package np

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

Test whether any array element along a given axis evaluates to True.

Returns single boolean unless `axis` is not ``None``

Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical OR reduction is performed. The default (``axis=None``) is to perform a logical OR over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis.

.. versionadded:: 1.7.0

If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if it is of type float, then it will remain so, returning 1.0 for True and 0.0 for False, regardless of the type of `a`). See `ufuncs-output-type` for more details.

keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then `keepdims` will not be passed through to the `any` method of sub-classes of `ndarray`, however any non-default value will be. If the sub-class' method does not implement `keepdims` any exceptions will be raised.

Returns ------- any : bool or ndarray A new boolean or `ndarray` is returned unless `out` is specified, in which case a reference to `out` is returned.

See Also -------- ndarray.any : equivalent method

all : Test whether all elements along a given axis evaluate to True.

Notes ----- Not a Number (NaN), positive infinity and negative infinity evaluate to `True` because these are not equal to zero.

Examples -------- >>> np.any([True, False], [True, True]) True

>>> np.any([True, False], [False, False], axis=0) array( True, False)

>>> np.any(-1, 0, 5) True

>>> np.any(np.nan) True

>>> o=np.array(False) >>> z=np.any(-1, 4, 5, out=o) >>> z, o (array(True), array(True)) >>> # Check now that z is a reference to o >>> z is o True >>> id(z), id(o) # identity of z and o # doctest: +SKIP (191614240, 191614240)

Compute the inverse cosine of x.

Return the 'principal value' (for a description of this, see `numpy.arccos`) of the inverse cosine of `x`. For real `x` such that `abs(x) <= 1`, this is a real number in the closed interval :math:`0, \pi`. Otherwise, the complex principle value is returned.

Parameters ---------- x : array_like or scalar The value(s) whose arccos is (are) required.

Returns ------- out : ndarray or scalar The inverse cosine(s) of the `x` value(s). If `x` was a scalar, so is `out`, otherwise an array object is returned.

See Also -------- numpy.arccos

Notes ----- For an arccos() that returns ``NAN`` when real `x` is not in the interval ``-1,1``, use `numpy.arccos`.

Examples -------- >>> np.set_printoptions(precision=4)

>>> np.emath.arccos(1) # a scalar is returned 0.0

>>> np.emath.arccos(1,2) array(0.-0.j , 0.-1.317j)

Compute the inverse sine of x.

Return the 'principal value' (for a description of this, see `numpy.arcsin`) of the inverse sine of `x`. For real `x` such that `abs(x) <= 1`, this is a real number in the closed interval :math:`-\pi/2, \pi/2`. Otherwise, the complex principle value is returned.

Parameters ---------- x : array_like or scalar The value(s) whose arcsin is (are) required.

Returns ------- out : ndarray or scalar The inverse sine(s) of the `x` value(s). If `x` was a scalar, so is `out`, otherwise an array object is returned.

See Also -------- numpy.arcsin

Notes ----- For an arcsin() that returns ``NAN`` when real `x` is not in the interval ``-1,1``, use `numpy.arcsin`.

Examples -------- >>> np.set_printoptions(precision=4)

>>> np.emath.arcsin(0) 0.0

>>> np.emath.arcsin(0,1) array(0. , 1.5708)

Compute the inverse hyperbolic tangent of `x`.

Return the 'principal value' (for a description of this, see `numpy.arctanh`) of `arctanh(x)`. For real `x` such that `abs(x) < 1`, this is a real number. If `abs(x) > 1`, or if `x` is complex, the result is complex. Finally, `x = 1` returns``inf`` and `x=-1` returns ``-inf``.

Parameters ---------- x : array_like The value(s) whose arctanh is (are) required.

Returns ------- out : ndarray or scalar The inverse hyperbolic tangent(s) of the `x` value(s). If `x` was a scalar so is `out`, otherwise an array is returned.

See Also -------- numpy.arctanh

Notes ----- For an arctanh() that returns ``NAN`` when real `x` is not in the interval ``(-1,1)``, use `numpy.arctanh` (this latter, however, does return +/-inf for `x = +/-1`).

Examples -------- >>> np.set_printoptions(precision=4)

>>> from numpy.testing import suppress_warnings >>> with suppress_warnings() as sup: ... sup.filter(RuntimeWarning) ... np.emath.arctanh(np.eye(2)) array([inf, 0.], [ 0., inf]) >>> np.emath.arctanh(1j) array(0.+0.7854j)

Decorator for adding dispatch with the __array_function__ protocol.

See NEP-18 for example usage.

Parameters ---------- dispatcher : callable Function that when called like ``dispatcher( *args, **kwargs)`` with arguments from the NumPy function call returns an iterable of array-like arguments to check for ``__array_function__``. module : str, optional __module__ attribute to set on new function, e.g., ``module='numpy'``. By default, module is copied from the decorated function. verify : bool, optional If True, verify the that the signature of the dispatcher and decorated function signatures match exactly: all required and optional arguments should appear in order with the same names, but the default values for all optional arguments should be ``None``. Only disable verification if the dispatcher's signature needs to deviate for some particular reason, e.g., because the function has a signature like ``func( *args, **kwargs)``. docs_from_dispatcher : bool, optional If True, copy docs from the dispatcher function onto the dispatched function, rather than from the implementation. This is useful for functions defined in C, which otherwise don't have docstrings.

Returns ------- Function suitable for decorating the implementation of a NumPy function.

Convert the input to an array.

Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : 'C', 'F', optional Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to 'C'.

Returns ------- out : ndarray Array interpretation of `a`. No copy is performed if the input is already an ndarray with matching dtype and order. If `a` is a subclass of ndarray, a base class ndarray is returned.

See Also -------- asanyarray : Similar function which passes through subclasses. ascontiguousarray : Convert input to a contiguous array. asfarray : Convert input to a floating point ndarray. asfortranarray : Convert input to an ndarray with column-major memory order. asarray_chkfinite : Similar function which checks input for NaNs and Infs. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions.

Examples -------- Convert a list into an array:

>>> a = 1, 2 >>> np.asarray(a) array(1, 2)

Existing arrays are not copied:

>>> a = np.array(1, 2) >>> np.asarray(a) is a True

If `dtype` is set, array is copied only if dtype does not match:

>>> a = np.array(1, 2, dtype=np.float32) >>> np.asarray(a, dtype=np.float32) is a True >>> np.asarray(a, dtype=np.float64) is a False

Contrary to `asanyarray`, ndarray subclasses are not passed through:

>>> issubclass(np.recarray, np.ndarray) True >>> a = np.array((1.0, 2), (3.0, 4), dtype='f4,i4').view(np.recarray) >>> np.asarray(a) is a False >>> np.asanyarray(a) is a True

Returns a bool array, where True if input element is real.

If element has complex type with zero complex part, the return value for that element is True.

Parameters ---------- x : array_like Input array.

Returns ------- out : ndarray, bool Boolean array of same shape as `x`.

See Also -------- iscomplex isrealobj : Return True if x is not a complex type.

Examples -------- >>> np.isreal(1+1j, 1+0j, 4.5, 3, 2, 2j) array(False, True, True, True, True, False)

Compute the natural logarithm of `x`.

Return the 'principal value' (for a description of this, see `numpy.log`) of :math:`log_e(x)`. For real `x > 0`, this is a real number (``log(0)`` returns ``-inf`` and ``log(np.inf)`` returns ``inf``). Otherwise, the complex principle value is returned.

Parameters ---------- x : array_like The value(s) whose log is (are) required.

Returns ------- out : ndarray or scalar The log of the `x` value(s). If `x` was a scalar, so is `out`, otherwise an array is returned.

See Also -------- numpy.log

Notes ----- For a log() that returns ``NAN`` when real `x < 0`, use `numpy.log` (note, however, that otherwise `numpy.log` and this `log` are identical, i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`, and, notably, the complex principle value if ``x.imag != 0``).

Examples -------- >>> np.emath.log(np.exp(1)) 1.0

Negative arguments are handled 'correctly' (recall that ``exp(log(x)) == x`` does *not* hold for real ``x < 0``):

>>> np.emath.log(-np.exp(1)) == (1 + np.pi * 1j) True

Compute the logarithm base 10 of `x`.

Return the 'principal value' (for a description of this, see `numpy.log10`) of :math:`log_

(x)`. For real `x > 0`, this is a real number (``log10(0)`` returns ``-inf`` and ``log10(np.inf)`` returns ``inf``). Otherwise, the complex principle value is returned.

Parameters ---------- x : array_like or scalar The value(s) whose log base 10 is (are) required.

Returns ------- out : ndarray or scalar The log base 10 of the `x` value(s). If `x` was a scalar, so is `out`, otherwise an array object is returned.

See Also -------- numpy.log10

Notes ----- For a log10() that returns ``NAN`` when real `x < 0`, use `numpy.log10` (note, however, that otherwise `numpy.log10` and this `log10` are identical, i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`, and, notably, the complex principle value if ``x.imag != 0``).

Examples --------

(We set the printing precision so the example can be auto-tested)

>>> np.set_printoptions(precision=4)

>>> np.emath.log10(10**1) 1.0

>>> np.emath.log10(-10**1, -10**2, 10**2) array(1.+1.3644j, 2.+1.3644j, 2.+0.j )

Compute the logarithm base 2 of `x`.

Return the 'principal value' (for a description of this, see `numpy.log2`) of :math:`log_2(x)`. For real `x > 0`, this is a real number (``log2(0)`` returns ``-inf`` and ``log2(np.inf)`` returns ``inf``). Otherwise, the complex principle value is returned.

Parameters ---------- x : array_like The value(s) whose log base 2 is (are) required.

Returns ------- out : ndarray or scalar The log base 2 of the `x` value(s). If `x` was a scalar, so is `out`, otherwise an array is returned.

See Also -------- numpy.log2

Notes ----- For a log2() that returns ``NAN`` when real `x < 0`, use `numpy.log2` (note, however, that otherwise `numpy.log2` and this `log2` are identical, i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`, and, notably, the complex principle value if ``x.imag != 0``).

Examples -------- We set the printing precision so the example can be auto-tested:

>>> np.set_printoptions(precision=4)

>>> np.emath.log2(8) 3.0 >>> np.emath.log2(-4, -8, 8) array(2.+4.5324j, 3.+4.5324j, 3.+0.j )

Take log base n of x.

If `x` contains negative inputs, the answer is computed and returned in the complex domain.

Parameters ---------- n : array_like The integer base(s) in which the log is taken. x : array_like The value(s) whose log base `n` is (are) required.

Returns ------- out : ndarray or scalar The log base `n` of the `x` value(s). If `x` was a scalar, so is `out`, otherwise an array is returned.

Examples -------- >>> np.set_printoptions(precision=4)

>>> np.lib.scimath.logn(2, 4, 8) array(2., 3.) >>> np.lib.scimath.logn(2, -4, -8, 8) array(2.+4.5324j, 3.+4.5324j, 3.+0.j )

Return x to the power p, (x**p).

If `x` contains negative values, the output is converted to the complex domain.

Parameters ---------- x : array_like The input value(s). p : array_like of ints The power(s) to which `x` is raised. If `x` contains multiple values, `p` has to either be a scalar, or contain the same number of values as `x`. In the latter case, the result is ``x0**p0, x1**p1, ...``.

Returns ------- out : ndarray or scalar The result of ``x**p``. If `x` and `p` are scalars, so is `out`, otherwise an array is returned.

See Also -------- numpy.power

Examples -------- >>> np.set_printoptions(precision=4)

>>> np.lib.scimath.power(2, 4, 2) array( 4, 16) >>> np.lib.scimath.power(2, 4, -2) array(0.25 , 0.0625) >>> np.lib.scimath.power(-2, 4, 2) array( 4.-0.j, 16.+0.j)

Compute the square root of x.

For negative input elements, a complex value is returned (unlike `numpy.sqrt` which returns NaN).

Parameters ---------- x : array_like The input value(s).

Returns ------- out : ndarray or scalar The square root of `x`. If `x` was a scalar, so is `out`, otherwise an array is returned.

See Also -------- numpy.sqrt

Examples -------- For real, non-negative inputs this works just like `numpy.sqrt`:

>>> np.lib.scimath.sqrt(1) 1.0 >>> np.lib.scimath.sqrt(1, 4) array(1., 2.)

But it automatically handles negative inputs:

>>> np.lib.scimath.sqrt(-1) 1j >>> np.lib.scimath.sqrt(-1,4) array(0.+1.j, 2.+0.j)