"""
=========
Constants
=========
Numpy includes several constants:
%(constant_list)s
"""
#
# Note: the docstring is autogenerated.
#
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
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