Python numpy.core.numeric.sqrt() Examples

The following are 20 code examples of numpy.core.numeric.sqrt(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy.core.numeric , or try the search function .
Example #1
Source File: scimath.py    From pySINDy with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #2
Source File: scimath.py    From keras-lambda with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #3
Source File: scimath.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #4
Source File: scimath.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #5
Source File: scimath.py    From Carnets with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #6
Source File: scimath.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #7
Source File: scimath.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #8
Source File: scimath.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #9
Source File: scimath.py    From ImageFusion with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #10
Source File: scimath.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #11
Source File: scimath.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #12
Source File: scimath.py    From Fluid-Designer with GNU General Public License v3.0 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #13
Source File: scimath.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #14
Source File: scimath.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #15
Source File: scimath.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #16
Source File: scimath.py    From Computable with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #17
Source File: scimath.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #18
Source File: scimath.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #19
Source File: scimath.py    From lambda-packs with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)
Example #20
Source File: scimath.py    From lambda-packs with MIT License 5 votes vote down vote up 
def sqrt(x):
    """
    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)
    (0.0+1.0j)
    >>> np.lib.scimath.sqrt([-1,4])
    array([ 0.+1.j,  2.+0.j])

    """
    x = _fix_real_lt_zero(x)
    return nx.sqrt(x)