""" ========= Constants ========= Numpy includes several constants: %(constant_list)s """ # # Note: the docstring is autogenerated. # from __future__ import division, absolute_import, print_function import textwrap, re # Maintain same format as in numpy.add_newdocs constants = [] def add_newdoc(module, name, doc): constants.append((name, doc)) add_newdoc('numpy', 'Inf', """ 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 """) add_newdoc('numpy', 'Infinity', """ 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 """) add_newdoc('numpy', 'NAN', """ 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 """) add_newdoc('numpy', 'NINF', """ 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 """) add_newdoc('numpy', 'NZERO', """ 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) """) add_newdoc('numpy', 'NaN', """ 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 """) add_newdoc('numpy', 'PINF', """ 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 """) add_newdoc('numpy', 'PZERO', """ 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) """) add_newdoc('numpy', 'e', """ 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 """) add_newdoc('numpy', 'inf', """ 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]) """) add_newdoc('numpy', 'infty', """ 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 """) add_newdoc('numpy', 'nan', """ 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]) """) add_newdoc('numpy', 'newaxis', """ 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) """) if __doc__: constants_str = [] constants.sort() for name, doc in constants: s = textwrap.dedent(doc).replace("\n", "\n ") # Replace sections by rubrics lines = s.split("\n") new_lines = [] for line in lines: m = re.match(r'^(\s+)[-=]+\s*\$', line) if m and new_lines: prev = textwrap.dedent(new_lines.pop()) new_lines.append('%s.. rubric:: %s' % (m.group(1), prev)) new_lines.append('') else: new_lines.append(line) s = "\n".join(new_lines) # Done. constants_str.append(""".. const:: %s\n %s""" % (name, s)) constants_str = "\n".join(constants_str) __doc__ = __doc__ % dict(constant_list=constants_str) del constants_str, name, doc del line, lines, new_lines, m, s, prev del constants, add_newdoc