"""

=========

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