Python numerictypes.number方法代码示例

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import number [as 别名]
def _hist_bin_rice(x):
    """
    Rice histogram bin estimator.

    Another simple estimator with no normality assumption. It has better
    performance for large data than Sturges, but tends to overestimate
    the number of bins. The number of bins is proportional to the cube
    root of data size (asymptotically optimal). The estimate depends
    only on size of the data.

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    return x.ptp() / (2.0 * x.size ** (1.0 / 3)) 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import number [as 别名]
def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N, it will be repeated, and if elements of `a` are to be masked,
        this sequence must be non-empty.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import number [as 别名]
def _hist_bin_fd(x):
    """
    The Freedman-Diaconis histogram bin estimator.

    The Freedman-Diaconis rule uses interquartile range (IQR) to
    estimate binwidth. It is considered a variation of the Scott rule
    with more robustness as the IQR is less affected by outliers than
    the standard deviation. However, the IQR depends on fewer points
    than the standard deviation, so it is less accurate, especially for
    long tailed distributions.

    If the IQR is 0, this function returns 1 for the number of bins.
    Binwidth is inversely proportional to the cube root of data size
    (asymptotically optimal).

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    iqr = np.subtract(*np.percentile(x, [75, 25]))
    return 2.0 * iqr * x.size ** (-1.0 / 3.0) 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import number [as 别名]
def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N it will be repeated.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import number [as 别名]
def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : array_like
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N it will be repeated.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    return _insert(arr, mask, vals) 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import number [as 别名]
def angle(z, deg=0):
    """
    Return the angle of the complex argument.

    Parameters
    ----------
    z : array_like
        A complex number or sequence of complex numbers.
    deg : bool, optional
        Return angle in degrees if True, radians if False (default).

    Returns
    -------
    angle : ndarray or scalar
        The counterclockwise angle from the positive real axis on
        the complex plane, with dtype as numpy.float64.

    See Also
    --------
    arctan2
    absolute

    Examples
    --------
    >>> np.angle([1.0, 1.0j, 1+1j])               # in radians
    array([ 0.        ,  1.57079633,  0.78539816])
    >>> np.angle(1+1j, deg=True)                  # in degrees
    45.0

    """
    if deg:
        fact = 180/pi
    else:
        fact = 1.0
    z = asarray(z)
    if (issubclass(z.dtype.type, _nx.complexfloating)):
        zimag = z.imag
        zreal = z.real
    else:
        zimag = 0
        zreal = z
    return arctan2(zimag, zreal) * fact 

# 需要导入模块: from numpy.core import numerictypes [as 别名]
# 或者: from numpy.core.numerictypes import number [as 别名]
def _get_ufunc_and_otypes(self, func, args):
        """Return (ufunc, otypes)."""
        # frompyfunc will fail if args is empty
        if not args:
            raise ValueError('args can not be empty')

        if self.otypes is not None:
            otypes = self.otypes
            nout = len(otypes)

            # Note logic here: We only *use* self._ufunc if func is self.pyfunc
            # even though we set self._ufunc regardless.
            if func is self.pyfunc and self._ufunc is not None:
                ufunc = self._ufunc
            else:
                ufunc = self._ufunc = frompyfunc(func, len(args), nout)
        else:
            # Get number of outputs and output types by calling the function on
            # the first entries of args.  We also cache the result to prevent
            # the subsequent call when the ufunc is evaluated.
            # Assumes that ufunc first evaluates the 0th elements in the input
            # arrays (the input values are not checked to ensure this)
            args = [asarray(arg) for arg in args]
            if builtins.any(arg.size == 0 for arg in args):
                raise ValueError('cannot call `vectorize` on size 0 inputs '
                                 'unless `otypes` is set')

            inputs = [arg.flat[0] for arg in args]
            outputs = func(*inputs)

            # Performance note: profiling indicates that -- for simple
            # functions at least -- this wrapping can almost double the
            # execution time.
            # Hence we make it optional.
            if self.cache:
                _cache = [outputs]

                def _func(*vargs):
                    if _cache:
                        return _cache.pop()
                    else:
                        return func(*vargs)
            else:
                _func = func

            if isinstance(outputs, tuple):
                nout = len(outputs)
            else:
                nout = 1
                outputs = (outputs,)

            otypes = ''.join([asarray(outputs[_k]).dtype.char
                              for _k in range(nout)])

            # Performance note: profiling indicates that creating the ufunc is
            # not a significant cost compared with wrapping so it seems not
            # worth trying to cache this.
            ufunc = frompyfunc(_func, len(args), nout)

        return ufunc, otypes