def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
# note: `:field: value` is rST syntax which renders as field lists.
o = getattr(_numerictypes, obj)
character_code = dtype(o).char
canonical_name_doc = (
"" if obj == o.__name__ else ":Canonical name: `numpy.{}`\n ".format(obj)
)
alias_doc = "".join(
":Alias: `numpy.{}`\n ".format(alias) for alias in fixed_aliases
)
alias_doc += "".join(
":Alias on this platform: `numpy.{}`: {}.\n ".format(alias, doc)
for (alias_type, alias, doc) in possible_aliases
if alias_type is o
)
docstring = """
{doc}
:Character code: ``'{character_code}'``
{canonical_name_doc}{alias_doc}
""".format(
doc=doc.strip(),
character_code=character_code,
canonical_name_doc=canonical_name_doc,
alias_doc=alias_doc,
)
add_newdoc("numpy.core.numerictypes", obj, docstring)
def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
# note: `:field: value` is rST syntax which renders as field lists.
o = getattr(_numerictypes, obj)
character_code = dtype(o).char
canonical_name_doc = "" if obj == o.__name__ else \
f":Canonical name: `numpy.{obj}`\n "
if fixed_aliases:
alias_doc = ''.join(f":Alias: `numpy.{alias}`\n "
for alias in fixed_aliases)
else:
alias_doc = ''
alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n "
for (alias_type, alias, doc) in possible_aliases
if alias_type is o)
docstring = f"""
{doc.strip()}
:Character code: ``'{character_code}'``
{canonical_name_doc}{alias_doc}
"""
add_newdoc('numpy.core.numerictypes', obj, docstring)
""")
add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
"""
Complex number type composed of two extended-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('object_', [],
"""
Any Python object.
""")
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
"""
{ftype}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
floating point number, and with a positive denominator.
Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.{ftype}(10.0).as_integer_ratio()
(10, 1)
>>> np.{ftype}(0.0).as_integer_ratio()
(0, 1)
>>> np.{ftype}(-.25).as_integer_ratio()
(-1, 4)
""".format(ftype=float_name)))
add_newdoc_for_scalar_type('timedelta64', [],
"""
A timedelta stored as a 64-bit integer.
See :ref:`arrays.datetime` for more information.
""")
add_newdoc('numpy.core.numerictypes', "integer", ('is_integer',
"""
integer.is_integer() -> bool
Return ``True`` if the number is finite with integral value.
.. versionadded:: 1.22
Examples
--------
>>> np.int64(-2).is_integer()
True
>>> np.uint32(5).is_integer()
True
"""))
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
"""
{ftype}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
""")
add_newdoc_for_scalar_type(
'timedelta64', [], """
A timedelta stored as a 64-bit integer.
See :ref:`arrays.datetime` for more information.
""")
add_newdoc('numpy.core.numerictypes', "integer", ('is_integer', """
integer.is_integer() -> bool
Return ``True`` if the number is finite with integral value.
.. versionadded:: 1.22
Examples
--------
>>> np.int64(-2).is_integer()
True
>>> np.uint32(5).is_integer()
True
"""))
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio', """
{ftype}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
floating point number, and with a positive denominator.
Raise `OverflowError` on infinities and a `ValueError` on NaNs.