def test_can_find_negative_and_signaling_nans(neg, snan):
find_any(
floats().filter(math.isnan),
lambda x:
(snan is
(float_to_int(abs(x)) != float_to_int(float("nan"))) and neg is
(math.copysign(1, x) == -1)),
settings=TRY_HARDER,
)
def test_draw_write_round_trip(f):
d = ConjectureData.for_buffer(bytes(10))
flt.write_float(d, f)
d2 = ConjectureData.for_buffer(d.buffer)
g = flt.draw_float(d2)
if f == f:
assert f == g
assert float_to_int(f) == float_to_int(g)
d3 = ConjectureData.for_buffer(d2.buffer)
flt.draw_float(d3)
assert d3.buffer == d2.buffer
def test_draw_write_round_trip(f):
d = ConjectureData.for_buffer(hbytes(10))
flt.write_float(d, f)
d2 = ConjectureData.for_buffer(d.buffer)
g = flt.draw_float(d2)
if f == f:
assert f == g
assert float_to_int(f) == float_to_int(g)
d3 = ConjectureData.for_buffer(d2.buffer)
flt.draw_float(d3)
assert d3.buffer == d2.buffer
def test_very_narrow_interval():
upper_bound = -1.0
lower_bound = int_to_float(float_to_int(upper_bound) + 10)
assert lower_bound < upper_bound
@given(st.floats(lower_bound, upper_bound))
def test(f):
assert lower_bound <= f <= upper_bound
test()
def base_float_to_lex(f):
i = float_to_int(f)
i &= (1 << 63) - 1
exponent = i >> 52
mantissa = i & MANTISSA_MASK
mantissa = update_mantissa(exponent - BIAS, mantissa)
exponent = encode_exponent(exponent)
assert mantissa.bit_length() <= 52
return (1 << 63) | (exponent << 52) | mantissa
def base_float_to_lex(f):
i = float_to_int(f)
i &= (1 << 63) - 1
exponent = i >> 52
mantissa = i & MANTISSA_MASK
mantissa = update_mantissa(exponent - BIAS, mantissa)
exponent = encode_exponent(exponent)
assert mantissa.bit_length() <= 52
return (1 << 63) | (exponent << 52) | mantissa
def float_to_lex(f):
if is_simple(f):
assert f >= 0
return int(f)
i = float_to_int(f)
i &= ((1 << 63) - 1)
exponent = i >> 52
mantissa = i & MANTISSA_MASK
mantissa = update_mantissa(exponent - BIAS, mantissa)
exponent = encode_exponent(exponent)
assert mantissa.bit_length() <= 52
return (1 << 63) | (exponent << 52) | mantissa
def float_to_lex(f):
if is_simple(f):
assert f >= 0
return int(f)
i = float_to_int(f)
i &= ((1 << 63) - 1)
exponent = i >> 52
mantissa = i & MANTISSA_MASK
mantissa = update_mantissa(exponent - BIAS, mantissa)
exponent = encode_exponent(exponent)
assert mantissa.bit_length() <= 52
return (1 << 63) | (exponent << 52) | mantissa
def write_float(data, f):
data.draw_bits(64, forced=float_to_lex(abs(f)))
sign = float_to_int(f) >> 63
data.draw_bits(1, forced=sign)
def __init__(self, f):
self.value = float_to_int(f)
def __init__(self, f):
self.value = float_to_int(f)
def floats(
min_value=None, max_value=None, allow_nan=None, allow_infinity=None
):
"""Returns a strategy which generates floats.
- If min_value is not None, all values will be >= min_value.
- If max_value is not None, all values will be <= max_value.
- If min_value or max_value is not None, it is an error to enable
allow_nan.
- If both min_value and max_value are not None, it is an error to enable
allow_infinity.
Where not explicitly ruled out by the bounds, all of infinity, -infinity
and NaN are possible values generated by this strategy.
"""
if allow_nan is None:
allow_nan = bool(min_value is None and max_value is None)
elif allow_nan:
if min_value is not None or max_value is not None:
raise InvalidArgument(
'Cannot have allow_nan=%r, with min_value or max_value' % (
allow_nan
))
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
if min_value is not None:
min_value = float(min_value)
if max_value is not None:
max_value = float(max_value)
if min_value == float(u'-inf'):
min_value = None
if max_value == float(u'inf'):
max_value = None
if allow_infinity is None:
allow_infinity = bool(min_value is None or max_value is None)
elif allow_infinity:
if min_value is not None and max_value is not None:
raise InvalidArgument(
'Cannot have allow_infinity=%r, with both min_value and '
'max_value' % (
allow_infinity
))
from hypothesis.searchstrategy.numbers import FloatStrategy, \
FixedBoundedFloatStrategy
if min_value is None and max_value is None:
return FloatStrategy(
allow_infinity=allow_infinity, allow_nan=allow_nan,
)
elif min_value is not None and max_value is not None:
if min_value == max_value:
return just(min_value)
elif math.isinf(max_value - min_value):
assert min_value < 0 and max_value > 0
return floats(min_value=0, max_value=max_value) | floats(
min_value=min_value, max_value=0
)
elif count_between_floats(min_value, max_value) > 1000:
return FixedBoundedFloatStrategy(
lower_bound=min_value, upper_bound=max_value
)
elif is_negative(max_value):
assert is_negative(min_value)
ub_int = float_to_int(max_value)
lb_int = float_to_int(min_value)
assert ub_int <= lb_int
return integers(min_value=ub_int, max_value=lb_int).map(
int_to_float
)
elif is_negative(min_value):
return floats(min_value=min_value, max_value=-0.0) | floats(
min_value=0, max_value=max_value
)
else:
ub_int = float_to_int(max_value)
lb_int = float_to_int(min_value)
assert lb_int <= ub_int
return integers(min_value=lb_int, max_value=ub_int).map(
int_to_float
)
elif min_value is not None:
if min_value < 0:
result = floats(
min_value=0.0
) | floats(min_value=min_value, max_value=0.0)
else:
result = (
floats(allow_infinity=allow_infinity, allow_nan=False).map(
lambda x: assume(not math.isnan(x)) and min_value + abs(x)
)
)
if min_value == 0 and not is_negative(min_value):
result = result.filter(lambda x: math.copysign(1.0, x) == 1)
return result
else:
assert max_value is not None
if max_value > 0:
result = floats(
min_value=0.0,
max_value=max_value,
) | floats(max_value=0.0)
else:
result = (
floats(allow_infinity=allow_infinity, allow_nan=False).map(
lambda x: assume(not math.isnan(x)) and max_value - abs(x)
)
)
if max_value == 0 and is_negative(max_value):
result = result.filter(is_negative)
return result
def adjust_float(self, source, offset):
return int_to_float(clamp(0, float_to_int(source) + offset, 2**64 - 1))
def test_floats_round_trip(f):
i = flt.float_to_lex(f)
g = flt.lex_to_float(i)
assert float_to_int(f) == float_to_int(g)
def adjust_float(self, source, offset):
return int_to_float(clamp(0, float_to_int(source) + offset, 2 ** 64 - 1))
def floats(
min_value: Optional[Real] = None,
max_value: Optional[Real] = None,
*,
allow_nan: Optional[bool] = None,
allow_infinity: Optional[bool] = None,
allow_subnormal: Optional[bool] = None,
width: int = 64,
exclude_min: bool = False,
exclude_max: bool = False,
) -> SearchStrategy[float]:
"""Returns a strategy which generates floats.
- If min_value is not None, all values will be ``>= min_value``
(or ``> min_value`` if ``exclude_min``).
- If max_value is not None, all values will be ``<= max_value``
(or ``< max_value`` if ``exclude_max``).
- If min_value or max_value is not None, it is an error to enable
allow_nan.
- If both min_value and max_value are not None, it is an error to enable
allow_infinity.
- If inferred values range does not include subnormal values, it is an error
to enable allow_subnormal.
Where not explicitly ruled out by the bounds,
:wikipedia:`subnormals <Subnormal_number>`, infinities, and NaNs are possible
values generated by this strategy.
The width argument specifies the maximum number of bits of precision
required to represent the generated float. Valid values are 16, 32, or 64.
Passing ``width=32`` will still use the builtin 64-bit ``float`` class,
but always for values which can be exactly represented as a 32-bit float.
The exclude_min and exclude_max argument can be used to generate numbers
from open or half-open intervals, by excluding the respective endpoints.
Excluding either signed zero will also exclude the other.
Attempting to exclude an endpoint which is None will raise an error;
use ``allow_infinity=False`` to generate finite floats. You can however
use e.g. ``min_value=-math.inf, exclude_min=True`` to exclude only
one infinite endpoint.
Examples from this strategy have a complicated and hard to explain
shrinking behaviour, but it tries to improve "human readability". Finite
numbers will be preferred to infinity and infinity will be preferred to
NaN.
"""
check_type(bool, exclude_min, "exclude_min")
check_type(bool, exclude_max, "exclude_max")
if allow_nan is None:
allow_nan = bool(min_value is None and max_value is None)
elif allow_nan and (min_value is not None or max_value is not None):
raise InvalidArgument(
f"Cannot have allow_nan={allow_nan!r}, with min_value or max_value"
)
if width not in (16, 32, 64):
raise InvalidArgument(
f"Got width={width!r}, but the only valid values "
"are the integers 16, 32, and 64.")
check_valid_bound(min_value, "min_value")
check_valid_bound(max_value, "max_value")
if math.copysign(1.0, -0.0) == 1.0: # pragma: no cover
raise FloatingPointError(
"You Python install can't represent -0.0, which is required by the "
"IEEE-754 floating-point specification. This is probably because it was "
"compiled with an unsafe option like -ffast-math; for a more detailed "
"explanation see https://simonbyrne.github.io/notes/fastmath/")
if allow_subnormal and next_up(0.0, width=width) == 0: # pragma: no cover
# Not worth having separate CI envs and dependencies just to cover this branch;
# discussion in https://github.com/HypothesisWorks/hypothesis/issues/3092
#
# Erroring out here ensures that the database contents are interpreted
# consistently - which matters for such a foundational strategy, even if it's
# not always true for all user-composed strategies further up the stack.
raise FloatingPointError(
f"Got allow_subnormal={allow_subnormal!r}, but we can't represent "
f"subnormal floats right now, in violation of the IEEE-754 floating-point "
f"specification. This is usually because something was compiled with "
f"-ffast-math or a similar option, which sets global processor state. "
f"See https://simonbyrne.github.io/notes/fastmath/ for a more detailed "
f"writeup - and good luck!")
min_arg, max_arg = min_value, max_value
if min_value is not None:
min_value = float_of(min_value, width)
assert isinstance(min_value, float)
if max_value is not None:
max_value = float_of(max_value, width)
assert isinstance(max_value, float)
if min_value != min_arg:
raise InvalidArgument(
f"min_value={min_arg!r} cannot be exactly represented as a float "
f"of width {width} - use min_value={min_value!r} instead.")
if max_value != max_arg:
raise InvalidArgument(
f"max_value={max_arg!r} cannot be exactly represented as a float "
f"of width {width} - use max_value={max_value!r} instead.")
if exclude_min and (min_value is None or min_value == math.inf):
raise InvalidArgument(f"Cannot exclude min_value={min_value!r}")
if exclude_max and (max_value is None or max_value == -math.inf):
raise InvalidArgument(f"Cannot exclude max_value={max_value!r}")
assumed_allow_subnormal = allow_subnormal is None or allow_subnormal
if min_value is not None and (exclude_min or (min_arg is not None
and min_value < min_arg)):
min_value = next_up_normal(min_value, width, assumed_allow_subnormal)
if min_value == min_arg:
assert min_value == min_arg == 0
assert is_negative(min_arg) and not is_negative(min_value)
min_value = next_up_normal(min_value, width,
assumed_allow_subnormal)
assert min_value > min_arg # type: ignore
if max_value is not None and (exclude_max or (max_arg is not None
and max_value > max_arg)):
max_value = next_down_normal(max_value, width, assumed_allow_subnormal)
if max_value == max_arg:
assert max_value == max_arg == 0
assert is_negative(max_value) and not is_negative(max_arg)
max_value = next_down_normal(max_value, width,
assumed_allow_subnormal)
assert max_value < max_arg # type: ignore
if min_value == -math.inf:
min_value = None
if max_value == math.inf:
max_value = None
bad_zero_bounds = (min_value == max_value == 0 and is_negative(max_value)
and not is_negative(min_value))
if (min_value is not None and max_value is not None
and (min_value > max_value or bad_zero_bounds)):
# This is a custom alternative to check_valid_interval, because we want
# to include the bit-width and exclusion information in the message.
msg = (
"There are no %s-bit floating-point values between min_value=%r "
"and max_value=%r" % (width, min_arg, max_arg))
if exclude_min or exclude_max:
msg += f", exclude_min={exclude_min!r} and exclude_max={exclude_max!r}"
raise InvalidArgument(msg)
if allow_infinity is None:
allow_infinity = bool(min_value is None or max_value is None)
elif allow_infinity:
if min_value is not None and max_value is not None:
raise InvalidArgument(
f"Cannot have allow_infinity={allow_infinity!r}, "
"with both min_value and max_value")
elif min_value == math.inf:
if min_arg == math.inf:
raise InvalidArgument(
"allow_infinity=False excludes min_value=inf")
raise InvalidArgument(
f"exclude_min=True turns min_value={min_arg!r} into inf, "
"but allow_infinity=False")
elif max_value == -math.inf:
if max_arg == -math.inf:
raise InvalidArgument(
"allow_infinity=False excludes max_value=-inf")
raise InvalidArgument(
f"exclude_max=True turns max_value={max_arg!r} into -inf, "
"but allow_infinity=False")
smallest_normal = width_smallest_normals[width]
if allow_subnormal is None:
if min_value is not None and max_value is not None:
if min_value == max_value:
allow_subnormal = -smallest_normal < min_value < smallest_normal
else:
allow_subnormal = (min_value < smallest_normal
and max_value > -smallest_normal)
elif min_value is not None:
allow_subnormal = min_value < smallest_normal
elif max_value is not None:
allow_subnormal = max_value > -smallest_normal
else:
allow_subnormal = True
if allow_subnormal:
if min_value is not None and min_value >= smallest_normal:
raise InvalidArgument(
f"allow_subnormal=True, but minimum value {min_value} "
f"excludes values below float{width}'s "
f"smallest positive normal {smallest_normal}")
if max_value is not None and max_value <= -smallest_normal:
raise InvalidArgument(
f"allow_subnormal=True, but maximum value {max_value} "
f"excludes values above float{width}'s "
f"smallest negative normal {-smallest_normal}")
# Any type hint silences mypy when we unpack these parameters
kw: Any = {"allow_subnormal": allow_subnormal, "width": width}
unbounded_floats = FloatStrategy(allow_infinity=allow_infinity,
allow_nan=allow_nan,
**kw)
if min_value is None and max_value is None:
return unbounded_floats
elif min_value is not None and max_value is not None:
if min_value == max_value:
assert isinstance(min_value, float)
result = just(min_value)
elif is_negative(min_value):
if is_negative(max_value):
return floats(min_value=-max_value, max_value=-min_value,
**kw).map(operator.neg)
else:
return floats(
min_value=0.0, max_value=max_value, **kw) | floats(
min_value=0.0, max_value=-min_value, **kw).map(
operator.neg # type: ignore
)
elif (count_between_floats(min_value, max_value, width) > 1000
or not allow_subnormal):
return FixedBoundedFloatStrategy(lower_bound=min_value,
upper_bound=max_value,
**kw)
else:
ub_int = float_to_int(max_value, width)
lb_int = float_to_int(min_value, width)
assert lb_int <= ub_int
result = integers(
min_value=lb_int,
max_value=ub_int).map(lambda x: int_to_float(x, width))
elif min_value is not None:
assert isinstance(min_value, float)
if is_negative(min_value):
# Ignore known bug https://github.com/python/mypy/issues/6697
return unbounded_floats.map(abs) | floats( # type: ignore
min_value=min_value, max_value=-0.0, **kw)
else:
result = unbounded_floats.map(lambda x: min_value + abs(x))
else:
assert isinstance(max_value, float)
if not is_negative(max_value):
return floats(min_value=0.0, max_value=max_value, **
kw) | unbounded_floats.map(lambda x: -abs(x))
else:
result = unbounded_floats.map(lambda x: max_value - abs(x))
if width < 64:
def downcast(x):
try:
return float_of(x, width)
except OverflowError: # pragma: no cover
reject()
result = result.map(downcast)
if not allow_infinity:
result = result.filter(lambda x: not math.isinf(x))
return result
def floats(
min_value: Optional[Real] = None,
max_value: Optional[Real] = None,
*,
allow_nan: Optional[bool] = None,
allow_infinity: Optional[bool] = None,
width: int = 64,
exclude_min: bool = False,
exclude_max: bool = False,
) -> SearchStrategy[float]:
"""Returns a strategy which generates floats.
- If min_value is not None, all values will be ``>= min_value``
(or ``> min_value`` if ``exclude_min``).
- If max_value is not None, all values will be ``<= max_value``
(or ``< max_value`` if ``exclude_max``).
- If min_value or max_value is not None, it is an error to enable
allow_nan.
- If both min_value and max_value are not None, it is an error to enable
allow_infinity.
Where not explicitly ruled out by the bounds, all of infinity, -infinity
and NaN are possible values generated by this strategy.
The width argument specifies the maximum number of bits of precision
required to represent the generated float. Valid values are 16, 32, or 64.
Passing ``width=32`` will still use the builtin 64-bit ``float`` class,
but always for values which can be exactly represented as a 32-bit float.
The exclude_min and exclude_max argument can be used to generate numbers
from open or half-open intervals, by excluding the respective endpoints.
Excluding either signed zero will also exclude the other.
Attempting to exclude an endpoint which is None will raise an error;
use ``allow_infinity=False`` to generate finite floats. You can however
use e.g. ``min_value=-math.inf, exclude_min=True`` to exclude only
one infinite endpoint.
Examples from this strategy have a complicated and hard to explain
shrinking behaviour, but it tries to improve "human readability". Finite
numbers will be preferred to infinity and infinity will be preferred to
NaN.
"""
check_type(bool, exclude_min, "exclude_min")
check_type(bool, exclude_max, "exclude_max")
if allow_nan is None:
allow_nan = bool(min_value is None and max_value is None)
elif allow_nan and (min_value is not None or max_value is not None):
raise InvalidArgument(
f"Cannot have allow_nan={allow_nan!r}, with min_value or max_value"
)
if width not in (16, 32, 64):
raise InvalidArgument(
f"Got width={width!r}, but the only valid values "
"are the integers 16, 32, and 64.")
check_valid_bound(min_value, "min_value")
check_valid_bound(max_value, "max_value")
min_arg, max_arg = min_value, max_value
if min_value is not None:
min_value = float_of(min_value, width)
assert isinstance(min_value, float)
if max_value is not None:
max_value = float_of(max_value, width)
assert isinstance(max_value, float)
if min_value != min_arg:
raise InvalidArgument(
f"min_value={min_arg!r} cannot be exactly represented as a float "
f"of width {width} - use min_value={min_value!r} instead.")
if max_value != max_arg:
raise InvalidArgument(
f"max_value={max_arg!r} cannot be exactly represented as a float "
f"of width {width} - use max_value={max_value!r} instead.")
if exclude_min and (min_value is None or min_value == math.inf):
raise InvalidArgument(f"Cannot exclude min_value={min_value!r}")
if exclude_max and (max_value is None or max_value == -math.inf):
raise InvalidArgument(f"Cannot exclude max_value={max_value!r}")
if min_value is not None and (exclude_min or (min_arg is not None
and min_value < min_arg)):
min_value = next_up(min_value, width)
if min_value == min_arg:
assert min_value == min_arg == 0
assert is_negative(min_arg) and not is_negative(min_value)
min_value = next_up(min_value, width)
assert min_value > min_arg # type: ignore
if max_value is not None and (exclude_max or (max_arg is not None
and max_value > max_arg)):
max_value = next_down(max_value, width)
if max_value == max_arg:
assert max_value == max_arg == 0
assert is_negative(max_value) and not is_negative(max_arg)
max_value = next_down(max_value, width)
assert max_value < max_arg # type: ignore
if min_value == -math.inf:
min_value = None
if max_value == math.inf:
max_value = None
bad_zero_bounds = (min_value == max_value == 0 and is_negative(max_value)
and not is_negative(min_value))
if (min_value is not None and max_value is not None
and (min_value > max_value or bad_zero_bounds)):
# This is a custom alternative to check_valid_interval, because we want
# to include the bit-width and exclusion information in the message.
msg = (
"There are no %s-bit floating-point values between min_value=%r "
"and max_value=%r" % (width, min_arg, max_arg))
if exclude_min or exclude_max:
msg += f", exclude_min={exclude_min!r} and exclude_max={exclude_max!r}"
raise InvalidArgument(msg)
if allow_infinity is None:
allow_infinity = bool(min_value is None or max_value is None)
elif allow_infinity:
if min_value is not None and max_value is not None:
raise InvalidArgument(
f"Cannot have allow_infinity={allow_infinity!r}, "
"with both min_value and max_value")
elif min_value == math.inf:
raise InvalidArgument("allow_infinity=False excludes min_value=inf")
elif max_value == -math.inf:
raise InvalidArgument("allow_infinity=False excludes max_value=-inf")
unbounded_floats = FloatStrategy(allow_infinity=allow_infinity,
allow_nan=allow_nan,
width=width)
if min_value is None and max_value is None:
return unbounded_floats
elif min_value is not None and max_value is not None:
if min_value == max_value:
assert isinstance(min_value, float)
result = just(min_value)
elif is_negative(min_value):
if is_negative(max_value):
return floats(min_value=-max_value,
max_value=-min_value,
width=width).map(operator.neg)
else:
return floats(
min_value=0.0, max_value=max_value, width=width) | floats(
min_value=0.0, max_value=-min_value, width=width).map(
operator.neg)
elif count_between_floats(min_value, max_value) > 1000:
return FixedBoundedFloatStrategy(lower_bound=min_value,
upper_bound=max_value,
width=width)
else:
ub_int = float_to_int(max_value, width)
lb_int = float_to_int(min_value, width)
assert lb_int <= ub_int
result = integers(
min_value=lb_int,
max_value=ub_int).map(lambda x: int_to_float(x, width))
elif min_value is not None:
assert isinstance(min_value, float)
if is_negative(min_value):
# Ignore known bug https://github.com/python/mypy/issues/6697
return unbounded_floats.map(abs) | floats( # type: ignore
min_value=min_value,
max_value=-0.0,
width=width)
else:
result = unbounded_floats.map(lambda x: min_value + abs(x))
else:
assert isinstance(max_value, float)
if not is_negative(max_value):
return floats(
min_value=0.0, max_value=max_value,
width=width) | unbounded_floats.map(lambda x: -abs(x))
else:
result = unbounded_floats.map(lambda x: max_value - abs(x))
if width < 64:
def downcast(x):
try:
return float_of(x, width)
except OverflowError: # pragma: no cover
reject()
result = result.map(downcast)
if not allow_infinity:
result = result.filter(lambda x: not math.isinf(x))
return result
def write_float(data, f):
data.write(int_to_bytes(float_to_lex(abs(f)), 8))
sign = float_to_int(f) >> 63
data.write(hbytes([sign]))
def test_floats_round_trip(f):
i = flt.float_to_lex(f)
g = flt.lex_to_float(i)
assert float_to_int(f) == float_to_int(g)
def write_float(data, f):
data.draw_bits(64, forced=float_to_lex(abs(f)))
sign = float_to_int(f) >> 63
data.draw_bits(1, forced=sign)
def write_float(data, f):
data.write(int_to_bytes(float_to_lex(abs(f)), 8))
sign = float_to_int(f) >> 63
data.write(hbytes([sign]))
def floats(min_value=None,
max_value=None,
allow_nan=None,
allow_infinity=None):
"""Returns a strategy which generates floats.
- If min_value is not None, all values will be >= min_value.
- If max_value is not None, all values will be <= max_value.
- If min_value or max_value is not None, it is an error to enable
allow_nan.
- If both min_value and max_value are not None, it is an error to enable
allow_infinity.
Where not explicitly ruled out by the bounds, all of infinity, -infinity
and NaN are possible values generated by this strategy.
"""
if allow_nan is None:
allow_nan = bool(min_value is None and max_value is None)
elif allow_nan:
if min_value is not None or max_value is not None:
raise InvalidArgument(
'Cannot have allow_nan=%r, with min_value or max_value' %
(allow_nan))
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
if min_value is not None:
min_value = float(min_value)
if max_value is not None:
max_value = float(max_value)
if min_value == float(u'-inf'):
min_value = None
if max_value == float(u'inf'):
max_value = None
if allow_infinity is None:
allow_infinity = bool(min_value is None or max_value is None)
elif allow_infinity:
if min_value is not None and max_value is not None:
raise InvalidArgument(
'Cannot have allow_infinity=%r, with both min_value and '
'max_value' % (allow_infinity))
from hypothesis.searchstrategy.numbers import FloatStrategy, \
FixedBoundedFloatStrategy
if min_value is None and max_value is None:
return FloatStrategy(
allow_infinity=allow_infinity,
allow_nan=allow_nan,
)
elif min_value is not None and max_value is not None:
if min_value == max_value:
return just(min_value)
elif math.isinf(max_value - min_value):
assert min_value < 0 and max_value > 0
return floats(min_value=0, max_value=max_value) | floats(
min_value=min_value, max_value=0)
elif count_between_floats(min_value, max_value) > 1000:
return FixedBoundedFloatStrategy(lower_bound=min_value,
upper_bound=max_value)
elif is_negative(max_value):
assert is_negative(min_value)
ub_int = float_to_int(max_value)
lb_int = float_to_int(min_value)
assert ub_int <= lb_int
return integers(min_value=ub_int,
max_value=lb_int).map(int_to_float)
elif is_negative(min_value):
return floats(min_value=min_value, max_value=-0.0) | floats(
min_value=0, max_value=max_value)
else:
ub_int = float_to_int(max_value)
lb_int = float_to_int(min_value)
assert lb_int <= ub_int
return integers(min_value=lb_int,
max_value=ub_int).map(int_to_float)
elif min_value is not None:
if min_value < 0:
result = floats(min_value=0.0) | floats(min_value=min_value,
max_value=0.0)
else:
result = (floats(allow_infinity=allow_infinity,
allow_nan=False).map(lambda x: assume(
not math.isnan(x)) and min_value + abs(x)))
if min_value == 0 and not is_negative(min_value):
result = result.filter(lambda x: math.copysign(1.0, x) == 1)
return result
else:
assert max_value is not None
if max_value > 0:
result = floats(
min_value=0.0,
max_value=max_value,
) | floats(max_value=0.0)
else:
result = (floats(allow_infinity=allow_infinity,
allow_nan=False).map(lambda x: assume(
not math.isnan(x)) and max_value - abs(x)))
if max_value == 0 and is_negative(max_value):
result = result.filter(is_negative)
return result
