Edit File: constants.cpython-37.pyc
B ��Fd�" � @ s� d Z ddlmZmZmZ ddlZddlZg Zdd� Zeddd� edd d� edd d � edd d � eddd� eddd� eddd� eddd� eddd� eddd� eddd� eddd� edd d � e �r�g Z e� � x�eD ]�\Z Z e� e ��d d �Ze�d �Zg ZxbeD ]ZZe�d e�Ze�rde�rde� e�� �Ze�d!e�d"�ef � e�d#� n e�e� �qW d �e�Ze �d$e ef � q�W d �e �Z e ee d%� Z [ [ [ [[[[[[[[dS )&zU ========= Constants ========= NumPy includes several constants: %(constant_list)s � )�division�absolute_import�print_functionNc C s t �||f� d S )N)� constants�append)�module�name�doc� r �F/opt/alt/python37/lib64/python3.7/site-packages/numpy/doc/constants.py� add_newdoc s r �numpyZInfz� IEEE 754 floating point representation of (positive) infinity. Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`. For more details, see `inf`. See Also -------- inf �InfinityZNANz� IEEE 754 floating point representation of Not a Number (NaN). `NaN` and `NAN` are equivalent definitions of `nan`. Please use `nan` instead of `NAN`. See Also -------- nan ZNINFa� IEEE 754 floating point representation of negative infinity. Returns ------- y : float A floating point representation of negative infinity. See Also -------- isinf : Shows which elements are positive or negative infinity isposinf : Shows which elements are positive infinity isneginf : Shows which elements are negative infinity isnan : Shows which elements are Not a Number isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity) Notes ----- NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. Examples -------- >>> np.NINF -inf >>> np.log(0) -inf ZNZEROa� IEEE 754 floating point representation of negative zero. Returns ------- y : float A floating point representation of negative zero. See Also -------- PZERO : Defines positive zero. isinf : Shows which elements are positive or negative infinity. isposinf : Shows which elements are positive infinity. isneginf : Shows which elements are negative infinity. isnan : Shows which elements are Not a Number. isfinite : Shows which elements are finite - not one of Not a Number, positive infinity and negative infinity. Notes ----- NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Negative zero is considered to be a finite number. Examples -------- >>> np.NZERO -0.0 >>> np.PZERO 0.0 >>> np.isfinite([np.NZERO]) array([ True], dtype=bool) >>> np.isnan([np.NZERO]) array([False], dtype=bool) >>> np.isinf([np.NZERO]) array([False], dtype=bool) �NaNz� IEEE 754 floating point representation of Not a Number (NaN). `NaN` and `NAN` are equivalent definitions of `nan`. Please use `nan` instead of `NaN`. See Also -------- nan ZPINFZPZEROa� IEEE 754 floating point representation of positive zero. Returns ------- y : float A floating point representation of positive zero. See Also -------- NZERO : Defines negative zero. isinf : Shows which elements are positive or negative infinity. isposinf : Shows which elements are positive infinity. isneginf : Shows which elements are negative infinity. isnan : Shows which elements are Not a Number. isfinite : Shows which elements are finite - not one of Not a Number, positive infinity and negative infinity. Notes ----- NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Positive zero is considered to be a finite number. Examples -------- >>> np.PZERO 0.0 >>> np.NZERO -0.0 >>> np.isfinite([np.PZERO]) array([ True], dtype=bool) >>> np.isnan([np.PZERO]) array([False], dtype=bool) >>> np.isinf([np.PZERO]) array([False], dtype=bool) �ea= Euler's constant, base of natural logarithms, Napier's constant. ``e = 2.71828182845904523536028747135266249775724709369995...`` See Also -------- exp : Exponential function log : Natural logarithm References ---------- .. [1] http://en.wikipedia.org/wiki/Napier_constant �infa� IEEE 754 floating point representation of (positive) infinity. Returns ------- y : float A floating point representation of positive infinity. See Also -------- isinf : Shows which elements are positive or negative infinity isposinf : Shows which elements are positive infinity isneginf : Shows which elements are negative infinity isnan : Shows which elements are Not a Number isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity) Notes ----- NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. `Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`. Examples -------- >>> np.inf inf >>> np.array([1]) / 0. array([ Inf]) Zinfty�nana� IEEE 754 floating point representation of Not a Number (NaN). Returns ------- y : A floating point representation of Not a Number. See Also -------- isnan : Shows which elements are Not a Number. isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity) Notes ----- NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. `NaN` and `NAN` are aliases of `nan`. Examples -------- >>> np.nan nan >>> np.log(-1) nan >>> np.log([-1, 1, 2]) array([ NaN, 0. , 0.69314718]) Znewaxisa9 A convenient alias for None, useful for indexing arrays. See Also -------- `numpy.doc.indexing` Examples -------- >>> newaxis is None True >>> x = np.arange(3) >>> x array([0, 1, 2]) >>> x[:, newaxis] array([[0], [1], [2]]) >>> x[:, newaxis, newaxis] array([[[0]], [[1]], [[2]]]) >>> x[:, newaxis] * x array([[0, 0, 0], [0, 1, 2], [0, 2, 4]]) Outer product, same as ``outer(x, y)``: >>> y = np.arange(3, 6) >>> x[:, newaxis] * y array([[ 0, 0, 0], [ 3, 4, 5], [ 6, 8, 10]]) ``x[newaxis, :]`` is equivalent to ``x[newaxis]`` and ``x[None]``: >>> x[newaxis, :].shape (1, 3) >>> x[newaxis].shape (1, 3) >>> x[None].shape (1, 3) >>> x[:, newaxis].shape (3, 1) � z z^(\s+)[-=]+\s*$z%s.. rubric:: %s� � z.. const:: %s %s)Z constant_list) �__doc__� __future__r r r �textwrap�rer r Z constants_str�sortr r �dedent�replace�s�split�linesZ new_lines�line�match�m�pop�prevr �group�join�dictr r r r �<module> sf $+ +& /