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a y[yc&< � @ s� d Z ddlZddlZddlmZmZmZmZ ze W n e yJ dZY n0 er`ej � d� �n.zddl m Z W n2 ey� Z zdZee�e�W Y dZ[n dZ[0 0 d d gZi Zdd lmZ dd lmZ dd lT ddlmZ ddlmZ dd lT ddlmZ ddlmZ ddlmZ ddlm Z ddlm Z ddlm Z ddlm Z dd l T ddl!Z"dZ#dZ$dZ%dde$�&d �fd e$�&d �fd e$�&d �fd!e$�&d"�fd#e%�&d#�fgZ'e�(d$d%� e'D �� ej)j*d&fed'< ej+j,d(fed)< d*Z#e-ed+�e#j&d+d#e%�&d+�d,�fed+< e-ed-�e#j&d-d!e$�&d"�d,�fed-< [#[$[%['["dd.lm.Z.m/Z/m0Z0m1Z1 ej2�3� e�4d/d0g� e�4ej� e�4e j� e�4ej� e�4g d1�� e�5d2� [6[7e�5d+� e�5d-� e�5d3� [8g d4�Z9d5d%� e9D �Z:ej;d6d7d8� ej;d6d9d8� ej;d6d:d8� d;Z<d;Z=d<d=� Z>d>d?� Z?dd@l@mAZA eAeB�ZC[AdAdB� ZDeD� [DdCdD� ZEddlZejFdEk�r�ejGdFdG��XZHeE� dHZIeJeH�dk�r�dI�&eHdJ jKjBeLeHdJ jM��ZIdK�&eI�ZeNe��W d � n1 �s�0 Y [EddlOZOeOjP�QdLd�ZRejFdMk�rVeRdu �rVz@dZReO�S� jT�UdN�ddO� ZVeWdPdQ� eVD ��ZVeVdRk �r8dZRW n eX�yR dZYY n0 neRdu �rfdZRneZeR�ZRej[�\eR� ej[j]�^� dSdT� Z_ddUl`maZambZc dS )VaV NumPy ===== Provides 1. An array object of arbitrary homogeneous items 2. Fast mathematical operations over arrays 3. Linear Algebra, Fourier Transforms, Random Number Generation How to use the documentation ---------------------------- Documentation is available in two forms: docstrings provided with the code, and a loose standing reference guide, available from `the NumPy homepage <https://numpy.org>`_. We recommend exploring the docstrings using `IPython <https://ipython.org>`_, an advanced Python shell with TAB-completion and introspection capabilities. See below for further instructions. The docstring examples assume that `numpy` has been imported as `np`:: >>> import numpy as np Code snippets are indicated by three greater-than signs:: >>> x = 42 >>> x = x + 1 Use the built-in ``help`` function to view a function's docstring:: >>> help(np.sort) ... # doctest: +SKIP For some objects, ``np.info(obj)`` may provide additional help. This is particularly true if you see the line "Help on ufunc object:" at the top of the help() page. Ufuncs are implemented in C, not Python, for speed. The native Python help() does not know how to view their help, but our np.info() function does. To search for documents containing a keyword, do:: >>> np.lookfor('keyword') ... # doctest: +SKIP General-purpose documents like a glossary and help on the basic concepts of numpy are available under the ``doc`` sub-module:: >>> from numpy import doc >>> help(doc) ... # doctest: +SKIP Available subpackages --------------------- lib Basic functions used by several sub-packages. random Core Random Tools linalg Core Linear Algebra Tools fft Core FFT routines polynomial Polynomial tools testing NumPy testing tools distutils Enhancements to distutils with support for Fortran compilers support and more. Utilities --------- test Run numpy unittests show_config Show numpy build configuration dual Overwrite certain functions with high-performance SciPy tools. Note: `numpy.dual` is deprecated. Use the functions from NumPy or Scipy directly instead of importing them from `numpy.dual`. matlib Make everything matrices. __version__ NumPy version string Viewing documentation using IPython ----------------------------------- Start IPython with the NumPy profile (``ipython -p numpy``), which will import `numpy` under the alias `np`. Then, use the ``cpaste`` command to paste examples into the shell. To see which functions are available in `numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use ``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow down the list. To view the docstring for a function, use ``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view the source code). Copies vs. in-place operation ----------------------------- Most of the functions in `numpy` return a copy of the array argument (e.g., `np.sort`). 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