Python numpy.core.numeric.array() Examples

def __getitem__(self, index):
        self._getitem = True

        try:
            out = N.ndarray.__getitem__(self, index)
        finally:
            self._getitem = False

        if not isinstance(out, N.ndarray):
            return out

        if out.ndim == 0:
            return out[()]
        if out.ndim == 1:
            sh = out.shape[0]
            # Determine when we should have a column array
            try:
                n = len(index)
            except Exception:
                n = 0
            if n > 1 and isscalar(index[1]):
                out.shape = (sh, 1)
            else:
                out.shape = (1, sh)
        return out 

def __getitem__(self, index):
        self._getitem = True

        try:
            out = N.ndarray.__getitem__(self, index)
        finally:
            self._getitem = False

        if not isinstance(out, N.ndarray):
            return out

        if out.ndim == 0:
            return out[()]
        if out.ndim == 1:
            sh = out.shape[0]
            # Determine when we should have a column array
            try:
                n = len(index)
            except Exception:
                n = 0
            if n > 1 and isscalar(index[1]):
                out.shape = (sh, 1)
            else:
                out.shape = (1, sh)
        return out 

def any(self, axis=None, out=None):
        """
        Test whether any array element along a given axis evaluates to True.

        Refer to `numpy.any` for full documentation.

        Parameters
        ----------
        axis : int, optional
            Axis along which logical OR is performed
        out : ndarray, optional
            Output to existing array instead of creating new one, must have
            same shape as expected output

        Returns
        -------
            any : bool, ndarray
                Returns a single bool if `axis` is ``None``; otherwise,
                returns `ndarray`

        """
        return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def asscalar(a):
    """
    Convert an array of size 1 to its scalar equivalent.

    Parameters
    ----------
    a : ndarray
        Input array of size 1.

    Returns
    -------
    out : scalar
        Scalar representation of `a`. The output data type is the same type
        returned by the input's `item` method.

    Examples
    --------
    >>> np.asscalar(np.array([24]))
    24

    """
    return a.item()

#----------------------------------------------------------------------------- 

def any(self, axis=None, out=None):
        """
        Test whether any array element along a given axis evaluates to True.

        Refer to `numpy.any` for full documentation.

        Parameters
        ----------
        axis : int, optional
            Axis along which logical OR is performed
        out : ndarray, optional
            Output to existing array instead of creating new one, must have
            same shape as expected output

        Returns
        -------
            any : bool, ndarray
                Returns a single bool if `axis` is ``None``; otherwise,
                returns `ndarray`

        """
        return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis) 

def __getitem__(self, index):
        self._getitem = True

        try:
            out = N.ndarray.__getitem__(self, index)
        finally:
            self._getitem = False

        if not isinstance(out, N.ndarray):
            return out

        if out.ndim == 0:
            return out[()]
        if out.ndim == 1:
            sh = out.shape[0]
            # Determine when we should have a column array
            try:
                n = len(index)
            except:
                n = 0
            if n > 1 and isscalar(index[1]):
                out.shape = (sh, 1)
            else:
                out.shape = (1, sh)
        return out 

def any(self, axis=None, out=None):
        """
        Test whether any array element along a given axis evaluates to True.

        Refer to `numpy.any` for full documentation.

        Parameters
        ----------
        axis : int, optional
            Axis along which logical OR is performed
        out : ndarray, optional
            Output to existing array instead of creating new one, must have
            same shape as expected output

        Returns
        -------
            any : bool, ndarray
                Returns a single bool if `axis` is ``None``; otherwise,
                returns `ndarray`

        """
        return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def __init__(self, c_or_r, r=0, variable=None):
        if isinstance(c_or_r, poly1d):
            for key in c_or_r.__dict__.keys():
                self.__dict__[key] = c_or_r.__dict__[key]
            if variable is not None:
                self.__dict__['variable'] = variable
            return
        if r:
            c_or_r = poly(c_or_r)
        c_or_r = atleast_1d(c_or_r)
        if len(c_or_r.shape) > 1:
            raise ValueError("Polynomial must be 1d only.")
        c_or_r = trim_zeros(c_or_r, trim='f')
        if len(c_or_r) == 0:
            c_or_r = NX.array([0.])
        self.__dict__['coeffs'] = c_or_r
        self.__dict__['order'] = len(c_or_r) - 1
        if variable is None:
            variable = 'x'
        self.__dict__['variable'] = variable 

def __init__(self, c_or_r, r=False, variable=None):
        if isinstance(c_or_r, poly1d):
            self._variable = c_or_r._variable
            self._coeffs = c_or_r._coeffs

            if set(c_or_r.__dict__) - set(self.__dict__):
                msg = ("In the future extra properties will not be copied "
                       "across when constructing one poly1d from another")
                warnings.warn(msg, FutureWarning, stacklevel=2)
                self.__dict__.update(c_or_r.__dict__)

            if variable is not None:
                self._variable = variable
            return
        if r:
            c_or_r = poly(c_or_r)
        c_or_r = atleast_1d(c_or_r)
        if c_or_r.ndim > 1:
            raise ValueError("Polynomial must be 1d only.")
        c_or_r = trim_zeros(c_or_r, trim='f')
        if len(c_or_r) == 0:
            c_or_r = NX.array([0.])
        self._coeffs = c_or_r
        if variable is None:
            variable = 'x'
        self._variable = variable 

def asmatrix(data, dtype=None):
    """
    Interpret the input as a matrix.

    Unlike `matrix`, `asmatrix` does not make a copy if the input is already
    a matrix or an ndarray.  Equivalent to ``matrix(data, copy=False)``.

    Parameters
    ----------
    data : array_like
        Input data.
    dtype : data-type
       Data-type of the output matrix.

    Returns
    -------
    mat : matrix
        `data` interpreted as a matrix.

    Examples
    --------
    >>> x = np.array([[1, 2], [3, 4]])

    >>> m = np.asmatrix(x)

    >>> x[0,0] = 5

    >>> m
    matrix([[5, 2],
            [3, 4]])

    """
    return matrix(data, dtype=dtype, copy=False) 

def column_stack(tup):
    """
    Stack 1-D arrays as columns into a 2-D array.

    Take a sequence of 1-D arrays and stack them as columns
    to make a single 2-D array. 2-D arrays are stacked as-is,
    just like with `hstack`.  1-D arrays are turned into 2-D columns
    first.

    Parameters
    ----------
    tup : sequence of 1-D or 2-D arrays.
        Arrays to stack. All of them must have the same first dimension.

    Returns
    -------
    stacked : 2-D array
        The array formed by stacking the given arrays.

    See Also
    --------
    stack, hstack, vstack, concatenate

    Examples
    --------
    >>> a = np.array((1,2,3))
    >>> b = np.array((2,3,4))
    >>> np.column_stack((a,b))
    array([[1, 2],
           [2, 3],
           [3, 4]])

    """
    arrays = []
    for v in tup:
        arr = array(v, copy=False, subok=True)
        if arr.ndim < 2:
            arr = array(arr, copy=False, subok=True, ndmin=2).T
        arrays.append(arr)
    return _nx.concatenate(arrays, 1) 

def iscomplexobj(x):
    """
    Check for a complex type or an array of complex numbers.

    The type of the input is checked, not the value. Even if the input
    has an imaginary part equal to zero, `iscomplexobj` evaluates to True.

    Parameters
    ----------
    x : any
        The input can be of any type and shape.

    Returns
    -------
    iscomplexobj : bool
        The return value, True if `x` is of a complex type or has at least
        one complex element.

    See Also
    --------
    isrealobj, iscomplex

    Examples
    --------
    >>> np.iscomplexobj(1)
    False
    >>> np.iscomplexobj(1+0j)
    True
    >>> np.iscomplexobj([3, 1+0j, True])
    True

    """
    try:
        dtype = x.dtype
        type_ = dtype.type
    except AttributeError:
        type_ = asarray(x).dtype.type
    return issubclass(type_, _nx.complexfloating) 

def isreal(x):
    """
    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])

    """
    return imag(x) == 0 

def isreal(x):
    """
    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], dtype=bool)

    """
    return imag(x) == 0 

def sum(self, axis=None, dtype=None, out=None):
        """
        Returns the sum of the matrix elements, along the given axis.

        Refer to `numpy.sum` for full documentation.

        See Also
        --------
        numpy.sum

        Notes
        -----
        This is the same as `ndarray.sum`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix([[1, 2], [4, 3]])
        >>> x.sum()
        10
        >>> x.sum(axis=1)
        matrix([[3],
                [7]])
        >>> x.sum(axis=1, dtype='float')
        matrix([[ 3.],
                [ 7.]])
        >>> out = np.zeros((1, 2), dtype='float')
        >>> x.sum(axis=1, dtype='float', out=out)
        matrix([[ 3.],
                [ 7.]])

        """
        return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)


    # To update docstring from array to matrix... 

def real(val):
    """
    Return the real part of the complex argument.

    Parameters
    ----------
    val : array_like
        Input array.

    Returns
    -------
    out : ndarray or scalar
        The real component of the complex argument. If `val` is real, the type
        of `val` is used for the output.  If `val` has complex elements, the
        returned type is float.

    See Also
    --------
    real_if_close, imag, angle

    Examples
    --------
    >>> a = np.array([1+2j, 3+4j, 5+6j])
    >>> a.real
    array([ 1.,  3.,  5.])
    >>> a.real = 9
    >>> a
    array([ 9.+2.j,  9.+4.j,  9.+6.j])
    >>> a.real = np.array([9, 8, 7])
    >>> a
    array([ 9.+2.j,  8.+4.j,  7.+6.j])
    >>> np.real(1 + 1j)
    1.0

    """
    try:
        return val.real
    except AttributeError:
        return asanyarray(val).real 

def flatten(self, order='C'):
        """
        Return a flattened copy of the matrix.

        All `N` elements of the matrix are placed into a single row.

        Parameters
        ----------
        order : {'C', 'F', 'A', 'K'}, optional
            'C' means to flatten in row-major (C-style) order. 'F' means to
            flatten in column-major (Fortran-style) order. 'A' means to
            flatten in column-major order if `m` is Fortran *contiguous* in
            memory, row-major order otherwise. 'K' means to flatten `m` in
            the order the elements occur in memory. The default is 'C'.

        Returns
        -------
        y : matrix
            A copy of the matrix, flattened to a `(1, N)` matrix where `N`
            is the number of elements in the original matrix.

        See Also
        --------
        ravel : Return a flattened array.
        flat : A 1-D flat iterator over the matrix.

        Examples
        --------
        >>> m = np.matrix([[1,2], [3,4]])
        >>> m.flatten()
        matrix([[1, 2, 3, 4]])
        >>> m.flatten('F')
        matrix([[1, 3, 2, 4]])

        """
        return N.ndarray.flatten(self, order=order) 

def __repr__(self):
        s = repr(self.__array__()).replace('array', 'matrix')
        # now, 'matrix' has 6 letters, and 'array' 5, so the columns don't
        # line up anymore. We need to add a space.
        l = s.splitlines()
        for i in range(1, len(l)):
            if l[i]:
                l[i] = ' ' + l[i]
        return '\n'.join(l) 

def get_array_prepare(*args):
    """Find the wrapper for the array with the highest priority.

    In case of ties, leftmost wins. If no wrapper is found, return None
    """
    wrappers = sorted((getattr(x, '__array_priority__', 0), -i,
                 x.__array_prepare__) for i, x in enumerate(args)
                                   if hasattr(x, '__array_prepare__'))
    if wrappers:
        return wrappers[-1][-1]
    return None 

def iscomplex(x):
    """
    Returns a bool array, where True if input element is complex.

    What is tested is whether the input has a non-zero imaginary part, not if
    the input type is complex.

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

    Returns
    -------
    out : ndarray of bools
        Output array.

    See Also
    --------
    isreal
    iscomplexobj : Return True if x is a complex type or an array of complex
                   numbers.

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

    """
    ax = asanyarray(x)
    if issubclass(ax.dtype.type, _nx.complexfloating):
        return ax.imag != 0
    res = zeros(ax.shape, bool)
    return +res  # convert to array-scalar if needed 

def asmatrix(data, dtype=None):
    """
    Interpret the input as a matrix.

    Unlike `matrix`, `asmatrix` does not make a copy if the input is already
    a matrix or an ndarray.  Equivalent to ``matrix(data, copy=False)``.

    Parameters
    ----------
    data : array_like
        Input data.
    dtype : data-type
       Data-type of the output matrix.

    Returns
    -------
    mat : matrix
        `data` interpreted as a matrix.

    Examples
    --------
    >>> x = np.array([[1, 2], [3, 4]])

    >>> m = np.asmatrix(x)

    >>> x[0,0] = 5

    >>> m
    matrix([[5, 2],
            [3, 4]])

    """
    return matrix(data, dtype=dtype, copy=False) 

def getA1(self):
        """
        Return `self` as a flattened `ndarray`.

        Equivalent to ``np.asarray(x).ravel()``

        Parameters
        ----------
        None

        Returns
        -------
        ret : ndarray
            `self`, 1-D, as an `ndarray`

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.getA1()
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

        """
        return self.__array__().ravel() 

def getA(self):
        """
        Return `self` as an `ndarray` object.

        Equivalent to ``np.asarray(self)``.

        Parameters
        ----------
        None

        Returns
        -------
        ret : ndarray
            `self` as an `ndarray`

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.getA()
        array([[ 0,  1,  2,  3],
               [ 4,  5,  6,  7],
               [ 8,  9, 10, 11]])

        """
        return self.__array__() 

def prod(self, axis=None, dtype=None, out=None):
        """
        Return the product of the array elements over the given axis.

        Refer to `prod` for full documentation.

        See Also
        --------
        prod, ndarray.prod

        Notes
        -----
        Same as `ndarray.prod`, except, where that returns an `ndarray`, this
        returns a `matrix` object instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.prod()
        0
        >>> x.prod(0)
        matrix([[  0,  45, 120, 231]])
        >>> x.prod(1)
        matrix([[   0],
                [ 840],
                [7920]])

        """
        return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis) 

def std(self, axis=None, dtype=None, out=None, ddof=0):
        """
        Return the standard deviation of the array elements along the given axis.

        Refer to `numpy.std` for full documentation.

        See Also
        --------
        numpy.std

        Notes
        -----
        This is the same as `ndarray.std`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.std()
        3.4520525295346629
        >>> x.std(0)
        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]])
        >>> x.std(1)
        matrix([[ 1.11803399],
                [ 1.11803399],
                [ 1.11803399]])

        """
        return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) 

def std(self, axis=None, dtype=None, out=None, ddof=0):
        """
        Return the standard deviation of the array elements along the given axis.

        Refer to `numpy.std` for full documentation.

        See Also
        --------
        numpy.std

        Notes
        -----
        This is the same as `ndarray.std`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.std()
        3.4520525295346629
        >>> x.std(0)
        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]])
        >>> x.std(1)
        matrix([[ 1.11803399],
                [ 1.11803399],
                [ 1.11803399]])

        """
        return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) 

def sum(self, axis=None, dtype=None, out=None):
        """
        Returns the sum of the matrix elements, along the given axis.

        Refer to `numpy.sum` for full documentation.

        See Also
        --------
        numpy.sum

        Notes
        -----
        This is the same as `ndarray.sum`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix([[1, 2], [4, 3]])
        >>> x.sum()
        10
        >>> x.sum(axis=1)
        matrix([[3],
                [7]])
        >>> x.sum(axis=1, dtype='float')
        matrix([[ 3.],
                [ 7.]])
        >>> out = np.zeros((1, 2), dtype='float')
        >>> x.sum(axis=1, dtype='float', out=out)
        matrix([[ 3.],
                [ 7.]])

        """
        return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)


    # To update docstring from array to matrix... 

def prod(self, axis=None, dtype=None, out=None):
        """
        Return the product of the array elements over the given axis.

        Refer to `prod` for full documentation.

        See Also
        --------
        prod, ndarray.prod

        Notes
        -----
        Same as `ndarray.prod`, except, where that returns an `ndarray`, this
        returns a `matrix` object instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.prod()
        0
        >>> x.prod(0)
        matrix([[  0,  45, 120, 231]])
        >>> x.prod(1)
        matrix([[   0],
                [ 840],
                [7920]])

        """
        return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis)