File Manager

X7ROOT File Manager

Current Path: /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/core

opt / cloudlinux / venv / lib / python3.11 / site-packages / numpy / core /

?? ..
?? __init__.py(5.64 KB)
?? __init__.pyi(126 B)
?? __pycache__
?? _add_newdocs.py(204.07 KB)
?? _add_newdocs_scalars.py(11.82 KB)
?? _asarray.py(3.79 KB)
?? _asarray.pyi(1.06 KB)
?? _dtype.py(10.36 KB)
?? _dtype_ctypes.py(3.59 KB)
?? _exceptions.py(5.25 KB)
?? _internal.py(27.68 KB)
?? _internal.pyi(1.01 KB)
?? _machar.py(11.29 KB)
?? _methods.py(8.41 KB)
?? _multiarray_tests.cpython-311-x86_64-linux-gnu.so(171.4 KB)
?? _multiarray_umath.cpython-311-x86_64-linux-gnu.so(6.64 MB)
?? _operand_flag_tests.cpython-311-x86_64-linux-gnu.so(16.55 KB)
?? _rational_tests.cpython-311-x86_64-linux-gnu.so(58.29 KB)
?? _simd.cpython-311-x86_64-linux-gnu.so(2.47 MB)
?? _string_helpers.py(2.79 KB)
?? _struct_ufunc_tests.cpython-311-x86_64-linux-gnu.so(16.65 KB)
?? _type_aliases.py(7.36 KB)
?? _type_aliases.pyi(404 B)
?? _ufunc_config.py(13.62 KB)
?? _ufunc_config.pyi(1.04 KB)
?? _umath_tests.cpython-311-x86_64-linux-gnu.so(41.01 KB)
?? arrayprint.py(62.12 KB)
?? arrayprint.pyi(4.32 KB)
?? cversions.py(347 B)
?? defchararray.py(71.89 KB)
?? defchararray.pyi(9 KB)
?? einsumfunc.py(50.65 KB)
?? einsumfunc.pyi(4.75 KB)
?? fromnumeric.py(125.8 KB)
?? fromnumeric.pyi(22.96 KB)
?? function_base.py(19.37 KB)
?? function_base.pyi(4.61 KB)
?? generate_numpy_api.py(7.47 KB)
?? getlimits.py(25.26 KB)
?? getlimits.pyi(82 B)
?? include
?? lib
?? memmap.py(11.5 KB)
?? memmap.pyi(55 B)
?? multiarray.py(54.78 KB)
?? multiarray.pyi(24.19 KB)
?? numeric.py(75.21 KB)
?? numeric.pyi(13.9 KB)
?? numerictypes.py(17.67 KB)
?? numerictypes.pyi(3.19 KB)
?? overrides.py(6.93 KB)
?? records.py(36.65 KB)
?? records.pyi(5.56 KB)
?? setup.py(47.05 KB)
?? setup_common.py(16.68 KB)
?? shape_base.py(29.05 KB)
?? shape_base.pyi(2.71 KB)
?? tests
?? umath.py(1.99 KB)
?? umath_tests.py(389 B)

Editing: function_base.py

import functools
import warnings
import operator
import types

import numpy as np
from . import numeric as _nx
from .numeric import result_type, NaN, asanyarray, ndim
from numpy.core.multiarray import add_docstring
from numpy.core import overrides

__all__ = ['logspace', 'linspace', 'geomspace']

array_function_dispatch = functools.partial(
    overrides.array_function_dispatch, module='numpy')

def _linspace_dispatcher(start, stop, num=None, endpoint=None, retstep=None,
                         dtype=None, axis=None):
    return (start, stop)

@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
             axis=0):
    """
    Return evenly spaced numbers over a specified interval.

Returns `num` evenly spaced samples, calculated over the
    interval [`start`, `stop`].

The endpoint of the interval can optionally be excluded.

.. versionchanged:: 1.16.0
        Non-scalar `start` and `stop` are now supported.

.. versionchanged:: 1.20.0
        Values are rounded towards ``-inf`` instead of ``0`` when an
        integer ``dtype`` is specified. The old behavior can
        still be obtained with ``np.linspace(start, stop, num).astype(int)``

Parameters
    ----------
    start : array_like
        The starting value of the sequence.
    stop : array_like
        The end value of the sequence, unless `endpoint` is set to False.
        In that case, the sequence consists of all but the last of ``num + 1``
        evenly spaced samples, so that `stop` is excluded.  Note that the step
        size changes when `endpoint` is False.
    num : int, optional
        Number of samples to generate. Default is 50. Must be non-negative.
    endpoint : bool, optional
        If True, `stop` is the last sample. Otherwise, it is not included.
        Default is True.
    retstep : bool, optional
        If True, return (`samples`, `step`), where `step` is the spacing
        between samples.
    dtype : dtype, optional
        The type of the output array.  If `dtype` is not given, the data type
        is inferred from `start` and `stop`. The inferred dtype will never be
        an integer; `float` is chosen even if the arguments would produce an
        array of integers.

.. versionadded:: 1.9.0

axis : int, optional
        The axis in the result to store the samples.  Relevant only if start
        or stop are array-like.  By default (0), the samples will be along a
        new axis inserted at the beginning. Use -1 to get an axis at the end.

.. versionadded:: 1.16.0

Returns
    -------
    samples : ndarray
        There are `num` equally spaced samples in the closed interval
        ``[start, stop]`` or the half-open interval ``[start, stop)``
        (depending on whether `endpoint` is True or False).
    step : float, optional
        Only returned if `retstep` is True

Size of spacing between samples.

See Also
    --------
    arange : Similar to `linspace`, but uses a step size (instead of the
             number of samples).
    geomspace : Similar to `linspace`, but with numbers spaced evenly on a log
                scale (a geometric progression).
    logspace : Similar to `geomspace`, but with the end points specified as
               logarithms.
    :ref:`how-to-partition`

Examples
    --------
    >>> np.linspace(2.0, 3.0, num=5)
    array([2.  , 2.25, 2.5 , 2.75, 3.  ])
    >>> np.linspace(2.0, 3.0, num=5, endpoint=False)
    array([2. ,  2.2,  2.4,  2.6,  2.8])
    >>> np.linspace(2.0, 3.0, num=5, retstep=True)
    (array([2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)

Graphical illustration:

>>> import matplotlib.pyplot as plt
    >>> N = 8
    >>> y = np.zeros(N)
    >>> x1 = np.linspace(0, 10, N, endpoint=True)
    >>> x2 = np.linspace(0, 10, N, endpoint=False)
    >>> plt.plot(x1, y, 'o')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.plot(x2, y + 0.5, 'o')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.ylim([-0.5, 1])
    (-0.5, 1)
    >>> plt.show()

"""
    num = operator.index(num)
    if num < 0:
        raise ValueError("Number of samples, %s, must be non-negative." % num)
    div = (num - 1) if endpoint else num

# Convert float/complex array scalars to float, gh-3504
    # and make sure one can use variables that have an __array_interface__, gh-6634
    start = asanyarray(start) * 1.0
    stop  = asanyarray(stop)  * 1.0

dt = result_type(start, stop, float(num))
    if dtype is None:
        dtype = dt
        integer_dtype = False
    else:
        integer_dtype = _nx.issubdtype(dtype, _nx.integer)

delta = stop - start
    y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
    # In-place multiplication y *= delta/div is faster, but prevents the multiplicant
    # from overriding what class is produced, and thus prevents, e.g. use of Quantities,
    # see gh-7142. Hence, we multiply in place only for standard scalar types.
    if div > 0:
        _mult_inplace = _nx.isscalar(delta)
        step = delta / div
        any_step_zero = (
            step == 0 if _mult_inplace else _nx.asanyarray(step == 0).any())
        if any_step_zero:
            # Special handling for denormal numbers, gh-5437
            y /= div
            if _mult_inplace:
                y *= delta
            else:
                y = y * delta
        else:
            if _mult_inplace:
                y *= step
            else:
                y = y * step
    else:
        # sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
        # have an undefined step
        step = NaN
        # Multiply with delta to allow possible override of output class.
        y = y * delta

y += start

if endpoint and num > 1:
        y[-1, ...] = stop

if axis != 0:
        y = _nx.moveaxis(y, 0, axis)

if integer_dtype:
        _nx.floor(y, out=y)

if retstep:
        return y.astype(dtype, copy=False), step
    else:
        return y.astype(dtype, copy=False)

def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None,
                         dtype=None, axis=None):
    return (start, stop, base)

@array_function_dispatch(_logspace_dispatcher)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
             axis=0):
    """
    Return numbers spaced evenly on a log scale.

In linear space, the sequence starts at ``base ** start``
    (`base` to the power of `start`) and ends with ``base ** stop``
    (see `endpoint` below).

.. versionchanged:: 1.16.0
        Non-scalar `start` and `stop` are now supported.

.. versionchanged:: 1.25.0
        Non-scalar 'base` is now supported

Parameters
    ----------
    start : array_like
        ``base ** start`` is the starting value of the sequence.
    stop : array_like
        ``base ** stop`` is the final value of the sequence, unless `endpoint`
        is False.  In that case, ``num + 1`` values are spaced over the
        interval in log-space, of which all but the last (a sequence of
        length `num`) are returned.
    num : integer, optional
        Number of samples to generate.  Default is 50.
    endpoint : boolean, optional
        If true, `stop` is the last sample. Otherwise, it is not included.
        Default is True.
    base : array_like, optional
        The base of the log space. The step size between the elements in
        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
        Default is 10.0.
    dtype : dtype
        The type of the output array.  If `dtype` is not given, the data type
        is inferred from `start` and `stop`. The inferred type will never be
        an integer; `float` is chosen even if the arguments would produce an
        array of integers.
    axis : int, optional
        The axis in the result to store the samples.  Relevant only if start,
        stop, or base are array-like.  By default (0), the samples will be
        along a new axis inserted at the beginning. Use -1 to get an axis at
        the end.

.. versionadded:: 1.16.0

Returns
    -------
    samples : ndarray
        `num` samples, equally spaced on a log scale.

See Also
    --------
    arange : Similar to linspace, with the step size specified instead of the
             number of samples. Note that, when used with a float endpoint, the
             endpoint may or may not be included.
    linspace : Similar to logspace, but with the samples uniformly distributed
               in linear space, instead of log space.
    geomspace : Similar to logspace, but with endpoints specified directly.
    :ref:`how-to-partition`

Notes
    -----
    If base is a scalar, logspace is equivalent to the code

>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
    ... # doctest: +SKIP
    >>> power(base, y).astype(dtype)
    ... # doctest: +SKIP

Examples
    --------
    >>> np.logspace(2.0, 3.0, num=4)
    array([ 100.        ,  215.443469  ,  464.15888336, 1000.        ])
    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
    array([100.        ,  177.827941  ,  316.22776602,  562.34132519])
    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
    array([4.        ,  5.0396842 ,  6.34960421,  8.        ])
    >>> np.logspace(2.0, 3.0, num=4, base=[2.0, 3.0], axis=-1)
    array([[ 4.        ,  5.0396842 ,  6.34960421,  8.        ],
           [ 9.        , 12.98024613, 18.72075441, 27.        ]])

Graphical illustration:

>>> import matplotlib.pyplot as plt
    >>> N = 10
    >>> x1 = np.logspace(0.1, 1, N, endpoint=True)
    >>> x2 = np.logspace(0.1, 1, N, endpoint=False)
    >>> y = np.zeros(N)
    >>> plt.plot(x1, y, 'o')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.plot(x2, y + 0.5, 'o')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.ylim([-0.5, 1])
    (-0.5, 1)
    >>> plt.show()

"""
    ndmax = np.broadcast(start, stop, base).ndim
    start, stop, base = (
        np.array(a, copy=False, subok=True, ndmin=ndmax)
        for a in (start, stop, base)
    )
    y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
    base = np.expand_dims(base, axis=axis)
    if dtype is None:
        return _nx.power(base, y)
    return _nx.power(base, y).astype(dtype, copy=False)

def _geomspace_dispatcher(start, stop, num=None, endpoint=None, dtype=None,
                          axis=None):
    return (start, stop)

@array_function_dispatch(_geomspace_dispatcher)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
    """
    Return numbers spaced evenly on a log scale (a geometric progression).

This is similar to `logspace`, but with endpoints specified directly.
    Each output sample is a constant multiple of the previous.

.. versionchanged:: 1.16.0
        Non-scalar `start` and `stop` are now supported.

Parameters
    ----------
    start : array_like
        The starting value of the sequence.
    stop : array_like
        The final value of the sequence, unless `endpoint` is False.
        In that case, ``num + 1`` values are spaced over the
        interval in log-space, of which all but the last (a sequence of
        length `num`) are returned.
    num : integer, optional
        Number of samples to generate.  Default is 50.
    endpoint : boolean, optional
        If true, `stop` is the last sample. Otherwise, it is not included.
        Default is True.
    dtype : dtype
        The type of the output array.  If `dtype` is not given, the data type
        is inferred from `start` and `stop`. The inferred dtype will never be
        an integer; `float` is chosen even if the arguments would produce an
        array of integers.
    axis : int, optional
        The axis in the result to store the samples.  Relevant only if start
        or stop are array-like.  By default (0), the samples will be along a
        new axis inserted at the beginning. Use -1 to get an axis at the end.

.. versionadded:: 1.16.0

Returns
    -------
    samples : ndarray
        `num` samples, equally spaced on a log scale.

See Also
    --------
    logspace : Similar to geomspace, but with endpoints specified using log
               and base.
    linspace : Similar to geomspace, but with arithmetic instead of geometric
               progression.
    arange : Similar to linspace, with the step size specified instead of the
             number of samples.
    :ref:`how-to-partition`

Notes
    -----
    If the inputs or dtype are complex, the output will follow a logarithmic
    spiral in the complex plane.  (There are an infinite number of spirals
    passing through two points; the output will follow the shortest such path.)

Examples
    --------
    >>> np.geomspace(1, 1000, num=4)
    array([    1.,    10.,   100.,  1000.])
    >>> np.geomspace(1, 1000, num=3, endpoint=False)
    array([   1.,   10.,  100.])
    >>> np.geomspace(1, 1000, num=4, endpoint=False)
    array([   1.        ,    5.62341325,   31.6227766 ,  177.827941  ])
    >>> np.geomspace(1, 256, num=9)
    array([   1.,    2.,    4.,    8.,   16.,   32.,   64.,  128.,  256.])

Note that the above may not produce exact integers:

>>> np.geomspace(1, 256, num=9, dtype=int)
    array([  1,   2,   4,   7,  16,  32,  63, 127, 256])
    >>> np.around(np.geomspace(1, 256, num=9)).astype(int)
    array([  1,   2,   4,   8,  16,  32,  64, 128, 256])

Negative, decreasing, and complex inputs are allowed:

>>> np.geomspace(1000, 1, num=4)
    array([1000.,  100.,   10.,    1.])
    >>> np.geomspace(-1000, -1, num=4)
    array([-1000.,  -100.,   -10.,    -1.])
    >>> np.geomspace(1j, 1000j, num=4)  # Straight line
    array([0.   +1.j, 0.  +10.j, 0. +100.j, 0.+1000.j])
    >>> np.geomspace(-1+0j, 1+0j, num=5)  # Circle
    array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
            6.12323400e-17+1.00000000e+00j,  7.07106781e-01+7.07106781e-01j,
            1.00000000e+00+0.00000000e+00j])

Graphical illustration of `endpoint` parameter:

>>> import matplotlib.pyplot as plt
    >>> N = 10
    >>> y = np.zeros(N)
    >>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.axis([0.5, 2000, 0, 3])
    [0.5, 2000, 0, 3]
    >>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
    >>> plt.show()

"""
    start = asanyarray(start)
    stop = asanyarray(stop)
    if _nx.any(start == 0) or _nx.any(stop == 0):
        raise ValueError('Geometric sequence cannot include zero')

dt = result_type(start, stop, float(num), _nx.zeros((), dtype))
    if dtype is None:
        dtype = dt
    else:
        # complex to dtype('complex128'), for instance
        dtype = _nx.dtype(dtype)

# Promote both arguments to the same dtype in case, for instance, one is
    # complex and another is negative and log would produce NaN otherwise.
    # Copy since we may change things in-place further down.
    start = start.astype(dt, copy=True)
    stop = stop.astype(dt, copy=True)

out_sign = _nx.ones(_nx.broadcast(start, stop).shape, dt)
    # Avoid negligible real or imaginary parts in output by rotating to
    # positive real, calculating, then undoing rotation
    if _nx.issubdtype(dt, _nx.complexfloating):
        all_imag = (start.real == 0.) & (stop.real == 0.)
        if _nx.any(all_imag):
            start[all_imag] = start[all_imag].imag
            stop[all_imag] = stop[all_imag].imag
            out_sign[all_imag] = 1j

both_negative = (_nx.sign(start) == -1) & (_nx.sign(stop) == -1)
    if _nx.any(both_negative):
        _nx.negative(start, out=start, where=both_negative)
        _nx.negative(stop, out=stop, where=both_negative)
        _nx.negative(out_sign, out=out_sign, where=both_negative)

log_start = _nx.log10(start)
    log_stop = _nx.log10(stop)
    result = logspace(log_start, log_stop, num=num,
                      endpoint=endpoint, base=10.0, dtype=dtype)

# Make sure the endpoints match the start and stop arguments. This is
    # necessary because np.exp(np.log(x)) is not necessarily equal to x.
    if num > 0:
        result[0] = start
        if num > 1 and endpoint:
            result[-1] = stop

result = out_sign * result

if axis != 0:
        result = _nx.moveaxis(result, 0, axis)

return result.astype(dtype, copy=False)

def _needs_add_docstring(obj):
    """
    Returns true if the only way to set the docstring of `obj` from python is
    via add_docstring.

This function errs on the side of being overly conservative.
    """
    Py_TPFLAGS_HEAPTYPE = 1 << 9

if isinstance(obj, (types.FunctionType, types.MethodType, property)):
        return False

if isinstance(obj, type) and obj.__flags__ & Py_TPFLAGS_HEAPTYPE:
        return False

return True

def _add_docstring(obj, doc, warn_on_python):
    if warn_on_python and not _needs_add_docstring(obj):
        warnings.warn(
            "add_newdoc was used on a pure-python object {}. "
            "Prefer to attach it directly to the source."
            .format(obj),
            UserWarning,
            stacklevel=3)
    try:
        add_docstring(obj, doc)
    except Exception:
        pass

def add_newdoc(place, obj, doc, warn_on_python=True):
    """
    Add documentation to an existing object, typically one defined in C

The purpose is to allow easier editing of the docstrings without requiring
    a re-compile. This exists primarily for internal use within numpy itself.

Parameters
    ----------
    place : str
        The absolute name of the module to import from
    obj : str
        The name of the object to add documentation to, typically a class or
        function name
    doc : {str, Tuple[str, str], List[Tuple[str, str]]}
        If a string, the documentation to apply to `obj`

If a tuple, then the first element is interpreted as an attribute of
        `obj` and the second as the docstring to apply - ``(method, docstring)``

If a list, then each element of the list should be a tuple of length
        two - ``[(method1, docstring1), (method2, docstring2), ...]``
    warn_on_python : bool
        If True, the default, emit `UserWarning` if this is used to attach
        documentation to a pure-python object.

Notes
    -----
    This routine never raises an error if the docstring can't be written, but
    will raise an error if the object being documented does not exist.

This routine cannot modify read-only docstrings, as appear
    in new-style classes or built-in functions. Because this
    routine never raises an error the caller must check manually
    that the docstrings were changed.

Since this function grabs the ``char *`` from a c-level str object and puts
    it into the ``tp_doc`` slot of the type of `obj`, it violates a number of
    C-API best-practices, by:

- modifying a `PyTypeObject` after calling `PyType_Ready`
    - calling `Py_INCREF` on the str and losing the reference, so the str
      will never be released

If possible it should be avoided.
    """
    new = getattr(__import__(place, globals(), {}, [obj]), obj)
    if isinstance(doc, str):
        _add_docstring(new, doc.strip(), warn_on_python)
    elif isinstance(doc, tuple):
        attr, docstring = doc
        _add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)
    elif isinstance(doc, list):
        for attr, docstring in doc:
            _add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)

X7ROOT File Manager

Editing: function_base.py

Upload File

Create Folder