Linux websever 5.15.0-153-generic #163-Ubuntu SMP Thu Aug 7 16:37:18 UTC 2025 x86_64
Apache/2.4.52 (Ubuntu)
: 192.168.3.70 | : 192.168.1.99
Cant Read [ /etc/named.conf ]
8.1.2-1ubuntu2.23
urlab
www.github.com/MadExploits
Terminal
AUTO ROOT
Adminer
Backdoor Destroyer
Linux Exploit
Lock Shell
Lock File
Create User
CREATE RDP
PHP Mailer
BACKCONNECT
UNLOCK SHELL
HASH IDENTIFIER
CPANEL RESET
CREATE WP USER
README
+ Create Folder
+ Create File
/
usr /
lib /
python3 /
dist-packages /
numpy /
core /
[ HOME SHELL ]
Name
Size
Permission
Action
__pycache__
[ DIR ]
drwxr-xr-x
include
[ DIR ]
drwxr-xr-x
lib
[ DIR ]
drwxr-xr-x
tests
[ DIR ]
drwxr-xr-x
__init__.py
5.24
KB
-rw-r--r--
__init__.pyi
126
B
-rw-r--r--
_add_newdocs.py
187.41
KB
-rw-r--r--
_add_newdocs_scalars.py
8.59
KB
-rw-r--r--
_asarray.py
4.08
KB
-rw-r--r--
_asarray.pyi
1.89
KB
-rw-r--r--
_dtype.py
9.61
KB
-rw-r--r--
_dtype_ctypes.py
3.59
KB
-rw-r--r--
_exceptions.py
5.99
KB
-rw-r--r--
_internal.py
26.73
KB
-rw-r--r--
_internal.pyi
1.34
KB
-rw-r--r--
_methods.py
10.54
KB
-rw-r--r--
_multiarray_tests.cpython-310-...
122.6
KB
-rw-r--r--
_multiarray_umath.cpython-310-...
3.63
MB
-rw-r--r--
_operand_flag_tests.cpython-31...
14.2
KB
-rw-r--r--
_rational_tests.cpython-310-x8...
43.46
KB
-rw-r--r--
_simd.cpython-310-x86_64-linux...
1.91
MB
-rw-r--r--
_string_helpers.py
2.79
KB
-rw-r--r--
_struct_ufunc_tests.cpython-31...
14.38
KB
-rw-r--r--
_type_aliases.py
7.1
KB
-rw-r--r--
_type_aliases.pyi
520
B
-rw-r--r--
_ufunc_config.py
13.07
KB
-rw-r--r--
_ufunc_config.pyi
1.22
KB
-rw-r--r--
_umath_tests.cpython-310-x86_6...
34.8
KB
-rw-r--r--
arrayprint.py
60.18
KB
-rw-r--r--
arrayprint.pyi
4.56
KB
-rw-r--r--
cversions.py
347
B
-rw-r--r--
defchararray.py
68.1
KB
-rw-r--r--
einsumfunc.py
50.24
KB
-rw-r--r--
einsumfunc.pyi
3.62
KB
-rw-r--r--
fromnumeric.py
119.9
KB
-rw-r--r--
fromnumeric.pyi
7.83
KB
-rw-r--r--
function_base.py
18.57
KB
-rw-r--r--
function_base.pyi
1.44
KB
-rw-r--r--
generate_numpy_api.py
6.94
KB
-rw-r--r--
getlimits.py
19.31
KB
-rw-r--r--
machar.py
10.56
KB
-rw-r--r--
memmap.py
11.41
KB
-rw-r--r--
multiarray.py
54.01
KB
-rw-r--r--
numeric.py
74.93
KB
-rw-r--r--
numeric.pyi
4.76
KB
-rw-r--r--
numerictypes.py
16.91
KB
-rw-r--r--
numerictypes.pyi
2.85
KB
-rw-r--r--
overrides.py
7.94
KB
-rw-r--r--
records.py
36.58
KB
-rw-r--r--
setup.py
44.62
KB
-rw-r--r--
setup_common.py
19.31
KB
-rw-r--r--
shape_base.py
28.32
KB
-rw-r--r--
shape_base.pyi
1.04
KB
-rw-r--r--
umath.py
1.99
KB
-rw-r--r--
umath_tests.py
389
B
-rw-r--r--
Delete
Unzip
Zip
${this.title}
Close
Code Editor : _add_newdocs_scalars.py
""" This file is separate from ``_add_newdocs.py`` so that it can be mocked out by our sphinx ``conf.py`` during doc builds, where we want to avoid showing platform-dependent information. """ from numpy.core import dtype from numpy.core import numerictypes as _numerictypes from numpy.core.function_base import add_newdoc import platform ############################################################################## # # Documentation for concrete scalar classes # ############################################################################## def numeric_type_aliases(aliases): def type_aliases_gen(): for alias, doc in aliases: try: alias_type = getattr(_numerictypes, alias) except AttributeError: # The set of aliases that actually exist varies between platforms pass else: yield (alias_type, alias, doc) return list(type_aliases_gen()) possible_aliases = numeric_type_aliases([ ('int8', '8-bit signed integer (``-128`` to ``127``)'), ('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'), ('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'), ('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'), ('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'), ('uint8', '8-bit unsigned integer (``0`` to ``255``)'), ('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'), ('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'), ('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'), ('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'), ('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'), ('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'), ('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'), ('float96', '96-bit extended-precision floating-point number type'), ('float128', '128-bit extended-precision floating-point number type'), ('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'), ('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'), ('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'), ('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'), ]) 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(platform.system(), platform.machine(), 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) add_newdoc_for_scalar_type('bool_', ['bool8'], """ Boolean type (True or False), stored as a byte. .. warning:: The :class:`bool_` type is not a subclass of the :class:`int_` type (the :class:`bool_` is not even a number type). This is different than Python's default implementation of :class:`bool` as a sub-class of :class:`int`. """) add_newdoc_for_scalar_type('byte', [], """ Signed integer type, compatible with C ``char``. """) add_newdoc_for_scalar_type('short', [], """ Signed integer type, compatible with C ``short``. """) add_newdoc_for_scalar_type('intc', [], """ Signed integer type, compatible with C ``int``. """) add_newdoc_for_scalar_type('int_', [], """ Signed integer type, compatible with Python `int` and C ``long``. """) add_newdoc_for_scalar_type('longlong', [], """ Signed integer type, compatible with C ``long long``. """) add_newdoc_for_scalar_type('ubyte', [], """ Unsigned integer type, compatible with C ``unsigned char``. """) add_newdoc_for_scalar_type('ushort', [], """ Unsigned integer type, compatible with C ``unsigned short``. """) add_newdoc_for_scalar_type('uintc', [], """ Unsigned integer type, compatible with C ``unsigned int``. """) add_newdoc_for_scalar_type('uint', [], """ Unsigned integer type, compatible with C ``unsigned long``. """) add_newdoc_for_scalar_type('ulonglong', [], """ Signed integer type, compatible with C ``unsigned long long``. """) add_newdoc_for_scalar_type('half', [], """ Half-precision floating-point number type. """) add_newdoc_for_scalar_type('single', [], """ Single-precision floating-point number type, compatible with C ``float``. """) add_newdoc_for_scalar_type('double', ['float_'], """ Double-precision floating-point number type, compatible with Python `float` and C ``double``. """) add_newdoc_for_scalar_type('longdouble', ['longfloat'], """ Extended-precision floating-point number type, compatible with C ``long double`` but not necessarily with IEEE 754 quadruple-precision. """) add_newdoc_for_scalar_type('csingle', ['singlecomplex'], """ Complex number type composed of two single-precision floating-point numbers. """) add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'], """ Complex number type composed of two double-precision floating-point numbers, compatible with Python `complex`. """) 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. """) add_newdoc_for_scalar_type('str_', ['unicode_'], r""" A unicode string. When used in arrays, this type strips trailing null codepoints. Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its contents as UCS4: >>> m = memoryview(np.str_("abc")) >>> m.format '3w' >>> m.tobytes() b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00' """) add_newdoc_for_scalar_type('bytes_', ['string_'], r""" A byte string. When used in arrays, this type strips trailing null bytes. """) add_newdoc_for_scalar_type('void', [], r""" Either an opaque sequence of bytes, or a structure. >>> np.void(b'abcd') void(b'\x61\x62\x63\x64') Structured `void` scalars can only be constructed via extraction from :ref:`structured_arrays`: >>> arr = np.array((1, 2), dtype=[('x', np.int8), ('y', np.int8)]) >>> arr[()] (1, 2) # looks like a tuple, but is `np.void` """) add_newdoc_for_scalar_type('datetime64', [], """ If created from a 64-bit integer, it represents an offset from ``1970-01-01T00:00:00``. If created from string, the string can be in ISO 8601 date or datetime format. >>> np.datetime64(10, 'Y') numpy.datetime64('1980') >>> np.datetime64('1980', 'Y') numpy.datetime64('1980') >>> np.datetime64(10, 'D') numpy.datetime64('1970-01-11') See :ref:`arrays.datetime` for more information. """) add_newdoc_for_scalar_type('timedelta64', [], """ A timedelta stored as a 64-bit integer. See :ref:`arrays.datetime` for more information. """) # 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)))
Close
