D g|M^ddlZddlZddlZddlZddlZddlmZddlm Z m Z m Z m Z ddl mZddlmZgdZejejd Z dd Zee ddZ ddZee ddZ ddZeeddZdZdZddZdS)N)numeric) result_typeNaN asanyarrayndim) add_docstring) overrides)logspacelinspace geomspacenumpy)modulec ||fSN)startstopnumendpointretstepdtypeaxiss M/var/www/sysmax/venv/lib/python3.11/site-packages/numpy/core/function_base.py_linspace_dispatcherr 4=2TFctj|}|dkrtd|z|r|dz n|}t|dz}t|dz}t ||t |}||}d} nt j|t j} ||z } t j d|| dd t| zz} |dkrnt j | } | |z } | r| dkn)t j| dk }|r| |z} | r| | z} n | | z} n| r| | z} n| | z} n t} | | z} | |z } |r |dkr|| d <|dkrt 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') [] >>> plt.plot(x2, y + 0.5, 'o') [] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() rz,Number of samples, %s, must be non-negative.rg?NF)r))r)r .)outcopy)operatorindex ValueErrorrrfloat_nx issubdtypeintegerarangereshaperisscalaranyrmoveaxisfloorastype)rrrrrrrdivdt integer_dtypedeltay _mult_inplacestep any_step_zeros rr r s$D .  C QwwG#MNNN (377SC u   #E t   #D UD%** - -B } uck:: 5LE 1c$$$,,UTDKK5G-GHHA Qww U++ s{& KDAIICN419,E,E,I,I,K,K    HA U I T H IJAC!GG'  qyy LAt $ $ !+xxEx**D00xxEx***rc |||fSrr)rrrrbaserrs r_logspace_dispatcherr<s 4 r$@c:tj|||jfd|||fD\}}}t|||||}tj||}|t j||St j|||dS)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') [] >>> plt.plot(x2, y + 0.5, 'o') [] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() c3HK|]}tj|ddVdS)FT)r#subokndminN)nparray).0andmaxs r zlogspace..#sJ  d%888r)rrr)rNFr")rB broadcastrr expand_dimsr(powerr1) rrrrr;rrr6rFs @rr r sH Ld + + 0Et$E4 #tDDDA >$T * * *D }yq!!! 9T1   $ $U $ 7 77rc ||fSrr)rrrrrrs r_geomspace_dispatcherrL.rrc t|}t|}tj|dkstj|dkrtdt ||t |tjd|}||}ntj|}||d}||d}tj tj ||j |}tj |tj rP|jdk|jdkz}tj|r%||j||<||j||<d||<tj|d ktj|d kz} tj| rEtj||| tj||| tj||| tj|} tj|} t'| | ||d | } |dkr|| d<|d kr|r|| d <|| z} |dkrtj| d|} | |dS)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') [] >>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o') [] >>> 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() rz&Geometric sequence cannot include zerorNTr"gy?r )r!wherer=)rrr;rrF)rr(r.r&rr'zerosrr1onesrHshaper)complexfloatingrealimagsignnegativelog10r r/) rrrrrrr3out_signall_imag both_negative log_startlog_stopresults rr r 3swP u  E d  D wuzCcgdai00CABBB UD%**ciE.B.B C CB } %   LL$L ' 'E ;;r; % %Dx eT228"==H ~b#-..$J"$b9 78   $#Ho2E(O!(^0DN!#HX Xe__*sx~~/CDM w}B U];;;; Tt=9999 X8=AAAA %  IyH is'd%AAAF  Qwwq 77x7F2J  F qyyfa.. ==U= + ++rcd}t|tjtjtfrdSt|t r |j|zrdSdS)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. iFT) isinstancetypes FunctionType MethodTypepropertytype __flags__)objPy_TPFLAGS_HEAPTYPEs r_needs_add_docstringrhsY!#*E,t|s/tjd|td t ||dS#t $rYdSwxYw)Nz[add_newdoc was used on a pure-python object {}. Prefer to attach it directly to the source.) stacklevel)rhwarningswarnformat UserWarningr Exception)rfdocwarn_on_pythons r_add_docstringrss2377  : VC[[        c3      sA A"!A"ctt|ti|g|}t|tr%t |||dSt|tr8|\}}t t||||dSt|tr9|D]8\}}t t||||7dSdS)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_strrsstriptuplelist)placerfrqrrnewattr docstrings r add_newdocrs \ *UGIIrC5993 ? ?C#sRsCIIKK88888 C  RiwsD))9??+<+rs 777777777777////// / / /,)+ %g777HL*. -..FJ]+]+]+/.]+@EI*. -..BFl8l8l8/.l8^GK# .//Z,Z,Z,0/Z,z$    6R6R6R6R6R6Rr