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function_base.cpython-311.pyc
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� � h|M � �^ � d dl Z d dlZd dlZd dlZd dlZddlmZ ddlm Z m Z m Z m Z d dl mZ d dlmZ g d�Z e j ej d� � � Z dd �Z ee� � dd�� � Z dd�Z ee� � dd�� � Z dd�Z ee� � dd�� � Zd� Zd� Zd d�Z dS ) � N� )�numeric)� result_type�NaN� asanyarray�ndim)� add_docstring)� overrides)�logspace�linspace� geomspace�numpy)�modulec � � | |fS �N� )�start�stop�num�endpoint�retstep�dtype�axiss �O/opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/core/function_base.py�_linspace_dispatcherr � � � �4�=�� �2 TFc � � t j |� � }|dk rt d|z � � �|r|dz n|}t | � � dz } t |� � dz }t | |t |� � � � }|�|}d} n t j |t j � � } || z } t j d||�� � � dd t | � � z z � � } |dk rnt j | � � } | |z } | r| dk n)t j | dk � � � � � }|r| |z } | r| | z } n | | z } n| r| | z } n| | z } n t } | | z } | | z } |r |dk r|| d < |dk rt j | d|� � } | rt j | | � � � |r| � |d� � � | fS | � |d� � � S ) aN 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() r z,Number of samples, %s, must be non-negative.r g �?NF)r )���)r )r .)�out��copy)�operator�index� ValueErrorr r �float�_nx� issubdtype�integer�arange�reshaper �isscalar�anyr �moveaxis�floor�astype)r r r r r r r �div�dt� integer_dtype�delta�y� _mult_inplace�step� any_step_zeros r r r s$ � �D �.�� � �C� �Q�w�w��G�#�M�N�N�N� � (�3��7�7�S�C� �u� � �� #�E� �t� � �� #�D� �U�D�%��*�*� -� -�B� �}��� � � ���u�c�k�:�:� � �5�L�E� � �1�c��$�$�$�,�,�U�T�D��K�K�5G�-G�H�H�A� �Q�w�w�� �U�+�+� ��s�{��&� K�D�A�I�I�C�N�4�1�9�,E�,E�,I�,I�,K�,K� � � � ��H�A� � ��U� �����I��� � ��T� �����H��� �� ��I����J�A�� �C�!�G�G���'� � �q�y�y� �L��A�t� $� $��� � � �!������� +��x�x��E�x�*�*�D�0�0��x�x��E�x�*�*�*r c � � | ||fS r r )r r r r �baser r s r �_logspace_dispatcherr< � s � � �4�� � r � $@c �: �� t j | ||� � j ��fd�| ||fD � � \ } }}t | ||||�� � }t j ||�� � }|�t j ||� � S t j ||� � � |d�� � S )a 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() c 3 �H �K � | ] }t j |d d��� � V � � dS )FT)r# �subok�ndminN)�np�array)�.0�a�ndmaxs �r � <genexpr>zlogspace.<locals>.<genexpr># sJ �� � � � � � � ����d�%�8�8�8�� � � � � r )r r r )r NFr" )rB � broadcastr r � expand_dimsr( �powerr1 ) r r r r r; r r r6 rF s @r r r � s� �� �H �L���d� +� +� 0�E�� � � ���t�$�� � ��E�4�� ���#��t�D�D�D�A� �>�$�T� *� *� *�D� �}��y��q�!�!�!� �9�T�1� � � $� $�U�� $� 7� 7�7r c � � | |fS r r )r r r r r r s r �_geomspace_dispatcherrL . r r c � � t | � � } t |� � }t j | dk � � st j |dk � � rt d� � �t | |t |� � t j d|� � � � }|�|}nt j |� � }| � |d�� � } |� |d�� � }t j t j | |� � j |� � }t j |t j � � rP| j dk |j dk z }t j |� � r%| | j | |< || j ||< d||< t j | � � d k t j |� � d k z } t j | � � rEt j | | | � � � t j ||| � � � t j ||| � � � t j | � � } t j |� � } t' | | ||d |� � � } |dk r| | d< |d k r|r|| d < || z } |dk rt j | d|� � } | � |d�� � S )a� 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() r z&Geometric sequence cannot include zeror NTr" g y �?r )r! �wherer= )r r r; r r F)r r( r. r& r r' �zerosr r1 �onesrH �shaper) �complexfloating�real�imag�sign�negative�log10r r/ ) r r r r r r r3 �out_sign�all_imag� both_negative� log_start�log_stop�results r r r 3 sw � �P �u� � �E� �d� � �D� �w�u��z��� C�c�g�d�a�i� 0� 0� C��A�B�B�B� �U�D�%��*�*�c�i��E�.B�.B� C� C�B� �}���� � �%� � �� �L�L��$�L� '� '�E� �;�;�r��;� %� %�D��x�� �e�T�2�2�8�"�=�=�H� �~�b�#�-�.�.� $��J�"�$���b� �9�� �7�8� � � $� #�H�o� 2�E�(�O� !�(�^� 0�D��N�!#�H�X� ��X�e�_�_��*�s�x��~�~��/C�D�M� �w�}� � � B� � �U��]�;�;�;�;� � �T�t�=�9�9�9�9� � �X�8�=�A�A�A�A�� �%� � �I��y����H� �i��s� '�d�%�A� A� A�F� �Q�w�w���q� � ��7�7�x�7� �F�2�J� �� �F� �q�y�y�� �f�a��.�.�� �=�=��U�=� +� +�+r c � � d}t | t j t j t f� � rdS t | t � � r | j |z rdS dS )z� 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. i FT)� isinstance�types� FunctionType� MethodType�property�type� __flags__)�obj�Py_TPFLAGS_HEAPTYPEs r �_needs_add_docstringrh � sY � � !���#��*�E�,<�h�G�H�H� ��u��#�t� � � �� �1D�!D� ��u� �4r c �� � |r>t | � � s/t j d� | � � t d�� � t | |� � d S # t $ r Y d S w xY w)Nz[add_newdoc was used on a pure-python object {}. Prefer to attach it directly to the source.� )� stacklevel)rh �warnings�warn�format� UserWarningr � Exception)rf �doc�warn_on_pythons r �_add_docstringrs � s� � �� � 2�3� 7� 7� �� � :� �V�C�[�[� �� � � � � ��c�3� � � � � �� � � � � ��� ���s �A � A"�!A"c � � t t | t � � i |g� � |� � }t |t � � r%t ||� � � |� � dS t |t � � r8|\ }}t t ||� � |� � � |� � dS t |t � � r9|D ]8\ }}t t ||� � |� � � |� � �7dS dS )a 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. N) �getattr� __import__�globalsr_ �strrs �strip�tuple�list)�placerf rq rr �new�attr� docstrings r � add_newdocr� � s � �\ �*�U�G�I�I�r�C�5�9�9�3� ?� ?�C��#�s��� R��s�C�I�I�K�K� �8�8�8�8�8� �C�� � � R� ���i��w�s�D�)�)�9�?�?�+<�+<�n�M�M�M�M�M� �C�� � � R� "� R� R�O�D�)� �7�3��-�-�y���/@�/@�.� Q� Q� Q� Q�R� R� R� Rr )NNNNN)r TFNr )r Tr= Nr )NNNN)r TNr )T) � functoolsrl r$ r` r rB � r r( r r r r �numpy.core.multiarrayr � numpy.corer �__all__�partial�array_function_dispatchr r r< r rL r rh rs r� r r r �<module>r� s� �� � � � � ���� ���� � � � � � � � � � � � � � � 7� 7� 7� 7� 7� 7� 7� 7� 7� 7� 7� 7� /� /� /� /� /� /� � � � � � � /� /� /�� ,�)�+� �%�g�7� 7� 7� � HL�*.�� � � � ��-�.�.�FJ��]+� ]+� ]+� /�.�]+�@ EI�*.� � � � � ��-�.�.�BF��l8� l8� l8� /�.�l8�^ GK� #�� � � � ��.�/�/�Z,� Z,� Z,� 0�/�Z,�z� � �$ � � � 6R� 6R� 6R� 6R� 6R� 6Rr
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