UP', 'UP' or 'DOWN',
the first one rounding up to the closest number with the rule that
number>=0.5 is rounded up to 1, the second always rounding up and the
latest one always rounding down.
:return: rounded float
"""
rounding_factor = _float_check_precision(precision_digits=precision_digits,
precision_rounding=precision_rounding)
if rounding_factor == 0 or value == 0:
return 0.0
# NORMALIZE - ROUND - DENORMALIZE
# In order to easily support rounding to arbitrary 'steps' (e.g. coin values),
# we normalize the value before rounding it as an integer, and de-normalize
# after rounding: e.g. float_round(1.3, precision_rounding=.5) == 1.5
# Due to IEE754 float/double representation limits, the approximation of the
# real value may be slightly below the tie limit, resulting in an error of
# 1 unit in the last place (ulp) after rounding.
# For example 2.675 == 2.6749999999999998.
# To correct this, we add a very small epsilon value, scaled to the
# the order of magnitude of the value, to tip the tie-break in the right
# direction.
# Credit: discussion with OpenERP community members on bug 882036
normalized_value = value / rounding_factor # normalize
sign = math.copysign(1.0, normalized_value)
epsilon_magnitude = math.log(abs(normalized_value), 2)
epsilon = 2**(epsilon_magnitude-52)
# TIE-BREAKING: UP/DOWN (for ceiling[resp. flooring] operations)
# When rounding the value up[resp. down], we instead subtract[resp. add] the epsilon value
# as the approximation of the real value may be slightly *above* the
# tie limit, this would result in incorrectly rounding up[resp. down] to the next number
# The math.ceil[resp. math.floor] operation is applied on the absolute value in order to
# round "away from zero" and not "towards infinity", then the sign is
# restored.
if rounding_method == 'UP':
normalized_value -= sign*epsilon
rounded_value = math.ceil(abs(normalized_value)) * sign
elif rounding_method == 'DOWN':
normalized_value += sign*epsilon
rounded_value = math.floor(abs(normalized_value)) * sign
# TIE-BREAKING: HALF-UP (for normal rounding)
# We want to apply HALF-UP tie-breaking rules, i.e. 0.5 rounds away from 0.
else:
normalized_value += math.copysign(epsilon, normalized_value)
rounded_value = round(normalized_value) # round to integer
result = rounded_value * rounding_factor # de-normalize
return result
def float_is_zero(value, precision_digits=None, precision_rounding=None):
"""Returns true if ``value`` is small enough to be treated as
zero at the given precision (smaller than the corresponding *epsilon*).
The precision (``10**-precision_digits`` or ``precision_rounding``)
is used as the zero *epsilon*: values less than that are considered
to be zero.
Precision must be given by ``precision_digits`` or ``precision_rounding``,
not both!
Warning: ``float_is_zero(value1-value2)`` is not equivalent to
``float_compare(value1,value2) == 0``, as the former will round after
computing the difference, while the latter will round before, giving
different results for e.g. 0.006 and 0.002 at 2 digits precision.
:param int precision_digits: number of fractional digits to round to.
:param float precision_rounding: decimal number representing the minimum
non-zero value at the desired precision (for example, 0.01 for a
2-digit precision).
:param float value: value to compare with the precision's zero
:return: True if ``value`` is considered zero
"""
epsilon = _float_check_precision(precision_digits=precision_digits,
precision_rounding=precision_rounding)
return abs(float_round(value, precision_rounding=epsilon)) < epsilon
def float_compare(value1, value2, precision_digits=None, precision_rounding=None):
"""Compare ``value1`` and ``value2`` after rounding them according to the
given precision. A value is considered lower/greater than another value
if their rounded value is different. This is not the same as having a
non-zero difference!
Precision must be given by ``precision_digits`` or ``precision_rounding``,
not both!
Example: 1.432 and 1.431 are equal at 2 digits precision,
so this method would return 0
However 0.006 and 0.002 are considered different (this method returns 1)
because they respectively round to 0.01 and 0.0, even though
0.006-0.002 = 0.004 which would be considered zero at 2 digits precision.
Warning: ``float_is_zero(value1-value2)`` is not equivalent to
``float_compare(value1,value2) == 0``, as the former will round after
computing the difference, while the latter will round before, giving
different results for e.g. 0.006 and 0.002 at 2 digits precision.
:param int precision_digits: number of fractional digits to round to.
:param float precision_rounding: decimal number representing the minimum
non-zero value at the desired precision (for example, 0.01 for a
2-digit precision).
:param float value1: first value to compare
:param float value2: second value to compare
:return: (resp.) -1, 0 or 1, if ``value1`` is (resp.) lower than,
equal to, or greater than ``value2``, at the given precision.
"""
rounding_factor = _float_check_precision(precision_digits=precision_digits,
precision_rounding=precision_rounding)
value1 = float_round(value1, precision_rounding=rounding_factor)
value2 = float_round(value2, precision_rounding=rounding_factor)
delta = value1 - value2
if float_is_zero(delta, precision_rounding=rounding_factor): return 0
return -1 if delta < 0.0 else 1
def float_repr(value, precision_digits):
"""Returns a string representation of a float with the
given number of fractional digits. This should not be
used to perform a rounding operation (this is done via
:func:`~.float_round`), but only to produce a suitable
string representation for a float.
:param float value:
:param int precision_digits: number of fractional digits to include in the output
"""
# Can't use str() here because it seems to have an intrinsic
# rounding to 12 significant digits, which causes a loss of
# precision. e.g. str(123456789.1234) == str(123456789.123)!!
return ("%%.%sf" % precision_digits) % value
_float_repr = float_repr
def float_split_str(value, precision_digits):
"""Splits the given float 'value' in its unitary and decimal parts,
returning each of them as a string, rounding the value using
the provided ``precision_digits`` argument.
The length of the string returned for decimal places will always
be equal to ``precision_digits``, adding zeros at the end if needed.
In case ``precision_digits`` is zero, an empty string is returned for
the decimal places.
Examples:
1.432 with precision 2 => ('1', '43')
1.49 with precision 1 => ('1', '5')
1.1 with precision 3 => ('1', '100')
1.12 with precision 0 => ('1', '')
:param float value: value to split.
:param int precision_digits: number of fractional digits to round to.
:return: returns the tuple(<unitary part>, <decimal part>) of the given value
:rtype: tuple(str, str)
"""
value = float_round(value, precision_digits=precision_digits)
value_repr = float_repr(value, precision_digits)
return tuple(value_repr.split('.')) if precision_digits else (value_repr, '')
def float_split(value, precision_digits):
""" same as float_split_str() except that it returns the unitary and decimal
parts as integers instead of strings. In case ``precision_digits`` is zero,
0 is always returned as decimal part.
:rtype: tuple(int, int)
"""
units, cents = float_split_str(value, precision_digits)
if not cents:
return int(units), 0
return int(units), int(cents)
def json_float_round(value, precision_digits, rounding_method='HALF-UP'):
"""Not suitable for float calculations! Similar to float_repr except that it
returns a float suitable for json dump
This may be necessary to produce "exact" representations of rounded float
values during serialization, such as what is done by `json.dumps()`.
Unfortunately `json.dumps` does not allow any form of custom float representation,
nor any custom types, everything is serialized from the basic JSON types.
:param int precision_digits: number of fractional digits to round to.
:param rounding_method: the rounding method used: 'HALF-UP', 'UP' or 'DOWN',
the first one rounding up to the closest number with the rule that
number>=0.5 is rounded up to 1, the second always rounding up and the
latest one always rounding down.
:return: a rounded float value that must not be used for calculations, but
is ready to be serialized in JSON with minimal chances of
representation errors.
"""
rounded_value = float_round(value, precision_digits=precision_digits, rounding_method=rounding_method)
rounded_repr = float_repr(rounded_value, precision_digits=precision_digits)
# As of Python 3.1, rounded_repr should be the shortest representation for our
# rounded float, so we create a new float whose repr is expected
# to be the same value, or a value that is semantically identical
# and will be used in the json serialization.
# e.g. if rounded_repr is '3.1750', the new float repr could be 3.175
# but not 3.174999999999322452.
# Cfr. bpo-1580: https://bugs.python.org/issue1580
return float(rounded_repr)
if __name__ == "__main__":
import time
start = time.time()
count = 0
errors = 0
def try_round(amount, expected, precision_digits=3):
global count, errors; count += 1
result = float_repr(float_round(amount, precision_digits=precision_digits),
precision_digits=precision_digits)
if result != expected:
errors += 1
print('###!!! Rounding error: got %s , expected %s' % (result, expected))
# Extended float range test, inspired by Cloves Almeida's test on bug #882036.
fractions = [.0, .015, .01499, .675, .67499, .4555, .4555, .45555]
expecteds = ['.00', '.02', '.01', '.68', '.67', '.46', '.456', '.4556']
precisions = [2, 2, 2, 2, 2, 2, 3, 4]
for magnitude in range(7):
for frac, exp, prec in zip(fractions, expecteds, precisions):
for sign in [-1,1]:
for x in range(0, 10000, 97):
n = x * 10**magnitude
f = sign * (n + frac)
f_exp = ('-' if f != 0 and sign == -1 else '') + str(n) + exp
try_round(f, f_exp, precision_digits=prec)
stop = time.time()
# Micro-bench results:
# 47130 round calls in 0.422306060791 secs, with Python 2.6.7 on Core i3 x64
# with decimal:
# 47130 round calls in 6.612248100021 secs, with Python 2.6.7 on Core i3 x64
print(count, " round calls, ", errors, "errors, done in ", (stop-start), 'secs')