File: numpy.py

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class getset_descriptor: pass
class dictproxy: pass
class member_descriptor: pass
ALLOW_THREADS = int()
BUFSIZE = int()
CLIP = int()
class ComplexWarning:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    args = getset_descriptor()
    message = getset_descriptor()
class DataSource:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    def _cache(self, _):
        """Cache the file specified by path.
        
                Creates a copy of the file in the datasource cache.
        
                """
        return None
    def _findfile(self, _):
        """Searches for ``path`` and returns full path if found.
        
                If path is an URL, _findfile will cache a local copy and return
                the path to the cached file.
                If path is a local file, _findfile will return a path to that local
                file.
        
                The search will include possible compressed versions of the file and
                return the first occurrence found.
        
                """
        return None
    def _isurl(self, _):
        """Test if path is a net location.  Tests the scheme and netloc."""
        return None
    def _iswritemode(self, _):
        """Test if the given mode will open a file for writing."""
        return None
    def _iszip(self, _):
        """Test if the filename is a zip file by looking at the file extension.
                """
        return None
    def _possible_names(self, _):
        """Return a tuple containing compressed filename variations."""
        return None
    def _sanitize_relative_path(self, _):
        """Return a sanitised relative path for which
                os.path.abspath(os.path.join(base, path)).startswith(base)
                """
        return None
    def _splitzipext(self, _):
        """Split zip extension from filename and return filename.
        
                *Returns*:
                    base, zip_ext : {tuple}
        
                """
        return None
    def abspath(self, path):
        """
                Return absolute path of file in the DataSource directory.
        
                If `path` is an URL, then `abspath` will return either the location
                the file exists locally or the location it would exist when opened
                using the `open` method.
        
                Parameters
                ----------
                path : str
                    Can be a local file or a remote URL.
        
                Returns
                -------
                out : str
                    Complete path, including the `DataSource` destination directory.
        
                Notes
                -----
                The functionality is based on `os.path.abspath`.
        
                """
        return str()
    def exists(self, path):
        """
                Test if path exists.
        
                Test if `path` exists as (and in this order):
        
                - a local file.
                - a remote URL that has been downloaded and stored locally in the
                  `DataSource` directory.
                - a remote URL that has not been downloaded, but is valid and accessible.
        
                Parameters
                ----------
                path : str
                    Can be a local file or a remote URL.
        
                Returns
                -------
                out : bool
                    True if `path` exists.
        
                Notes
                -----
                When `path` is an URL, `exists` will return True if it's either stored
                locally in the `DataSource` directory, or is a valid remote URL.
                `DataSource` does not discriminate between the two, the file is accessible
                if it exists in either location.
        
                """
        return bool()
    def open(self, path="r", mode="r"):
        """
                Open and return file-like object.
        
                If `path` is an URL, it will be downloaded, stored in the `DataSource`
                directory and opened from there.
        
                Parameters
                ----------
                path : str
                    Local file path or URL to open.
                mode : {'r', 'w', 'a'}, optional
                    Mode to open `path`.  Mode 'r' for reading, 'w' for writing, 'a' to
                    append. Available modes depend on the type of object specified by
                    `path`. Default is 'r'.
        
                Returns
                -------
                out : file object
                    File object.
        
                """
        return file()
ERR_CALL = int()
ERR_DEFAULT = int()
ERR_DEFAULT2 = int()
ERR_IGNORE = int()
ERR_LOG = int()
ERR_PRINT = int()
ERR_RAISE = int()
ERR_WARN = int()
FLOATING_POINT_SUPPORT = int()
FPE_DIVIDEBYZERO = int()
FPE_INVALID = int()
FPE_OVERFLOW = int()
FPE_UNDERFLOW = int()
False_ = bool_()
Inf = float()
Infinity = float()
MAXDIMS = int()
class MachAr:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    def _do_init(self, _):
        """None"""
        return None
class ModuleDeprecationWarning:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    args = getset_descriptor()
    message = getset_descriptor()
NAN = float()
NINF = float()
NZERO = float()
NaN = float()
PINF = float()
PZERO = float()
class PackageLoader:
    __dict__ = dictproxy()
    __doc__ = None
    __module__ = str()
    __weakref__ = getset_descriptor()
    def _execcmd(self, _):
        """ Execute command in parent_frame."""
        return None
    def _format_titles(self, titles="---", colsep="---"):
        """None"""
        return None
    def _get_doc_title(self, _):
        """ Get the title from a package info.py file.
                """
        return None
    def _get_info_files(self, package_dir, parent_path=None, parent_package=None):
        """ Return list of (package name,info.py file) from parent_path subdirectories.
                """
        return None
    def _get_sorted_names(self, _):
        """ Return package names sorted in the order as they should be
                imported due to dependence relations between packages.
                """
        return None
    def _init_info_modules(self=None, packages=None):
        """Initialize info_modules = {<package_name>: <package info.py module>}.
                """
        return None
    def _obj2repr(self, obj):
        """ Return repr(obj) with"""
        return None
    def error(self, _):
        """None"""
        return None
    def get_pkgdocs(self, _):
        """ Return documentation summary of subpackages.
                """
        return None
    def log(self, _):
        """None"""
        return None
    def warn(self, _):
        """None"""
        return None
RAISE = int()
class RankWarning:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    args = getset_descriptor()
    message = getset_descriptor()
SHIFT_DIVIDEBYZERO = int()
SHIFT_INVALID = int()
SHIFT_OVERFLOW = int()
SHIFT_UNDERFLOW = int()
ScalarType = tuple()
class NoseTester:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    def _get_custom_doctester(self, _):
        """ Return instantiated plugin for doctests
        
                Allows subclassing of this class to override doctester
        
                A return value of None means use the nose builtin doctest plugin
                """
        return None
    def _show_system_info(self, _):
        """None"""
        return None
    def _test_argv(self, label, verbose, extra_argv):
        """ Generate argv for nosetest command
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    see ``test`` docstring
                verbose : int, optional
                    Verbosity value for test outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
        
                Returns
                -------
                argv : list
                    command line arguments that will be passed to nose
                """
        return list()
    def bench(self=None, label="fast", verbose=1, extra_argv=None):
        """
                Run benchmarks for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the benchmarks to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow benchmarks as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
        
                Returns
                -------
                success : bool
                    Returns True if running the benchmarks works, False if an error
                    occurred.
        
                Notes
                -----
                Benchmarks are like tests, but have names starting with "bench" instead
                of "test", and can be found under the "benchmarks" sub-directory of the
                module.
        
                Each NumPy module exposes `bench` in its namespace to run all benchmarks
                for it.
        
                Examples
                --------
                >>> success = np.lib.bench() #doctest: +SKIP
                Running benchmarks for numpy.lib
                ...
                using 562341 items:
                unique:
                0.11
                unique1d:
                0.11
                ratio: 1.0
                nUnique: 56230 == 56230
                ...
                OK
        
                >>> success #doctest: +SKIP
                True
        
                """
        return bool()
    excludes = list()
    def prepare_test_args(self=False, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False):
        """
                Run tests for module using nose.
        
                This method does the heavy lifting for the `test` method. It takes all
                the same arguments, for details see `test`.
        
                See Also
                --------
                test
        
                """
        return None
    def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
        """
                Run tests for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the tests to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow tests as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for test outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
                doctests : bool, optional
                    If True, run doctests in module. Default is False.
                coverage : bool, optional
                    If True, report coverage of NumPy code. Default is False.
                    (This requires the `coverage module:
                     <http://nedbatchelder.com/code/modules/coverage.html>`_).
                raise_warnings : str or sequence of warnings, optional
                    This specifies which warnings to configure as 'raise' instead
                    of 'warn' during the test execution.  Valid strings are:
        
                      - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                      - "release" : equals ``()``, don't raise on any warnings.
        
                Returns
                -------
                result : object
                    Returns the result of running the tests as a
                    ``nose.result.TextTestResult`` object.
        
                Notes
                -----
                Each NumPy module exposes `test` in its namespace to run all tests for it.
                For example, to run all tests for numpy.lib:
        
                >>> np.lib.test() #doctest: +SKIP
        
                Examples
                --------
                >>> result = np.lib.test() #doctest: +SKIP
                Running unit tests for numpy.lib
                ...
                Ran 976 tests in 3.933s
        
                OK
        
                >>> result.errors #doctest: +SKIP
                []
                >>> result.knownfail #doctest: +SKIP
                []
                """
        return object()
True_ = bool_()
UFUNC_BUFSIZE_DEFAULT = int()
UFUNC_PYVALS_NAME = str()
WRAP = int()
__NUMPY_SETUP__ = bool()
__all__ = list()
__builtins__ = dict()
__doc__ = str()
__file__ = str()
__git_revision__ = str()
__name__ = str()
__package__ = str()
__path__ = list()
__version__ = str()
def absolute(x, out=None):
    """absolute(x[, out])
    
    Calculate the absolute value element-wise.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    absolute : ndarray
        An ndarray containing the absolute value of
        each element in `x`.  For complex input, ``a + ib``, the
        absolute value is :math:`\sqrt{ a^2 + b^2 }`.
    
    Examples
    --------
    >>> x = np.array([-1.2, 1.2])
    >>> np.absolute(x)
    array([ 1.2,  1.2])
    >>> np.absolute(1.2 + 1j)
    1.5620499351813308
    
    Plot the function over ``[-10, 10]``:
    
    >>> import matplotlib.pyplot as plt
    
    >>> x = np.linspace(start=-10, stop=10, num=101)
    >>> plt.plot(x, np.absolute(x))
    >>> plt.show()
    
    Plot the function over the complex plane:
    
    >>> xx = x + 1j * x[:, np.newaxis]
    >>> plt.imshow(np.abs(xx), extent=[-10, 10, -10, 10])
    >>> plt.show()"""
    return ndarray()
def absolute(x, out=None):
    """absolute(x[, out])
    
    Calculate the absolute value element-wise.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    absolute : ndarray
        An ndarray containing the absolute value of
        each element in `x`.  For complex input, ``a + ib``, the
        absolute value is :math:`\sqrt{ a^2 + b^2 }`.
    
    Examples
    --------
    >>> x = np.array([-1.2, 1.2])
    >>> np.absolute(x)
    array([ 1.2,  1.2])
    >>> np.absolute(1.2 + 1j)
    1.5620499351813308
    
    Plot the function over ``[-10, 10]``:
    
    >>> import matplotlib.pyplot as plt
    
    >>> x = np.linspace(start=-10, stop=10, num=101)
    >>> plt.plot(x, np.absolute(x))
    >>> plt.show()
    
    Plot the function over the complex plane:
    
    >>> xx = x + 1j * x[:, np.newaxis]
    >>> plt.imshow(np.abs(xx), extent=[-10, 10, -10, 10])
    >>> plt.show()"""
    return ndarray()
absolute_import = instance()
def add(x1, x2, out=None):
    """add(x1, x2[, out])
    
    Add arguments element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        The arrays to be added.  If ``x1.shape != x2.shape``, they must be
        broadcastable to a common shape (which may be the shape of one or
        the other).
    
    Returns
    -------
    add : ndarray or scalar
        The sum of `x1` and `x2`, element-wise.  Returns a scalar if
        both  `x1` and `x2` are scalars.
    
    Notes
    -----
    Equivalent to `x1` + `x2` in terms of array broadcasting.
    
    Examples
    --------
    >>> np.add(1.0, 4.0)
    5.0
    >>> x1 = np.arange(9.0).reshape((3, 3))
    >>> x2 = np.arange(3.0)
    >>> np.add(x1, x2)
    array([[  0.,   2.,   4.],
           [  3.,   5.,   7.],
           [  6.,   8.,  10.]])"""
    return ndarray() if False else float()
def add_docstring(obj, docstring):
    """add_docstring(obj, docstring)
    
        Add a docstring to a built-in obj if possible.
        If the obj already has a docstring raise a RuntimeError
        If this routine does not know how to add a docstring to the object
        raise a TypeError"""
    return None
def add_newdoc():
    """Adds documentation to obj which is in module place.
    
        If doc is a string add it to obj as a docstring
    
        If doc is a tuple, then the first element is interpreted as
           an attribute of obj and the second as the docstring
              (method, docstring)
    
        If doc is a list, then each element of the list should be a
           sequence of length two --> [(method1, docstring1),
           (method2, docstring2), ...]
    
        This routine never raises an error.
    
        This routine cannot modify read-only docstrings, as appear
        in new-style classes or built-in functions. Because this
        routine never raises an error the caller must check manually
        that the docstrings were changed.
           """
    return None
def add_newdoc_ufunc(ufunc, new_docstring):
    """add_ufunc_docstring(ufunc, new_docstring)
    
        Replace the docstring for a ufunc with new_docstring.
        This method will only work if the current docstring for
        the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.)
    
        Parameters
        ----------
        ufunc : numpy.ufunc
            A ufunc whose current doc is NULL.
        new_docstring : string
            The new docstring for the ufunc.
    
        Notes
        -----
        This method allocates memory for new_docstring on
        the heap. Technically this creates a mempory leak, since this
        memory will not be reclaimed until the end of the program
        even if the ufunc itself is removed. However this will only
        be a problem if the user is repeatedly creating ufuncs with
        no documentation, adding documentation via add_newdoc_ufunc,
        and then throwing away the ufunc."""
    return None
def alen(a):
    """
        Return the length of the first dimension of the input array.
    
        Parameters
        ----------
        a : array_like
           Input array.
    
        Returns
        -------
        alen : int
           Length of the first dimension of `a`.
    
        See Also
        --------
        shape, size
    
        Examples
        --------
        >>> a = np.zeros((7,4,5))
        >>> a.shape[0]
        7
        >>> np.alen(a)
        7
    
        """
    return int()
def all(a=False, axis=None, out=None, keepdims=False):
    """
        Test whether all array elements along a given axis evaluate to True.
    
        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 AND reduction is performed.
            The default (`axis` = `None`) is 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 ``dtype(out)`` is float, the result
            will consist of 0.0's and 1.0's).  See `doc.ufuncs` (Section
            "Output arguments") 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 original `arr`.
    
        Returns
        -------
        all : ndarray, bool
            A new boolean or array is returned unless `out` is specified,
            in which case a reference to `out` is returned.
    
        See Also
        --------
        ndarray.all : equivalent method
    
        any : Test whether any element along a given axis evaluates to True.
    
        Notes
        -----
        Not a Number (NaN), positive infinity and negative infinity
        evaluate to `True` because these are not equal to zero.
    
        Examples
        --------
        >>> np.all([[True,False],[True,True]])
        False
    
        >>> np.all([[True,False],[True,True]], axis=0)
        array([ True, False], dtype=bool)
    
        >>> np.all([-1, 4, 5])
        True
    
        >>> np.all([1.0, np.nan])
        True
    
        >>> o=np.array([False])
        >>> z=np.all([-1, 4, 5], out=o)
        >>> id(z), id(o), z                             # doctest: +SKIP
        (28293632, 28293632, array([ True], dtype=bool))
    
        """
    return ndarray()
def allclose(a, b=1e-08, rtol=1e-05, atol=1e-08):
    """
        Returns True if two arrays are element-wise equal within a tolerance.
    
        The tolerance values are positive, typically very small numbers.  The
        relative difference (`rtol` * abs(`b`)) and the absolute difference
        `atol` are added together to compare against the absolute difference
        between `a` and `b`.
    
        If either array contains one or more NaNs, False is returned.
        Infs are treated as equal if they are in the same place and of the same
        sign in both arrays.
    
        Parameters
        ----------
        a, b : array_like
            Input arrays to compare.
        rtol : float
            The relative tolerance parameter (see Notes).
        atol : float
            The absolute tolerance parameter (see Notes).
    
        Returns
        -------
        allclose : bool
            Returns True if the two arrays are equal within the given
            tolerance; False otherwise.
    
        See Also
        --------
        isclose, all, any
    
        Notes
        -----
        If the following equation is element-wise True, then allclose returns
        True.
    
         absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
    
        The above equation is not symmetric in `a` and `b`, so that
        `allclose(a, b)` might be different from `allclose(b, a)` in
        some rare cases.
    
        Examples
        --------
        >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])
        False
        >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])
        True
        >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])
        False
        >>> np.allclose([1.0, np.nan], [1.0, np.nan])
        False
    
        """
    return bool()
def alltrue(a=False, axis=None, out=None, keepdims=False):
    """
        Check if all elements of input array are true.
    
        See Also
        --------
        numpy.all : Equivalent function; see for details.
    
        """
    return None
def alterdot():
    """Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions.
    
        Typically, as a user of Numpy, you do not explicitly call this function. If
        Numpy is built with an accelerated BLAS, this function is automatically
        called when Numpy is imported.
    
        When Numpy is built with an accelerated BLAS like ATLAS, these functions
        are replaced to make use of the faster implementations.  The faster
        implementations only affect float32, float64, complex64, and complex128
        arrays. Furthermore, the BLAS API only includes matrix-matrix,
        matrix-vector, and vector-vector products. Products of arrays with larger
        dimensionalities use the built in functions and are not accelerated.
    
        See Also
        --------
        restoredot : `restoredot` undoes the effects of `alterdot`."""
    return None
def amax(a=False, axis=None, out=None, keepdims=False):
    """
        Return the maximum of an array or maximum along an axis.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which to operate.  By default, flattened input is used.
        out : ndarray, optional
            Alternative output array in which to place the result.  Must
            be of the same shape and buffer length as the expected output.
            See `doc.ufuncs` (Section "Output arguments") 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 original `arr`.
    
        Returns
        -------
        amax : ndarray or scalar
            Maximum of `a`. If `axis` is None, the result is a scalar value.
            If `axis` is given, the result is an array of dimension
            ``a.ndim - 1``.
    
        See Also
        --------
        amin :
            The minimum value of an array along a given axis, propagating any NaNs.
        nanmax :
            The maximum value of an array along a given axis, ignoring any NaNs.
        maximum :
            Element-wise maximum of two arrays, propagating any NaNs.
        fmax :
            Element-wise maximum of two arrays, ignoring any NaNs.
        argmax :
            Return the indices of the maximum values.
    
        nanmin, minimum, fmin
    
        Notes
        -----
        NaN values are propagated, that is if at least one item is NaN, the
        corresponding max value will be NaN as well. To ignore NaN values
        (MATLAB behavior), please use nanmax.
    
        Don't use `amax` for element-wise comparison of 2 arrays; when
        ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
        ``amax(a, axis=0)``.
    
        Examples
        --------
        >>> a = np.arange(4).reshape((2,2))
        >>> a
        array([[0, 1],
               [2, 3]])
        >>> np.amax(a)           # Maximum of the flattened array
        3
        >>> np.amax(a, axis=0)   # Maxima along the first axis
        array([2, 3])
        >>> np.amax(a, axis=1)   # Maxima along the second axis
        array([1, 3])
    
        >>> b = np.arange(5, dtype=np.float)
        >>> b[2] = np.NaN
        >>> np.amax(b)
        nan
        >>> np.nanmax(b)
        4.0
    
        """
    return ndarray() if False else float()
def amin(a=False, axis=None, out=None, keepdims=False):
    """
        Return the minimum of an array or minimum along an axis.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which to operate.  By default, flattened input is used.
        out : ndarray, optional
            Alternative output array in which to place the result.  Must
            be of the same shape and buffer length as the expected output.
            See `doc.ufuncs` (Section "Output arguments") 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 original `arr`.
    
        Returns
        -------
        amin : ndarray or scalar
            Minimum of `a`. If `axis` is None, the result is a scalar value.
            If `axis` is given, the result is an array of dimension
            ``a.ndim - 1``.
    
        See Also
        --------
        amax :
            The maximum value of an array along a given axis, propagating any NaNs.
        nanmin :
            The minimum value of an array along a given axis, ignoring any NaNs.
        minimum :
            Element-wise minimum of two arrays, propagating any NaNs.
        fmin :
            Element-wise minimum of two arrays, ignoring any NaNs.
        argmin :
            Return the indices of the minimum values.
    
        nanmax, maximum, fmax
    
        Notes
        -----
        NaN values are propagated, that is if at least one item is NaN, the
        corresponding min value will be NaN as well. To ignore NaN values
        (MATLAB behavior), please use nanmin.
    
        Don't use `amin` for element-wise comparison of 2 arrays; when
        ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
        ``amin(a, axis=0)``.
    
        Examples
        --------
        >>> a = np.arange(4).reshape((2,2))
        >>> a
        array([[0, 1],
               [2, 3]])
        >>> np.amin(a)           # Minimum of the flattened array
        0
        >>> np.amin(a, axis=0)   # Minima along the first axis
        array([0, 1])
        >>> np.amin(a, axis=1)   # Minima along the second axis
        array([0, 2])
    
        >>> b = np.arange(5, dtype=np.float)
        >>> b[2] = np.NaN
        >>> np.amin(b)
        nan
        >>> np.nanmin(b)
        0.0
    
        """
    return ndarray() if False else float()
def angle(z=0, 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, 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
    
        """
    return ndarray()
def any(a=False, axis=None, out=None, keepdims=False):
    """
        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 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 `doc.ufuncs` (Section "Output arguments") for 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 original `arr`.
    
        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], dtype=bool)
    
        >>> 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], dtype=bool), array([ True], dtype=bool))
        >>> # 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)
    
        """
    return bool() if False else ndarray()
def append(arr, values=None, axis=None):
    """
        Append values to the end of an array.
    
        Parameters
        ----------
        arr : array_like
            Values are appended to a copy of this array.
        values : array_like
            These values are appended to a copy of `arr`.  It must be of the
            correct shape (the same shape as `arr`, excluding `axis`).  If `axis`
            is not specified, `values` can be any shape and will be flattened
            before use.
        axis : int, optional
            The axis along which `values` are appended.  If `axis` is not given,
            both `arr` and `values` are flattened before use.
    
        Returns
        -------
        append : ndarray
            A copy of `arr` with `values` appended to `axis`.  Note that `append`
            does not occur in-place: a new array is allocated and filled.  If
            `axis` is None, `out` is a flattened array.
    
        See Also
        --------
        insert : Insert elements into an array.
        delete : Delete elements from an array.
    
        Examples
        --------
        >>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
        array([1, 2, 3, 4, 5, 6, 7, 8, 9])
    
        When `axis` is specified, `values` must have the correct shape.
    
        >>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
        array([[1, 2, 3],
               [4, 5, 6],
               [7, 8, 9]])
        >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
        Traceback (most recent call last):
        ...
        ValueError: arrays must have same number of dimensions
    
        """
    return ndarray()
def apply_along_axis(func1d, axis, arr, args):
    """
        Apply a function to 1-D slices along the given axis.
    
        Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`
        is a 1-D slice of `arr` along `axis`.
    
        Parameters
        ----------
        func1d : function
            This function should accept 1-D arrays. It is applied to 1-D
            slices of `arr` along the specified axis.
        axis : integer
            Axis along which `arr` is sliced.
        arr : ndarray
            Input array.
        args : any
            Additional arguments to `func1d`.
    
        Returns
        -------
        apply_along_axis : ndarray
            The output array. The shape of `outarr` is identical to the shape of
            `arr`, except along the `axis` dimension, where the length of `outarr`
            is equal to the size of the return value of `func1d`.  If `func1d`
            returns a scalar `outarr` will have one fewer dimensions than `arr`.
    
        See Also
        --------
        apply_over_axes : Apply a function repeatedly over multiple axes.
    
        Examples
        --------
        >>> def my_func(a):
        ...     ___Average first and last element of a 1-D array___
        ...     return (a[0] + a[-1]) * 0.5
        >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
        >>> np.apply_along_axis(my_func, 0, b)
        array([ 4.,  5.,  6.])
        >>> np.apply_along_axis(my_func, 1, b)
        array([ 2.,  5.,  8.])
    
        For a function that doesn't return a scalar, the number of dimensions in
        `outarr` is the same as `arr`.
    
        >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
        >>> np.apply_along_axis(sorted, 1, b)
        array([[1, 7, 8],
               [3, 4, 9],
               [2, 5, 6]])
    
        """
    return ndarray()
def apply_over_axes(func, a, axes):
    """
        Apply a function repeatedly over multiple axes.
    
        `func` is called as `res = func(a, axis)`, where `axis` is the first
        element of `axes`.  The result `res` of the function call must have
        either the same dimensions as `a` or one less dimension.  If `res`
        has one less dimension than `a`, a dimension is inserted before
        `axis`.  The call to `func` is then repeated for each axis in `axes`,
        with `res` as the first argument.
    
        Parameters
        ----------
        func : function
            This function must take two arguments, `func(a, axis)`.
        a : array_like
            Input array.
        axes : array_like
            Axes over which `func` is applied; the elements must be integers.
    
        Returns
        -------
        apply_over_axis : ndarray
            The output array.  The number of dimensions is the same as `a`,
            but the shape can be different.  This depends on whether `func`
            changes the shape of its output with respect to its input.
    
        See Also
        --------
        apply_along_axis :
            Apply a function to 1-D slices of an array along the given axis.
    
        Examples
        --------
        >>> a = np.arange(24).reshape(2,3,4)
        >>> a
        array([[[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]],
               [[12, 13, 14, 15],
                [16, 17, 18, 19],
                [20, 21, 22, 23]]])
    
        Sum over axes 0 and 2. The result has same number of dimensions
        as the original array:
    
        >>> np.apply_over_axes(np.sum, a, [0,2])
        array([[[ 60],
                [ 92],
                [124]]])
    
        """
    return ndarray()
def arange(start=None, stop=None, step=None, _=None, dtype=None):
    """arange([start,] stop[, step,], dtype=None)
    
        Return evenly spaced values within a given interval.
    
        Values are generated within the half-open interval ``[start, stop)``
        (in other words, the interval including `start` but excluding `stop`).
        For integer arguments the function is equivalent to the Python built-in
        `range <http://docs.python.org/lib/built-in-funcs.html>`_ function,
        but returns an ndarray rather than a list.
    
        When using a non-integer step, such as 0.1, the results will often not
        be consistent.  It is better to use ``linspace`` for these cases.
    
        Parameters
        ----------
        start : number, optional
            Start of interval.  The interval includes this value.  The default
            start value is 0.
        stop : number
            End of interval.  The interval does not include this value, except
            in some cases where `step` is not an integer and floating point
            round-off affects the length of `out`.
        step : number, optional
            Spacing between values.  For any output `out`, this is the distance
            between two adjacent values, ``out[i+1] - out[i]``.  The default
            step size is 1.  If `step` is specified, `start` must also be given.
        dtype : dtype
            The type of the output array.  If `dtype` is not given, infer the data
            type from the other input arguments.
    
        Returns
        -------
        arange : ndarray
            Array of evenly spaced values.
    
            For floating point arguments, the length of the result is
            ``ceil((stop - start)/step)``.  Because of floating point overflow,
            this rule may result in the last element of `out` being greater
            than `stop`.
    
        See Also
        --------
        linspace : Evenly spaced numbers with careful handling of endpoints.
        ogrid: Arrays of evenly spaced numbers in N-dimensions.
        mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
    
        Examples
        --------
        >>> np.arange(3)
        array([0, 1, 2])
        >>> np.arange(3.0)
        array([ 0.,  1.,  2.])
        >>> np.arange(3,7)
        array([3, 4, 5, 6])
        >>> np.arange(3,7,2)
        array([3, 5])"""
    return ndarray()
def arccos(x, out):
    """arccos(x[, out])
    
    Trigonometric inverse cosine, element-wise.
    
    The inverse of `cos` so that, if ``y = cos(x)``, then ``x = arccos(y)``.
    
    Parameters
    ----------
    x : array_like
        `x`-coordinate on the unit circle.
        For real arguments, the domain is [-1, 1].
    
    out : ndarray, optional
        Array of the same shape as `a`, to store results in. See
        `doc.ufuncs` (Section "Output arguments") for more details.
    
    Returns
    -------
    angle : ndarray
        The angle of the ray intersecting the unit circle at the given
        `x`-coordinate in radians [0, pi]. If `x` is a scalar then a
        scalar is returned, otherwise an array of the same shape as `x`
        is returned.
    
    See Also
    --------
    cos, arctan, arcsin, emath.arccos
    
    Notes
    -----
    `arccos` is a multivalued function: for each `x` there are infinitely
    many numbers `z` such that `cos(z) = x`. The convention is to return
    the angle `z` whose real part lies in `[0, pi]`.
    
    For real-valued input data types, `arccos` always returns real output.
    For each value that cannot be expressed as a real number or infinity,
    it yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `arccos` is a complex analytic function that
    has branch cuts `[-inf, -1]` and `[1, inf]` and is continuous from
    above on the former and from below on the latter.
    
    The inverse `cos` is also known as `acos` or cos^-1.
    
    References
    ----------
    M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
    10th printing, 1964, pp. 79. http://www.math.sfu.ca/~cbm/aands/
    
    Examples
    --------
    We expect the arccos of 1 to be 0, and of -1 to be pi:
    
    >>> np.arccos([1, -1])
    array([ 0.        ,  3.14159265])
    
    Plot arccos:
    
    >>> import matplotlib.pyplot as plt
    >>> x = np.linspace(-1, 1, num=100)
    >>> plt.plot(x, np.arccos(x))
    >>> plt.axis('tight')
    >>> plt.show()"""
    return ndarray()
def arccosh(x, out):
    """arccosh(x[, out])
    
    Inverse hyperbolic cosine, elementwise.
    
    Parameters
    ----------
    x : array_like
        Input array.
    out : ndarray, optional
        Array of the same shape as `x`, to store results in.
        See `doc.ufuncs` (Section "Output arguments") for details.
    
    
    Returns
    -------
    arccosh : ndarray
        Array of the same shape as `x`.
    
    See Also
    --------
    
    cosh, arcsinh, sinh, arctanh, tanh
    
    Notes
    -----
    `arccosh` is a multivalued function: for each `x` there are infinitely
    many numbers `z` such that `cosh(z) = x`. The convention is to return the
    `z` whose imaginary part lies in `[-pi, pi]` and the real part in
    ``[0, inf]``.
    
    For real-valued input data types, `arccosh` always returns real output.
    For each value that cannot be expressed as a real number or infinity, it
    yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `arccosh` is a complex analytical function that
    has a branch cut `[-inf, 1]` and is continuous from above on it.
    
    References
    ----------
    .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
           10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/
    .. [2] Wikipedia, "Inverse hyperbolic function",
           http://en.wikipedia.org/wiki/Arccosh
    
    Examples
    --------
    >>> np.arccosh([np.e, 10.0])
    array([ 1.65745445,  2.99322285])
    >>> np.arccosh(1)
    0.0"""
    return ndarray()
def arcsin(x, out):
    """arcsin(x[, out])
    
    Inverse sine, element-wise.
    
    Parameters
    ----------
    x : array_like
        `y`-coordinate on the unit circle.
    
    out : ndarray, optional
        Array of the same shape as `x`, in which to store the results.
        See `doc.ufuncs` (Section "Output arguments") for more details.
    
    Returns
    -------
    angle : ndarray
        The inverse sine of each element in `x`, in radians and in the
        closed interval ``[-pi/2, pi/2]``.  If `x` is a scalar, a scalar
        is returned, otherwise an array.
    
    See Also
    --------
    sin, cos, arccos, tan, arctan, arctan2, emath.arcsin
    
    Notes
    -----
    `arcsin` is a multivalued function: for each `x` there are infinitely
    many numbers `z` such that :math:`sin(z) = x`.  The convention is to
    return the angle `z` whose real part lies in [-pi/2, pi/2].
    
    For real-valued input data types, *arcsin* always returns real output.
    For each value that cannot be expressed as a real number or infinity,
    it yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `arcsin` is a complex analytic function that
    has, by convention, the branch cuts [-inf, -1] and [1, inf]  and is
    continuous from above on the former and from below on the latter.
    
    The inverse sine is also known as `asin` or sin^{-1}.
    
    References
    ----------
    Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
    10th printing, New York: Dover, 1964, pp. 79ff.
    http://www.math.sfu.ca/~cbm/aands/
    
    Examples
    --------
    >>> np.arcsin(1)     # pi/2
    1.5707963267948966
    >>> np.arcsin(-1)    # -pi/2
    -1.5707963267948966
    >>> np.arcsin(0)
    0.0"""
    return ndarray()
def arcsinh(x, out):
    """arcsinh(x[, out])
    
    Inverse hyperbolic sine elementwise.
    
    Parameters
    ----------
    x : array_like
        Input array.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See `doc.ufuncs`.
    
    Returns
    -------
    out : ndarray
        Array of of the same shape as `x`.
    
    Notes
    -----
    `arcsinh` is a multivalued function: for each `x` there are infinitely
    many numbers `z` such that `sinh(z) = x`. The convention is to return the
    `z` whose imaginary part lies in `[-pi/2, pi/2]`.
    
    For real-valued input data types, `arcsinh` always returns real output.
    For each value that cannot be expressed as a real number or infinity, it
    returns ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `arccos` is a complex analytical function that
    has branch cuts `[1j, infj]` and `[-1j, -infj]` and is continuous from
    the right on the former and from the left on the latter.
    
    The inverse hyperbolic sine is also known as `asinh` or ``sinh^-1``.
    
    References
    ----------
    .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
           10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/
    .. [2] Wikipedia, "Inverse hyperbolic function",
           http://en.wikipedia.org/wiki/Arcsinh
    
    Examples
    --------
    >>> np.arcsinh(np.array([np.e, 10.0]))
    array([ 1.72538256,  2.99822295])"""
    return ndarray()
def arctan(x, out=None):
    """arctan(x[, out])
    
    Trigonometric inverse tangent, element-wise.
    
    The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.
    
    Parameters
    ----------
    x : array_like
        Input values.  `arctan` is applied to each element of `x`.
    
    Returns
    -------
    out : ndarray
        Out has the same shape as `x`.  Its real part is in
        ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
        It is a scalar if `x` is a scalar.
    
    See Also
    --------
    arctan2 : The "four quadrant" arctan of the angle formed by (`x`, `y`)
        and the positive `x`-axis.
    angle : Argument of complex values.
    
    Notes
    -----
    `arctan` is a multi-valued function: for each `x` there are infinitely
    many numbers `z` such that tan(`z`) = `x`.  The convention is to return
    the angle `z` whose real part lies in [-pi/2, pi/2].
    
    For real-valued input data types, `arctan` always returns real output.
    For each value that cannot be expressed as a real number or infinity,
    it yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `arctan` is a complex analytic function that
    has [`1j, infj`] and [`-1j, -infj`] as branch cuts, and is continuous
    from the left on the former and from the right on the latter.
    
    The inverse tangent is also known as `atan` or tan^{-1}.
    
    References
    ----------
    Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
    10th printing, New York: Dover, 1964, pp. 79.
    http://www.math.sfu.ca/~cbm/aands/
    
    Examples
    --------
    We expect the arctan of 0 to be 0, and of 1 to be pi/4:
    
    >>> np.arctan([0, 1])
    array([ 0.        ,  0.78539816])
    
    >>> np.pi/4
    0.78539816339744828
    
    Plot arctan:
    
    >>> import matplotlib.pyplot as plt
    >>> x = np.linspace(-10, 10)
    >>> plt.plot(x, np.arctan(x))
    >>> plt.axis('tight')
    >>> plt.show()"""
    return ndarray()
def arctan2(x1, x2, out=None):
    """arctan2(x1, x2[, out])
    
    Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
    
    The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is
    the signed angle in radians between the ray ending at the origin and
    passing through the point (1,0), and the ray ending at the origin and
    passing through the point (`x2`, `x1`).  (Note the role reversal: the
    "`y`-coordinate" is the first function parameter, the "`x`-coordinate"
    is the second.)  By IEEE convention, this function is defined for
    `x2` = +/-0 and for either or both of `x1` and `x2` = +/-inf (see
    Notes for specific values).
    
    This function is not defined for complex-valued arguments; for the
    so-called argument of complex values, use `angle`.
    
    Parameters
    ----------
    x1 : array_like, real-valued
        `y`-coordinates.
    x2 : array_like, real-valued
        `x`-coordinates. `x2` must be broadcastable to match the shape of
        `x1` or vice versa.
    
    Returns
    -------
    angle : ndarray
        Array of angles in radians, in the range ``[-pi, pi]``.
    
    See Also
    --------
    arctan, tan, angle
    
    Notes
    -----
    *arctan2* is identical to the `atan2` function of the underlying
    C library.  The following special values are defined in the C
    standard: [1]_
    
    ====== ====== ================
    `x1`   `x2`   `arctan2(x1,x2)`
    ====== ====== ================
    +/- 0  +0     +/- 0
    +/- 0  -0     +/- pi
     > 0   +/-inf +0 / +pi
     < 0   +/-inf -0 / -pi
    +/-inf +inf   +/- (pi/4)
    +/-inf -inf   +/- (3*pi/4)
    ====== ====== ================
    
    Note that +0 and -0 are distinct floating point numbers, as are +inf
    and -inf.
    
    References
    ----------
    .. [1] ISO/IEC standard 9899:1999, "Programming language C."
    
    Examples
    --------
    Consider four points in different quadrants:
    
    >>> x = np.array([-1, +1, +1, -1])
    >>> y = np.array([-1, -1, +1, +1])
    >>> np.arctan2(y, x) * 180 / np.pi
    array([-135.,  -45.,   45.,  135.])
    
    Note the order of the parameters. `arctan2` is defined also when `x2` = 0
    and at several other special points, obtaining values in
    the range ``[-pi, pi]``:
    
    >>> np.arctan2([1., -1.], [0., 0.])
    array([ 1.57079633, -1.57079633])
    >>> np.arctan2([0., 0., np.inf], [+0., -0., np.inf])
    array([ 0.        ,  3.14159265,  0.78539816])"""
    return ndarray()
def arctanh(x, out=None):
    """arctanh(x[, out])
    
    Inverse hyperbolic tangent elementwise.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    out : ndarray
        Array of the same shape as `x`.
    
    See Also
    --------
    emath.arctanh
    
    Notes
    -----
    `arctanh` is a multivalued function: for each `x` there are infinitely
    many numbers `z` such that `tanh(z) = x`. The convention is to return the
    `z` whose imaginary part lies in `[-pi/2, pi/2]`.
    
    For real-valued input data types, `arctanh` always returns real output.
    For each value that cannot be expressed as a real number or infinity, it
    yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `arctanh` is a complex analytical function that
    has branch cuts `[-1, -inf]` and `[1, inf]` and is continuous from
    above on the former and from below on the latter.
    
    The inverse hyperbolic tangent is also known as `atanh` or ``tanh^-1``.
    
    References
    ----------
    .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
           10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/
    .. [2] Wikipedia, "Inverse hyperbolic function",
           http://en.wikipedia.org/wiki/Arctanh
    
    Examples
    --------
    >>> np.arctanh([0, -0.5])
    array([ 0.        , -0.54930614])"""
    return ndarray()
def argmax(a=None, axis=None):
    """
        Indices of the maximum values along an axis.
    
        Parameters
        ----------
        a : array_like
            Input array.
        axis : int, optional
            By default, the index is into the flattened array, otherwise
            along the specified axis.
    
        Returns
        -------
        index_array : ndarray of ints
            Array of indices into the array. It has the same shape as `a.shape`
            with the dimension along `axis` removed.
    
        See Also
        --------
        ndarray.argmax, argmin
        amax : The maximum value along a given axis.
        unravel_index : Convert a flat index into an index tuple.
    
        Notes
        -----
        In case of multiple occurrences of the maximum values, the indices
        corresponding to the first occurrence are returned.
    
        Examples
        --------
        >>> a = np.arange(6).reshape(2,3)
        >>> a
        array([[0, 1, 2],
               [3, 4, 5]])
        >>> np.argmax(a)
        5
        >>> np.argmax(a, axis=0)
        array([1, 1, 1])
        >>> np.argmax(a, axis=1)
        array([2, 2])
    
        >>> b = np.arange(6)
        >>> b[1] = 5
        >>> b
        array([0, 5, 2, 3, 4, 5])
        >>> np.argmax(b) # Only the first occurrence is returned.
        1
    
        """
    return ndarray()
def argmin(a=None, axis=None):
    """
        Return the indices of the minimum values along an axis.
    
        See Also
        --------
        argmax : Similar function.  Please refer to `numpy.argmax` for detailed
            documentation.
    
        """
    return None
def argpartition(a, kth=None, axis=-1, kind="introselect", order=None):
    """
        Perform an indirect partition along the given axis using the algorithm
        specified by the `kind` keyword. It returns an array of indices of the
        same shape as `a` that index data along the given axis in partitioned
        order.
    
        .. versionadded:: 1.8.0
    
        Parameters
        ----------
        a : array_like
            Array to sort.
        kth : int or sequence of ints
            Element index to partition by. The kth element will be in its final
            sorted position and all smaller elements will be moved before it and
            all larger elements behind it.
            The order all elements in the partitions is undefined.
            If provided with a sequence of kth it will partition all of them into
            their sorted position at once.
        axis : int or None, optional
            Axis along which to sort.  The default is -1 (the last axis). If None,
            the flattened array is used.
        kind : {'introselect'}, optional
            Selection algorithm. Default is 'introselect'
        order : list, optional
            When `a` is an array with fields defined, this argument specifies
            which fields to compare first, second, etc.  Not all fields need be
            specified.
    
        Returns
        -------
        index_array : ndarray, int
            Array of indices that partition `a` along the specified axis.
            In other words, ``a[index_array]`` yields a sorted `a`.
    
        See Also
        --------
        partition : Describes partition algorithms used.
        ndarray.partition : Inplace partition.
        argsort : Full indirect sort
    
        Notes
        -----
        See `partition` for notes on the different selection algorithms.
    
        Examples
        --------
        One dimensional array:
    
        >>> x = np.array([3, 4, 2, 1])
        >>> x[np.argpartition(x, 3)]
        array([2, 1, 3, 4])
        >>> x[np.argpartition(x, (1, 3))]
        array([1, 2, 3, 4])
    
        """
    return ndarray()
def argsort(a=None, axis=-1, kind="quicksort", order=None):
    """
        Returns the indices that would sort an array.
    
        Perform an indirect sort along the given axis using the algorithm specified
        by the `kind` keyword. It returns an array of indices of the same shape as
        `a` that index data along the given axis in sorted order.
    
        Parameters
        ----------
        a : array_like
            Array to sort.
        axis : int or None, optional
            Axis along which to sort.  The default is -1 (the last axis). If None,
            the flattened array is used.
        kind : {'quicksort', 'mergesort', 'heapsort'}, optional
            Sorting algorithm.
        order : list, optional
            When `a` is an array with fields defined, this argument specifies
            which fields to compare first, second, etc.  Not all fields need be
            specified.
    
        Returns
        -------
        index_array : ndarray, int
            Array of indices that sort `a` along the specified axis.
            In other words, ``a[index_array]`` yields a sorted `a`.
    
        See Also
        --------
        sort : Describes sorting algorithms used.
        lexsort : Indirect stable sort with multiple keys.
        ndarray.sort : Inplace sort.
        argpartition : Indirect partial sort.
    
        Notes
        -----
        See `sort` for notes on the different sorting algorithms.
    
        As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
        nan values. The enhanced sort order is documented in `sort`.
    
        Examples
        --------
        One dimensional array:
    
        >>> x = np.array([3, 1, 2])
        >>> np.argsort(x)
        array([1, 2, 0])
    
        Two-dimensional array:
    
        >>> x = np.array([[0, 3], [2, 2]])
        >>> x
        array([[0, 3],
               [2, 2]])
    
        >>> np.argsort(x, axis=0)
        array([[0, 1],
               [1, 0]])
    
        >>> np.argsort(x, axis=1)
        array([[0, 1],
               [0, 1]])
    
        Sorting with keys:
    
        >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
        >>> x
        array([(1, 0), (0, 1)],
              dtype=[('x', '<i4'), ('y', '<i4')])
    
        >>> np.argsort(x, order=('x','y'))
        array([1, 0])
    
        >>> np.argsort(x, order=('y','x'))
        array([0, 1])
    
        """
    return ndarray()
def argwhere(a):
    """
        Find the indices of array elements that are non-zero, grouped by element.
    
        Parameters
        ----------
        a : array_like
            Input data.
    
        Returns
        -------
        index_array : ndarray
            Indices of elements that are non-zero. Indices are grouped by element.
    
        See Also
        --------
        where, nonzero
    
        Notes
        -----
        ``np.argwhere(a)`` is the same as ``np.transpose(np.nonzero(a))``.
    
        The output of ``argwhere`` is not suitable for indexing arrays.
        For this purpose use ``where(a)`` instead.
    
        Examples
        --------
        >>> x = np.arange(6).reshape(2,3)
        >>> x
        array([[0, 1, 2],
               [3, 4, 5]])
        >>> np.argwhere(x>1)
        array([[0, 2],
               [1, 0],
               [1, 1],
               [1, 2]])
    
        """
    return ndarray()
def around(a=None, decimals=0, out=None):
    """
        Evenly round to the given number of decimals.
    
        Parameters
        ----------
        a : array_like
            Input data.
        decimals : int, optional
            Number of decimal places to round to (default: 0).  If
            decimals is negative, it specifies the number of positions to
            the left of the decimal point.
        out : ndarray, optional
            Alternative output array in which to place the result. It must have
            the same shape as the expected output, but the type of the output
            values will be cast if necessary. See `doc.ufuncs` (Section
            "Output arguments") for details.
    
        Returns
        -------
        rounded_array : ndarray
            An array of the same type as `a`, containing the rounded values.
            Unless `out` was specified, a new array is created.  A reference to
            the result is returned.
    
            The real and imaginary parts of complex numbers are rounded
            separately.  The result of rounding a float is a float.
    
        See Also
        --------
        ndarray.round : equivalent method
    
        ceil, fix, floor, rint, trunc
    
    
        Notes
        -----
        For values exactly halfway between rounded decimal values, Numpy
        rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
        -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due
        to the inexact representation of decimal fractions in the IEEE
        floating point standard [1]_ and errors introduced when scaling
        by powers of ten.
    
        References
        ----------
        .. [1] "Lecture Notes on the Status of  IEEE 754", William Kahan,
               http://www.cs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
        .. [2] "How Futile are Mindless Assessments of
               Roundoff in Floating-Point Computation?", William Kahan,
               http://www.cs.berkeley.edu/~wkahan/Mindless.pdf
    
        Examples
        --------
        >>> np.around([0.37, 1.64])
        array([ 0.,  2.])
        >>> np.around([0.37, 1.64], decimals=1)
        array([ 0.4,  1.6])
        >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
        array([ 0.,  2.,  2.,  4.,  4.])
        >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
        array([ 1,  2,  3, 11])
        >>> np.around([1,2,3,11], decimals=-1)
        array([ 0,  0,  0, 10])
    
        """
    return ndarray()
def array(object, dtype, copy, order, subok, ndmin):
    """array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)
    
        Create an array.
    
        Parameters
        ----------
        object : array_like
            An array, any object exposing the array interface, an
            object whose __array__ method returns an array, or any
            (nested) sequence.
        dtype : data-type, optional
            The desired data-type for the array.  If not given, then
            the type will be determined as the minimum type required
            to hold the objects in the sequence.  This argument can only
            be used to 'upcast' the array.  For downcasting, use the
            .astype(t) method.
        copy : bool, optional
            If true (default), then the object is copied.  Otherwise, a copy
            will only be made if __array__ returns a copy, if obj is a
            nested sequence, or if a copy is needed to satisfy any of the other
            requirements (`dtype`, `order`, etc.).
        order : {'C', 'F', 'A'}, optional
            Specify the order of the array.  If order is 'C' (default), then the
            array will be in C-contiguous order (last-index varies the
            fastest).  If order is 'F', then the returned array
            will be in Fortran-contiguous order (first-index varies the
            fastest).  If order is 'A', then the returned array may
            be in any order (either C-, Fortran-contiguous, or even
            discontiguous).
        subok : bool, optional
            If True, then sub-classes will be passed-through, otherwise
            the returned array will be forced to be a base-class array (default).
        ndmin : int, optional
            Specifies the minimum number of dimensions that the resulting
            array should have.  Ones will be pre-pended to the shape as
            needed to meet this requirement.
    
        Returns
        -------
        out : ndarray
            An array object satisfying the specified requirements.
    
        See Also
        --------
        empty, empty_like, zeros, zeros_like, ones, ones_like, fill
    
        Examples
        --------
        >>> np.array([1, 2, 3])
        array([1, 2, 3])
    
        Upcasting:
    
        >>> np.array([1, 2, 3.0])
        array([ 1.,  2.,  3.])
    
        More than one dimension:
    
        >>> np.array([[1, 2], [3, 4]])
        array([[1, 2],
               [3, 4]])
    
        Minimum dimensions 2:
    
        >>> np.array([1, 2, 3], ndmin=2)
        array([[1, 2, 3]])
    
        Type provided:
    
        >>> np.array([1, 2, 3], dtype=complex)
        array([ 1.+0.j,  2.+0.j,  3.+0.j])
    
        Data-type consisting of more than one element:
    
        >>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
        >>> x['a']
        array([1, 3])
    
        Creating an array from sub-classes:
    
        >>> np.array(np.mat('1 2; 3 4'))
        array([[1, 2],
               [3, 4]])
    
        >>> np.array(np.mat('1 2; 3 4'), subok=True)
        matrix([[1, 2],
                [3, 4]])"""
    return ndarray()
def array2string(a=None, max_line_width=None, precision=None, suppress_small=None, separator=" ", prefix="", style="<built-in function repr>", formatter=None):
    """
        Return a string representation of an array.
    
        Parameters
        ----------
        a : ndarray
            Input array.
        max_line_width : int, optional
            The maximum number of columns the string should span. Newline
            characters splits the string appropriately after array elements.
        precision : int, optional
            Floating point precision. Default is the current printing
            precision (usually 8), which can be altered using `set_printoptions`.
        suppress_small : bool, optional
            Represent very small numbers as zero. A number is "very small" if it
            is smaller than the current printing precision.
        separator : str, optional
            Inserted between elements.
        prefix : str, optional
            An array is typically printed as::
    
              'prefix(' + array2string(a) + ')'
    
            The length of the prefix string is used to align the
            output correctly.
        style : function, optional
            A function that accepts an ndarray and returns a string.  Used only
            when the shape of `a` is equal to ``()``, i.e. for 0-D arrays.
        formatter : dict of callables, optional
            If not None, the keys should indicate the type(s) that the respective
            formatting function applies to.  Callables should return a string.
            Types that are not specified (by their corresponding keys) are handled
            by the default formatters.  Individual types for which a formatter
            can be set are::
    
                - 'bool'
                - 'int'
                - 'timedelta' : a `numpy.timedelta64`
                - 'datetime' : a `numpy.datetime64`
                - 'float'
                - 'longfloat' : 128-bit floats
                - 'complexfloat'
                - 'longcomplexfloat' : composed of two 128-bit floats
                - 'numpy_str' : types `numpy.string_` and `numpy.unicode_`
                - 'str' : all other strings
    
            Other keys that can be used to set a group of types at once are::
    
                - 'all' : sets all types
                - 'int_kind' : sets 'int'
                - 'float_kind' : sets 'float' and 'longfloat'
                - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
                - 'str_kind' : sets 'str' and 'numpystr'
    
        Returns
        -------
        array_str : str
            String representation of the array.
    
        Raises
        ------
        TypeError
            if a callable in `formatter` does not return a string.
    
        See Also
        --------
        array_str, array_repr, set_printoptions, get_printoptions
    
        Notes
        -----
        If a formatter is specified for a certain type, the `precision` keyword is
        ignored for that type.
    
        Examples
        --------
        >>> x = np.array([1e-16,1,2,3])
        >>> print np.array2string(x, precision=2, separator=',',
        ...                       suppress_small=True)
        [ 0., 1., 2., 3.]
    
        >>> x  = np.arange(3.)
        >>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
        '[0.00 1.00 2.00]'
    
        >>> x  = np.arange(3)
        >>> np.array2string(x, formatter={'int':lambda x: hex(x)})
        '[0x0L 0x1L 0x2L]'
    
        """
    return str()
def array_equal(a1a2):
    """
        True if two arrays have the same shape and elements, False otherwise.
    
        Parameters
        ----------
        a1, a2 : array_like
            Input arrays.
    
        Returns
        -------
        b : bool
            Returns True if the arrays are equal.
    
        See Also
        --------
        allclose: Returns True if two arrays are element-wise equal within a
                  tolerance.
        array_equiv: Returns True if input arrays are shape consistent and all
                     elements equal.
    
        Examples
        --------
        >>> np.array_equal([1, 2], [1, 2])
        True
        >>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
        True
        >>> np.array_equal([1, 2], [1, 2, 3])
        False
        >>> np.array_equal([1, 2], [1, 4])
        False
    
        """
    return bool()
def array_equiv(a1a2):
    """
        Returns True if input arrays are shape consistent and all elements equal.
    
        Shape consistent means they are either the same shape, or one input array
        can be broadcasted to create the same shape as the other one.
    
        Parameters
        ----------
        a1, a2 : array_like
            Input arrays.
    
        Returns
        -------
        out : bool
            True if equivalent, False otherwise.
    
        Examples
        --------
        >>> np.array_equiv([1, 2], [1, 2])
        True
        >>> np.array_equiv([1, 2], [1, 3])
        False
    
        Showing the shape equivalence:
    
        >>> np.array_equiv([1, 2], [[1, 2], [1, 2]])
        True
        >>> np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])
        False
    
        >>> np.array_equiv([1, 2], [[1, 2], [1, 3]])
        False
    
        """
    return bool()
def array_repr(arr=None, max_line_width=None, precision=None, suppress_small=None):
    """
        Return the string representation of an array.
    
        Parameters
        ----------
        arr : ndarray
            Input array.
        max_line_width : int, optional
            The maximum number of columns the string should span. Newline
            characters split the string appropriately after array elements.
        precision : int, optional
            Floating point precision. Default is the current printing precision
            (usually 8), which can be altered using `set_printoptions`.
        suppress_small : bool, optional
            Represent very small numbers as zero, default is False. Very small
            is defined by `precision`, if the precision is 8 then
            numbers smaller than 5e-9 are represented as zero.
    
        Returns
        -------
        string : str
          The string representation of an array.
    
        See Also
        --------
        array_str, array2string, set_printoptions
    
        Examples
        --------
        >>> np.array_repr(np.array([1,2]))
        'array([1, 2])'
        >>> np.array_repr(np.ma.array([0.]))
        'MaskedArray([ 0.])'
        >>> np.array_repr(np.array([], np.int32))
        'array([], dtype=int32)'
    
        >>> x = np.array([1e-6, 4e-7, 2, 3])
        >>> np.array_repr(x, precision=6, suppress_small=True)
        'array([ 0.000001,  0.      ,  2.      ,  3.      ])'
    
        """
    return str()
def array_split(ary, indices_or_sections=0, axis=0):
    """
        Split an array into multiple sub-arrays.
    
        Please refer to the ``split`` documentation.  The only difference
        between these functions is that ``array_split`` allows
        `indices_or_sections` to be an integer that does *not* equally
        divide the axis.
    
        See Also
        --------
        split : Split array into multiple sub-arrays of equal size.
    
        Examples
        --------
        >>> x = np.arange(8.0)
        >>> np.array_split(x, 3)
            [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]
    
        """
    return None
def array_str(a=None, max_line_width=None, precision=None, suppress_small=None):
    """
        Return a string representation of the data in an array.
    
        The data in the array is returned as a single string.  This function is
        similar to `array_repr`, the difference being that `array_repr` also
        returns information on the kind of array and its data type.
    
        Parameters
        ----------
        a : ndarray
            Input array.
        max_line_width : int, optional
            Inserts newlines if text is longer than `max_line_width`.  The
            default is, indirectly, 75.
        precision : int, optional
            Floating point precision.  Default is the current printing precision
            (usually 8), which can be altered using `set_printoptions`.
        suppress_small : bool, optional
            Represent numbers "very close" to zero as zero; default is False.
            Very close is defined by precision: if the precision is 8, e.g.,
            numbers smaller (in absolute value) than 5e-9 are represented as
            zero.
    
        See Also
        --------
        array2string, array_repr, set_printoptions
    
        Examples
        --------
        >>> np.array_str(np.arange(3))
        '[0 1 2]'
    
        """
    return None
def asanyarray(a=None, dtype=None, order=None):
    """
        Convert the input to an ndarray, but pass ndarray subclasses through.
    
        Parameters
        ----------
        a : array_like
            Input data, in any form that can be converted to an array.  This
            includes scalars, 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') or column-major ('F') memory
            representation.  Defaults to 'C'.
    
        Returns
        -------
        out : ndarray or an ndarray subclass
            Array interpretation of `a`.  If `a` is an ndarray or a subclass
            of ndarray, it is returned as-is and no copy is performed.
    
        See Also
        --------
        asarray : Similar function which always returns ndarrays.
        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.asanyarray(a)
        array([1, 2])
    
        Instances of `ndarray` subclasses are passed through as-is:
    
        >>> a = np.matrix([1, 2])
        >>> np.asanyarray(a) is a
        True
    
        """
    return ndarray() if False else an()
def asarray(a=None, dtype=None, order=None):
    """
        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') or column-major ('F' for FORTRAN)
            memory representation.  Defaults to 'C'.
    
        Returns
        -------
        out : ndarray
            Array interpretation of `a`.  No copy is performed if the input
            is already an ndarray.  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.matrix, np.ndarray)
        True
        >>> a = np.matrix([[1, 2]])
        >>> np.asarray(a) is a
        False
        >>> np.asanyarray(a) is a
        True
    
        """
    return ndarray()
def asarray_chkfinite(a=None, dtype=None, order=None):
    """
        Convert the input to an array, checking for NaNs or Infs.
    
        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.  Success requires no NaNs or Infs.
        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') or column-major ('FORTRAN') memory
            representation.  Defaults to 'C'.
    
        Returns
        -------
        out : ndarray
            Array interpretation of `a`.  No copy is performed if the input
            is already an ndarray.  If `a` is a subclass of ndarray, a base
            class ndarray is returned.
    
        Raises
        ------
        ValueError
            Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity).
    
        See Also
        --------
        asarray : Create and array.
        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.
        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.  If all elements are finite
        ``asarray_chkfinite`` is identical to ``asarray``.
    
        >>> a = [1, 2]
        >>> np.asarray_chkfinite(a, dtype=float)
        array([1., 2.])
    
        Raises ValueError if array_like contains Nans or Infs.
    
        >>> a = [1, 2, np.inf]
        >>> try:
        ...     np.asarray_chkfinite(a)
        ... except ValueError:
        ...     print 'ValueError'
        ...
        ValueError
    
        """
    return ndarray()
def ascontiguousarray(a=None, dtype=None):
    """
        Return a contiguous array in memory (C order).
    
        Parameters
        ----------
        a : array_like
            Input array.
        dtype : str or dtype object, optional
            Data-type of returned array.
    
        Returns
        -------
        out : ndarray
            Contiguous array of same shape and content as `a`, with type `dtype`
            if specified.
    
        See Also
        --------
        asfortranarray : Convert input to an ndarray with column-major
                         memory order.
        require : Return an ndarray that satisfies requirements.
        ndarray.flags : Information about the memory layout of the array.
    
        Examples
        --------
        >>> x = np.arange(6).reshape(2,3)
        >>> np.ascontiguousarray(x, dtype=np.float32)
        array([[ 0.,  1.,  2.],
               [ 3.,  4.,  5.]], dtype=float32)
        >>> x.flags['C_CONTIGUOUS']
        True
    
        """
    return ndarray()
def asfarray(a=typenumpy.float64(), dtype=typenumpy.float64()):
    """
        Return an array converted to a float type.
    
        Parameters
        ----------
        a : array_like
            The input array.
        dtype : str or dtype object, optional
            Float type code to coerce input array `a`.  If `dtype` is one of the
            'int' dtypes, it is replaced with float64.
    
        Returns
        -------
        out : ndarray
            The input `a` as a float ndarray.
    
        Examples
        --------
        >>> np.asfarray([2, 3])
        array([ 2.,  3.])
        >>> np.asfarray([2, 3], dtype='float')
        array([ 2.,  3.])
        >>> np.asfarray([2, 3], dtype='int8')
        array([ 2.,  3.])
    
        """
    return ndarray()
def asfortranarray(a=None, dtype=None):
    """
        Return an array laid out in Fortran order in memory.
    
        Parameters
        ----------
        a : array_like
            Input array.
        dtype : str or dtype object, optional
            By default, the data-type is inferred from the input data.
    
        Returns
        -------
        out : ndarray
            The input `a` in Fortran, or column-major, order.
    
        See Also
        --------
        ascontiguousarray : Convert input to a contiguous (C order) array.
        asanyarray : Convert input to an ndarray with either row or
            column-major memory order.
        require : Return an ndarray that satisfies requirements.
        ndarray.flags : Information about the memory layout of the array.
    
        Examples
        --------
        >>> x = np.arange(6).reshape(2,3)
        >>> y = np.asfortranarray(x)
        >>> x.flags['F_CONTIGUOUS']
        False
        >>> y.flags['F_CONTIGUOUS']
        True
    
        """
    return ndarray()
def asmatrix(data=None, 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.
    
        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()
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 None
def atleast_1d():
    """
        Convert inputs to arrays with at least one dimension.
    
        Scalar inputs are converted to 1-dimensional arrays, whilst
        higher-dimensional inputs are preserved.
    
        Parameters
        ----------
        arys1, arys2, ... : array_like
            One or more input arrays.
    
        Returns
        -------
        ret : ndarray
            An array, or sequence of arrays, each with ``a.ndim >= 1``.
            Copies are made only if necessary.
    
        See Also
        --------
        atleast_2d, atleast_3d
    
        Examples
        --------
        >>> np.atleast_1d(1.0)
        array([ 1.])
    
        >>> x = np.arange(9.0).reshape(3,3)
        >>> np.atleast_1d(x)
        array([[ 0.,  1.,  2.],
               [ 3.,  4.,  5.],
               [ 6.,  7.,  8.]])
        >>> np.atleast_1d(x) is x
        True
    
        >>> np.atleast_1d(1, [3, 4])
        [array([1]), array([3, 4])]
    
        """
    return ndarray()
def atleast_2d():
    """
        View inputs as arrays with at least two dimensions.
    
        Parameters
        ----------
        arys1, arys2, ... : array_like
            One or more array-like sequences.  Non-array inputs are converted
            to arrays.  Arrays that already have two or more dimensions are
            preserved.
    
        Returns
        -------
        res, res2, ... : ndarray
            An array, or tuple of arrays, each with ``a.ndim >= 2``.
            Copies are avoided where possible, and views with two or more
            dimensions are returned.
    
        See Also
        --------
        atleast_1d, atleast_3d
    
        Examples
        --------
        >>> np.atleast_2d(3.0)
        array([[ 3.]])
    
        >>> x = np.arange(3.0)
        >>> np.atleast_2d(x)
        array([[ 0.,  1.,  2.]])
        >>> np.atleast_2d(x).base is x
        True
    
        >>> np.atleast_2d(1, [1, 2], [[1, 2]])
        [array([[1]]), array([[1, 2]]), array([[1, 2]])]
    
        """
    return ndarray()
def atleast_3d():
    """
        View inputs as arrays with at least three dimensions.
    
        Parameters
        ----------
        arys1, arys2, ... : array_like
            One or more array-like sequences.  Non-array inputs are converted to
            arrays.  Arrays that already have three or more dimensions are
            preserved.
    
        Returns
        -------
        res1, res2, ... : ndarray
            An array, or tuple of arrays, each with ``a.ndim >= 3``.  Copies are
            avoided where possible, and views with three or more dimensions are
            returned.  For example, a 1-D array of shape ``(N,)`` becomes a view
            of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a
            view of shape ``(M, N, 1)``.
    
        See Also
        --------
        atleast_1d, atleast_2d
    
        Examples
        --------
        >>> np.atleast_3d(3.0)
        array([[[ 3.]]])
    
        >>> x = np.arange(3.0)
        >>> np.atleast_3d(x).shape
        (1, 3, 1)
    
        >>> x = np.arange(12.0).reshape(4,3)
        >>> np.atleast_3d(x).shape
        (4, 3, 1)
        >>> np.atleast_3d(x).base is x
        True
    
        >>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):
        ...     print arr, arr.shape
        ...
        [[[1]
          [2]]] (1, 2, 1)
        [[[1]
          [2]]] (1, 2, 1)
        [[[1 2]]] (1, 1, 2)
    
        """
    return ndarray()
def average(a=False, axis=None, weights=None, returned=False):
    """
        Compute the weighted average along the specified axis.
    
        Parameters
        ----------
        a : array_like
            Array containing data to be averaged. If `a` is not an array, a
            conversion is attempted.
        axis : int, optional
            Axis along which to average `a`. If `None`, averaging is done over
            the flattened array.
        weights : array_like, optional
            An array of weights associated with the values in `a`. Each value in
            `a` contributes to the average according to its associated weight.
            The weights array can either be 1-D (in which case its length must be
            the size of `a` along the given axis) or of the same shape as `a`.
            If `weights=None`, then all data in `a` are assumed to have a
            weight equal to one.
        returned : bool, optional
            Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`)
            is returned, otherwise only the average is returned.
            If `weights=None`, `sum_of_weights` is equivalent to the number of
            elements over which the average is taken.
    
    
        Returns
        -------
        average, [sum_of_weights] : {array_type, double}
            Return the average along the specified axis. When returned is `True`,
            return a tuple with the average as the first element and the sum
            of the weights as the second element. The return type is `Float`
            if `a` is of integer type, otherwise it is of the same type as `a`.
            `sum_of_weights` is of the same type as `average`.
    
        Raises
        ------
        ZeroDivisionError
            When all weights along axis are zero. See `numpy.ma.average` for a
            version robust to this type of error.
        TypeError
            When the length of 1D `weights` is not the same as the shape of `a`
            along axis.
    
        See Also
        --------
        mean
    
        ma.average : average for masked arrays -- useful if your data contains
                     "missing" values
    
        Examples
        --------
        >>> data = range(1,5)
        >>> data
        [1, 2, 3, 4]
        >>> np.average(data)
        2.5
        >>> np.average(range(1,11), weights=range(10,0,-1))
        4.0
    
        >>> data = np.arange(6).reshape((3,2))
        >>> data
        array([[0, 1],
               [2, 3],
               [4, 5]])
        >>> np.average(data, axis=1, weights=[1./4, 3./4])
        array([ 0.75,  2.75,  4.75])
        >>> np.average(data, weights=[1./4, 3./4])
        Traceback (most recent call last):
        ...
        TypeError: Axis must be specified when shapes of a and weights differ.
    
        """
    return array_type()
def bartlett(M):
    """
        Return the Bartlett window.
    
        The Bartlett window is very similar to a triangular window, except
        that the end points are at zero.  It is often used in signal
        processing for tapering a signal, without generating too much
        ripple in the frequency domain.
    
        Parameters
        ----------
        M : int
            Number of points in the output window. If zero or less, an
            empty array is returned.
    
        Returns
        -------
        out : array
            The triangular window, with the maximum value normalized to one
            (the value one appears only if the number of samples is odd), with
            the first and last samples equal to zero.
    
        See Also
        --------
        blackman, hamming, hanning, kaiser
    
        Notes
        -----
        The Bartlett window is defined as
    
        .. math:: w(n) = \frac{2}{M-1} \left(
                  \frac{M-1}{2} - \left|n - \frac{M-1}{2}\right|
                  \right)
    
        Most references to the Bartlett window come from the signal
        processing literature, where it is used as one of many windowing
        functions for smoothing values.  Note that convolution with this
        window produces linear interpolation.  It is also known as an
        apodization (which means"removing the foot", i.e. smoothing
        discontinuities at the beginning and end of the sampled signal) or
        tapering function. The fourier transform of the Bartlett is the product
        of two sinc functions.
        Note the excellent discussion in Kanasewich.
    
        References
        ----------
        .. [1] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra",
               Biometrika 37, 1-16, 1950.
        .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
               The University of Alberta Press, 1975, pp. 109-110.
        .. [3] A.V. Oppenheim and R.W. Schafer, "Discrete-Time Signal
               Processing", Prentice-Hall, 1999, pp. 468-471.
        .. [4] Wikipedia, "Window function",
               http://en.wikipedia.org/wiki/Window_function
        .. [5] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
               "Numerical Recipes", Cambridge University Press, 1986, page 429.
    
    
        Examples
        --------
        >>> np.bartlett(12)
        array([ 0.        ,  0.18181818,  0.36363636,  0.54545455,  0.72727273,
                0.90909091,  0.90909091,  0.72727273,  0.54545455,  0.36363636,
                0.18181818,  0.        ])
    
        Plot the window and its frequency response (requires SciPy and matplotlib):
    
        >>> from numpy.fft import fft, fftshift
        >>> window = np.bartlett(51)
        >>> plt.plot(window)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Bartlett window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Amplitude")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Sample")
        <matplotlib.text.Text object at 0x...>
        >>> plt.show()
    
        >>> plt.figure()
        <matplotlib.figure.Figure object at 0x...>
        >>> A = fft(window, 2048) / 25.5
        >>> mag = np.abs(fftshift(A))
        >>> freq = np.linspace(-0.5, 0.5, len(A))
        >>> response = 20 * np.log10(mag)
        >>> response = np.clip(response, -100, 100)
        >>> plt.plot(freq, response)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Frequency response of Bartlett window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Magnitude [dB]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Normalized frequency [cycles per sample]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.axis('tight')
        (-0.5, 0.5, -100.0, ...)
        >>> plt.show()
    
        """
    return array()
def base_repr(number=0, base=2, padding=0):
    """
        Return a string representation of a number in the given base system.
    
        Parameters
        ----------
        number : int
            The value to convert. Only positive values are handled.
        base : int, optional
            Convert `number` to the `base` number system. The valid range is 2-36,
            the default value is 2.
        padding : int, optional
            Number of zeros padded on the left. Default is 0 (no padding).
    
        Returns
        -------
        out : str
            String representation of `number` in `base` system.
    
        See Also
        --------
        binary_repr : Faster version of `base_repr` for base 2.
    
        Examples
        --------
        >>> np.base_repr(5)
        '101'
        >>> np.base_repr(6, 5)
        '11'
        >>> np.base_repr(7, base=5, padding=3)
        '00012'
    
        >>> np.base_repr(10, base=16)
        'A'
        >>> np.base_repr(32, base=16)
        '20'
    
        """
    return str()
def bench(self=None, label="fast", verbose=1, extra_argv=None):
    """
            Run benchmarks for module using nose.
    
            Parameters
            ----------
            label : {'fast', 'full', '', attribute identifier}, optional
                Identifies the benchmarks to run. This can be a string to pass to
                the nosetests executable with the '-A' option, or one of several
                special values.  Special values are:
                * 'fast' - the default - which corresponds to the ``nosetests -A``
                  option of 'not slow'.
                * 'full' - fast (as above) and slow benchmarks as in the
                  'no -A' option to nosetests - this is the same as ''.
                * None or '' - run all tests.
                attribute_identifier - string passed directly to nosetests as '-A'.
            verbose : int, optional
                Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
            extra_argv : list, optional
                List with any extra arguments to pass to nosetests.
    
            Returns
            -------
            success : bool
                Returns True if running the benchmarks works, False if an error
                occurred.
    
            Notes
            -----
            Benchmarks are like tests, but have names starting with "bench" instead
            of "test", and can be found under the "benchmarks" sub-directory of the
            module.
    
            Each NumPy module exposes `bench` in its namespace to run all benchmarks
            for it.
    
            Examples
            --------
            >>> success = np.lib.bench() #doctest: +SKIP
            Running benchmarks for numpy.lib
            ...
            using 562341 items:
            unique:
            0.11
            unique1d:
            0.11
            ratio: 1.0
            nUnique: 56230 == 56230
            ...
            OK
    
            >>> success #doctest: +SKIP
            True
    
            """
    return bool()
def binary_repr(num=None, width=None):
    """
        Return the binary representation of the input number as a string.
    
        For negative numbers, if width is not given, a minus sign is added to the
        front. If width is given, the two's complement of the number is
        returned, with respect to that width.
    
        In a two's-complement system negative numbers are represented by the two's
        complement of the absolute value. This is the most common method of
        representing signed integers on computers [1]_. A N-bit two's-complement
        system can represent every integer in the range
        :math:`-2^{N-1}` to :math:`+2^{N-1}-1`.
    
        Parameters
        ----------
        num : int
            Only an integer decimal number can be used.
        width : int, optional
            The length of the returned string if `num` is positive, the length of
            the two's complement if `num` is negative.
    
        Returns
        -------
        bin : str
            Binary representation of `num` or two's complement of `num`.
    
        See Also
        --------
        base_repr: Return a string representation of a number in the given base
                   system.
    
        Notes
        -----
        `binary_repr` is equivalent to using `base_repr` with base 2, but about 25x
        faster.
    
        References
        ----------
        .. [1] Wikipedia, "Two's complement",
            http://en.wikipedia.org/wiki/Two's_complement
    
        Examples
        --------
        >>> np.binary_repr(3)
        '11'
        >>> np.binary_repr(-3)
        '-11'
        >>> np.binary_repr(3, width=4)
        '0011'
    
        The two's complement is returned when the input number is negative and
        width is specified:
    
        >>> np.binary_repr(-3, width=4)
        '1101'
    
        """
    return str()
def bincount(x, weights, minlength):
    """bincount(x, weights=None, minlength=None)
    
        Count number of occurrences of each value in array of non-negative ints.
    
        The number of bins (of size 1) is one larger than the largest value in
        `x`. If `minlength` is specified, there will be at least this number
        of bins in the output array (though it will be longer if necessary,
        depending on the contents of `x`).
        Each bin gives the number of occurrences of its index value in `x`.
        If `weights` is specified the input array is weighted by it, i.e. if a
        value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
        of ``out[n] += 1``.
    
        Parameters
        ----------
        x : array_like, 1 dimension, nonnegative ints
            Input array.
        weights : array_like, optional
            Weights, array of the same shape as `x`.
        minlength : int, optional
            .. versionadded:: 1.6.0
    
            A minimum number of bins for the output array.
    
        Returns
        -------
        out : ndarray of ints
            The result of binning the input array.
            The length of `out` is equal to ``np.amax(x)+1``.
    
        Raises
        ------
        ValueError
            If the input is not 1-dimensional, or contains elements with negative
            values, or if `minlength` is non-positive.
        TypeError
            If the type of the input is float or complex.
    
        See Also
        --------
        histogram, digitize, unique
    
        Examples
        --------
        >>> np.bincount(np.arange(5))
        array([1, 1, 1, 1, 1])
        >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
        array([1, 3, 1, 1, 0, 0, 0, 1])
    
        >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
        >>> np.bincount(x).size == np.amax(x)+1
        True
    
        The input array needs to be of integer dtype, otherwise a
        TypeError is raised:
    
        >>> np.bincount(np.arange(5, dtype=np.float))
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        TypeError: array cannot be safely cast to required type
    
        A possible use of ``bincount`` is to perform sums over
        variable-size chunks of an array, using the ``weights`` keyword.
    
        >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
        >>> x = np.array([0, 1, 1, 2, 2, 2])
        >>> np.bincount(x,  weights=w)
        array([ 0.3,  0.7,  1.1])"""
    return ndarray()
def bitwise_and(x1, x2, out=None):
    """bitwise_and(x1, x2[, out])
    
    Compute the bit-wise AND of two arrays element-wise.
    
    Computes the bit-wise AND of the underlying binary representation of
    the integers in the input arrays. This ufunc implements the C/Python
    operator ``&``.
    
    Parameters
    ----------
    x1, x2 : array_like
        Only integer types are handled (including booleans).
    
    Returns
    -------
    out : array_like
        Result.
    
    See Also
    --------
    logical_and
    bitwise_or
    bitwise_xor
    binary_repr :
        Return the binary representation of the input number as a string.
    
    Examples
    --------
    The number 13 is represented by ``00001101``.  Likewise, 17 is
    represented by ``00010001``.  The bit-wise AND of 13 and 17 is
    therefore ``000000001``, or 1:
    
    >>> np.bitwise_and(13, 17)
    1
    
    >>> np.bitwise_and(14, 13)
    12
    >>> np.binary_repr(12)
    '1100'
    >>> np.bitwise_and([14,3], 13)
    array([12,  1])
    
    >>> np.bitwise_and([11,7], [4,25])
    array([0, 1])
    >>> np.bitwise_and(np.array([2,5,255]), np.array([3,14,16]))
    array([ 2,  4, 16])
    >>> np.bitwise_and([True, True], [False, True])
    array([False,  True], dtype=bool)"""
    return ndarray()
def invert(x, out=None):
    """invert(x[, out])
    
    Compute bit-wise inversion, or bit-wise NOT, element-wise.
    
    Computes the bit-wise NOT of the underlying binary representation of
    the integers in the input arrays. This ufunc implements the C/Python
    operator ``~``.
    
    For signed integer inputs, the two's complement is returned.
    In a two's-complement system negative numbers are represented by the two's
    complement of the absolute value. This is the most common method of
    representing signed integers on computers [1]_. A N-bit two's-complement
    system can represent every integer in the range
    :math:`-2^{N-1}` to :math:`+2^{N-1}-1`.
    
    Parameters
    ----------
    x1 : array_like
        Only integer types are handled (including booleans).
    
    Returns
    -------
    out : array_like
        Result.
    
    See Also
    --------
    bitwise_and, bitwise_or, bitwise_xor
    logical_not
    binary_repr :
        Return the binary representation of the input number as a string.
    
    Notes
    -----
    `bitwise_not` is an alias for `invert`:
    
    >>> np.bitwise_not is np.invert
    True
    
    References
    ----------
    .. [1] Wikipedia, "Two's complement",
        http://en.wikipedia.org/wiki/Two's_complement
    
    Examples
    --------
    We've seen that 13 is represented by ``00001101``.
    The invert or bit-wise NOT of 13 is then:
    
    >>> np.invert(np.array([13], dtype=uint8))
    array([242], dtype=uint8)
    >>> np.binary_repr(x, width=8)
    '00001101'
    >>> np.binary_repr(242, width=8)
    '11110010'
    
    The result depends on the bit-width:
    
    >>> np.invert(np.array([13], dtype=uint16))
    array([65522], dtype=uint16)
    >>> np.binary_repr(x, width=16)
    '0000000000001101'
    >>> np.binary_repr(65522, width=16)
    '1111111111110010'
    
    When using signed integer types the result is the two's complement of
    the result for the unsigned type:
    
    >>> np.invert(np.array([13], dtype=int8))
    array([-14], dtype=int8)
    >>> np.binary_repr(-14, width=8)
    '11110010'
    
    Booleans are accepted as well:
    
    >>> np.invert(array([True, False]))
    array([False,  True], dtype=bool)"""
    return ndarray()
def bitwise_or(x1, x2, out=None):
    """bitwise_or(x1, x2[, out])
    
    Compute the bit-wise OR of two arrays element-wise.
    
    Computes the bit-wise OR of the underlying binary representation of
    the integers in the input arrays. This ufunc implements the C/Python
    operator ``|``.
    
    Parameters
    ----------
    x1, x2 : array_like
        Only integer types are handled (including booleans).
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    out : array_like
        Result.
    
    See Also
    --------
    logical_or
    bitwise_and
    bitwise_xor
    binary_repr :
        Return the binary representation of the input number as a string.
    
    Examples
    --------
    The number 13 has the binaray representation ``00001101``. Likewise,
    16 is represented by ``00010000``.  The bit-wise OR of 13 and 16 is
    then ``000111011``, or 29:
    
    >>> np.bitwise_or(13, 16)
    29
    >>> np.binary_repr(29)
    '11101'
    
    >>> np.bitwise_or(32, 2)
    34
    >>> np.bitwise_or([33, 4], 1)
    array([33,  5])
    >>> np.bitwise_or([33, 4], [1, 2])
    array([33,  6])
    
    >>> np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))
    array([  6,   5, 255])
    >>> np.array([2, 5, 255]) | np.array([4, 4, 4])
    array([  6,   5, 255])
    >>> np.bitwise_or(np.array([2, 5, 255, 2147483647L], dtype=np.int32),
    ...               np.array([4, 4, 4, 2147483647L], dtype=np.int32))
    array([         6,          5,        255, 2147483647])
    >>> np.bitwise_or([True, True], [False, True])
    array([ True,  True], dtype=bool)"""
    return ndarray()
def bitwise_xor(x1, x2, out=None):
    """bitwise_xor(x1, x2[, out])
    
    Compute the bit-wise XOR of two arrays element-wise.
    
    Computes the bit-wise XOR of the underlying binary representation of
    the integers in the input arrays. This ufunc implements the C/Python
    operator ``^``.
    
    Parameters
    ----------
    x1, x2 : array_like
        Only integer types are handled (including booleans).
    
    Returns
    -------
    out : array_like
        Result.
    
    See Also
    --------
    logical_xor
    bitwise_and
    bitwise_or
    binary_repr :
        Return the binary representation of the input number as a string.
    
    Examples
    --------
    The number 13 is represented by ``00001101``. Likewise, 17 is
    represented by ``00010001``.  The bit-wise XOR of 13 and 17 is
    therefore ``00011100``, or 28:
    
    >>> np.bitwise_xor(13, 17)
    28
    >>> np.binary_repr(28)
    '11100'
    
    >>> np.bitwise_xor(31, 5)
    26
    >>> np.bitwise_xor([31,3], 5)
    array([26,  6])
    
    >>> np.bitwise_xor([31,3], [5,6])
    array([26,  5])
    >>> np.bitwise_xor([True, True], [False, True])
    array([ True, False], dtype=bool)"""
    return ndarray()
def blackman(M):
    """
        Return the Blackman window.
    
        The Blackman window is a taper formed by using the the first three
        terms of a summation of cosines. It was designed to have close to the
        minimal leakage possible.  It is close to optimal, only slightly worse
        than a Kaiser window.
    
        Parameters
        ----------
        M : int
            Number of points in the output window. If zero or less, an empty
            array is returned.
    
        Returns
        -------
        out : ndarray
            The window, with the maximum value normalized to one (the value one
            appears only if the number of samples is odd).
    
        See Also
        --------
        bartlett, hamming, hanning, kaiser
    
        Notes
        -----
        The Blackman window is defined as
    
        .. math::  w(n) = 0.42 - 0.5 \cos(2\pi n/M) + 0.08 \cos(4\pi n/M)
    
        Most references to the Blackman window come from the signal processing
        literature, where it is used as one of many windowing functions for
        smoothing values.  It is also known as an apodization (which means
        "removing the foot", i.e. smoothing discontinuities at the beginning
        and end of the sampled signal) or tapering function. It is known as a
        "near optimal" tapering function, almost as good (by some measures)
        as the kaiser window.
    
        References
        ----------
        Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
        Dover Publications, New York.
    
        Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
        Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
    
        Examples
        --------
        >>> np.blackman(12)
        array([ -1.38777878e-17,   3.26064346e-02,   1.59903635e-01,
                 4.14397981e-01,   7.36045180e-01,   9.67046769e-01,
                 9.67046769e-01,   7.36045180e-01,   4.14397981e-01,
                 1.59903635e-01,   3.26064346e-02,  -1.38777878e-17])
    
    
        Plot the window and the frequency response:
    
        >>> from numpy.fft import fft, fftshift
        >>> window = np.blackman(51)
        >>> plt.plot(window)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Blackman window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Amplitude")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Sample")
        <matplotlib.text.Text object at 0x...>
        >>> plt.show()
    
        >>> plt.figure()
        <matplotlib.figure.Figure object at 0x...>
        >>> A = fft(window, 2048) / 25.5
        >>> mag = np.abs(fftshift(A))
        >>> freq = np.linspace(-0.5, 0.5, len(A))
        >>> response = 20 * np.log10(mag)
        >>> response = np.clip(response, -100, 100)
        >>> plt.plot(freq, response)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Frequency response of Blackman window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Magnitude [dB]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Normalized frequency [cycles per sample]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.axis('tight')
        (-0.5, 0.5, -100.0, ...)
        >>> plt.show()
    
        """
    return ndarray()
def bmat(obj=None, ldict=None, gdict=None):
    """
        Build a matrix object from a string, nested sequence, or array.
    
        Parameters
        ----------
        obj : str or array_like
            Input data.  Names of variables in the current scope may be
            referenced, even if `obj` is a string.
    
        Returns
        -------
        out : matrix
            Returns a matrix object, which is a specialized 2-D array.
    
        See Also
        --------
        matrix
    
        Examples
        --------
        >>> A = np.mat('1 1; 1 1')
        >>> B = np.mat('2 2; 2 2')
        >>> C = np.mat('3 4; 5 6')
        >>> D = np.mat('7 8; 9 0')
    
        All the following expressions construct the same block matrix:
    
        >>> np.bmat([[A, B], [C, D]])
        matrix([[1, 1, 2, 2],
                [1, 1, 2, 2],
                [3, 4, 7, 8],
                [5, 6, 9, 0]])
        >>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])
        matrix([[1, 1, 2, 2],
                [1, 1, 2, 2],
                [3, 4, 7, 8],
                [5, 6, 9, 0]])
        >>> np.bmat('A,B; C,D')
        matrix([[1, 1, 2, 2],
                [1, 1, 2, 2],
                [3, 4, 7, 8],
                [5, 6, 9, 0]])
    
        """
    return matrix()
class bool:
    __doc__ = str()
    def bit_length(self, _):
        """int.bit_length() -> int
        
        Number of bits necessary to represent self in binary.
        >>> bin(37)
        '0b100101'
        >>> (37).bit_length()
        6"""
        return None
    def conjugate(self, _):
        """Returns self, the complex conjugate of any int."""
        return None
    denominator = getset_descriptor()
    imag = getset_descriptor()
    numerator = getset_descriptor()
    real = getset_descriptor()
class bool_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class bool_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class broadcast:
    __doc__ = str()
    index = getset_descriptor()
    iters = getset_descriptor()
    nd = member_descriptor()
    def next(self, _):
        """x.next() -> the next value, or raise StopIteration"""
        return None
    numiter = member_descriptor()
    def reset(self, _):
        """reset()
        
            Reset the broadcasted result's iterator(s).
        
            Parameters
            ----------
            None
        
            Returns
            -------
            None
        
            Examples
            --------
            >>> x = np.array([1, 2, 3])
            >>> y = np.array([[4], [5], [6]]
            >>> b = np.broadcast(x, y)
            >>> b.index
            0
            >>> b.next(), b.next(), b.next()
            ((1, 4), (2, 4), (3, 4))
            >>> b.index
            3
            >>> b.reset()
            >>> b.index
            0"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
def broadcast_arrays():
    """
        Broadcast any number of arrays against each other.
    
        Parameters
        ----------
        `*args` : array_likes
            The arrays to broadcast.
    
        Returns
        -------
        broadcasted : list of arrays
            These arrays are views on the original arrays.  They are typically
            not contiguous.  Furthermore, more than one element of a
            broadcasted array may refer to a single memory location.  If you
            need to write to the arrays, make copies first.
    
        Examples
        --------
        >>> x = np.array([[1,2,3]])
        >>> y = np.array([[1],[2],[3]])
        >>> np.broadcast_arrays(x, y)
        [array([[1, 2, 3],
               [1, 2, 3],
               [1, 2, 3]]), array([[1, 1, 1],
               [2, 2, 2],
               [3, 3, 3]])]
    
        Here is a useful idiom for getting contiguous copies instead of
        non-contiguous views.
    
        >>> [np.array(a) for a in np.broadcast_arrays(x, y)]
        [array([[1, 2, 3],
               [1, 2, 3],
               [1, 2, 3]]), array([[1, 1, 1],
               [2, 2, 2],
               [3, 3, 3]])]
    
        """
    return list()
def busday_count(begindates, enddates, weekmask, holidays, busdaycal, out):
    """busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)
    
        Counts the number of valid days between `begindates` and
        `enddates`, not including the day of `enddates`.
    
        If ``enddates`` specifies a date value that is earlier than the
        corresponding ``begindates`` date value, the count will be negative.
    
        .. versionadded:: 1.7.0
    
        Parameters
        ----------
        begindates : array_like of datetime64[D]
            The array of the first dates for counting.
        enddates : array_like of datetime64[D]
            The array of the end dates for counting, which are excluded
            from the count themselves.
        weekmask : str or array_like of bool, optional
            A seven-element array indicating which of Monday through Sunday are
            valid days. May be specified as a length-seven list or array, like
            [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
            like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
            weekdays, optionally separated by white space. Valid abbreviations
            are: Mon Tue Wed Thu Fri Sat Sun
        holidays : array_like of datetime64[D], optional
            An array of dates to consider as invalid dates.  They may be
            specified in any order, and NaT (not-a-time) dates are ignored.
            This list is saved in a normalized form that is suited for
            fast calculations of valid days.
        busdaycal : busdaycalendar, optional
            A `busdaycalendar` object which specifies the valid days. If this
            parameter is provided, neither weekmask nor holidays may be
            provided.
        out : array of int, optional
            If provided, this array is filled with the result.
    
        Returns
        -------
        out : array of int
            An array with a shape from broadcasting ``begindates`` and ``enddates``
            together, containing the number of valid days between
            the begin and end dates.
    
        See Also
        --------
        busdaycalendar: An object that specifies a custom set of valid days.
        is_busday : Returns a boolean array indicating valid days.
        busday_offset : Applies an offset counted in valid days.
    
        Examples
        --------
        >>> # Number of weekdays in January 2011
        ... np.busday_count('2011-01', '2011-02')
        21
        >>> # Number of weekdays in 2011
        ...  np.busday_count('2011', '2012')
        260
        >>> # Number of Saturdays in 2011
        ... np.busday_count('2011', '2012', weekmask='Sat')
        53"""
    return array()
def busday_offset(dates, offsets, roll, weekmask, holidays, busdaycal, out):
    """busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)
    
        First adjusts the date to fall on a valid day according to
        the ``roll`` rule, then applies offsets to the given dates
        counted in valid days.
    
        .. versionadded:: 1.7.0
    
        Parameters
        ----------
        dates : array_like of datetime64[D]
            The array of dates to process.
        offsets : array_like of int
            The array of offsets, which is broadcast with ``dates``.
        roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional
            How to treat dates that do not fall on a valid day. The default
            is 'raise'.
    
              * 'raise' means to raise an exception for an invalid day.
              * 'nat' means to return a NaT (not-a-time) for an invalid day.
              * 'forward' and 'following' mean to take the first valid day
                later in time.
              * 'backward' and 'preceding' mean to take the first valid day
                earlier in time.
              * 'modifiedfollowing' means to take the first valid day
                later in time unless it is across a Month boundary, in which
                case to take the first valid day earlier in time.
              * 'modifiedpreceding' means to take the first valid day
                earlier in time unless it is across a Month boundary, in which
                case to take the first valid day later in time.
        weekmask : str or array_like of bool, optional
            A seven-element array indicating which of Monday through Sunday are
            valid days. May be specified as a length-seven list or array, like
            [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
            like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
            weekdays, optionally separated by white space. Valid abbreviations
            are: Mon Tue Wed Thu Fri Sat Sun
        holidays : array_like of datetime64[D], optional
            An array of dates to consider as invalid dates.  They may be
            specified in any order, and NaT (not-a-time) dates are ignored.
            This list is saved in a normalized form that is suited for
            fast calculations of valid days.
        busdaycal : busdaycalendar, optional
            A `busdaycalendar` object which specifies the valid days. If this
            parameter is provided, neither weekmask nor holidays may be
            provided.
        out : array of datetime64[D], optional
            If provided, this array is filled with the result.
    
        Returns
        -------
        out : array of datetime64[D]
            An array with a shape from broadcasting ``dates`` and ``offsets``
            together, containing the dates with offsets applied.
    
        See Also
        --------
        busdaycalendar: An object that specifies a custom set of valid days.
        is_busday : Returns a boolean array indicating valid days.
        busday_count : Counts how many valid days are in a half-open date range.
    
        Examples
        --------
        >>> # First business day in October 2011 (not accounting for holidays)
        ... np.busday_offset('2011-10', 0, roll='forward')
        numpy.datetime64('2011-10-03','D')
        >>> # Last business day in February 2012 (not accounting for holidays)
        ... np.busday_offset('2012-03', -1, roll='forward')
        numpy.datetime64('2012-02-29','D')
        >>> # Third Wednesday in January 2011
        ... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')
        numpy.datetime64('2011-01-19','D')
        >>> # 2012 Mother's Day in Canada and the U.S.
        ... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
        numpy.datetime64('2012-05-13','D')
    
        >>> # First business day on or after a date
        ... np.busday_offset('2011-03-20', 0, roll='forward')
        numpy.datetime64('2011-03-21','D')
        >>> np.busday_offset('2011-03-22', 0, roll='forward')
        numpy.datetime64('2011-03-22','D')
        >>> # First business day after a date
        ... np.busday_offset('2011-03-20', 1, roll='backward')
        numpy.datetime64('2011-03-21','D')
        >>> np.busday_offset('2011-03-22', 1, roll='backward')
        numpy.datetime64('2011-03-23','D')"""
    return array()
class busdaycalendar:
    __doc__ = str()
    holidays = getset_descriptor()
    weekmask = getset_descriptor()
class int8:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def byte_bounds(a):
    """
        Returns pointers to the end-points of an array.
    
        Parameters
        ----------
        a : ndarray
            Input array. It must conform to the Python-side of the array interface.
    
        Returns
        -------
        (low, high) : tuple of 2 integers
            The first integer is the first byte of the array, the second integer is
            just past the last byte of the array.  If `a` is not contiguous it
            will not use every byte between the (`low`, `high`) values.
    
        Examples
        --------
        >>> I = np.eye(2, dtype='f'); I.dtype
        dtype('float32')
        >>> low, high = np.byte_bounds(I)
        >>> high - low == I.size*I.itemsize
        True
        >>> I = np.eye(2, dtype='G'); I.dtype
        dtype('complex192')
        >>> low, high = np.byte_bounds(I)
        >>> high - low == I.size*I.itemsize
        True
    
        """
    return tuple()
class string_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    base = getset_descriptor()
    def capitalize(self, _):
        """S.capitalize() -> string
        
        Return a copy of the string S with only its first character
        capitalized."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> string
        
        Return S centered in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def conj(self, _):
        """None"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        string S[start:end].  Optional arguments start and end are interpreted
        as in slice notation."""
        return None
    data = getset_descriptor()
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> object
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    dtype = getset_descriptor()
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> object
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that is able to handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> string
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> string
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    imag = getset_descriptor()
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. uppercase characters may only follow uncased
        characters and lowercase characters only cased ones. Return False
        otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    itemsize = getset_descriptor()
    def join(self, iterable):
        """S.join(iterable) -> string
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> string
        
        Return S left-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> string
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> string or unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    real = getset_descriptor()
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> string
        
        Return a copy of string S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> string
        
        Return S right-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string, starting at the end of the string and working
        to the front.  If maxsplit is given, at most maxsplit splits are
        done. If sep is not specified or is None, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> string or unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are removed
        from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    strides = getset_descriptor()
    def strip(self, chars):
        """S.strip([chars]) -> string or unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> string
        
        Return a copy of the string S with uppercase characters
        converted to lowercase and vice versa."""
        return None
    def title(self, _):
        """S.title() -> string
        
        Return a titlecased version of S, i.e. words start with uppercase
        characters, all remaining cased characters have lowercase."""
        return None
    def translate(self, table, deletechars):
        """S.translate(table [,deletechars]) -> string
        
        Return a copy of the string S, where all characters occurring
        in the optional argument deletechars are removed, and the
        remaining characters have been mapped through the given
        translation table, which must be a string of length 256 or None.
        If the table argument is None, no translation is applied and
        the operation simply removes the characters in deletechars."""
        return None
    def upper(self, _):
        """S.upper() -> string
        
        Return a copy of the string S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> string
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width.  The string S is never truncated."""
        return None
c_ = CClass()
def can_cast(_from, totype, casting):
    """can_cast(from, totype, casting = 'safe')
    
        Returns True if cast between data types can occur according to the
        casting rule.  If from is a scalar or array scalar, also returns
        True if the scalar value can be cast without overflow or truncation
        to an integer.
    
        Parameters
        ----------
        from : dtype, dtype specifier, scalar, or array
            Data type, scalar, or array to cast from.
        totype : dtype or dtype specifier
            Data type to cast to.
        casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
            Controls what kind of data casting may occur.
    
              * 'no' means the data types should not be cast at all.
              * 'equiv' means only byte-order changes are allowed.
              * 'safe' means only casts which can preserve values are allowed.
              * 'same_kind' means only safe casts or casts within a kind,
                like float64 to float32, are allowed.
              * 'unsafe' means any data conversions may be done.
    
        Returns
        -------
        out : bool
            True if cast can occur according to the casting rule.
    
        See also
        --------
        dtype, result_type
    
        Examples
        --------
        Basic examples
    
        >>> np.can_cast(np.int32, np.int64)
        True
        >>> np.can_cast(np.float64, np.complex)
        True
        >>> np.can_cast(np.complex, np.float)
        False
    
        >>> np.can_cast('i8', 'f8')
        True
        >>> np.can_cast('i8', 'f4')
        False
        >>> np.can_cast('i4', 'S4')
        True
    
        Casting scalars
    
        >>> np.can_cast(100, 'i1')
        True
        >>> np.can_cast(150, 'i1')
        False
        >>> np.can_cast(150, 'u1')
        True
    
        >>> np.can_cast(3.5e100, np.float32)
        False
        >>> np.can_cast(1000.0, np.float32)
        True
    
        Array scalar checks the value, array does not
    
        >>> np.can_cast(np.array(1000.0), np.float32)
        True
        >>> np.can_cast(np.array([1000.0]), np.float32)
        False
    
        Using the casting rules
    
        >>> np.can_cast('i8', 'i8', 'no')
        True
        >>> np.can_cast('<i8', '>i8', 'no')
        False
    
        >>> np.can_cast('<i8', '>i8', 'equiv')
        True
        >>> np.can_cast('<i4', '>i8', 'equiv')
        False
    
        >>> np.can_cast('<i4', '>i8', 'safe')
        True
        >>> np.can_cast('<i8', '>i4', 'safe')
        False
    
        >>> np.can_cast('<i8', '>i4', 'same_kind')
        True
        >>> np.can_cast('<i8', '>u4', 'same_kind')
        False
    
        >>> np.can_cast('<i8', '>u4', 'unsafe')
        True"""
    return bool()
cast = _typedict()
class complex128:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def ceil(x, out=None):
    """ceil(x[, out])
    
    Return the ceiling of the input, element-wise.
    
    The ceil of the scalar `x` is the smallest integer `i`, such that
    `i >= x`.  It is often denoted as :math:`\lceil x \rceil`.
    
    Parameters
    ----------
    x : array_like
        Input data.
    
    Returns
    -------
    y : {ndarray, scalar}
        The ceiling of each element in `x`, with `float` dtype.
    
    See Also
    --------
    floor, trunc, rint
    
    Examples
    --------
    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
    >>> np.ceil(a)
    array([-1., -1., -0.,  1.,  2.,  2.,  2.])"""
    return None
class complex128:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class character:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class chararray:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    def all(self, axis=None, out=None):
        """a.all(axis=None, out=None)
        
            Returns True if all elements evaluate to True.
        
            Refer to `numpy.all` for full documentation.
        
            See Also
            --------
            numpy.all : equivalent function"""
        return None
    def any(self, axis=None, out=None):
        """a.any(axis=None, out=None)
        
            Returns True if any of the elements of `a` evaluate to True.
        
            Refer to `numpy.any` for full documentation.
        
            See Also
            --------
            numpy.any : equivalent function"""
        return None
    def argmax(self, axis=None, out=None):
        """a.argmax(axis=None, out=None)
        
            Return indices of the maximum values along the given axis.
        
            Refer to `numpy.argmax` for full documentation.
        
            See Also
            --------
            numpy.argmax : equivalent function"""
        return None
    def argmin(self, axis=None, out=None):
        """a.argmin(axis=None, out=None)
        
            Return indices of the minimum values along the given axis of `a`.
        
            Refer to `numpy.argmin` for detailed documentation.
        
            See Also
            --------
            numpy.argmin : equivalent function"""
        return None
    def argpartition(self, kth, axis=_1, kind=quickselect, order=None):
        """a.argpartition(kth, axis=-1, kind='quickselect', order=None)
        
            Returns the indices that would partition this array.
        
            Refer to `numpy.argpartition` for full documentation.
        
            .. versionadded:: 1.8.0
        
            See Also
            --------
            numpy.argpartition : equivalent function"""
        return None
    def argsort(self=None, axis=-1, kind="quicksort", order=None):
        """None"""
        return None
    def astype(self, dtype, order, casting, subok, copy):
        """a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
        
            Copy of the array, cast to a specified type.
        
            Parameters
            ----------
            dtype : str or dtype
                Typecode or data-type to which the array is cast.
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout order of the result.
                'C' means C order, 'F' means Fortran order, 'A'
                means 'F' order if all the arrays are Fortran contiguous,
                'C' order otherwise, and 'K' means as close to the
                order the array elements appear in memory as possible.
                Default is 'K'.
            casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
                Controls what kind of data casting may occur. Defaults to 'unsafe'
                for backwards compatibility.
        
                  * 'no' means the data types should not be cast at all.
                  * 'equiv' means only byte-order changes are allowed.
                  * 'safe' means only casts which can preserve values are allowed.
                  * 'same_kind' means only safe casts or casts within a kind,
                    like float64 to float32, are allowed.
                  * 'unsafe' means any data conversions may be done.
            subok : bool, optional
                If True, then sub-classes will be passed-through (default), otherwise
                the returned array will be forced to be a base-class array.
            copy : bool, optional
                By default, astype always returns a newly allocated array. If this
                is set to false, and the `dtype`, `order`, and `subok`
                requirements are satisfied, the input array is returned instead
                of a copy.
        
            Returns
            -------
            arr_t : ndarray
                Unless `copy` is False and the other conditions for returning the input
                array are satisfied (see description for `copy` input paramter), `arr_t`
                is a new array of the same shape as the input array, with dtype, order
                given by `dtype`, `order`.
        
            Raises
            ------
            ComplexWarning
                When casting from complex to float or int. To avoid this,
                one should use ``a.real.astype(t)``.
        
            Examples
            --------
            >>> x = np.array([1, 2, 2.5])
            >>> x
            array([ 1. ,  2. ,  2.5])
        
            >>> x.astype(int)
            array([1, 2, 2])"""
        return ndarray()
    base = getset_descriptor()
    def byteswap(self, inplace):
        """a.byteswap(inplace)
        
            Swap the bytes of the array elements
        
            Toggle between low-endian and big-endian data representation by
            returning a byteswapped array, optionally swapped in-place.
        
            Parameters
            ----------
            inplace : bool, optional
                If ``True``, swap bytes in-place, default is ``False``.
        
            Returns
            -------
            out : ndarray
                The byteswapped array. If `inplace` is ``True``, this is
                a view to self.
        
            Examples
            --------
            >>> A = np.array([1, 256, 8755], dtype=np.int16)
            >>> map(hex, A)
            ['0x1', '0x100', '0x2233']
            >>> A.byteswap(True)
            array([  256,     1, 13090], dtype=int16)
            >>> map(hex, A)
            ['0x100', '0x1', '0x3322']
        
            Arrays of strings are not swapped
        
            >>> A = np.array(['ceg', 'fac'])
            >>> A.byteswap()
            array(['ceg', 'fac'],
                  dtype='|S3')"""
        return ndarray()
    def capitalize(self, _):
        """
                Return a copy of `self` with only the first character of each element
                capitalized.
        
                See also
                --------
                char.capitalize
        
                """
        return None
    def center(self, width=" ", fillchar=" "):
        """
                Return a copy of `self` with its elements centered in a
                string of length `width`.
        
                See also
                --------
                center
                """
        return None
    def choose(self, choices, out=None, mode=_raise):
        """a.choose(choices, out=None, mode='raise')
        
            Use an index array to construct a new array from a set of choices.
        
            Refer to `numpy.choose` for full documentation.
        
            See Also
            --------
            numpy.choose : equivalent function"""
        return None
    def clip(self, a_min, a_max, out=None):
        """a.clip(a_min, a_max, out=None)
        
            Return an array whose values are limited to ``[a_min, a_max]``.
        
            Refer to `numpy.clip` for full documentation.
        
            See Also
            --------
            numpy.clip : equivalent function"""
        return None
    def compress(self, condition, axis=None, out=None):
        """a.compress(condition, axis=None, out=None)
        
            Return selected slices of this array along given axis.
        
            Refer to `numpy.compress` for full documentation.
        
            See Also
            --------
            numpy.compress : equivalent function"""
        return None
    def conj(self, _):
        """a.conj()
        
            Complex-conjugate all elements.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def conjugate(self, _):
        """a.conjugate()
        
            Return the complex conjugate, element-wise.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def copy(self, order):
        """a.copy(order='C')
        
            Return a copy of the array.
        
            Parameters
            ----------
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout of the copy. 'C' means C-order,
                'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
                'C' otherwise. 'K' means match the layout of `a` as closely
                as possible. (Note that this function and :func:numpy.copy are very
                similar, but have different default values for their order=
                arguments.)
        
            See also
            --------
            numpy.copy
            numpy.copyto
        
            Examples
            --------
            >>> x = np.array([[1,2,3],[4,5,6]], order='F')
        
            >>> y = x.copy()
        
            >>> x.fill(0)
        
            >>> x
            array([[0, 0, 0],
                   [0, 0, 0]])
        
            >>> y
            array([[1, 2, 3],
                   [4, 5, 6]])
        
            >>> y.flags['C_CONTIGUOUS']
            True"""
        return None
    def count(self, sub=None, start=0, end=None):
        """
                Returns an array with the number of non-overlapping occurrences of
                substring `sub` in the range [`start`, `end`].
        
                See also
                --------
                char.count
        
                """
        return None
    ctypes = getset_descriptor()
    def cumprod(self, axis=None, dtype=None, out=None):
        """a.cumprod(axis=None, dtype=None, out=None)
        
            Return the cumulative product of the elements along the given axis.
        
            Refer to `numpy.cumprod` for full documentation.
        
            See Also
            --------
            numpy.cumprod : equivalent function"""
        return None
    def cumsum(self, axis=None, dtype=None, out=None):
        """a.cumsum(axis=None, dtype=None, out=None)
        
            Return the cumulative sum of the elements along the given axis.
        
            Refer to `numpy.cumsum` for full documentation.
        
            See Also
            --------
            numpy.cumsum : equivalent function"""
        return None
    data = getset_descriptor()
    def decode(self=None, encoding=None, errors=None):
        """
                Calls `str.decode` element-wise.
        
                See also
                --------
                char.decode
        
                """
        return None
    def diagonal(self, offset=0, axis1=0, axis2=1):
        """a.diagonal(offset=0, axis1=0, axis2=1)
        
            Return specified diagonals.
        
            Refer to :func:`numpy.diagonal` for full documentation.
        
            See Also
            --------
            numpy.diagonal : equivalent function"""
        return None
    def dot(self, b, out=None):
        """a.dot(b, out=None)
        
            Dot product of two arrays.
        
            Refer to `numpy.dot` for full documentation.
        
            See Also
            --------
            numpy.dot : equivalent function
        
            Examples
            --------
            >>> a = np.eye(2)
            >>> b = np.ones((2, 2)) * 2
            >>> a.dot(b)
            array([[ 2.,  2.],
                   [ 2.,  2.]])
        
            This array method can be conveniently chained:
        
            >>> a.dot(b).dot(b)
            array([[ 8.,  8.],
                   [ 8.,  8.]])"""
        return None
    dtype = getset_descriptor()
    def dump(self, file):
        """a.dump(file)
        
            Dump a pickle of the array to the specified file.
            The array can be read back with pickle.load or numpy.load.
        
            Parameters
            ----------
            file : str
                A string naming the dump file."""
        return None
    def dumps(self, _):
        """a.dumps()
        
            Returns the pickle of the array as a string.
            pickle.loads or numpy.loads will convert the string back to an array.
        
            Parameters
            ----------
            None"""
        return None
    def encode(self=None, encoding=None, errors=None):
        """
                Calls `str.encode` element-wise.
        
                See also
                --------
                char.encode
        
                """
        return None
    def endswith(self, suffix=None, start=0, end=None):
        """
                Returns a boolean array which is `True` where the string element
                in `self` ends with `suffix`, otherwise `False`.
        
                See also
                --------
                char.endswith
        
                """
        return None
    def expandtabs(self=8, tabsize=8):
        """
                Return a copy of each string element where all tab characters are
                replaced by one or more spaces.
        
                See also
                --------
                char.expandtabs
        
                """
        return None
    def fill(self, value):
        """a.fill(value)
        
            Fill the array with a scalar value.
        
            Parameters
            ----------
            value : scalar
                All elements of `a` will be assigned this value.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.fill(0)
            >>> a
            array([0, 0])
            >>> a = np.empty(2)
            >>> a.fill(1)
            >>> a
            array([ 1.,  1.])"""
        return None
    def find(self, sub=None, start=0, end=None):
        """
                For each element, return the lowest index in the string where
                substring `sub` is found.
        
                See also
                --------
                char.find
        
                """
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def flatten(self, order):
        """a.flatten(order='C')
        
            Return a copy of the array collapsed into one dimension.
        
            Parameters
            ----------
            order : {'C', 'F', 'A'}, optional
                Whether to flatten in C (row-major), Fortran (column-major) order,
                or preserve the C/Fortran ordering from `a`.
                The default is 'C'.
        
            Returns
            -------
            y : ndarray
                A copy of the input array, flattened to one dimension.
        
            See Also
            --------
            ravel : Return a flattened array.
            flat : A 1-D flat iterator over the array.
        
            Examples
            --------
            >>> a = np.array([[1,2], [3,4]])
            >>> a.flatten()
            array([1, 2, 3, 4])
            >>> a.flatten('F')
            array([1, 3, 2, 4])"""
        return ndarray()
    def getfield(self, dtype, offset):
        """a.getfield(dtype, offset=0)
        
            Returns a field of the given array as a certain type.
        
            A field is a view of the array data with a given data-type. The values in
            the view are determined by the given type and the offset into the current
            array in bytes. The offset needs to be such that the view dtype fits in the
            array dtype; for example an array of dtype complex128 has 16-byte elements.
            If taking a view with a 32-bit integer (4 bytes), the offset needs to be
            between 0 and 12 bytes.
        
            Parameters
            ----------
            dtype : str or dtype
                The data type of the view. The dtype size of the view can not be larger
                than that of the array itself.
            offset : int
                Number of bytes to skip before beginning the element view.
        
            Examples
            --------
            >>> x = np.diag([1.+1.j]*2)
            >>> x[1, 1] = 2 + 4.j
            >>> x
            array([[ 1.+1.j,  0.+0.j],
                   [ 0.+0.j,  2.+4.j]])
            >>> x.getfield(np.float64)
            array([[ 1.,  0.],
                   [ 0.,  2.]])
        
            By choosing an offset of 8 bytes we can select the complex part of the
            array for our view:
        
            >>> x.getfield(np.float64, offset=8)
            array([[ 1.,  0.],
               [ 0.,  4.]])"""
        return array()
    imag = getset_descriptor()
    def index(self, sub=None, start=0, end=None):
        """
                Like `find`, but raises `ValueError` when the substring is not found.
        
                See also
                --------
                char.index
        
                """
        return None
    def isalnum(self, _):
        """
                Returns true for each element if all characters in the string
                are alphanumeric and there is at least one character, false
                otherwise.
        
                See also
                --------
                char.isalnum
        
                """
        return None
    def isalpha(self, _):
        """
                Returns true for each element if all characters in the string
                are alphabetic and there is at least one character, false
                otherwise.
        
                See also
                --------
                char.isalpha
        
                """
        return None
    def isdecimal(self, _):
        """
                For each element in `self`, return True if there are only
                decimal characters in the element.
        
                See also
                --------
                char.isdecimal
        
                """
        return None
    def isdigit(self, _):
        """
                Returns true for each element if all characters in the string are
                digits and there is at least one character, false otherwise.
        
                See also
                --------
                char.isdigit
        
                """
        return None
    def islower(self, _):
        """
                Returns true for each element if all cased characters in the
                string are lowercase and there is at least one cased character,
                false otherwise.
        
                See also
                --------
                char.islower
        
                """
        return None
    def isnumeric(self, _):
        """
                For each element in `self`, return True if there are only
                numeric characters in the element.
        
                See also
                --------
                char.isnumeric
        
                """
        return None
    def isspace(self, _):
        """
                Returns true for each element if there are only whitespace
                characters in the string and there is at least one character,
                false otherwise.
        
                See also
                --------
                char.isspace
        
                """
        return None
    def istitle(self, _):
        """
                Returns true for each element if the element is a titlecased
                string and there is at least one character, false otherwise.
        
                See also
                --------
                char.istitle
        
                """
        return None
    def isupper(self, _):
        """
                Returns true for each element if all cased characters in the
                string are uppercase and there is at least one character, false
                otherwise.
        
                See also
                --------
                char.isupper
        
                """
        return None
    def item(self, ESCargs):
        """a.item(*args)
        
            Copy an element of an array to a standard Python scalar and return it.
        
            Parameters
            ----------
            \*args : Arguments (variable number and type)
        
                * none: in this case, the method only works for arrays
                  with one element (`a.size == 1`), which element is
                  copied into a standard Python scalar object and returned.
        
                * int_type: this argument is interpreted as a flat index into
                  the array, specifying which element to copy and return.
        
                * tuple of int_types: functions as does a single int_type argument,
                  except that the argument is interpreted as an nd-index into the
                  array.
        
            Returns
            -------
            z : Standard Python scalar object
                A copy of the specified element of the array as a suitable
                Python scalar
        
            Notes
            -----
            When the data type of `a` is longdouble or clongdouble, item() returns
            a scalar array object because there is no available Python scalar that
            would not lose information. Void arrays return a buffer object for item(),
            unless fields are defined, in which case a tuple is returned.
        
            `item` is very similar to a[args], except, instead of an array scalar,
            a standard Python scalar is returned. This can be useful for speeding up
            access to elements of the array and doing arithmetic on elements of the
            array using Python's optimized math.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.item(3)
            2
            >>> x.item(7)
            5
            >>> x.item((0, 1))
            1
            >>> x.item((2, 2))
            3"""
        return Standard()
    def itemset(self, ESCargs):
        """a.itemset(*args)
        
            Insert scalar into an array (scalar is cast to array's dtype, if possible)
        
            There must be at least 1 argument, and define the last argument
            as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
            than ``a[args] = item``.  The item should be a scalar value and `args`
            must select a single item in the array `a`.
        
            Parameters
            ----------
            \*args : Arguments
                If one argument: a scalar, only used in case `a` is of size 1.
                If two arguments: the last argument is the value to be set
                and must be a scalar, the first argument specifies a single array
                element location. It is either an int or a tuple.
        
            Notes
            -----
            Compared to indexing syntax, `itemset` provides some speed increase
            for placing a scalar into a particular location in an `ndarray`,
            if you must do this.  However, generally this is discouraged:
            among other problems, it complicates the appearance of the code.
            Also, when using `itemset` (and `item`) inside a loop, be sure
            to assign the methods to a local variable to avoid the attribute
            look-up at each loop iteration.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.itemset(4, 0)
            >>> x.itemset((2, 2), 9)
            >>> x
            array([[3, 1, 7],
                   [2, 0, 3],
                   [8, 5, 9]])"""
        return None
    itemsize = getset_descriptor()
    def join(self, _):
        """
                Return a string which is the concatenation of the strings in the
                sequence `seq`.
        
                See also
                --------
                char.join
        
                """
        return None
    def ljust(self, width=" ", fillchar=" "):
        """
                Return an array with the elements of `self` left-justified in a
                string of length `width`.
        
                See also
                --------
                char.ljust
        
                """
        return None
    def lower(self, _):
        """
                Return an array with the elements of `self` converted to
                lowercase.
        
                See also
                --------
                char.lower
        
                """
        return None
    def lstrip(self=None, chars=None):
        """
                For each element in `self`, return a copy with the leading characters
                removed.
        
                See also
                --------
                char.lstrip
        
                """
        return None
    def max(self, axis=None, out=None):
        """a.max(axis=None, out=None)
        
            Return the maximum along a given axis.
        
            Refer to `numpy.amax` for full documentation.
        
            See Also
            --------
            numpy.amax : equivalent function"""
        return None
    def mean(self, axis=None, dtype=None, out=None):
        """a.mean(axis=None, dtype=None, out=None)
        
            Returns the average of the array elements along given axis.
        
            Refer to `numpy.mean` for full documentation.
        
            See Also
            --------
            numpy.mean : equivalent function"""
        return None
    def min(self, axis=None, out=None):
        """a.min(axis=None, out=None)
        
            Return the minimum along a given axis.
        
            Refer to `numpy.amin` for full documentation.
        
            See Also
            --------
            numpy.amin : equivalent function"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """arr.newbyteorder(new_order='S')
        
            Return the array with the same data viewed with a different byte order.
        
            Equivalent to::
        
                arr.view(arr.dtype.newbytorder(new_order))
        
            Changes are also made in all fields and sub-arrays of the array data
            type.
        
        
        
            Parameters
            ----------
            new_order : string, optional
                Byte order to force; a value from the byte order specifications
                above. `new_order` codes can be any of::
        
                 * 'S' - swap dtype from current to opposite endian
                 * {'<', 'L'} - little endian
                 * {'>', 'B'} - big endian
                 * {'=', 'N'} - native order
                 * {'|', 'I'} - ignore (no change to byte order)
        
                The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_arr : array
                New array object with the dtype reflecting given change to the
                byte order."""
        return array()
    def nonzero(self, _):
        """a.nonzero()
        
            Return the indices of the elements that are non-zero.
        
            Refer to `numpy.nonzero` for full documentation.
        
            See Also
            --------
            numpy.nonzero : equivalent function"""
        return None
    def partition(self, _):
        """
                Partition each element in `self` around `sep`.
        
                See also
                --------
                partition
                """
        return None
    def prod(self, axis=None, dtype=None, out=None):
        """a.prod(axis=None, dtype=None, out=None)
        
            Return the product of the array elements over the given axis
        
            Refer to `numpy.prod` for full documentation.
        
            See Also
            --------
            numpy.prod : equivalent function"""
        return None
    def ptp(self, axis=None, out=None):
        """a.ptp(axis=None, out=None)
        
            Peak to peak (maximum - minimum) value along a given axis.
        
            Refer to `numpy.ptp` for full documentation.
        
            See Also
            --------
            numpy.ptp : equivalent function"""
        return None
    def put(self, indices, values, mode=_raise):
        """a.put(indices, values, mode='raise')
        
            Set ``a.flat[n] = values[n]`` for all `n` in indices.
        
            Refer to `numpy.put` for full documentation.
        
            See Also
            --------
            numpy.put : equivalent function"""
        return None
    def ravel(self, order):
        """a.ravel([order])
        
            Return a flattened array.
        
            Refer to `numpy.ravel` for full documentation.
        
            See Also
            --------
            numpy.ravel : equivalent function
        
            ndarray.flat : a flat iterator on the array."""
        return None
    real = getset_descriptor()
    def repeat(self, repeats, axis=None):
        """a.repeat(repeats, axis=None)
        
            Repeat elements of an array.
        
            Refer to `numpy.repeat` for full documentation.
        
            See Also
            --------
            numpy.repeat : equivalent function"""
        return None
    def replace(self, old, new=None, count=None):
        """
                For each element in `self`, return a copy of the string with all
                occurrences of substring `old` replaced by `new`.
        
                See also
                --------
                char.replace
        
                """
        return None
    def reshape(self, shape, order=C):
        """a.reshape(shape, order='C')
        
            Returns an array containing the same data with a new shape.
        
            Refer to `numpy.reshape` for full documentation.
        
            See Also
            --------
            numpy.reshape : equivalent function"""
        return None
    def resize(self, new_shape, refcheck):
        """a.resize(new_shape, refcheck=True)
        
            Change shape and size of array in-place.
        
            Parameters
            ----------
            new_shape : tuple of ints, or `n` ints
                Shape of resized array.
            refcheck : bool, optional
                If False, reference count will not be checked. Default is True.
        
            Returns
            -------
            None
        
            Raises
            ------
            ValueError
                If `a` does not own its own data or references or views to it exist,
                and the data memory must be changed.
        
            SystemError
                If the `order` keyword argument is specified. This behaviour is a
                bug in NumPy.
        
            See Also
            --------
            resize : Return a new array with the specified shape.
        
            Notes
            -----
            This reallocates space for the data area if necessary.
        
            Only contiguous arrays (data elements consecutive in memory) can be
            resized.
        
            The purpose of the reference count check is to make sure you
            do not use this array as a buffer for another Python object and then
            reallocate the memory. However, reference counts can increase in
            other ways so if you are sure that you have not shared the memory
            for this array with another Python object, then you may safely set
            `refcheck` to False.
        
            Examples
            --------
            Shrinking an array: array is flattened (in the order that the data are
            stored in memory), resized, and reshaped:
        
            >>> a = np.array([[0, 1], [2, 3]], order='C')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [1]])
        
            >>> a = np.array([[0, 1], [2, 3]], order='F')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [2]])
        
            Enlarging an array: as above, but missing entries are filled with zeros:
        
            >>> b = np.array([[0, 1], [2, 3]])
            >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
            >>> b
            array([[0, 1, 2],
                   [3, 0, 0]])
        
            Referencing an array prevents resizing...
        
            >>> c = a
            >>> a.resize((1, 1))
            Traceback (most recent call last):
            ...
            ValueError: cannot resize an array that has been referenced ...
        
            Unless `refcheck` is False:
        
            >>> a.resize((1, 1), refcheck=False)
            >>> a
            array([[0]])
            >>> c
            array([[0]])"""
        return None
    def rfind(self, sub=None, start=0, end=None):
        """
                For each element in `self`, return the highest index in the string
                where substring `sub` is found, such that `sub` is contained
                within [`start`, `end`].
        
                See also
                --------
                char.rfind
        
                """
        return None
    def rindex(self, sub=None, start=0, end=None):
        """
                Like `rfind`, but raises `ValueError` when the substring `sub` is
                not found.
        
                See also
                --------
                char.rindex
        
                """
        return None
    def rjust(self, width=" ", fillchar=" "):
        """
                Return an array with the elements of `self`
                right-justified in a string of length `width`.
        
                See also
                --------
                char.rjust
        
                """
        return None
    def round(self, decimals=0, out=None):
        """a.round(decimals=0, out=None)
        
            Return `a` with each element rounded to the given number of decimals.
        
            Refer to `numpy.around` for full documentation.
        
            See Also
            --------
            numpy.around : equivalent function"""
        return None
    def rpartition(self, _):
        """
                Partition each element in `self` around `sep`.
        
                See also
                --------
                rpartition
                """
        return None
    def rsplit(self=None, sep=None, maxsplit=None):
        """
                For each element in `self`, return a list of the words in
                the string, using `sep` as the delimiter string.
        
                See also
                --------
                char.rsplit
        
                """
        return None
    def rstrip(self=None, chars=None):
        """
                For each element in `self`, return a copy with the trailing
                characters removed.
        
                See also
                --------
                char.rstrip
        
                """
        return None
    def searchsorted(self, v, side=left, sorter=None):
        """a.searchsorted(v, side='left', sorter=None)
        
            Find indices where elements of v should be inserted in a to maintain order.
        
            For full documentation, see `numpy.searchsorted`
        
            See Also
            --------
            numpy.searchsorted : equivalent function"""
        return None
    def setfield(self, val, dtype, offset):
        """a.setfield(val, dtype, offset=0)
        
            Put a value into a specified place in a field defined by a data-type.
        
            Place `val` into `a`'s field defined by `dtype` and beginning `offset`
            bytes into the field.
        
            Parameters
            ----------
            val : object
                Value to be placed in field.
            dtype : dtype object
                Data-type of the field in which to place `val`.
            offset : int, optional
                The number of bytes into the field at which to place `val`.
        
            Returns
            -------
            None
        
            See Also
            --------
            getfield
        
            Examples
            --------
            >>> x = np.eye(3)
            >>> x.getfield(np.float64)
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])
            >>> x.setfield(3, np.int32)
            >>> x.getfield(np.int32)
            array([[3, 3, 3],
                   [3, 3, 3],
                   [3, 3, 3]])
            >>> x
            array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
                   [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
                   [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
            >>> x.setfield(np.eye(3), np.int32)
            >>> x
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])"""
        return None
    def setflags(self, write, align, uic):
        """a.setflags(write=None, align=None, uic=None)
        
            Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
        
            These Boolean-valued flags affect how numpy interprets the memory
            area used by `a` (see Notes below). The ALIGNED flag can only
            be set to True if the data is actually aligned according to the type.
            The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE
            can only be set to True if the array owns its own memory, or the
            ultimate owner of the memory exposes a writeable buffer interface,
            or is a string. (The exception for string is made so that unpickling
            can be done without copying memory.)
        
            Parameters
            ----------
            write : bool, optional
                Describes whether or not `a` can be written to.
            align : bool, optional
                Describes whether or not `a` is aligned properly for its type.
            uic : bool, optional
                Describes whether or not `a` is a copy of another "base" array.
        
            Notes
            -----
            Array flags provide information about how the memory area used
            for the array is to be interpreted. There are 6 Boolean flags
            in use, only three of which can be changed by the user:
            UPDATEIFCOPY, WRITEABLE, and ALIGNED.
        
            WRITEABLE (W) the data area can be written to;
        
            ALIGNED (A) the data and strides are aligned appropriately for the hardware
            (as determined by the compiler);
        
            UPDATEIFCOPY (U) this array is a copy of some other array (referenced
            by .base). When this array is deallocated, the base array will be
            updated with the contents of this array.
        
            All flags can be accessed using their first (upper case) letter as well
            as the full name.
        
            Examples
            --------
            >>> y
            array([[3, 1, 7],
                   [2, 0, 0],
                   [8, 5, 9]])
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : True
              ALIGNED : True
              UPDATEIFCOPY : False
            >>> y.setflags(write=0, align=0)
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : False
              ALIGNED : False
              UPDATEIFCOPY : False
            >>> y.setflags(uic=1)
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: cannot set UPDATEIFCOPY flag to True"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def sort(self, axis, kind, order):
        """a.sort(axis=-1, kind='quicksort', order=None)
        
            Sort an array, in-place.
        
            Parameters
            ----------
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'quicksort', 'mergesort', 'heapsort'}, optional
                Sorting algorithm. Default is 'quicksort'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.sort : Return a sorted copy of an array.
            argsort : Indirect sort.
            lexsort : Indirect stable sort on multiple keys.
            searchsorted : Find elements in sorted array.
            partition: Partial sort.
        
            Notes
            -----
            See ``sort`` for notes on the different sorting algorithms.
        
            Examples
            --------
            >>> a = np.array([[1,4], [3,1]])
            >>> a.sort(axis=1)
            >>> a
            array([[1, 4],
                   [1, 3]])
            >>> a.sort(axis=0)
            >>> a
            array([[1, 3],
                   [1, 4]])
        
            Use the `order` keyword to specify a field to use when sorting a
            structured array:
        
            >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
            >>> a.sort(order='y')
            >>> a
            array([('c', 1), ('a', 2)],
                  dtype=[('x', '|S1'), ('y', '<i4')])"""
        return None
    def split(self=None, sep=None, maxsplit=None):
        """
                For each element in `self`, return a list of the words in the
                string, using `sep` as the delimiter string.
        
                See also
                --------
                char.split
        
                """
        return None
    def splitlines(self=None, keepends=None):
        """
                For each element in `self`, return a list of the lines in the
                element, breaking at line boundaries.
        
                See also
                --------
                char.splitlines
        
                """
        return None
    def squeeze(self, axis=None):
        """a.squeeze(axis=None)
        
            Remove single-dimensional entries from the shape of `a`.
        
            Refer to `numpy.squeeze` for full documentation.
        
            See Also
            --------
            numpy.squeeze : equivalent function"""
        return None
    def startswith(self, prefix=None, start=0, end=None):
        """
                Returns a boolean array which is `True` where the string element
                in `self` starts with `prefix`, otherwise `False`.
        
                See also
                --------
                char.startswith
        
                """
        return None
    def std(self, axis=None, dtype=None, out=None, ddof=0):
        """a.std(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the standard deviation of the array elements along given axis.
        
            Refer to `numpy.std` for full documentation.
        
            See Also
            --------
            numpy.std : equivalent function"""
        return None
    strides = getset_descriptor()
    def strip(self=None, chars=None):
        """
                For each element in `self`, return a copy with the leading and
                trailing characters removed.
        
                See also
                --------
                char.strip
        
                """
        return None
    def sum(self, axis=None, dtype=None, out=None):
        """a.sum(axis=None, dtype=None, out=None)
        
            Return the sum of the array elements over the given axis.
        
            Refer to `numpy.sum` for full documentation.
        
            See Also
            --------
            numpy.sum : equivalent function"""
        return None
    def swapaxes(self, axis1, axis2):
        """a.swapaxes(axis1, axis2)
        
            Return a view of the array with `axis1` and `axis2` interchanged.
        
            Refer to `numpy.swapaxes` for full documentation.
        
            See Also
            --------
            numpy.swapaxes : equivalent function"""
        return None
    def swapcase(self, _):
        """
                For each element in `self`, return a copy of the string with
                uppercase characters converted to lowercase and vice versa.
        
                See also
                --------
                char.swapcase
        
                """
        return None
    def take(self, indices, axis=None, out=None, mode=_raise):
        """a.take(indices, axis=None, out=None, mode='raise')
        
            Return an array formed from the elements of `a` at the given indices.
        
            Refer to `numpy.take` for full documentation.
        
            See Also
            --------
            numpy.take : equivalent function"""
        return None
    def title(self, _):
        """
                For each element in `self`, return a titlecased version of the
                string: words start with uppercase characters, all remaining cased
                characters are lowercase.
        
                See also
                --------
                char.title
        
                """
        return None
    def tofile(self, fid, sep, format):
        """a.tofile(fid, sep="", format="%s")
        
            Write array to a file as text or binary (default).
        
            Data is always written in 'C' order, independent of the order of `a`.
            The data produced by this method can be recovered using the function
            fromfile().
        
            Parameters
            ----------
            fid : file or str
                An open file object, or a string containing a filename.
            sep : str
                Separator between array items for text output.
                If "" (empty), a binary file is written, equivalent to
                ``file.write(a.tostring())``.
            format : str
                Format string for text file output.
                Each entry in the array is formatted to text by first converting
                it to the closest Python type, and then using "format" % item.
        
            Notes
            -----
            This is a convenience function for quick storage of array data.
            Information on endianness and precision is lost, so this method is not a
            good choice for files intended to archive data or transport data between
            machines with different endianness. Some of these problems can be overcome
            by outputting the data as text files, at the expense of speed and file
            size."""
        return None
    def tolist(self, _):
        """a.tolist()
        
            Return the array as a (possibly nested) list.
        
            Return a copy of the array data as a (nested) Python list.
            Data items are converted to the nearest compatible Python type.
        
            Parameters
            ----------
            none
        
            Returns
            -------
            y : list
                The possibly nested list of array elements.
        
            Notes
            -----
            The array may be recreated, ``a = np.array(a.tolist())``.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.tolist()
            [1, 2]
            >>> a = np.array([[1, 2], [3, 4]])
            >>> list(a)
            [array([1, 2]), array([3, 4])]
            >>> a.tolist()
            [[1, 2], [3, 4]]"""
        return list()
    def tostring(self, order):
        """a.tostring(order='C')
        
            Construct a Python string containing the raw data bytes in the array.
        
            Constructs a Python string showing a copy of the raw contents of
            data memory. The string can be produced in either 'C' or 'Fortran',
            or 'Any' order (the default is 'C'-order). 'Any' order means C-order
            unless the F_CONTIGUOUS flag in the array is set, in which case it
            means 'Fortran' order.
        
            Parameters
            ----------
            order : {'C', 'F', None}, optional
                Order of the data for multidimensional arrays:
                C, Fortran, or the same as for the original array.
        
            Returns
            -------
            s : str
                A Python string exhibiting a copy of `a`'s raw data.
        
            Examples
            --------
            >>> x = np.array([[0, 1], [2, 3]])
            >>> x.tostring()
            '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
            >>> x.tostring('C') == x.tostring()
            True
            >>> x.tostring('F')
            '\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'"""
        return str()
    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
        """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
        
            Return the sum along diagonals of the array.
        
            Refer to `numpy.trace` for full documentation.
        
            See Also
            --------
            numpy.trace : equivalent function"""
        return None
    def translate(self, table=None, deletechars=None):
        """
                For each element in `self`, return a copy of the string where
                all characters occurring in the optional argument
                `deletechars` are removed, and the remaining characters have
                been mapped through the given translation table.
        
                See also
                --------
                char.translate
        
                """
        return None
    def transpose(self, axes):
        """a.transpose(*axes)
        
            Returns a view of the array with axes transposed.
        
            For a 1-D array, this has no effect. (To change between column and
            row vectors, first cast the 1-D array into a matrix object.)
            For a 2-D array, this is the usual matrix transpose.
            For an n-D array, if axes are given, their order indicates how the
            axes are permuted (see Examples). If axes are not provided and
            ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
            ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
        
            Parameters
            ----------
            axes : None, tuple of ints, or `n` ints
        
             * None or no argument: reverses the order of the axes.
        
             * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
               `i`-th axis becomes `a.transpose()`'s `j`-th axis.
        
             * `n` ints: same as an n-tuple of the same ints (this form is
               intended simply as a "convenience" alternative to the tuple form)
        
            Returns
            -------
            out : ndarray
                View of `a`, with axes suitably permuted.
        
            See Also
            --------
            ndarray.T : Array property returning the array transposed.
        
            Examples
            --------
            >>> a = np.array([[1, 2], [3, 4]])
            >>> a
            array([[1, 2],
                   [3, 4]])
            >>> a.transpose()
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose((1, 0))
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose(1, 0)
            array([[1, 3],
                   [2, 4]])"""
        return ndarray()
    def upper(self, _):
        """
                Return an array with the elements of `self` converted to
                uppercase.
        
                See also
                --------
                char.upper
        
                """
        return None
    def var(self, axis=None, dtype=None, out=None, ddof=0):
        """a.var(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the variance of the array elements, along given axis.
        
            Refer to `numpy.var` for full documentation.
        
            See Also
            --------
            numpy.var : equivalent function"""
        return None
    def view(self, dtype, type):
        """a.view(dtype=None, type=None)
        
            New view of array with the same data.
        
            Parameters
            ----------
            dtype : data-type or ndarray sub-class, optional
                Data-type descriptor of the returned view, e.g., float32 or int16. The
                default, None, results in the view having the same data-type as `a`.
                This argument can also be specified as an ndarray sub-class, which
                then specifies the type of the returned object (this is equivalent to
                setting the ``type`` parameter).
            type : Python type, optional
                Type of the returned view, e.g., ndarray or matrix.  Again, the
                default None results in type preservation.
        
            Notes
            -----
            ``a.view()`` is used two different ways:
        
            ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
            of the array's memory with a different data-type.  This can cause a
            reinterpretation of the bytes of memory.
        
            ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
            returns an instance of `ndarray_subclass` that looks at the same array
            (same shape, dtype, etc.)  This does not cause a reinterpretation of the
            memory.
        
            For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
            bytes per entry than the previous dtype (for example, converting a
            regular array to a structured array), then the behavior of the view
            cannot be predicted just from the superficial appearance of ``a`` (shown
            by ``print(a)``). It also depends on exactly how ``a`` is stored in
            memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
            defined as a slice or transpose, etc., the view may give different
            results.
        
        
            Examples
            --------
            >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
        
            Viewing array data using a different type and dtype:
        
            >>> y = x.view(dtype=np.int16, type=np.matrix)
            >>> y
            matrix([[513]], dtype=int16)
            >>> print type(y)
            <class 'numpy.matrixlib.defmatrix.matrix'>
        
            Creating a view on a structured array so it can be used in calculations
        
            >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
            >>> xv = x.view(dtype=np.int8).reshape(-1,2)
            >>> xv
            array([[1, 2],
                   [3, 4]], dtype=int8)
            >>> xv.mean(0)
            array([ 2.,  3.])
        
            Making changes to the view changes the underlying array
        
            >>> xv[0,1] = 20
            >>> print x
            [(1, 20) (3, 4)]
        
            Using a view to convert an array to a record array:
        
            >>> z = x.view(np.recarray)
            >>> z.a
            array([1], dtype=int8)
        
            Views share data:
        
            >>> x[0] = (9, 10)
            >>> z[0]
            (9, 10)
        
            Views that change the dtype size (bytes per entry) should normally be
            avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
        
            >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
            >>> y = x[:, 0:2]
            >>> y
            array([[1, 2],
                   [4, 5]], dtype=int16)
            >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: new type not compatible with array.
            >>> z = y.copy()
            >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
            array([[(1, 2)],
                   [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])"""
        return None
    def zfill(self, _):
        """
                Return the numeric string left-filled with zeros in a string of
                length `width`.
        
                See also
                --------
                char.zfill
        
                """
        return None
def choose(a, choices="raise", out=None, mode="raise"):
    """
        Construct an array from an index array and a set of arrays to choose from.
    
        First of all, if confused or uncertain, definitely look at the Examples -
        in its full generality, this function is less simple than it might
        seem from the following code description (below ndi =
        `numpy.lib.index_tricks`):
    
        ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.
    
        But this omits some subtleties.  Here is a fully general summary:
    
        Given an "index" array (`a`) of integers and a sequence of `n` arrays
        (`choices`), `a` and each choice array are first broadcast, as necessary,
        to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =
        0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
        for each `i`.  Then, a new array with shape ``Ba.shape`` is created as
        follows:
    
        * if ``mode=raise`` (the default), then, first of all, each element of
          `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that
          `i` (in that range) is the value at the `(j0, j1, ..., jm)` position
          in `Ba` - then the value at the same position in the new array is the
          value in `Bchoices[i]` at that same position;
    
        * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)
          integer; modular arithmetic is used to map integers outside the range
          `[0, n-1]` back into that range; and then the new array is constructed
          as above;
    
        * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)
          integer; negative integers are mapped to 0; values greater than `n-1`
          are mapped to `n-1`; and then the new array is constructed as above.
    
        Parameters
        ----------
        a : int array
            This array must contain integers in `[0, n-1]`, where `n` is the number
            of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any
            integers are permissible.
        choices : sequence of arrays
            Choice arrays. `a` and all of the choices must be broadcastable to the
            same shape.  If `choices` is itself an array (not recommended), then
            its outermost dimension (i.e., the one corresponding to
            ``choices.shape[0]``) is taken as defining the "sequence".
        out : array, optional
            If provided, the result will be inserted into this array. It should
            be of the appropriate shape and dtype.
        mode : {'raise' (default), 'wrap', 'clip'}, optional
            Specifies how indices outside `[0, n-1]` will be treated:
    
              * 'raise' : an exception is raised
              * 'wrap' : value becomes value mod `n`
              * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1
    
        Returns
        -------
        merged_array : array
            The merged result.
    
        Raises
        ------
        ValueError: shape mismatch
            If `a` and each choice array are not all broadcastable to the same
            shape.
    
        See Also
        --------
        ndarray.choose : equivalent method
    
        Notes
        -----
        To reduce the chance of misinterpretation, even though the following
        "abuse" is nominally supported, `choices` should neither be, nor be
        thought of as, a single array, i.e., the outermost sequence-like container
        should be either a list or a tuple.
    
        Examples
        --------
    
        >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
        ...   [20, 21, 22, 23], [30, 31, 32, 33]]
        >>> np.choose([2, 3, 1, 0], choices
        ... # the first element of the result will be the first element of the
        ... # third (2+1) "array" in choices, namely, 20; the second element
        ... # will be the second element of the fourth (3+1) choice array, i.e.,
        ... # 31, etc.
        ... )
        array([20, 31, 12,  3])
        >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
        array([20, 31, 12,  3])
        >>> # because there are 4 choice arrays
        >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
        array([20,  1, 12,  3])
        >>> # i.e., 0
    
        A couple examples illustrating how choose broadcasts:
    
        >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
        >>> choices = [-10, 10]
        >>> np.choose(a, choices)
        array([[ 10, -10,  10],
               [-10,  10, -10],
               [ 10, -10,  10]])
    
        >>> # With thanks to Anne Archibald
        >>> a = np.array([0, 1]).reshape((2,1,1))
        >>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
        >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
        >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
        array([[[ 1,  1,  1,  1,  1],
                [ 2,  2,  2,  2,  2],
                [ 3,  3,  3,  3,  3]],
               [[-1, -2, -3, -4, -5],
                [-1, -2, -3, -4, -5],
                [-1, -2, -3, -4, -5]]])
    
        """
    return array()
def clip(a, a_min, a_max=None, out=None):
    """
        Clip (limit) the values in an array.
    
        Given an interval, values outside the interval are clipped to
        the interval edges.  For example, if an interval of ``[0, 1]``
        is specified, values smaller than 0 become 0, and values larger
        than 1 become 1.
    
        Parameters
        ----------
        a : array_like
            Array containing elements to clip.
        a_min : scalar or array_like
            Minimum value.
        a_max : scalar or array_like
            Maximum value.  If `a_min` or `a_max` are array_like, then they will
            be broadcasted to the shape of `a`.
        out : ndarray, optional
            The results will be placed in this array. It may be the input
            array for in-place clipping.  `out` must be of the right shape
            to hold the output.  Its type is preserved.
    
        Returns
        -------
        clipped_array : ndarray
            An array with the elements of `a`, but where values
            < `a_min` are replaced with `a_min`, and those > `a_max`
            with `a_max`.
    
        See Also
        --------
        numpy.doc.ufuncs : Section "Output arguments"
    
        Examples
        --------
        >>> a = np.arange(10)
        >>> np.clip(a, 1, 8)
        array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
        >>> a
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
        >>> np.clip(a, 3, 6, out=a)
        array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
        >>> a = np.arange(10)
        >>> a
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
        >>> np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8)
        array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])
    
        """
    return ndarray()
class complex256:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class complex256:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
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
        --------
        hstack, vstack, concatenate
    
        Notes
        -----
        This function is equivalent to ``np.vstack(tup).T``.
    
        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]])
    
        """
    return _2_D()
def common_type():
    """
        Return a scalar type which is common to the input arrays.
    
        The return type will always be an inexact (i.e. floating point) scalar
        type, even if all the arrays are integer arrays. If one of the inputs is
        an integer array, the minimum precision type that is returned is a
        64-bit floating point dtype.
    
        All input arrays can be safely cast to the returned dtype without loss
        of information.
    
        Parameters
        ----------
        array1, array2, ... : ndarrays
            Input arrays.
    
        Returns
        -------
        out : data type code
            Data type code.
    
        See Also
        --------
        dtype, mintypecode
    
        Examples
        --------
        >>> np.common_type(np.arange(2, dtype=np.float32))
        <type 'numpy.float32'>
        >>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
        <type 'numpy.float64'>
        >>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
        <type 'numpy.complex128'>
    
        """
    return data()
def compare_chararrays():
    """None"""
    return None
class complex:
    __doc__ = str()
    def conjugate(self, _):
        """complex.conjugate() -> complex
        
        Return the complex conjugate of its argument. (3-4j).conjugate() == 3+4j."""
        return None
    imag = member_descriptor()
    real = member_descriptor()
class complex128:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class complex256:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class complex64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class complex128:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class complexfloating:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def compress(condition, a=None, axis=None, out=None):
    """
        Return selected slices of an array along given axis.
    
        When working along a given axis, a slice along that axis is returned in
        `output` for each index where `condition` evaluates to True. When
        working on a 1-D array, `compress` is equivalent to `extract`.
    
        Parameters
        ----------
        condition : 1-D array of bools
            Array that selects which entries to return. If len(condition)
            is less than the size of `a` along the given axis, then output is
            truncated to the length of the condition array.
        a : array_like
            Array from which to extract a part.
        axis : int, optional
            Axis along which to take slices. If None (default), work on the
            flattened array.
        out : ndarray, optional
            Output array.  Its type is preserved and it must be of the right
            shape to hold the output.
    
        Returns
        -------
        compressed_array : ndarray
            A copy of `a` without the slices along axis for which `condition`
            is false.
    
        See Also
        --------
        take, choose, diag, diagonal, select
        ndarray.compress : Equivalent method in ndarray
        np.extract: Equivalent method when working on 1-D arrays
        numpy.doc.ufuncs : Section "Output arguments"
    
        Examples
        --------
        >>> a = np.array([[1, 2], [3, 4], [5, 6]])
        >>> a
        array([[1, 2],
               [3, 4],
               [5, 6]])
        >>> np.compress([0, 1], a, axis=0)
        array([[3, 4]])
        >>> np.compress([False, True, True], a, axis=0)
        array([[3, 4],
               [5, 6]])
        >>> np.compress([False, True], a, axis=1)
        array([[2],
               [4],
               [6]])
    
        Working on the flattened array does not return slices along an axis but
        selects elements.
    
        >>> np.compress([False, True], a)
        array([2])
    
        """
    return ndarray()
def concatenate(a1, a2, more_args, axis=0):
    """concatenate((a1, a2, ...), axis=0)
    
        Join a sequence of arrays together.
    
        Parameters
        ----------
        a1, a2, ... : sequence of array_like
            The arrays must have the same shape, except in the dimension
            corresponding to `axis` (the first, by default).
        axis : int, optional
            The axis along which the arrays will be joined.  Default is 0.
    
        Returns
        -------
        res : ndarray
            The concatenated array.
    
        See Also
        --------
        ma.concatenate : Concatenate function that preserves input masks.
        array_split : Split an array into multiple sub-arrays of equal or
                      near-equal size.
        split : Split array into a list of multiple sub-arrays of equal size.
        hsplit : Split array into multiple sub-arrays horizontally (column wise)
        vsplit : Split array into multiple sub-arrays vertically (row wise)
        dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
        hstack : Stack arrays in sequence horizontally (column wise)
        vstack : Stack arrays in sequence vertically (row wise)
        dstack : Stack arrays in sequence depth wise (along third dimension)
    
        Notes
        -----
        When one or more of the arrays to be concatenated is a MaskedArray,
        this function will return a MaskedArray object instead of an ndarray,
        but the input masks are *not* preserved. In cases where a MaskedArray
        is expected as input, use the ma.concatenate function from the masked
        array module instead.
    
        Examples
        --------
        >>> a = np.array([[1, 2], [3, 4]])
        >>> b = np.array([[5, 6]])
        >>> np.concatenate((a, b), axis=0)
        array([[1, 2],
               [3, 4],
               [5, 6]])
        >>> np.concatenate((a, b.T), axis=1)
        array([[1, 2, 5],
               [3, 4, 6]])
    
        This function will not preserve masking of MaskedArray inputs.
    
        >>> a = np.ma.arange(3)
        >>> a[1] = np.ma.masked
        >>> b = np.arange(2, 5)
        >>> a
        masked_array(data = [0 -- 2],
                     mask = [False  True False],
               fill_value = 999999)
        >>> b
        array([2, 3, 4])
        >>> np.concatenate([a, b])
        masked_array(data = [0 1 2 2 3 4],
                     mask = False,
               fill_value = 999999)
        >>> np.ma.concatenate([a, b])
        masked_array(data = [0 -- 2 2 3 4],
                     mask = [False  True False False False False],
               fill_value = 999999)"""
    return ndarray()
def conjugate(x, out=None):
    """conjugate(x[, out])
    
    Return the complex conjugate, element-wise.
    
    The complex conjugate of a complex number is obtained by changing the
    sign of its imaginary part.
    
    Parameters
    ----------
    x : array_like
        Input value.
    
    Returns
    -------
    y : ndarray
        The complex conjugate of `x`, with same dtype as `y`.
    
    Examples
    --------
    >>> np.conjugate(1+2j)
    (1-2j)
    
    >>> x = np.eye(2) + 1j * np.eye(2)
    >>> np.conjugate(x)
    array([[ 1.-1.j,  0.-0.j],
           [ 0.-0.j,  1.-1.j]])"""
    return ndarray()
def conjugate(x, out=None):
    """conjugate(x[, out])
    
    Return the complex conjugate, element-wise.
    
    The complex conjugate of a complex number is obtained by changing the
    sign of its imaginary part.
    
    Parameters
    ----------
    x : array_like
        Input value.
    
    Returns
    -------
    y : ndarray
        The complex conjugate of `x`, with same dtype as `y`.
    
    Examples
    --------
    >>> np.conjugate(1+2j)
    (1-2j)
    
    >>> x = np.eye(2) + 1j * np.eye(2)
    >>> np.conjugate(x)
    array([[ 1.-1.j,  0.-0.j],
           [ 0.-0.j,  1.-1.j]])"""
    return ndarray()
def convolve(a, v="full", mode="full"):
    """
        Returns the discrete, linear convolution of two one-dimensional sequences.
    
        The convolution operator is often seen in signal processing, where it
        models the effect of a linear time-invariant system on a signal [1]_.  In
        probability theory, the sum of two independent random variables is
        distributed according to the convolution of their individual
        distributions.
    
        Parameters
        ----------
        a : (N,) array_like
            First one-dimensional input array.
        v : (M,) array_like
            Second one-dimensional input array.
        mode : {'full', 'valid', 'same'}, optional
            'full':
              By default, mode is 'full'.  This returns the convolution
              at each point of overlap, with an output shape of (N+M-1,). At
              the end-points of the convolution, the signals do not overlap
              completely, and boundary effects may be seen.
    
            'same':
              Mode `same` returns output of length ``max(M, N)``.  Boundary
              effects are still visible.
    
            'valid':
              Mode `valid` returns output of length
              ``max(M, N) - min(M, N) + 1``.  The convolution product is only given
              for points where the signals overlap completely.  Values outside
              the signal boundary have no effect.
    
        Returns
        -------
        out : ndarray
            Discrete, linear convolution of `a` and `v`.
    
        See Also
        --------
        scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier
                                   Transform.
        scipy.linalg.toeplitz : Used to construct the convolution operator.
    
        Notes
        -----
        The discrete convolution operation is defined as
    
        .. math:: (f * g)[n] = \sum_{m = -\infty}^{\infty} f[m] g[n - m]
    
        It can be shown that a convolution :math:`x(t) * y(t)` in time/space
        is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier
        domain, after appropriate padding (padding is necessary to prevent
        circular convolution).  Since multiplication is more efficient (faster)
        than convolution, the function `scipy.signal.fftconvolve` exploits the
        FFT to calculate the convolution of large data-sets.
    
        References
        ----------
        .. [1] Wikipedia, "Convolution", http://en.wikipedia.org/wiki/Convolution.
    
        Examples
        --------
        Note how the convolution operator flips the second array
        before "sliding" the two across one another:
    
        >>> np.convolve([1, 2, 3], [0, 1, 0.5])
        array([ 0. ,  1. ,  2.5,  4. ,  1.5])
    
        Only return the middle values of the convolution.
        Contains boundary effects, where zeros are taken
        into account:
    
        >>> np.convolve([1,2,3],[0,1,0.5], 'same')
        array([ 1. ,  2.5,  4. ])
    
        The two arrays are of the same length, so there
        is only one position where they completely overlap:
    
        >>> np.convolve([1,2,3],[0,1,0.5], 'valid')
        array([ 2.5])
    
        """
    return ndarray()
def copy(a="K", order="K"):
    """
        Return an array copy of the given object.
    
        Parameters
        ----------
        a : array_like
            Input data.
        order : {'C', 'F', 'A', 'K'}, optional
            Controls the memory layout of the copy. 'C' means C-order,
            'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
            'C' otherwise. 'K' means match the layout of `a` as closely
            as possible. (Note that this function and :meth:ndarray.copy are very
            similar, but have different default values for their order=
            arguments.)
    
        Returns
        -------
        arr : ndarray
            Array interpretation of `a`.
    
        Notes
        -----
        This is equivalent to
    
        >>> np.array(a, copy=True)                              #doctest: +SKIP
    
        Examples
        --------
        Create an array x, with a reference y and a copy z:
    
        >>> x = np.array([1, 2, 3])
        >>> y = x
        >>> z = np.copy(x)
    
        Note that, when we modify x, y changes, but not z:
    
        >>> x[0] = 10
        >>> x[0] == y[0]
        True
        >>> x[0] == z[0]
        False
    
        """
    return ndarray()
def copysign(x1, x2, out):
    """copysign(x1, x2[, out])
    
    Change the sign of x1 to that of x2, element-wise.
    
    If both arguments are arrays or sequences, they have to be of the same
    length. If `x2` is a scalar, its sign will be copied to all elements of
    `x1`.
    
    Parameters
    ----------
    x1 : array_like
        Values to change the sign of.
    x2 : array_like
        The sign of `x2` is copied to `x1`.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    out : array_like
        The values of `x1` with the sign of `x2`.
    
    Examples
    --------
    >>> np.copysign(1.3, -1)
    -1.3
    >>> 1/np.copysign(0, 1)
    inf
    >>> 1/np.copysign(0, -1)
    -inf
    
    >>> np.copysign([-1, 0, 1], -1.1)
    array([-1., -0., -1.])
    >>> np.copysign([-1, 0, 1], np.arange(3)-1)
    array([-1.,  0.,  1.])"""
    return ndarray()
def copyto(dst, src, casting=same_kind, where=None, preservena=False):
    """copyto(dst, src, casting='same_kind', where=None, preservena=False)
    
        Copies values from one array to another, broadcasting as necessary.
    
        Raises a TypeError if the `casting` rule is violated, and if
        `where` is provided, it selects which elements to copy.
    
        .. versionadded:: 1.7.0
    
        Parameters
        ----------
        dst : ndarray
            The array into which values are copied.
        src : array_like
            The array from which values are copied.
        casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
            Controls what kind of data casting may occur when copying.
    
              * 'no' means the data types should not be cast at all.
              * 'equiv' means only byte-order changes are allowed.
              * 'safe' means only casts which can preserve values are allowed.
              * 'same_kind' means only safe casts or casts within a kind,
                like float64 to float32, are allowed.
              * 'unsafe' means any data conversions may be done.
        where : array_like of bool, optional
            A boolean array which is broadcasted to match the dimensions
            of `dst`, and selects elements to copy from `src` to `dst`
            wherever it contains the value True.
        preservena : bool, optional
            If set to True, leaves any NA values in `dst` untouched. This
            is similar to the "hard mask" feature in numpy.ma."""
    return None
def corrcoef(x=None, y=None, rowvar=1, bias=0, ddof=None):
    """
        Return correlation coefficients.
    
        Please refer to the documentation for `cov` for more detail.  The
        relationship between the correlation coefficient matrix, `P`, and the
        covariance matrix, `C`, is
    
        .. math:: P_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }
    
        The values of `P` are between -1 and 1, inclusive.
    
        Parameters
        ----------
        x : array_like
            A 1-D or 2-D array containing multiple variables and observations.
            Each row of `m` represents a variable, and each column a single
            observation of all those variables. Also see `rowvar` below.
        y : array_like, optional
            An additional set of variables and observations. `y` has the same
            shape as `m`.
        rowvar : int, optional
            If `rowvar` is non-zero (default), then each row represents a
            variable, with observations in the columns. Otherwise, the relationship
            is transposed: each column represents a variable, while the rows
            contain observations.
        bias : int, optional
            Default normalization is by ``(N - 1)``, where ``N`` is the number of
            observations (unbiased estimate). If `bias` is 1, then
            normalization is by ``N``. These values can be overridden by using
            the keyword ``ddof`` in numpy versions >= 1.5.
        ddof : {None, int}, optional
            .. versionadded:: 1.5
            If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
            the number of observations; this overrides the value implied by
            ``bias``. The default value is ``None``.
    
        Returns
        -------
        out : ndarray
            The correlation coefficient matrix of the variables.
    
        See Also
        --------
        cov : Covariance matrix
    
        """
    return ndarray()
def correlate(a, v=False, mode="valid", old_behavior=False):
    """
        Cross-correlation of two 1-dimensional sequences.
    
        This function computes the correlation as generally defined in signal
        processing texts::
    
            z[k] = sum_n a[n] * conj(v[n+k])
    
        with a and v sequences being zero-padded where necessary and conj being
        the conjugate.
    
        Parameters
        ----------
        a, v : array_like
            Input sequences.
        mode : {'valid', 'same', 'full'}, optional
            Refer to the `convolve` docstring.  Note that the default
            is `valid`, unlike `convolve`, which uses `full`.
        old_behavior : bool
            If True, uses the old behavior from Numeric,
            (correlate(a,v) == correlate(v,a), and the conjugate is not taken
            for complex arrays). If False, uses the conventional signal
            processing definition.
    
        See Also
        --------
        convolve : Discrete, linear convolution of two one-dimensional sequences.
    
        Examples
        --------
        >>> np.correlate([1, 2, 3], [0, 1, 0.5])
        array([ 3.5])
        >>> np.correlate([1, 2, 3], [0, 1, 0.5], "same")
        array([ 2. ,  3.5,  3. ])
        >>> np.correlate([1, 2, 3], [0, 1, 0.5], "full")
        array([ 0.5,  2. ,  3.5,  3. ,  0. ])
    
        """
    return None
def cos(x, out):
    """cos(x[, out])
    
    Cosine elementwise.
    
    Parameters
    ----------
    x : array_like
        Input array in radians.
    out : ndarray, optional
        Output array of same shape as `x`.
    
    Returns
    -------
    y : ndarray
        The corresponding cosine values.
    
    Raises
    ------
    ValueError: invalid return array shape
        if `out` is provided and `out.shape` != `x.shape` (See Examples)
    
    Notes
    -----
    If `out` is provided, the function writes the result into it,
    and returns a reference to `out`.  (See Examples)
    
    References
    ----------
    M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
    New York, NY: Dover, 1972.
    
    Examples
    --------
    >>> np.cos(np.array([0, np.pi/2, np.pi]))
    array([  1.00000000e+00,   6.12303177e-17,  -1.00000000e+00])
    >>>
    >>> # Example of providing the optional output parameter
    >>> out2 = np.cos([0.1], out1)
    >>> out2 is out1
    True
    >>>
    >>> # Example of ValueError due to provision of shape mis-matched `out`
    >>> np.cos(np.zeros((3,3)),np.zeros((2,2)))
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ValueError: invalid return array shape"""
    return ndarray()
def cosh(x, out=None):
    """cosh(x[, out])
    
    Hyperbolic cosine, element-wise.
    
    Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    out : ndarray
        Output array of same shape as `x`.
    
    Examples
    --------
    >>> np.cosh(0)
    1.0
    
    The hyperbolic cosine describes the shape of a hanging cable:
    
    >>> import matplotlib.pyplot as plt
    >>> x = np.linspace(-4, 4, 1000)
    >>> plt.plot(x, np.cosh(x))
    >>> plt.show()"""
    return ndarray()
def count_nonzero(a):
    """count_nonzero(a)
    
        Counts the number of non-zero values in the array ``a``.
    
        Parameters
        ----------
        a : array_like
            The array for which to count non-zeros.
    
        Returns
        -------
        count : int or array of int
            Number of non-zero values in the array.
    
        See Also
        --------
        nonzero : Return the coordinates of all the non-zero values.
    
        Examples
        --------
        >>> np.count_nonzero(np.eye(4))
        4
        >>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]])
        5"""
    return int() if False else array()
def cov(m=None, y=None, rowvar=1, bias=0, ddof=None):
    """
        Estimate a covariance matrix, given data.
    
        Covariance indicates the level to which two variables vary together.
        If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,
        then the covariance matrix element :math:`C_{ij}` is the covariance of
        :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance
        of :math:`x_i`.
    
        Parameters
        ----------
        m : array_like
            A 1-D or 2-D array containing multiple variables and observations.
            Each row of `m` represents a variable, and each column a single
            observation of all those variables. Also see `rowvar` below.
        y : array_like, optional
            An additional set of variables and observations. `y` has the same
            form as that of `m`.
        rowvar : int, optional
            If `rowvar` is non-zero (default), then each row represents a
            variable, with observations in the columns. Otherwise, the relationship
            is transposed: each column represents a variable, while the rows
            contain observations.
        bias : int, optional
            Default normalization is by ``(N - 1)``, where ``N`` is the number of
            observations given (unbiased estimate). If `bias` is 1, then
            normalization is by ``N``. These values can be overridden by using
            the keyword ``ddof`` in numpy versions >= 1.5.
        ddof : int, optional
            .. versionadded:: 1.5
            If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
            the number of observations; this overrides the value implied by
            ``bias``. The default value is ``None``.
    
        Returns
        -------
        out : ndarray
            The covariance matrix of the variables.
    
        See Also
        --------
        corrcoef : Normalized covariance matrix
    
        Examples
        --------
        Consider two variables, :math:`x_0` and :math:`x_1`, which
        correlate perfectly, but in opposite directions:
    
        >>> x = np.array([[0, 2], [1, 1], [2, 0]]).T
        >>> x
        array([[0, 1, 2],
               [2, 1, 0]])
    
        Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance
        matrix shows this clearly:
    
        >>> np.cov(x)
        array([[ 1., -1.],
               [-1.,  1.]])
    
        Note that element :math:`C_{0,1}`, which shows the correlation between
        :math:`x_0` and :math:`x_1`, is negative.
    
        Further, note how `x` and `y` are combined:
    
        >>> x = [-2.1, -1,  4.3]
        >>> y = [3,  1.1,  0.12]
        >>> X = np.vstack((x,y))
        >>> print np.cov(X)
        [[ 11.71        -4.286     ]
         [ -4.286        2.14413333]]
        >>> print np.cov(x, y)
        [[ 11.71        -4.286     ]
         [ -4.286        2.14413333]]
        >>> print np.cov(x)
        11.71
    
        """
    return ndarray()
def cross(a, b=None, axisa=-1, axisb=-1, axisc=-1, axis=None):
    """
        Return the cross product of two (arrays of) vectors.
    
        The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular
        to both `a` and `b`.  If `a` and `b` are arrays of vectors, the vectors
        are defined by the last axis of `a` and `b` by default, and these axes
        can have dimensions 2 or 3.  Where the dimension of either `a` or `b` is
        2, the third component of the input vector is assumed to be zero and the
        cross product calculated accordingly.  In cases where both input vectors
        have dimension 2, the z-component of the cross product is returned.
    
        Parameters
        ----------
        a : array_like
            Components of the first vector(s).
        b : array_like
            Components of the second vector(s).
        axisa : int, optional
            Axis of `a` that defines the vector(s).  By default, the last axis.
        axisb : int, optional
            Axis of `b` that defines the vector(s).  By default, the last axis.
        axisc : int, optional
            Axis of `c` containing the cross product vector(s).  By default, the
            last axis.
        axis : int, optional
            If defined, the axis of `a`, `b` and `c` that defines the vector(s)
            and cross product(s).  Overrides `axisa`, `axisb` and `axisc`.
    
        Returns
        -------
        c : ndarray
            Vector cross product(s).
    
        Raises
        ------
        ValueError
            When the dimension of the vector(s) in `a` and/or `b` does not
            equal 2 or 3.
    
        See Also
        --------
        inner : Inner product
        outer : Outer product.
        ix_ : Construct index arrays.
    
        Examples
        --------
        Vector cross-product.
    
        >>> x = [1, 2, 3]
        >>> y = [4, 5, 6]
        >>> np.cross(x, y)
        array([-3,  6, -3])
    
        One vector with dimension 2.
    
        >>> x = [1, 2]
        >>> y = [4, 5, 6]
        >>> np.cross(x, y)
        array([12, -6, -3])
    
        Equivalently:
    
        >>> x = [1, 2, 0]
        >>> y = [4, 5, 6]
        >>> np.cross(x, y)
        array([12, -6, -3])
    
        Both vectors with dimension 2.
    
        >>> x = [1,2]
        >>> y = [4,5]
        >>> np.cross(x, y)
        -3
    
        Multiple vector cross-products. Note that the direction of the cross
        product vector is defined by the `right-hand rule`.
    
        >>> x = np.array([[1,2,3], [4,5,6]])
        >>> y = np.array([[4,5,6], [1,2,3]])
        >>> np.cross(x, y)
        array([[-3,  6, -3],
               [ 3, -6,  3]])
    
        The orientation of `c` can be changed using the `axisc` keyword.
    
        >>> np.cross(x, y, axisc=0)
        array([[-3,  3],
               [ 6, -6],
               [-3,  3]])
    
        Change the vector definition of `x` and `y` using `axisa` and `axisb`.
    
        >>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])
        >>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])
        >>> np.cross(x, y)
        array([[ -6,  12,  -6],
               [  0,   0,   0],
               [  6, -12,   6]])
        >>> np.cross(x, y, axisa=0, axisb=0)
        array([[-24,  48, -24],
               [-30,  60, -30],
               [-36,  72, -36]])
    
        """
    return ndarray()
class complex64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def cumprod(a=None, axis=None, dtype=None, out=None):
    """
        Return the cumulative product of elements along a given axis.
    
        Parameters
        ----------
        a : array_like
            Input array.
        axis : int, optional
            Axis along which the cumulative product is computed.  By default
            the input is flattened.
        dtype : dtype, optional
            Type of the returned array, as well as of the accumulator in which
            the elements are multiplied.  If *dtype* is not specified, it
            defaults to the dtype of `a`, unless `a` has an integer dtype with
            a precision less than that of the default platform integer.  In
            that case, the default platform integer is used instead.
        out : ndarray, optional
            Alternative output array in which to place the result. It must
            have the same shape and buffer length as the expected output
            but the type of the resulting values will be cast if necessary.
    
        Returns
        -------
        cumprod : ndarray
            A new array holding the result is returned unless `out` is
            specified, in which case a reference to out is returned.
    
        See Also
        --------
        numpy.doc.ufuncs : Section "Output arguments"
    
        Notes
        -----
        Arithmetic is modular when using integer types, and no error is
        raised on overflow.
    
        Examples
        --------
        >>> a = np.array([1,2,3])
        >>> np.cumprod(a) # intermediate results 1, 1*2
        ...               # total product 1*2*3 = 6
        array([1, 2, 6])
        >>> a = np.array([[1, 2, 3], [4, 5, 6]])
        >>> np.cumprod(a, dtype=float) # specify type of output
        array([   1.,    2.,    6.,   24.,  120.,  720.])
    
        The cumulative product for each column (i.e., over the rows) of `a`:
    
        >>> np.cumprod(a, axis=0)
        array([[ 1,  2,  3],
               [ 4, 10, 18]])
    
        The cumulative product for each row (i.e. over the columns) of `a`:
    
        >>> np.cumprod(a,axis=1)
        array([[  1,   2,   6],
               [  4,  20, 120]])
    
        """
    return ndarray()
def cumproduct(a=None, axis=None, dtype=None, out=None):
    """
        Return the cumulative product over the given axis.
    
    
        See Also
        --------
        cumprod : equivalent function; see for details.
    
        """
    return None
def cumsum(a=None, axis=None, dtype=None, out=None):
    """
        Return the cumulative sum of the elements along a given axis.
    
        Parameters
        ----------
        a : array_like
            Input array.
        axis : int, optional
            Axis along which the cumulative sum is computed. The default
            (None) is to compute the cumsum over the flattened array.
        dtype : dtype, optional
            Type of the returned array and of the accumulator in which the
            elements are summed.  If `dtype` is not specified, it defaults
            to the dtype of `a`, unless `a` has an integer dtype with a
            precision less than that of the default platform integer.  In
            that case, the default platform integer is used.
        out : ndarray, optional
            Alternative output array in which to place the result. It must
            have the same shape and buffer length as the expected output
            but the type will be cast if necessary. See `doc.ufuncs`
            (Section "Output arguments") for more details.
    
        Returns
        -------
        cumsum_along_axis : ndarray.
            A new array holding the result is returned unless `out` is
            specified, in which case a reference to `out` is returned. The
            result has the same size as `a`, and the same shape as `a` if
            `axis` is not None or `a` is a 1-d array.
    
    
        See Also
        --------
        sum : Sum array elements.
    
        trapz : Integration of array values using the composite trapezoidal rule.
    
        diff :  Calculate the n-th order discrete difference along given axis.
    
        Notes
        -----
        Arithmetic is modular when using integer types, and no error is
        raised on overflow.
    
        Examples
        --------
        >>> a = np.array([[1,2,3], [4,5,6]])
        >>> a
        array([[1, 2, 3],
               [4, 5, 6]])
        >>> np.cumsum(a)
        array([ 1,  3,  6, 10, 15, 21])
        >>> np.cumsum(a, dtype=float)     # specifies type of output value(s)
        array([  1.,   3.,   6.,  10.,  15.,  21.])
    
        >>> np.cumsum(a,axis=0)      # sum over rows for each of the 3 columns
        array([[1, 2, 3],
               [5, 7, 9]])
        >>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows
        array([[ 1,  3,  6],
               [ 4,  9, 15]])
    
        """
    return ndarray()
class datetime64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def datetime_as_string():
    """None"""
    return None
def datetime_data():
    """None"""
    return None
def deg2rad(x, out=None):
    """deg2rad(x[, out])
    
    Convert angles from degrees to radians.
    
    Parameters
    ----------
    x : array_like
        Angles in degrees.
    
    Returns
    -------
    y : ndarray
        The corresponding angle in radians.
    
    See Also
    --------
    rad2deg : Convert angles from radians to degrees.
    unwrap : Remove large jumps in angle by wrapping.
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    ``deg2rad(x)`` is ``x * pi / 180``.
    
    Examples
    --------
    >>> np.deg2rad(180)
    3.1415926535897931"""
    return ndarray()
def degrees(x, out):
    """degrees(x[, out])
    
    Convert angles from radians to degrees.
    
    Parameters
    ----------
    x : array_like
        Input array in radians.
    out : ndarray, optional
        Output array of same shape as x.
    
    Returns
    -------
    y : ndarray of floats
        The corresponding degree values; if `out` was supplied this is a
        reference to it.
    
    See Also
    --------
    rad2deg : equivalent function
    
    Examples
    --------
    Convert a radian array to degrees
    
    >>> rad = np.arange(12.)*np.pi/6
    >>> np.degrees(rad)
    array([   0.,   30.,   60.,   90.,  120.,  150.,  180.,  210.,  240.,
            270.,  300.,  330.])
    
    >>> out = np.zeros((rad.shape))
    >>> r = degrees(rad, out)
    >>> np.all(r == out)
    True"""
    return ndarray()
def delete(arr, obj=None, axis=None):
    """
        Return a new array with sub-arrays along an axis deleted. For a one
        dimensional array, this returns those entries not returned by `arr[obj]`.
    
        Parameters
        ----------
        arr : array_like
          Input array.
        obj : slice, int or array of ints
          Indicate which sub-arrays to remove.
        axis : int, optional
          The axis along which to delete the subarray defined by `obj`.
          If `axis` is None, `obj` is applied to the flattened array.
    
        Returns
        -------
        out : ndarray
            A copy of `arr` with the elements specified by `obj` removed. Note
            that `delete` does not occur in-place. If `axis` is None, `out` is
            a flattened array.
    
        See Also
        --------
        insert : Insert elements into an array.
        append : Append elements at the end of an array.
    
        Notes
        -----
        Often it is preferable to use a boolean mask. For example:
        >>> mask = np.ones(len(arr), dtype=bool)
        >>> mask[[0,2,4]] = False
        >>> result = arr[mask,...]
        Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further
        use of `mask`.
    
        Examples
        --------
        >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
        >>> arr
        array([[ 1,  2,  3,  4],
               [ 5,  6,  7,  8],
               [ 9, 10, 11, 12]])
        >>> np.delete(arr, 1, 0)
        array([[ 1,  2,  3,  4],
               [ 9, 10, 11, 12]])
    
        >>> np.delete(arr, np.s_[::2], 1)
        array([[ 2,  4],
               [ 6,  8],
               [10, 12]])
        >>> np.delete(arr, [1,3,5], None)
        array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])
    
        """
    return ndarray()
def deprecate():
    """
        Issues a DeprecationWarning, adds warning to `old_name`'s
        docstring, rebinds ``old_name.__name__`` and returns the new
        function object.
    
        This function may also be used as a decorator.
    
        Parameters
        ----------
        func : function
            The function to be deprecated.
        old_name : str, optional
            The name of the function to be deprecated. Default is None, in which
            case the name of `func` is used.
        new_name : str, optional
            The new name for the function. Default is None, in which case
            the deprecation message is that `old_name` is deprecated. If given,
            the deprecation message is that `old_name` is deprecated and `new_name`
            should be used instead.
        message : str, optional
            Additional explanation of the deprecation.  Displayed in the docstring
            after the warning.
    
        Returns
        -------
        old_func : function
            The deprecated function.
    
        Examples
        --------
        Note that ``olduint`` returns a value after printing Deprecation Warning:
    
        >>> olduint = np.deprecate(np.uint)
        >>> olduint(6)
        /usr/lib/python2.5/site-packages/numpy/lib/utils.py:114:
        DeprecationWarning: uint32 is deprecated
          warnings.warn(str1, DeprecationWarning)
        6
    
        """
    return None
def _lambda():
    """None"""
    return None
def diag(v=0, k=0):
    """
        Extract a diagonal or construct a diagonal array.
    
        See the more detailed documentation for ``numpy.diagonal`` if you use this
        function to extract a diagonal and wish to write to the resulting array;
        whether it returns a copy or a view depends on what version of numpy you
        are using.
    
        Parameters
        ----------
        v : array_like
            If `v` is a 2-D array, return a copy of its `k`-th diagonal.
            If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th
            diagonal.
        k : int, optional
            Diagonal in question. The default is 0. Use `k>0` for diagonals
            above the main diagonal, and `k<0` for diagonals below the main
            diagonal.
    
        Returns
        -------
        out : ndarray
            The extracted diagonal or constructed diagonal array.
    
        See Also
        --------
        diagonal : Return specified diagonals.
        diagflat : Create a 2-D array with the flattened input as a diagonal.
        trace : Sum along diagonals.
        triu : Upper triangle of an array.
        tril : Lower triange of an array.
    
        Examples
        --------
        >>> x = np.arange(9).reshape((3,3))
        >>> x
        array([[0, 1, 2],
               [3, 4, 5],
               [6, 7, 8]])
    
        >>> np.diag(x)
        array([0, 4, 8])
        >>> np.diag(x, k=1)
        array([1, 5])
        >>> np.diag(x, k=-1)
        array([3, 7])
    
        >>> np.diag(np.diag(x))
        array([[0, 0, 0],
               [0, 4, 0],
               [0, 0, 8]])
    
        """
    return ndarray()
def diag_indices(n=2, ndim=2):
    """
        Return the indices to access the main diagonal of an array.
    
        This returns a tuple of indices that can be used to access the main
        diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape
        (n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for
        ``a.ndim > 2`` this is the set of indices to access ``a[i, i, ..., i]``
        for ``i = [0..n-1]``.
    
        Parameters
        ----------
        n : int
          The size, along each dimension, of the arrays for which the returned
          indices can be used.
    
        ndim : int, optional
          The number of dimensions.
    
        See also
        --------
        diag_indices_from
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        Examples
        --------
        Create a set of indices to access the diagonal of a (4, 4) array:
    
        >>> di = np.diag_indices(4)
        >>> di
        (array([0, 1, 2, 3]), array([0, 1, 2, 3]))
        >>> a = np.arange(16).reshape(4, 4)
        >>> a
        array([[ 0,  1,  2,  3],
               [ 4,  5,  6,  7],
               [ 8,  9, 10, 11],
               [12, 13, 14, 15]])
        >>> a[di] = 100
        >>> a
        array([[100,   1,   2,   3],
               [  4, 100,   6,   7],
               [  8,   9, 100,  11],
               [ 12,  13,  14, 100]])
    
        Now, we create indices to manipulate a 3-D array:
    
        >>> d3 = np.diag_indices(2, 3)
        >>> d3
        (array([0, 1]), array([0, 1]), array([0, 1]))
    
        And use it to set the diagonal of an array of zeros to 1:
    
        >>> a = np.zeros((2, 2, 2), dtype=np.int)
        >>> a[d3] = 1
        >>> a
        array([[[1, 0],
                [0, 0]],
               [[0, 0],
                [0, 1]]])
    
        """
    return None
def diag_indices__from(arr):
    """
        Return the indices to access the main diagonal of an n-dimensional array.
    
        See `diag_indices` for full details.
    
        Parameters
        ----------
        arr : array, at least 2-D
    
        See Also
        --------
        diag_indices
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        """
    return None
def diagflat(v=0, k=0):
    """
        Create a two-dimensional array with the flattened input as a diagonal.
    
        Parameters
        ----------
        v : array_like
            Input data, which is flattened and set as the `k`-th
            diagonal of the output.
        k : int, optional
            Diagonal to set; 0, the default, corresponds to the "main" diagonal,
            a positive (negative) `k` giving the number of the diagonal above
            (below) the main.
    
        Returns
        -------
        out : ndarray
            The 2-D output array.
    
        See Also
        --------
        diag : MATLAB work-alike for 1-D and 2-D arrays.
        diagonal : Return specified diagonals.
        trace : Sum along diagonals.
    
        Examples
        --------
        >>> np.diagflat([[1,2], [3,4]])
        array([[1, 0, 0, 0],
               [0, 2, 0, 0],
               [0, 0, 3, 0],
               [0, 0, 0, 4]])
    
        >>> np.diagflat([1,2], 1)
        array([[0, 1, 0],
               [0, 0, 2],
               [0, 0, 0]])
    
        """
    return ndarray()
def diagonal(a=1, offset=0, axis1=0, axis2=1):
    """
        Return specified diagonals.
    
        If `a` is 2-D, returns the diagonal of `a` with the given offset,
        i.e., the collection of elements of the form ``a[i, i+offset]``.  If
        `a` has more than two dimensions, then the axes specified by `axis1`
        and `axis2` are used to determine the 2-D sub-array whose diagonal is
        returned.  The shape of the resulting array can be determined by
        removing `axis1` and `axis2` and appending an index to the right equal
        to the size of the resulting diagonals.
    
        In versions of NumPy prior to 1.7, this function always returned a new,
        independent array containing a copy of the values in the diagonal.
    
        In NumPy 1.7, it continues to return a copy of the diagonal, but depending
        on this fact is deprecated. Writing to the resulting array continues to
        work as it used to, but a FutureWarning will be issued.
    
        In NumPy 1.9, it will switch to returning a read-only view on the original
        array. Attempting to write to the resulting array will produce an error.
    
        In NumPy 1.10, it will still return a view, but this view will no longer be
        marked read-only. Writing to the returned array will alter your original
        array as well.
    
        If you don't write to the array returned by this function, then you can
        just ignore all of the above.
    
        If you depend on the current behavior, then we suggest copying the
        returned array explicitly, i.e., use ``np.diagonal(a).copy()`` instead of
        just ``np.diagonal(a)``. This will work with both past and future versions
        of NumPy.
    
        Parameters
        ----------
        a : array_like
            Array from which the diagonals are taken.
        offset : int, optional
            Offset of the diagonal from the main diagonal.  Can be positive or
            negative.  Defaults to main diagonal (0).
        axis1 : int, optional
            Axis to be used as the first axis of the 2-D sub-arrays from which
            the diagonals should be taken.  Defaults to first axis (0).
        axis2 : int, optional
            Axis to be used as the second axis of the 2-D sub-arrays from
            which the diagonals should be taken. Defaults to second axis (1).
    
        Returns
        -------
        array_of_diagonals : ndarray
            If `a` is 2-D, a 1-D array containing the diagonal is returned.
            If the dimension of `a` is larger, then an array of diagonals is
            returned, "packed" from left-most dimension to right-most (e.g.,
            if `a` is 3-D, then the diagonals are "packed" along rows).
    
        Raises
        ------
        ValueError
            If the dimension of `a` is less than 2.
    
        See Also
        --------
        diag : MATLAB work-a-like for 1-D and 2-D arrays.
        diagflat : Create diagonal arrays.
        trace : Sum along diagonals.
    
        Examples
        --------
        >>> a = np.arange(4).reshape(2,2)
        >>> a
        array([[0, 1],
               [2, 3]])
        >>> a.diagonal()
        array([0, 3])
        >>> a.diagonal(1)
        array([1])
    
        A 3-D example:
    
        >>> a = np.arange(8).reshape(2,2,2); a
        array([[[0, 1],
                [2, 3]],
               [[4, 5],
                [6, 7]]])
        >>> a.diagonal(0, # Main diagonals of two arrays created by skipping
        ...            0, # across the outer(left)-most axis last and
        ...            1) # the "middle" (row) axis first.
        array([[0, 6],
               [1, 7]])
    
        The sub-arrays whose main diagonals we just obtained; note that each
        corresponds to fixing the right-most (column) axis, and that the
        diagonals are "packed" in rows.
    
        >>> a[:,:,0] # main diagonal is [0 6]
        array([[0, 2],
               [4, 6]])
        >>> a[:,:,1] # main diagonal is [1 7]
        array([[1, 3],
               [5, 7]])
    
        """
    return ndarray()
def diff(a=-1, n=1, axis=-1):
    """
        Calculate the n-th order discrete difference along given axis.
    
        The first order difference is given by ``out[n] = a[n+1] - a[n]`` along
        the given axis, higher order differences are calculated by using `diff`
        recursively.
    
        Parameters
        ----------
        a : array_like
            Input array
        n : int, optional
            The number of times values are differenced.
        axis : int, optional
            The axis along which the difference is taken, default is the last axis.
    
        Returns
        -------
        diff : ndarray
            The `n` order differences. The shape of the output is the same as `a`
            except along `axis` where the dimension is smaller by `n`.
    
        See Also
        --------
        gradient, ediff1d, cumsum
    
        Examples
        --------
        >>> x = np.array([1, 2, 4, 7, 0])
        >>> np.diff(x)
        array([ 1,  2,  3, -7])
        >>> np.diff(x, n=2)
        array([  1,   1, -10])
    
        >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
        >>> np.diff(x)
        array([[2, 3, 4],
               [5, 1, 2]])
        >>> np.diff(x, axis=0)
        array([[-1,  2,  0, -2]])
    
        """
    return ndarray()
def digitize(x, bins, right):
    """digitize(x, bins, right=False)
    
        Return the indices of the bins to which each value in input array belongs.
    
        Each index ``i`` returned is such that ``bins[i-1] <= x < bins[i]`` if
        `bins` is monotonically increasing, or ``bins[i-1] > x >= bins[i]`` if
        `bins` is monotonically decreasing. If values in `x` are beyond the
        bounds of `bins`, 0 or ``len(bins)`` is returned as appropriate. If right
        is True, then the right bin is closed so that the index ``i`` is such
        that ``bins[i-1] < x <= bins[i]`` or bins[i-1] >= x > bins[i]`` if `bins`
        is monotonically increasing or decreasing, respectively.
    
        Parameters
        ----------
        x : array_like
            Input array to be binned. It has to be 1-dimensional.
        bins : array_like
            Array of bins. It has to be 1-dimensional and monotonic.
        right : bool, optional
            Indicating whether the intervals include the right or the left bin
            edge. Default behavior is (right==False) indicating that the interval
            does not include the right edge. The left bin and is open in this
            case. Ie., bins[i-1] <= x < bins[i] is the default behavior for
            monotonically increasing bins.
    
        Returns
        -------
        out : ndarray of ints
            Output array of indices, of same shape as `x`.
    
        Raises
        ------
        ValueError
            If the input is not 1-dimensional, or if `bins` is not monotonic.
        TypeError
            If the type of the input is complex.
    
        See Also
        --------
        bincount, histogram, unique
    
        Notes
        -----
        If values in `x` are such that they fall outside the bin range,
        attempting to index `bins` with the indices that `digitize` returns
        will result in an IndexError.
    
        Examples
        --------
        >>> x = np.array([0.2, 6.4, 3.0, 1.6])
        >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
        >>> inds = np.digitize(x, bins)
        >>> inds
        array([1, 4, 3, 2])
        >>> for n in range(x.size):
        ...   print bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]]
        ...
        0.0 <= 0.2 < 1.0
        4.0 <= 6.4 < 10.0
        2.5 <= 3.0 < 4.0
        1.0 <= 1.6 < 2.5
    
        >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
        >>> bins = np.array([0,5,10,15,20])
        >>> np.digitize(x,bins,right=True)
        array([1, 2, 3, 4, 4])
        >>> np.digitize(x,bins,right=False)
        array([1, 3, 3, 4, 5])"""
    return ndarray()
def disp(mesg=True, device=None, linefeed=True):
    """
        Display a message on a device.
    
        Parameters
        ----------
        mesg : str
            Message to display.
        device : object
            Device to write message. If None, defaults to ``sys.stdout`` which is
            very similar to ``print``. `device` needs to have ``write()`` and
            ``flush()`` methods.
        linefeed : bool, optional
            Option whether to print a line feed or not. Defaults to True.
    
        Raises
        ------
        AttributeError
            If `device` does not have a ``write()`` or ``flush()`` method.
    
        Examples
        --------
        Besides ``sys.stdout``, a file-like object can also be used as it has
        both required methods:
    
        >>> from StringIO import StringIO
        >>> buf = StringIO()
        >>> np.disp('"Display" in a file', device=buf)
        >>> buf.getvalue()
        '"Display" in a file\n'
    
        """
    return None
def divide(x1, x2, out):
    """divide(x1, x2[, out])
    
    Divide arguments element-wise.
    
    Parameters
    ----------
    x1 : array_like
        Dividend array.
    x2 : array_like
        Divisor array.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    y : {ndarray, scalar}
        The quotient `x1/x2`, element-wise. Returns a scalar if
        both  `x1` and `x2` are scalars.
    
    See Also
    --------
    seterr : Set whether to raise or warn on overflow, underflow and division
             by zero.
    
    Notes
    -----
    Equivalent to `x1` / `x2` in terms of array-broadcasting.
    
    Behavior on division by zero can be changed using `seterr`.
    
    When both `x1` and `x2` are of an integer type, `divide` will return
    integers and throw away the fractional part. Moreover, division by zero
    always yields zero in integer arithmetic.
    
    Examples
    --------
    >>> np.divide(2.0, 4.0)
    0.5
    >>> x1 = np.arange(9.0).reshape((3, 3))
    >>> x2 = np.arange(3.0)
    >>> np.divide(x1, x2)
    array([[ NaN,  1. ,  1. ],
           [ Inf,  4. ,  2.5],
           [ Inf,  7. ,  4. ]])
    
    Note the behavior with integer types:
    
    >>> np.divide(2, 4)
    0
    >>> np.divide(2, 4.)
    0.5
    
    Division by zero always yields zero in integer arithmetic, and does not
    raise an exception or a warning:
    
    >>> np.divide(np.array([0, 1], dtype=int), np.array([0, 0], dtype=int))
    array([0, 0])
    
    Division by zero can, however, be caught using `seterr`:
    
    >>> old_err_state = np.seterr(divide='raise')
    >>> np.divide(1, 0)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    FloatingPointError: divide by zero encountered in divide
    
    >>> ignored_states = np.seterr(**old_err_state)
    >>> np.divide(1, 0)
    0"""
    return ndarray()
division = instance()
def dot(a, b, out):
    """dot(a, b, out=None)
    
        Dot product of two arrays.
    
        For 2-D arrays it is equivalent to matrix multiplication, and for 1-D
        arrays to inner product of vectors (without complex conjugation). For
        N dimensions it is a sum product over the last axis of `a` and
        the second-to-last of `b`::
    
            dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
    
        Parameters
        ----------
        a : array_like
            First argument.
        b : array_like
            Second argument.
        out : ndarray, optional
            Output argument. This must have the exact kind that would be returned
            if it was not used. In particular, it must have the right type, must be
            C-contiguous, and its dtype must be the dtype that would be returned
            for `dot(a,b)`. This is a performance feature. Therefore, if these
            conditions are not met, an exception is raised, instead of attempting
            to be flexible.
    
        Returns
        -------
        output : ndarray
            Returns the dot product of `a` and `b`.  If `a` and `b` are both
            scalars or both 1-D arrays then a scalar is returned; otherwise
            an array is returned.
            If `out` is given, then it is returned.
    
        Raises
        ------
        ValueError
            If the last dimension of `a` is not the same size as
            the second-to-last dimension of `b`.
    
        See Also
        --------
        vdot : Complex-conjugating dot product.
        tensordot : Sum products over arbitrary axes.
        einsum : Einstein summation convention.
    
        Examples
        --------
        >>> np.dot(3, 4)
        12
    
        Neither argument is complex-conjugated:
    
        >>> np.dot([2j, 3j], [2j, 3j])
        (-13+0j)
    
        For 2-D arrays it's the matrix product:
    
        >>> a = [[1, 0], [0, 1]]
        >>> b = [[4, 1], [2, 2]]
        >>> np.dot(a, b)
        array([[4, 1],
               [2, 2]])
    
        >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
        >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
        >>> np.dot(a, b)[2,3,2,1,2,2]
        499128
        >>> sum(a[2,3,2,:] * b[1,2,:,2])
        499128"""
    return ndarray()
class float64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    def as_integer_ratio(self, _):
        """float.as_integer_ratio() -> (int, int)
        
        Return a pair of integers, whose ratio is exactly equal to the original
        float and with a positive denominator.
        Raise OverflowError on infinities and a ValueError on NaNs.
        
        >>> (10.0).as_integer_ratio()
        (10, 1)
        >>> (0.0).as_integer_ratio()
        (0, 1)
        >>> (-.25).as_integer_ratio()
        (-1, 4)"""
        return None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    def _fromhex(self, string):
        """float.fromhex(string) -> float
        
        Create a floating-point number from a hexadecimal string.
        >>> float.fromhex('0x1.ffffp10')
        2047.984375
        >>> float.fromhex('-0x1p-1074')
        -4.9406564584124654e-324"""
        return None
    def hex(self, _):
        """float.hex() -> string
        
        Return a hexadecimal representation of a floating-point number.
        >>> (-0.1).hex()
        '-0x1.999999999999ap-4'
        >>> 3.14159.hex()
        '0x1.921f9f01b866ep+1'"""
        return None
    imag = getset_descriptor()
    def is_integer(self, _):
        """Return True if the float is an integer."""
        return None
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def dsplit():
    """
        Split array into multiple sub-arrays along the 3rd axis (depth).
    
        Please refer to the `split` documentation.  `dsplit` is equivalent
        to `split` with ``axis=2``, the array is always split along the third
        axis provided the array dimension is greater than or equal to 3.
    
        See Also
        --------
        split : Split an array into multiple sub-arrays of equal size.
    
        Examples
        --------
        >>> x = np.arange(16.0).reshape(2, 2, 4)
        >>> x
        array([[[  0.,   1.,   2.,   3.],
                [  4.,   5.,   6.,   7.]],
               [[  8.,   9.,  10.,  11.],
                [ 12.,  13.,  14.,  15.]]])
        >>> np.dsplit(x, 2)
        [array([[[  0.,   1.],
                [  4.,   5.]],
               [[  8.,   9.],
                [ 12.,  13.]]]),
         array([[[  2.,   3.],
                [  6.,   7.]],
               [[ 10.,  11.],
                [ 14.,  15.]]])]
        >>> np.dsplit(x, np.array([3, 6]))
        [array([[[  0.,   1.,   2.],
                [  4.,   5.,   6.]],
               [[  8.,   9.,  10.],
                [ 12.,  13.,  14.]]]),
         array([[[  3.],
                [  7.]],
               [[ 11.],
                [ 15.]]]),
         array([], dtype=float64)]
    
        """
    return None
def dstack(tup):
    """
        Stack arrays in sequence depth wise (along third axis).
    
        Takes a sequence of arrays and stack them along the third axis
        to make a single array. Rebuilds arrays divided by `dsplit`.
        This is a simple way to stack 2D arrays (images) into a single
        3D array for processing.
    
        Parameters
        ----------
        tup : sequence of arrays
            Arrays to stack. All of them must have the same shape along all
            but the third axis.
    
        Returns
        -------
        stacked : ndarray
            The array formed by stacking the given arrays.
    
        See Also
        --------
        vstack : Stack along first axis.
        hstack : Stack along second axis.
        concatenate : Join arrays.
        dsplit : Split array along third axis.
    
        Notes
        -----
        Equivalent to ``np.concatenate(tup, axis=2)``.
    
        Examples
        --------
        >>> a = np.array((1,2,3))
        >>> b = np.array((2,3,4))
        >>> np.dstack((a,b))
        array([[[1, 2],
                [2, 3],
                [3, 4]]])
    
        >>> a = np.array([[1],[2],[3]])
        >>> b = np.array([[2],[3],[4]])
        >>> np.dstack((a,b))
        array([[[1, 2]],
               [[2, 3]],
               [[3, 4]]])
    
        """
    return ndarray()
class dtype:
    __doc__ = str()
    alignment = member_descriptor()
    base = getset_descriptor()
    byteorder = member_descriptor()
    char = member_descriptor()
    descr = getset_descriptor()
    fields = getset_descriptor()
    flags = member_descriptor()
    hasobject = getset_descriptor()
    isalignedstruct = getset_descriptor()
    isbuiltin = getset_descriptor()
    isnative = getset_descriptor()
    itemsize = member_descriptor()
    kind = member_descriptor()
    metadata = getset_descriptor()
    name = getset_descriptor()
    names = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new dtype with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            Parameters
            ----------
            new_order : string, optional
                Byte order to force; a value from the byte order
                specifications below.  The default value ('S') results in
                swapping the current byte order.
                `new_order` codes can be any of::
        
                 * 'S' - swap dtype from current to opposite endian
                 * {'<', 'L'} - little endian
                 * {'>', 'B'} - big endian
                 * {'=', 'N'} - native order
                 * {'|', 'I'} - ignore (no change to byte order)
        
                The code does a case-insensitive check on the first letter of
                `new_order` for these alternatives.  For example, any of '>'
                or 'B' or 'b' or 'brian' are valid to specify big-endian.
        
            Returns
            -------
            new_dtype : dtype
                New dtype object with the given change to the byte order.
        
            Notes
            -----
            Changes are also made in all fields and sub-arrays of the data type.
        
            Examples
            --------
            >>> import sys
            >>> sys_is_le = sys.byteorder == 'little'
            >>> native_code = sys_is_le and '<' or '>'
            >>> swapped_code = sys_is_le and '>' or '<'
            >>> native_dt = np.dtype(native_code+'i2')
            >>> swapped_dt = np.dtype(swapped_code+'i2')
            >>> native_dt.newbyteorder('S') == swapped_dt
            True
            >>> native_dt.newbyteorder() == swapped_dt
            True
            >>> native_dt == swapped_dt.newbyteorder('S')
            True
            >>> native_dt == swapped_dt.newbyteorder('=')
            True
            >>> native_dt == swapped_dt.newbyteorder('N')
            True
            >>> native_dt == native_dt.newbyteorder('|')
            True
            >>> np.dtype('<i2') == native_dt.newbyteorder('<')
            True
            >>> np.dtype('<i2') == native_dt.newbyteorder('L')
            True
            >>> np.dtype('>i2') == native_dt.newbyteorder('>')
            True
            >>> np.dtype('>i2') == native_dt.newbyteorder('B')
            True"""
        return dtype()
    num = member_descriptor()
    shape = getset_descriptor()
    str = getset_descriptor()
    subdtype = getset_descriptor()
    type = member_descriptor()
e = float()
def ediff1d(ary=None, to_end=None, to_begin=None):
    """
        The differences between consecutive elements of an array.
    
        Parameters
        ----------
        ary : array_like
            If necessary, will be flattened before the differences are taken.
        to_end : array_like, optional
            Number(s) to append at the end of the returned differences.
        to_begin : array_like, optional
            Number(s) to prepend at the beginning of the returned differences.
    
        Returns
        -------
        ediff1d : ndarray
            The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.
    
        See Also
        --------
        diff, gradient
    
        Notes
        -----
        When applied to masked arrays, this function drops the mask information
        if the `to_begin` and/or `to_end` parameters are used.
    
        Examples
        --------
        >>> x = np.array([1, 2, 4, 7, 0])
        >>> np.ediff1d(x)
        array([ 1,  2,  3, -7])
    
        >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
        array([-99,   1,   2,   3,  -7,  88,  99])
    
        The returned array is always 1D.
    
        >>> y = [[1, 2, 4], [1, 6, 24]]
        >>> np.ediff1d(y)
        array([ 1,  2, -3,  5, 18])
    
        """
    return ndarray()
def einsum(subscripts, operands, out, dtype, order, casting):
    """einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe')
    
        Evaluates the Einstein summation convention on the operands.
    
        Using the Einstein summation convention, many common multi-dimensional
        array operations can be represented in a simple fashion.  This function
        provides a way compute such summations. The best way to understand this
        function is to try the examples below, which show how many common NumPy
        functions can be implemented as calls to `einsum`.
    
        Parameters
        ----------
        subscripts : str
            Specifies the subscripts for summation.
        operands : list of array_like
            These are the arrays for the operation.
        out : ndarray, optional
            If provided, the calculation is done into this array.
        dtype : data-type, optional
            If provided, forces the calculation to use the data type specified.
            Note that you may have to also give a more liberal `casting`
            parameter to allow the conversions.
        order : {'C', 'F', 'A', 'K'}, optional
            Controls the memory layout of the output. 'C' means it should
            be C contiguous. 'F' means it should be Fortran contiguous,
            'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
            'K' means it should be as close to the layout as the inputs as
            is possible, including arbitrarily permuted axes.
            Default is 'K'.
        casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
            Controls what kind of data casting may occur.  Setting this to
            'unsafe' is not recommended, as it can adversely affect accumulations.
    
              * 'no' means the data types should not be cast at all.
              * 'equiv' means only byte-order changes are allowed.
              * 'safe' means only casts which can preserve values are allowed.
              * 'same_kind' means only safe casts or casts within a kind,
                like float64 to float32, are allowed.
              * 'unsafe' means any data conversions may be done.
    
        Returns
        -------
        output : ndarray
            The calculation based on the Einstein summation convention.
    
        See Also
        --------
        dot, inner, outer, tensordot
    
        Notes
        -----
        .. versionadded:: 1.6.0
    
        The subscripts string is a comma-separated list of subscript labels,
        where each label refers to a dimension of the corresponding operand.
        Repeated subscripts labels in one operand take the diagonal.  For example,
        ``np.einsum('ii', a)`` is equivalent to ``np.trace(a)``.
    
        Whenever a label is repeated, it is summed, so ``np.einsum('i,i', a, b)``
        is equivalent to ``np.inner(a,b)``.  If a label appears only once,
        it is not summed, so ``np.einsum('i', a)`` produces a view of ``a``
        with no changes.
    
        The order of labels in the output is by default alphabetical.  This
        means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
        ``np.einsum('ji', a)`` takes its transpose.
    
        The output can be controlled by specifying output subscript labels
        as well.  This specifies the label order, and allows summing to
        be disallowed or forced when desired.  The call ``np.einsum('i->', a)``
        is like ``np.sum(a, axis=-1)``, and ``np.einsum('ii->i', a)``
        is like ``np.diag(a)``.  The difference is that `einsum` does not
        allow broadcasting by default.
    
        To enable and control broadcasting, use an ellipsis.  Default
        NumPy-style broadcasting is done by adding an ellipsis
        to the left of each term, like ``np.einsum('...ii->...i', a)``.
        To take the trace along the first and last axes,
        you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
        product with the left-most indices instead of rightmost, you can do
        ``np.einsum('ij...,jk...->ik...', a, b)``.
    
        When there is only one operand, no axes are summed, and no output
        parameter is provided, a view into the operand is returned instead
        of a new array.  Thus, taking the diagonal as ``np.einsum('ii->i', a)``
        produces a view.
    
        An alternative way to provide the subscripts and operands is as
        ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples
        below have corresponding `einsum` calls with the two parameter methods.
    
        Examples
        --------
        >>> a = np.arange(25).reshape(5,5)
        >>> b = np.arange(5)
        >>> c = np.arange(6).reshape(2,3)
    
        >>> np.einsum('ii', a)
        60
        >>> np.einsum(a, [0,0])
        60
        >>> np.trace(a)
        60
    
        >>> np.einsum('ii->i', a)
        array([ 0,  6, 12, 18, 24])
        >>> np.einsum(a, [0,0], [0])
        array([ 0,  6, 12, 18, 24])
        >>> np.diag(a)
        array([ 0,  6, 12, 18, 24])
    
        >>> np.einsum('ij,j', a, b)
        array([ 30,  80, 130, 180, 230])
        >>> np.einsum(a, [0,1], b, [1])
        array([ 30,  80, 130, 180, 230])
        >>> np.dot(a, b)
        array([ 30,  80, 130, 180, 230])
    
        >>> np.einsum('ji', c)
        array([[0, 3],
               [1, 4],
               [2, 5]])
        >>> np.einsum(c, [1,0])
        array([[0, 3],
               [1, 4],
               [2, 5]])
        >>> c.T
        array([[0, 3],
               [1, 4],
               [2, 5]])
    
        >>> np.einsum('..., ...', 3, c)
        array([[ 0,  3,  6],
               [ 9, 12, 15]])
        >>> np.einsum(3, [Ellipsis], c, [Ellipsis])
        array([[ 0,  3,  6],
               [ 9, 12, 15]])
        >>> np.multiply(3, c)
        array([[ 0,  3,  6],
               [ 9, 12, 15]])
    
        >>> np.einsum('i,i', b, b)
        30
        >>> np.einsum(b, [0], b, [0])
        30
        >>> np.inner(b,b)
        30
    
        >>> np.einsum('i,j', np.arange(2)+1, b)
        array([[0, 1, 2, 3, 4],
               [0, 2, 4, 6, 8]])
        >>> np.einsum(np.arange(2)+1, [0], b, [1])
        array([[0, 1, 2, 3, 4],
               [0, 2, 4, 6, 8]])
        >>> np.outer(np.arange(2)+1, b)
        array([[0, 1, 2, 3, 4],
               [0, 2, 4, 6, 8]])
    
        >>> np.einsum('i...->...', a)
        array([50, 55, 60, 65, 70])
        >>> np.einsum(a, [0,Ellipsis], [Ellipsis])
        array([50, 55, 60, 65, 70])
        >>> np.sum(a, axis=0)
        array([50, 55, 60, 65, 70])
    
        >>> a = np.arange(60.).reshape(3,4,5)
        >>> b = np.arange(24.).reshape(4,3,2)
        >>> np.einsum('ijk,jil->kl', a, b)
        array([[ 4400.,  4730.],
               [ 4532.,  4874.],
               [ 4664.,  5018.],
               [ 4796.,  5162.],
               [ 4928.,  5306.]])
        >>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
        array([[ 4400.,  4730.],
               [ 4532.,  4874.],
               [ 4664.,  5018.],
               [ 4796.,  5162.],
               [ 4928.,  5306.]])
        >>> np.tensordot(a,b, axes=([1,0],[0,1]))
        array([[ 4400.,  4730.],
               [ 4532.,  4874.],
               [ 4664.,  5018.],
               [ 4796.,  5162.],
               [ 4928.,  5306.]])"""
    return ndarray()
def empty(shape, dtype, order):
    """empty(shape, dtype=float, order='C')
    
        Return a new array of given shape and type, without initializing entries.
    
        Parameters
        ----------
        shape : int or tuple of int
            Shape of the empty array
        dtype : data-type, optional
            Desired output data-type.
        order : {'C', 'F'}, optional
            Whether to store multi-dimensional data in C (row-major) or
            Fortran (column-major) order in memory.
    
        See Also
        --------
        empty_like, zeros, ones
    
        Notes
        -----
        `empty`, unlike `zeros`, does not set the array values to zero,
        and may therefore be marginally faster.  On the other hand, it requires
        the user to manually set all the values in the array, and should be
        used with caution.
    
        Examples
        --------
        >>> np.empty([2, 2])
        array([[ -9.74499359e+001,   6.69583040e-309],
               [  2.13182611e-314,   3.06959433e-309]])         #random
    
        >>> np.empty([2, 2], dtype=int)
        array([[-1073741821, -1067949133],
               [  496041986,    19249760]])                     #random"""
    return None
def empty_like(a, dtype, order, subok):
    """empty_like(a, dtype=None, order='K', subok=True)
    
        Return a new array with the same shape and type as a given array.
    
        Parameters
        ----------
        a : array_like
            The shape and data-type of `a` define these same attributes of the
            returned array.
        dtype : data-type, optional
            .. versionadded:: 1.6.0
            Overrides the data type of the result.
        order : {'C', 'F', 'A', or 'K'}, optional
            .. versionadded:: 1.6.0
            Overrides the memory layout of the result. 'C' means C-order,
            'F' means F-order, 'A' means 'F' if ``a`` is Fortran contiguous,
            'C' otherwise. 'K' means match the layout of ``a`` as closely
            as possible.
        subok : bool, optional.
            If True, then the newly created array will use the sub-class
            type of 'a', otherwise it will be a base-class array. Defaults
            to True.
    
        Returns
        -------
        out : ndarray
            Array of uninitialized (arbitrary) data with the same
            shape and type as `a`.
    
        See Also
        --------
        ones_like : Return an array of ones with shape and type of input.
        zeros_like : Return an array of zeros with shape and type of input.
        empty : Return a new uninitialized array.
        ones : Return a new array setting values to one.
        zeros : Return a new array setting values to zero.
    
        Notes
        -----
        This function does *not* initialize the returned array; to do that use
        `zeros_like` or `ones_like` instead.  It may be marginally faster than
        the functions that do set the array values.
    
        Examples
        --------
        >>> a = ([1,2,3], [4,5,6])                         # a is array-like
        >>> np.empty_like(a)
        array([[-1073741821, -1073741821,           3],    #random
               [          0,           0, -1073741821]])
        >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
        >>> np.empty_like(a)
        array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000],#random
               [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])"""
    return ndarray()
def equal(x1, x2, out=None):
    """equal(x1, x2[, out])
    
    Return (x1 == x2) element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input arrays of the same shape.
    
    Returns
    -------
    out : {ndarray, bool}
        Output array of bools, or a single bool if x1 and x2 are scalars.
    
    See Also
    --------
    not_equal, greater_equal, less_equal, greater, less
    
    Examples
    --------
    >>> np.equal([0, 1, 3], np.arange(3))
    array([ True,  True, False], dtype=bool)
    
    What is compared are values, not types. So an int (1) and an array of
    length one can evaluate as True:
    
    >>> np.equal(1, np.ones(1))
    array([ True], dtype=bool)"""
    return ndarray()
class errstate:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
euler_gamma = float()
def exp(x, out=None):
    """exp(x[, out])
    
    Calculate the exponential of all elements in the input array.
    
    Parameters
    ----------
    x : array_like
        Input values.
    
    Returns
    -------
    out : ndarray
        Output array, element-wise exponential of `x`.
    
    See Also
    --------
    expm1 : Calculate ``exp(x) - 1`` for all elements in the array.
    exp2  : Calculate ``2**x`` for all elements in the array.
    
    Notes
    -----
    The irrational number ``e`` is also known as Euler's number.  It is
    approximately 2.718281, and is the base of the natural logarithm,
    ``ln`` (this means that, if :math:`x = \ln y = \log_e y`,
    then :math:`e^x = y`. For real input, ``exp(x)`` is always positive.
    
    For complex arguments, ``x = a + ib``, we can write
    :math:`e^x = e^a e^{ib}`.  The first term, :math:`e^a`, is already
    known (it is the real argument, described above).  The second term,
    :math:`e^{ib}`, is :math:`\cos b + i \sin b`, a function with magnitude
    1 and a periodic phase.
    
    References
    ----------
    .. [1] Wikipedia, "Exponential function",
           http://en.wikipedia.org/wiki/Exponential_function
    .. [2] M. Abramovitz and I. A. Stegun, "Handbook of Mathematical Functions
           with Formulas, Graphs, and Mathematical Tables," Dover, 1964, p. 69,
           http://www.math.sfu.ca/~cbm/aands/page_69.htm
    
    Examples
    --------
    Plot the magnitude and phase of ``exp(x)`` in the complex plane:
    
    >>> import matplotlib.pyplot as plt
    
    >>> x = np.linspace(-2*np.pi, 2*np.pi, 100)
    >>> xx = x + 1j * x[:, np.newaxis] # a + ib over complex plane
    >>> out = np.exp(xx)
    
    >>> plt.subplot(121)
    >>> plt.imshow(np.abs(out),
    ...            extent=[-2*np.pi, 2*np.pi, -2*np.pi, 2*np.pi])
    >>> plt.title('Magnitude of exp(x)')
    
    >>> plt.subplot(122)
    >>> plt.imshow(np.angle(out),
    ...            extent=[-2*np.pi, 2*np.pi, -2*np.pi, 2*np.pi])
    >>> plt.title('Phase (angle) of exp(x)')
    >>> plt.show()"""
    return ndarray()
def exp2(x, out):
    """exp2(x[, out])
    
    Calculate `2**p` for all `p` in the input array.
    
    Parameters
    ----------
    x : array_like
        Input values.
    
    out : ndarray, optional
        Array to insert results into.
    
    Returns
    -------
    out : ndarray
        Element-wise 2 to the power `x`.
    
    See Also
    --------
    power
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    
    
    Examples
    --------
    >>> np.exp2([2, 3])
    array([ 4.,  8.])"""
    return ndarray()
def expand_dims(a, axis):
    """
        Expand the shape of an array.
    
        Insert a new axis, corresponding to a given position in the array shape.
    
        Parameters
        ----------
        a : array_like
            Input array.
        axis : int
            Position (amongst axes) where new axis is to be inserted.
    
        Returns
        -------
        res : ndarray
            Output array. The number of dimensions is one greater than that of
            the input array.
    
        See Also
        --------
        doc.indexing, atleast_1d, atleast_2d, atleast_3d
    
        Examples
        --------
        >>> x = np.array([1,2])
        >>> x.shape
        (2,)
    
        The following is equivalent to ``x[np.newaxis,:]`` or ``x[np.newaxis]``:
    
        >>> y = np.expand_dims(x, axis=0)
        >>> y
        array([[1, 2]])
        >>> y.shape
        (1, 2)
    
        >>> y = np.expand_dims(x, axis=1)  # Equivalent to x[:,newaxis]
        >>> y
        array([[1],
               [2]])
        >>> y.shape
        (2, 1)
    
        Note that some examples may use ``None`` instead of ``np.newaxis``.  These
        are the same objects:
    
        >>> np.newaxis is None
        True
    
        """
    return ndarray()
def expm1(x, out=None):
    """expm1(x[, out])
    
    Calculate ``exp(x) - 1`` for all elements in the array.
    
    Parameters
    ----------
    x : array_like
       Input values.
    
    Returns
    -------
    out : ndarray
        Element-wise exponential minus one: ``out = exp(x) - 1``.
    
    See Also
    --------
    log1p : ``log(1 + x)``, the inverse of expm1.
    
    
    Notes
    -----
    This function provides greater precision than the formula ``exp(x) - 1``
    for small values of ``x``.
    
    Examples
    --------
    The true value of ``exp(1e-10) - 1`` is ``1.00000000005e-10`` to
    about 32 significant digits. This example shows the superiority of
    expm1 in this case.
    
    >>> np.expm1(1e-10)
    1.00000000005e-10
    >>> np.exp(1e-10) - 1
    1.000000082740371e-10"""
    return ndarray()
def extract(condition, arr):
    """
        Return the elements of an array that satisfy some condition.
    
        This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
        `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.
    
        Parameters
        ----------
        condition : array_like
            An array whose nonzero or True entries indicate the elements of `arr`
            to extract.
        arr : array_like
            Input array of the same size as `condition`.
    
        Returns
        -------
        extract : ndarray
            Rank 1 array of values from `arr` where `condition` is True.
    
        See Also
        --------
        take, put, copyto, compress
    
        Examples
        --------
        >>> arr = np.arange(12).reshape((3, 4))
        >>> arr
        array([[ 0,  1,  2,  3],
               [ 4,  5,  6,  7],
               [ 8,  9, 10, 11]])
        >>> condition = np.mod(arr, 3)==0
        >>> condition
        array([[ True, False, False,  True],
               [False, False,  True, False],
               [False,  True, False, False]], dtype=bool)
        >>> np.extract(condition, arr)
        array([0, 3, 6, 9])
    
    
        If `condition` is boolean:
    
        >>> arr[condition]
        array([0, 3, 6, 9])
    
        """
    return ndarray()
def eye(N=typefloat(), M=None, k=0, dtype=typefloat()):
    """
        Return a 2-D array with ones on the diagonal and zeros elsewhere.
    
        Parameters
        ----------
        N : int
          Number of rows in the output.
        M : int, optional
          Number of columns in the output. If None, defaults to `N`.
        k : int, optional
          Index of the diagonal: 0 (the default) refers to the main diagonal,
          a positive value refers to an upper diagonal, and a negative value
          to a lower diagonal.
        dtype : data-type, optional
          Data-type of the returned array.
    
        Returns
        -------
        I : ndarray of shape (N,M)
          An array where all elements are equal to zero, except for the `k`-th
          diagonal, whose values are equal to one.
    
        See Also
        --------
        identity : (almost) equivalent function
        diag : diagonal 2-D array from a 1-D array specified by the user.
    
        Examples
        --------
        >>> np.eye(2, dtype=int)
        array([[1, 0],
               [0, 1]])
        >>> np.eye(3, k=1)
        array([[ 0.,  1.,  0.],
               [ 0.,  0.,  1.],
               [ 0.,  0.,  0.]])
    
        """
    return ndarray()
def fabs(x, out):
    """fabs(x[, out])
    
    Compute the absolute values elementwise.
    
    This function returns the absolute values (positive magnitude) of the data
    in `x`. Complex values are not handled, use `absolute` to find the
    absolute values of complex data.
    
    Parameters
    ----------
    x : array_like
        The array of numbers for which the absolute values are required. If
        `x` is a scalar, the result `y` will also be a scalar.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    y : {ndarray, scalar}
        The absolute values of `x`, the returned values are always floats.
    
    See Also
    --------
    absolute : Absolute values including `complex` types.
    
    Examples
    --------
    >>> np.fabs(-1)
    1.0
    >>> np.fabs([-1.2, 1.2])
    array([ 1.2,  1.2])"""
    return ndarray()
def _fastCopyAndTranspose(a):
    """_fastCopyAndTranspose(a)"""
    return None
def fill_diagonal(a, val=False, wrap=False):
    """Fill the main diagonal of the given array of any dimensionality.
    
        For an array `a` with ``a.ndim > 2``, the diagonal is the list of
        locations with indices ``a[i, i, ..., i]`` all identical. This function
        modifies the input array in-place, it does not return a value.
    
        Parameters
        ----------
        a : array, at least 2-D.
          Array whose diagonal is to be filled, it gets modified in-place.
    
        val : scalar
          Value to be written on the diagonal, its type must be compatible with
          that of the array a.
    
        wrap : bool
          For tall matrices in NumPy version up to 1.6.2, the
          diagonal "wrapped" after N columns. You can have this behavior
          with this option. This affect only tall matrices.
    
        See also
        --------
        diag_indices, diag_indices_from
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        This functionality can be obtained via `diag_indices`, but internally
        this version uses a much faster implementation that never constructs the
        indices and uses simple slicing.
    
        Examples
        --------
        >>> a = np.zeros((3, 3), int)
        >>> np.fill_diagonal(a, 5)
        >>> a
        array([[5, 0, 0],
               [0, 5, 0],
               [0, 0, 5]])
    
        The same function can operate on a 4-D array:
    
        >>> a = np.zeros((3, 3, 3, 3), int)
        >>> np.fill_diagonal(a, 4)
    
        We only show a few blocks for clarity:
    
        >>> a[0, 0]
        array([[4, 0, 0],
               [0, 0, 0],
               [0, 0, 0]])
        >>> a[1, 1]
        array([[0, 0, 0],
               [0, 4, 0],
               [0, 0, 0]])
        >>> a[2, 2]
        array([[0, 0, 0],
               [0, 0, 0],
               [0, 0, 4]])
    
        # tall matrices no wrap
        >>> a = np.zeros((5, 3),int)
        >>> fill_diagonal(a, 4)
        array([[4, 0, 0],
               [0, 4, 0],
               [0, 0, 4],
               [0, 0, 0],
               [0, 0, 0]])
    
        # tall matrices wrap
        >>> a = np.zeros((5, 3),int)
        >>> fill_diagonal(a, 4)
        array([[4, 0, 0],
               [0, 4, 0],
               [0, 0, 4],
               [0, 0, 0],
               [4, 0, 0]])
    
        # wide matrices
        >>> a = np.zeros((3, 5),int)
        >>> fill_diagonal(a, 4)
        array([[4, 0, 0, 0, 0],
               [0, 4, 0, 0, 0],
               [0, 0, 4, 0, 0]])
    
        """
    return None
def find_common_type(array_types, scalar_types):
    """
        Determine common type following standard coercion rules.
    
        Parameters
        ----------
        array_types : sequence
            A list of dtypes or dtype convertible objects representing arrays.
        scalar_types : sequence
            A list of dtypes or dtype convertible objects representing scalars.
    
        Returns
        -------
        datatype : dtype
            The common data type, which is the maximum of `array_types` ignoring
            `scalar_types`, unless the maximum of `scalar_types` is of a
            different kind (`dtype.kind`). If the kind is not understood, then
            None is returned.
    
        See Also
        --------
        dtype, common_type, can_cast, mintypecode
    
        Examples
        --------
        >>> np.find_common_type([], [np.int64, np.float32, np.complex])
        dtype('complex128')
        >>> np.find_common_type([np.int64, np.float32], [])
        dtype('float64')
    
        The standard casting rules ensure that a scalar cannot up-cast an
        array unless the scalar is of a fundamentally different kind of data
        (i.e. under a different hierarchy in the data type hierarchy) then
        the array:
    
        >>> np.find_common_type([np.float32], [np.int64, np.float64])
        dtype('float32')
    
        Complex is of a different type, so it up-casts the float in the
        `array_types` argument:
    
        >>> np.find_common_type([np.float32], [np.complex])
        dtype('complex128')
    
        Type specifier strings are convertible to dtypes and can therefore
        be used instead of dtypes:
    
        >>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
        dtype('complex128')
    
        """
    return dtype()
class finfo:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    _finfo_cache = dict()
    def _init(self, _):
        """None"""
        return None
def fix(x=None, y=None):
    """
        Round to nearest integer towards zero.
    
        Round an array of floats element-wise to nearest integer towards zero.
        The rounded values are returned as floats.
    
        Parameters
        ----------
        x : array_like
            An array of floats to be rounded
        y : ndarray, optional
            Output array
    
        Returns
        -------
        out : ndarray of floats
            The array of rounded numbers
    
        See Also
        --------
        trunc, floor, ceil
        around : Round to given number of decimals
    
        Examples
        --------
        >>> np.fix(3.14)
        3.0
        >>> np.fix(3)
        3.0
        >>> np.fix([2.1, 2.9, -2.1, -2.9])
        array([ 2.,  2., -2., -2.])
    
        """
    return ndarray()
class flatiter:
    __doc__ = str()
    base = member_descriptor()
    coords = getset_descriptor()
    def copy(self, _):
        """copy()
        
            Get a copy of the iterator as a 1-D array.
        
            Examples
            --------
            >>> x = np.arange(6).reshape(2, 3)
            >>> x
            array([[0, 1, 2],
                   [3, 4, 5]])
            >>> fl = x.flat
            >>> fl.copy()
            array([0, 1, 2, 3, 4, 5])"""
        return None
    index = member_descriptor()
    def next(self, _):
        """x.next() -> the next value, or raise StopIteration"""
        return None
def flatnonzero(a):
    """
        Return indices that are non-zero in the flattened version of a.
    
        This is equivalent to a.ravel().nonzero()[0].
    
        Parameters
        ----------
        a : ndarray
            Input array.
    
        Returns
        -------
        res : ndarray
            Output array, containing the indices of the elements of `a.ravel()`
            that are non-zero.
    
        See Also
        --------
        nonzero : Return the indices of the non-zero elements of the input array.
        ravel : Return a 1-D array containing the elements of the input array.
    
        Examples
        --------
        >>> x = np.arange(-2, 3)
        >>> x
        array([-2, -1,  0,  1,  2])
        >>> np.flatnonzero(x)
        array([0, 1, 3, 4])
    
        Use the indices of the non-zero elements as an index array to extract
        these elements:
    
        >>> x.ravel()[np.flatnonzero(x)]
        array([-2, -1,  1,  2])
    
        """
    return ndarray()
class flexible:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def fliplr(m):
    """
        Flip array in the left/right direction.
    
        Flip the entries in each row in the left/right direction.
        Columns are preserved, but appear in a different order than before.
    
        Parameters
        ----------
        m : array_like
            Input array.
    
        Returns
        -------
        f : ndarray
            A view of `m` with the columns reversed.  Since a view
            is returned, this operation is :math:`\mathcal O(1)`.
    
        See Also
        --------
        flipud : Flip array in the up/down direction.
        rot90 : Rotate array counterclockwise.
    
        Notes
        -----
        Equivalent to A[:,::-1]. Does not require the array to be
        two-dimensional.
    
        Examples
        --------
        >>> A = np.diag([1.,2.,3.])
        >>> A
        array([[ 1.,  0.,  0.],
               [ 0.,  2.,  0.],
               [ 0.,  0.,  3.]])
        >>> np.fliplr(A)
        array([[ 0.,  0.,  1.],
               [ 0.,  2.,  0.],
               [ 3.,  0.,  0.]])
    
        >>> A = np.random.randn(2,3,5)
        >>> np.all(np.fliplr(A)==A[:,::-1,...])
        True
    
        """
    return ndarray()
def flipud(m):
    """
        Flip array in the up/down direction.
    
        Flip the entries in each column in the up/down direction.
        Rows are preserved, but appear in a different order than before.
    
        Parameters
        ----------
        m : array_like
            Input array.
    
        Returns
        -------
        out : array_like
            A view of `m` with the rows reversed.  Since a view is
            returned, this operation is :math:`\mathcal O(1)`.
    
        See Also
        --------
        fliplr : Flip array in the left/right direction.
        rot90 : Rotate array counterclockwise.
    
        Notes
        -----
        Equivalent to ``A[::-1,...]``.
        Does not require the array to be two-dimensional.
    
        Examples
        --------
        >>> A = np.diag([1.0, 2, 3])
        >>> A
        array([[ 1.,  0.,  0.],
               [ 0.,  2.,  0.],
               [ 0.,  0.,  3.]])
        >>> np.flipud(A)
        array([[ 0.,  0.,  3.],
               [ 0.,  2.,  0.],
               [ 1.,  0.,  0.]])
    
        >>> A = np.random.randn(2,3,5)
        >>> np.all(np.flipud(A)==A[::-1,...])
        True
    
        >>> np.flipud([1,2])
        array([2, 1])
    
        """
    return ndarray()
class float:
    __doc__ = str()
    def as_integer_ratio(self, _):
        """float.as_integer_ratio() -> (int, int)
        
        Return a pair of integers, whose ratio is exactly equal to the original
        float and with a positive denominator.
        Raise OverflowError on infinities and a ValueError on NaNs.
        
        >>> (10.0).as_integer_ratio()
        (10, 1)
        >>> (0.0).as_integer_ratio()
        (0, 1)
        >>> (-.25).as_integer_ratio()
        (-1, 4)"""
        return None
    def conjugate(self, _):
        """Return self, the complex conjugate of any float."""
        return None
    def _fromhex(self, string):
        """float.fromhex(string) -> float
        
        Create a floating-point number from a hexadecimal string.
        >>> float.fromhex('0x1.ffffp10')
        2047.984375
        >>> float.fromhex('-0x1p-1074')
        -4.9406564584124654e-324"""
        return None
    def hex(self, _):
        """float.hex() -> string
        
        Return a hexadecimal representation of a floating-point number.
        >>> (-0.1).hex()
        '-0x1.999999999999ap-4'
        >>> 3.14159.hex()
        '0x1.921f9f01b866ep+1'"""
        return None
    imag = getset_descriptor()
    def is_integer(self, _):
        """Return True if the float is an integer."""
        return None
    real = getset_descriptor()
class float128:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class float16:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class float32:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class float64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    def as_integer_ratio(self, _):
        """float.as_integer_ratio() -> (int, int)
        
        Return a pair of integers, whose ratio is exactly equal to the original
        float and with a positive denominator.
        Raise OverflowError on infinities and a ValueError on NaNs.
        
        >>> (10.0).as_integer_ratio()
        (10, 1)
        >>> (0.0).as_integer_ratio()
        (0, 1)
        >>> (-.25).as_integer_ratio()
        (-1, 4)"""
        return None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    def _fromhex(self, string):
        """float.fromhex(string) -> float
        
        Create a floating-point number from a hexadecimal string.
        >>> float.fromhex('0x1.ffffp10')
        2047.984375
        >>> float.fromhex('-0x1p-1074')
        -4.9406564584124654e-324"""
        return None
    def hex(self, _):
        """float.hex() -> string
        
        Return a hexadecimal representation of a floating-point number.
        >>> (-0.1).hex()
        '-0x1.999999999999ap-4'
        >>> 3.14159.hex()
        '0x1.921f9f01b866ep+1'"""
        return None
    imag = getset_descriptor()
    def is_integer(self, _):
        """Return True if the float is an integer."""
        return None
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class float64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    def as_integer_ratio(self, _):
        """float.as_integer_ratio() -> (int, int)
        
        Return a pair of integers, whose ratio is exactly equal to the original
        float and with a positive denominator.
        Raise OverflowError on infinities and a ValueError on NaNs.
        
        >>> (10.0).as_integer_ratio()
        (10, 1)
        >>> (0.0).as_integer_ratio()
        (0, 1)
        >>> (-.25).as_integer_ratio()
        (-1, 4)"""
        return None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    def _fromhex(self, string):
        """float.fromhex(string) -> float
        
        Create a floating-point number from a hexadecimal string.
        >>> float.fromhex('0x1.ffffp10')
        2047.984375
        >>> float.fromhex('-0x1p-1074')
        -4.9406564584124654e-324"""
        return None
    def hex(self, _):
        """float.hex() -> string
        
        Return a hexadecimal representation of a floating-point number.
        >>> (-0.1).hex()
        '-0x1.999999999999ap-4'
        >>> 3.14159.hex()
        '0x1.921f9f01b866ep+1'"""
        return None
    imag = getset_descriptor()
    def is_integer(self, _):
        """Return True if the float is an integer."""
        return None
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class floating:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def floor(x, out=None):
    """floor(x[, out])
    
    Return the floor of the input, element-wise.
    
    The floor of the scalar `x` is the largest integer `i`, such that
    `i <= x`.  It is often denoted as :math:`\lfloor x \rfloor`.
    
    Parameters
    ----------
    x : array_like
        Input data.
    
    Returns
    -------
    y : {ndarray, scalar}
        The floor of each element in `x`.
    
    See Also
    --------
    ceil, trunc, rint
    
    Notes
    -----
    Some spreadsheet programs calculate the "floor-towards-zero", in other
    words ``floor(-2.5) == -2``.  NumPy, however, uses the a definition of
    `floor` such that `floor(-2.5) == -3`.
    
    Examples
    --------
    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
    >>> np.floor(a)
    array([-2., -2., -1.,  0.,  1.,  1.,  2.])"""
    return ndarray()
def floor_divide(x1, x2, out=None):
    """floor_divide(x1, x2[, out])
    
    Return the largest integer smaller or equal to the division of the inputs.
    
    Parameters
    ----------
    x1 : array_like
        Numerator.
    x2 : array_like
        Denominator.
    
    Returns
    -------
    y : ndarray
        y = floor(`x1`/`x2`)
    
    
    See Also
    --------
    divide : Standard division.
    floor : Round a number to the nearest integer toward minus infinity.
    ceil : Round a number to the nearest integer toward infinity.
    
    Examples
    --------
    >>> np.floor_divide(7,3)
    2
    >>> np.floor_divide([1., 2., 3., 4.], 2.5)
    array([ 0.,  0.,  1.,  1.])"""
    return ndarray()
def fmax(x1, x2, out=None):
    """fmax(x1, x2[, out])
    
    Element-wise maximum of array elements.
    
    Compare two arrays and returns a new array containing the element-wise
    maxima. If one of the elements being compared is a nan, then the non-nan
    element is returned. If both elements are nans then the first is returned.
    The latter distinction is important for complex nans, which are defined as
    at least one of the real or imaginary parts being a nan. The net effect is
    that nans are ignored when possible.
    
    Parameters
    ----------
    x1, x2 : array_like
        The arrays holding the elements to be compared. They must have
        the same shape.
    
    Returns
    -------
    y : {ndarray, scalar}
        The minimum of `x1` and `x2`, element-wise.  Returns scalar if
        both  `x1` and `x2` are scalars.
    
    See Also
    --------
    fmin :
        Element-wise minimum of two arrays, ignoring any NaNs.
    maximum :
        Element-wise maximum of two arrays, propagating any NaNs.
    amax :
        The maximum value of an array along a given axis, propagating any NaNs.
    nanmax :
        The maximum value of an array along a given axis, ignoring any NaNs.
    
    minimum, amin, nanmin
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    The fmax is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither
    x1 nor x2 are nans, but it is faster and does proper broadcasting.
    
    Examples
    --------
    >>> np.fmax([2, 3, 4], [1, 5, 2])
    array([ 2.,  5.,  4.])
    
    >>> np.fmax(np.eye(2), [0.5, 2])
    array([[ 1. ,  2. ],
           [ 0.5,  2. ]])
    
    >>> np.fmax([np.nan, 0, np.nan],[0, np.nan, np.nan])
    array([  0.,   0.,  NaN])"""
    return ndarray()
def fmin(x1, x2, out=None):
    """fmin(x1, x2[, out])
    
    Element-wise minimum of array elements.
    
    Compare two arrays and returns a new array containing the element-wise
    minima. If one of the elements being compared is a nan, then the non-nan
    element is returned. If both elements are nans then the first is returned.
    The latter distinction is important for complex nans, which are defined as
    at least one of the real or imaginary parts being a nan. The net effect is
    that nans are ignored when possible.
    
    Parameters
    ----------
    x1, x2 : array_like
        The arrays holding the elements to be compared. They must have
        the same shape.
    
    Returns
    -------
    y : {ndarray, scalar}
        The minimum of `x1` and `x2`, element-wise.  Returns scalar if
        both  `x1` and `x2` are scalars.
    
    See Also
    --------
    fmax :
        Element-wise maximum of two arrays, ignoring any NaNs.
    minimum :
        Element-wise minimum of two arrays, propagating any NaNs.
    amin :
        The minimum value of an array along a given axis, propagating any NaNs.
    nanmin :
        The minimum value of an array along a given axis, ignoring any NaNs.
    
    maximum, amax, nanmax
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither
    x1 nor x2 are nans, but it is faster and does proper broadcasting.
    
    Examples
    --------
    >>> np.fmin([2, 3, 4], [1, 5, 2])
    array([2, 5, 4])
    
    >>> np.fmin(np.eye(2), [0.5, 2])
    array([[ 1. ,  2. ],
           [ 0.5,  2. ]])
    
    >>> np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])
    array([  0.,   0.,  NaN])"""
    return ndarray()
def fmod(x1, x2, out=None):
    """fmod(x1, x2[, out])
    
    Return the element-wise remainder of division.
    
    This is the NumPy implementation of the Python modulo operator `%`.
    
    Parameters
    ----------
    x1 : array_like
      Dividend.
    x2 : array_like
      Divisor.
    
    Returns
    -------
    y : array_like
      The remainder of the division of `x1` by `x2`.
    
    See Also
    --------
    remainder : Modulo operation where the quotient is `floor(x1/x2)`.
    divide
    
    Notes
    -----
    The result of the modulo operation for negative dividend and divisors is
    bound by conventions. In `fmod`, the sign of the remainder is the sign of
    the dividend. In `remainder`, the sign of the divisor does not affect the
    sign of the result.
    
    Examples
    --------
    >>> np.fmod([-3, -2, -1, 1, 2, 3], 2)
    array([-1,  0, -1,  1,  0,  1])
    >>> np.remainder([-3, -2, -1, 1, 2, 3], 2)
    array([1, 0, 1, 1, 0, 1])
    
    >>> np.fmod([5, 3], [2, 2.])
    array([ 1.,  1.])
    >>> a = np.arange(-3, 3).reshape(3, 2)
    >>> a
    array([[-3, -2],
           [-1,  0],
           [ 1,  2]])
    >>> np.fmod(a, [2,2])
    array([[-1,  0],
           [-1,  0],
           [ 1,  0]])"""
    return ndarray()
class format_parser:
    __doc__ = str()
    __module__ = str()
    def _createdescr(self, _):
        """None"""
        return None
    def _parseFormats(self, formats=0, aligned=0):
        """ Parse the field formats """
        return None
    def _setfieldnames(self, _):
        """convert input field names into a list and assign to the _names
                attribute """
        return None
def frexp(x, out1, out2):
    """frexp(x[, out1, out2])
    
    Split the number, x, into a normalized fraction (y1) and exponent (y2)"""
    return None
def _frombuffer(buffer, dtype, count, offset):
    """frombuffer(buffer, dtype=float, count=-1, offset=0)
    
        Interpret a buffer as a 1-dimensional array.
    
        Parameters
        ----------
        buffer : buffer_like
            An object that exposes the buffer interface.
        dtype : data-type, optional
            Data-type of the returned array; default: float.
        count : int, optional
            Number of items to read. ``-1`` means all data in the buffer.
        offset : int, optional
            Start reading the buffer from this offset; default: 0.
    
        Notes
        -----
        If the buffer has data that is not in machine byte-order, this should
        be specified as part of the data-type, e.g.::
    
          >>> dt = np.dtype(int)
          >>> dt = dt.newbyteorder('>')
          >>> np.frombuffer(buf, dtype=dt)
    
        The data of the resulting array will not be byteswapped, but will be
        interpreted correctly.
    
        Examples
        --------
        >>> s = 'hello world'
        >>> np.frombuffer(s, dtype='S1', count=5, offset=6)
        array(['w', 'o', 'r', 'l', 'd'],
              dtype='|S1')"""
    return None
def _fromfile(file, dtype, count, sep):
    """fromfile(file, dtype=float, count=-1, sep='')
    
        Construct an array from data in a text or binary file.
    
        A highly efficient way of reading binary data with a known data-type,
        as well as parsing simply formatted text files.  Data written using the
        `tofile` method can be read using this function.
    
        Parameters
        ----------
        file : file or str
            Open file object or filename.
        dtype : data-type
            Data type of the returned array.
            For binary files, it is used to determine the size and byte-order
            of the items in the file.
        count : int
            Number of items to read. ``-1`` means all items (i.e., the complete
            file).
        sep : str
            Separator between items if file is a text file.
            Empty ("") separator means the file should be treated as binary.
            Spaces (" ") in the separator match zero or more whitespace characters.
            A separator consisting only of spaces must match at least one
            whitespace.
    
        See also
        --------
        load, save
        ndarray.tofile
        loadtxt : More flexible way of loading data from a text file.
    
        Notes
        -----
        Do not rely on the combination of `tofile` and `fromfile` for
        data storage, as the binary files generated are are not platform
        independent.  In particular, no byte-order or data-type information is
        saved.  Data can be stored in the platform independent ``.npy`` format
        using `save` and `load` instead.
    
        Examples
        --------
        Construct an ndarray:
    
        >>> dt = np.dtype([('time', [('min', int), ('sec', int)]),
        ...                ('temp', float)])
        >>> x = np.zeros((1,), dtype=dt)
        >>> x['time']['min'] = 10; x['temp'] = 98.25
        >>> x
        array([((10, 0), 98.25)],
              dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
    
        Save the raw data to disk:
    
        >>> import os
        >>> fname = os.tmpnam()
        >>> x.tofile(fname)
    
        Read the raw data from disk:
    
        >>> np.fromfile(fname, dtype=dt)
        array([((10, 0), 98.25)],
              dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
    
        The recommended way to store and load data:
    
        >>> np.save(fname, x)
        >>> np.load(fname + '.npy')
        array([((10, 0), 98.25)],
              dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])"""
    return None
def _fromfunction(function, shape, dtype):
    """
        Construct an array by executing a function over each coordinate.
    
        The resulting array therefore has a value ``fn(x, y, z)`` at
        coordinate ``(x, y, z)``.
    
        Parameters
        ----------
        function : callable
            The function is called with N parameters, where N is the rank of
            `shape`.  Each parameter represents the coordinates of the array
            varying along a specific axis.  For example, if `shape`
            were ``(2, 2)``, then the parameters in turn be (0, 0), (0, 1),
            (1, 0), (1, 1).
        shape : (N,) tuple of ints
            Shape of the output array, which also determines the shape of
            the coordinate arrays passed to `function`.
        dtype : data-type, optional
            Data-type of the coordinate arrays passed to `function`.
            By default, `dtype` is float.
    
        Returns
        -------
        fromfunction : any
            The result of the call to `function` is passed back directly.
            Therefore the shape of `fromfunction` is completely determined by
            `function`.  If `function` returns a scalar value, the shape of
            `fromfunction` would match the `shape` parameter.
    
        See Also
        --------
        indices, meshgrid
    
        Notes
        -----
        Keywords other than `dtype` are passed to `function`.
    
        Examples
        --------
        >>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
        array([[ True, False, False],
               [False,  True, False],
               [False, False,  True]], dtype=bool)
    
        >>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
        array([[0, 1, 2],
               [1, 2, 3],
               [2, 3, 4]])
    
        """
    return any()
def _fromiter(iterable, dtype, count):
    """fromiter(iterable, dtype, count=-1)
    
        Create a new 1-dimensional array from an iterable object.
    
        Parameters
        ----------
        iterable : iterable object
            An iterable object providing data for the array.
        dtype : data-type
            The data-type of the returned array.
        count : int, optional
            The number of items to read from *iterable*.  The default is -1,
            which means all data is read.
    
        Returns
        -------
        out : ndarray
            The output array.
    
        Notes
        -----
        Specify `count` to improve performance.  It allows ``fromiter`` to
        pre-allocate the output array, instead of resizing it on demand.
    
        Examples
        --------
        >>> iterable = (x*x for x in range(5))
        >>> np.fromiter(iterable, np.float)
        array([  0.,   1.,   4.,   9.,  16.])"""
    return ndarray()
def _frompyfunc(func, nin, nout):
    """frompyfunc(func, nin, nout)
    
        Takes an arbitrary Python function and returns a Numpy ufunc.
    
        Can be used, for example, to add broadcasting to a built-in Python
        function (see Examples section).
    
        Parameters
        ----------
        func : Python function object
            An arbitrary Python function.
        nin : int
            The number of input arguments.
        nout : int
            The number of objects returned by `func`.
    
        Returns
        -------
        out : ufunc
            Returns a Numpy universal function (``ufunc``) object.
    
        Notes
        -----
        The returned ufunc always returns PyObject arrays.
    
        Examples
        --------
        Use frompyfunc to add broadcasting to the Python function ``oct``:
    
        >>> oct_array = np.frompyfunc(oct, 1, 1)
        >>> oct_array(np.array((10, 30, 100)))
        array([012, 036, 0144], dtype=object)
        >>> np.array((oct(10), oct(30), oct(100))) # for comparison
        array(['012', '036', '0144'],
              dtype='|S4')"""
    return ufunc()
def _fromregex(file, regexp, dtype):
    """
        Construct an array from a text file, using regular expression parsing.
    
        The returned array is always a structured array, and is constructed from
        all matches of the regular expression in the file. Groups in the regular
        expression are converted to fields of the structured array.
    
        Parameters
        ----------
        file : str or file
            File name or file object to read.
        regexp : str or regexp
            Regular expression used to parse the file.
            Groups in the regular expression correspond to fields in the dtype.
        dtype : dtype or list of dtypes
            Dtype for the structured array.
    
        Returns
        -------
        output : ndarray
            The output array, containing the part of the content of `file` that
            was matched by `regexp`. `output` is always a structured array.
    
        Raises
        ------
        TypeError
            When `dtype` is not a valid dtype for a structured array.
    
        See Also
        --------
        fromstring, loadtxt
    
        Notes
        -----
        Dtypes for structured arrays can be specified in several forms, but all
        forms specify at least the data type and field name. For details see
        `doc.structured_arrays`.
    
        Examples
        --------
        >>> f = open('test.dat', 'w')
        >>> f.write("1312 foo\n1534  bar\n444   qux")
        >>> f.close()
    
        >>> regexp = r"(\d+)\s+(...)"  # match [digits, whitespace, anything]
        >>> output = np.fromregex('test.dat', regexp,
        ...                       [('num', np.int64), ('key', 'S3')])
        >>> output
        array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],
              dtype=[('num', '<i8'), ('key', '|S3')])
        >>> output['num']
        array([1312, 1534,  444], dtype=int64)
    
        """
    return ndarray()
def _fromstring(string, dtype, count, sep):
    """fromstring(string, dtype=float, count=-1, sep='')
    
        A new 1-D array initialized from raw binary or text data in a string.
    
        Parameters
        ----------
        string : str
            A string containing the data.
        dtype : data-type, optional
            The data type of the array; default: float.  For binary input data,
            the data must be in exactly this format.
        count : int, optional
            Read this number of `dtype` elements from the data.  If this is
            negative (the default), the count will be determined from the
            length of the data.
        sep : str, optional
            If not provided or, equivalently, the empty string, the data will
            be interpreted as binary data; otherwise, as ASCII text with
            decimal numbers.  Also in this latter case, this argument is
            interpreted as the string separating numbers in the data; extra
            whitespace between elements is also ignored.
    
        Returns
        -------
        arr : ndarray
            The constructed array.
    
        Raises
        ------
        ValueError
            If the string is not the correct size to satisfy the requested
            `dtype` and `count`.
    
        See Also
        --------
        frombuffer, fromfile, fromiter
    
        Examples
        --------
        >>> np.fromstring('\x01\x02', dtype=np.uint8)
        array([1, 2], dtype=uint8)
        >>> np.fromstring('1 2', dtype=int, sep=' ')
        array([1, 2])
        >>> np.fromstring('1, 2', dtype=int, sep=',')
        array([1, 2])
        >>> np.fromstring('\x01\x02\x03\x04\x05', dtype=np.uint8, count=3)
        array([1, 2, 3], dtype=uint8)"""
    return ndarray()
def full(shape, fill_value="C", dtype=None, order="C"):
    """
        Return a new array of given shape and type, filled with `fill_value`.
    
        Parameters
        ----------
        shape : int or sequence of ints
            Shape of the new array, e.g., ``(2, 3)`` or ``2``.
        fill_value : scalar
            Fill value.
        dtype : data-type, optional
            The desired data-type for the array, e.g., `numpy.int8`.  Default is
            is chosen as `np.array(fill_value).dtype`.
        order : {'C', 'F'}, optional
            Whether to store multidimensional data in C- or Fortran-contiguous
            (row- or column-wise) order in memory.
    
        Returns
        -------
        out : ndarray
            Array of `fill_value` with the given shape, dtype, and order.
    
        See Also
        --------
        zeros_like : Return an array of zeros with shape and type of input.
        ones_like : Return an array of ones with shape and type of input.
        empty_like : Return an empty array with shape and type of input.
        full_like : Fill an array with shape and type of input.
        zeros : Return a new array setting values to zero.
        ones : Return a new array setting values to one.
        empty : Return a new uninitialized array.
    
        Examples
        --------
        >>> np.full((2, 2), np.inf)
        array([[ inf,  inf],
               [ inf,  inf]])
        >>> np.full((2, 2), 10, dtype=np.int)
        array([[10, 10],
               [10, 10]])
    
        """
    return ndarray()
def full_like(a, fill_value=True, dtype=None, order="K", subok=True):
    """
        Return a full array with the same shape and type as a given array.
    
        Parameters
        ----------
        a : array_like
            The shape and data-type of `a` define these same attributes of
            the returned array.
        fill_value : scalar
            Fill value.
        dtype : data-type, optional
            Overrides the data type of the result.
        order : {'C', 'F', 'A', or 'K'}, optional
            Overrides the memory layout of the result. 'C' means C-order,
            'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
            'C' otherwise. 'K' means match the layout of `a` as closely
            as possible.
        subok : bool, optional.
            If True, then the newly created array will use the sub-class
            type of 'a', otherwise it will be a base-class array. Defaults
            to True.
    
        Returns
        -------
        out : ndarray
            Array of `fill_value` with the same shape and type as `a`.
    
        See Also
        --------
        zeros_like : Return an array of zeros with shape and type of input.
        ones_like : Return an array of ones with shape and type of input.
        empty_like : Return an empty array with shape and type of input.
        zeros : Return a new array setting values to zero.
        ones : Return a new array setting values to one.
        empty : Return a new uninitialized array.
        full : Fill a new array.
    
        Examples
        --------
        >>> x = np.arange(6, dtype=np.int)
        >>> np.full_like(x, 1)
        array([1, 1, 1, 1, 1, 1])
        >>> np.full_like(x, 0.1)
        array([0, 0, 0, 0, 0, 0])
        >>> np.full_like(x, 0.1, dtype=np.double)
        array([ 0.1,  0.1,  0.1,  0.1,  0.1,  0.1])
        >>> np.full_like(x, np.nan, dtype=np.double)
        array([ nan,  nan,  nan,  nan,  nan,  nan])
    
        >>> y = np.arange(6, dtype=np.double)
        >>> np.full_like(y, 0.1)
        array([ 0.1,  0.1,  0.1,  0.1,  0.1,  0.1])
    
        """
    return ndarray()
def fv(rate, nper, pmt, pv="end", when="end"):
    """
        Compute the future value.
    
        Given:
         * a present value, `pv`
         * an interest `rate` compounded once per period, of which
           there are
         * `nper` total
         * a (fixed) payment, `pmt`, paid either
         * at the beginning (`when` = {'begin', 1}) or the end
           (`when` = {'end', 0}) of each period
    
        Return:
           the value at the end of the `nper` periods
    
        Parameters
        ----------
        rate : scalar or array_like of shape(M, )
            Rate of interest as decimal (not per cent) per period
        nper : scalar or array_like of shape(M, )
            Number of compounding periods
        pmt : scalar or array_like of shape(M, )
            Payment
        pv : scalar or array_like of shape(M, )
            Present value
        when : {{'begin', 1}, {'end', 0}}, {string, int}, optional
            When payments are due ('begin' (1) or 'end' (0)).
            Defaults to {'end', 0}.
    
        Returns
        -------
        out : ndarray
            Future values.  If all input is scalar, returns a scalar float.  If
            any input is array_like, returns future values for each input element.
            If multiple inputs are array_like, they all must have the same shape.
    
        Notes
        -----
        The future value is computed by solving the equation::
    
         fv +
         pv*(1+rate)**nper +
         pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0
    
        or, when ``rate == 0``::
    
         fv + pv + pmt * nper == 0
    
        References
        ----------
        .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
           Open Document Format for Office Applications (OpenDocument)v1.2,
           Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,
           Pre-Draft 12. Organization for the Advancement of Structured Information
           Standards (OASIS). Billerica, MA, USA. [ODT Document].
           Available:
           http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula
           OpenDocument-formula-20090508.odt
    
        Examples
        --------
        What is the future value after 10 years of saving $100 now, with
        an additional monthly savings of $100.  Assume the interest rate is
        5% (annually) compounded monthly?
    
        >>> np.fv(0.05/12, 10*12, -100, -100)
        15692.928894335748
    
        By convention, the negative sign represents cash flow out (i.e. money not
        available today).  Thus, saving $100 a month at 5% annual interest leads
        to $15,692.93 available to spend in 10 years.
    
        If any input is array_like, returns an array of equal shape.  Let's
        compare different interest rates from the example above.
    
        >>> a = np.array((0.05, 0.06, 0.07))/12
        >>> np.fv(a, 10*12, -100, -100)
        array([ 15692.92889434,  16569.87435405,  17509.44688102])
    
        """
    return ndarray()
class generic:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def gen_fromtxt(fname=True, dtype=typefloat(), comments="#", delimiter=None, skiprows=0, skip_header=0, skip_footer=0, converters=None, missing="", missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space="_", autostrip=False, case_sensitive=True, defaultfmt="f%i", unpack=None, usemask=False, loose=True, invalid_raise=True):
    """
        Load data from a text file, with missing values handled as specified.
    
        Each line past the first `skip_header` lines is split at the `delimiter`
        character, and characters following the `comments` character are discarded.
    
        Parameters
        ----------
        fname : file or str
            File, filename, or generator to read.  If the filename extension is
            `.gz` or `.bz2`, the file is first decompressed. Note that
            generators must return byte strings in Python 3k.
        dtype : dtype, optional
            Data type of the resulting array.
            If None, the dtypes will be determined by the contents of each
            column, individually.
        comments : str, optional
            The character used to indicate the start of a comment.
            All the characters occurring on a line after a comment are discarded
        delimiter : str, int, or sequence, optional
            The string used to separate values.  By default, any consecutive
            whitespaces act as delimiter.  An integer or sequence of integers
            can also be provided as width(s) of each field.
        skip_header : int, optional
            The numbers of lines to skip at the beginning of the file.
        skip_footer : int, optional
            The numbers of lines to skip at the end of the file
        converters : variable, optional
            The set of functions that convert the data of a column to a value.
            The converters can also be used to provide a default value
            for missing data: ``converters = {3: lambda s: float(s or 0)}``.
        missing_values : variable, optional
            The set of strings corresponding to missing data.
        filling_values : variable, optional
            The set of values to be used as default when the data are missing.
        usecols : sequence, optional
            Which columns to read, with 0 being the first.  For example,
            ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
        names : {None, True, str, sequence}, optional
            If `names` is True, the field names are read from the first valid line
            after the first `skip_header` lines.
            If `names` is a sequence or a single-string of comma-separated names,
            the names will be used to define the field names in a structured dtype.
            If `names` is None, the names of the dtype fields will be used, if any.
        excludelist : sequence, optional
            A list of names to exclude. This list is appended to the default list
            ['return','file','print']. Excluded names are appended an underscore:
            for example, `file` would become `file_`.
        deletechars : str, optional
            A string combining invalid characters that must be deleted from the
            names.
        defaultfmt : str, optional
            A format used to define default field names, such as "f%i" or "f_%02i".
        autostrip : bool, optional
            Whether to automatically strip white spaces from the variables.
        replace_space : char, optional
            Character(s) used in replacement of white spaces in the variables
            names. By default, use a '_'.
        case_sensitive : {True, False, 'upper', 'lower'}, optional
            If True, field names are case sensitive.
            If False or 'upper', field names are converted to upper case.
            If 'lower', field names are converted to lower case.
        unpack : bool, optional
            If True, the returned array is transposed, so that arguments may be
            unpacked using ``x, y, z = loadtxt(...)``
        usemask : bool, optional
            If True, return a masked array.
            If False, return a regular array.
        invalid_raise : bool, optional
            If True, an exception is raised if an inconsistency is detected in the
            number of columns.
            If False, a warning is emitted and the offending lines are skipped.
    
        Returns
        -------
        out : ndarray
            Data read from the text file. If `usemask` is True, this is a
            masked array.
    
        See Also
        --------
        numpy.loadtxt : equivalent function when no data is missing.
    
        Notes
        -----
        * When spaces are used as delimiters, or when no delimiter has been given
          as input, there should not be any missing data between two fields.
        * When the variables are named (either by a flexible dtype or with `names`,
          there must not be any header in the file (else a ValueError
          exception is raised).
        * Individual values are not stripped of spaces by default.
          When using a custom converter, make sure the function does remove spaces.
    
        References
        ----------
        .. [1] Numpy User Guide, section `I/O with Numpy
               <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
    
        Examples
        ---------
        >>> from StringIO import StringIO
        >>> import numpy as np
    
        Comma delimited file with mixed dtype
    
        >>> s = StringIO("1,1.3,abcde")
        >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
        ... ('mystring','S5')], delimiter=",")
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
    
        Using dtype = None
    
        >>> s.seek(0) # needed for StringIO example only
        >>> data = np.genfromtxt(s, dtype=None,
        ... names = ['myint','myfloat','mystring'], delimiter=",")
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
    
        Specifying dtype and names
    
        >>> s.seek(0)
        >>> data = np.genfromtxt(s, dtype="i8,f8,S5",
        ... names=['myint','myfloat','mystring'], delimiter=",")
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
    
        An example with fixed-width columns
    
        >>> s = StringIO("11.3abcde")
        >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
        ...     delimiter=[1,3,5])
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])
    
        """
    return ndarray()
def get_array_wrap():
    """Find the wrapper for the array with the highest priority.
    
        In case of ties, leftmost wins. If no wrapper is found, return None
        """
    return None
def get_include():
    """
        Return the directory that contains the NumPy \*.h header files.
    
        Extension modules that need to compile against NumPy should use this
        function to locate the appropriate include directory.
    
        Notes
        -----
        When using ``distutils``, for example in ``setup.py``.
        ::
    
            import numpy as np
            ...
            Extension('extension_name', ...
                    include_dirs=[np.get_include()])
            ...
    
        """
    return None
def get_numarray_include(type=None):
    """
        Return the directory that contains the numarray \*.h header files.
    
        Extension modules that need to compile against numarray should use this
        function to locate the appropriate include directory.
    
        Parameters
        ----------
        type : any, optional
            If `type` is not None, the location of the NumPy headers is returned
            as well.
    
        Returns
        -------
        dirs : str or list of str
            If `type` is None, `dirs` is a string containing the path to the
            numarray headers.
            If `type` is not None, `dirs` is a list of strings with first the
            path(s) to the numarray headers, followed by the path to the NumPy
            headers.
    
        Notes
        -----
        Useful when using ``distutils``, for example in ``setup.py``.
        ::
    
            import numpy as np
            ...
            Extension('extension_name', ...
                    include_dirs=[np.get_numarray_include()])
            ...
    
        """
    return str() if False else list()
def get_printoptions():
    """
        Return the current print options.
    
        Returns
        -------
        print_opts : dict
            Dictionary of current print options with keys
    
              - precision : int
              - threshold : int
              - edgeitems : int
              - linewidth : int
              - suppress : bool
              - nanstr : str
              - infstr : str
              - formatter : dict of callables
    
            For a full description of these options, see `set_printoptions`.
    
        See Also
        --------
        set_printoptions, set_string_function
    
        """
    return None
def getbuffer(obj, offset, size):
    """getbuffer(obj [,offset[, size]])
    
        Create a buffer object from the given object referencing a slice of
        length size starting at offset.
    
        Default is the entire buffer. A read-write buffer is attempted followed
        by a read-only buffer.
    
        Parameters
        ----------
        obj : object
    
        offset : int, optional
    
        size : int, optional
    
        Returns
        -------
        buffer_obj : buffer
    
        Examples
        --------
        >>> buf = np.getbuffer(np.ones(5), 1, 3)
        >>> len(buf)
        3
        >>> buf[0]
        '\x00'
        >>> buf
        <read-write buffer for 0x8af1e70, size 3, offset 1 at 0x8ba4ec0>"""
    return buffer()
def getbufsize():
    """
        Return the size of the buffer used in ufuncs.
    
        Returns
        -------
        getbufsize : int
            Size of ufunc buffer in bytes.
    
        """
    return None
def geterr():
    """
        Get the current way of handling floating-point errors.
    
        Returns
        -------
        res : dict
            A dictionary with keys "divide", "over", "under", and "invalid",
            whose values are from the strings "ignore", "print", "log", "warn",
            "raise", and "call". The keys represent possible floating-point
            exceptions, and the values define how these exceptions are handled.
    
        See Also
        --------
        geterrcall, seterr, seterrcall
    
        Notes
        -----
        For complete documentation of the types of floating-point exceptions and
        treatment options, see `seterr`.
    
        Examples
        --------
        >>> np.geterr()
        {'over': 'warn', 'divide': 'warn', 'invalid': 'warn',
        'under': 'ignore'}
        >>> np.arange(3.) / np.arange(3.)
        array([ NaN,   1.,   1.])
    
        >>> oldsettings = np.seterr(all='warn', over='raise')
        >>> np.geterr()
        {'over': 'raise', 'divide': 'warn', 'invalid': 'warn', 'under': 'warn'}
        >>> np.arange(3.) / np.arange(3.)
        __main__:1: RuntimeWarning: invalid value encountered in divide
        array([ NaN,   1.,   1.])
    
        """
    return None
def geterrcall():
    """
        Return the current callback function used on floating-point errors.
    
        When the error handling for a floating-point error (one of "divide",
        "over", "under", or "invalid") is set to 'call' or 'log', the function
        that is called or the log instance that is written to is returned by
        `geterrcall`. This function or log instance has been set with
        `seterrcall`.
    
        Returns
        -------
        errobj : callable, log instance or None
            The current error handler. If no handler was set through `seterrcall`,
            ``None`` is returned.
    
        See Also
        --------
        seterrcall, seterr, geterr
    
        Notes
        -----
        For complete documentation of the types of floating-point exceptions and
        treatment options, see `seterr`.
    
        Examples
        --------
        >>> np.geterrcall()  # we did not yet set a handler, returns None
    
        >>> oldsettings = np.seterr(all='call')
        >>> def err_handler(type, flag):
        ...     print "Floating point error (%s), with flag %s" % (type, flag)
        >>> oldhandler = np.seterrcall(err_handler)
        >>> np.array([1, 2, 3]) / 0.0
        Floating point error (divide by zero), with flag 1
        array([ Inf,  Inf,  Inf])
    
        >>> cur_handler = np.geterrcall()
        >>> cur_handler is err_handler
        True
    
        """
    return None
def geterrobj(_):
    """geterrobj()
    
        Return the current object that defines floating-point error handling.
    
        The error object contains all information that defines the error handling
        behavior in Numpy. `geterrobj` is used internally by the other
        functions that get and set error handling behavior (`geterr`, `seterr`,
        `geterrcall`, `seterrcall`).
    
        Returns
        -------
        errobj : list
            The error object, a list containing three elements:
            [internal numpy buffer size, error mask, error callback function].
    
            The error mask is a single integer that holds the treatment information
            on all four floating point errors. The information for each error type
            is contained in three bits of the integer. If we print it in base 8, we
            can see what treatment is set for "invalid", "under", "over", and
            "divide" (in that order). The printed string can be interpreted with
    
            * 0 : 'ignore'
            * 1 : 'warn'
            * 2 : 'raise'
            * 3 : 'call'
            * 4 : 'print'
            * 5 : 'log'
    
        See Also
        --------
        seterrobj, seterr, geterr, seterrcall, geterrcall
        getbufsize, setbufsize
    
        Notes
        -----
        For complete documentation of the types of floating-point exceptions and
        treatment options, see `seterr`.
    
        Examples
        --------
        >>> np.geterrobj()  # first get the defaults
        [10000, 0, None]
    
        >>> def err_handler(type, flag):
        ...     print "Floating point error (%s), with flag %s" % (type, flag)
        ...
        >>> old_bufsize = np.setbufsize(20000)
        >>> old_err = np.seterr(divide='raise')
        >>> old_handler = np.seterrcall(err_handler)
        >>> np.geterrobj()
        [20000, 2, <function err_handler at 0x91dcaac>]
    
        >>> old_err = np.seterr(all='ignore')
        >>> np.base_repr(np.geterrobj()[1], 8)
        '0'
        >>> old_err = np.seterr(divide='warn', over='log', under='call',
                                invalid='print')
        >>> np.base_repr(np.geterrobj()[1], 8)
        '4351'"""
    return None
def gradient(f, varargs):
    """
        Return the gradient of an N-dimensional array.
    
        The gradient is computed using central differences in the interior
        and first differences at the boundaries. The returned gradient hence has
        the same shape as the input array.
    
        Parameters
        ----------
        f : array_like
          An N-dimensional array containing samples of a scalar function.
        `*varargs` : scalars
          0, 1, or N scalars specifying the sample distances in each direction,
          that is: `dx`, `dy`, `dz`, ... The default distance is 1.
    
    
        Returns
        -------
        gradient : ndarray
          N arrays of the same shape as `f` giving the derivative of `f` with
          respect to each dimension.
    
        Examples
        --------
        >>> x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
        >>> np.gradient(x)
        array([ 1. ,  1.5,  2.5,  3.5,  4.5,  5. ])
        >>> np.gradient(x, 2)
        array([ 0.5 ,  0.75,  1.25,  1.75,  2.25,  2.5 ])
    
        >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
        [array([[ 2.,  2., -1.],
               [ 2.,  2., -1.]]),
        array([[ 1. ,  2.5,  4. ],
               [ 1. ,  1. ,  1. ]])]
    
        """
    return ndarray()
def greater(x1, x2, out=None):
    """greater(x1, x2[, out])
    
    Return the truth value of (x1 > x2) element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input arrays.  If ``x1.shape != x2.shape``, they must be
        broadcastable to a common shape (which may be the shape of one or
        the other).
    
    Returns
    -------
    out : bool or ndarray of bool
        Array of bools, or a single bool if `x1` and `x2` are scalars.
    
    
    See Also
    --------
    greater_equal, less, less_equal, equal, not_equal
    
    Examples
    --------
    >>> np.greater([4,2],[2,2])
    array([ True, False], dtype=bool)
    
    If the inputs are ndarrays, then np.greater is equivalent to '>'.
    
    >>> a = np.array([4,2])
    >>> b = np.array([2,2])
    >>> a > b
    array([ True, False], dtype=bool)"""
    return bool() if False else ndarray()
def greater_equal(x1, x2, out=None):
    """greater_equal(x1, x2[, out])
    
    Return the truth value of (x1 >= x2) element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input arrays.  If ``x1.shape != x2.shape``, they must be
        broadcastable to a common shape (which may be the shape of one or
        the other).
    
    Returns
    -------
    out : bool or ndarray of bool
        Array of bools, or a single bool if `x1` and `x2` are scalars.
    
    See Also
    --------
    greater, less, less_equal, equal, not_equal
    
    Examples
    --------
    >>> np.greater_equal([4, 2, 1], [2, 2, 2])
    array([ True, True, False], dtype=bool)"""
    return bool() if False else ndarray()
class float16:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def hamming(M):
    """
        Return the Hamming window.
    
        The Hamming window is a taper formed by using a weighted cosine.
    
        Parameters
        ----------
        M : int
            Number of points in the output window. If zero or less, an
            empty array is returned.
    
        Returns
        -------
        out : ndarray
            The window, with the maximum value normalized to one (the value
            one appears only if the number of samples is odd).
    
        See Also
        --------
        bartlett, blackman, hanning, kaiser
    
        Notes
        -----
        The Hamming window is defined as
    
        .. math::  w(n) = 0.54 - 0.46cos\left(\frac{2\pi{n}}{M-1}\right)
                   \qquad 0 \leq n \leq M-1
    
        The Hamming was named for R. W. Hamming, an associate of J. W. Tukey and
        is described in Blackman and Tukey. It was recommended for smoothing the
        truncated autocovariance function in the time domain.
        Most references to the Hamming window come from the signal processing
        literature, where it is used as one of many windowing functions for
        smoothing values.  It is also known as an apodization (which means
        "removing the foot", i.e. smoothing discontinuities at the beginning
        and end of the sampled signal) or tapering function.
    
        References
        ----------
        .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
               spectra, Dover Publications, New York.
        .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
               University of Alberta Press, 1975, pp. 109-110.
        .. [3] Wikipedia, "Window function",
               http://en.wikipedia.org/wiki/Window_function
        .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
               "Numerical Recipes", Cambridge University Press, 1986, page 425.
    
        Examples
        --------
        >>> np.hamming(12)
        array([ 0.08      ,  0.15302337,  0.34890909,  0.60546483,  0.84123594,
                0.98136677,  0.98136677,  0.84123594,  0.60546483,  0.34890909,
                0.15302337,  0.08      ])
    
        Plot the window and the frequency response:
    
        >>> from numpy.fft import fft, fftshift
        >>> window = np.hamming(51)
        >>> plt.plot(window)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Hamming window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Amplitude")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Sample")
        <matplotlib.text.Text object at 0x...>
        >>> plt.show()
    
        >>> plt.figure()
        <matplotlib.figure.Figure object at 0x...>
        >>> A = fft(window, 2048) / 25.5
        >>> mag = np.abs(fftshift(A))
        >>> freq = np.linspace(-0.5, 0.5, len(A))
        >>> response = 20 * np.log10(mag)
        >>> response = np.clip(response, -100, 100)
        >>> plt.plot(freq, response)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Frequency response of Hamming window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Magnitude [dB]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Normalized frequency [cycles per sample]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.axis('tight')
        (-0.5, 0.5, -100.0, ...)
        >>> plt.show()
    
        """
    return ndarray()
def hanning(M):
    """
        Return the Hanning window.
    
        The Hanning window is a taper formed by using a weighted cosine.
    
        Parameters
        ----------
        M : int
            Number of points in the output window. If zero or less, an
            empty array is returned.
    
        Returns
        -------
        out : ndarray, shape(M,)
            The window, with the maximum value normalized to one (the value
            one appears only if `M` is odd).
    
        See Also
        --------
        bartlett, blackman, hamming, kaiser
    
        Notes
        -----
        The Hanning window is defined as
    
        .. math::  w(n) = 0.5 - 0.5cos\left(\frac{2\pi{n}}{M-1}\right)
                   \qquad 0 \leq n \leq M-1
    
        The Hanning was named for Julius van Hann, an Austrian meterologist. It is
        also known as the Cosine Bell. Some authors prefer that it be called a
        Hann window, to help avoid confusion with the very similar Hamming window.
    
        Most references to the Hanning window come from the signal processing
        literature, where it is used as one of many windowing functions for
        smoothing values.  It is also known as an apodization (which means
        "removing the foot", i.e. smoothing discontinuities at the beginning
        and end of the sampled signal) or tapering function.
    
        References
        ----------
        .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
               spectra, Dover Publications, New York.
        .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
               The University of Alberta Press, 1975, pp. 106-108.
        .. [3] Wikipedia, "Window function",
               http://en.wikipedia.org/wiki/Window_function
        .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
               "Numerical Recipes", Cambridge University Press, 1986, page 425.
    
        Examples
        --------
        >>> np.hanning(12)
        array([ 0.        ,  0.07937323,  0.29229249,  0.57115742,  0.82743037,
                0.97974649,  0.97974649,  0.82743037,  0.57115742,  0.29229249,
                0.07937323,  0.        ])
    
        Plot the window and its frequency response:
    
        >>> from numpy.fft import fft, fftshift
        >>> window = np.hanning(51)
        >>> plt.plot(window)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Hann window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Amplitude")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Sample")
        <matplotlib.text.Text object at 0x...>
        >>> plt.show()
    
        >>> plt.figure()
        <matplotlib.figure.Figure object at 0x...>
        >>> A = fft(window, 2048) / 25.5
        >>> mag = np.abs(fftshift(A))
        >>> freq = np.linspace(-0.5, 0.5, len(A))
        >>> response = 20 * np.log10(mag)
        >>> response = np.clip(response, -100, 100)
        >>> plt.plot(freq, response)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Frequency response of the Hann window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Magnitude [dB]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Normalized frequency [cycles per sample]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.axis('tight')
        (-0.5, 0.5, -100.0, ...)
        >>> plt.show()
    
        """
    return ndarray()
def histogram(a=None, bins=10, range=None, normed=False, weights=None, density=None):
    """
        Compute the histogram of a set of data.
    
        Parameters
        ----------
        a : array_like
            Input data. The histogram is computed over the flattened array.
        bins : int or sequence of scalars, optional
            If `bins` is an int, it defines the number of equal-width
            bins in the given range (10, by default). If `bins` is a sequence,
            it defines the bin edges, including the rightmost edge, allowing
            for non-uniform bin widths.
        range : (float, float), optional
            The lower and upper range of the bins.  If not provided, range
            is simply ``(a.min(), a.max())``.  Values outside the range are
            ignored.
        normed : bool, optional
            This keyword is deprecated in Numpy 1.6 due to confusing/buggy
            behavior. It will be removed in Numpy 2.0. Use the density keyword
            instead.
            If False, the result will contain the number of samples
            in each bin.  If True, the result is the value of the
            probability *density* function at the bin, normalized such that
            the *integral* over the range is 1. Note that this latter behavior is
            known to be buggy with unequal bin widths; use `density` instead.
        weights : array_like, optional
            An array of weights, of the same shape as `a`.  Each value in `a`
            only contributes its associated weight towards the bin count
            (instead of 1).  If `normed` is True, the weights are normalized,
            so that the integral of the density over the range remains 1
        density : bool, optional
            If False, the result will contain the number of samples
            in each bin.  If True, the result is the value of the
            probability *density* function at the bin, normalized such that
            the *integral* over the range is 1. Note that the sum of the
            histogram values will not be equal to 1 unless bins of unity
            width are chosen; it is not a probability *mass* function.
            Overrides the `normed` keyword if given.
    
        Returns
        -------
        hist : array
            The values of the histogram. See `normed` and `weights` for a
            description of the possible semantics.
        bin_edges : array of dtype float
            Return the bin edges ``(length(hist)+1)``.
    
    
        See Also
        --------
        histogramdd, bincount, searchsorted, digitize
    
        Notes
        -----
        All but the last (righthand-most) bin is half-open.  In other words, if
        `bins` is::
    
          [1, 2, 3, 4]
    
        then the first bin is ``[1, 2)`` (including 1, but excluding 2) and the
        second ``[2, 3)``.  The last bin, however, is ``[3, 4]``, which *includes*
        4.
    
        Examples
        --------
        >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
        (array([0, 2, 1]), array([0, 1, 2, 3]))
        >>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
        (array([ 0.25,  0.25,  0.25,  0.25]), array([0, 1, 2, 3, 4]))
        >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
        (array([1, 4, 1]), array([0, 1, 2, 3]))
    
        >>> a = np.arange(5)
        >>> hist, bin_edges = np.histogram(a, density=True)
        >>> hist
        array([ 0.5,  0. ,  0.5,  0. ,  0. ,  0.5,  0. ,  0.5,  0. ,  0.5])
        >>> hist.sum()
        2.4999999999999996
        >>> np.sum(hist*np.diff(bin_edges))
        1.0
    
        """
    return array()
def histogram2d(x, y=None, bins=10, range=None, normed=False, weights=None):
    """
        Compute the bi-dimensional histogram of two data samples.
    
        Parameters
        ----------
        x : array_like, shape (N,)
            An array containing the x coordinates of the points to be histogrammed.
        y : array_like, shape (N,)
            An array containing the y coordinates of the points to be histogrammed.
        bins : int or [int, int] or array_like or [array, array], optional
            The bin specification:
    
              * If int, the number of bins for the two dimensions (nx=ny=bins).
              * If [int, int], the number of bins in each dimension (nx, ny = bins).
              * If array_like, the bin edges for the two dimensions
                (x_edges=y_edges=bins).
              * If [array, array], the bin edges in each dimension
                (x_edges, y_edges = bins).
    
        range : array_like, shape(2,2), optional
            The leftmost and rightmost edges of the bins along each dimension
            (if not specified explicitly in the `bins` parameters):
            ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
            will be considered outliers and not tallied in the histogram.
        normed : bool, optional
            If False, returns the number of samples in each bin. If True, returns
            the bin density, i.e. the bin count divided by the bin area.
        weights : array_like, shape(N,), optional
            An array of values ``w_i`` weighing each sample ``(x_i, y_i)``. Weights
            are normalized to 1 if `normed` is True. If `normed` is False, the
            values of the returned histogram are equal to the sum of the weights
            belonging to the samples falling into each bin.
    
        Returns
        -------
        H : ndarray, shape(nx, ny)
            The bi-dimensional histogram of samples `x` and `y`. Values in `x`
            are histogrammed along the first dimension and values in `y` are
            histogrammed along the second dimension.
        xedges : ndarray, shape(nx,)
            The bin edges along the first dimension.
        yedges : ndarray, shape(ny,)
            The bin edges along the second dimension.
    
        See Also
        --------
        histogram : 1D histogram
        histogramdd : Multidimensional histogram
    
        Notes
        -----
        When `normed` is True, then the returned histogram is the sample density,
        defined such that:
    
        .. math::
          \sum_{i=0}^{nx-1} \sum_{j=0}^{ny-1} H_{i,j} \Delta x_i \Delta y_j = 1
    
        where `H` is the histogram array and :math:`\Delta x_i \Delta y_i`
        the area of bin ``{i,j}``.
    
        Please note that the histogram does not follow the Cartesian convention
        where `x` values are on the abcissa and `y` values on the ordinate axis.
        Rather, `x` is histogrammed along the first dimension of the array
        (vertical), and `y` along the second dimension of the array (horizontal).
        This ensures compatibility with `histogramdd`.
    
        Examples
        --------
        >>> import matplotlib as mpl
        >>> import matplotlib.pyplot as plt
    
        Construct a 2D-histogram with variable bin width. First define the bin
        edges:
    
        >>> xedges = [0, 1, 1.5, 3, 5]
        >>> yedges = [0, 2, 3, 4, 6]
    
        Next we create a histogram H with random bin content:
    
        >>> x = np.random.normal(3, 1, 100)
        >>> y = np.random.normal(1, 1, 100)
        >>> H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges))
    
        Or we fill the histogram H with a determined bin content:
    
        >>> H = np.ones((4, 4)).cumsum().reshape(4, 4)
        >>> print H[::-1]  # This shows the bin content in the order as plotted
        [[ 13.  14.  15.  16.]
         [  9.  10.  11.  12.]
         [  5.   6.   7.   8.]
         [  1.   2.   3.   4.]]
    
        Imshow can only do an equidistant representation of bins:
    
        >>> fig = plt.figure(figsize=(7, 3))
        >>> ax = fig.add_subplot(131)
        >>> ax.set_title('imshow:
    equidistant')
        >>> im = plt.imshow(H, interpolation='nearest', origin='low',
                            extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
    
        pcolormesh can displaying exact bin edges:
    
        >>> ax = fig.add_subplot(132)
        >>> ax.set_title('pcolormesh:
    exact bin edges')
        >>> X, Y = np.meshgrid(xedges, yedges)
        >>> ax.pcolormesh(X, Y, H)
        >>> ax.set_aspect('equal')
    
        NonUniformImage displays exact bin edges with interpolation:
    
        >>> ax = fig.add_subplot(133)
        >>> ax.set_title('NonUniformImage:
    interpolated')
        >>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear')
        >>> xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1])
        >>> ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1])
        >>> im.set_data(xcenters, ycenters, H)
        >>> ax.images.append(im)
        >>> ax.set_xlim(xedges[0], xedges[-1])
        >>> ax.set_ylim(yedges[0], yedges[-1])
        >>> ax.set_aspect('equal')
        >>> plt.show()
    
        """
    return ndarray()
def histogramdd(sample=None, bins=10, range=None, normed=False, weights=None):
    """
        Compute the multidimensional histogram of some data.
    
        Parameters
        ----------
        sample : array_like
            The data to be histogrammed. It must be an (N,D) array or data
            that can be converted to such. The rows of the resulting array
            are the coordinates of points in a D dimensional polytope.
        bins : sequence or int, optional
            The bin specification:
    
            * A sequence of arrays describing the bin edges along each dimension.
            * The number of bins for each dimension (nx, ny, ... =bins)
            * The number of bins for all dimensions (nx=ny=...=bins).
    
        range : sequence, optional
            A sequence of lower and upper bin edges to be used if the edges are
            not given explicitely in `bins`. Defaults to the minimum and maximum
            values along each dimension.
        normed : bool, optional
            If False, returns the number of samples in each bin. If True, returns
            the bin density, ie, the bin count divided by the bin hypervolume.
        weights : array_like (N,), optional
            An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
            Weights are normalized to 1 if normed is True. If normed is False, the
            values of the returned histogram are equal to the sum of the weights
            belonging to the samples falling into each bin.
    
        Returns
        -------
        H : ndarray
            The multidimensional histogram of sample x. See normed and weights for
            the different possible semantics.
        edges : list
            A list of D arrays describing the bin edges for each dimension.
    
        See Also
        --------
        histogram: 1-D histogram
        histogram2d: 2-D histogram
    
        Examples
        --------
        >>> r = np.random.randn(100,3)
        >>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
        >>> H.shape, edges[0].size, edges[1].size, edges[2].size
        ((5, 8, 4), 6, 9, 5)
    
        """
    return ndarray()
def hsplit():
    """
        Split an array into multiple sub-arrays horizontally (column-wise).
    
        Please refer to the `split` documentation.  `hsplit` is equivalent
        to `split` with ``axis=1``, the array is always split along the second
        axis regardless of the array dimension.
    
        See Also
        --------
        split : Split an array into multiple sub-arrays of equal size.
    
        Examples
        --------
        >>> x = np.arange(16.0).reshape(4, 4)
        >>> x
        array([[  0.,   1.,   2.,   3.],
               [  4.,   5.,   6.,   7.],
               [  8.,   9.,  10.,  11.],
               [ 12.,  13.,  14.,  15.]])
        >>> np.hsplit(x, 2)
        [array([[  0.,   1.],
               [  4.,   5.],
               [  8.,   9.],
               [ 12.,  13.]]),
         array([[  2.,   3.],
               [  6.,   7.],
               [ 10.,  11.],
               [ 14.,  15.]])]
        >>> np.hsplit(x, np.array([3, 6]))
        [array([[  0.,   1.,   2.],
               [  4.,   5.,   6.],
               [  8.,   9.,  10.],
               [ 12.,  13.,  14.]]),
         array([[  3.],
               [  7.],
               [ 11.],
               [ 15.]]),
         array([], dtype=float64)]
    
        With a higher dimensional array the split is still along the second axis.
    
        >>> x = np.arange(8.0).reshape(2, 2, 2)
        >>> x
        array([[[ 0.,  1.],
                [ 2.,  3.]],
               [[ 4.,  5.],
                [ 6.,  7.]]])
        >>> np.hsplit(x, 2)
        [array([[[ 0.,  1.]],
               [[ 4.,  5.]]]),
         array([[[ 2.,  3.]],
               [[ 6.,  7.]]])]
    
        """
    return None
def hstack(tup):
    """
        Stack arrays in sequence horizontally (column wise).
    
        Take a sequence of arrays and stack them horizontally to make
        a single array. Rebuild arrays divided by `hsplit`.
    
        Parameters
        ----------
        tup : sequence of ndarrays
            All arrays must have the same shape along all but the second axis.
    
        Returns
        -------
        stacked : ndarray
            The array formed by stacking the given arrays.
    
        See Also
        --------
        vstack : Stack arrays in sequence vertically (row wise).
        dstack : Stack arrays in sequence depth wise (along third axis).
        concatenate : Join a sequence of arrays together.
        hsplit : Split array along second axis.
    
        Notes
        -----
        Equivalent to ``np.concatenate(tup, axis=1)``
    
        Examples
        --------
        >>> a = np.array((1,2,3))
        >>> b = np.array((2,3,4))
        >>> np.hstack((a,b))
        array([1, 2, 3, 2, 3, 4])
        >>> a = np.array([[1],[2],[3]])
        >>> b = np.array([[2],[3],[4]])
        >>> np.hstack((a,b))
        array([[1, 2],
               [2, 3],
               [3, 4]])
    
        """
    return ndarray()
def hypot(x1, x2, out=None):
    """hypot(x1, x2[, out])
    
    Given the "legs" of a right triangle, return its hypotenuse.
    
    Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise.  If `x1` or
    `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),
    it is broadcast for use with each element of the other argument.
    (See Examples)
    
    Parameters
    ----------
    x1, x2 : array_like
        Leg of the triangle(s).
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    z : ndarray
        The hypotenuse of the triangle(s).
    
    Examples
    --------
    >>> np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3)))
    array([[ 5.,  5.,  5.],
           [ 5.,  5.,  5.],
           [ 5.,  5.,  5.]])
    
    Example showing broadcast of scalar_like argument:
    
    >>> np.hypot(3*np.ones((3, 3)), [4])
    array([[ 5.,  5.,  5.],
           [ 5.,  5.,  5.],
           [ 5.,  5.,  5.]])"""
    return ndarray()
def i0(x):
    """
        Modified Bessel function of the first kind, order 0.
    
        Usually denoted :math:`I_0`.  This function does broadcast, but will *not*
        "up-cast" int dtype arguments unless accompanied by at least one float or
        complex dtype argument (see Raises below).
    
        Parameters
        ----------
        x : array_like, dtype float or complex
            Argument of the Bessel function.
    
        Returns
        -------
        out : ndarray, shape = x.shape, dtype = x.dtype
            The modified Bessel function evaluated at each of the elements of `x`.
    
        Raises
        ------
        TypeError: array cannot be safely cast to required type
            If argument consists exclusively of int dtypes.
    
        See Also
        --------
        scipy.special.iv, scipy.special.ive
    
        Notes
        -----
        We use the algorithm published by Clenshaw [1]_ and referenced by
        Abramowitz and Stegun [2]_, for which the function domain is partitioned
        into the two intervals [0,8] and (8,inf), and Chebyshev polynomial
        expansions are employed in each interval. Relative error on the domain
        [0,30] using IEEE arithmetic is documented [3]_ as having a peak of 5.8e-16
        with an rms of 1.4e-16 (n = 30000).
    
        References
        ----------
        .. [1] C. W. Clenshaw, "Chebyshev series for mathematical functions", in
               *National Physical Laboratory Mathematical Tables*, vol. 5, London:
               Her Majesty's Stationery Office, 1962.
        .. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical
               Functions*, 10th printing, New York: Dover, 1964, pp. 379.
               http://www.math.sfu.ca/~cbm/aands/page_379.htm
        .. [3] http://kobesearch.cpan.org/htdocs/Math-Cephes/Math/Cephes.html
    
        Examples
        --------
        >>> np.i0([0.])
        array(1.0)
        >>> np.i0([0., 1. + 2j])
        array([ 1.00000000+0.j        ,  0.18785373+0.64616944j])
    
        """
    return ndarray()
def identity(n=None, dtype=None):
    """
        Return the identity array.
    
        The identity array is a square array with ones on
        the main diagonal.
    
        Parameters
        ----------
        n : int
            Number of rows (and columns) in `n` x `n` output.
        dtype : data-type, optional
            Data-type of the output.  Defaults to ``float``.
    
        Returns
        -------
        out : ndarray
            `n` x `n` array with its main diagonal set to one,
            and all other elements 0.
    
        Examples
        --------
        >>> np.identity(3)
        array([[ 1.,  0.,  0.],
               [ 0.,  1.,  0.],
               [ 0.,  0.,  1.]])
    
        """
    return ndarray()
class iinfo:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    _max_vals = dict()
    _min_vals = dict()
    max = property()
    min = property()
def imag(val):
    """
        Return the imaginary part of the elements of the array.
    
        Parameters
        ----------
        val : array_like
            Input array.
    
        Returns
        -------
        out : ndarray
            Output array. 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, angle, real_if_close
    
        Examples
        --------
        >>> a = np.array([1+2j, 3+4j, 5+6j])
        >>> a.imag
        array([ 2.,  4.,  6.])
        >>> a.imag = np.array([8, 10, 12])
        >>> a
        array([ 1. +8.j,  3.+10.j,  5.+12.j])
    
        """
    return ndarray()
def in1d(ar1, ar2=False, assume_unique=False, invert=False):
    """
        Test whether each element of a 1-D array is also present in a second array.
    
        Returns a boolean array the same length as `ar1` that is True
        where an element of `ar1` is in `ar2` and False otherwise.
    
        Parameters
        ----------
        ar1 : (M,) array_like
            Input array.
        ar2 : array_like
            The values against which to test each value of `ar1`.
        assume_unique : bool, optional
            If True, the input arrays are both assumed to be unique, which
            can speed up the calculation.  Default is False.
        invert : bool, optional
            If True, the values in the returned array are inverted (that is,
            False where an element of `ar1` is in `ar2` and True otherwise).
            Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
            to (but is faster than) ``np.invert(in1d(a, b))``.
    
            .. versionadded:: 1.8.0
    
        Returns
        -------
        in1d : (M,) ndarray, bool
            The values `ar1[in1d]` are in `ar2`.
    
        See Also
        --------
        numpy.lib.arraysetops : Module with a number of other functions for
                                performing set operations on arrays.
    
        Notes
        -----
        `in1d` can be considered as an element-wise function version of the
        python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
        equivalent to ``np.array([item in b for item in a])``.
    
        .. versionadded:: 1.4.0
    
        Examples
        --------
        >>> test = np.array([0, 1, 2, 5, 0])
        >>> states = [0, 2]
        >>> mask = np.in1d(test, states)
        >>> mask
        array([ True, False,  True, False,  True], dtype=bool)
        >>> test[mask]
        array([0, 2, 0])
        >>> mask = np.in1d(test, states, invert=True)
        >>> mask
        array([False,  True, False,  True, False], dtype=bool)
        >>> test[mask]
        array([1, 5])
        """
    return M()
index_exp = IndexExpression()
def indices(dimensions=typeint(), dtype=typeint()):
    """
        Return an array representing the indices of a grid.
    
        Compute an array where the subarrays contain index values 0,1,...
        varying only along the corresponding axis.
    
        Parameters
        ----------
        dimensions : sequence of ints
            The shape of the grid.
        dtype : dtype, optional
            Data type of the result.
    
        Returns
        -------
        grid : ndarray
            The array of grid indices,
            ``grid.shape = (len(dimensions),) + tuple(dimensions)``.
    
        See Also
        --------
        mgrid, meshgrid
    
        Notes
        -----
        The output shape is obtained by prepending the number of dimensions
        in front of the tuple of dimensions, i.e. if `dimensions` is a tuple
        ``(r0, ..., rN-1)`` of length ``N``, the output shape is
        ``(N,r0,...,rN-1)``.
    
        The subarrays ``grid[k]`` contains the N-D array of indices along the
        ``k-th`` axis. Explicitly::
    
            grid[k,i0,i1,...,iN-1] = ik
    
        Examples
        --------
        >>> grid = np.indices((2, 3))
        >>> grid.shape
        (2, 2, 3)
        >>> grid[0]        # row indices
        array([[0, 0, 0],
               [1, 1, 1]])
        >>> grid[1]        # column indices
        array([[0, 1, 2],
               [0, 1, 2]])
    
        The indices can be used as an index into an array.
    
        >>> x = np.arange(20).reshape(5, 4)
        >>> row, col = np.indices((2, 3))
        >>> x[row, col]
        array([[0, 1, 2],
               [4, 5, 6]])
    
        Note that it would be more straightforward in the above example to
        extract the required elements directly with ``x[:2, :3]``.
    
        """
    return ndarray()
class inexact:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
inf = float()
def info(object=None, maxwidth=76, output="<open file '../../documentation_files/numpy.py', mode 'w' at 0x7ff32be959c0>", toplevel="numpy"):
    """
        Get help information for a function, class, or module.
    
        Parameters
        ----------
        object : object or str, optional
            Input object or name to get information about. If `object` is a
            numpy object, its docstring is given. If it is a string, available
            modules are searched for matching objects.
            If None, information about `info` itself is returned.
        maxwidth : int, optional
            Printing width.
        output : file like object, optional
            File like object that the output is written to, default is ``stdout``.
            The object has to be opened in 'w' or 'a' mode.
        toplevel : str, optional
            Start search at this level.
    
        See Also
        --------
        source, lookfor
    
        Notes
        -----
        When used interactively with an object, ``np.info(obj)`` is equivalent to
        ``help(obj)`` on the Python prompt or ``obj?`` on the IPython prompt.
    
        Examples
        --------
        >>> np.info(np.polyval) # doctest: +SKIP
           polyval(p, x)
             Evaluate the polynomial p at x.
             ...
    
        When using a string for `object` it is possible to get multiple results.
    
        >>> np.info('fft') # doctest: +SKIP
             *** Found in numpy ***
        Core FFT routines
        ...
             *** Found in numpy.fft ***
         fft(a, n=None, axis=-1)
        ...
             *** Repeat reference found in numpy.fft.fftpack ***
             *** Total of 3 references found. ***
    
        """
    return None
infty = float()
def inner(a, b):
    """inner(a, b)
    
        Inner product of two arrays.
    
        Ordinary inner product of vectors for 1-D arrays (without complex
        conjugation), in higher dimensions a sum product over the last axes.
    
        Parameters
        ----------
        a, b : array_like
            If `a` and `b` are nonscalar, their last dimensions of must match.
    
        Returns
        -------
        out : ndarray
            `out.shape = a.shape[:-1] + b.shape[:-1]`
    
        Raises
        ------
        ValueError
            If the last dimension of `a` and `b` has different size.
    
        See Also
        --------
        tensordot : Sum products over arbitrary axes.
        dot : Generalised matrix product, using second last dimension of `b`.
        einsum : Einstein summation convention.
    
        Notes
        -----
        For vectors (1-D arrays) it computes the ordinary inner-product::
    
            np.inner(a, b) = sum(a[:]*b[:])
    
        More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
    
            np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
    
        or explicitly::
    
            np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
                 = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])
    
        In addition `a` or `b` may be scalars, in which case::
    
           np.inner(a,b) = a*b
    
        Examples
        --------
        Ordinary inner product for vectors:
    
        >>> a = np.array([1,2,3])
        >>> b = np.array([0,1,0])
        >>> np.inner(a, b)
        2
    
        A multidimensional example:
    
        >>> a = np.arange(24).reshape((2,3,4))
        >>> b = np.arange(4)
        >>> np.inner(a, b)
        array([[ 14,  38,  62],
               [ 86, 110, 134]])
    
        An example where `b` is a scalar:
    
        >>> np.inner(np.eye(2), 7)
        array([[ 7.,  0.],
               [ 0.,  7.]])"""
    return ndarray()
def insert(arr, obj, values=None, axis=None):
    """
        Insert values along the given axis before the given indices.
    
        Parameters
        ----------
        arr : array_like
            Input array.
        obj : int, slice or sequence of ints
            Object that defines the index or indices before which `values` is
            inserted.
    
            .. versionadded:: 1.8.0
    
            Support for multiple insertions when `obj` is a single scalar or a
            sequence with one element (similar to calling insert multiple times).
        values : array_like
            Values to insert into `arr`. If the type of `values` is different
            from that of `arr`, `values` is converted to the type of `arr`.
            `values` should be shaped so that ``arr[...,obj,...] = values``
            is legal.
        axis : int, optional
            Axis along which to insert `values`.  If `axis` is None then `arr`
            is flattened first.
    
        Returns
        -------
        out : ndarray
            A copy of `arr` with `values` inserted.  Note that `insert`
            does not occur in-place: a new array is returned. If
            `axis` is None, `out` is a flattened array.
    
        See Also
        --------
        append : Append elements at the end of an array.
        concatenate : Join a sequence of arrays together.
        delete : Delete elements from an array.
    
        Notes
        -----
        Note that for higher dimensional inserts `obj=0` behaves very different
        from `obj=[0]` just like `arr[:,0,:] = values` is different from
        `arr[:,[0],:] = values`.
    
        Examples
        --------
        >>> a = np.array([[1, 1], [2, 2], [3, 3]])
        >>> a
        array([[1, 1],
               [2, 2],
               [3, 3]])
        >>> np.insert(a, 1, 5)
        array([1, 5, 1, 2, 2, 3, 3])
        >>> np.insert(a, 1, 5, axis=1)
        array([[1, 5, 1],
               [2, 5, 2],
               [3, 5, 3]])
    
        Difference between sequence and scalars:
        >>> np.insert(a, [1], [[1],[2],[3]], axis=1)
        array([[1, 1, 1],
               [2, 2, 2],
               [3, 3, 3]])
        >>> np.array_equal(np.insert(a, 1, [1, 2, 3], axis=1),
        ...                np.insert(a, [1], [[1],[2],[3]], axis=1))
        True
    
        >>> b = a.flatten()
        >>> b
        array([1, 1, 2, 2, 3, 3])
        >>> np.insert(b, [2, 2], [5, 6])
        array([1, 1, 5, 6, 2, 2, 3, 3])
    
        >>> np.insert(b, slice(2, 4), [5, 6])
        array([1, 1, 5, 2, 6, 2, 3, 3])
    
        >>> np.insert(b, [2, 2], [7.13, False]) # type casting
        array([1, 1, 7, 0, 2, 2, 3, 3])
    
        >>> x = np.arange(8).reshape(2, 4)
        >>> idx = (1, 3)
        >>> np.insert(x, idx, 999, axis=1)
        array([[  0, 999,   1,   2, 999,   3],
               [  4, 999,   5,   6, 999,   7]])
    
        """
    return ndarray()
class int:
    __doc__ = str()
    def bit_length(self, _):
        """int.bit_length() -> int
        
        Number of bits necessary to represent self in binary.
        >>> bin(37)
        '0b100101'
        >>> (37).bit_length()
        6"""
        return None
    def conjugate(self, _):
        """Returns self, the complex conjugate of any int."""
        return None
    denominator = getset_descriptor()
    imag = getset_descriptor()
    numerator = getset_descriptor()
    real = getset_descriptor()
class int64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def bit_length(self, _):
        """int.bit_length() -> int
        
        Number of bits necessary to represent self in binary.
        >>> bin(37)
        '0b100101'
        >>> (37).bit_length()
        6"""
        return None
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    denominator = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    numerator = getset_descriptor()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class int16:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class int32:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class int64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def bit_length(self, _):
        """int.bit_length() -> int
        
        Number of bits necessary to represent self in binary.
        >>> bin(37)
        '0b100101'
        >>> (37).bit_length()
        6"""
        return None
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    denominator = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    numerator = getset_descriptor()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class int8:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class int64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def bit_length(self, _):
        """int.bit_length() -> int
        
        Number of bits necessary to represent self in binary.
        >>> bin(37)
        '0b100101'
        >>> (37).bit_length()
        6"""
        return None
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    denominator = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    numerator = getset_descriptor()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def int_asbuffer():
    """None"""
    return None
class int32:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class integer:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def interp(x, xp, fp=None, left=None, right=None):
    """
        One-dimensional linear interpolation.
    
        Returns the one-dimensional piecewise linear interpolant to a function
        with given values at discrete data-points.
    
        Parameters
        ----------
        x : array_like
            The x-coordinates of the interpolated values.
    
        xp : 1-D sequence of floats
            The x-coordinates of the data points, must be increasing.
    
        fp : 1-D sequence of floats
            The y-coordinates of the data points, same length as `xp`.
    
        left : float, optional
            Value to return for `x < xp[0]`, default is `fp[0]`.
    
        right : float, optional
            Value to return for `x > xp[-1]`, defaults is `fp[-1]`.
    
        Returns
        -------
        y : {float, ndarray}
            The interpolated values, same shape as `x`.
    
        Raises
        ------
        ValueError
            If `xp` and `fp` have different length
    
        Notes
        -----
        Does not check that the x-coordinate sequence `xp` is increasing.
        If `xp` is not increasing, the results are nonsense.
        A simple check for increasingness is::
    
            np.all(np.diff(xp) > 0)
    
    
        Examples
        --------
        >>> xp = [1, 2, 3]
        >>> fp = [3, 2, 0]
        >>> np.interp(2.5, xp, fp)
        1.0
        >>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
        array([ 3. ,  3. ,  2.5 ,  0.56,  0. ])
        >>> UNDEF = -99.0
        >>> np.interp(3.14, xp, fp, right=UNDEF)
        -99.0
    
        Plot an interpolant to the sine function:
    
        >>> x = np.linspace(0, 2*np.pi, 10)
        >>> y = np.sin(x)
        >>> xvals = np.linspace(0, 2*np.pi, 50)
        >>> yinterp = np.interp(xvals, x, y)
        >>> import matplotlib.pyplot as plt
        >>> plt.plot(x, y, 'o')
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.plot(xvals, yinterp, '-x')
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.show()
    
        """
    return float()
def intersect1d(ar1, ar2=False, assume_unique=False):
    """
        Find the intersection of two arrays.
    
        Return the sorted, unique values that are in both of the input arrays.
    
        Parameters
        ----------
        ar1, ar2 : array_like
            Input arrays.
        assume_unique : bool
            If True, the input arrays are both assumed to be unique, which
            can speed up the calculation.  Default is False.
    
        Returns
        -------
        intersect1d : ndarray
            Sorted 1D array of common and unique elements.
    
        See Also
        --------
        numpy.lib.arraysetops : Module with a number of other functions for
                                performing set operations on arrays.
    
        Examples
        --------
        >>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])
        array([1, 3])
    
        """
    return ndarray()
class int64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def bit_length(self, _):
        """int.bit_length() -> int
        
        Number of bits necessary to represent self in binary.
        >>> bin(37)
        '0b100101'
        >>> (37).bit_length()
        6"""
        return None
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    denominator = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    numerator = getset_descriptor()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def invert(x, out=None):
    """invert(x[, out])
    
    Compute bit-wise inversion, or bit-wise NOT, element-wise.
    
    Computes the bit-wise NOT of the underlying binary representation of
    the integers in the input arrays. This ufunc implements the C/Python
    operator ``~``.
    
    For signed integer inputs, the two's complement is returned.
    In a two's-complement system negative numbers are represented by the two's
    complement of the absolute value. This is the most common method of
    representing signed integers on computers [1]_. A N-bit two's-complement
    system can represent every integer in the range
    :math:`-2^{N-1}` to :math:`+2^{N-1}-1`.
    
    Parameters
    ----------
    x1 : array_like
        Only integer types are handled (including booleans).
    
    Returns
    -------
    out : array_like
        Result.
    
    See Also
    --------
    bitwise_and, bitwise_or, bitwise_xor
    logical_not
    binary_repr :
        Return the binary representation of the input number as a string.
    
    Notes
    -----
    `bitwise_not` is an alias for `invert`:
    
    >>> np.bitwise_not is np.invert
    True
    
    References
    ----------
    .. [1] Wikipedia, "Two's complement",
        http://en.wikipedia.org/wiki/Two's_complement
    
    Examples
    --------
    We've seen that 13 is represented by ``00001101``.
    The invert or bit-wise NOT of 13 is then:
    
    >>> np.invert(np.array([13], dtype=uint8))
    array([242], dtype=uint8)
    >>> np.binary_repr(x, width=8)
    '00001101'
    >>> np.binary_repr(242, width=8)
    '11110010'
    
    The result depends on the bit-width:
    
    >>> np.invert(np.array([13], dtype=uint16))
    array([65522], dtype=uint16)
    >>> np.binary_repr(x, width=16)
    '0000000000001101'
    >>> np.binary_repr(65522, width=16)
    '1111111111110010'
    
    When using signed integer types the result is the two's complement of
    the result for the unsigned type:
    
    >>> np.invert(np.array([13], dtype=int8))
    array([-14], dtype=int8)
    >>> np.binary_repr(-14, width=8)
    '11110010'
    
    Booleans are accepted as well:
    
    >>> np.invert(array([True, False]))
    array([False,  True], dtype=bool)"""
    return ndarray()
def ipmt(rate, per, nper, pv="end", fv=0.0, when="end"):
    """
        Compute the interest portion of a payment.
    
        Parameters
        ----------
        rate : scalar or array_like of shape(M, )
            Rate of interest as decimal (not per cent) per period
        per : scalar or array_like of shape(M, )
            Interest paid against the loan changes during the life or the loan.
            The `per` is the payment period to calculate the interest amount.
        nper : scalar or array_like of shape(M, )
            Number of compounding periods
        pv : scalar or array_like of shape(M, )
            Present value
        fv : scalar or array_like of shape(M, ), optional
            Future value
        when : {{'begin', 1}, {'end', 0}}, {string, int}, optional
            When payments are due ('begin' (1) or 'end' (0)).
            Defaults to {'end', 0}.
    
        Returns
        -------
        out : ndarray
            Interest portion of payment.  If all input is scalar, returns a scalar
            float.  If any input is array_like, returns interest payment for each
            input element. If multiple inputs are array_like, they all must have
            the same shape.
    
        See Also
        --------
        ppmt, pmt, pv
    
        Notes
        -----
        The total payment is made up of payment against principal plus interest.
    
        ``pmt = ppmt + ipmt``
    
        Examples
        --------
        What is the amortization schedule for a 1 year loan of $2500 at
        8.24% interest per year compounded monthly?
    
        >>> principal = 2500.00
    
        The 'per' variable represents the periods of the loan.  Remember that
        financial equations start the period count at 1!
    
        >>> per = np.arange(1*12) + 1
        >>> ipmt = np.ipmt(0.0824/12, per, 1*12, principal)
        >>> ppmt = np.ppmt(0.0824/12, per, 1*12, principal)
    
        Each element of the sum of the 'ipmt' and 'ppmt' arrays should equal
        'pmt'.
    
        >>> pmt = np.pmt(0.0824/12, 1*12, principal)
        >>> np.allclose(ipmt + ppmt, pmt)
        True
    
        >>> fmt = '{0:2d} {1:8.2f} {2:8.2f} {3:8.2f}'
        >>> for payment in per:
        ...     index = payment - 1
        ...     principal = principal + ppmt[index]
        ...     print fmt.format(payment, ppmt[index], ipmt[index], principal)
         1  -200.58   -17.17  2299.42
         2  -201.96   -15.79  2097.46
         3  -203.35   -14.40  1894.11
         4  -204.74   -13.01  1689.37
         5  -206.15   -11.60  1483.22
         6  -207.56   -10.18  1275.66
         7  -208.99    -8.76  1066.67
         8  -210.42    -7.32   856.25
         9  -211.87    -5.88   644.38
        10  -213.32    -4.42   431.05
        11  -214.79    -2.96   216.26
        12  -216.26    -1.49    -0.00
    
        >>> interestpd = np.sum(ipmt)
        >>> np.round(interestpd, 2)
        -112.98
    
        """
    return ndarray()
def irr(values):
    """
        Return the Internal Rate of Return (IRR).
    
        This is the "average" periodically compounded rate of return
        that gives a net present value of 0.0; for a more complete explanation,
        see Notes below.
    
        Parameters
        ----------
        values : array_like, shape(N,)
            Input cash flows per time period.  By convention, net "deposits"
            are negative and net "withdrawals" are positive.  Thus, for example,
            at least the first element of `values`, which represents the initial
            investment, will typically be negative.
    
        Returns
        -------
        out : float
            Internal Rate of Return for periodic input values.
    
        Notes
        -----
        The IRR is perhaps best understood through an example (illustrated
        using np.irr in the Examples section below).  Suppose one invests
        100 units and then makes the following withdrawals at regular
        (fixed) intervals: 39, 59, 55, 20.  Assuming the ending value is 0,
        one's 100 unit investment yields 173 units; however, due to the
        combination of compounding and the periodic withdrawals, the
        "average" rate of return is neither simply 0.73/4 nor (1.73)^0.25-1.
        Rather, it is the solution (for :math:`r`) of the equation:
    
        .. math:: -100 + \frac{39}{1+r} + \frac{59}{(1+r)^2}
         + \frac{55}{(1+r)^3} + \frac{20}{(1+r)^4} = 0
    
        In general, for `values` :math:`= [v_0, v_1, ... v_M]`,
        irr is the solution of the equation: [G]_
    
        .. math:: \sum_{t=0}^M{\frac{v_t}{(1+irr)^{t}}} = 0
    
        References
        ----------
        .. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
           Addison-Wesley, 2003, pg. 348.
    
        Examples
        --------
        >>> print round(np.irr([-100, 39, 59, 55, 20]), 5)
        0.28095
    
        (Compare with the Example given for numpy.lib.financial.npv)
    
        """
    return float()
def is_busday(dates, weekmask, holidays, busdaycal, out):
    """is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)
    
        Calculates which of the given dates are valid days, and which are not.
    
        .. versionadded:: 1.7.0
    
        Parameters
        ----------
        dates : array_like of datetime64[D]
            The array of dates to process.
        weekmask : str or array_like of bool, optional
            A seven-element array indicating which of Monday through Sunday are
            valid days. May be specified as a length-seven list or array, like
            [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
            like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
            weekdays, optionally separated by white space. Valid abbreviations
            are: Mon Tue Wed Thu Fri Sat Sun
        holidays : array_like of datetime64[D], optional
            An array of dates to consider as invalid dates.  They may be
            specified in any order, and NaT (not-a-time) dates are ignored.
            This list is saved in a normalized form that is suited for
            fast calculations of valid days.
        busdaycal : busdaycalendar, optional
            A `busdaycalendar` object which specifies the valid days. If this
            parameter is provided, neither weekmask nor holidays may be
            provided.
        out : array of bool, optional
            If provided, this array is filled with the result.
    
        Returns
        -------
        out : array of bool
            An array with the same shape as ``dates``, containing True for
            each valid day, and False for each invalid day.
    
        See Also
        --------
        busdaycalendar: An object that specifies a custom set of valid days.
        busday_offset : Applies an offset counted in valid days.
        busday_count : Counts how many valid days are in a half-open date range.
    
        Examples
        --------
        >>> # The weekdays are Friday, Saturday, and Monday
        ... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],
        ...                 holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
        array([False, False,  True], dtype='bool')"""
    return array()
def isclose(a, b=False, rtol=1e-05, atol=1e-08, equal_nan=False):
    """
        Returns a boolean array where two arrays are element-wise equal within a
        tolerance.
    
        The tolerance values are positive, typically very small numbers.  The
        relative difference (`rtol` * abs(`b`)) and the absolute difference
        `atol` are added together to compare against the absolute difference
        between `a` and `b`.
    
        Parameters
        ----------
        a, b : array_like
            Input arrays to compare.
        rtol : float
            The relative tolerance parameter (see Notes).
        atol : float
            The absolute tolerance parameter (see Notes).
        equal_nan : bool
            Whether to compare NaN's as equal.  If True, NaN's in `a` will be
            considered equal to NaN's in `b` in the output array.
    
        Returns
        -------
        y : array_like
            Returns a boolean array of where `a` and `b` are equal within the
            given tolerance. If both `a` and `b` are scalars, returns a single
            boolean value.
    
        See Also
        --------
        allclose
    
        Notes
        -----
        .. versionadded:: 1.7.0
    
        For finite values, isclose uses the following equation to test whether
        two floating point values are equivalent.
    
         absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
    
        The above equation is not symmetric in `a` and `b`, so that
        `isclose(a, b)` might be different from `isclose(b, a)` in
        some rare cases.
    
        Examples
        --------
        >>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])
        array([True, False])
        >>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])
        array([True, True])
        >>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])
        array([False, True])
        >>> np.isclose([1.0, np.nan], [1.0, np.nan])
        array([True, False])
        >>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
        array([True, True])
        """
    return ndarray()
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], dtype=bool)
    
        """
    return ndarray()
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
    
        """
    return bool()
def isfinite(x, out):
    """isfinite(x[, out])
    
    Test element-wise for finite-ness (not infinity or not Not a Number).
    
    The result is returned as a boolean array.
    
    Parameters
    ----------
    x : array_like
        Input values.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See `doc.ufuncs`.
    
    Returns
    -------
    y : ndarray, bool
        For scalar input, the result is a new boolean with value True
        if the input is finite; otherwise the value is False (input is
        either positive infinity, negative infinity or Not a Number).
    
        For array input, the result is a boolean array with the same
        dimensions as the input and the values are True if the corresponding
        element of the input is finite; otherwise the values are False (element
        is either positive infinity, negative infinity or Not a Number).
    
    See Also
    --------
    isinf, isneginf, isposinf, isnan
    
    Notes
    -----
    Not a Number, positive infinity and negative infinity are considered
    to be non-finite.
    
    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.
    Errors result if the second argument is also supplied when `x` is a scalar
    input, or if first and second arguments have different shapes.
    
    Examples
    --------
    >>> np.isfinite(1)
    True
    >>> np.isfinite(0)
    True
    >>> np.isfinite(np.nan)
    False
    >>> np.isfinite(np.inf)
    False
    >>> np.isfinite(np.NINF)
    False
    >>> np.isfinite([np.log(-1.),1.,np.log(0)])
    array([False,  True, False], dtype=bool)
    
    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isfinite(x, y)
    array([0, 1, 0])
    >>> y
    array([0, 1, 0])"""
    return ndarray()
def isfortran(a):
    """
        Returns True if array is arranged in Fortran-order in memory
        and not C-order.
    
        Parameters
        ----------
        a : ndarray
            Input array.
    
    
        Examples
        --------
    
        np.array allows to specify whether the array is written in C-contiguous
        order (last index varies the fastest), or FORTRAN-contiguous order in
        memory (first index varies the fastest).
    
        >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
        >>> a
        array([[1, 2, 3],
               [4, 5, 6]])
        >>> np.isfortran(a)
        False
    
        >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN')
        >>> b
        array([[1, 2, 3],
               [4, 5, 6]])
        >>> np.isfortran(b)
        True
    
    
        The transpose of a C-ordered array is a FORTRAN-ordered array.
    
        >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
        >>> a
        array([[1, 2, 3],
               [4, 5, 6]])
        >>> np.isfortran(a)
        False
        >>> b = a.T
        >>> b
        array([[1, 4],
               [2, 5],
               [3, 6]])
        >>> np.isfortran(b)
        True
    
        C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.
    
        >>> np.isfortran(np.array([1, 2], order='FORTRAN'))
        False
    
        """
    return bool()
def isinf(x, out):
    """isinf(x[, out])
    
    Test element-wise for positive or negative infinity.
    
    Return a bool-type array, the same shape as `x`, True where ``x ==
    +/-inf``, False everywhere else.
    
    Parameters
    ----------
    x : array_like
        Input values
    out : array_like, optional
        An array with the same shape as `x` to store the result.
    
    Returns
    -------
    y : bool (scalar) or bool-type ndarray
        For scalar input, the result is a new boolean with value True
        if the input is positive or negative infinity; otherwise the value
        is False.
    
        For array input, the result is a boolean array with the same
        shape as the input and the values are True where the
        corresponding element of the input is positive or negative
        infinity; elsewhere the values are False.  If a second argument
        was supplied the result is stored there.  If the type of that array
        is a numeric type the result is represented as zeros and ones, if
        the type is boolean then as False and True, respectively.
        The return value `y` is then a reference to that array.
    
    See Also
    --------
    isneginf, isposinf, isnan, isfinite
    
    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).
    
    Errors result if the second argument is supplied when the first
    argument is a scalar, or if the first and second arguments have
    different shapes.
    
    Examples
    --------
    >>> np.isinf(np.inf)
    True
    >>> np.isinf(np.nan)
    False
    >>> np.isinf(np.NINF)
    True
    >>> np.isinf([np.inf, -np.inf, 1.0, np.nan])
    array([ True,  True, False, False], dtype=bool)
    
    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isinf(x, y)
    array([1, 0, 1])
    >>> y
    array([1, 0, 1])"""
    return bool() if False else bool_type()
def isnan(x, out=None):
    """isnan(x[, out])
    
    Test element-wise for Not a Number (NaN), return result as a bool array.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    y : {ndarray, bool}
        For scalar input, the result is a new boolean with value True
        if the input is NaN; otherwise the value is False.
    
        For array input, the result is a boolean array with the same
        dimensions as the input and the values are True if the corresponding
        element of the input is NaN; otherwise the values are False.
    
    See Also
    --------
    isinf, isneginf, isposinf, isfinite
    
    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.
    
    Examples
    --------
    >>> np.isnan(np.nan)
    True
    >>> np.isnan(np.inf)
    False
    >>> np.isnan([np.log(-1.),1.,np.log(0)])
    array([ True, False, False], dtype=bool)"""
    return ndarray()
def isneginf(x=None, y=None):
    """
        Test element-wise for negative infinity, return result as bool array.
    
        Parameters
        ----------
        x : array_like
            The input array.
        y : array_like, optional
            A boolean array with the same shape and type as `x` to store the
            result.
    
        Returns
        -------
        y : ndarray
            A boolean array with the same dimensions as the input.
            If second argument is not supplied then a numpy boolean array is
            returned with values True where the corresponding element of the
            input is negative infinity and values False where the element of
            the input is not negative infinity.
    
            If a second argument is supplied the result is stored there. If the
            type of that array is a numeric type the result is represented as
            zeros and ones, if the type is boolean then as False and True. The
            return value `y` is then a reference to that array.
    
        See Also
        --------
        isinf, isposinf, isnan, isfinite
    
        Notes
        -----
        Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
        (IEEE 754).
    
        Errors result if the second argument is also supplied when x is a scalar
        input, or if first and second arguments have different shapes.
    
        Examples
        --------
        >>> np.isneginf(np.NINF)
        array(True, dtype=bool)
        >>> np.isneginf(np.inf)
        array(False, dtype=bool)
        >>> np.isneginf(np.PINF)
        array(False, dtype=bool)
        >>> np.isneginf([-np.inf, 0., np.inf])
        array([ True, False, False], dtype=bool)
    
        >>> x = np.array([-np.inf, 0., np.inf])
        >>> y = np.array([2, 2, 2])
        >>> np.isneginf(x, y)
        array([1, 0, 0])
        >>> y
        array([1, 0, 0])
    
        """
    return ndarray()
def isposinf(x=None, y=None):
    """
        Test element-wise for positive infinity, return result as bool array.
    
        Parameters
        ----------
        x : array_like
            The input array.
        y : array_like, optional
            A boolean array with the same shape as `x` to store the result.
    
        Returns
        -------
        y : ndarray
            A boolean array with the same dimensions as the input.
            If second argument is not supplied then a boolean array is returned
            with values True where the corresponding element of the input is
            positive infinity and values False where the element of the input is
            not positive infinity.
    
            If a second argument is supplied the result is stored there. If the
            type of that array is a numeric type the result is represented as zeros
            and ones, if the type is boolean then as False and True.
            The return value `y` is then a reference to that array.
    
        See Also
        --------
        isinf, isneginf, isfinite, isnan
    
        Notes
        -----
        Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
        (IEEE 754).
    
        Errors result if the second argument is also supplied when `x` is a
        scalar input, or if first and second arguments have different shapes.
    
        Examples
        --------
        >>> np.isposinf(np.PINF)
        array(True, dtype=bool)
        >>> np.isposinf(np.inf)
        array(True, dtype=bool)
        >>> np.isposinf(np.NINF)
        array(False, dtype=bool)
        >>> np.isposinf([-np.inf, 0., np.inf])
        array([False, False,  True], dtype=bool)
    
        >>> x = np.array([-np.inf, 0., np.inf])
        >>> y = np.array([2, 2, 2])
        >>> np.isposinf(x, y)
        array([0, 0, 1])
        >>> y
        array([0, 0, 1])
    
        """
    return ndarray()
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 ndarray()
def isrealobj(x):
    """
        Return True if x is a not complex type or an array of complex numbers.
    
        The type of the input is checked, not the value. So even if the input
        has an imaginary part equal to zero, `isrealobj` evaluates to False
        if the data type is complex.
    
        Parameters
        ----------
        x : any
            The input can be of any type and shape.
    
        Returns
        -------
        y : bool
            The return value, False if `x` is of a complex type.
    
        See Also
        --------
        iscomplexobj, isreal
    
        Examples
        --------
        >>> np.isrealobj(1)
        True
        >>> np.isrealobj(1+0j)
        False
        >>> np.isrealobj([3, 1+0j, True])
        False
    
        """
    return bool()
def isscalar(num):
    """
        Returns True if the type of `num` is a scalar type.
    
        Parameters
        ----------
        num : any
            Input argument, can be of any type and shape.
    
        Returns
        -------
        val : bool
            True if `num` is a scalar type, False if it is not.
    
        Examples
        --------
        >>> np.isscalar(3.1)
        True
        >>> np.isscalar([3.1])
        False
        >>> np.isscalar(False)
        True
    
        """
    return bool()
def issctype(rep):
    """
        Determines whether the given object represents a scalar data-type.
    
        Parameters
        ----------
        rep : any
            If `rep` is an instance of a scalar dtype, True is returned. If not,
            False is returned.
    
        Returns
        -------
        out : bool
            Boolean result of check whether `rep` is a scalar dtype.
    
        See Also
        --------
        issubsctype, issubdtype, obj2sctype, sctype2char
    
        Examples
        --------
        >>> np.issctype(np.int32)
        True
        >>> np.issctype(list)
        False
        >>> np.issctype(1.1)
        False
    
        Strings are also a scalar type:
    
        >>> np.issctype(np.dtype('str'))
        True
    
        """
    return bool()
def issub_class_(arg1, arg2):
    """
        Determine if a class is a subclass of a second class.
    
        `issubclass_` is equivalent to the Python built-in ``issubclass``,
        except that it returns False instead of raising a TypeError is one
        of the arguments is not a class.
    
        Parameters
        ----------
        arg1 : class
            Input class. True is returned if `arg1` is a subclass of `arg2`.
        arg2 : class or tuple of classes.
            Input class. If a tuple of classes, True is returned if `arg1` is a
            subclass of any of the tuple elements.
    
        Returns
        -------
        out : bool
            Whether `arg1` is a subclass of `arg2` or not.
    
        See Also
        --------
        issubsctype, issubdtype, issctype
    
        Examples
        --------
        >>> np.issubclass_(np.int32, np.int)
        True
        >>> np.issubclass_(np.int32, np.float)
        False
    
        """
    return bool()
def issubdtype(arg1arg2):
    """
        Returns True if first argument is a typecode lower/equal in type hierarchy.
    
        Parameters
        ----------
        arg1, arg2 : dtype_like
            dtype or string representing a typecode.
    
        Returns
        -------
        out : bool
    
        See Also
        --------
        issubsctype, issubclass_
        numpy.core.numerictypes : Overview of numpy type hierarchy.
    
        Examples
        --------
        >>> np.issubdtype('S1', str)
        True
        >>> np.issubdtype(np.float64, np.float32)
        False
    
        """
    return bool()
def issubsctype(arg1arg2):
    """
        Determine if the first argument is a subclass of the second argument.
    
        Parameters
        ----------
        arg1, arg2 : dtype or dtype specifier
            Data-types.
    
        Returns
        -------
        out : bool
            The result.
    
        See Also
        --------
        issctype, issubdtype,obj2sctype
    
        Examples
        --------
        >>> np.issubsctype('S8', str)
        True
        >>> np.issubsctype(np.array([1]), np.int)
        True
        >>> np.issubsctype(np.array([1]), np.float)
        False
    
        """
    return bool()
def iterable(y):
    """
        Check whether or not an object can be iterated over.
    
        Parameters
        ----------
        y : object
          Input object.
    
        Returns
        -------
        b : {0, 1}
          Return 1 if the object has an iterator method or is a sequence,
          and 0 otherwise.
    
    
        Examples
        --------
        >>> np.iterable([1, 2, 3])
        1
        >>> np.iterable(2)
        0
    
        """
    return _0()
def ix_():
    """
        Construct an open mesh from multiple sequences.
    
        This function takes N 1-D sequences and returns N outputs with N
        dimensions each, such that the shape is 1 in all but one dimension
        and the dimension with the non-unit shape value cycles through all
        N dimensions.
    
        Using `ix_` one can quickly construct index arrays that will index
        the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array
        ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``.
    
        Parameters
        ----------
        args : 1-D sequences
    
        Returns
        -------
        out : tuple of ndarrays
            N arrays with N dimensions each, with N the number of input
            sequences. Together these arrays form an open mesh.
    
        See Also
        --------
        ogrid, mgrid, meshgrid
    
        Examples
        --------
        >>> a = np.arange(10).reshape(2, 5)
        >>> a
        array([[0, 1, 2, 3, 4],
               [5, 6, 7, 8, 9]])
        >>> ixgrid = np.ix_([0,1], [2,4])
        >>> ixgrid
        (array([[0],
               [1]]), array([[2, 4]]))
        >>> ixgrid[0].shape, ixgrid[1].shape
        ((2, 1), (1, 2))
        >>> a[ixgrid]
        array([[2, 4],
               [7, 9]])
    
        """
    return tuple()
def kaiser(M, beta):
    """
        Return the Kaiser window.
    
        The Kaiser window is a taper formed by using a Bessel function.
    
        Parameters
        ----------
        M : int
            Number of points in the output window. If zero or less, an
            empty array is returned.
        beta : float
            Shape parameter for window.
    
        Returns
        -------
        out : array
            The window, with the maximum value normalized to one (the value
            one appears only if the number of samples is odd).
    
        See Also
        --------
        bartlett, blackman, hamming, hanning
    
        Notes
        -----
        The Kaiser window is defined as
    
        .. math::  w(n) = I_0\left( \beta \sqrt{1-\frac{4n^2}{(M-1)^2}}
                   \right)/I_0(\beta)
    
        with
    
        .. math:: \quad -\frac{M-1}{2} \leq n \leq \frac{M-1}{2},
    
        where :math:`I_0` is the modified zeroth-order Bessel function.
    
        The Kaiser was named for Jim Kaiser, who discovered a simple approximation
        to the DPSS window based on Bessel functions.
        The Kaiser window is a very good approximation to the Digital Prolate
        Spheroidal Sequence, or Slepian window, which is the transform which
        maximizes the energy in the main lobe of the window relative to total
        energy.
    
        The Kaiser can approximate many other windows by varying the beta
        parameter.
    
        ====  =======================
        beta  Window shape
        ====  =======================
        0     Rectangular
        5     Similar to a Hamming
        6     Similar to a Hanning
        8.6   Similar to a Blackman
        ====  =======================
    
        A beta value of 14 is probably a good starting point. Note that as beta
        gets large, the window narrows, and so the number of samples needs to be
        large enough to sample the increasingly narrow spike, otherwise NaNs will
        get returned.
    
        Most references to the Kaiser window come from the signal processing
        literature, where it is used as one of many windowing functions for
        smoothing values.  It is also known as an apodization (which means
        "removing the foot", i.e. smoothing discontinuities at the beginning
        and end of the sampled signal) or tapering function.
    
        References
        ----------
        .. [1] J. F. Kaiser, "Digital Filters" - Ch 7 in "Systems analysis by
               digital computer", Editors: F.F. Kuo and J.F. Kaiser, p 218-285.
               John Wiley and Sons, New York, (1966).
        .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
               University of Alberta Press, 1975, pp. 177-178.
        .. [3] Wikipedia, "Window function",
               http://en.wikipedia.org/wiki/Window_function
    
        Examples
        --------
        >>> np.kaiser(12, 14)
        array([  7.72686684e-06,   3.46009194e-03,   4.65200189e-02,
                 2.29737120e-01,   5.99885316e-01,   9.45674898e-01,
                 9.45674898e-01,   5.99885316e-01,   2.29737120e-01,
                 4.65200189e-02,   3.46009194e-03,   7.72686684e-06])
    
    
        Plot the window and the frequency response:
    
        >>> from numpy.fft import fft, fftshift
        >>> window = np.kaiser(51, 14)
        >>> plt.plot(window)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Kaiser window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Amplitude")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Sample")
        <matplotlib.text.Text object at 0x...>
        >>> plt.show()
    
        >>> plt.figure()
        <matplotlib.figure.Figure object at 0x...>
        >>> A = fft(window, 2048) / 25.5
        >>> mag = np.abs(fftshift(A))
        >>> freq = np.linspace(-0.5, 0.5, len(A))
        >>> response = 20 * np.log10(mag)
        >>> response = np.clip(response, -100, 100)
        >>> plt.plot(freq, response)
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Frequency response of Kaiser window")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Magnitude [dB]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("Normalized frequency [cycles per sample]")
        <matplotlib.text.Text object at 0x...>
        >>> plt.axis('tight')
        (-0.5, 0.5, -100.0, ...)
        >>> plt.show()
    
        """
    return array()
def kron(ab):
    """
        Kronecker product of two arrays.
    
        Computes the Kronecker product, a composite array made of blocks of the
        second array scaled by the first.
    
        Parameters
        ----------
        a, b : array_like
    
        Returns
        -------
        out : ndarray
    
        See Also
        --------
        outer : The outer product
    
        Notes
        -----
        The function assumes that the number of dimenensions of `a` and `b`
        are the same, if necessary prepending the smallest with ones.
        If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`,
        the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`.
        The elements are products of elements from `a` and `b`, organized
        explicitly by::
    
            kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]
    
        where::
    
            kt = it * st + jt,  t = 0,...,N
    
        In the common 2-D case (N=1), the block structure can be visualized::
    
            [[ a[0,0]*b,   a[0,1]*b,  ... , a[0,-1]*b  ],
             [  ...                              ...   ],
             [ a[-1,0]*b,  a[-1,1]*b, ... , a[-1,-1]*b ]]
    
    
        Examples
        --------
        >>> np.kron([1,10,100], [5,6,7])
        array([  5,   6,   7,  50,  60,  70, 500, 600, 700])
        >>> np.kron([5,6,7], [1,10,100])
        array([  5,  50, 500,   6,  60, 600,   7,  70, 700])
    
        >>> np.kron(np.eye(2), np.ones((2,2)))
        array([[ 1.,  1.,  0.,  0.],
               [ 1.,  1.,  0.,  0.],
               [ 0.,  0.,  1.,  1.],
               [ 0.,  0.,  1.,  1.]])
    
        >>> a = np.arange(100).reshape((2,5,2,5))
        >>> b = np.arange(24).reshape((2,3,4))
        >>> c = np.kron(a,b)
        >>> c.shape
        (2, 10, 6, 20)
        >>> I = (1,3,0,2)
        >>> J = (0,2,1)
        >>> J1 = (0,) + J             # extend to ndim=4
        >>> S1 = (1,) + b.shape
        >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1))
        >>> c[K] == a[I]*b[J]
        True
    
        """
    return ndarray()
def ldexp(x1, x2, out):
    """ldexp(x1, x2[, out])
    
    Compute y = x1 * 2**x2."""
    return None
def left_shift(x1, x2, out=None):
    """left_shift(x1, x2[, out])
    
    Shift the bits of an integer to the left.
    
    Bits are shifted to the left by appending `x2` 0s at the right of `x1`.
    Since the internal representation of numbers is in binary format, this
    operation is equivalent to multiplying `x1` by ``2**x2``.
    
    Parameters
    ----------
    x1 : array_like of integer type
        Input values.
    x2 : array_like of integer type
        Number of zeros to append to `x1`. Has to be non-negative.
    
    Returns
    -------
    out : array of integer type
        Return `x1` with bits shifted `x2` times to the left.
    
    See Also
    --------
    right_shift : Shift the bits of an integer to the right.
    binary_repr : Return the binary representation of the input number
        as a string.
    
    Examples
    --------
    >>> np.binary_repr(5)
    '101'
    >>> np.left_shift(5, 2)
    20
    >>> np.binary_repr(20)
    '10100'
    
    >>> np.left_shift(5, [1,2,3])
    array([10, 20, 40])"""
    return array()
def less(x1, x2, out=None):
    """less(x1, x2[, out])
    
    Return the truth value of (x1 < x2) element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input arrays.  If ``x1.shape != x2.shape``, they must be
        broadcastable to a common shape (which may be the shape of one or
        the other).
    
    Returns
    -------
    out : bool or ndarray of bool
        Array of bools, or a single bool if `x1` and `x2` are scalars.
    
    See Also
    --------
    greater, less_equal, greater_equal, equal, not_equal
    
    Examples
    --------
    >>> np.less([1, 2], [2, 2])
    array([ True, False], dtype=bool)"""
    return bool() if False else ndarray()
def less_equal(x1, x2, out=None):
    """less_equal(x1, x2[, out])
    
    Return the truth value of (x1 =< x2) element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input arrays.  If ``x1.shape != x2.shape``, they must be
        broadcastable to a common shape (which may be the shape of one or
        the other).
    
    Returns
    -------
    out : bool or ndarray of bool
        Array of bools, or a single bool if `x1` and `x2` are scalars.
    
    See Also
    --------
    greater, less, greater_equal, equal, not_equal
    
    Examples
    --------
    >>> np.less_equal([4, 2, 1], [2, 2, 2])
    array([False,  True,  True], dtype=bool)"""
    return bool() if False else ndarray()
def lexsort(keys, axis):
    """lexsort(keys, axis=-1)
    
        Perform an indirect sort using a sequence of keys.
    
        Given multiple sorting keys, which can be interpreted as columns in a
        spreadsheet, lexsort returns an array of integer indices that describes
        the sort order by multiple columns. The last key in the sequence is used
        for the primary sort order, the second-to-last key for the secondary sort
        order, and so on. The keys argument must be a sequence of objects that
        can be converted to arrays of the same shape. If a 2D array is provided
        for the keys argument, it's rows are interpreted as the sorting keys and
        sorting is according to the last row, second last row etc.
    
        Parameters
        ----------
        keys : (k, N) array or tuple containing k (N,)-shaped sequences
            The `k` different "columns" to be sorted.  The last column (or row if
            `keys` is a 2D array) is the primary sort key.
        axis : int, optional
            Axis to be indirectly sorted.  By default, sort over the last axis.
    
        Returns
        -------
        indices : (N,) ndarray of ints
            Array of indices that sort the keys along the specified axis.
    
        See Also
        --------
        argsort : Indirect sort.
        ndarray.sort : In-place sort.
        sort : Return a sorted copy of an array.
    
        Examples
        --------
        Sort names: first by surname, then by name.
    
        >>> surnames =    ('Hertz',    'Galilei', 'Hertz')
        >>> first_names = ('Heinrich', 'Galileo', 'Gustav')
        >>> ind = np.lexsort((first_names, surnames))
        >>> ind
        array([1, 2, 0])
    
        >>> [surnames[i] + ", " + first_names[i] for i in ind]
        ['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']
    
        Sort two columns of numbers:
    
        >>> a = [1,5,1,4,3,4,4] # First column
        >>> b = [9,4,0,4,0,2,1] # Second column
        >>> ind = np.lexsort((b,a)) # Sort by a, then by b
        >>> print ind
        [2 0 4 6 5 3 1]
    
        >>> [(a[i],b[i]) for i in ind]
        [(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]
    
        Note that sorting is first according to the elements of ``a``.
        Secondary sorting is according to the elements of ``b``.
    
        A normal ``argsort`` would have yielded:
    
        >>> [(a[i],b[i]) for i in np.argsort(a)]
        [(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]
    
        Structured arrays are sorted lexically by ``argsort``:
    
        >>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
        ...              dtype=np.dtype([('x', int), ('y', int)]))
    
        >>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
        array([2, 0, 4, 6, 5, 3, 1])"""
    return N()
def linspace(start, stop=False, num=50, endpoint=True, retstep=False):
    """
        Return evenly spaced numbers over a specified interval.
    
        Returns `num` evenly spaced samples, calculated over the
        interval [`start`, `stop` ].
    
        The endpoint of the interval can optionally be excluded.
    
        Parameters
        ----------
        start : scalar
            The starting value of the sequence.
        stop : scalar
            The end value of the sequence, unless `endpoint` is set to False.
            In that case, the sequence consists of all but the last of ``num + 1``
            evenly spaced samples, so that `stop` is excluded.  Note that the step
            size changes when `endpoint` is False.
        num : int, optional
            Number of samples to generate. Default is 50.
        endpoint : bool, optional
            If True, `stop` is the last sample. Otherwise, it is not included.
            Default is True.
        retstep : bool, optional
            If True, return (`samples`, `step`), where `step` is the spacing
            between samples.
    
        Returns
        -------
        samples : ndarray
            There are `num` equally spaced samples in the closed interval
            ``[start, stop]`` or the half-open interval ``[start, stop)``
            (depending on whether `endpoint` is True or False).
        step : float (only if `retstep` is True)
            Size of spacing between samples.
    
    
        See Also
        --------
        arange : Similar to `linspace`, but uses a step size (instead of the
                 number of samples).
        logspace : Samples uniformly distributed in log space.
    
        Examples
        --------
        >>> np.linspace(2.0, 3.0, num=5)
            array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ])
        >>> np.linspace(2.0, 3.0, num=5, endpoint=False)
            array([ 2. ,  2.2,  2.4,  2.6,  2.8])
        >>> np.linspace(2.0, 3.0, num=5, retstep=True)
            (array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)
    
        Graphical illustration:
    
        >>> import matplotlib.pyplot as plt
        >>> N = 8
        >>> y = np.zeros(N)
        >>> x1 = np.linspace(0, 10, N, endpoint=True)
        >>> x2 = np.linspace(0, 10, N, endpoint=False)
        >>> plt.plot(x1, y, 'o')
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.plot(x2, y + 0.5, 'o')
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.ylim([-0.5, 1])
        (-0.5, 1)
        >>> plt.show()
    
        """
    return ndarray()
little_endian = bool()
def load(file=None, mmap_mode=None):
    """
        Load an array(s) or pickled objects from .npy, .npz, or pickled files.
    
        Parameters
        ----------
        file : file-like object or string
            The file to read.  It must support ``seek()`` and ``read()`` methods.
            If the filename extension is ``.gz``, the file is first decompressed.
        mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
            If not None, then memory-map the file, using the given mode
            (see `numpy.memmap` for a detailed description of the modes).
            A memory-mapped array is kept on disk. However, it can be accessed
            and sliced like any ndarray.  Memory mapping is especially useful for
            accessing small fragments of large files without reading the entire
            file into memory.
    
        Returns
        -------
        result : array, tuple, dict, etc.
            Data stored in the file. For '.npz' files, the returned instance of
            NpzFile class must be closed to avoid leaking file descriptors.
    
        Raises
        ------
        IOError
            If the input file does not exist or cannot be read.
    
        See Also
        --------
        save, savez, loadtxt
        memmap : Create a memory-map to an array stored in a file on disk.
    
        Notes
        -----
        - If the file contains pickle data, then whatever object is stored
          in the pickle is returned.
        - If the file is a ``.npy`` file, then a single array is returned.
        - If the file is a ``.npz`` file, then a dictionary-like object is
          returned, containing ``{filename: array}`` key-value pairs, one for
          each file in the archive.
        - If the file is a ``.npz`` file, the returned value supports the context
          manager protocol in a similar fashion to the open function::
    
            with load('foo.npz') as data:
                a = data['a']
    
          The underlyling file descriptor is closed when exiting the 'with' block.
    
        Examples
        --------
        Store data to disk, and load it again:
    
        >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
        >>> np.load('/tmp/123.npy')
        array([[1, 2, 3],
               [4, 5, 6]])
    
        Store compressed data to disk, and load it again:
    
        >>> a=np.array([[1, 2, 3], [4, 5, 6]])
        >>> b=np.array([1, 2])
        >>> np.savez('/tmp/123.npz', a=a, b=b)
        >>> data = np.load('/tmp/123.npz')
        >>> data['a']
        array([[1, 2, 3],
               [4, 5, 6]])
        >>> data['b']
        array([1, 2])
        >>> data.close()
    
        Mem-map the stored array, and then access the second row
        directly from disk:
    
        >>> X = np.load('/tmp/123.npy', mmap_mode='r')
        >>> X[1, :]
        memmap([4, 5, 6])
    
        """
    return array()
def loads(string):
    """loads(string) -- Load a pickle from the given string"""
    return None
def loadtxt(fname=0, dtype=typefloat(), comments="#", delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0):
    """
        Load data from a text file.
    
        Each row in the text file must have the same number of values.
    
        Parameters
        ----------
        fname : file or str
            File, filename, or generator to read.  If the filename extension is
            ``.gz`` or ``.bz2``, the file is first decompressed. Note that
            generators should return byte strings for Python 3k.
        dtype : data-type, optional
            Data-type of the resulting array; default: float.  If this is a
            record data-type, the resulting array will be 1-dimensional, and
            each row will be interpreted as an element of the array.  In this
            case, the number of columns used must match the number of fields in
            the data-type.
        comments : str, optional
            The character used to indicate the start of a comment;
            default: '#'.
        delimiter : str, optional
            The string used to separate values.  By default, this is any
            whitespace.
        converters : dict, optional
            A dictionary mapping column number to a function that will convert
            that column to a float.  E.g., if column 0 is a date string:
            ``converters = {0: datestr2num}``.  Converters can also be used to
            provide a default value for missing data (but see also `genfromtxt`):
            ``converters = {3: lambda s: float(s.strip() or 0)}``.  Default: None.
        skiprows : int, optional
            Skip the first `skiprows` lines; default: 0.
        usecols : sequence, optional
            Which columns to read, with 0 being the first.  For example,
            ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
            The default, None, results in all columns being read.
        unpack : bool, optional
            If True, the returned array is transposed, so that arguments may be
            unpacked using ``x, y, z = loadtxt(...)``.  When used with a record
            data-type, arrays are returned for each field.  Default is False.
        ndmin : int, optional
            The returned array will have at least `ndmin` dimensions.
            Otherwise mono-dimensional axes will be squeezed.
            Legal values: 0 (default), 1 or 2.
            .. versionadded:: 1.6.0
    
        Returns
        -------
        out : ndarray
            Data read from the text file.
    
        See Also
        --------
        load, fromstring, fromregex
        genfromtxt : Load data with missing values handled as specified.
        scipy.io.loadmat : reads MATLAB data files
    
        Notes
        -----
        This function aims to be a fast reader for simply formatted files.  The
        `genfromtxt` function provides more sophisticated handling of, e.g.,
        lines with missing values.
    
        Examples
        --------
        >>> from StringIO import StringIO   # StringIO behaves like a file object
        >>> c = StringIO("0 1\n2 3")
        >>> np.loadtxt(c)
        array([[ 0.,  1.],
               [ 2.,  3.]])
    
        >>> d = StringIO("M 21 72\nF 35 58")
        >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
        ...                      'formats': ('S1', 'i4', 'f4')})
        array([('M', 21, 72.0), ('F', 35, 58.0)],
              dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
    
        >>> c = StringIO("1,0,2\n3,0,4")
        >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
        >>> x
        array([ 1.,  3.])
        >>> y
        array([ 2.,  4.])
    
        """
    return ndarray()
def log(x, out=None):
    """log(x[, out])
    
    Natural logarithm, element-wise.
    
    The natural logarithm `log` is the inverse of the exponential function,
    so that `log(exp(x)) = x`. The natural logarithm is logarithm in base `e`.
    
    Parameters
    ----------
    x : array_like
        Input value.
    
    Returns
    -------
    y : ndarray
        The natural logarithm of `x`, element-wise.
    
    See Also
    --------
    log10, log2, log1p, emath.log
    
    Notes
    -----
    Logarithm is a multivalued function: for each `x` there is an infinite
    number of `z` such that `exp(z) = x`. The convention is to return the `z`
    whose imaginary part lies in `[-pi, pi]`.
    
    For real-valued input data types, `log` always returns real output. For
    each value that cannot be expressed as a real number or infinity, it
    yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `log` is a complex analytical function that
    has a branch cut `[-inf, 0]` and is continuous from above on it. `log`
    handles the floating-point negative zero as an infinitesimal negative
    number, conforming to the C99 standard.
    
    References
    ----------
    .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
           10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/
    .. [2] Wikipedia, "Logarithm". http://en.wikipedia.org/wiki/Logarithm
    
    Examples
    --------
    >>> np.log([1, np.e, np.e**2, 0])
    array([  0.,   1.,   2., -Inf])"""
    return ndarray()
def log10(x, out=None):
    """log10(x[, out])
    
    Return the base 10 logarithm of the input array, element-wise.
    
    Parameters
    ----------
    x : array_like
        Input values.
    
    Returns
    -------
    y : ndarray
        The logarithm to the base 10 of `x`, element-wise. NaNs are
        returned where x is negative.
    
    See Also
    --------
    emath.log10
    
    Notes
    -----
    Logarithm is a multivalued function: for each `x` there is an infinite
    number of `z` such that `10**z = x`. The convention is to return the `z`
    whose imaginary part lies in `[-pi, pi]`.
    
    For real-valued input data types, `log10` always returns real output. For
    each value that cannot be expressed as a real number or infinity, it
    yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `log10` is a complex analytical function that
    has a branch cut `[-inf, 0]` and is continuous from above on it. `log10`
    handles the floating-point negative zero as an infinitesimal negative
    number, conforming to the C99 standard.
    
    References
    ----------
    .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
           10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/
    .. [2] Wikipedia, "Logarithm". http://en.wikipedia.org/wiki/Logarithm
    
    Examples
    --------
    >>> np.log10([1e-15, -3.])
    array([-15.,  NaN])"""
    return ndarray()
def log1p(x, out=None):
    """log1p(x[, out])
    
    Return the natural logarithm of one plus the input array, element-wise.
    
    Calculates ``log(1 + x)``.
    
    Parameters
    ----------
    x : array_like
        Input values.
    
    Returns
    -------
    y : ndarray
        Natural logarithm of `1 + x`, element-wise.
    
    See Also
    --------
    expm1 : ``exp(x) - 1``, the inverse of `log1p`.
    
    Notes
    -----
    For real-valued input, `log1p` is accurate also for `x` so small
    that `1 + x == 1` in floating-point accuracy.
    
    Logarithm is a multivalued function: for each `x` there is an infinite
    number of `z` such that `exp(z) = 1 + x`. The convention is to return
    the `z` whose imaginary part lies in `[-pi, pi]`.
    
    For real-valued input data types, `log1p` always returns real output. For
    each value that cannot be expressed as a real number or infinity, it
    yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `log1p` is a complex analytical function that
    has a branch cut `[-inf, -1]` and is continuous from above on it. `log1p`
    handles the floating-point negative zero as an infinitesimal negative
    number, conforming to the C99 standard.
    
    References
    ----------
    .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
           10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/
    .. [2] Wikipedia, "Logarithm". http://en.wikipedia.org/wiki/Logarithm
    
    Examples
    --------
    >>> np.log1p(1e-99)
    1e-99
    >>> np.log(1 + 1e-99)
    0.0"""
    return ndarray()
def log2(x, out=None):
    """log2(x[, out])
    
    Base-2 logarithm of `x`.
    
    Parameters
    ----------
    x : array_like
        Input values.
    
    Returns
    -------
    y : ndarray
        Base-2 logarithm of `x`.
    
    See Also
    --------
    log, log10, log1p, emath.log2
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    Logarithm is a multivalued function: for each `x` there is an infinite
    number of `z` such that `2**z = x`. The convention is to return the `z`
    whose imaginary part lies in `[-pi, pi]`.
    
    For real-valued input data types, `log2` always returns real output. For
    each value that cannot be expressed as a real number or infinity, it
    yields ``nan`` and sets the `invalid` floating point error flag.
    
    For complex-valued input, `log2` is a complex analytical function that
    has a branch cut `[-inf, 0]` and is continuous from above on it. `log2`
    handles the floating-point negative zero as an infinitesimal negative
    number, conforming to the C99 standard.
    
    Examples
    --------
    >>> x = np.array([0, 1, 2, 2**4])
    >>> np.log2(x)
    array([-Inf,   0.,   1.,   4.])
    
    >>> xi = np.array([0+1.j, 1, 2+0.j, 4.j])
    >>> np.log2(xi)
    array([ 0.+2.26618007j,  0.+0.j        ,  1.+0.j        ,  2.+2.26618007j])"""
    return ndarray()
def logaddexp(x1, x2, out=None):
    """logaddexp(x1, x2[, out])
    
    Logarithm of the sum of exponentiations of the inputs.
    
    Calculates ``log(exp(x1) + exp(x2))``. This function is useful in
    statistics where the calculated probabilities of events may be so small
    as to exceed the range of normal floating point numbers.  In such cases
    the logarithm of the calculated probability is stored. This function
    allows adding probabilities stored in such a fashion.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input values.
    
    Returns
    -------
    result : ndarray
        Logarithm of ``exp(x1) + exp(x2)``.
    
    See Also
    --------
    logaddexp2: Logarithm of the sum of exponentiations of inputs in base-2.
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    Examples
    --------
    >>> prob1 = np.log(1e-50)
    >>> prob2 = np.log(2.5e-50)
    >>> prob12 = np.logaddexp(prob1, prob2)
    >>> prob12
    -113.87649168120691
    >>> np.exp(prob12)
    3.5000000000000057e-50"""
    return ndarray()
def logaddexp2(x1, x2, out=None):
    """logaddexp2(x1, x2[, out])
    
    Logarithm of the sum of exponentiations of the inputs in base-2.
    
    Calculates ``log2(2**x1 + 2**x2)``. This function is useful in machine
    learning when the calculated probabilities of events may be so small
    as to exceed the range of normal floating point numbers.  In such cases
    the base-2 logarithm of the calculated probability can be used instead.
    This function allows adding probabilities stored in such a fashion.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input values.
    out : ndarray, optional
        Array to store results in.
    
    Returns
    -------
    result : ndarray
        Base-2 logarithm of ``2**x1 + 2**x2``.
    
    See Also
    --------
    logaddexp: Logarithm of the sum of exponentiations of the inputs.
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    Examples
    --------
    >>> prob1 = np.log2(1e-50)
    >>> prob2 = np.log2(2.5e-50)
    >>> prob12 = np.logaddexp2(prob1, prob2)
    >>> prob1, prob2, prob12
    (-166.09640474436813, -164.77447664948076, -164.28904982231052)
    >>> 2**prob12
    3.4999999999999914e-50"""
    return ndarray()
def logical_and(x1, x2, out=None):
    """logical_and(x1, x2[, out])
    
    Compute the truth value of x1 AND x2 elementwise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input arrays. `x1` and `x2` must be of the same shape.
    
    
    Returns
    -------
    y : {ndarray, bool}
        Boolean result with the same shape as `x1` and `x2` of the logical
        AND operation on corresponding elements of `x1` and `x2`.
    
    See Also
    --------
    logical_or, logical_not, logical_xor
    bitwise_and
    
    Examples
    --------
    >>> np.logical_and(True, False)
    False
    >>> np.logical_and([True, False], [False, False])
    array([False, False], dtype=bool)
    
    >>> x = np.arange(5)
    >>> np.logical_and(x>1, x<4)
    array([False, False,  True,  True, False], dtype=bool)"""
    return None
def logical_not(x, out=None):
    """logical_not(x[, out])
    
    Compute the truth value of NOT x elementwise.
    
    Parameters
    ----------
    x : array_like
        Logical NOT is applied to the elements of `x`.
    
    Returns
    -------
    y : bool or ndarray of bool
        Boolean result with the same shape as `x` of the NOT operation
        on elements of `x`.
    
    See Also
    --------
    logical_and, logical_or, logical_xor
    
    Examples
    --------
    >>> np.logical_not(3)
    False
    >>> np.logical_not([True, False, 0, 1])
    array([False,  True,  True, False], dtype=bool)
    
    >>> x = np.arange(5)
    >>> np.logical_not(x<3)
    array([False, False, False,  True,  True], dtype=bool)"""
    return bool() if False else ndarray()
def logical_or(x1, x2, out=None):
    """logical_or(x1, x2[, out])
    
    Compute the truth value of x1 OR x2 elementwise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Logical OR is applied to the elements of `x1` and `x2`.
        They have to be of the same shape.
    
    Returns
    -------
    y : {ndarray, bool}
        Boolean result with the same shape as `x1` and `x2` of the logical
        OR operation on elements of `x1` and `x2`.
    
    See Also
    --------
    logical_and, logical_not, logical_xor
    bitwise_or
    
    Examples
    --------
    >>> np.logical_or(True, False)
    True
    >>> np.logical_or([True, False], [False, False])
    array([ True, False], dtype=bool)
    
    >>> x = np.arange(5)
    >>> np.logical_or(x < 1, x > 3)
    array([ True, False, False, False,  True], dtype=bool)"""
    return None
def logical_xor(x1, x2, out=None):
    """logical_xor(x1, x2[, out])
    
    Compute the truth value of x1 XOR x2, element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Logical XOR is applied to the elements of `x1` and `x2`.  They must
        be broadcastable to the same shape.
    
    Returns
    -------
    y : bool or ndarray of bool
        Boolean result of the logical XOR operation applied to the elements
        of `x1` and `x2`; the shape is determined by whether or not
        broadcasting of one or both arrays was required.
    
    See Also
    --------
    logical_and, logical_or, logical_not, bitwise_xor
    
    Examples
    --------
    >>> np.logical_xor(True, False)
    True
    >>> np.logical_xor([True, True, False, False], [True, False, True, False])
    array([False,  True,  True, False], dtype=bool)
    
    >>> x = np.arange(5)
    >>> np.logical_xor(x < 1, x > 3)
    array([ True, False, False, False,  True], dtype=bool)
    
    Simple example showing support of broadcasting
    
    >>> np.logical_xor(0, np.eye(2))
    array([[ True, False],
           [False,  True]], dtype=bool)"""
    return bool() if False else ndarray()
def logspace(start, stop=10.0, num=50, endpoint=True, base=10.0):
    """
        Return numbers spaced evenly on a log scale.
    
        In linear space, the sequence starts at ``base ** start``
        (`base` to the power of `start`) and ends with ``base ** stop``
        (see `endpoint` below).
    
        Parameters
        ----------
        start : float
            ``base ** start`` is the starting value of the sequence.
        stop : float
            ``base ** stop`` is the final value of the sequence, unless `endpoint`
            is False.  In that case, ``num + 1`` values are spaced over the
            interval in log-space, of which all but the last (a sequence of
            length ``num``) are returned.
        num : integer, optional
            Number of samples to generate.  Default is 50.
        endpoint : boolean, optional
            If true, `stop` is the last sample. Otherwise, it is not included.
            Default is True.
        base : float, optional
            The base of the log space. The step size between the elements in
            ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
            Default is 10.0.
    
        Returns
        -------
        samples : ndarray
            `num` samples, equally spaced on a log scale.
    
        See Also
        --------
        arange : Similar to linspace, with the step size specified instead of the
                 number of samples. Note that, when used with a float endpoint, the
                 endpoint may or may not be included.
        linspace : Similar to logspace, but with the samples uniformly distributed
                   in linear space, instead of log space.
    
        Notes
        -----
        Logspace is equivalent to the code
    
        >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
        ... # doctest: +SKIP
        >>> power(base, y)
        ... # doctest: +SKIP
    
        Examples
        --------
        >>> np.logspace(2.0, 3.0, num=4)
            array([  100.        ,   215.443469  ,   464.15888336,  1000.        ])
        >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
            array([ 100.        ,  177.827941  ,  316.22776602,  562.34132519])
        >>> np.logspace(2.0, 3.0, num=4, base=2.0)
            array([ 4.        ,  5.0396842 ,  6.34960421,  8.        ])
    
        Graphical illustration:
    
        >>> import matplotlib.pyplot as plt
        >>> N = 10
        >>> x1 = np.logspace(0.1, 1, N, endpoint=True)
        >>> x2 = np.logspace(0.1, 1, N, endpoint=False)
        >>> y = np.zeros(N)
        >>> plt.plot(x1, y, 'o')
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.plot(x2, y + 0.5, 'o')
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.ylim([-0.5, 1])
        (-0.5, 1)
        >>> plt.show()
    
        """
    return ndarray()
class long:
    __doc__ = str()
    def bit_length(self, _):
        """long.bit_length() -> int or long
        
        Number of bits necessary to represent self in binary.
        >>> bin(37L)
        '0b100101'
        >>> (37L).bit_length()
        6"""
        return None
    def conjugate(self, _):
        """Returns self, the complex conjugate of any long."""
        return None
    denominator = getset_descriptor()
    imag = getset_descriptor()
    numerator = getset_descriptor()
    real = getset_descriptor()
class complex256:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class float128:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class float128:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class int64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def bit_length(self, _):
        """int.bit_length() -> int
        
        Number of bits necessary to represent self in binary.
        >>> bin(37)
        '0b100101'
        >>> (37).bit_length()
        6"""
        return None
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    denominator = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    numerator = getset_descriptor()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def lookfor(what=None, module=None, import_modules=True, regenerate=False, output=None):
    """
        Do a keyword search on docstrings.
    
        A list of of objects that matched the search is displayed,
        sorted by relevance. All given keywords need to be found in the
        docstring for it to be returned as a result, but the order does
        not matter.
    
        Parameters
        ----------
        what : str
            String containing words to look for.
        module : str or list, optional
            Name of module(s) whose docstrings to go through.
        import_modules : bool, optional
            Whether to import sub-modules in packages. Default is True.
        regenerate : bool, optional
            Whether to re-generate the docstring cache. Default is False.
        output : file-like, optional
            File-like object to write the output to. If omitted, use a pager.
    
        See Also
        --------
        source, info
    
        Notes
        -----
        Relevance is determined only roughly, by checking if the keywords occur
        in the function name, at the start of a docstring, etc.
    
        Examples
        --------
        >>> np.lookfor('binary representation')
        Search results for 'binary representation'
        ------------------------------------------
        numpy.binary_repr
            Return the binary representation of the input number as a string.
        numpy.core.setup_common.long_double_representation
            Given a binary dump as given by GNU od -b, look for long double
        numpy.base_repr
            Return a string representation of a number in the given base system.
        ...
    
        """
    return None
def ma_fromtxt(fnamekwargs):
    """
        Load ASCII data stored in a text file and return a masked array.
    
        Parameters
        ----------
        fname, kwargs : For a description of input parameters, see `genfromtxt`.
    
        See Also
        --------
        numpy.genfromtxt : generic function to load ASCII data.
    
        """
    return None
def mask_indices(n, mask_func=0, k=0):
    """
        Return the indices to access (n, n) arrays, given a masking function.
    
        Assume `mask_func` is a function that, for a square array a of size
        ``(n, n)`` with a possible offset argument `k`, when called as
        ``mask_func(a, k)`` returns a new array with zeros in certain locations
        (functions like `triu` or `tril` do precisely this). Then this function
        returns the indices where the non-zero values would be located.
    
        Parameters
        ----------
        n : int
            The returned indices will be valid to access arrays of shape (n, n).
        mask_func : callable
            A function whose call signature is similar to that of `triu`, `tril`.
            That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`.
            `k` is an optional argument to the function.
        k : scalar
            An optional argument which is passed through to `mask_func`. Functions
            like `triu`, `tril` take a second argument that is interpreted as an
            offset.
    
        Returns
        -------
        indices : tuple of arrays.
            The `n` arrays of indices corresponding to the locations where
            ``mask_func(np.ones((n, n)), k)`` is True.
    
        See Also
        --------
        triu, tril, triu_indices, tril_indices
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        Examples
        --------
        These are the indices that would allow you to access the upper triangular
        part of any 3x3 array:
    
        >>> iu = np.mask_indices(3, np.triu)
    
        For example, if `a` is a 3x3 array:
    
        >>> a = np.arange(9).reshape(3, 3)
        >>> a
        array([[0, 1, 2],
               [3, 4, 5],
               [6, 7, 8]])
        >>> a[iu]
        array([0, 1, 2, 4, 5, 8])
    
        An offset can be passed also to the masking function.  This gets us the
        indices starting on the first diagonal right of the main one:
    
        >>> iu1 = np.mask_indices(3, np.triu, 1)
    
        with which we now extract only three elements:
    
        >>> a[iu1]
        array([1, 2, 5])
    
        """
    return tuple()
def asmatrix(data=None, 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.
    
        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()
class matrix:
    A = property()
    A1 = property()
    H = property()
    I = property()
    T = property()
    __array_interface__ = getset_descriptor()
    __array_priority__ = float()
    __array_struct__ = getset_descriptor()
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    def _align(self, _):
        """A convenience function for operations that need to preserve axis
                orientation.
                """
        return None
    def _collapse(self, _):
        """A convenience function for operations that want to collapse
                to a scalar like _align, but are using keepdims=True
                """
        return None
    def all(self=None, axis=None, out=None):
        """
                Test whether all matrix elements along a given axis evaluate to True.
        
                Parameters
                ----------
                See `numpy.all` for complete descriptions
        
                See Also
                --------
                numpy.all
        
                Notes
                -----
                This is the same as `ndarray.all`, but it returns a `matrix` object.
        
                Examples
                --------
                >>> x = np.matrix(np.arange(12).reshape((3,4))); x
                matrix([[ 0,  1,  2,  3],
                        [ 4,  5,  6,  7],
                        [ 8,  9, 10, 11]])
                >>> y = x[0]; y
                matrix([[0, 1, 2, 3]])
                >>> (x == y)
                matrix([[ True,  True,  True,  True],
                        [False, False, False, False],
                        [False, False, False, False]], dtype=bool)
                >>> (x == y).all()
                False
                >>> (x == y).all(0)
                matrix([[False, False, False, False]], dtype=bool)
                >>> (x == y).all(1)
                matrix([[ True],
                        [False],
                        [False]], dtype=bool)
        
                """
        return None
    def any(self=None, 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 bool()
    def argmax(self=None, axis=None, out=None):
        """
                Indices of the maximum values along an axis.
        
                Parameters
                ----------
                See `numpy.argmax` for complete descriptions
        
                See Also
                --------
                numpy.argmax
        
                Notes
                -----
                This is the same as `ndarray.argmax`, but returns a `matrix` object
                where `ndarray.argmax` would return 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.argmax()
                11
                >>> x.argmax(0)
                matrix([[2, 2, 2, 2]])
                >>> x.argmax(1)
                matrix([[3],
                        [3],
                        [3]])
        
                """
        return None
    def argmin(self=None, axis=None, out=None):
        """
                Return the indices of the minimum values along an axis.
        
                Parameters
                ----------
                See `numpy.argmin` for complete descriptions.
        
                See Also
                --------
                numpy.argmin
        
                Notes
                -----
                This is the same as `ndarray.argmin`, but returns a `matrix` object
                where `ndarray.argmin` would return 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.argmin()
                11
                >>> x.argmin(0)
                matrix([[2, 2, 2, 2]])
                >>> x.argmin(1)
                matrix([[3],
                        [3],
                        [3]])
        
                """
        return None
    def argpartition(self, kth, axis=_1, kind=quickselect, order=None):
        """a.argpartition(kth, axis=-1, kind='quickselect', order=None)
        
            Returns the indices that would partition this array.
        
            Refer to `numpy.argpartition` for full documentation.
        
            .. versionadded:: 1.8.0
        
            See Also
            --------
            numpy.argpartition : equivalent function"""
        return None
    def argsort(self, axis=_1, kind=quicksort, order=None):
        """a.argsort(axis=-1, kind='quicksort', order=None)
        
            Returns the indices that would sort this array.
        
            Refer to `numpy.argsort` for full documentation.
        
            See Also
            --------
            numpy.argsort : equivalent function"""
        return None
    def astype(self, dtype, order, casting, subok, copy):
        """a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
        
            Copy of the array, cast to a specified type.
        
            Parameters
            ----------
            dtype : str or dtype
                Typecode or data-type to which the array is cast.
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout order of the result.
                'C' means C order, 'F' means Fortran order, 'A'
                means 'F' order if all the arrays are Fortran contiguous,
                'C' order otherwise, and 'K' means as close to the
                order the array elements appear in memory as possible.
                Default is 'K'.
            casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
                Controls what kind of data casting may occur. Defaults to 'unsafe'
                for backwards compatibility.
        
                  * 'no' means the data types should not be cast at all.
                  * 'equiv' means only byte-order changes are allowed.
                  * 'safe' means only casts which can preserve values are allowed.
                  * 'same_kind' means only safe casts or casts within a kind,
                    like float64 to float32, are allowed.
                  * 'unsafe' means any data conversions may be done.
            subok : bool, optional
                If True, then sub-classes will be passed-through (default), otherwise
                the returned array will be forced to be a base-class array.
            copy : bool, optional
                By default, astype always returns a newly allocated array. If this
                is set to false, and the `dtype`, `order`, and `subok`
                requirements are satisfied, the input array is returned instead
                of a copy.
        
            Returns
            -------
            arr_t : ndarray
                Unless `copy` is False and the other conditions for returning the input
                array are satisfied (see description for `copy` input paramter), `arr_t`
                is a new array of the same shape as the input array, with dtype, order
                given by `dtype`, `order`.
        
            Raises
            ------
            ComplexWarning
                When casting from complex to float or int. To avoid this,
                one should use ``a.real.astype(t)``.
        
            Examples
            --------
            >>> x = np.array([1, 2, 2.5])
            >>> x
            array([ 1. ,  2. ,  2.5])
        
            >>> x.astype(int)
            array([1, 2, 2])"""
        return ndarray()
    base = getset_descriptor()
    def byteswap(self, inplace):
        """a.byteswap(inplace)
        
            Swap the bytes of the array elements
        
            Toggle between low-endian and big-endian data representation by
            returning a byteswapped array, optionally swapped in-place.
        
            Parameters
            ----------
            inplace : bool, optional
                If ``True``, swap bytes in-place, default is ``False``.
        
            Returns
            -------
            out : ndarray
                The byteswapped array. If `inplace` is ``True``, this is
                a view to self.
        
            Examples
            --------
            >>> A = np.array([1, 256, 8755], dtype=np.int16)
            >>> map(hex, A)
            ['0x1', '0x100', '0x2233']
            >>> A.byteswap(True)
            array([  256,     1, 13090], dtype=int16)
            >>> map(hex, A)
            ['0x100', '0x1', '0x3322']
        
            Arrays of strings are not swapped
        
            >>> A = np.array(['ceg', 'fac'])
            >>> A.byteswap()
            array(['ceg', 'fac'],
                  dtype='|S3')"""
        return ndarray()
    def choose(self, choices, out=None, mode=_raise):
        """a.choose(choices, out=None, mode='raise')
        
            Use an index array to construct a new array from a set of choices.
        
            Refer to `numpy.choose` for full documentation.
        
            See Also
            --------
            numpy.choose : equivalent function"""
        return None
    def clip(self, a_min, a_max, out=None):
        """a.clip(a_min, a_max, out=None)
        
            Return an array whose values are limited to ``[a_min, a_max]``.
        
            Refer to `numpy.clip` for full documentation.
        
            See Also
            --------
            numpy.clip : equivalent function"""
        return None
    def compress(self, condition, axis=None, out=None):
        """a.compress(condition, axis=None, out=None)
        
            Return selected slices of this array along given axis.
        
            Refer to `numpy.compress` for full documentation.
        
            See Also
            --------
            numpy.compress : equivalent function"""
        return None
    def conj(self, _):
        """a.conj()
        
            Complex-conjugate all elements.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def conjugate(self, _):
        """a.conjugate()
        
            Return the complex conjugate, element-wise.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def copy(self, order):
        """a.copy(order='C')
        
            Return a copy of the array.
        
            Parameters
            ----------
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout of the copy. 'C' means C-order,
                'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
                'C' otherwise. 'K' means match the layout of `a` as closely
                as possible. (Note that this function and :func:numpy.copy are very
                similar, but have different default values for their order=
                arguments.)
        
            See also
            --------
            numpy.copy
            numpy.copyto
        
            Examples
            --------
            >>> x = np.array([[1,2,3],[4,5,6]], order='F')
        
            >>> y = x.copy()
        
            >>> x.fill(0)
        
            >>> x
            array([[0, 0, 0],
                   [0, 0, 0]])
        
            >>> y
            array([[1, 2, 3],
                   [4, 5, 6]])
        
            >>> y.flags['C_CONTIGUOUS']
            True"""
        return None
    ctypes = getset_descriptor()
    def cumprod(self, axis=None, dtype=None, out=None):
        """a.cumprod(axis=None, dtype=None, out=None)
        
            Return the cumulative product of the elements along the given axis.
        
            Refer to `numpy.cumprod` for full documentation.
        
            See Also
            --------
            numpy.cumprod : equivalent function"""
        return None
    def cumsum(self, axis=None, dtype=None, out=None):
        """a.cumsum(axis=None, dtype=None, out=None)
        
            Return the cumulative sum of the elements along the given axis.
        
            Refer to `numpy.cumsum` for full documentation.
        
            See Also
            --------
            numpy.cumsum : equivalent function"""
        return None
    data = getset_descriptor()
    def diagonal(self, offset=0, axis1=0, axis2=1):
        """a.diagonal(offset=0, axis1=0, axis2=1)
        
            Return specified diagonals.
        
            Refer to :func:`numpy.diagonal` for full documentation.
        
            See Also
            --------
            numpy.diagonal : equivalent function"""
        return None
    def dot(self, b, out=None):
        """a.dot(b, out=None)
        
            Dot product of two arrays.
        
            Refer to `numpy.dot` for full documentation.
        
            See Also
            --------
            numpy.dot : equivalent function
        
            Examples
            --------
            >>> a = np.eye(2)
            >>> b = np.ones((2, 2)) * 2
            >>> a.dot(b)
            array([[ 2.,  2.],
                   [ 2.,  2.]])
        
            This array method can be conveniently chained:
        
            >>> a.dot(b).dot(b)
            array([[ 8.,  8.],
                   [ 8.,  8.]])"""
        return None
    dtype = getset_descriptor()
    def dump(self, file):
        """a.dump(file)
        
            Dump a pickle of the array to the specified file.
            The array can be read back with pickle.load or numpy.load.
        
            Parameters
            ----------
            file : str
                A string naming the dump file."""
        return None
    def dumps(self, _):
        """a.dumps()
        
            Returns the pickle of the array as a string.
            pickle.loads or numpy.loads will convert the string back to an array.
        
            Parameters
            ----------
            None"""
        return None
    def fill(self, value):
        """a.fill(value)
        
            Fill the array with a scalar value.
        
            Parameters
            ----------
            value : scalar
                All elements of `a` will be assigned this value.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.fill(0)
            >>> a
            array([0, 0])
            >>> a = np.empty(2)
            >>> a.fill(1)
            >>> a
            array([ 1.,  1.])"""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def flatten(self, order):
        """a.flatten(order='C')
        
            Return a copy of the array collapsed into one dimension.
        
            Parameters
            ----------
            order : {'C', 'F', 'A'}, optional
                Whether to flatten in C (row-major), Fortran (column-major) order,
                or preserve the C/Fortran ordering from `a`.
                The default is 'C'.
        
            Returns
            -------
            y : ndarray
                A copy of the input array, flattened to one dimension.
        
            See Also
            --------
            ravel : Return a flattened array.
            flat : A 1-D flat iterator over the array.
        
            Examples
            --------
            >>> a = np.array([[1,2], [3,4]])
            >>> a.flatten()
            array([1, 2, 3, 4])
            >>> a.flatten('F')
            array([1, 3, 2, 4])"""
        return ndarray()
    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 ndarray()
    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 ndarray()
    def getH(self, _):
        """
                Returns the (complex) conjugate transpose of `self`.
        
                Equivalent to ``np.transpose(self)`` if `self` is real-valued.
        
                Parameters
                ----------
                None
        
                Returns
                -------
                ret : matrix object
                    complex conjugate transpose of `self`
        
                Examples
                --------
                >>> x = np.matrix(np.arange(12).reshape((3,4)))
                >>> z = x - 1j*x; z
                matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
                        [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
                        [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
                >>> z.getH()
                matrix([[  0. +0.j,   4. +4.j,   8. +8.j],
                        [  1. +1.j,   5. +5.j,   9. +9.j],
                        [  2. +2.j,   6. +6.j,  10.+10.j],
                        [  3. +3.j,   7. +7.j,  11.+11.j]])
        
                """
        return matrix()
    def getI(self, _):
        """
                Returns the (multiplicative) inverse of invertible `self`.
        
                Parameters
                ----------
                None
        
                Returns
                -------
                ret : matrix object
                    If `self` is non-singular, `ret` is such that ``ret * self`` ==
                    ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return
                    ``True``.
        
                Raises
                ------
                numpy.linalg.LinAlgError: Singular matrix
                    If `self` is singular.
        
                See Also
                --------
                linalg.inv
        
                Examples
                --------
                >>> m = np.matrix('[1, 2; 3, 4]'); m
                matrix([[1, 2],
                        [3, 4]])
                >>> m.getI()
                matrix([[-2. ,  1. ],
                        [ 1.5, -0.5]])
                >>> m.getI() * m
                matrix([[ 1.,  0.],
                        [ 0.,  1.]])
        
                """
        return matrix()
    def getT(self, _):
        """
                Returns the transpose of the matrix.
        
                Does *not* conjugate!  For the complex conjugate transpose, use `getH`.
        
                Parameters
                ----------
                None
        
                Returns
                -------
                ret : matrix object
                    The (non-conjugated) transpose of the matrix.
        
                See Also
                --------
                transpose, getH
        
                Examples
                --------
                >>> m = np.matrix('[1, 2; 3, 4]')
                >>> m
                matrix([[1, 2],
                        [3, 4]])
                >>> m.getT()
                matrix([[1, 3],
                        [2, 4]])
        
                """
        return matrix()
    def getfield(self, dtype, offset):
        """a.getfield(dtype, offset=0)
        
            Returns a field of the given array as a certain type.
        
            A field is a view of the array data with a given data-type. The values in
            the view are determined by the given type and the offset into the current
            array in bytes. The offset needs to be such that the view dtype fits in the
            array dtype; for example an array of dtype complex128 has 16-byte elements.
            If taking a view with a 32-bit integer (4 bytes), the offset needs to be
            between 0 and 12 bytes.
        
            Parameters
            ----------
            dtype : str or dtype
                The data type of the view. The dtype size of the view can not be larger
                than that of the array itself.
            offset : int
                Number of bytes to skip before beginning the element view.
        
            Examples
            --------
            >>> x = np.diag([1.+1.j]*2)
            >>> x[1, 1] = 2 + 4.j
            >>> x
            array([[ 1.+1.j,  0.+0.j],
                   [ 0.+0.j,  2.+4.j]])
            >>> x.getfield(np.float64)
            array([[ 1.,  0.],
                   [ 0.,  2.]])
        
            By choosing an offset of 8 bytes we can select the complex part of the
            array for our view:
        
            >>> x.getfield(np.float64, offset=8)
            array([[ 1.,  0.],
               [ 0.,  4.]])"""
        return array()
    imag = getset_descriptor()
    def item(self, ESCargs):
        """a.item(*args)
        
            Copy an element of an array to a standard Python scalar and return it.
        
            Parameters
            ----------
            \*args : Arguments (variable number and type)
        
                * none: in this case, the method only works for arrays
                  with one element (`a.size == 1`), which element is
                  copied into a standard Python scalar object and returned.
        
                * int_type: this argument is interpreted as a flat index into
                  the array, specifying which element to copy and return.
        
                * tuple of int_types: functions as does a single int_type argument,
                  except that the argument is interpreted as an nd-index into the
                  array.
        
            Returns
            -------
            z : Standard Python scalar object
                A copy of the specified element of the array as a suitable
                Python scalar
        
            Notes
            -----
            When the data type of `a` is longdouble or clongdouble, item() returns
            a scalar array object because there is no available Python scalar that
            would not lose information. Void arrays return a buffer object for item(),
            unless fields are defined, in which case a tuple is returned.
        
            `item` is very similar to a[args], except, instead of an array scalar,
            a standard Python scalar is returned. This can be useful for speeding up
            access to elements of the array and doing arithmetic on elements of the
            array using Python's optimized math.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.item(3)
            2
            >>> x.item(7)
            5
            >>> x.item((0, 1))
            1
            >>> x.item((2, 2))
            3"""
        return Standard()
    def itemset(self, ESCargs):
        """a.itemset(*args)
        
            Insert scalar into an array (scalar is cast to array's dtype, if possible)
        
            There must be at least 1 argument, and define the last argument
            as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
            than ``a[args] = item``.  The item should be a scalar value and `args`
            must select a single item in the array `a`.
        
            Parameters
            ----------
            \*args : Arguments
                If one argument: a scalar, only used in case `a` is of size 1.
                If two arguments: the last argument is the value to be set
                and must be a scalar, the first argument specifies a single array
                element location. It is either an int or a tuple.
        
            Notes
            -----
            Compared to indexing syntax, `itemset` provides some speed increase
            for placing a scalar into a particular location in an `ndarray`,
            if you must do this.  However, generally this is discouraged:
            among other problems, it complicates the appearance of the code.
            Also, when using `itemset` (and `item`) inside a loop, be sure
            to assign the methods to a local variable to avoid the attribute
            look-up at each loop iteration.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.itemset(4, 0)
            >>> x.itemset((2, 2), 9)
            >>> x
            array([[3, 1, 7],
                   [2, 0, 3],
                   [8, 5, 9]])"""
        return None
    itemsize = getset_descriptor()
    def max(self=None, axis=None, out=None):
        """
                Return the maximum value along an axis.
        
                Parameters
                ----------
                See `amax` for complete descriptions
        
                See Also
                --------
                amax, ndarray.max
        
                Notes
                -----
                This is the same as `ndarray.max`, but returns a `matrix` object
                where `ndarray.max` would return 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.max()
                11
                >>> x.max(0)
                matrix([[ 8,  9, 10, 11]])
                >>> x.max(1)
                matrix([[ 3],
                        [ 7],
                        [11]])
        
                """
        return None
    def mean(self=None, axis=None, dtype=None, out=None):
        """
                Returns the average of the matrix elements along the given axis.
        
                Refer to `numpy.mean` for full documentation.
        
                See Also
                --------
                numpy.mean
        
                Notes
                -----
                Same as `ndarray.mean` except that, where that returns an `ndarray`,
                this returns a `matrix` object.
        
                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.mean()
                5.5
                >>> x.mean(0)
                matrix([[ 4.,  5.,  6.,  7.]])
                >>> x.mean(1)
                matrix([[ 1.5],
                        [ 5.5],
                        [ 9.5]])
        
                """
        return None
    def min(self=None, axis=None, out=None):
        """
                Return the minimum value along an axis.
        
                Parameters
                ----------
                See `amin` for complete descriptions.
        
                See Also
                --------
                amin, ndarray.min
        
                Notes
                -----
                This is the same as `ndarray.min`, but returns a `matrix` object
                where `ndarray.min` would return 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.min()
                -11
                >>> x.min(0)
                matrix([[ -8,  -9, -10, -11]])
                >>> x.min(1)
                matrix([[ -3],
                        [ -7],
                        [-11]])
        
                """
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """arr.newbyteorder(new_order='S')
        
            Return the array with the same data viewed with a different byte order.
        
            Equivalent to::
        
                arr.view(arr.dtype.newbytorder(new_order))
        
            Changes are also made in all fields and sub-arrays of the array data
            type.
        
        
        
            Parameters
            ----------
            new_order : string, optional
                Byte order to force; a value from the byte order specifications
                above. `new_order` codes can be any of::
        
                 * 'S' - swap dtype from current to opposite endian
                 * {'<', 'L'} - little endian
                 * {'>', 'B'} - big endian
                 * {'=', 'N'} - native order
                 * {'|', 'I'} - ignore (no change to byte order)
        
                The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_arr : array
                New array object with the dtype reflecting given change to the
                byte order."""
        return array()
    def nonzero(self, _):
        """a.nonzero()
        
            Return the indices of the elements that are non-zero.
        
            Refer to `numpy.nonzero` for full documentation.
        
            See Also
            --------
            numpy.nonzero : equivalent function"""
        return None
    def partition(self, kth, axis, kind, order):
        """a.partition(kth, axis=-1, kind='introselect', order=None)
        
            Rearranges the elements in the array in such a way that value of the
            element in kth position is in the position it would be in a sorted array.
            All elements smaller than the kth element are moved before this element and
            all equal or greater are moved behind it. The ordering of the elements in
            the two partitions is undefined.
        
            .. versionadded:: 1.8.0
        
            Parameters
            ----------
            kth : int or sequence of ints
                Element index to partition by. The kth element value will be in its
                final sorted position and all smaller elements will be moved before it
                and all equal or greater elements behind it.
                The order all elements in the partitions is undefined.
                If provided with a sequence of kth it will partition all elements
                indexed by kth of them into their sorted position at once.
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'introselect'}, optional
                Selection algorithm. Default is 'introselect'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.partition : Return a parititioned copy of an array.
            argpartition : Indirect partition.
            sort : Full sort.
        
            Notes
            -----
            See ``np.partition`` for notes on the different algorithms.
        
            Examples
            --------
            >>> a = np.array([3, 4, 2, 1])
            >>> a.partition(a, 3)
            >>> a
            array([2, 1, 3, 4])
        
            >>> a.partition((1, 3))
            array([1, 2, 3, 4])"""
        return None
    def prod(self=None, 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 None
    def ptp(self=None, axis=None, out=None):
        """
                Peak-to-peak (maximum - minimum) value along the given axis.
        
                Refer to `numpy.ptp` for full documentation.
        
                See Also
                --------
                numpy.ptp
        
                Notes
                -----
                Same as `ndarray.ptp`, except, where that would return an `ndarray` object,
                this returns a `matrix` object.
        
                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.ptp()
                11
                >>> x.ptp(0)
                matrix([[8, 8, 8, 8]])
                >>> x.ptp(1)
                matrix([[3],
                        [3],
                        [3]])
        
                """
        return None
    def put(self, indices, values, mode=_raise):
        """a.put(indices, values, mode='raise')
        
            Set ``a.flat[n] = values[n]`` for all `n` in indices.
        
            Refer to `numpy.put` for full documentation.
        
            See Also
            --------
            numpy.put : equivalent function"""
        return None
    def ravel(self, order):
        """a.ravel([order])
        
            Return a flattened array.
        
            Refer to `numpy.ravel` for full documentation.
        
            See Also
            --------
            numpy.ravel : equivalent function
        
            ndarray.flat : a flat iterator on the array."""
        return None
    real = getset_descriptor()
    def repeat(self, repeats, axis=None):
        """a.repeat(repeats, axis=None)
        
            Repeat elements of an array.
        
            Refer to `numpy.repeat` for full documentation.
        
            See Also
            --------
            numpy.repeat : equivalent function"""
        return None
    def reshape(self, shape, order=C):
        """a.reshape(shape, order='C')
        
            Returns an array containing the same data with a new shape.
        
            Refer to `numpy.reshape` for full documentation.
        
            See Also
            --------
            numpy.reshape : equivalent function"""
        return None
    def resize(self, new_shape, refcheck):
        """a.resize(new_shape, refcheck=True)
        
            Change shape and size of array in-place.
        
            Parameters
            ----------
            new_shape : tuple of ints, or `n` ints
                Shape of resized array.
            refcheck : bool, optional
                If False, reference count will not be checked. Default is True.
        
            Returns
            -------
            None
        
            Raises
            ------
            ValueError
                If `a` does not own its own data or references or views to it exist,
                and the data memory must be changed.
        
            SystemError
                If the `order` keyword argument is specified. This behaviour is a
                bug in NumPy.
        
            See Also
            --------
            resize : Return a new array with the specified shape.
        
            Notes
            -----
            This reallocates space for the data area if necessary.
        
            Only contiguous arrays (data elements consecutive in memory) can be
            resized.
        
            The purpose of the reference count check is to make sure you
            do not use this array as a buffer for another Python object and then
            reallocate the memory. However, reference counts can increase in
            other ways so if you are sure that you have not shared the memory
            for this array with another Python object, then you may safely set
            `refcheck` to False.
        
            Examples
            --------
            Shrinking an array: array is flattened (in the order that the data are
            stored in memory), resized, and reshaped:
        
            >>> a = np.array([[0, 1], [2, 3]], order='C')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [1]])
        
            >>> a = np.array([[0, 1], [2, 3]], order='F')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [2]])
        
            Enlarging an array: as above, but missing entries are filled with zeros:
        
            >>> b = np.array([[0, 1], [2, 3]])
            >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
            >>> b
            array([[0, 1, 2],
                   [3, 0, 0]])
        
            Referencing an array prevents resizing...
        
            >>> c = a
            >>> a.resize((1, 1))
            Traceback (most recent call last):
            ...
            ValueError: cannot resize an array that has been referenced ...
        
            Unless `refcheck` is False:
        
            >>> a.resize((1, 1), refcheck=False)
            >>> a
            array([[0]])
            >>> c
            array([[0]])"""
        return None
    def round(self, decimals=0, out=None):
        """a.round(decimals=0, out=None)
        
            Return `a` with each element rounded to the given number of decimals.
        
            Refer to `numpy.around` for full documentation.
        
            See Also
            --------
            numpy.around : equivalent function"""
        return None
    def searchsorted(self, v, side=left, sorter=None):
        """a.searchsorted(v, side='left', sorter=None)
        
            Find indices where elements of v should be inserted in a to maintain order.
        
            For full documentation, see `numpy.searchsorted`
        
            See Also
            --------
            numpy.searchsorted : equivalent function"""
        return None
    def setfield(self, val, dtype, offset):
        """a.setfield(val, dtype, offset=0)
        
            Put a value into a specified place in a field defined by a data-type.
        
            Place `val` into `a`'s field defined by `dtype` and beginning `offset`
            bytes into the field.
        
            Parameters
            ----------
            val : object
                Value to be placed in field.
            dtype : dtype object
                Data-type of the field in which to place `val`.
            offset : int, optional
                The number of bytes into the field at which to place `val`.
        
            Returns
            -------
            None
        
            See Also
            --------
            getfield
        
            Examples
            --------
            >>> x = np.eye(3)
            >>> x.getfield(np.float64)
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])
            >>> x.setfield(3, np.int32)
            >>> x.getfield(np.int32)
            array([[3, 3, 3],
                   [3, 3, 3],
                   [3, 3, 3]])
            >>> x
            array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
                   [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
                   [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
            >>> x.setfield(np.eye(3), np.int32)
            >>> x
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])"""
        return None
    def setflags(self, write, align, uic):
        """a.setflags(write=None, align=None, uic=None)
        
            Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
        
            These Boolean-valued flags affect how numpy interprets the memory
            area used by `a` (see Notes below). The ALIGNED flag can only
            be set to True if the data is actually aligned according to the type.
            The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE
            can only be set to True if the array owns its own memory, or the
            ultimate owner of the memory exposes a writeable buffer interface,
            or is a string. (The exception for string is made so that unpickling
            can be done without copying memory.)
        
            Parameters
            ----------
            write : bool, optional
                Describes whether or not `a` can be written to.
            align : bool, optional
                Describes whether or not `a` is aligned properly for its type.
            uic : bool, optional
                Describes whether or not `a` is a copy of another "base" array.
        
            Notes
            -----
            Array flags provide information about how the memory area used
            for the array is to be interpreted. There are 6 Boolean flags
            in use, only three of which can be changed by the user:
            UPDATEIFCOPY, WRITEABLE, and ALIGNED.
        
            WRITEABLE (W) the data area can be written to;
        
            ALIGNED (A) the data and strides are aligned appropriately for the hardware
            (as determined by the compiler);
        
            UPDATEIFCOPY (U) this array is a copy of some other array (referenced
            by .base). When this array is deallocated, the base array will be
            updated with the contents of this array.
        
            All flags can be accessed using their first (upper case) letter as well
            as the full name.
        
            Examples
            --------
            >>> y
            array([[3, 1, 7],
                   [2, 0, 0],
                   [8, 5, 9]])
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : True
              ALIGNED : True
              UPDATEIFCOPY : False
            >>> y.setflags(write=0, align=0)
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : False
              ALIGNED : False
              UPDATEIFCOPY : False
            >>> y.setflags(uic=1)
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: cannot set UPDATEIFCOPY flag to True"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def sort(self, axis, kind, order):
        """a.sort(axis=-1, kind='quicksort', order=None)
        
            Sort an array, in-place.
        
            Parameters
            ----------
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'quicksort', 'mergesort', 'heapsort'}, optional
                Sorting algorithm. Default is 'quicksort'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.sort : Return a sorted copy of an array.
            argsort : Indirect sort.
            lexsort : Indirect stable sort on multiple keys.
            searchsorted : Find elements in sorted array.
            partition: Partial sort.
        
            Notes
            -----
            See ``sort`` for notes on the different sorting algorithms.
        
            Examples
            --------
            >>> a = np.array([[1,4], [3,1]])
            >>> a.sort(axis=1)
            >>> a
            array([[1, 4],
                   [1, 3]])
            >>> a.sort(axis=0)
            >>> a
            array([[1, 3],
                   [1, 4]])
        
            Use the `order` keyword to specify a field to use when sorting a
            structured array:
        
            >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
            >>> a.sort(order='y')
            >>> a
            array([('c', 1), ('a', 2)],
                  dtype=[('x', '|S1'), ('y', '<i4')])"""
        return None
    def squeeze(self, axis=None):
        """a.squeeze(axis=None)
        
            Remove single-dimensional entries from the shape of `a`.
        
            Refer to `numpy.squeeze` for full documentation.
        
            See Also
            --------
            numpy.squeeze : equivalent function"""
        return None
    def std(self=0, 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 None
    strides = getset_descriptor()
    def sum(self=None, 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 None
    def swapaxes(self, axis1, axis2):
        """a.swapaxes(axis1, axis2)
        
            Return a view of the array with `axis1` and `axis2` interchanged.
        
            Refer to `numpy.swapaxes` for full documentation.
        
            See Also
            --------
            numpy.swapaxes : equivalent function"""
        return None
    def take(self, indices, axis=None, out=None, mode=_raise):
        """a.take(indices, axis=None, out=None, mode='raise')
        
            Return an array formed from the elements of `a` at the given indices.
        
            Refer to `numpy.take` for full documentation.
        
            See Also
            --------
            numpy.take : equivalent function"""
        return None
    def tofile(self, fid, sep, format):
        """a.tofile(fid, sep="", format="%s")
        
            Write array to a file as text or binary (default).
        
            Data is always written in 'C' order, independent of the order of `a`.
            The data produced by this method can be recovered using the function
            fromfile().
        
            Parameters
            ----------
            fid : file or str
                An open file object, or a string containing a filename.
            sep : str
                Separator between array items for text output.
                If "" (empty), a binary file is written, equivalent to
                ``file.write(a.tostring())``.
            format : str
                Format string for text file output.
                Each entry in the array is formatted to text by first converting
                it to the closest Python type, and then using "format" % item.
        
            Notes
            -----
            This is a convenience function for quick storage of array data.
            Information on endianness and precision is lost, so this method is not a
            good choice for files intended to archive data or transport data between
            machines with different endianness. Some of these problems can be overcome
            by outputting the data as text files, at the expense of speed and file
            size."""
        return None
    def tolist(self, _):
        """
                Return the matrix as a (possibly nested) list.
        
                See `ndarray.tolist` for full documentation.
        
                See Also
                --------
                ndarray.tolist
        
                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.tolist()
                [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]
        
                """
        return None
    def tostring(self, order):
        """a.tostring(order='C')
        
            Construct a Python string containing the raw data bytes in the array.
        
            Constructs a Python string showing a copy of the raw contents of
            data memory. The string can be produced in either 'C' or 'Fortran',
            or 'Any' order (the default is 'C'-order). 'Any' order means C-order
            unless the F_CONTIGUOUS flag in the array is set, in which case it
            means 'Fortran' order.
        
            Parameters
            ----------
            order : {'C', 'F', None}, optional
                Order of the data for multidimensional arrays:
                C, Fortran, or the same as for the original array.
        
            Returns
            -------
            s : str
                A Python string exhibiting a copy of `a`'s raw data.
        
            Examples
            --------
            >>> x = np.array([[0, 1], [2, 3]])
            >>> x.tostring()
            '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
            >>> x.tostring('C') == x.tostring()
            True
            >>> x.tostring('F')
            '\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'"""
        return str()
    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
        """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
        
            Return the sum along diagonals of the array.
        
            Refer to `numpy.trace` for full documentation.
        
            See Also
            --------
            numpy.trace : equivalent function"""
        return None
    def transpose(self, axes):
        """a.transpose(*axes)
        
            Returns a view of the array with axes transposed.
        
            For a 1-D array, this has no effect. (To change between column and
            row vectors, first cast the 1-D array into a matrix object.)
            For a 2-D array, this is the usual matrix transpose.
            For an n-D array, if axes are given, their order indicates how the
            axes are permuted (see Examples). If axes are not provided and
            ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
            ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
        
            Parameters
            ----------
            axes : None, tuple of ints, or `n` ints
        
             * None or no argument: reverses the order of the axes.
        
             * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
               `i`-th axis becomes `a.transpose()`'s `j`-th axis.
        
             * `n` ints: same as an n-tuple of the same ints (this form is
               intended simply as a "convenience" alternative to the tuple form)
        
            Returns
            -------
            out : ndarray
                View of `a`, with axes suitably permuted.
        
            See Also
            --------
            ndarray.T : Array property returning the array transposed.
        
            Examples
            --------
            >>> a = np.array([[1, 2], [3, 4]])
            >>> a
            array([[1, 2],
                   [3, 4]])
            >>> a.transpose()
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose((1, 0))
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose(1, 0)
            array([[1, 3],
                   [2, 4]])"""
        return ndarray()
    def var(self=0, axis=None, dtype=None, out=None, ddof=0):
        """
                Returns the variance of the matrix elements, along the given axis.
        
                Refer to `numpy.var` for full documentation.
        
                See Also
                --------
                numpy.var
        
                Notes
                -----
                This is the same as `ndarray.var`, 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.var()
                11.916666666666666
                >>> x.var(0)
                matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]])
                >>> x.var(1)
                matrix([[ 1.25],
                        [ 1.25],
                        [ 1.25]])
        
                """
        return None
    def view(self, dtype, type):
        """a.view(dtype=None, type=None)
        
            New view of array with the same data.
        
            Parameters
            ----------
            dtype : data-type or ndarray sub-class, optional
                Data-type descriptor of the returned view, e.g., float32 or int16. The
                default, None, results in the view having the same data-type as `a`.
                This argument can also be specified as an ndarray sub-class, which
                then specifies the type of the returned object (this is equivalent to
                setting the ``type`` parameter).
            type : Python type, optional
                Type of the returned view, e.g., ndarray or matrix.  Again, the
                default None results in type preservation.
        
            Notes
            -----
            ``a.view()`` is used two different ways:
        
            ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
            of the array's memory with a different data-type.  This can cause a
            reinterpretation of the bytes of memory.
        
            ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
            returns an instance of `ndarray_subclass` that looks at the same array
            (same shape, dtype, etc.)  This does not cause a reinterpretation of the
            memory.
        
            For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
            bytes per entry than the previous dtype (for example, converting a
            regular array to a structured array), then the behavior of the view
            cannot be predicted just from the superficial appearance of ``a`` (shown
            by ``print(a)``). It also depends on exactly how ``a`` is stored in
            memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
            defined as a slice or transpose, etc., the view may give different
            results.
        
        
            Examples
            --------
            >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
        
            Viewing array data using a different type and dtype:
        
            >>> y = x.view(dtype=np.int16, type=np.matrix)
            >>> y
            matrix([[513]], dtype=int16)
            >>> print type(y)
            <class 'numpy.matrixlib.defmatrix.matrix'>
        
            Creating a view on a structured array so it can be used in calculations
        
            >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
            >>> xv = x.view(dtype=np.int8).reshape(-1,2)
            >>> xv
            array([[1, 2],
                   [3, 4]], dtype=int8)
            >>> xv.mean(0)
            array([ 2.,  3.])
        
            Making changes to the view changes the underlying array
        
            >>> xv[0,1] = 20
            >>> print x
            [(1, 20) (3, 4)]
        
            Using a view to convert an array to a record array:
        
            >>> z = x.view(np.recarray)
            >>> z.a
            array([1], dtype=int8)
        
            Views share data:
        
            >>> x[0] = (9, 10)
            >>> z[0]
            (9, 10)
        
            Views that change the dtype size (bytes per entry) should normally be
            avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
        
            >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
            >>> y = x[:, 0:2]
            >>> y
            array([[1, 2],
                   [4, 5]], dtype=int16)
            >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: new type not compatible with array.
            >>> z = y.copy()
            >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
            array([[(1, 2)],
                   [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])"""
        return None
def amax(a=False, axis=None, out=None, keepdims=False):
    """
        Return the maximum of an array or maximum along an axis.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which to operate.  By default, flattened input is used.
        out : ndarray, optional
            Alternative output array in which to place the result.  Must
            be of the same shape and buffer length as the expected output.
            See `doc.ufuncs` (Section "Output arguments") 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 original `arr`.
    
        Returns
        -------
        amax : ndarray or scalar
            Maximum of `a`. If `axis` is None, the result is a scalar value.
            If `axis` is given, the result is an array of dimension
            ``a.ndim - 1``.
    
        See Also
        --------
        amin :
            The minimum value of an array along a given axis, propagating any NaNs.
        nanmax :
            The maximum value of an array along a given axis, ignoring any NaNs.
        maximum :
            Element-wise maximum of two arrays, propagating any NaNs.
        fmax :
            Element-wise maximum of two arrays, ignoring any NaNs.
        argmax :
            Return the indices of the maximum values.
    
        nanmin, minimum, fmin
    
        Notes
        -----
        NaN values are propagated, that is if at least one item is NaN, the
        corresponding max value will be NaN as well. To ignore NaN values
        (MATLAB behavior), please use nanmax.
    
        Don't use `amax` for element-wise comparison of 2 arrays; when
        ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
        ``amax(a, axis=0)``.
    
        Examples
        --------
        >>> a = np.arange(4).reshape((2,2))
        >>> a
        array([[0, 1],
               [2, 3]])
        >>> np.amax(a)           # Maximum of the flattened array
        3
        >>> np.amax(a, axis=0)   # Maxima along the first axis
        array([2, 3])
        >>> np.amax(a, axis=1)   # Maxima along the second axis
        array([1, 3])
    
        >>> b = np.arange(5, dtype=np.float)
        >>> b[2] = np.NaN
        >>> np.amax(b)
        nan
        >>> np.nanmax(b)
        4.0
    
        """
    return ndarray() if False else float()
def maximum(x1, x2, out=None):
    """maximum(x1, x2[, out])
    
    Element-wise maximum of array elements.
    
    Compare two arrays and returns a new array containing the element-wise
    maxima. If one of the elements being compared is a nan, then that element
    is returned. If both elements are nans then the first is returned. The
    latter distinction is important for complex nans, which are defined as at
    least one of the real or imaginary parts being a nan. The net effect is
    that nans are propagated.
    
    Parameters
    ----------
    x1, x2 : array_like
        The arrays holding the elements to be compared. They must have
        the same shape, or shapes that can be broadcast to a single shape.
    
    Returns
    -------
    y : {ndarray, scalar}
        The maximum of `x1` and `x2`, element-wise.  Returns scalar if
        both  `x1` and `x2` are scalars.
    
    See Also
    --------
    minimum :
        Element-wise minimum of two arrays, propagating any NaNs.
    fmax :
        Element-wise maximum of two arrays, ignoring any NaNs.
    amax :
        The maximum value of an array along a given axis, propagating any NaNs.
    nanmax :
        The maximum value of an array along a given axis, ignoring any NaNs.
    
    fmin, amin, nanmin
    
    Notes
    -----
    The maximum is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither
    x1 nor x2 are nans, but it is faster and does proper broadcasting.
    
    Examples
    --------
    >>> np.maximum([2, 3, 4], [1, 5, 2])
    array([2, 5, 4])
    
    >>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting
    array([[ 1. ,  2. ],
           [ 0.5,  2. ]])
    
    >>> np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])
    array([ NaN,  NaN,  NaN])
    >>> np.maximum(np.Inf, 1)
    inf"""
    return ndarray()
def maximum_sctype(t):
    """
        Return the scalar type of highest precision of the same kind as the input.
    
        Parameters
        ----------
        t : dtype or dtype specifier
            The input data type. This can be a `dtype` object or an object that
            is convertible to a `dtype`.
    
        Returns
        -------
        out : dtype
            The highest precision data type of the same kind (`dtype.kind`) as `t`.
    
        See Also
        --------
        obj2sctype, mintypecode, sctype2char
        dtype
    
        Examples
        --------
        >>> np.maximum_sctype(np.int)
        <type 'numpy.int64'>
        >>> np.maximum_sctype(np.uint8)
        <type 'numpy.uint64'>
        >>> np.maximum_sctype(np.complex)
        <type 'numpy.complex192'>
    
        >>> np.maximum_sctype(str)
        <type 'numpy.string_'>
    
        >>> np.maximum_sctype('i2')
        <type 'numpy.int64'>
        >>> np.maximum_sctype('f4')
        <type 'numpy.float96'>
    
        """
    return dtype()
def may_share_memory(ab):
    """Determine if two arrays can share memory
    
        The memory-bounds of a and b are computed.  If they overlap then
        this function returns True.  Otherwise, it returns False.
    
        A return of True does not necessarily mean that the two arrays
        share any element.  It just means that they *might*.
    
        Parameters
        ----------
        a, b : ndarray
    
        Returns
        -------
        out : bool
    
        Examples
        --------
        >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
        False"""
    return bool()
def mean(a=False, axis=None, dtype=None, out=None, keepdims=False):
    """
        Compute the arithmetic mean along the specified axis.
    
        Returns the average of the array elements.  The average is taken over
        the flattened array by default, otherwise over the specified axis.
        `float64` intermediate and return values are used for integer inputs.
    
        Parameters
        ----------
        a : array_like
            Array containing numbers whose mean is desired. If `a` is not an
            array, a conversion is attempted.
        axis : int, optional
            Axis along which the means are computed. The default is to compute
            the mean of the flattened array.
        dtype : data-type, optional
            Type to use in computing the mean.  For integer inputs, the default
            is `float64`; for floating point inputs, it is the same as the
            input dtype.
        out : ndarray, optional
            Alternate output array in which to place the result.  The default
            is ``None``; if provided, it must have the same shape as the
            expected output, but the type will be cast if necessary.
            See `doc.ufuncs` for 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 original `arr`.
    
        Returns
        -------
        m : ndarray, see dtype parameter above
            If `out=None`, returns a new array containing the mean values,
            otherwise a reference to the output array is returned.
    
        See Also
        --------
        average : Weighted average
        std, var, nanmean, nanstd, nanvar
    
        Notes
        -----
        The arithmetic mean is the sum of the elements along the axis divided
        by the number of elements.
    
        Note that for floating-point input, the mean is computed using the
        same precision the input has.  Depending on the input data, this can
        cause the results to be inaccurate, especially for `float32` (see
        example below).  Specifying a higher-precision accumulator using the
        `dtype` keyword can alleviate this issue.
    
        Examples
        --------
        >>> a = np.array([[1, 2], [3, 4]])
        >>> np.mean(a)
        2.5
        >>> np.mean(a, axis=0)
        array([ 2.,  3.])
        >>> np.mean(a, axis=1)
        array([ 1.5,  3.5])
    
        In single precision, `mean` can be inaccurate:
    
        >>> a = np.zeros((2, 512*512), dtype=np.float32)
        >>> a[0, :] = 1.0
        >>> a[1, :] = 0.1
        >>> np.mean(a)
        0.546875
    
        Computing the mean in float64 is more accurate:
    
        >>> np.mean(a, dtype=np.float64)
        0.55000000074505806
    
        """
    return ndarray()
def median(a=False, axis=None, out=None, overwrite_input=False):
    """
        Compute the median along the specified axis.
    
        Returns the median of the array elements.
    
        Parameters
        ----------
        a : array_like
            Input array or object that can be converted to an array.
        axis : int, optional
            Axis along which the medians are computed. The default (axis=None)
            is to compute the median along a flattened version of the array.
        out : ndarray, optional
            Alternative output array in which to place the result. It must
            have the same shape and buffer length as the expected output,
            but the type (of the output) will be cast if necessary.
        overwrite_input : bool, optional
           If True, then allow use of memory of input array (a) for
           calculations. The input array will be modified by the call to
           median. This will save memory when you do not need to preserve
           the contents of the input array. Treat the input as undefined,
           but it will probably be fully or partially sorted. Default is
           False. Note that, if `overwrite_input` is True and the input
           is not already an ndarray, an error will be raised.
    
        Returns
        -------
        median : ndarray
            A new array holding the result (unless `out` is specified, in
            which case that array is returned instead).  If the input contains
            integers, or floats of smaller precision than 64, then the output
            data-type is float64.  Otherwise, the output data-type is the same
            as that of the input.
    
        See Also
        --------
        mean, percentile
    
        Notes
        -----
        Given a vector V of length N, the median of V is the middle value of
        a sorted copy of V, ``V_sorted`` - i.e., ``V_sorted[(N-1)/2]``, when N is
        odd.  When N is even, it is the average of the two middle values of
        ``V_sorted``.
    
        Examples
        --------
        >>> a = np.array([[10, 7, 4], [3, 2, 1]])
        >>> a
        array([[10,  7,  4],
               [ 3,  2,  1]])
        >>> np.median(a)
        3.5
        >>> np.median(a, axis=0)
        array([ 6.5,  4.5,  2.5])
        >>> np.median(a, axis=1)
        array([ 7.,  2.])
        >>> m = np.median(a, axis=0)
        >>> out = np.zeros_like(m)
        >>> np.median(a, axis=0, out=m)
        array([ 6.5,  4.5,  2.5])
        >>> m
        array([ 6.5,  4.5,  2.5])
        >>> b = a.copy()
        >>> np.median(b, axis=1, overwrite_input=True)
        array([ 7.,  2.])
        >>> assert not np.all(a==b)
        >>> b = a.copy()
        >>> np.median(b, axis=None, overwrite_input=True)
        3.5
        >>> assert not np.all(a==b)
    
        """
    return ndarray()
class memmap:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = float()
    __array_struct__ = getset_descriptor()
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    def all(self, axis=None, out=None):
        """a.all(axis=None, out=None)
        
            Returns True if all elements evaluate to True.
        
            Refer to `numpy.all` for full documentation.
        
            See Also
            --------
            numpy.all : equivalent function"""
        return None
    def any(self, axis=None, out=None):
        """a.any(axis=None, out=None)
        
            Returns True if any of the elements of `a` evaluate to True.
        
            Refer to `numpy.any` for full documentation.
        
            See Also
            --------
            numpy.any : equivalent function"""
        return None
    def argmax(self, axis=None, out=None):
        """a.argmax(axis=None, out=None)
        
            Return indices of the maximum values along the given axis.
        
            Refer to `numpy.argmax` for full documentation.
        
            See Also
            --------
            numpy.argmax : equivalent function"""
        return None
    def argmin(self, axis=None, out=None):
        """a.argmin(axis=None, out=None)
        
            Return indices of the minimum values along the given axis of `a`.
        
            Refer to `numpy.argmin` for detailed documentation.
        
            See Also
            --------
            numpy.argmin : equivalent function"""
        return None
    def argpartition(self, kth, axis=_1, kind=quickselect, order=None):
        """a.argpartition(kth, axis=-1, kind='quickselect', order=None)
        
            Returns the indices that would partition this array.
        
            Refer to `numpy.argpartition` for full documentation.
        
            .. versionadded:: 1.8.0
        
            See Also
            --------
            numpy.argpartition : equivalent function"""
        return None
    def argsort(self, axis=_1, kind=quicksort, order=None):
        """a.argsort(axis=-1, kind='quicksort', order=None)
        
            Returns the indices that would sort this array.
        
            Refer to `numpy.argsort` for full documentation.
        
            See Also
            --------
            numpy.argsort : equivalent function"""
        return None
    def astype(self, dtype, order, casting, subok, copy):
        """a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
        
            Copy of the array, cast to a specified type.
        
            Parameters
            ----------
            dtype : str or dtype
                Typecode or data-type to which the array is cast.
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout order of the result.
                'C' means C order, 'F' means Fortran order, 'A'
                means 'F' order if all the arrays are Fortran contiguous,
                'C' order otherwise, and 'K' means as close to the
                order the array elements appear in memory as possible.
                Default is 'K'.
            casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
                Controls what kind of data casting may occur. Defaults to 'unsafe'
                for backwards compatibility.
        
                  * 'no' means the data types should not be cast at all.
                  * 'equiv' means only byte-order changes are allowed.
                  * 'safe' means only casts which can preserve values are allowed.
                  * 'same_kind' means only safe casts or casts within a kind,
                    like float64 to float32, are allowed.
                  * 'unsafe' means any data conversions may be done.
            subok : bool, optional
                If True, then sub-classes will be passed-through (default), otherwise
                the returned array will be forced to be a base-class array.
            copy : bool, optional
                By default, astype always returns a newly allocated array. If this
                is set to false, and the `dtype`, `order`, and `subok`
                requirements are satisfied, the input array is returned instead
                of a copy.
        
            Returns
            -------
            arr_t : ndarray
                Unless `copy` is False and the other conditions for returning the input
                array are satisfied (see description for `copy` input paramter), `arr_t`
                is a new array of the same shape as the input array, with dtype, order
                given by `dtype`, `order`.
        
            Raises
            ------
            ComplexWarning
                When casting from complex to float or int. To avoid this,
                one should use ``a.real.astype(t)``.
        
            Examples
            --------
            >>> x = np.array([1, 2, 2.5])
            >>> x
            array([ 1. ,  2. ,  2.5])
        
            >>> x.astype(int)
            array([1, 2, 2])"""
        return ndarray()
    base = getset_descriptor()
    def byteswap(self, inplace):
        """a.byteswap(inplace)
        
            Swap the bytes of the array elements
        
            Toggle between low-endian and big-endian data representation by
            returning a byteswapped array, optionally swapped in-place.
        
            Parameters
            ----------
            inplace : bool, optional
                If ``True``, swap bytes in-place, default is ``False``.
        
            Returns
            -------
            out : ndarray
                The byteswapped array. If `inplace` is ``True``, this is
                a view to self.
        
            Examples
            --------
            >>> A = np.array([1, 256, 8755], dtype=np.int16)
            >>> map(hex, A)
            ['0x1', '0x100', '0x2233']
            >>> A.byteswap(True)
            array([  256,     1, 13090], dtype=int16)
            >>> map(hex, A)
            ['0x100', '0x1', '0x3322']
        
            Arrays of strings are not swapped
        
            >>> A = np.array(['ceg', 'fac'])
            >>> A.byteswap()
            array(['ceg', 'fac'],
                  dtype='|S3')"""
        return ndarray()
    def choose(self, choices, out=None, mode=_raise):
        """a.choose(choices, out=None, mode='raise')
        
            Use an index array to construct a new array from a set of choices.
        
            Refer to `numpy.choose` for full documentation.
        
            See Also
            --------
            numpy.choose : equivalent function"""
        return None
    def clip(self, a_min, a_max, out=None):
        """a.clip(a_min, a_max, out=None)
        
            Return an array whose values are limited to ``[a_min, a_max]``.
        
            Refer to `numpy.clip` for full documentation.
        
            See Also
            --------
            numpy.clip : equivalent function"""
        return None
    def compress(self, condition, axis=None, out=None):
        """a.compress(condition, axis=None, out=None)
        
            Return selected slices of this array along given axis.
        
            Refer to `numpy.compress` for full documentation.
        
            See Also
            --------
            numpy.compress : equivalent function"""
        return None
    def conj(self, _):
        """a.conj()
        
            Complex-conjugate all elements.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def conjugate(self, _):
        """a.conjugate()
        
            Return the complex conjugate, element-wise.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def copy(self, order):
        """a.copy(order='C')
        
            Return a copy of the array.
        
            Parameters
            ----------
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout of the copy. 'C' means C-order,
                'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
                'C' otherwise. 'K' means match the layout of `a` as closely
                as possible. (Note that this function and :func:numpy.copy are very
                similar, but have different default values for their order=
                arguments.)
        
            See also
            --------
            numpy.copy
            numpy.copyto
        
            Examples
            --------
            >>> x = np.array([[1,2,3],[4,5,6]], order='F')
        
            >>> y = x.copy()
        
            >>> x.fill(0)
        
            >>> x
            array([[0, 0, 0],
                   [0, 0, 0]])
        
            >>> y
            array([[1, 2, 3],
                   [4, 5, 6]])
        
            >>> y.flags['C_CONTIGUOUS']
            True"""
        return None
    ctypes = getset_descriptor()
    def cumprod(self, axis=None, dtype=None, out=None):
        """a.cumprod(axis=None, dtype=None, out=None)
        
            Return the cumulative product of the elements along the given axis.
        
            Refer to `numpy.cumprod` for full documentation.
        
            See Also
            --------
            numpy.cumprod : equivalent function"""
        return None
    def cumsum(self, axis=None, dtype=None, out=None):
        """a.cumsum(axis=None, dtype=None, out=None)
        
            Return the cumulative sum of the elements along the given axis.
        
            Refer to `numpy.cumsum` for full documentation.
        
            See Also
            --------
            numpy.cumsum : equivalent function"""
        return None
    data = getset_descriptor()
    def diagonal(self, offset=0, axis1=0, axis2=1):
        """a.diagonal(offset=0, axis1=0, axis2=1)
        
            Return specified diagonals.
        
            Refer to :func:`numpy.diagonal` for full documentation.
        
            See Also
            --------
            numpy.diagonal : equivalent function"""
        return None
    def dot(self, b, out=None):
        """a.dot(b, out=None)
        
            Dot product of two arrays.
        
            Refer to `numpy.dot` for full documentation.
        
            See Also
            --------
            numpy.dot : equivalent function
        
            Examples
            --------
            >>> a = np.eye(2)
            >>> b = np.ones((2, 2)) * 2
            >>> a.dot(b)
            array([[ 2.,  2.],
                   [ 2.,  2.]])
        
            This array method can be conveniently chained:
        
            >>> a.dot(b).dot(b)
            array([[ 8.,  8.],
                   [ 8.,  8.]])"""
        return None
    dtype = getset_descriptor()
    def dump(self, file):
        """a.dump(file)
        
            Dump a pickle of the array to the specified file.
            The array can be read back with pickle.load or numpy.load.
        
            Parameters
            ----------
            file : str
                A string naming the dump file."""
        return None
    def dumps(self, _):
        """a.dumps()
        
            Returns the pickle of the array as a string.
            pickle.loads or numpy.loads will convert the string back to an array.
        
            Parameters
            ----------
            None"""
        return None
    def fill(self, value):
        """a.fill(value)
        
            Fill the array with a scalar value.
        
            Parameters
            ----------
            value : scalar
                All elements of `a` will be assigned this value.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.fill(0)
            >>> a
            array([0, 0])
            >>> a = np.empty(2)
            >>> a.fill(1)
            >>> a
            array([ 1.,  1.])"""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def flatten(self, order):
        """a.flatten(order='C')
        
            Return a copy of the array collapsed into one dimension.
        
            Parameters
            ----------
            order : {'C', 'F', 'A'}, optional
                Whether to flatten in C (row-major), Fortran (column-major) order,
                or preserve the C/Fortran ordering from `a`.
                The default is 'C'.
        
            Returns
            -------
            y : ndarray
                A copy of the input array, flattened to one dimension.
        
            See Also
            --------
            ravel : Return a flattened array.
            flat : A 1-D flat iterator over the array.
        
            Examples
            --------
            >>> a = np.array([[1,2], [3,4]])
            >>> a.flatten()
            array([1, 2, 3, 4])
            >>> a.flatten('F')
            array([1, 3, 2, 4])"""
        return ndarray()
    def flush(self, _):
        """
                Write any changes in the array to the file on disk.
        
                For further information, see `memmap`.
        
                Parameters
                ----------
                None
        
                See Also
                --------
                memmap
        
                """
        return None
    def getfield(self, dtype, offset):
        """a.getfield(dtype, offset=0)
        
            Returns a field of the given array as a certain type.
        
            A field is a view of the array data with a given data-type. The values in
            the view are determined by the given type and the offset into the current
            array in bytes. The offset needs to be such that the view dtype fits in the
            array dtype; for example an array of dtype complex128 has 16-byte elements.
            If taking a view with a 32-bit integer (4 bytes), the offset needs to be
            between 0 and 12 bytes.
        
            Parameters
            ----------
            dtype : str or dtype
                The data type of the view. The dtype size of the view can not be larger
                than that of the array itself.
            offset : int
                Number of bytes to skip before beginning the element view.
        
            Examples
            --------
            >>> x = np.diag([1.+1.j]*2)
            >>> x[1, 1] = 2 + 4.j
            >>> x
            array([[ 1.+1.j,  0.+0.j],
                   [ 0.+0.j,  2.+4.j]])
            >>> x.getfield(np.float64)
            array([[ 1.,  0.],
                   [ 0.,  2.]])
        
            By choosing an offset of 8 bytes we can select the complex part of the
            array for our view:
        
            >>> x.getfield(np.float64, offset=8)
            array([[ 1.,  0.],
               [ 0.,  4.]])"""
        return array()
    imag = getset_descriptor()
    def item(self, ESCargs):
        """a.item(*args)
        
            Copy an element of an array to a standard Python scalar and return it.
        
            Parameters
            ----------
            \*args : Arguments (variable number and type)
        
                * none: in this case, the method only works for arrays
                  with one element (`a.size == 1`), which element is
                  copied into a standard Python scalar object and returned.
        
                * int_type: this argument is interpreted as a flat index into
                  the array, specifying which element to copy and return.
        
                * tuple of int_types: functions as does a single int_type argument,
                  except that the argument is interpreted as an nd-index into the
                  array.
        
            Returns
            -------
            z : Standard Python scalar object
                A copy of the specified element of the array as a suitable
                Python scalar
        
            Notes
            -----
            When the data type of `a` is longdouble or clongdouble, item() returns
            a scalar array object because there is no available Python scalar that
            would not lose information. Void arrays return a buffer object for item(),
            unless fields are defined, in which case a tuple is returned.
        
            `item` is very similar to a[args], except, instead of an array scalar,
            a standard Python scalar is returned. This can be useful for speeding up
            access to elements of the array and doing arithmetic on elements of the
            array using Python's optimized math.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.item(3)
            2
            >>> x.item(7)
            5
            >>> x.item((0, 1))
            1
            >>> x.item((2, 2))
            3"""
        return Standard()
    def itemset(self, ESCargs):
        """a.itemset(*args)
        
            Insert scalar into an array (scalar is cast to array's dtype, if possible)
        
            There must be at least 1 argument, and define the last argument
            as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
            than ``a[args] = item``.  The item should be a scalar value and `args`
            must select a single item in the array `a`.
        
            Parameters
            ----------
            \*args : Arguments
                If one argument: a scalar, only used in case `a` is of size 1.
                If two arguments: the last argument is the value to be set
                and must be a scalar, the first argument specifies a single array
                element location. It is either an int or a tuple.
        
            Notes
            -----
            Compared to indexing syntax, `itemset` provides some speed increase
            for placing a scalar into a particular location in an `ndarray`,
            if you must do this.  However, generally this is discouraged:
            among other problems, it complicates the appearance of the code.
            Also, when using `itemset` (and `item`) inside a loop, be sure
            to assign the methods to a local variable to avoid the attribute
            look-up at each loop iteration.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.itemset(4, 0)
            >>> x.itemset((2, 2), 9)
            >>> x
            array([[3, 1, 7],
                   [2, 0, 3],
                   [8, 5, 9]])"""
        return None
    itemsize = getset_descriptor()
    def max(self, axis=None, out=None):
        """a.max(axis=None, out=None)
        
            Return the maximum along a given axis.
        
            Refer to `numpy.amax` for full documentation.
        
            See Also
            --------
            numpy.amax : equivalent function"""
        return None
    def mean(self, axis=None, dtype=None, out=None):
        """a.mean(axis=None, dtype=None, out=None)
        
            Returns the average of the array elements along given axis.
        
            Refer to `numpy.mean` for full documentation.
        
            See Also
            --------
            numpy.mean : equivalent function"""
        return None
    def min(self, axis=None, out=None):
        """a.min(axis=None, out=None)
        
            Return the minimum along a given axis.
        
            Refer to `numpy.amin` for full documentation.
        
            See Also
            --------
            numpy.amin : equivalent function"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """arr.newbyteorder(new_order='S')
        
            Return the array with the same data viewed with a different byte order.
        
            Equivalent to::
        
                arr.view(arr.dtype.newbytorder(new_order))
        
            Changes are also made in all fields and sub-arrays of the array data
            type.
        
        
        
            Parameters
            ----------
            new_order : string, optional
                Byte order to force; a value from the byte order specifications
                above. `new_order` codes can be any of::
        
                 * 'S' - swap dtype from current to opposite endian
                 * {'<', 'L'} - little endian
                 * {'>', 'B'} - big endian
                 * {'=', 'N'} - native order
                 * {'|', 'I'} - ignore (no change to byte order)
        
                The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_arr : array
                New array object with the dtype reflecting given change to the
                byte order."""
        return array()
    def nonzero(self, _):
        """a.nonzero()
        
            Return the indices of the elements that are non-zero.
        
            Refer to `numpy.nonzero` for full documentation.
        
            See Also
            --------
            numpy.nonzero : equivalent function"""
        return None
    def partition(self, kth, axis, kind, order):
        """a.partition(kth, axis=-1, kind='introselect', order=None)
        
            Rearranges the elements in the array in such a way that value of the
            element in kth position is in the position it would be in a sorted array.
            All elements smaller than the kth element are moved before this element and
            all equal or greater are moved behind it. The ordering of the elements in
            the two partitions is undefined.
        
            .. versionadded:: 1.8.0
        
            Parameters
            ----------
            kth : int or sequence of ints
                Element index to partition by. The kth element value will be in its
                final sorted position and all smaller elements will be moved before it
                and all equal or greater elements behind it.
                The order all elements in the partitions is undefined.
                If provided with a sequence of kth it will partition all elements
                indexed by kth of them into their sorted position at once.
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'introselect'}, optional
                Selection algorithm. Default is 'introselect'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.partition : Return a parititioned copy of an array.
            argpartition : Indirect partition.
            sort : Full sort.
        
            Notes
            -----
            See ``np.partition`` for notes on the different algorithms.
        
            Examples
            --------
            >>> a = np.array([3, 4, 2, 1])
            >>> a.partition(a, 3)
            >>> a
            array([2, 1, 3, 4])
        
            >>> a.partition((1, 3))
            array([1, 2, 3, 4])"""
        return None
    def prod(self, axis=None, dtype=None, out=None):
        """a.prod(axis=None, dtype=None, out=None)
        
            Return the product of the array elements over the given axis
        
            Refer to `numpy.prod` for full documentation.
        
            See Also
            --------
            numpy.prod : equivalent function"""
        return None
    def ptp(self, axis=None, out=None):
        """a.ptp(axis=None, out=None)
        
            Peak to peak (maximum - minimum) value along a given axis.
        
            Refer to `numpy.ptp` for full documentation.
        
            See Also
            --------
            numpy.ptp : equivalent function"""
        return None
    def put(self, indices, values, mode=_raise):
        """a.put(indices, values, mode='raise')
        
            Set ``a.flat[n] = values[n]`` for all `n` in indices.
        
            Refer to `numpy.put` for full documentation.
        
            See Also
            --------
            numpy.put : equivalent function"""
        return None
    def ravel(self, order):
        """a.ravel([order])
        
            Return a flattened array.
        
            Refer to `numpy.ravel` for full documentation.
        
            See Also
            --------
            numpy.ravel : equivalent function
        
            ndarray.flat : a flat iterator on the array."""
        return None
    real = getset_descriptor()
    def repeat(self, repeats, axis=None):
        """a.repeat(repeats, axis=None)
        
            Repeat elements of an array.
        
            Refer to `numpy.repeat` for full documentation.
        
            See Also
            --------
            numpy.repeat : equivalent function"""
        return None
    def reshape(self, shape, order=C):
        """a.reshape(shape, order='C')
        
            Returns an array containing the same data with a new shape.
        
            Refer to `numpy.reshape` for full documentation.
        
            See Also
            --------
            numpy.reshape : equivalent function"""
        return None
    def resize(self, new_shape, refcheck):
        """a.resize(new_shape, refcheck=True)
        
            Change shape and size of array in-place.
        
            Parameters
            ----------
            new_shape : tuple of ints, or `n` ints
                Shape of resized array.
            refcheck : bool, optional
                If False, reference count will not be checked. Default is True.
        
            Returns
            -------
            None
        
            Raises
            ------
            ValueError
                If `a` does not own its own data or references or views to it exist,
                and the data memory must be changed.
        
            SystemError
                If the `order` keyword argument is specified. This behaviour is a
                bug in NumPy.
        
            See Also
            --------
            resize : Return a new array with the specified shape.
        
            Notes
            -----
            This reallocates space for the data area if necessary.
        
            Only contiguous arrays (data elements consecutive in memory) can be
            resized.
        
            The purpose of the reference count check is to make sure you
            do not use this array as a buffer for another Python object and then
            reallocate the memory. However, reference counts can increase in
            other ways so if you are sure that you have not shared the memory
            for this array with another Python object, then you may safely set
            `refcheck` to False.
        
            Examples
            --------
            Shrinking an array: array is flattened (in the order that the data are
            stored in memory), resized, and reshaped:
        
            >>> a = np.array([[0, 1], [2, 3]], order='C')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [1]])
        
            >>> a = np.array([[0, 1], [2, 3]], order='F')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [2]])
        
            Enlarging an array: as above, but missing entries are filled with zeros:
        
            >>> b = np.array([[0, 1], [2, 3]])
            >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
            >>> b
            array([[0, 1, 2],
                   [3, 0, 0]])
        
            Referencing an array prevents resizing...
        
            >>> c = a
            >>> a.resize((1, 1))
            Traceback (most recent call last):
            ...
            ValueError: cannot resize an array that has been referenced ...
        
            Unless `refcheck` is False:
        
            >>> a.resize((1, 1), refcheck=False)
            >>> a
            array([[0]])
            >>> c
            array([[0]])"""
        return None
    def round(self, decimals=0, out=None):
        """a.round(decimals=0, out=None)
        
            Return `a` with each element rounded to the given number of decimals.
        
            Refer to `numpy.around` for full documentation.
        
            See Also
            --------
            numpy.around : equivalent function"""
        return None
    def searchsorted(self, v, side=left, sorter=None):
        """a.searchsorted(v, side='left', sorter=None)
        
            Find indices where elements of v should be inserted in a to maintain order.
        
            For full documentation, see `numpy.searchsorted`
        
            See Also
            --------
            numpy.searchsorted : equivalent function"""
        return None
    def setfield(self, val, dtype, offset):
        """a.setfield(val, dtype, offset=0)
        
            Put a value into a specified place in a field defined by a data-type.
        
            Place `val` into `a`'s field defined by `dtype` and beginning `offset`
            bytes into the field.
        
            Parameters
            ----------
            val : object
                Value to be placed in field.
            dtype : dtype object
                Data-type of the field in which to place `val`.
            offset : int, optional
                The number of bytes into the field at which to place `val`.
        
            Returns
            -------
            None
        
            See Also
            --------
            getfield
        
            Examples
            --------
            >>> x = np.eye(3)
            >>> x.getfield(np.float64)
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])
            >>> x.setfield(3, np.int32)
            >>> x.getfield(np.int32)
            array([[3, 3, 3],
                   [3, 3, 3],
                   [3, 3, 3]])
            >>> x
            array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
                   [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
                   [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
            >>> x.setfield(np.eye(3), np.int32)
            >>> x
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])"""
        return None
    def setflags(self, write, align, uic):
        """a.setflags(write=None, align=None, uic=None)
        
            Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
        
            These Boolean-valued flags affect how numpy interprets the memory
            area used by `a` (see Notes below). The ALIGNED flag can only
            be set to True if the data is actually aligned according to the type.
            The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE
            can only be set to True if the array owns its own memory, or the
            ultimate owner of the memory exposes a writeable buffer interface,
            or is a string. (The exception for string is made so that unpickling
            can be done without copying memory.)
        
            Parameters
            ----------
            write : bool, optional
                Describes whether or not `a` can be written to.
            align : bool, optional
                Describes whether or not `a` is aligned properly for its type.
            uic : bool, optional
                Describes whether or not `a` is a copy of another "base" array.
        
            Notes
            -----
            Array flags provide information about how the memory area used
            for the array is to be interpreted. There are 6 Boolean flags
            in use, only three of which can be changed by the user:
            UPDATEIFCOPY, WRITEABLE, and ALIGNED.
        
            WRITEABLE (W) the data area can be written to;
        
            ALIGNED (A) the data and strides are aligned appropriately for the hardware
            (as determined by the compiler);
        
            UPDATEIFCOPY (U) this array is a copy of some other array (referenced
            by .base). When this array is deallocated, the base array will be
            updated with the contents of this array.
        
            All flags can be accessed using their first (upper case) letter as well
            as the full name.
        
            Examples
            --------
            >>> y
            array([[3, 1, 7],
                   [2, 0, 0],
                   [8, 5, 9]])
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : True
              ALIGNED : True
              UPDATEIFCOPY : False
            >>> y.setflags(write=0, align=0)
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : False
              ALIGNED : False
              UPDATEIFCOPY : False
            >>> y.setflags(uic=1)
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: cannot set UPDATEIFCOPY flag to True"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def sort(self, axis, kind, order):
        """a.sort(axis=-1, kind='quicksort', order=None)
        
            Sort an array, in-place.
        
            Parameters
            ----------
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'quicksort', 'mergesort', 'heapsort'}, optional
                Sorting algorithm. Default is 'quicksort'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.sort : Return a sorted copy of an array.
            argsort : Indirect sort.
            lexsort : Indirect stable sort on multiple keys.
            searchsorted : Find elements in sorted array.
            partition: Partial sort.
        
            Notes
            -----
            See ``sort`` for notes on the different sorting algorithms.
        
            Examples
            --------
            >>> a = np.array([[1,4], [3,1]])
            >>> a.sort(axis=1)
            >>> a
            array([[1, 4],
                   [1, 3]])
            >>> a.sort(axis=0)
            >>> a
            array([[1, 3],
                   [1, 4]])
        
            Use the `order` keyword to specify a field to use when sorting a
            structured array:
        
            >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
            >>> a.sort(order='y')
            >>> a
            array([('c', 1), ('a', 2)],
                  dtype=[('x', '|S1'), ('y', '<i4')])"""
        return None
    def squeeze(self, axis=None):
        """a.squeeze(axis=None)
        
            Remove single-dimensional entries from the shape of `a`.
        
            Refer to `numpy.squeeze` for full documentation.
        
            See Also
            --------
            numpy.squeeze : equivalent function"""
        return None
    def std(self, axis=None, dtype=None, out=None, ddof=0):
        """a.std(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the standard deviation of the array elements along given axis.
        
            Refer to `numpy.std` for full documentation.
        
            See Also
            --------
            numpy.std : equivalent function"""
        return None
    strides = getset_descriptor()
    def sum(self, axis=None, dtype=None, out=None):
        """a.sum(axis=None, dtype=None, out=None)
        
            Return the sum of the array elements over the given axis.
        
            Refer to `numpy.sum` for full documentation.
        
            See Also
            --------
            numpy.sum : equivalent function"""
        return None
    def swapaxes(self, axis1, axis2):
        """a.swapaxes(axis1, axis2)
        
            Return a view of the array with `axis1` and `axis2` interchanged.
        
            Refer to `numpy.swapaxes` for full documentation.
        
            See Also
            --------
            numpy.swapaxes : equivalent function"""
        return None
    def take(self, indices, axis=None, out=None, mode=_raise):
        """a.take(indices, axis=None, out=None, mode='raise')
        
            Return an array formed from the elements of `a` at the given indices.
        
            Refer to `numpy.take` for full documentation.
        
            See Also
            --------
            numpy.take : equivalent function"""
        return None
    def tofile(self, fid, sep, format):
        """a.tofile(fid, sep="", format="%s")
        
            Write array to a file as text or binary (default).
        
            Data is always written in 'C' order, independent of the order of `a`.
            The data produced by this method can be recovered using the function
            fromfile().
        
            Parameters
            ----------
            fid : file or str
                An open file object, or a string containing a filename.
            sep : str
                Separator between array items for text output.
                If "" (empty), a binary file is written, equivalent to
                ``file.write(a.tostring())``.
            format : str
                Format string for text file output.
                Each entry in the array is formatted to text by first converting
                it to the closest Python type, and then using "format" % item.
        
            Notes
            -----
            This is a convenience function for quick storage of array data.
            Information on endianness and precision is lost, so this method is not a
            good choice for files intended to archive data or transport data between
            machines with different endianness. Some of these problems can be overcome
            by outputting the data as text files, at the expense of speed and file
            size."""
        return None
    def tolist(self, _):
        """a.tolist()
        
            Return the array as a (possibly nested) list.
        
            Return a copy of the array data as a (nested) Python list.
            Data items are converted to the nearest compatible Python type.
        
            Parameters
            ----------
            none
        
            Returns
            -------
            y : list
                The possibly nested list of array elements.
        
            Notes
            -----
            The array may be recreated, ``a = np.array(a.tolist())``.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.tolist()
            [1, 2]
            >>> a = np.array([[1, 2], [3, 4]])
            >>> list(a)
            [array([1, 2]), array([3, 4])]
            >>> a.tolist()
            [[1, 2], [3, 4]]"""
        return list()
    def tostring(self, order):
        """a.tostring(order='C')
        
            Construct a Python string containing the raw data bytes in the array.
        
            Constructs a Python string showing a copy of the raw contents of
            data memory. The string can be produced in either 'C' or 'Fortran',
            or 'Any' order (the default is 'C'-order). 'Any' order means C-order
            unless the F_CONTIGUOUS flag in the array is set, in which case it
            means 'Fortran' order.
        
            Parameters
            ----------
            order : {'C', 'F', None}, optional
                Order of the data for multidimensional arrays:
                C, Fortran, or the same as for the original array.
        
            Returns
            -------
            s : str
                A Python string exhibiting a copy of `a`'s raw data.
        
            Examples
            --------
            >>> x = np.array([[0, 1], [2, 3]])
            >>> x.tostring()
            '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
            >>> x.tostring('C') == x.tostring()
            True
            >>> x.tostring('F')
            '\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'"""
        return str()
    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
        """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
        
            Return the sum along diagonals of the array.
        
            Refer to `numpy.trace` for full documentation.
        
            See Also
            --------
            numpy.trace : equivalent function"""
        return None
    def transpose(self, axes):
        """a.transpose(*axes)
        
            Returns a view of the array with axes transposed.
        
            For a 1-D array, this has no effect. (To change between column and
            row vectors, first cast the 1-D array into a matrix object.)
            For a 2-D array, this is the usual matrix transpose.
            For an n-D array, if axes are given, their order indicates how the
            axes are permuted (see Examples). If axes are not provided and
            ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
            ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
        
            Parameters
            ----------
            axes : None, tuple of ints, or `n` ints
        
             * None or no argument: reverses the order of the axes.
        
             * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
               `i`-th axis becomes `a.transpose()`'s `j`-th axis.
        
             * `n` ints: same as an n-tuple of the same ints (this form is
               intended simply as a "convenience" alternative to the tuple form)
        
            Returns
            -------
            out : ndarray
                View of `a`, with axes suitably permuted.
        
            See Also
            --------
            ndarray.T : Array property returning the array transposed.
        
            Examples
            --------
            >>> a = np.array([[1, 2], [3, 4]])
            >>> a
            array([[1, 2],
                   [3, 4]])
            >>> a.transpose()
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose((1, 0))
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose(1, 0)
            array([[1, 3],
                   [2, 4]])"""
        return ndarray()
    def var(self, axis=None, dtype=None, out=None, ddof=0):
        """a.var(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the variance of the array elements, along given axis.
        
            Refer to `numpy.var` for full documentation.
        
            See Also
            --------
            numpy.var : equivalent function"""
        return None
    def view(self, dtype, type):
        """a.view(dtype=None, type=None)
        
            New view of array with the same data.
        
            Parameters
            ----------
            dtype : data-type or ndarray sub-class, optional
                Data-type descriptor of the returned view, e.g., float32 or int16. The
                default, None, results in the view having the same data-type as `a`.
                This argument can also be specified as an ndarray sub-class, which
                then specifies the type of the returned object (this is equivalent to
                setting the ``type`` parameter).
            type : Python type, optional
                Type of the returned view, e.g., ndarray or matrix.  Again, the
                default None results in type preservation.
        
            Notes
            -----
            ``a.view()`` is used two different ways:
        
            ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
            of the array's memory with a different data-type.  This can cause a
            reinterpretation of the bytes of memory.
        
            ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
            returns an instance of `ndarray_subclass` that looks at the same array
            (same shape, dtype, etc.)  This does not cause a reinterpretation of the
            memory.
        
            For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
            bytes per entry than the previous dtype (for example, converting a
            regular array to a structured array), then the behavior of the view
            cannot be predicted just from the superficial appearance of ``a`` (shown
            by ``print(a)``). It also depends on exactly how ``a`` is stored in
            memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
            defined as a slice or transpose, etc., the view may give different
            results.
        
        
            Examples
            --------
            >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
        
            Viewing array data using a different type and dtype:
        
            >>> y = x.view(dtype=np.int16, type=np.matrix)
            >>> y
            matrix([[513]], dtype=int16)
            >>> print type(y)
            <class 'numpy.matrixlib.defmatrix.matrix'>
        
            Creating a view on a structured array so it can be used in calculations
        
            >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
            >>> xv = x.view(dtype=np.int8).reshape(-1,2)
            >>> xv
            array([[1, 2],
                   [3, 4]], dtype=int8)
            >>> xv.mean(0)
            array([ 2.,  3.])
        
            Making changes to the view changes the underlying array
        
            >>> xv[0,1] = 20
            >>> print x
            [(1, 20) (3, 4)]
        
            Using a view to convert an array to a record array:
        
            >>> z = x.view(np.recarray)
            >>> z.a
            array([1], dtype=int8)
        
            Views share data:
        
            >>> x[0] = (9, 10)
            >>> z[0]
            (9, 10)
        
            Views that change the dtype size (bytes per entry) should normally be
            avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
        
            >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
            >>> y = x[:, 0:2]
            >>> y
            array([[1, 2],
                   [4, 5]], dtype=int16)
            >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: new type not compatible with array.
            >>> z = y.copy()
            >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
            array([[(1, 2)],
                   [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])"""
        return None
def meshgrid():
    """
        Return coordinate matrices from two or more coordinate vectors.
    
        Make N-D coordinate arrays for vectorized evaluations of
        N-D scalar/vector fields over N-D grids, given
        one-dimensional coordinate arrays x1, x2,..., xn.
    
        Parameters
        ----------
        x1, x2,..., xn : array_like
            1-D arrays representing the coordinates of a grid.
        indexing : {'xy', 'ij'}, optional
            Cartesian ('xy', default) or matrix ('ij') indexing of output.
            See Notes for more details.
        sparse : bool, optional
             If True a sparse grid is returned in order to conserve memory.
             Default is False.
        copy : bool, optional
            If False, a view into the original arrays are returned in
            order to conserve memory.  Default is True.  Please note that
            ``sparse=False, copy=False`` will likely return non-contiguous arrays.
            Furthermore, more than one element of a broadcast array may refer to
            a single memory location.  If you need to write to the arrays, make
            copies first.
    
        Returns
        -------
        X1, X2,..., XN : ndarray
            For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,
            return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'
            or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'
            with the elements of `xi` repeated to fill the matrix along
            the first dimension for `x1`, the second for `x2` and so on.
    
        Notes
        -----
        This function supports both indexing conventions through the indexing keyword
        argument.  Giving the string 'ij' returns a meshgrid with matrix indexing,
        while 'xy' returns a meshgrid with Cartesian indexing.  In the 2-D case
        with inputs of length M and N, the outputs are of shape (N, M) for 'xy'
        indexing and (M, N) for 'ij' indexing.  In the 3-D case with inputs of
        length M, N and P, outputs are of shape (N, M, P) for 'xy' indexing and (M,
        N, P) for 'ij' indexing.  The difference is illustrated by the following
        code snippet::
    
            xv, yv = meshgrid(x, y, sparse=False, indexing='ij')
            for i in range(nx):
                for j in range(ny):
                    # treat xv[i,j], yv[i,j]
    
            xv, yv = meshgrid(x, y, sparse=False, indexing='xy')
            for i in range(nx):
                for j in range(ny):
                    # treat xv[j,i], yv[j,i]
    
        See Also
        --------
        index_tricks.mgrid : Construct a multi-dimensional "meshgrid"
                         using indexing notation.
        index_tricks.ogrid : Construct an open multi-dimensional "meshgrid"
                         using indexing notation.
    
        Examples
        --------
        >>> nx, ny = (3, 2)
        >>> x = np.linspace(0, 1, nx)
        >>> y = np.linspace(0, 1, ny)
        >>> xv, yv = meshgrid(x, y)
        >>> xv
        array([[ 0. ,  0.5,  1. ],
               [ 0. ,  0.5,  1. ]])
        >>> yv
        array([[ 0.,  0.,  0.],
               [ 1.,  1.,  1.]])
        >>> xv, yv = meshgrid(x, y, sparse=True)  # make sparse output arrays
        >>> xv
        array([[ 0. ,  0.5,  1. ]])
        >>> yv
        array([[ 0.],
               [ 1.]])
    
        `meshgrid` is very useful to evaluate functions on a grid.
    
        >>> x = np.arange(-5, 5, 0.1)
        >>> y = np.arange(-5, 5, 0.1)
        >>> xx, yy = meshgrid(x, y, sparse=True)
        >>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
        >>> h = plt.contourf(x,y,z)
    
        """
    return ndarray()
mgrid = nd_grid()
def amin(a=False, axis=None, out=None, keepdims=False):
    """
        Return the minimum of an array or minimum along an axis.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which to operate.  By default, flattened input is used.
        out : ndarray, optional
            Alternative output array in which to place the result.  Must
            be of the same shape and buffer length as the expected output.
            See `doc.ufuncs` (Section "Output arguments") 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 original `arr`.
    
        Returns
        -------
        amin : ndarray or scalar
            Minimum of `a`. If `axis` is None, the result is a scalar value.
            If `axis` is given, the result is an array of dimension
            ``a.ndim - 1``.
    
        See Also
        --------
        amax :
            The maximum value of an array along a given axis, propagating any NaNs.
        nanmin :
            The minimum value of an array along a given axis, ignoring any NaNs.
        minimum :
            Element-wise minimum of two arrays, propagating any NaNs.
        fmin :
            Element-wise minimum of two arrays, ignoring any NaNs.
        argmin :
            Return the indices of the minimum values.
    
        nanmax, maximum, fmax
    
        Notes
        -----
        NaN values are propagated, that is if at least one item is NaN, the
        corresponding min value will be NaN as well. To ignore NaN values
        (MATLAB behavior), please use nanmin.
    
        Don't use `amin` for element-wise comparison of 2 arrays; when
        ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
        ``amin(a, axis=0)``.
    
        Examples
        --------
        >>> a = np.arange(4).reshape((2,2))
        >>> a
        array([[0, 1],
               [2, 3]])
        >>> np.amin(a)           # Minimum of the flattened array
        0
        >>> np.amin(a, axis=0)   # Minima along the first axis
        array([0, 1])
        >>> np.amin(a, axis=1)   # Minima along the second axis
        array([0, 2])
    
        >>> b = np.arange(5, dtype=np.float)
        >>> b[2] = np.NaN
        >>> np.amin(b)
        nan
        >>> np.nanmin(b)
        0.0
    
        """
    return ndarray() if False else float()
def min_scalar_type(a):
    """min_scalar_type(a)
    
        For scalar ``a``, returns the data type with the smallest size
        and smallest scalar kind which can hold its value.  For non-scalar
        array ``a``, returns the vector's dtype unmodified.
    
        Floating point values are not demoted to integers,
        and complex values are not demoted to floats.
    
        Parameters
        ----------
        a : scalar or array_like
            The value whose minimal data type is to be found.
    
        Returns
        -------
        out : dtype
            The minimal data type.
    
        Notes
        -----
        .. versionadded:: 1.6.0
    
        See Also
        --------
        result_type, promote_types, dtype, can_cast
    
        Examples
        --------
        >>> np.min_scalar_type(10)
        dtype('uint8')
    
        >>> np.min_scalar_type(-260)
        dtype('int16')
    
        >>> np.min_scalar_type(3.1)
        dtype('float16')
    
        >>> np.min_scalar_type(1e50)
        dtype('float64')
    
        >>> np.min_scalar_type(np.arange(4,dtype='f8'))
        dtype('float64')"""
    return dtype()
def minimum(x1, x2, out=None):
    """minimum(x1, x2[, out])
    
    Element-wise minimum of array elements.
    
    Compare two arrays and returns a new array containing the element-wise
    minima. If one of the elements being compared is a nan, then that element
    is returned. If both elements are nans then the first is returned. The
    latter distinction is important for complex nans, which are defined as at
    least one of the real or imaginary parts being a nan. The net effect is
    that nans are propagated.
    
    Parameters
    ----------
    x1, x2 : array_like
        The arrays holding the elements to be compared. They must have
        the same shape, or shapes that can be broadcast to a single shape.
    
    Returns
    -------
    y : {ndarray, scalar}
        The minimum of `x1` and `x2`, element-wise.  Returns scalar if
        both  `x1` and `x2` are scalars.
    
    See Also
    --------
    maximum :
        Element-wise maximum of two arrays, propagating any NaNs.
    fmin :
        Element-wise minimum of two arrays, ignoring any NaNs.
    amin :
        The minimum value of an array along a given axis, propagating any NaNs.
    nanmin :
        The minimum value of an array along a given axis, ignoring any NaNs.
    
    fmax, amax, nanmax
    
    Notes
    -----
    The minimum is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither
    x1 nor x2 are nans, but it is faster and does proper broadcasting.
    
    Examples
    --------
    >>> np.minimum([2, 3, 4], [1, 5, 2])
    array([1, 3, 2])
    
    >>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting
    array([[ 0.5,  0. ],
           [ 0. ,  1. ]])
    
    >>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])
    array([ NaN,  NaN,  NaN])
    >>> np.minimum(-np.Inf, 1)
    -inf"""
    return ndarray()
def mintypecode(typechars="d", typeset="GDFgdf", default="d"):
    """
        Return the character for the minimum-size type to which given types can
        be safely cast.
    
        The returned type character must represent the smallest size dtype such
        that an array of the returned type can handle the data from an array of
        all types in `typechars` (or if `typechars` is an array, then its
        dtype.char).
    
        Parameters
        ----------
        typechars : list of str or array_like
            If a list of strings, each string should represent a dtype.
            If array_like, the character representation of the array dtype is used.
        typeset : str or list of str, optional
            The set of characters that the returned character is chosen from.
            The default set is 'GDFgdf'.
        default : str, optional
            The default character, this is returned if none of the characters in
            `typechars` matches a character in `typeset`.
    
        Returns
        -------
        typechar : str
            The character representing the minimum-size type that was found.
    
        See Also
        --------
        dtype, sctype2char, maximum_sctype
    
        Examples
        --------
        >>> np.mintypecode(['d', 'f', 'S'])
        'd'
        >>> x = np.array([1.1, 2-3.j])
        >>> np.mintypecode(x)
        'D'
    
        >>> np.mintypecode('abceh', default='G')
        'G'
    
        """
    return str()
def mirr(values, finance_rate, reinvest_rate):
    """
        Modified internal rate of return.
    
        Parameters
        ----------
        values : array_like
            Cash flows (must contain at least one positive and one negative value)
            or nan is returned.  The first value is considered a sunk cost at time zero.
        finance_rate : scalar
            Interest rate paid on the cash flows
        reinvest_rate : scalar
            Interest rate received on the cash flows upon reinvestment
    
        Returns
        -------
        out : float
            Modified internal rate of return
    
        """
    return float()
def remainder(x1, x2, out):
    """remainder(x1, x2[, out])
    
    Return element-wise remainder of division.
    
    Computes ``x1 - floor(x1 / x2) * x2``.
    
    Parameters
    ----------
    x1 : array_like
        Dividend array.
    x2 : array_like
        Divisor array.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    y : ndarray
        The remainder of the quotient ``x1/x2``, element-wise. Returns a scalar
        if both  `x1` and `x2` are scalars.
    
    See Also
    --------
    divide, floor
    
    Notes
    -----
    Returns 0 when `x2` is 0 and both `x1` and `x2` are (arrays of) integers.
    
    Examples
    --------
    >>> np.remainder([4, 7], [2, 3])
    array([0, 1])
    >>> np.remainder(np.arange(7), 5)
    array([0, 1, 2, 3, 4, 0, 1])"""
    return ndarray()
def modf(x, out1=None, out2=None):
    """modf(x[, out1, out2])
    
    Return the fractional and integral parts of an array, element-wise.
    
    The fractional and integral parts are negative if the given number is
    negative.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    y1 : ndarray
        Fractional part of `x`.
    y2 : ndarray
        Integral part of `x`.
    
    Notes
    -----
    For integer input the return values are floats.
    
    Examples
    --------
    >>> np.modf([0, 3.5])
    (array([ 0. ,  0.5]), array([ 0.,  3.]))
    >>> np.modf(-0.5)
    (-0.5, -0)"""
    return ndarray()
def msort(a):
    """
        Return a copy of an array sorted along the first axis.
    
        Parameters
        ----------
        a : array_like
            Array to be sorted.
    
        Returns
        -------
        sorted_array : ndarray
            Array of the same type and shape as `a`.
    
        See Also
        --------
        sort
    
        Notes
        -----
        ``np.msort(a)`` is equivalent to  ``np.sort(a, axis=0)``.
    
        """
    return ndarray()
def multiply(x1, x2, out=None):
    """multiply(x1, x2[, out])
    
    Multiply arguments element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        Input arrays to be multiplied.
    
    Returns
    -------
    y : ndarray
        The product of `x1` and `x2`, element-wise. Returns a scalar if
        both  `x1` and `x2` are scalars.
    
    Notes
    -----
    Equivalent to `x1` * `x2` in terms of array broadcasting.
    
    Examples
    --------
    >>> np.multiply(2.0, 4.0)
    8.0
    
    >>> x1 = np.arange(9.0).reshape((3, 3))
    >>> x2 = np.arange(3.0)
    >>> np.multiply(x1, x2)
    array([[  0.,   1.,   4.],
           [  0.,   4.,  10.],
           [  0.,   7.,  16.]])"""
    return ndarray()
nan = float()
def nan_to_num(x):
    """
        Replace nan with zero and inf with finite numbers.
    
        Returns an array or scalar replacing Not a Number (NaN) with zero,
        (positive) infinity with a very large number and negative infinity
        with a very small (or negative) number.
    
        Parameters
        ----------
        x : array_like
            Input data.
    
        Returns
        -------
        out : ndarray, float
            Array with the same shape as `x` and dtype of the element in `x`  with
            the greatest precision. NaN is replaced by zero, and infinity
            (-infinity) is replaced by the largest (smallest or most negative)
            floating point value that fits in the output dtype. All finite numbers
            are upcast to the output dtype (default float64).
    
        See Also
        --------
        isinf : Shows which elements are negative or negative infinity.
        isneginf : Shows which elements are negative infinity.
        isposinf : Shows which elements are positive infinity.
        isnan : Shows which elements are Not a Number (NaN).
        isfinite : Shows which elements are finite (not NaN, not 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.
    
    
        Examples
        --------
        >>> np.set_printoptions(precision=8)
        >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
        >>> np.nan_to_num(x)
        array([  1.79769313e+308,  -1.79769313e+308,   0.00000000e+000,
                -1.28000000e+002,   1.28000000e+002])
    
        """
    return ndarray()
def nanargmax(a=None, axis=None):
    """
        Return the indices of the maximum values in the specified axis ignoring
        NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
        results cannot be trusted if a slice contains only NaNs and -Infs.
    
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which to operate.  By default flattened input is used.
    
        Returns
        -------
        index_array : ndarray
            An array of indices or a single index value.
    
        See Also
        --------
        argmax, nanargmin
    
        Examples
        --------
        >>> a = np.array([[np.nan, 4], [2, 3]])
        >>> np.argmax(a)
        0
        >>> np.nanargmax(a)
        1
        >>> np.nanargmax(a, axis=0)
        array([1, 0])
        >>> np.nanargmax(a, axis=1)
        array([1, 1])
    
        """
    return ndarray()
def nanargmin(a=None, axis=None):
    """
        Return the indices of the minimum values in the specified axis ignoring
        NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
        cannot be trusted if a slice contains only NaNs and Infs.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which to operate.  By default flattened input is used.
    
        Returns
        -------
        index_array : ndarray
            An array of indices or a single index value.
    
        See Also
        --------
        argmin, nanargmax
    
        Examples
        --------
        >>> a = np.array([[np.nan, 4], [2, 3]])
        >>> np.argmin(a)
        0
        >>> np.nanargmin(a)
        2
        >>> np.nanargmin(a, axis=0)
        array([1, 1])
        >>> np.nanargmin(a, axis=1)
        array([1, 0])
    
        """
    return ndarray()
def nanmax(a=False, axis=None, out=None, keepdims=False):
    """
        Return the maximum of an array or maximum along an axis, ignoring any
        NaNs.  When all-NaN slices are encountered a ``RuntimeWarning`` is
        raised and NaN is returned for that slice.
    
        Parameters
        ----------
        a : array_like
            Array containing numbers whose maximum is desired. If `a` is not an
            array, a conversion is attempted.
        axis : int, optional
            Axis along which the maximum is computed. The default is to compute
            the maximum of the flattened array.
        out : ndarray, optional
            Alternate output array in which to place the result.  The default
            is ``None``; if provided, it must have the same shape as the
            expected output, but the type will be cast if necessary.  See
            `doc.ufuncs` for details.
    
            .. versionadded:: 1.8.0
        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 original `a`.
    
            .. versionadded:: 1.8.0
    
        Returns
        -------
        nanmax : ndarray
            An array with the same shape as `a`, with the specified axis removed.
            If `a` is a 0-d array, or if axis is None, an ndarray scalar is
            returned.  The same dtype as `a` is returned.
    
        See Also
        --------
        nanmin :
            The minimum value of an array along a given axis, ignoring any NaNs.
        amax :
            The maximum value of an array along a given axis, propagating any NaNs.
        fmax :
            Element-wise maximum of two arrays, ignoring any NaNs.
        maximum :
            Element-wise maximum of two arrays, propagating any NaNs.
        isnan :
            Shows which elements are Not a Number (NaN).
        isfinite:
            Shows which elements are neither NaN nor infinity.
    
        amin, fmin, minimum
    
        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.
        Positive infinity is treated as a very large number and negative
        infinity is treated as a very small (i.e. negative) number.
    
        If the input has a integer type the function is equivalent to np.max.
    
        Examples
        --------
        >>> a = np.array([[1, 2], [3, np.nan]])
        >>> np.nanmax(a)
        3.0
        >>> np.nanmax(a, axis=0)
        array([ 3.,  2.])
        >>> np.nanmax(a, axis=1)
        array([ 2.,  3.])
    
        When positive infinity and negative infinity are present:
    
        >>> np.nanmax([1, 2, np.nan, np.NINF])
        2.0
        >>> np.nanmax([1, 2, np.nan, np.inf])
        inf
    
        """
    return ndarray()
def nanmean(a=False, axis=None, dtype=None, out=None, keepdims=False):
    """
        Compute the arithmetic mean along the specified axis, ignoring NaNs.
    
        Returns the average of the array elements.  The average is taken over
        the flattened array by default, otherwise over the specified axis.
        `float64` intermediate and return values are used for integer inputs.
    
        For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
    
        .. versionadded:: 1.8.0
    
        Parameters
        ----------
        a : array_like
            Array containing numbers whose mean is desired. If `a` is not an
            array, a conversion is attempted.
        axis : int, optional
            Axis along which the means are computed. The default is to compute
            the mean of the flattened array.
        dtype : data-type, optional
            Type to use in computing the mean.  For integer inputs, the default
            is `float64`; for inexact inputs, it is the same as the input
            dtype.
        out : ndarray, optional
            Alternate output array in which to place the result.  The default
            is ``None``; if provided, it must have the same shape as the
            expected output, but the type will be cast if necessary.  See
            `doc.ufuncs` for 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 original `arr`.
    
        Returns
        -------
        m : ndarray, see dtype parameter above
            If `out=None`, returns a new array containing the mean values,
            otherwise a reference to the output array is returned. Nan is
            returned for slices that contain only NaNs.
    
        See Also
        --------
        average : Weighted average
        mean : Arithmetic mean taken while not ignoring NaNs
        var, nanvar
    
        Notes
        -----
        The arithmetic mean is the sum of the non-NaN elements along the axis
        divided by the number of non-NaN elements.
    
        Note that for floating-point input, the mean is computed using the same
        precision the input has.  Depending on the input data, this can cause
        the results to be inaccurate, especially for `float32`.  Specifying a
        higher-precision accumulator using the `dtype` keyword can alleviate
        this issue.
    
        Examples
        --------
        >>> a = np.array([[1, np.nan], [3, 4]])
        >>> np.nanmean(a)
        2.6666666666666665
        >>> np.nanmean(a, axis=0)
        array([ 2.,  4.])
        >>> np.nanmean(a, axis=1)
        array([ 1.,  3.5])
    
        """
    return ndarray()
def nanmin(a=False, axis=None, out=None, keepdims=False):
    """
        Return minimum of an array or minimum along an axis, ignoring any NaNs.
        When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
        Nan is returned for that slice.
    
        Parameters
        ----------
        a : array_like
            Array containing numbers whose minimum is desired. If `a` is not an
            array, a conversion is attempted.
        axis : int, optional
            Axis along which the minimum is computed. The default is to compute
            the minimum of the flattened array.
        out : ndarray, optional
            Alternate output array in which to place the result.  The default
            is ``None``; if provided, it must have the same shape as the
            expected output, but the type will be cast if necessary.  See
            `doc.ufuncs` for details.
    
            .. versionadded:: 1.8.0
        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 original `a`.
    
            .. versionadded:: 1.8.0
    
        Returns
        -------
        nanmin : ndarray
            An array with the same shape as `a`, with the specified axis
            removed.  If `a` is a 0-d array, or if axis is None, an ndarray
            scalar is returned.  The same dtype as `a` is returned.
    
        See Also
        --------
        nanmax :
            The maximum value of an array along a given axis, ignoring any NaNs.
        amin :
            The minimum value of an array along a given axis, propagating any NaNs.
        fmin :
            Element-wise minimum of two arrays, ignoring any NaNs.
        minimum :
            Element-wise minimum of two arrays, propagating any NaNs.
        isnan :
            Shows which elements are Not a Number (NaN).
        isfinite:
            Shows which elements are neither NaN nor infinity.
    
        amax, fmax, maximum
    
        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.
        Positive infinity is treated as a very large number and negative
        infinity is treated as a very small (i.e. negative) number.
    
        If the input has a integer type the function is equivalent to np.min.
    
        Examples
        --------
        >>> a = np.array([[1, 2], [3, np.nan]])
        >>> np.nanmin(a)
        1.0
        >>> np.nanmin(a, axis=0)
        array([ 1.,  2.])
        >>> np.nanmin(a, axis=1)
        array([ 1.,  3.])
    
        When positive infinity and negative infinity are present:
    
        >>> np.nanmin([1, 2, np.nan, np.inf])
        1.0
        >>> np.nanmin([1, 2, np.nan, np.NINF])
        -inf
    
        """
    return ndarray()
def nanstd(a=False, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    """
        Compute the standard deviation along the specified axis, while
        ignoring NaNs.
    
        Returns the standard deviation, a measure of the spread of a
        distribution, of the non-NaN array elements. The standard deviation is
        computed for the flattened array by default, otherwise over the
        specified axis.
    
        For all-NaN slices or slices with zero degrees of freedom, NaN is
        returned and a `RuntimeWarning` is raised.
    
        .. versionadded:: 1.8.0
    
        Parameters
        ----------
        a : array_like
            Calculate the standard deviation of the non-NaN values.
        axis : int, optional
            Axis along which the standard deviation is computed. The default is
            to compute the standard deviation of the flattened array.
        dtype : dtype, optional
            Type to use in computing the standard deviation. For arrays of
            integer type the default is float64, for arrays of float types it
            is the same as the array type.
        out : ndarray, optional
            Alternative output array in which to place the result. It must have
            the same shape as the expected output but the type (of the
            calculated values) will be cast if necessary.
        ddof : int, optional
            Means Delta Degrees of Freedom.  The divisor used in calculations
            is ``N - ddof``, where ``N`` represents the number of non-NaN
            elements.  By default `ddof` is zero.
        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 original `arr`.
    
        Returns
        -------
        standard_deviation : ndarray, see dtype parameter above.
            If `out` is None, return a new array containing the standard
            deviation, otherwise return a reference to the output array. If
            ddof is >= the number of non-NaN elements in a slice or the slice
            contains only NaNs, then the result for that slice is NaN.
    
        See Also
        --------
        var, mean, std
        nanvar, nanmean
        numpy.doc.ufuncs : Section "Output arguments"
    
        Notes
        -----
        The standard deviation is the square root of the average of the squared
        deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.
    
        The average squared deviation is normally calculated as
        ``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is
        specified, the divisor ``N - ddof`` is used instead. In standard
        statistical practice, ``ddof=1`` provides an unbiased estimator of the
        variance of the infinite population. ``ddof=0`` provides a maximum
        likelihood estimate of the variance for normally distributed variables.
        The standard deviation computed in this function is the square root of
        the estimated variance, so even with ``ddof=1``, it will not be an
        unbiased estimate of the standard deviation per se.
    
        Note that, for complex numbers, `std` takes the absolute value before
        squaring, so that the result is always real and nonnegative.
    
        For floating-point input, the *std* is computed using the same
        precision the input has. Depending on the input data, this can cause
        the results to be inaccurate, especially for float32 (see example
        below).  Specifying a higher-accuracy accumulator using the `dtype`
        keyword can alleviate this issue.
    
        Examples
        --------
        >>> a = np.array([[1, np.nan], [3, 4]])
        >>> np.nanstd(a)
        1.247219128924647
        >>> np.nanstd(a, axis=0)
        array([ 1.,  0.])
        >>> np.nanstd(a, axis=1)
        array([ 0.,  0.5])
    
        """
    return ndarray()
def nansum(a=0, axis=None, dtype=None, out=None, keepdims=0):
    """
        Return the sum of array elements over a given axis treating Not a
        Numbers (NaNs) as zero.
    
        FutureWarning: In Numpy versions <= 1.8 Nan is returned for slices that
        are all-NaN or empty. In later versions zero will be returned.
    
        Parameters
        ----------
        a : array_like
            Array containing numbers whose sum is desired. If `a` is not an
            array, a conversion is attempted.
        axis : int, optional
            Axis along which the sum is computed. The default is to compute the
            sum of the flattened array.
        dtype : data-type, optional
            The type of the returned array and of the accumulator in which the
            elements are summed.  By default, the dtype of `a` is used.  An
            exception is when `a` has an integer type with less precision than
            the platform (u)intp. In that case, the default will be either
            (u)int32 or (u)int64 depending on whether the platform is 32 or 64
            bits. For inexact inputs, dtype must be inexact.
    
            .. versionadded:: 1.8.0
        out : ndarray, optional
            Alternate output array in which to place the result.  The default
            is ``None``. If provided, it must have the same shape as the
            expected output, but the type will be cast if necessary.  See
            `doc.ufuncs` for details. The casting of NaN to integer can yield
            unexpected results.
    
            .. versionadded:: 1.8.0
        keepdims : bool, optional
            If 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 original `arr`.
    
            .. versionadded:: 1.8.0
    
        Returns
        -------
        y : ndarray or numpy scalar
    
        See Also
        --------
        numpy.sum : Sum across array propagating NaNs.
        isnan : Show which elements are NaN.
        isfinite: Show which elements are not NaN or +/-inf.
    
        Notes
        -----
        If both positive and negative infinity are present, the sum will be Not
        A Number (NaN).
    
        Numpy integer arithmetic is modular. If the size of a sum exceeds the
        size of an integer accumulator, its value will wrap around and the
        result will be incorrect. Specifying ``dtype=double`` can alleviate
        that problem.
    
        Examples
        --------
        >>> np.nansum(1)
        1
        >>> np.nansum([1])
        1
        >>> np.nansum([1, np.nan])
        1.0
        >>> a = np.array([[1, 1], [1, np.nan]])
        >>> np.nansum(a)
        3.0
        >>> np.nansum(a, axis=0)
        array([ 2.,  1.])
        >>> np.nansum([1, np.nan, np.inf])
        inf
        >>> np.nansum([1, np.nan, np.NINF])
        -inf
        >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
        nan
    
        """
    return ndarray() if False else numpy()
def nanvar(a=False, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    """
        Compute the variance along the specified axis, while ignoring NaNs.
    
        Returns the variance of the array elements, a measure of the spread of
        a distribution.  The variance is computed for the flattened array by
        default, otherwise over the specified axis.
    
        For all-NaN slices or slices with zero degrees of freedom, NaN is
        returned and a `RuntimeWarning` is raised.
    
        .. versionadded:: 1.8.0
    
        Parameters
        ----------
        a : array_like
            Array containing numbers whose variance is desired.  If `a` is not an
            array, a conversion is attempted.
        axis : int, optional
            Axis along which the variance is computed.  The default is to compute
            the variance of the flattened array.
        dtype : data-type, optional
            Type to use in computing the variance.  For arrays of integer type
            the default is `float32`; for arrays of float types it is the same as
            the array type.
        out : ndarray, optional
            Alternate output array in which to place the result.  It must have
            the same shape as the expected output, but the type is cast if
            necessary.
        ddof : int, optional
            "Delta Degrees of Freedom": the divisor used in the calculation is
            ``N - ddof``, where ``N`` represents the number of non-NaN
            elements. By default `ddof` is zero.
        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 original `arr`.
    
        Returns
        -------
        variance : ndarray, see dtype parameter above
            If `out` is None, return a new array containing the variance,
            otherwise return a reference to the output array. If ddof is >= the
            number of non-NaN elements in a slice or the slice contains only
            NaNs, then the result for that slice is NaN.
    
        See Also
        --------
        std : Standard deviation
        mean : Average
        var : Variance while not ignoring NaNs
        nanstd, nanmean
        numpy.doc.ufuncs : Section "Output arguments"
    
        Notes
        -----
        The variance is the average of the squared deviations from the mean,
        i.e.,  ``var = mean(abs(x - x.mean())**2)``.
    
        The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
        If, however, `ddof` is specified, the divisor ``N - ddof`` is used
        instead.  In standard statistical practice, ``ddof=1`` provides an
        unbiased estimator of the variance of a hypothetical infinite
        population.  ``ddof=0`` provides a maximum likelihood estimate of the
        variance for normally distributed variables.
    
        Note that for complex numbers, the absolute value is taken before
        squaring, so that the result is always real and nonnegative.
    
        For floating-point input, the variance is computed using the same
        precision the input has.  Depending on the input data, this can cause
        the results to be inaccurate, especially for `float32` (see example
        below).  Specifying a higher-accuracy accumulator using the ``dtype``
        keyword can alleviate this issue.
    
        Examples
        --------
        >>> a = np.array([[1, np.nan], [3, 4]])
        >>> np.var(a)
        1.5555555555555554
        >>> np.nanvar(a, axis=0)
        array([ 1.,  0.])
        >>> np.nanvar(a, axis=1)
        array([ 0.,  0.25])
    
        """
    return ndarray()
nbytes = _typedict()
class ndarray:
    T = getset_descriptor()
    __array_finalize__ = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    def all(self, axis=None, out=None):
        """a.all(axis=None, out=None)
        
            Returns True if all elements evaluate to True.
        
            Refer to `numpy.all` for full documentation.
        
            See Also
            --------
            numpy.all : equivalent function"""
        return None
    def any(self, axis=None, out=None):
        """a.any(axis=None, out=None)
        
            Returns True if any of the elements of `a` evaluate to True.
        
            Refer to `numpy.any` for full documentation.
        
            See Also
            --------
            numpy.any : equivalent function"""
        return None
    def argmax(self, axis=None, out=None):
        """a.argmax(axis=None, out=None)
        
            Return indices of the maximum values along the given axis.
        
            Refer to `numpy.argmax` for full documentation.
        
            See Also
            --------
            numpy.argmax : equivalent function"""
        return None
    def argmin(self, axis=None, out=None):
        """a.argmin(axis=None, out=None)
        
            Return indices of the minimum values along the given axis of `a`.
        
            Refer to `numpy.argmin` for detailed documentation.
        
            See Also
            --------
            numpy.argmin : equivalent function"""
        return None
    def argpartition(self, kth, axis=_1, kind=quickselect, order=None):
        """a.argpartition(kth, axis=-1, kind='quickselect', order=None)
        
            Returns the indices that would partition this array.
        
            Refer to `numpy.argpartition` for full documentation.
        
            .. versionadded:: 1.8.0
        
            See Also
            --------
            numpy.argpartition : equivalent function"""
        return None
    def argsort(self, axis=_1, kind=quicksort, order=None):
        """a.argsort(axis=-1, kind='quicksort', order=None)
        
            Returns the indices that would sort this array.
        
            Refer to `numpy.argsort` for full documentation.
        
            See Also
            --------
            numpy.argsort : equivalent function"""
        return None
    def astype(self, dtype, order, casting, subok, copy):
        """a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
        
            Copy of the array, cast to a specified type.
        
            Parameters
            ----------
            dtype : str or dtype
                Typecode or data-type to which the array is cast.
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout order of the result.
                'C' means C order, 'F' means Fortran order, 'A'
                means 'F' order if all the arrays are Fortran contiguous,
                'C' order otherwise, and 'K' means as close to the
                order the array elements appear in memory as possible.
                Default is 'K'.
            casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
                Controls what kind of data casting may occur. Defaults to 'unsafe'
                for backwards compatibility.
        
                  * 'no' means the data types should not be cast at all.
                  * 'equiv' means only byte-order changes are allowed.
                  * 'safe' means only casts which can preserve values are allowed.
                  * 'same_kind' means only safe casts or casts within a kind,
                    like float64 to float32, are allowed.
                  * 'unsafe' means any data conversions may be done.
            subok : bool, optional
                If True, then sub-classes will be passed-through (default), otherwise
                the returned array will be forced to be a base-class array.
            copy : bool, optional
                By default, astype always returns a newly allocated array. If this
                is set to false, and the `dtype`, `order`, and `subok`
                requirements are satisfied, the input array is returned instead
                of a copy.
        
            Returns
            -------
            arr_t : ndarray
                Unless `copy` is False and the other conditions for returning the input
                array are satisfied (see description for `copy` input paramter), `arr_t`
                is a new array of the same shape as the input array, with dtype, order
                given by `dtype`, `order`.
        
            Raises
            ------
            ComplexWarning
                When casting from complex to float or int. To avoid this,
                one should use ``a.real.astype(t)``.
        
            Examples
            --------
            >>> x = np.array([1, 2, 2.5])
            >>> x
            array([ 1. ,  2. ,  2.5])
        
            >>> x.astype(int)
            array([1, 2, 2])"""
        return ndarray()
    base = getset_descriptor()
    def byteswap(self, inplace):
        """a.byteswap(inplace)
        
            Swap the bytes of the array elements
        
            Toggle between low-endian and big-endian data representation by
            returning a byteswapped array, optionally swapped in-place.
        
            Parameters
            ----------
            inplace : bool, optional
                If ``True``, swap bytes in-place, default is ``False``.
        
            Returns
            -------
            out : ndarray
                The byteswapped array. If `inplace` is ``True``, this is
                a view to self.
        
            Examples
            --------
            >>> A = np.array([1, 256, 8755], dtype=np.int16)
            >>> map(hex, A)
            ['0x1', '0x100', '0x2233']
            >>> A.byteswap(True)
            array([  256,     1, 13090], dtype=int16)
            >>> map(hex, A)
            ['0x100', '0x1', '0x3322']
        
            Arrays of strings are not swapped
        
            >>> A = np.array(['ceg', 'fac'])
            >>> A.byteswap()
            array(['ceg', 'fac'],
                  dtype='|S3')"""
        return ndarray()
    def choose(self, choices, out=None, mode=_raise):
        """a.choose(choices, out=None, mode='raise')
        
            Use an index array to construct a new array from a set of choices.
        
            Refer to `numpy.choose` for full documentation.
        
            See Also
            --------
            numpy.choose : equivalent function"""
        return None
    def clip(self, a_min, a_max, out=None):
        """a.clip(a_min, a_max, out=None)
        
            Return an array whose values are limited to ``[a_min, a_max]``.
        
            Refer to `numpy.clip` for full documentation.
        
            See Also
            --------
            numpy.clip : equivalent function"""
        return None
    def compress(self, condition, axis=None, out=None):
        """a.compress(condition, axis=None, out=None)
        
            Return selected slices of this array along given axis.
        
            Refer to `numpy.compress` for full documentation.
        
            See Also
            --------
            numpy.compress : equivalent function"""
        return None
    def conj(self, _):
        """a.conj()
        
            Complex-conjugate all elements.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def conjugate(self, _):
        """a.conjugate()
        
            Return the complex conjugate, element-wise.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def copy(self, order):
        """a.copy(order='C')
        
            Return a copy of the array.
        
            Parameters
            ----------
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout of the copy. 'C' means C-order,
                'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
                'C' otherwise. 'K' means match the layout of `a` as closely
                as possible. (Note that this function and :func:numpy.copy are very
                similar, but have different default values for their order=
                arguments.)
        
            See also
            --------
            numpy.copy
            numpy.copyto
        
            Examples
            --------
            >>> x = np.array([[1,2,3],[4,5,6]], order='F')
        
            >>> y = x.copy()
        
            >>> x.fill(0)
        
            >>> x
            array([[0, 0, 0],
                   [0, 0, 0]])
        
            >>> y
            array([[1, 2, 3],
                   [4, 5, 6]])
        
            >>> y.flags['C_CONTIGUOUS']
            True"""
        return None
    ctypes = getset_descriptor()
    def cumprod(self, axis=None, dtype=None, out=None):
        """a.cumprod(axis=None, dtype=None, out=None)
        
            Return the cumulative product of the elements along the given axis.
        
            Refer to `numpy.cumprod` for full documentation.
        
            See Also
            --------
            numpy.cumprod : equivalent function"""
        return None
    def cumsum(self, axis=None, dtype=None, out=None):
        """a.cumsum(axis=None, dtype=None, out=None)
        
            Return the cumulative sum of the elements along the given axis.
        
            Refer to `numpy.cumsum` for full documentation.
        
            See Also
            --------
            numpy.cumsum : equivalent function"""
        return None
    data = getset_descriptor()
    def diagonal(self, offset=0, axis1=0, axis2=1):
        """a.diagonal(offset=0, axis1=0, axis2=1)
        
            Return specified diagonals.
        
            Refer to :func:`numpy.diagonal` for full documentation.
        
            See Also
            --------
            numpy.diagonal : equivalent function"""
        return None
    def dot(self, b, out=None):
        """a.dot(b, out=None)
        
            Dot product of two arrays.
        
            Refer to `numpy.dot` for full documentation.
        
            See Also
            --------
            numpy.dot : equivalent function
        
            Examples
            --------
            >>> a = np.eye(2)
            >>> b = np.ones((2, 2)) * 2
            >>> a.dot(b)
            array([[ 2.,  2.],
                   [ 2.,  2.]])
        
            This array method can be conveniently chained:
        
            >>> a.dot(b).dot(b)
            array([[ 8.,  8.],
                   [ 8.,  8.]])"""
        return None
    dtype = getset_descriptor()
    def dump(self, file):
        """a.dump(file)
        
            Dump a pickle of the array to the specified file.
            The array can be read back with pickle.load or numpy.load.
        
            Parameters
            ----------
            file : str
                A string naming the dump file."""
        return None
    def dumps(self, _):
        """a.dumps()
        
            Returns the pickle of the array as a string.
            pickle.loads or numpy.loads will convert the string back to an array.
        
            Parameters
            ----------
            None"""
        return None
    def fill(self, value):
        """a.fill(value)
        
            Fill the array with a scalar value.
        
            Parameters
            ----------
            value : scalar
                All elements of `a` will be assigned this value.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.fill(0)
            >>> a
            array([0, 0])
            >>> a = np.empty(2)
            >>> a.fill(1)
            >>> a
            array([ 1.,  1.])"""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def flatten(self, order):
        """a.flatten(order='C')
        
            Return a copy of the array collapsed into one dimension.
        
            Parameters
            ----------
            order : {'C', 'F', 'A'}, optional
                Whether to flatten in C (row-major), Fortran (column-major) order,
                or preserve the C/Fortran ordering from `a`.
                The default is 'C'.
        
            Returns
            -------
            y : ndarray
                A copy of the input array, flattened to one dimension.
        
            See Also
            --------
            ravel : Return a flattened array.
            flat : A 1-D flat iterator over the array.
        
            Examples
            --------
            >>> a = np.array([[1,2], [3,4]])
            >>> a.flatten()
            array([1, 2, 3, 4])
            >>> a.flatten('F')
            array([1, 3, 2, 4])"""
        return ndarray()
    def getfield(self, dtype, offset):
        """a.getfield(dtype, offset=0)
        
            Returns a field of the given array as a certain type.
        
            A field is a view of the array data with a given data-type. The values in
            the view are determined by the given type and the offset into the current
            array in bytes. The offset needs to be such that the view dtype fits in the
            array dtype; for example an array of dtype complex128 has 16-byte elements.
            If taking a view with a 32-bit integer (4 bytes), the offset needs to be
            between 0 and 12 bytes.
        
            Parameters
            ----------
            dtype : str or dtype
                The data type of the view. The dtype size of the view can not be larger
                than that of the array itself.
            offset : int
                Number of bytes to skip before beginning the element view.
        
            Examples
            --------
            >>> x = np.diag([1.+1.j]*2)
            >>> x[1, 1] = 2 + 4.j
            >>> x
            array([[ 1.+1.j,  0.+0.j],
                   [ 0.+0.j,  2.+4.j]])
            >>> x.getfield(np.float64)
            array([[ 1.,  0.],
                   [ 0.,  2.]])
        
            By choosing an offset of 8 bytes we can select the complex part of the
            array for our view:
        
            >>> x.getfield(np.float64, offset=8)
            array([[ 1.,  0.],
               [ 0.,  4.]])"""
        return array()
    imag = getset_descriptor()
    def item(self, ESCargs):
        """a.item(*args)
        
            Copy an element of an array to a standard Python scalar and return it.
        
            Parameters
            ----------
            \*args : Arguments (variable number and type)
        
                * none: in this case, the method only works for arrays
                  with one element (`a.size == 1`), which element is
                  copied into a standard Python scalar object and returned.
        
                * int_type: this argument is interpreted as a flat index into
                  the array, specifying which element to copy and return.
        
                * tuple of int_types: functions as does a single int_type argument,
                  except that the argument is interpreted as an nd-index into the
                  array.
        
            Returns
            -------
            z : Standard Python scalar object
                A copy of the specified element of the array as a suitable
                Python scalar
        
            Notes
            -----
            When the data type of `a` is longdouble or clongdouble, item() returns
            a scalar array object because there is no available Python scalar that
            would not lose information. Void arrays return a buffer object for item(),
            unless fields are defined, in which case a tuple is returned.
        
            `item` is very similar to a[args], except, instead of an array scalar,
            a standard Python scalar is returned. This can be useful for speeding up
            access to elements of the array and doing arithmetic on elements of the
            array using Python's optimized math.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.item(3)
            2
            >>> x.item(7)
            5
            >>> x.item((0, 1))
            1
            >>> x.item((2, 2))
            3"""
        return Standard()
    def itemset(self, ESCargs):
        """a.itemset(*args)
        
            Insert scalar into an array (scalar is cast to array's dtype, if possible)
        
            There must be at least 1 argument, and define the last argument
            as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
            than ``a[args] = item``.  The item should be a scalar value and `args`
            must select a single item in the array `a`.
        
            Parameters
            ----------
            \*args : Arguments
                If one argument: a scalar, only used in case `a` is of size 1.
                If two arguments: the last argument is the value to be set
                and must be a scalar, the first argument specifies a single array
                element location. It is either an int or a tuple.
        
            Notes
            -----
            Compared to indexing syntax, `itemset` provides some speed increase
            for placing a scalar into a particular location in an `ndarray`,
            if you must do this.  However, generally this is discouraged:
            among other problems, it complicates the appearance of the code.
            Also, when using `itemset` (and `item`) inside a loop, be sure
            to assign the methods to a local variable to avoid the attribute
            look-up at each loop iteration.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.itemset(4, 0)
            >>> x.itemset((2, 2), 9)
            >>> x
            array([[3, 1, 7],
                   [2, 0, 3],
                   [8, 5, 9]])"""
        return None
    itemsize = getset_descriptor()
    def max(self, axis=None, out=None):
        """a.max(axis=None, out=None)
        
            Return the maximum along a given axis.
        
            Refer to `numpy.amax` for full documentation.
        
            See Also
            --------
            numpy.amax : equivalent function"""
        return None
    def mean(self, axis=None, dtype=None, out=None):
        """a.mean(axis=None, dtype=None, out=None)
        
            Returns the average of the array elements along given axis.
        
            Refer to `numpy.mean` for full documentation.
        
            See Also
            --------
            numpy.mean : equivalent function"""
        return None
    def min(self, axis=None, out=None):
        """a.min(axis=None, out=None)
        
            Return the minimum along a given axis.
        
            Refer to `numpy.amin` for full documentation.
        
            See Also
            --------
            numpy.amin : equivalent function"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """arr.newbyteorder(new_order='S')
        
            Return the array with the same data viewed with a different byte order.
        
            Equivalent to::
        
                arr.view(arr.dtype.newbytorder(new_order))
        
            Changes are also made in all fields and sub-arrays of the array data
            type.
        
        
        
            Parameters
            ----------
            new_order : string, optional
                Byte order to force; a value from the byte order specifications
                above. `new_order` codes can be any of::
        
                 * 'S' - swap dtype from current to opposite endian
                 * {'<', 'L'} - little endian
                 * {'>', 'B'} - big endian
                 * {'=', 'N'} - native order
                 * {'|', 'I'} - ignore (no change to byte order)
        
                The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_arr : array
                New array object with the dtype reflecting given change to the
                byte order."""
        return array()
    def nonzero(self, _):
        """a.nonzero()
        
            Return the indices of the elements that are non-zero.
        
            Refer to `numpy.nonzero` for full documentation.
        
            See Also
            --------
            numpy.nonzero : equivalent function"""
        return None
    def partition(self, kth, axis, kind, order):
        """a.partition(kth, axis=-1, kind='introselect', order=None)
        
            Rearranges the elements in the array in such a way that value of the
            element in kth position is in the position it would be in a sorted array.
            All elements smaller than the kth element are moved before this element and
            all equal or greater are moved behind it. The ordering of the elements in
            the two partitions is undefined.
        
            .. versionadded:: 1.8.0
        
            Parameters
            ----------
            kth : int or sequence of ints
                Element index to partition by. The kth element value will be in its
                final sorted position and all smaller elements will be moved before it
                and all equal or greater elements behind it.
                The order all elements in the partitions is undefined.
                If provided with a sequence of kth it will partition all elements
                indexed by kth of them into their sorted position at once.
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'introselect'}, optional
                Selection algorithm. Default is 'introselect'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.partition : Return a parititioned copy of an array.
            argpartition : Indirect partition.
            sort : Full sort.
        
            Notes
            -----
            See ``np.partition`` for notes on the different algorithms.
        
            Examples
            --------
            >>> a = np.array([3, 4, 2, 1])
            >>> a.partition(a, 3)
            >>> a
            array([2, 1, 3, 4])
        
            >>> a.partition((1, 3))
            array([1, 2, 3, 4])"""
        return None
    def prod(self, axis=None, dtype=None, out=None):
        """a.prod(axis=None, dtype=None, out=None)
        
            Return the product of the array elements over the given axis
        
            Refer to `numpy.prod` for full documentation.
        
            See Also
            --------
            numpy.prod : equivalent function"""
        return None
    def ptp(self, axis=None, out=None):
        """a.ptp(axis=None, out=None)
        
            Peak to peak (maximum - minimum) value along a given axis.
        
            Refer to `numpy.ptp` for full documentation.
        
            See Also
            --------
            numpy.ptp : equivalent function"""
        return None
    def put(self, indices, values, mode=_raise):
        """a.put(indices, values, mode='raise')
        
            Set ``a.flat[n] = values[n]`` for all `n` in indices.
        
            Refer to `numpy.put` for full documentation.
        
            See Also
            --------
            numpy.put : equivalent function"""
        return None
    def ravel(self, order):
        """a.ravel([order])
        
            Return a flattened array.
        
            Refer to `numpy.ravel` for full documentation.
        
            See Also
            --------
            numpy.ravel : equivalent function
        
            ndarray.flat : a flat iterator on the array."""
        return None
    real = getset_descriptor()
    def repeat(self, repeats, axis=None):
        """a.repeat(repeats, axis=None)
        
            Repeat elements of an array.
        
            Refer to `numpy.repeat` for full documentation.
        
            See Also
            --------
            numpy.repeat : equivalent function"""
        return None
    def reshape(self, shape, order=C):
        """a.reshape(shape, order='C')
        
            Returns an array containing the same data with a new shape.
        
            Refer to `numpy.reshape` for full documentation.
        
            See Also
            --------
            numpy.reshape : equivalent function"""
        return None
    def resize(self, new_shape, refcheck):
        """a.resize(new_shape, refcheck=True)
        
            Change shape and size of array in-place.
        
            Parameters
            ----------
            new_shape : tuple of ints, or `n` ints
                Shape of resized array.
            refcheck : bool, optional
                If False, reference count will not be checked. Default is True.
        
            Returns
            -------
            None
        
            Raises
            ------
            ValueError
                If `a` does not own its own data or references or views to it exist,
                and the data memory must be changed.
        
            SystemError
                If the `order` keyword argument is specified. This behaviour is a
                bug in NumPy.
        
            See Also
            --------
            resize : Return a new array with the specified shape.
        
            Notes
            -----
            This reallocates space for the data area if necessary.
        
            Only contiguous arrays (data elements consecutive in memory) can be
            resized.
        
            The purpose of the reference count check is to make sure you
            do not use this array as a buffer for another Python object and then
            reallocate the memory. However, reference counts can increase in
            other ways so if you are sure that you have not shared the memory
            for this array with another Python object, then you may safely set
            `refcheck` to False.
        
            Examples
            --------
            Shrinking an array: array is flattened (in the order that the data are
            stored in memory), resized, and reshaped:
        
            >>> a = np.array([[0, 1], [2, 3]], order='C')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [1]])
        
            >>> a = np.array([[0, 1], [2, 3]], order='F')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [2]])
        
            Enlarging an array: as above, but missing entries are filled with zeros:
        
            >>> b = np.array([[0, 1], [2, 3]])
            >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
            >>> b
            array([[0, 1, 2],
                   [3, 0, 0]])
        
            Referencing an array prevents resizing...
        
            >>> c = a
            >>> a.resize((1, 1))
            Traceback (most recent call last):
            ...
            ValueError: cannot resize an array that has been referenced ...
        
            Unless `refcheck` is False:
        
            >>> a.resize((1, 1), refcheck=False)
            >>> a
            array([[0]])
            >>> c
            array([[0]])"""
        return None
    def round(self, decimals=0, out=None):
        """a.round(decimals=0, out=None)
        
            Return `a` with each element rounded to the given number of decimals.
        
            Refer to `numpy.around` for full documentation.
        
            See Also
            --------
            numpy.around : equivalent function"""
        return None
    def searchsorted(self, v, side=left, sorter=None):
        """a.searchsorted(v, side='left', sorter=None)
        
            Find indices where elements of v should be inserted in a to maintain order.
        
            For full documentation, see `numpy.searchsorted`
        
            See Also
            --------
            numpy.searchsorted : equivalent function"""
        return None
    def setfield(self, val, dtype, offset):
        """a.setfield(val, dtype, offset=0)
        
            Put a value into a specified place in a field defined by a data-type.
        
            Place `val` into `a`'s field defined by `dtype` and beginning `offset`
            bytes into the field.
        
            Parameters
            ----------
            val : object
                Value to be placed in field.
            dtype : dtype object
                Data-type of the field in which to place `val`.
            offset : int, optional
                The number of bytes into the field at which to place `val`.
        
            Returns
            -------
            None
        
            See Also
            --------
            getfield
        
            Examples
            --------
            >>> x = np.eye(3)
            >>> x.getfield(np.float64)
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])
            >>> x.setfield(3, np.int32)
            >>> x.getfield(np.int32)
            array([[3, 3, 3],
                   [3, 3, 3],
                   [3, 3, 3]])
            >>> x
            array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
                   [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
                   [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
            >>> x.setfield(np.eye(3), np.int32)
            >>> x
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])"""
        return None
    def setflags(self, write, align, uic):
        """a.setflags(write=None, align=None, uic=None)
        
            Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
        
            These Boolean-valued flags affect how numpy interprets the memory
            area used by `a` (see Notes below). The ALIGNED flag can only
            be set to True if the data is actually aligned according to the type.
            The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE
            can only be set to True if the array owns its own memory, or the
            ultimate owner of the memory exposes a writeable buffer interface,
            or is a string. (The exception for string is made so that unpickling
            can be done without copying memory.)
        
            Parameters
            ----------
            write : bool, optional
                Describes whether or not `a` can be written to.
            align : bool, optional
                Describes whether or not `a` is aligned properly for its type.
            uic : bool, optional
                Describes whether or not `a` is a copy of another "base" array.
        
            Notes
            -----
            Array flags provide information about how the memory area used
            for the array is to be interpreted. There are 6 Boolean flags
            in use, only three of which can be changed by the user:
            UPDATEIFCOPY, WRITEABLE, and ALIGNED.
        
            WRITEABLE (W) the data area can be written to;
        
            ALIGNED (A) the data and strides are aligned appropriately for the hardware
            (as determined by the compiler);
        
            UPDATEIFCOPY (U) this array is a copy of some other array (referenced
            by .base). When this array is deallocated, the base array will be
            updated with the contents of this array.
        
            All flags can be accessed using their first (upper case) letter as well
            as the full name.
        
            Examples
            --------
            >>> y
            array([[3, 1, 7],
                   [2, 0, 0],
                   [8, 5, 9]])
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : True
              ALIGNED : True
              UPDATEIFCOPY : False
            >>> y.setflags(write=0, align=0)
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : False
              ALIGNED : False
              UPDATEIFCOPY : False
            >>> y.setflags(uic=1)
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: cannot set UPDATEIFCOPY flag to True"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def sort(self, axis, kind, order):
        """a.sort(axis=-1, kind='quicksort', order=None)
        
            Sort an array, in-place.
        
            Parameters
            ----------
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'quicksort', 'mergesort', 'heapsort'}, optional
                Sorting algorithm. Default is 'quicksort'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.sort : Return a sorted copy of an array.
            argsort : Indirect sort.
            lexsort : Indirect stable sort on multiple keys.
            searchsorted : Find elements in sorted array.
            partition: Partial sort.
        
            Notes
            -----
            See ``sort`` for notes on the different sorting algorithms.
        
            Examples
            --------
            >>> a = np.array([[1,4], [3,1]])
            >>> a.sort(axis=1)
            >>> a
            array([[1, 4],
                   [1, 3]])
            >>> a.sort(axis=0)
            >>> a
            array([[1, 3],
                   [1, 4]])
        
            Use the `order` keyword to specify a field to use when sorting a
            structured array:
        
            >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
            >>> a.sort(order='y')
            >>> a
            array([('c', 1), ('a', 2)],
                  dtype=[('x', '|S1'), ('y', '<i4')])"""
        return None
    def squeeze(self, axis=None):
        """a.squeeze(axis=None)
        
            Remove single-dimensional entries from the shape of `a`.
        
            Refer to `numpy.squeeze` for full documentation.
        
            See Also
            --------
            numpy.squeeze : equivalent function"""
        return None
    def std(self, axis=None, dtype=None, out=None, ddof=0):
        """a.std(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the standard deviation of the array elements along given axis.
        
            Refer to `numpy.std` for full documentation.
        
            See Also
            --------
            numpy.std : equivalent function"""
        return None
    strides = getset_descriptor()
    def sum(self, axis=None, dtype=None, out=None):
        """a.sum(axis=None, dtype=None, out=None)
        
            Return the sum of the array elements over the given axis.
        
            Refer to `numpy.sum` for full documentation.
        
            See Also
            --------
            numpy.sum : equivalent function"""
        return None
    def swapaxes(self, axis1, axis2):
        """a.swapaxes(axis1, axis2)
        
            Return a view of the array with `axis1` and `axis2` interchanged.
        
            Refer to `numpy.swapaxes` for full documentation.
        
            See Also
            --------
            numpy.swapaxes : equivalent function"""
        return None
    def take(self, indices, axis=None, out=None, mode=_raise):
        """a.take(indices, axis=None, out=None, mode='raise')
        
            Return an array formed from the elements of `a` at the given indices.
        
            Refer to `numpy.take` for full documentation.
        
            See Also
            --------
            numpy.take : equivalent function"""
        return None
    def tofile(self, fid, sep, format):
        """a.tofile(fid, sep="", format="%s")
        
            Write array to a file as text or binary (default).
        
            Data is always written in 'C' order, independent of the order of `a`.
            The data produced by this method can be recovered using the function
            fromfile().
        
            Parameters
            ----------
            fid : file or str
                An open file object, or a string containing a filename.
            sep : str
                Separator between array items for text output.
                If "" (empty), a binary file is written, equivalent to
                ``file.write(a.tostring())``.
            format : str
                Format string for text file output.
                Each entry in the array is formatted to text by first converting
                it to the closest Python type, and then using "format" % item.
        
            Notes
            -----
            This is a convenience function for quick storage of array data.
            Information on endianness and precision is lost, so this method is not a
            good choice for files intended to archive data or transport data between
            machines with different endianness. Some of these problems can be overcome
            by outputting the data as text files, at the expense of speed and file
            size."""
        return None
    def tolist(self, _):
        """a.tolist()
        
            Return the array as a (possibly nested) list.
        
            Return a copy of the array data as a (nested) Python list.
            Data items are converted to the nearest compatible Python type.
        
            Parameters
            ----------
            none
        
            Returns
            -------
            y : list
                The possibly nested list of array elements.
        
            Notes
            -----
            The array may be recreated, ``a = np.array(a.tolist())``.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.tolist()
            [1, 2]
            >>> a = np.array([[1, 2], [3, 4]])
            >>> list(a)
            [array([1, 2]), array([3, 4])]
            >>> a.tolist()
            [[1, 2], [3, 4]]"""
        return list()
    def tostring(self, order):
        """a.tostring(order='C')
        
            Construct a Python string containing the raw data bytes in the array.
        
            Constructs a Python string showing a copy of the raw contents of
            data memory. The string can be produced in either 'C' or 'Fortran',
            or 'Any' order (the default is 'C'-order). 'Any' order means C-order
            unless the F_CONTIGUOUS flag in the array is set, in which case it
            means 'Fortran' order.
        
            Parameters
            ----------
            order : {'C', 'F', None}, optional
                Order of the data for multidimensional arrays:
                C, Fortran, or the same as for the original array.
        
            Returns
            -------
            s : str
                A Python string exhibiting a copy of `a`'s raw data.
        
            Examples
            --------
            >>> x = np.array([[0, 1], [2, 3]])
            >>> x.tostring()
            '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
            >>> x.tostring('C') == x.tostring()
            True
            >>> x.tostring('F')
            '\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'"""
        return str()
    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
        """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
        
            Return the sum along diagonals of the array.
        
            Refer to `numpy.trace` for full documentation.
        
            See Also
            --------
            numpy.trace : equivalent function"""
        return None
    def transpose(self, axes):
        """a.transpose(*axes)
        
            Returns a view of the array with axes transposed.
        
            For a 1-D array, this has no effect. (To change between column and
            row vectors, first cast the 1-D array into a matrix object.)
            For a 2-D array, this is the usual matrix transpose.
            For an n-D array, if axes are given, their order indicates how the
            axes are permuted (see Examples). If axes are not provided and
            ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
            ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
        
            Parameters
            ----------
            axes : None, tuple of ints, or `n` ints
        
             * None or no argument: reverses the order of the axes.
        
             * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
               `i`-th axis becomes `a.transpose()`'s `j`-th axis.
        
             * `n` ints: same as an n-tuple of the same ints (this form is
               intended simply as a "convenience" alternative to the tuple form)
        
            Returns
            -------
            out : ndarray
                View of `a`, with axes suitably permuted.
        
            See Also
            --------
            ndarray.T : Array property returning the array transposed.
        
            Examples
            --------
            >>> a = np.array([[1, 2], [3, 4]])
            >>> a
            array([[1, 2],
                   [3, 4]])
            >>> a.transpose()
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose((1, 0))
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose(1, 0)
            array([[1, 3],
                   [2, 4]])"""
        return ndarray()
    def var(self, axis=None, dtype=None, out=None, ddof=0):
        """a.var(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the variance of the array elements, along given axis.
        
            Refer to `numpy.var` for full documentation.
        
            See Also
            --------
            numpy.var : equivalent function"""
        return None
    def view(self, dtype, type):
        """a.view(dtype=None, type=None)
        
            New view of array with the same data.
        
            Parameters
            ----------
            dtype : data-type or ndarray sub-class, optional
                Data-type descriptor of the returned view, e.g., float32 or int16. The
                default, None, results in the view having the same data-type as `a`.
                This argument can also be specified as an ndarray sub-class, which
                then specifies the type of the returned object (this is equivalent to
                setting the ``type`` parameter).
            type : Python type, optional
                Type of the returned view, e.g., ndarray or matrix.  Again, the
                default None results in type preservation.
        
            Notes
            -----
            ``a.view()`` is used two different ways:
        
            ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
            of the array's memory with a different data-type.  This can cause a
            reinterpretation of the bytes of memory.
        
            ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
            returns an instance of `ndarray_subclass` that looks at the same array
            (same shape, dtype, etc.)  This does not cause a reinterpretation of the
            memory.
        
            For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
            bytes per entry than the previous dtype (for example, converting a
            regular array to a structured array), then the behavior of the view
            cannot be predicted just from the superficial appearance of ``a`` (shown
            by ``print(a)``). It also depends on exactly how ``a`` is stored in
            memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
            defined as a slice or transpose, etc., the view may give different
            results.
        
        
            Examples
            --------
            >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
        
            Viewing array data using a different type and dtype:
        
            >>> y = x.view(dtype=np.int16, type=np.matrix)
            >>> y
            matrix([[513]], dtype=int16)
            >>> print type(y)
            <class 'numpy.matrixlib.defmatrix.matrix'>
        
            Creating a view on a structured array so it can be used in calculations
        
            >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
            >>> xv = x.view(dtype=np.int8).reshape(-1,2)
            >>> xv
            array([[1, 2],
                   [3, 4]], dtype=int8)
            >>> xv.mean(0)
            array([ 2.,  3.])
        
            Making changes to the view changes the underlying array
        
            >>> xv[0,1] = 20
            >>> print x
            [(1, 20) (3, 4)]
        
            Using a view to convert an array to a record array:
        
            >>> z = x.view(np.recarray)
            >>> z.a
            array([1], dtype=int8)
        
            Views share data:
        
            >>> x[0] = (9, 10)
            >>> z[0]
            (9, 10)
        
            Views that change the dtype size (bytes per entry) should normally be
            avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
        
            >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
            >>> y = x[:, 0:2]
            >>> y
            array([[1, 2],
                   [4, 5]], dtype=int16)
            >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: new type not compatible with array.
            >>> z = y.copy()
            >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
            array([[(1, 2)],
                   [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])"""
        return None
class ndenumerate:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
def nd_fromtxt(fnamekwargs):
    """
        Load ASCII data stored in a file and return it as a single array.
    
        Parameters
        ----------
        fname, kwargs : For a description of input parameters, see `genfromtxt`.
    
        See Also
        --------
        numpy.genfromtxt : generic function.
    
        """
    return None
def ndim(a):
    """
        Return the number of dimensions of an array.
    
        Parameters
        ----------
        a : array_like
            Input array.  If it is not already an ndarray, a conversion is
            attempted.
    
        Returns
        -------
        number_of_dimensions : int
            The number of dimensions in `a`.  Scalars are zero-dimensional.
    
        See Also
        --------
        ndarray.ndim : equivalent method
        shape : dimensions of array
        ndarray.shape : dimensions of array
    
        Examples
        --------
        >>> np.ndim([[1,2,3],[4,5,6]])
        2
        >>> np.ndim(np.array([[1,2,3],[4,5,6]]))
        2
        >>> np.ndim(1)
        0
    
        """
    return int()
class ndindex:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    def ndincr(self, _):
        """
                Increment the multi-dimensional index by one.
        
                This method is for backward compatibility only: do not use.
                """
        return None
class nditer:
    __doc__ = str()
    def copy(self, _):
        """copy()
        
            Get a copy of the iterator in its current state.
        
            Examples
            --------
            >>> x = np.arange(10)
            >>> y = x + 1
            >>> it = np.nditer([x, y])
            >>> it.next()
            (array(0), array(1))
            >>> it2 = it.copy()
            >>> it2.next()
            (array(1), array(2))"""
        return None
    def debug_print(self, _):
        """debug_print()
        
            Print the current state of the `nditer` instance and debug info to stdout."""
        return None
    dtypes = getset_descriptor()
    def enable_external_loop(self, _):
        """enable_external_loop()
        
            When the "external_loop" was not used during construction, but
            is desired, this modifies the iterator to behave as if the flag
            was specified."""
        return None
    finished = getset_descriptor()
    has_delayed_bufalloc = getset_descriptor()
    has_index = getset_descriptor()
    has_multi_index = getset_descriptor()
    index = getset_descriptor()
    iterationneedsapi = getset_descriptor()
    iterindex = getset_descriptor()
    def iternext(self, _):
        """iternext()
        
            Check whether iterations are left, and perform a single internal iteration
            without returning the result.  Used in the C-style pattern do-while
            pattern.  For an example, see `nditer`.
        
            Returns
            -------
            iternext : bool
                Whether or not there are iterations left."""
        return None
    iterrange = getset_descriptor()
    itersize = getset_descriptor()
    itviews = getset_descriptor()
    multi_index = getset_descriptor()
    ndim = getset_descriptor()
    def next(self, _):
        """x.next() -> the next value, or raise StopIteration"""
        return None
    nop = getset_descriptor()
    operands = getset_descriptor()
    def remove_axis(self, i):
        """remove_axis(i)
        
            Removes axis `i` from the iterator. Requires that the flag "multi_index"
            be enabled."""
        return None
    def remove_multi_index(self, _):
        """remove_multi_index()
        
            When the "multi_index" flag was specified, this removes it, allowing
            the internal iteration structure to be optimized further."""
        return None
    def reset(self, _):
        """reset()
        
            Reset the iterator to its initial state."""
        return None
    shape = getset_descriptor()
    value = getset_descriptor()
def negative(x, out=None):
    """negative(x[, out])
    
    Returns an array with the negative of each element of the original array.
    
    Parameters
    ----------
    x : array_like or scalar
        Input array.
    
    Returns
    -------
    y : ndarray or scalar
        Returned array or scalar: `y = -x`.
    
    Examples
    --------
    >>> np.negative([1.,-1.])
    array([-1.,  1.])"""
    return ndarray() if False else float()
def nested_iters():
    """None"""
    return None
newaxis = None
def newbuffer(size):
    """newbuffer(size)
    
        Return a new uninitialized buffer object.
    
        Parameters
        ----------
        size : int
            Size in bytes of returned buffer object.
    
        Returns
        -------
        newbuffer : buffer object
            Returned, uninitialized buffer object of `size` bytes."""
    return buffer()
def nextafter(x1, x2, out):
    """nextafter(x1, x2[, out])
    
    Return the next representable floating-point value after x1 in the direction
    of x2 element-wise.
    
    Parameters
    ----------
    x1 : array_like
        Values to find the next representable value of.
    x2 : array_like
        The direction where to look for the next representable value of `x1`.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See `doc.ufuncs`.
    
    Returns
    -------
    out : array_like
        The next representable values of `x1` in the direction of `x2`.
    
    Examples
    --------
    >>> eps = np.finfo(np.float64).eps
    >>> np.nextafter(1, 2) == eps + 1
    True
    >>> np.nextafter([1, 2], [2, 1]) == [eps + 1, 2 - eps]
    array([ True,  True], dtype=bool)"""
    return ndarray()
def nonzero(a):
    """
        Return the indices of the elements that are non-zero.
    
        Returns a tuple of arrays, one for each dimension of `a`, containing
        the indices of the non-zero elements in that dimension. The
        corresponding non-zero values can be obtained with::
    
            a[nonzero(a)]
    
        To group the indices by element, rather than dimension, use::
    
            transpose(nonzero(a))
    
        The result of this is always a 2-D array, with a row for
        each non-zero element.
    
        Parameters
        ----------
        a : array_like
            Input array.
    
        Returns
        -------
        tuple_of_arrays : tuple
            Indices of elements that are non-zero.
    
        See Also
        --------
        flatnonzero :
            Return indices that are non-zero in the flattened version of the input
            array.
        ndarray.nonzero :
            Equivalent ndarray method.
        count_nonzero :
            Counts the number of non-zero elements in the input array.
    
        Examples
        --------
        >>> x = np.eye(3)
        >>> x
        array([[ 1.,  0.,  0.],
               [ 0.,  1.,  0.],
               [ 0.,  0.,  1.]])
        >>> np.nonzero(x)
        (array([0, 1, 2]), array([0, 1, 2]))
    
        >>> x[np.nonzero(x)]
        array([ 1.,  1.,  1.])
        >>> np.transpose(np.nonzero(x))
        array([[0, 0],
               [1, 1],
               [2, 2]])
    
        A common use for ``nonzero`` is to find the indices of an array, where
        a condition is True.  Given an array `a`, the condition `a` > 3 is a
        boolean array and since False is interpreted as 0, np.nonzero(a > 3)
        yields the indices of the `a` where the condition is true.
    
        >>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])
        >>> a > 3
        array([[False, False, False],
               [ True,  True,  True],
               [ True,  True,  True]], dtype=bool)
        >>> np.nonzero(a > 3)
        (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
    
        The ``nonzero`` method of the boolean array can also be called.
    
        >>> (a > 3).nonzero()
        (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
    
        """
    return tuple()
def not_equal(x1, x2, out=None):
    """not_equal(x1, x2[, out])
    
    Return (x1 != x2) element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
      Input arrays.
    out : ndarray, optional
      A placeholder the same shape as `x1` to store the result.
      See `doc.ufuncs` (Section "Output arguments") for more details.
    
    Returns
    -------
    not_equal : ndarray bool, scalar bool
      For each element in `x1, x2`, return True if `x1` is not equal
      to `x2` and False otherwise.
    
    
    See Also
    --------
    equal, greater, greater_equal, less, less_equal
    
    Examples
    --------
    >>> np.not_equal([1.,2.], [1., 3.])
    array([False,  True], dtype=bool)
    >>> np.not_equal([1, 2], [[1, 3],[1, 4]])
    array([[False,  True],
           [False,  True]], dtype=bool)"""
    return ndarray()
def nper(rate, pmt, pv="end", fv=0, when="end"):
    """
        Compute the number of periodic payments.
    
        Parameters
        ----------
        rate : array_like
            Rate of interest (per period)
        pmt : array_like
            Payment
        pv : array_like
            Present value
        fv : array_like, optional
            Future value
        when : {{'begin', 1}, {'end', 0}}, {string, int}, optional
            When payments are due ('begin' (1) or 'end' (0))
    
        Notes
        -----
        The number of periods ``nper`` is computed by solving the equation::
    
         fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate*((1+rate)**nper-1) = 0
    
        but if ``rate = 0`` then::
    
         fv + pv + pmt*nper = 0
    
        Examples
        --------
        If you only had $150/month to pay towards the loan, how long would it take
        to pay-off a loan of $8,000 at 7% annual interest?
    
        >>> print round(np.nper(0.07/12, -150, 8000), 5)
        64.07335
    
        So, over 64 months would be required to pay off the loan.
    
        The same analysis could be done with several different interest rates
        and/or payments and/or total amounts to produce an entire table.
    
        >>> np.nper(*(np.ogrid[0.07/12: 0.08/12: 0.01/12,
        ...                    -150   : -99     : 50    ,
        ...                    8000   : 9001    : 1000]))
        array([[[  64.07334877,   74.06368256],
                [ 108.07548412,  127.99022654]],
               [[  66.12443902,   76.87897353],
                [ 114.70165583,  137.90124779]]])
    
        """
    return None
def npv(rate, values):
    """
        Returns the NPV (Net Present Value) of a cash flow series.
    
        Parameters
        ----------
        rate : scalar
            The discount rate.
        values : array_like, shape(M, )
            The values of the time series of cash flows.  The (fixed) time
            interval between cash flow "events" must be the same as that
            for which `rate` is given (i.e., if `rate` is per year, then
            precisely a year is understood to elapse between each cash flow
            event).  By convention, investments or "deposits" are negative,
            income or "withdrawals" are positive; `values` must begin with
            the initial investment, thus `values[0]` will typically be
            negative.
    
        Returns
        -------
        out : float
            The NPV of the input cash flow series `values` at the discount `rate`.
    
        Notes
        -----
        Returns the result of: [G]_
    
        .. math :: \sum_{t=0}^{M-1}{\frac{values_t}{(1+rate)^{t}}}
    
        References
        ----------
        .. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
           Addison-Wesley, 2003, pg. 346.
    
        Examples
        --------
        >>> np.npv(0.281,[-100, 39, 59, 55, 20])
        -0.0084785916384548798
    
        (Compare with the Example given for numpy.lib.financial.irr)
    
        """
    return float()
class number:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def obj2sctype(rep=None, default=None):
    """
        Return the scalar dtype or NumPy equivalent of Python type of an object.
    
        Parameters
        ----------
        rep : any
            The object of which the type is returned.
        default : any, optional
            If given, this is returned for objects whose types can not be
            determined. If not given, None is returned for those objects.
    
        Returns
        -------
        dtype : dtype or Python type
            The data type of `rep`.
    
        See Also
        --------
        sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
    
        Examples
        --------
        >>> np.obj2sctype(np.int32)
        <type 'numpy.int32'>
        >>> np.obj2sctype(np.array([1., 2.]))
        <type 'numpy.float64'>
        >>> np.obj2sctype(np.array([1.j]))
        <type 'numpy.complex128'>
    
        >>> np.obj2sctype(dict)
        <type 'numpy.object_'>
        >>> np.obj2sctype('string')
        <type 'numpy.string_'>
    
        >>> np.obj2sctype(1, default=list)
        <type 'list'>
    
        """
    return dtype() if False else Python()
class object:
    __doc__ = str()
class object_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class object_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
ogrid = nd_grid()
def ones(shape="C", dtype=None, order="C"):
    """
        Return a new array of given shape and type, filled with ones.
    
        Parameters
        ----------
        shape : int or sequence of ints
            Shape of the new array, e.g., ``(2, 3)`` or ``2``.
        dtype : data-type, optional
            The desired data-type for the array, e.g., `numpy.int8`.  Default is
            `numpy.float64`.
        order : {'C', 'F'}, optional
            Whether to store multidimensional data in C- or Fortran-contiguous
            (row- or column-wise) order in memory.
    
        Returns
        -------
        out : ndarray
            Array of ones with the given shape, dtype, and order.
    
        See Also
        --------
        zeros, ones_like
    
        Examples
        --------
        >>> np.ones(5)
        array([ 1.,  1.,  1.,  1.,  1.])
    
        >>> np.ones((5,), dtype=np.int)
        array([1, 1, 1, 1, 1])
    
        >>> np.ones((2, 1))
        array([[ 1.],
               [ 1.]])
    
        >>> s = (2,2)
        >>> np.ones(s)
        array([[ 1.,  1.],
               [ 1.,  1.]])
    
        """
    return ndarray()
def ones_like(a=True, dtype=None, order="K", subok=True):
    """
        Return an array of ones with the same shape and type as a given array.
    
        Parameters
        ----------
        a : array_like
            The shape and data-type of `a` define these same attributes of
            the returned array.
        dtype : data-type, optional
            .. versionadded:: 1.6.0
            Overrides the data type of the result.
        order : {'C', 'F', 'A', or 'K'}, optional
            .. versionadded:: 1.6.0
            Overrides the memory layout of the result. 'C' means C-order,
            'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
            'C' otherwise. 'K' means match the layout of `a` as closely
            as possible.
        subok : bool, optional.
            If True, then the newly created array will use the sub-class
            type of 'a', otherwise it will be a base-class array. Defaults
            to True.
    
        Returns
        -------
        out : ndarray
            Array of ones with the same shape and type as `a`.
    
        See Also
        --------
        zeros_like : Return an array of zeros with shape and type of input.
        empty_like : Return an empty array with shape and type of input.
        zeros : Return a new array setting values to zero.
        ones : Return a new array setting values to one.
        empty : Return a new uninitialized array.
    
        Examples
        --------
        >>> x = np.arange(6)
        >>> x = x.reshape((2, 3))
        >>> x
        array([[0, 1, 2],
               [3, 4, 5]])
        >>> np.ones_like(x)
        array([[1, 1, 1],
               [1, 1, 1]])
    
        >>> y = np.arange(3, dtype=np.float)
        >>> y
        array([ 0.,  1.,  2.])
        >>> np.ones_like(y)
        array([ 1.,  1.,  1.])
    
        """
    return ndarray()
def outer(a, b):
    """
        Compute the outer product of two vectors.
    
        Given two vectors, ``a = [a0, a1, ..., aM]`` and
        ``b = [b0, b1, ..., bN]``,
        the outer product [1]_ is::
    
          [[a0*b0  a0*b1 ... a0*bN ]
           [a1*b0    .
           [ ...          .
           [aM*b0            aM*bN ]]
    
        Parameters
        ----------
        a : (M,) array_like
            First input vector.  Input is flattened if
            not already 1-dimensional.
        b : (N,) array_like
            Second input vector.  Input is flattened if
            not already 1-dimensional.
    
        Returns
        -------
        out : (M, N) ndarray
            ``out[i, j] = a[i] * b[j]``
    
        See also
        --------
        inner, einsum
    
        References
        ----------
        .. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd
                 ed., Baltimore, MD, Johns Hopkins University Press, 1996,
                 pg. 8.
    
        Examples
        --------
        Make a (*very* coarse) grid for computing a Mandelbrot set:
    
        >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
        >>> rl
        array([[-2., -1.,  0.,  1.,  2.],
               [-2., -1.,  0.,  1.,  2.],
               [-2., -1.,  0.,  1.,  2.],
               [-2., -1.,  0.,  1.,  2.],
               [-2., -1.,  0.,  1.,  2.]])
        >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
        >>> im
        array([[ 0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j],
               [ 0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j],
               [ 0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],
               [ 0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j],
               [ 0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j]])
        >>> grid = rl + im
        >>> grid
        array([[-2.+2.j, -1.+2.j,  0.+2.j,  1.+2.j,  2.+2.j],
               [-2.+1.j, -1.+1.j,  0.+1.j,  1.+1.j,  2.+1.j],
               [-2.+0.j, -1.+0.j,  0.+0.j,  1.+0.j,  2.+0.j],
               [-2.-1.j, -1.-1.j,  0.-1.j,  1.-1.j,  2.-1.j],
               [-2.-2.j, -1.-2.j,  0.-2.j,  1.-2.j,  2.-2.j]])
    
        An example using a "vector" of letters:
    
        >>> x = np.array(['a', 'b', 'c'], dtype=object)
        >>> np.outer(x, [1, 2, 3])
        array([[a, aa, aaa],
               [b, bb, bbb],
               [c, cc, ccc]], dtype=object)
    
        """
    return M()
def packbits(myarray, axis):
    """packbits(myarray, axis=None)
    
        Packs the elements of a binary-valued array into bits in a uint8 array.
    
        The result is padded to full bytes by inserting zero bits at the end.
    
        Parameters
        ----------
        myarray : array_like
            An integer type array whose elements should be packed to bits.
        axis : int, optional
            The dimension over which bit-packing is done.
            ``None`` implies packing the flattened array.
    
        Returns
        -------
        packed : ndarray
            Array of type uint8 whose elements represent bits corresponding to the
            logical (0 or nonzero) value of the input elements. The shape of
            `packed` has the same number of dimensions as the input (unless `axis`
            is None, in which case the output is 1-D).
    
        See Also
        --------
        unpackbits: Unpacks elements of a uint8 array into a binary-valued output
                    array.
    
        Examples
        --------
        >>> a = np.array([[[1,0,1],
        ...                [0,1,0]],
        ...               [[1,1,0],
        ...                [0,0,1]]])
        >>> b = np.packbits(a, axis=-1)
        >>> b
        array([[[160],[64]],[[192],[32]]], dtype=uint8)
    
        Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,
        and 32 = 0010 0000."""
    return ndarray()
def pad(array, pad_width=None, mode=None):
    """
        Pads an array.
    
        Parameters
        ----------
        array : array_like of rank N
            Input array
        pad_width : {sequence, int}
            Number of values padded to the edges of each axis.
            ((before_1, after_1), ... (before_N, after_N)) unique pad widths
            for each axis.
            ((before, after),) yields same before and after pad for each axis.
            (pad,) or int is a shortcut for before = after = pad width for all
            axes.
        mode : {str, function}
            One of the following string values or a user supplied function.
    
            'constant'      Pads with a constant value.
            'edge'          Pads with the edge values of array.
            'linear_ramp'   Pads with the linear ramp between end_value and the
                            array edge value.
            'maximum'       Pads with the maximum value of all or part of the
                            vector along each axis.
            'mean'          Pads with the mean value of all or part of the
                            vector along each axis.
            'median'        Pads with the median value of all or part of the
                            vector along each axis.
            'minimum'       Pads with the minimum value of all or part of the
                            vector along each axis.
            'reflect'       Pads with the reflection of the vector mirrored on
                            the first and last values of the vector along each
                            axis.
            'symmetric'     Pads with the reflection of the vector mirrored
                            along the edge of the array.
            'wrap'          Pads with the wrap of the vector along the axis.
                            The first values are used to pad the end and the
                            end values are used to pad the beginning.
            <function>      Padding function, see Notes.
        stat_length : {sequence, int}, optional
            Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
            values at edge of each axis used to calculate the statistic value.
    
            ((before_1, after_1), ... (before_N, after_N)) unique statistic
            lengths for each axis.
    
            ((before, after),) yields same before and after statistic lengths
            for each axis.
    
            (stat_length,) or int is a shortcut for before = after = statistic
            length for all axes.
    
            Default is ``None``, to use the entire axis.
        constant_values : {sequence, int}, optional
            Used in 'constant'.  The values to set the padded values for each
            axis.
    
            ((before_1, after_1), ... (before_N, after_N)) unique pad constants
            for each axis.
    
            ((before, after),) yields same before and after constants for each
            axis.
    
            (constant,) or int is a shortcut for before = after = constant for
            all axes.
    
            Default is 0.
        end_values : {sequence, int}, optional
            Used in 'linear_ramp'.  The values used for the ending value of the
            linear_ramp and that will form the edge of the padded array.
    
            ((before_1, after_1), ... (before_N, after_N)) unique end values
            for each axis.
    
            ((before, after),) yields same before and after end values for each
            axis.
    
            (constant,) or int is a shortcut for before = after = end value for
            all axes.
    
            Default is 0.
        reflect_type : str {'even', 'odd'}, optional
            Used in 'reflect', and 'symmetric'.  The 'even' style is the
            default with an unaltered reflection around the edge value.  For
            the 'odd' style, the extented part of the array is created by
            subtracting the reflected values from two times the edge value.
    
        Returns
        -------
        pad : ndarray
            Padded array of rank equal to `array` with shape increased
            according to `pad_width`.
    
        Notes
        -----
        .. versionadded:: 1.7.0
    
        For an array with rank greater than 1, some of the padding of later
        axes is calculated from padding of previous axes.  This is easiest to
        think about with a rank 2 array where the corners of the padded array
        are calculated by using padded values from the first axis.
    
        The padding function, if used, should return a rank 1 array equal in
        length to the vector argument with padded values replaced. It has the
        following signature:
    
            padding_func(vector, iaxis_pad_width, iaxis, **kwargs)
    
        where
    
            vector : ndarray
                A rank 1 array already padded with zeros.  Padded values are
                vector[:pad_tuple[0]] and vector[-pad_tuple[1]:].
            iaxis_pad_width : tuple
                A 2-tuple of ints, iaxis_pad_width[0] represents the number of
                values padded at the beginning of vector where
                iaxis_pad_width[1] represents the number of values padded at
                the end of vector.
            iaxis : int
                The axis currently being calculated.
            kwargs : misc
                Any keyword arguments the function requires.
    
        Examples
        --------
        >>> a = [1, 2, 3, 4, 5]
        >>> np.lib.pad(a, (2,3), 'constant', constant_values=(4,6))
        array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
    
        >>> np.lib.pad(a, (2,3), 'edge')
        array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])
    
        >>> np.lib.pad(a, (2,3), 'linear_ramp', end_values=(5,-4))
        array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])
    
        >>> np.lib.pad(a, (2,), 'maximum')
        array([5, 5, 1, 2, 3, 4, 5, 5, 5])
    
        >>> np.lib.pad(a, (2,), 'mean')
        array([3, 3, 1, 2, 3, 4, 5, 3, 3])
    
        >>> np.lib.pad(a, (2,), 'median')
        array([3, 3, 1, 2, 3, 4, 5, 3, 3])
    
        >>> a = [[1,2], [3,4]]
        >>> np.lib.pad(a, ((3, 2), (2, 3)), 'minimum')
        array([[1, 1, 1, 2, 1, 1, 1],
               [1, 1, 1, 2, 1, 1, 1],
               [1, 1, 1, 2, 1, 1, 1],
               [1, 1, 1, 2, 1, 1, 1],
               [3, 3, 3, 4, 3, 3, 3],
               [1, 1, 1, 2, 1, 1, 1],
               [1, 1, 1, 2, 1, 1, 1]])
    
        >>> a = [1, 2, 3, 4, 5]
        >>> np.lib.pad(a, (2,3), 'reflect')
        array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
    
        >>> np.lib.pad(a, (2,3), 'reflect', reflect_type='odd')
        array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])
    
        >>> np.lib.pad(a, (2,3), 'symmetric')
        array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
    
        >>> np.lib.pad(a, (2,3), 'symmetric', reflect_type='odd')
        array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
    
        >>> np.lib.pad(a, (2,3), 'wrap')
        array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
    
        >>> def padwithtens(vector, pad_width, iaxis, kwargs):
        ...     vector[:pad_width[0]] = 10
        ...     vector[-pad_width[1]:] = 10
        ...     return vector
    
        >>> a = np.arange(6)
        >>> a = a.reshape((2,3))
    
        >>> np.lib.pad(a, 2, padwithtens)
        array([[10, 10, 10, 10, 10, 10, 10],
               [10, 10, 10, 10, 10, 10, 10],
               [10, 10,  0,  1,  2, 10, 10],
               [10, 10,  3,  4,  5, 10, 10],
               [10, 10, 10, 10, 10, 10, 10],
               [10, 10, 10, 10, 10, 10, 10]])
    
        """
    return ndarray()
def partition(a, kth=None, axis=-1, kind="introselect", order=None):
    """
        Return a partitioned copy of an array.
    
        Creates a copy of the array with its elements rearranged in such a way that
        the value of the element in kth position is in the position it would be in
        a sorted array. All elements smaller than the kth element are moved before
        this element and all equal or greater are moved behind it. The ordering of
        the elements in the two partitions is undefined.
    
        .. versionadded:: 1.8.0
    
        Parameters
        ----------
        a : array_like
            Array to be sorted.
        kth : int or sequence of ints
            Element index to partition by. The kth value of the element will be in
            its final sorted position and all smaller elements will be moved before
            it and all equal or greater elements behind it.
            The order all elements in the partitions is undefined.
            If provided with a sequence of kth it will partition all elements
            indexed by kth  of them into their sorted position at once.
        axis : int or None, optional
            Axis along which to sort. If None, the array is flattened before
            sorting. The default is -1, which sorts along the last axis.
        kind : {'introselect'}, optional
            Selection algorithm. Default is 'introselect'.
        order : list, optional
            When `a` is a structured array, this argument specifies which fields
            to compare first, second, and so on.  This list does not need to
            include all of the fields.
    
        Returns
        -------
        partitioned_array : ndarray
            Array of the same type and shape as `a`.
    
        See Also
        --------
        ndarray.partition : Method to sort an array in-place.
        argpartition : Indirect partition.
        sort : Full sorting
    
        Notes
        -----
        The various selection algorithms are characterized by their average speed,
        worst case performance, work space size, and whether they are stable. A
        stable sort keeps items with the same key in the same relative order. The
        three available algorithms have the following properties:
    
        ================= ======= ============= ============ =======
           kind            speed   worst case    work space  stable
        ================= ======= ============= ============ =======
        'introselect'        1        O(n)           0         no
        ================= ======= ============= ============ =======
    
        All the partition algorithms make temporary copies of the data when
        partitioning along any but the last axis.  Consequently, partitioning
        along the last axis is faster and uses less space than partitioning
        along any other axis.
    
        The sort order for complex numbers is lexicographic. If both the real
        and imaginary parts are non-nan then the order is determined by the
        real parts except when they are equal, in which case the order is
        determined by the imaginary parts.
    
        Examples
        --------
        >>> a = np.array([3, 4, 2, 1])
        >>> np.partition(a, 3)
        array([2, 1, 3, 4])
    
        >>> np.partition(a, (1, 3))
        array([1, 2, 3, 4])
    
        """
    return ndarray()
def percentile(a, q=False, axis=None, out=None, overwrite_input=False):
    """
        Compute the qth percentile of the data along the specified axis.
    
        Returns the qth percentile of the array elements.
    
        Parameters
        ----------
        a : array_like
            Input array or object that can be converted to an array.
        q : float in range of [0,100] (or sequence of floats)
            Percentile to compute which must be between 0 and 100 inclusive.
        axis : int, optional
            Axis along which the percentiles are computed. The default (None)
            is to compute the median along a flattened version of the array.
        out : ndarray, optional
            Alternative output array in which to place the result. It must
            have the same shape and buffer length as the expected output,
            but the type (of the output) will be cast if necessary.
        overwrite_input : bool, optional
           If True, then allow use of memory of input array `a` for
           calculations. The input array will be modified by the call to
           median. This will save memory when you do not need to preserve
           the contents of the input array. Treat the input as undefined,
           but it will probably be fully or partially sorted.
           Default is False. Note that, if `overwrite_input` is True and the
           input is not already an array, an error will be raised.
    
        Returns
        -------
        pcntile : ndarray
            A new array holding the result (unless `out` is specified, in
            which case that array is returned instead).  If the input contains
            integers, or floats of smaller precision than 64, then the output
            data-type is float64.  Otherwise, the output data-type is the same
            as that of the input.
    
        See Also
        --------
        mean, median
    
        Notes
        -----
        Given a vector V of length N, the qth percentile of V is the qth ranked
        value in a sorted copy of V.  A weighted average of the two nearest
        neighbors is used if the normalized ranking does not match q exactly.
        The same as the median if ``q=50``, the same as the minimum if ``q=0``
        and the same as the maximum if ``q=100``.
    
        Examples
        --------
        >>> a = np.array([[10, 7, 4], [3, 2, 1]])
        >>> a
        array([[10,  7,  4],
               [ 3,  2,  1]])
        >>> np.percentile(a, 50)
        3.5
        >>> np.percentile(a, 50, axis=0)
        array([ 6.5,  4.5,  2.5])
        >>> np.percentile(a, 50, axis=1)
        array([ 7.,  2.])
    
        >>> m = np.percentile(a, 50, axis=0)
        >>> out = np.zeros_like(m)
        >>> np.percentile(a, 50, axis=0, out=m)
        array([ 6.5,  4.5,  2.5])
        >>> m
        array([ 6.5,  4.5,  2.5])
    
        >>> b = a.copy()
        >>> np.percentile(b, 50, axis=1, overwrite_input=True)
        array([ 7.,  2.])
        >>> assert not np.all(a==b)
        >>> b = a.copy()
        >>> np.percentile(b, 50, axis=None, overwrite_input=True)
        3.5
    
        """
    return ndarray()
pi = float()
def piecewise(x, condlist, funclist, args, kw):
    """
        Evaluate a piecewise-defined function.
    
        Given a set of conditions and corresponding functions, evaluate each
        function on the input data wherever its condition is true.
    
        Parameters
        ----------
        x : ndarray
            The input domain.
        condlist : list of bool arrays
            Each boolean array corresponds to a function in `funclist`.  Wherever
            `condlist[i]` is True, `funclist[i](x)` is used as the output value.
    
            Each boolean array in `condlist` selects a piece of `x`,
            and should therefore be of the same shape as `x`.
    
            The length of `condlist` must correspond to that of `funclist`.
            If one extra function is given, i.e. if
            ``len(funclist) - len(condlist) == 1``, then that extra function
            is the default value, used wherever all conditions are false.
        funclist : list of callables, f(x,*args,**kw), or scalars
            Each function is evaluated over `x` wherever its corresponding
            condition is True.  It should take an array as input and give an array
            or a scalar value as output.  If, instead of a callable,
            a scalar is provided then a constant function (``lambda x: scalar``) is
            assumed.
        args : tuple, optional
            Any further arguments given to `piecewise` are passed to the functions
            upon execution, i.e., if called ``piecewise(..., ..., 1, 'a')``, then
            each function is called as ``f(x, 1, 'a')``.
        kw : dict, optional
            Keyword arguments used in calling `piecewise` are passed to the
            functions upon execution, i.e., if called
            ``piecewise(..., ..., lambda=1)``, then each function is called as
            ``f(x, lambda=1)``.
    
        Returns
        -------
        out : ndarray
            The output is the same shape and type as x and is found by
            calling the functions in `funclist` on the appropriate portions of `x`,
            as defined by the boolean arrays in `condlist`.  Portions not covered
            by any condition have undefined values.
    
    
        See Also
        --------
        choose, select, where
    
        Notes
        -----
        This is similar to choose or select, except that functions are
        evaluated on elements of `x` that satisfy the corresponding condition from
        `condlist`.
    
        The result is::
    
                |--
                |funclist[0](x[condlist[0]])
          out = |funclist[1](x[condlist[1]])
                |...
                |funclist[n2](x[condlist[n2]])
                |--
    
        Examples
        --------
        Define the sigma function, which is -1 for ``x < 0`` and +1 for ``x >= 0``.
    
        >>> x = np.linspace(-2.5, 2.5, 6)
        >>> np.piecewise(x, [x < 0, x >= 0], [-1, 1])
        array([-1., -1., -1.,  1.,  1.,  1.])
    
        Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for
        ``x >= 0``.
    
        >>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])
        array([ 2.5,  1.5,  0.5,  0.5,  1.5,  2.5])
    
        """
    return ndarray()
def pkgload():
    """Load one or more packages into parent package top-level namespace.
    
           This function is intended to shorten the need to import many
           subpackages, say of scipy, constantly with statements such as
    
             import scipy.linalg, scipy.fftpack, scipy.etc...
    
           Instead, you can say:
    
             import scipy
             scipy.pkgload('linalg','fftpack',...)
    
           or
    
             scipy.pkgload()
    
           to load all of them in one call.
    
           If a name which doesn't exist in scipy's namespace is
           given, a warning is shown.
    
           Parameters
           ----------
            *packages : arg-tuple
                 the names (one or more strings) of all the modules one
                 wishes to load into the top-level namespace.
            verbose= : integer
                 verbosity level [default: -1].
                 verbose=-1 will suspend also warnings.
            force= : bool
                 when True, force reloading loaded packages [default: False].
            postpone= : bool
                 when True, don't load packages [default: False]
    
         """
    return None
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 None
def pmt(rate, nper, pv="end", fv=0, when="end"):
    """
        Compute the payment against loan principal plus interest.
    
        Given:
         * a present value, `pv` (e.g., an amount borrowed)
         * a future value, `fv` (e.g., 0)
         * an interest `rate` compounded once per period, of which
           there are
         * `nper` total
         * and (optional) specification of whether payment is made
           at the beginning (`when` = {'begin', 1}) or the end
           (`when` = {'end', 0}) of each period
    
        Return:
           the (fixed) periodic payment.
    
        Parameters
        ----------
        rate : array_like
            Rate of interest (per period)
        nper : array_like
            Number of compounding periods
        pv : array_like
            Present value
        fv : array_like (optional)
            Future value (default = 0)
        when : {{'begin', 1}, {'end', 0}}, {string, int}
            When payments are due ('begin' (1) or 'end' (0))
    
        Returns
        -------
        out : ndarray
            Payment against loan plus interest.  If all input is scalar, returns a
            scalar float.  If any input is array_like, returns payment for each
            input element. If multiple inputs are array_like, they all must have
            the same shape.
    
        Notes
        -----
        The payment is computed by solving the equation::
    
         fv +
         pv*(1 + rate)**nper +
         pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0
    
        or, when ``rate == 0``::
    
          fv + pv + pmt * nper == 0
    
        for ``pmt``.
    
        Note that computing a monthly mortgage payment is only
        one use for this function.  For example, pmt returns the
        periodic deposit one must make to achieve a specified
        future balance given an initial deposit, a fixed,
        periodically compounded interest rate, and the total
        number of periods.
    
        References
        ----------
        .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
           Open Document Format for Office Applications (OpenDocument)v1.2,
           Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,
           Pre-Draft 12. Organization for the Advancement of Structured Information
           Standards (OASIS). Billerica, MA, USA. [ODT Document].
           Available:
           http://www.oasis-open.org/committees/documents.php
           ?wg_abbrev=office-formulaOpenDocument-formula-20090508.odt
    
        Examples
        --------
        What is the monthly payment needed to pay off a $200,000 loan in 15
        years at an annual interest rate of 7.5%?
    
        >>> np.pmt(0.075/12, 12*15, 200000)
        -1854.0247200054619
    
        In order to pay-off (i.e., have a future-value of 0) the $200,000 obtained
        today, a monthly payment of $1,854.02 would be required.  Note that this
        example illustrates usage of `fv` having a default value of 0.
    
        """
    return ndarray()
def poly(seq_of_zeros):
    """
        Find the coefficients of a polynomial with the given sequence of roots.
    
        Returns the coefficients of the polynomial whose leading coefficient
        is one for the given sequence of zeros (multiple roots must be included
        in the sequence as many times as their multiplicity; see Examples).
        A square matrix (or array, which will be treated as a matrix) can also
        be given, in which case the coefficients of the characteristic polynomial
        of the matrix are returned.
    
        Parameters
        ----------
        seq_of_zeros : array_like, shape (N,) or (N, N)
            A sequence of polynomial roots, or a square array or matrix object.
    
        Returns
        -------
        c : ndarray
            1D array of polynomial coefficients from highest to lowest degree:
    
            ``c[0] * x**(N) + c[1] * x**(N-1) + ... + c[N-1] * x + c[N]``
            where c[0] always equals 1.
    
        Raises
        ------
        ValueError
            If input is the wrong shape (the input must be a 1-D or square
            2-D array).
    
        See Also
        --------
        polyval : Evaluate a polynomial at a point.
        roots : Return the roots of a polynomial.
        polyfit : Least squares polynomial fit.
        poly1d : A one-dimensional polynomial class.
    
        Notes
        -----
        Specifying the roots of a polynomial still leaves one degree of
        freedom, typically represented by an undetermined leading
        coefficient. [1]_ In the case of this function, that coefficient -
        the first one in the returned array - is always taken as one. (If
        for some reason you have one other point, the only automatic way
        presently to leverage that information is to use ``polyfit``.)
    
        The characteristic polynomial, :math:`p_a(t)`, of an `n`-by-`n`
        matrix **A** is given by
    
            :math:`p_a(t) = \mathrm{det}(t\, \mathbf{I} - \mathbf{A})`,
    
        where **I** is the `n`-by-`n` identity matrix. [2]_
    
        References
        ----------
        .. [1] M. Sullivan and M. Sullivan, III, "Algebra and Trignometry,
           Enhanced With Graphing Utilities," Prentice-Hall, pg. 318, 1996.
    
        .. [2] G. Strang, "Linear Algebra and Its Applications, 2nd Edition,"
           Academic Press, pg. 182, 1980.
    
        Examples
        --------
        Given a sequence of a polynomial's zeros:
    
        >>> np.poly((0, 0, 0)) # Multiple root example
        array([1, 0, 0, 0])
    
        The line above represents z**3 + 0*z**2 + 0*z + 0.
    
        >>> np.poly((-1./2, 0, 1./2))
        array([ 1.  ,  0.  , -0.25,  0.  ])
    
        The line above represents z**3 - z/4
    
        >>> np.poly((np.random.random(1.)[0], 0, np.random.random(1.)[0]))
        array([ 1.        , -0.77086955,  0.08618131,  0.        ]) #random
    
        Given a square array object:
    
        >>> P = np.array([[0, 1./3], [-1./2, 0]])
        >>> np.poly(P)
        array([ 1.        ,  0.        ,  0.16666667])
    
        Or a square matrix object:
    
        >>> np.poly(np.matrix(P))
        array([ 1.        ,  0.        ,  0.16666667])
    
        Note how in all cases the leading coefficient is always 1.
    
        """
    return ndarray()
class poly1d:
    __dict__ = dictproxy()
    __doc__ = str()
    __hash__ = None
    __module__ = str()
    __weakref__ = getset_descriptor()
    coeffs = None
    def deriv(self=1, m=1):
        """
                Return a derivative of this polynomial.
        
                Refer to `polyder` for full documentation.
        
                See Also
                --------
                polyder : equivalent function
        
                """
        return None
    def integ(self=0, m=1, k=0):
        """
                Return an antiderivative (indefinite integral) of this polynomial.
        
                Refer to `polyint` for full documentation.
        
                See Also
                --------
                polyint : equivalent function
        
                """
        return None
    order = None
    variable = None
def polyadd(a1a2):
    """
        Find the sum of two polynomials.
    
        Returns the polynomial resulting from the sum of two input polynomials.
        Each input must be either a poly1d object or a 1D sequence of polynomial
        coefficients, from highest to lowest degree.
    
        Parameters
        ----------
        a1, a2 : array_like or poly1d object
            Input polynomials.
    
        Returns
        -------
        out : ndarray or poly1d object
            The sum of the inputs. If either input is a poly1d object, then the
            output is also a poly1d object. Otherwise, it is a 1D array of
            polynomial coefficients from highest to lowest degree.
    
        See Also
        --------
        poly1d : A one-dimensional polynomial class.
        poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval
    
        Examples
        --------
        >>> np.polyadd([1, 2], [9, 5, 4])
        array([9, 6, 6])
    
        Using poly1d objects:
    
        >>> p1 = np.poly1d([1, 2])
        >>> p2 = np.poly1d([9, 5, 4])
        >>> print p1
        1 x + 2
        >>> print p2
           2
        9 x + 5 x + 4
        >>> print np.polyadd(p1, p2)
           2
        9 x + 6 x + 6
    
        """
    return ndarray() if False else poly1d()
def polyder(p=1, m=1):
    """
        Return the derivative of the specified order of a polynomial.
    
        Parameters
        ----------
        p : poly1d or sequence
            Polynomial to differentiate.
            A sequence is interpreted as polynomial coefficients, see `poly1d`.
        m : int, optional
            Order of differentiation (default: 1)
    
        Returns
        -------
        der : poly1d
            A new polynomial representing the derivative.
    
        See Also
        --------
        polyint : Anti-derivative of a polynomial.
        poly1d : Class for one-dimensional polynomials.
    
        Examples
        --------
        The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is:
    
        >>> p = np.poly1d([1,1,1,1])
        >>> p2 = np.polyder(p)
        >>> p2
        poly1d([3, 2, 1])
    
        which evaluates to:
    
        >>> p2(2.)
        17.0
    
        We can verify this, approximating the derivative with
        ``(f(x + h) - f(x))/h``:
    
        >>> (p(2. + 0.001) - p(2.)) / 0.001
        17.007000999997857
    
        The fourth-order derivative of a 3rd-order polynomial is zero:
    
        >>> np.polyder(p, 2)
        poly1d([6, 2])
        >>> np.polyder(p, 3)
        poly1d([6])
        >>> np.polyder(p, 4)
        poly1d([ 0.])
    
        """
    return poly1d()
def polydiv(u, v):
    """
        Returns the quotient and remainder of polynomial division.
    
        The input arrays are the coefficients (including any coefficients
        equal to zero) of the "numerator" (dividend) and "denominator"
        (divisor) polynomials, respectively.
    
        Parameters
        ----------
        u : array_like or poly1d
            Dividend polynomial's coefficients.
    
        v : array_like or poly1d
            Divisor polynomial's coefficients.
    
        Returns
        -------
        q : ndarray
            Coefficients, including those equal to zero, of the quotient.
        r : ndarray
            Coefficients, including those equal to zero, of the remainder.
    
        See Also
        --------
        poly, polyadd, polyder, polydiv, polyfit, polyint, polymul, polysub,
        polyval
    
        Notes
        -----
        Both `u` and `v` must be 0-d or 1-d (ndim = 0 or 1), but `u.ndim` need
        not equal `v.ndim`. In other words, all four possible combinations -
        ``u.ndim = v.ndim = 0``, ``u.ndim = v.ndim = 1``,
        ``u.ndim = 1, v.ndim = 0``, and ``u.ndim = 0, v.ndim = 1`` - work.
    
        Examples
        --------
        .. math:: \frac{3x^2 + 5x + 2}{2x + 1} = 1.5x + 1.75, remainder 0.25
    
        >>> x = np.array([3.0, 5.0, 2.0])
        >>> y = np.array([2.0, 1.0])
        >>> np.polydiv(x, y)
        (array([ 1.5 ,  1.75]), array([ 0.25]))
    
        """
    return ndarray()
def polyfit(x, y, deg=False, rcond=None, full=False, w=None, cov=False):
    """
        Least squares polynomial fit.
    
        Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg`
        to points `(x, y)`. Returns a vector of coefficients `p` that minimises
        the squared error.
    
        Parameters
        ----------
        x : array_like, shape (M,)
            x-coordinates of the M sample points ``(x[i], y[i])``.
        y : array_like, shape (M,) or (M, K)
            y-coordinates of the sample points. Several data sets of sample
            points sharing the same x-coordinates can be fitted at once by
            passing in a 2D-array that contains one dataset per column.
        deg : int
            Degree of the fitting polynomial
        rcond : float, optional
            Relative condition number of the fit. Singular values smaller than this
            relative to the largest singular value will be ignored. The default
            value is len(x)*eps, where eps is the relative precision of the float
            type, about 2e-16 in most cases.
        full : bool, optional
            Switch determining nature of return value. When it is
            False (the default) just the coefficients are returned, when True
            diagnostic information from the singular value decomposition is also
            returned.
        w : array_like, shape (M,), optional
            weights to apply to the y-coordinates of the sample points.
        cov : bool, optional
            Return the estimate and the covariance matrix of the estimate
            If full is True, then cov is not returned.
    
        Returns
        -------
        p : ndarray, shape (M,) or (M, K)
            Polynomial coefficients, highest power first.
            If `y` was 2-D, the coefficients for `k`-th data set are in ``p[:,k]``.
    
        residuals, rank, singular_values, rcond : present only if `full` = True
            Residuals of the least-squares fit, the effective rank of the scaled
            Vandermonde coefficient matrix, its singular values, and the specified
            value of `rcond`. For more details, see `linalg.lstsq`.
    
        V : ndaray, shape (M,M) or (M,M,K) : present only if `full` = False and `cov`=True
            The covariance matrix of the polynomial coefficient estimates.  The diagonal
            of this matrix are the variance estimates for each coefficient.  If y is a 2-d
            array, then the covariance matrix for the `k`-th data set are in ``V[:,:,k]``
    
    
        Warns
        -----
        RankWarning
            The rank of the coefficient matrix in the least-squares fit is
            deficient. The warning is only raised if `full` = False.
    
            The warnings can be turned off by
    
            >>> import warnings
            >>> warnings.simplefilter('ignore', np.RankWarning)
    
        See Also
        --------
        polyval : Computes polynomial values.
        linalg.lstsq : Computes a least-squares fit.
        scipy.interpolate.UnivariateSpline : Computes spline fits.
    
        Notes
        -----
        The solution minimizes the squared error
    
        .. math ::
            E = \sum_{j=0}^k |p(x_j) - y_j|^2
    
        in the equations::
    
            x[0]**n * p[n] + ... + x[0] * p[1] + p[0] = y[0]
            x[1]**n * p[n] + ... + x[1] * p[1] + p[0] = y[1]
            ...
            x[k]**n * p[n] + ... + x[k] * p[1] + p[0] = y[k]
    
        The coefficient matrix of the coefficients `p` is a Vandermonde matrix.
    
        `polyfit` issues a `RankWarning` when the least-squares fit is badly
        conditioned. This implies that the best fit is not well-defined due
        to numerical error. The results may be improved by lowering the polynomial
        degree or by replacing `x` by `x` - `x`.mean(). The `rcond` parameter
        can also be set to a value smaller than its default, but the resulting
        fit may be spurious: including contributions from the small singular
        values can add numerical noise to the result.
    
        Note that fitting polynomial coefficients is inherently badly conditioned
        when the degree of the polynomial is large or the interval of sample points
        is badly centered. The quality of the fit should always be checked in these
        cases. When polynomial fits are not satisfactory, splines may be a good
        alternative.
    
        References
        ----------
        .. [1] Wikipedia, "Curve fitting",
               http://en.wikipedia.org/wiki/Curve_fitting
        .. [2] Wikipedia, "Polynomial interpolation",
               http://en.wikipedia.org/wiki/Polynomial_interpolation
    
        Examples
        --------
        >>> x = np.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
        >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
        >>> z = np.polyfit(x, y, 3)
        >>> z
        array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254])
    
        It is convenient to use `poly1d` objects for dealing with polynomials:
    
        >>> p = np.poly1d(z)
        >>> p(0.5)
        0.6143849206349179
        >>> p(3.5)
        -0.34732142857143039
        >>> p(10)
        22.579365079365115
    
        High-order polynomials may oscillate wildly:
    
        >>> p30 = np.poly1d(np.polyfit(x, y, 30))
        /... RankWarning: Polyfit may be poorly conditioned...
        >>> p30(4)
        -0.80000000000000204
        >>> p30(5)
        -0.99999999999999445
        >>> p30(4.5)
        -0.10547061179440398
    
        Illustration:
    
        >>> import matplotlib.pyplot as plt
        >>> xp = np.linspace(-2, 6, 100)
        >>> plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
        [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>]
        >>> plt.ylim(-2,2)
        (-2, 2)
        >>> plt.show()
    
        """
    return ndarray() if False else M()
def polyint(p=None, m=1, k=None):
    """
        Return an antiderivative (indefinite integral) of a polynomial.
    
        The returned order `m` antiderivative `P` of polynomial `p` satisfies
        :math:`\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1`
        integration constants `k`. The constants determine the low-order
        polynomial part
    
        .. math:: \frac{k_{m-1}}{0!} x^0 + \ldots + \frac{k_0}{(m-1)!}x^{m-1}
    
        of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`.
    
        Parameters
        ----------
        p : {array_like, poly1d}
            Polynomial to differentiate.
            A sequence is interpreted as polynomial coefficients, see `poly1d`.
        m : int, optional
            Order of the antiderivative. (Default: 1)
        k : {None, list of `m` scalars, scalar}, optional
            Integration constants. They are given in the order of integration:
            those corresponding to highest-order terms come first.
    
            If ``None`` (default), all constants are assumed to be zero.
            If `m = 1`, a single scalar can be given instead of a list.
    
        See Also
        --------
        polyder : derivative of a polynomial
        poly1d.integ : equivalent method
    
        Examples
        --------
        The defining property of the antiderivative:
    
        >>> p = np.poly1d([1,1,1])
        >>> P = np.polyint(p)
        >>> P
        poly1d([ 0.33333333,  0.5       ,  1.        ,  0.        ])
        >>> np.polyder(P) == p
        True
    
        The integration constants default to zero, but can be specified:
    
        >>> P = np.polyint(p, 3)
        >>> P(0)
        0.0
        >>> np.polyder(P)(0)
        0.0
        >>> np.polyder(P, 2)(0)
        0.0
        >>> P = np.polyint(p, 3, k=[6,5,3])
        >>> P
        poly1d([ 0.01666667,  0.04166667,  0.16666667,  3. ,  5. ,  3. ])
    
        Note that 3 = 6 / 2!, and that the constants are given in the order of
        integrations. Constant of the highest-order polynomial term comes first:
    
        >>> np.polyder(P, 2)(0)
        6.0
        >>> np.polyder(P, 1)(0)
        5.0
        >>> P(0)
        3.0
    
        """
    return None
def polymul(a1a2):
    """
        Find the product of two polynomials.
    
        Finds the polynomial resulting from the multiplication of the two input
        polynomials. Each input must be either a poly1d object or a 1D sequence
        of polynomial coefficients, from highest to lowest degree.
    
        Parameters
        ----------
        a1, a2 : array_like or poly1d object
            Input polynomials.
    
        Returns
        -------
        out : ndarray or poly1d object
            The polynomial resulting from the multiplication of the inputs. If
            either inputs is a poly1d object, then the output is also a poly1d
            object. Otherwise, it is a 1D array of polynomial coefficients from
            highest to lowest degree.
    
        See Also
        --------
        poly1d : A one-dimensional polynomial class.
        poly, polyadd, polyder, polydiv, polyfit, polyint, polysub,
        polyval
    
        Examples
        --------
        >>> np.polymul([1, 2, 3], [9, 5, 1])
        array([ 9, 23, 38, 17,  3])
    
        Using poly1d objects:
    
        >>> p1 = np.poly1d([1, 2, 3])
        >>> p2 = np.poly1d([9, 5, 1])
        >>> print p1
           2
        1 x + 2 x + 3
        >>> print p2
           2
        9 x + 5 x + 1
        >>> print np.polymul(p1, p2)
           4      3      2
        9 x + 23 x + 38 x + 17 x + 3
    
        """
    return ndarray() if False else poly1d()
def polysub(a1a2):
    """
        Difference (subtraction) of two polynomials.
    
        Given two polynomials `a1` and `a2`, returns ``a1 - a2``.
        `a1` and `a2` can be either array_like sequences of the polynomials'
        coefficients (including coefficients equal to zero), or `poly1d` objects.
    
        Parameters
        ----------
        a1, a2 : array_like or poly1d
            Minuend and subtrahend polynomials, respectively.
    
        Returns
        -------
        out : ndarray or poly1d
            Array or `poly1d` object of the difference polynomial's coefficients.
    
        See Also
        --------
        polyval, polydiv, polymul, polyadd
    
        Examples
        --------
        .. math:: (2 x^2 + 10 x - 2) - (3 x^2 + 10 x -4) = (-x^2 + 2)
    
        >>> np.polysub([2, 10, -2], [3, 10, -4])
        array([-1,  0,  2])
    
        """
    return ndarray() if False else poly1d()
def polyval(p, x):
    """
        Evaluate a polynomial at specific values.
    
        If `p` is of length N, this function returns the value:
    
            ``p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]``
    
        If `x` is a sequence, then `p(x)` is returned for each element of `x`.
        If `x` is another polynomial then the composite polynomial `p(x(t))`
        is returned.
    
        Parameters
        ----------
        p : array_like or poly1d object
           1D array of polynomial coefficients (including coefficients equal
           to zero) from highest degree to the constant term, or an
           instance of poly1d.
        x : array_like or poly1d object
           A number, a 1D array of numbers, or an instance of poly1d, "at"
           which to evaluate `p`.
    
        Returns
        -------
        values : ndarray or poly1d
           If `x` is a poly1d instance, the result is the composition of the two
           polynomials, i.e., `x` is "substituted" in `p` and the simplified
           result is returned. In addition, the type of `x` - array_like or
           poly1d - governs the type of the output: `x` array_like => `values`
           array_like, `x` a poly1d object => `values` is also.
    
        See Also
        --------
        poly1d: A polynomial class.
    
        Notes
        -----
        Horner's scheme [1]_ is used to evaluate the polynomial. Even so,
        for polynomials of high degree the values may be inaccurate due to
        rounding errors. Use carefully.
    
        References
        ----------
        .. [1] I. N. Bronshtein, K. A. Semendyayev, and K. A. Hirsch (Eng.
           trans. Ed.), *Handbook of Mathematics*, New York, Van Nostrand
           Reinhold Co., 1985, pg. 720.
    
        Examples
        --------
        >>> np.polyval([3,0,1], 5)  # 3 * 5**2 + 0 * 5**1 + 1
        76
        >>> np.polyval([3,0,1], np.poly1d(5))
        poly1d([ 76.])
        >>> np.polyval(np.poly1d([3,0,1]), 5)
        76
        >>> np.polyval(np.poly1d([3,0,1]), np.poly1d(5))
        poly1d([ 76.])
    
        """
    return ndarray() if False else poly1d()
def power(x1, x2, out=None):
    """power(x1, x2[, out])
    
    First array elements raised to powers from second array, element-wise.
    
    Raise each base in `x1` to the positionally-corresponding power in
    `x2`.  `x1` and `x2` must be broadcastable to the same shape.
    
    Parameters
    ----------
    x1 : array_like
        The bases.
    x2 : array_like
        The exponents.
    
    Returns
    -------
    y : ndarray
        The bases in `x1` raised to the exponents in `x2`.
    
    Examples
    --------
    Cube each element in a list.
    
    >>> x1 = range(6)
    >>> x1
    [0, 1, 2, 3, 4, 5]
    >>> np.power(x1, 3)
    array([  0,   1,   8,  27,  64, 125])
    
    Raise the bases to different exponents.
    
    >>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
    >>> np.power(x1, x2)
    array([  0.,   1.,   8.,  27.,  16.,   5.])
    
    The effect of broadcasting.
    
    >>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
    >>> x2
    array([[1, 2, 3, 3, 2, 1],
           [1, 2, 3, 3, 2, 1]])
    >>> np.power(x1, x2)
    array([[ 0,  1,  8, 27, 16,  5],
           [ 0,  1,  8, 27, 16,  5]])"""
    return ndarray()
def ppmt(rate, per, nper, pv="end", fv=0.0, when="end"):
    """
        Compute the payment against loan principal.
    
        Parameters
        ----------
        rate : array_like
            Rate of interest (per period)
        per : array_like, int
            Amount paid against the loan changes.  The `per` is the period of
            interest.
        nper : array_like
            Number of compounding periods
        pv : array_like
            Present value
        fv : array_like, optional
            Future value
        when : {{'begin', 1}, {'end', 0}}, {string, int}
            When payments are due ('begin' (1) or 'end' (0))
    
        See Also
        --------
        pmt, pv, ipmt
    
        """
    return None
print_function = instance()
def prod(a=False, axis=None, dtype=None, out=None, keepdims=False):
    """
        Return the product of array elements over a given axis.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : None or int or tuple of ints, optional
            Axis or axes along which a product is performed.
            The default (`axis` = `None`) is perform a product 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 product is performed on multiple
            axes, instead of a single axis or all the axes as before.
        dtype : data-type, optional
            The data-type of the returned array, as well as of the accumulator
            in which the elements are multiplied.  By default, if `a` is of
            integer type, `dtype` is the default platform integer. (Note: if
            the type of `a` is unsigned, then so is `dtype`.)  Otherwise,
            the dtype is the same as that of `a`.
        out : ndarray, optional
            Alternative output array in which to place the result. It must have
            the same shape as the expected output, but the type of the
            output values will be cast if necessary.
        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 original `arr`.
    
        Returns
        -------
        product_along_axis : ndarray, see `dtype` parameter above.
            An array shaped as `a` but with the specified axis removed.
            Returns a reference to `out` if specified.
    
        See Also
        --------
        ndarray.prod : equivalent method
        numpy.doc.ufuncs : Section "Output arguments"
    
        Notes
        -----
        Arithmetic is modular when using integer types, and no error is
        raised on overflow.  That means that, on a 32-bit platform:
    
        >>> x = np.array([536870910, 536870910, 536870910, 536870910])
        >>> np.prod(x) #random
        16
    
        Examples
        --------
        By default, calculate the product of all elements:
    
        >>> np.prod([1.,2.])
        2.0
    
        Even when the input array is two-dimensional:
    
        >>> np.prod([[1.,2.],[3.,4.]])
        24.0
    
        But we can also specify the axis over which to multiply:
    
        >>> np.prod([[1.,2.],[3.,4.]], axis=1)
        array([  2.,  12.])
    
        If the type of `x` is unsigned, then the output type is
        the unsigned platform integer:
    
        >>> x = np.array([1, 2, 3], dtype=np.uint8)
        >>> np.prod(x).dtype == np.uint
        True
    
        If `x` is of a signed integer type, then the output type
        is the default platform integer:
    
        >>> x = np.array([1, 2, 3], dtype=np.int8)
        >>> np.prod(x).dtype == np.int
        True
    
        """
    return ndarray()
def product(a=False, axis=None, dtype=None, out=None, keepdims=False):
    """
        Return the product of array elements over a given axis.
    
        See Also
        --------
        prod : equivalent function; see for details.
    
        """
    return None
def promote_types(type1, type2):
    """promote_types(type1, type2)
    
        Returns the data type with the smallest size and smallest scalar
        kind to which both ``type1`` and ``type2`` may be safely cast.
        The returned data type is always in native byte order.
    
        This function is symmetric and associative.
    
        Parameters
        ----------
        type1 : dtype or dtype specifier
            First data type.
        type2 : dtype or dtype specifier
            Second data type.
    
        Returns
        -------
        out : dtype
            The promoted data type.
    
        Notes
        -----
        .. versionadded:: 1.6.0
    
        See Also
        --------
        result_type, dtype, can_cast
    
        Examples
        --------
        >>> np.promote_types('f4', 'f8')
        dtype('float64')
    
        >>> np.promote_types('i8', 'f4')
        dtype('float64')
    
        >>> np.promote_types('>i8', '<c8')
        dtype('complex128')
    
        >>> np.promote_types('i1', 'S8')
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        TypeError: invalid type promotion"""
    return dtype()
def ptp(a=None, axis=None, out=None):
    """
        Range of values (maximum - minimum) along an axis.
    
        The name of the function comes from the acronym for 'peak to peak'.
    
        Parameters
        ----------
        a : array_like
            Input values.
        axis : int, optional
            Axis along which to find the peaks.  By default, flatten the
            array.
        out : array_like
            Alternative output array in which to place the result. It must
            have the same shape and buffer length as the expected output,
            but the type of the output values will be cast if necessary.
    
        Returns
        -------
        ptp : ndarray
            A new array holding the result, unless `out` was
            specified, in which case a reference to `out` is returned.
    
        Examples
        --------
        >>> x = np.arange(4).reshape((2,2))
        >>> x
        array([[0, 1],
               [2, 3]])
    
        >>> np.ptp(x, axis=0)
        array([2, 2])
    
        >>> np.ptp(x, axis=1)
        array([1, 1])
    
        """
    return ndarray()
def put(a, ind, v="raise", mode="raise"):
    """
        Replaces specified elements of an array with given values.
    
        The indexing works on the flattened target array. `put` is roughly
        equivalent to:
    
        ::
    
            a.flat[ind] = v
    
        Parameters
        ----------
        a : ndarray
            Target array.
        ind : array_like
            Target indices, interpreted as integers.
        v : array_like
            Values to place in `a` at target indices. If `v` is shorter than
            `ind` it will be repeated as necessary.
        mode : {'raise', 'wrap', 'clip'}, optional
            Specifies how out-of-bounds indices will behave.
    
            * 'raise' -- raise an error (default)
            * 'wrap' -- wrap around
            * 'clip' -- clip to the range
    
            'clip' mode means that all indices that are too large are replaced
            by the index that addresses the last element along that axis. Note
            that this disables indexing with negative numbers.
    
        See Also
        --------
        putmask, place
    
        Examples
        --------
        >>> a = np.arange(5)
        >>> np.put(a, [0, 2], [-44, -55])
        >>> a
        array([-44,   1, -55,   3,   4])
    
        >>> a = np.arange(5)
        >>> np.put(a, 22, -5, mode='clip')
        >>> a
        array([ 0,  1,  2,  3, -5])
    
        """
    return None
def putmask(a, mask, values):
    """putmask(a, mask, values)
    
        Changes elements of an array based on conditional and input values.
    
        Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.
    
        If `values` is not the same size as `a` and `mask` then it will repeat.
        This gives behavior different from ``a[mask] = values``.
    
        .. note:: The `putmask` functionality is also provided by `copyto`, which
                  can be significantly faster and in addition is NA-aware
                  (`preservena` keyword).  Replacing `putmask` with
                  ``np.copyto(a, values, where=mask)`` is recommended.
    
        Parameters
        ----------
        a : array_like
            Target array.
        mask : array_like
            Boolean mask array. It has to be the same shape as `a`.
        values : array_like
            Values to put into `a` where `mask` is True. If `values` is smaller
            than `a` it will be repeated.
    
        See Also
        --------
        place, put, take, copyto
    
        Examples
        --------
        >>> x = np.arange(6).reshape(2, 3)
        >>> np.putmask(x, x>2, x**2)
        >>> x
        array([[ 0,  1,  2],
               [ 9, 16, 25]])
    
        If `values` is smaller than `a` it is repeated:
    
        >>> x = np.arange(5)
        >>> np.putmask(x, x>1, [-33, -44])
        >>> x
        array([  0,   1, -33, -44, -33])"""
    return None
def pv(rate, nper, pmt="end", fv=0.0, when="end"):
    """
        Compute the present value.
    
        Given:
         * a future value, `fv`
         * an interest `rate` compounded once per period, of which
           there are
         * `nper` total
         * a (fixed) payment, `pmt`, paid either
         * at the beginning (`when` = {'begin', 1}) or the end
           (`when` = {'end', 0}) of each period
    
        Return:
           the value now
    
        Parameters
        ----------
        rate : array_like
            Rate of interest (per period)
        nper : array_like
            Number of compounding periods
        pmt : array_like
            Payment
        fv : array_like, optional
            Future value
        when : {{'begin', 1}, {'end', 0}}, {string, int}, optional
            When payments are due ('begin' (1) or 'end' (0))
    
        Returns
        -------
        out : ndarray, float
            Present value of a series of payments or investments.
    
        Notes
        -----
        The present value is computed by solving the equation::
    
         fv +
         pv*(1 + rate)**nper +
         pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) = 0
    
        or, when ``rate = 0``::
    
         fv + pv + pmt * nper = 0
    
        for `pv`, which is then returned.
    
        References
        ----------
        .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
           Open Document Format for Office Applications (OpenDocument)v1.2,
           Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,
           Pre-Draft 12. Organization for the Advancement of Structured Information
           Standards (OASIS). Billerica, MA, USA. [ODT Document].
           Available:
           http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula
           OpenDocument-formula-20090508.odt
    
        Examples
        --------
        What is the present value (e.g., the initial investment)
        of an investment that needs to total $15692.93
        after 10 years of saving $100 every month?  Assume the
        interest rate is 5% (annually) compounded monthly.
    
        >>> np.pv(0.05/12, 10*12, -100, 15692.93)
        -100.00067131625819
    
        By convention, the negative sign represents cash flow out
        (i.e., money not available today).  Thus, to end up with
        $15,692.93 in 10 years saving $100 a month at 5% annual
        interest, one's initial deposit should also be $100.
    
        If any input is array_like, ``pv`` returns an array of equal shape.
        Let's compare different interest rates in the example above:
    
        >>> a = np.array((0.05, 0.04, 0.03))/12
        >>> np.pv(a, 10*12, -100, 15692.93)
        array([ -100.00067132,  -649.26771385, -1273.78633713])
    
        So, to end up with the same $15692.93 under the same $100 per month
        "savings plan," for annual interest rates of 4% and 3%, one would
        need initial investments of $649.27 and $1273.79, respectively.
    
        """
    return ndarray()
r_ = RClass()
def rad2deg(x, out):
    """rad2deg(x[, out])
    
    Convert angles from radians to degrees.
    
    Parameters
    ----------
    x : array_like
        Angle in radians.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    y : ndarray
        The corresponding angle in degrees.
    
    See Also
    --------
    deg2rad : Convert angles from degrees to radians.
    unwrap : Remove large jumps in angle by wrapping.
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    rad2deg(x) is ``180 * x / pi``.
    
    Examples
    --------
    >>> np.rad2deg(np.pi/2)
    90.0"""
    return ndarray()
def radians(x, out):
    """radians(x[, out])
    
    Convert angles from degrees to radians.
    
    Parameters
    ----------
    x : array_like
        Input array in degrees.
    out : ndarray, optional
        Output array of same shape as `x`.
    
    Returns
    -------
    y : ndarray
        The corresponding radian values.
    
    See Also
    --------
    deg2rad : equivalent function
    
    Examples
    --------
    Convert a degree array to radians
    
    >>> deg = np.arange(12.) * 30.
    >>> np.radians(deg)
    array([ 0.        ,  0.52359878,  1.04719755,  1.57079633,  2.0943951 ,
            2.61799388,  3.14159265,  3.66519143,  4.1887902 ,  4.71238898,
            5.23598776,  5.75958653])
    
    >>> out = np.zeros((deg.shape))
    >>> ret = np.radians(deg, out)
    >>> ret is out
    True"""
    return ndarray()
def rank(a):
    """
        Return the number of dimensions of an array.
    
        If `a` is not already an array, a conversion is attempted.
        Scalars are zero dimensional.
    
        Parameters
        ----------
        a : array_like
            Array whose number of dimensions is desired. If `a` is not an array,
            a conversion is attempted.
    
        Returns
        -------
        number_of_dimensions : int
            The number of dimensions in the array.
    
        See Also
        --------
        ndim : equivalent function
        ndarray.ndim : equivalent property
        shape : dimensions of array
        ndarray.shape : dimensions of array
    
        Notes
        -----
        In the old Numeric package, `rank` was the term used for the number of
        dimensions, but in Numpy `ndim` is used instead.
    
        Examples
        --------
        >>> np.rank([1,2,3])
        1
        >>> np.rank(np.array([[1,2,3],[4,5,6]]))
        2
        >>> np.rank(1)
        0
    
        """
    return int()
def rate(nper, pmt, pv, fv=100, when="end", guess=0.1, tol=1e-06, maxiter=100):
    """
        Compute the rate of interest per period.
    
        Parameters
        ----------
        nper : array_like
            Number of compounding periods
        pmt : array_like
            Payment
        pv : array_like
            Present value
        fv : array_like
            Future value
        when : {{'begin', 1}, {'end', 0}}, {string, int}, optional
            When payments are due ('begin' (1) or 'end' (0))
        guess : float, optional
            Starting guess for solving the rate of interest
        tol : float, optional
            Required tolerance for the solution
        maxiter : int, optional
            Maximum iterations in finding the solution
    
        Notes
        -----
        The rate of interest is computed by iteratively solving the
        (non-linear) equation::
    
         fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate * ((1+rate)**nper - 1) = 0
    
        for ``rate``.
    
        References
        ----------
        Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document
        Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated
        Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12.
        Organization for the Advancement of Structured Information Standards
        (OASIS). Billerica, MA, USA. [ODT Document]. Available:
        http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula
        OpenDocument-formula-20090508.odt
    
        """
    return None
def ravel(a="C", order="C"):
    """
        Return a flattened array.
    
        A 1-D array, containing the elements of the input, is returned.  A copy is
        made only if needed.
    
        Parameters
        ----------
        a : array_like
            Input array.  The elements in `a` are read in the order specified by
            `order`, and packed as a 1-D array.
        order : {'C','F', 'A', 'K'}, optional
            The elements of `a` are read using this index order. 'C' means to
            index the elements in C-like order, with the last axis index changing
            fastest, back to the first axis index changing slowest.   'F' means to
            index the elements in Fortran-like index order, with the first index
            changing fastest, and the last index changing slowest. Note that the 'C'
            and 'F' options take no account of the memory layout of the underlying
            array, and only refer to the order of axis indexing.  'A' means to read
            the elements in Fortran-like index order if `a` is Fortran *contiguous*
            in memory, C-like order otherwise.  'K' means to read the elements in
            the order they occur in memory, except for reversing the data when
            strides are negative.  By default, 'C' index order is used.
    
        Returns
        -------
        1d_array : ndarray
            Output of the same dtype as `a`, and of shape ``(a.size,)``.
    
        See Also
        --------
        ndarray.flat : 1-D iterator over an array.
        ndarray.flatten : 1-D array copy of the elements of an array
                          in row-major order.
    
        Notes
        -----
        In C-like (row-major) order, in two dimensions, the row index varies the
        slowest, and the column index the quickest.  This can be generalized to
        multiple dimensions, where row-major order implies that the index along the
        first axis varies slowest, and the index along the last quickest.  The
        opposite holds for Fortran-like, or column-major, index ordering.
    
        Examples
        --------
        It is equivalent to ``reshape(-1, order=order)``.
    
        >>> x = np.array([[1, 2, 3], [4, 5, 6]])
        >>> print np.ravel(x)
        [1 2 3 4 5 6]
    
        >>> print x.reshape(-1)
        [1 2 3 4 5 6]
    
        >>> print np.ravel(x, order='F')
        [1 4 2 5 3 6]
    
        When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:
    
        >>> print np.ravel(x.T)
        [1 4 2 5 3 6]
        >>> print np.ravel(x.T, order='A')
        [1 2 3 4 5 6]
    
        When ``order`` is 'K', it will preserve orderings that are neither 'C'
        nor 'F', but won't reverse axes:
    
        >>> a = np.arange(3)[::-1]; a
        array([2, 1, 0])
        >>> a.ravel(order='C')
        array([2, 1, 0])
        >>> a.ravel(order='K')
        array([2, 1, 0])
    
        >>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
        array([[[ 0,  2,  4],
                [ 1,  3,  5]],
               [[ 6,  8, 10],
                [ 7,  9, 11]]])
        >>> a.ravel(order='C')
        array([ 0,  2,  4,  1,  3,  5,  6,  8, 10,  7,  9, 11])
        >>> a.ravel(order='K')
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
    
        """
    return ndarray()
def ravel_multi_index(multi_index, dims, mode, order):
    """ravel_multi_index(multi_index, dims, mode='raise', order='C')
    
        Converts a tuple of index arrays into an array of flat
        indices, applying boundary modes to the multi-index.
    
        Parameters
        ----------
        multi_index : tuple of array_like
            A tuple of integer arrays, one array for each dimension.
        dims : tuple of ints
            The shape of array into which the indices from ``multi_index`` apply.
        mode : {'raise', 'wrap', 'clip'}, optional
            Specifies how out-of-bounds indices are handled.  Can specify
            either one mode or a tuple of modes, one mode per index.
    
            * 'raise' -- raise an error (default)
            * 'wrap' -- wrap around
            * 'clip' -- clip to the range
    
            In 'clip' mode, a negative index which would normally
            wrap will clip to 0 instead.
        order : {'C', 'F'}, optional
            Determines whether the multi-index should be viewed as indexing in
            C (row-major) order or FORTRAN (column-major) order.
    
        Returns
        -------
        raveled_indices : ndarray
            An array of indices into the flattened version of an array
            of dimensions ``dims``.
    
        See Also
        --------
        unravel_index
    
        Notes
        -----
        .. versionadded:: 1.6.0
    
        Examples
        --------
        >>> arr = np.array([[3,6,6],[4,5,1]])
        >>> np.ravel_multi_index(arr, (7,6))
        array([22, 41, 37])
        >>> np.ravel_multi_index(arr, (7,6), order='F')
        array([31, 41, 13])
        >>> np.ravel_multi_index(arr, (4,6), mode='clip')
        array([22, 23, 19])
        >>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))
        array([12, 13, 13])
    
        >>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))
        1621"""
    return ndarray()
def real(val):
    """
        Return the real part of the elements of the array.
    
        Parameters
        ----------
        val : array_like
            Input array.
    
        Returns
        -------
        out : ndarray
            Output array. 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])
    
        """
    return ndarray()
def real_if_close(a=100, tol=100):
    """
        If complex input returns a real array if complex parts are close to zero.
    
        "Close to zero" is defined as `tol` * (machine epsilon of the type for
        `a`).
    
        Parameters
        ----------
        a : array_like
            Input array.
        tol : float
            Tolerance in machine epsilons for the complex part of the elements
            in the array.
    
        Returns
        -------
        out : ndarray
            If `a` is real, the type of `a` is used for the output.  If `a`
            has complex elements, the returned type is float.
    
        See Also
        --------
        real, imag, angle
    
        Notes
        -----
        Machine epsilon varies from machine to machine and between data types
        but Python floats on most platforms have a machine epsilon equal to
        2.2204460492503131e-16.  You can use 'np.finfo(np.float).eps' to print
        out the machine epsilon for floats.
    
        Examples
        --------
        >>> np.finfo(np.float).eps
        2.2204460492503131e-16
    
        >>> np.real_if_close([2.1 + 4e-14j], tol=1000)
        array([ 2.1])
        >>> np.real_if_close([2.1 + 4e-13j], tol=1000)
        array([ 2.1 +4.00000000e-13j])
    
        """
    return ndarray()
class recarray:
    T = getset_descriptor()
    __array_finalize__ = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    def all(self, axis=None, out=None):
        """a.all(axis=None, out=None)
        
            Returns True if all elements evaluate to True.
        
            Refer to `numpy.all` for full documentation.
        
            See Also
            --------
            numpy.all : equivalent function"""
        return None
    def any(self, axis=None, out=None):
        """a.any(axis=None, out=None)
        
            Returns True if any of the elements of `a` evaluate to True.
        
            Refer to `numpy.any` for full documentation.
        
            See Also
            --------
            numpy.any : equivalent function"""
        return None
    def argmax(self, axis=None, out=None):
        """a.argmax(axis=None, out=None)
        
            Return indices of the maximum values along the given axis.
        
            Refer to `numpy.argmax` for full documentation.
        
            See Also
            --------
            numpy.argmax : equivalent function"""
        return None
    def argmin(self, axis=None, out=None):
        """a.argmin(axis=None, out=None)
        
            Return indices of the minimum values along the given axis of `a`.
        
            Refer to `numpy.argmin` for detailed documentation.
        
            See Also
            --------
            numpy.argmin : equivalent function"""
        return None
    def argpartition(self, kth, axis=_1, kind=quickselect, order=None):
        """a.argpartition(kth, axis=-1, kind='quickselect', order=None)
        
            Returns the indices that would partition this array.
        
            Refer to `numpy.argpartition` for full documentation.
        
            .. versionadded:: 1.8.0
        
            See Also
            --------
            numpy.argpartition : equivalent function"""
        return None
    def argsort(self, axis=_1, kind=quicksort, order=None):
        """a.argsort(axis=-1, kind='quicksort', order=None)
        
            Returns the indices that would sort this array.
        
            Refer to `numpy.argsort` for full documentation.
        
            See Also
            --------
            numpy.argsort : equivalent function"""
        return None
    def astype(self, dtype, order, casting, subok, copy):
        """a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
        
            Copy of the array, cast to a specified type.
        
            Parameters
            ----------
            dtype : str or dtype
                Typecode or data-type to which the array is cast.
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout order of the result.
                'C' means C order, 'F' means Fortran order, 'A'
                means 'F' order if all the arrays are Fortran contiguous,
                'C' order otherwise, and 'K' means as close to the
                order the array elements appear in memory as possible.
                Default is 'K'.
            casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
                Controls what kind of data casting may occur. Defaults to 'unsafe'
                for backwards compatibility.
        
                  * 'no' means the data types should not be cast at all.
                  * 'equiv' means only byte-order changes are allowed.
                  * 'safe' means only casts which can preserve values are allowed.
                  * 'same_kind' means only safe casts or casts within a kind,
                    like float64 to float32, are allowed.
                  * 'unsafe' means any data conversions may be done.
            subok : bool, optional
                If True, then sub-classes will be passed-through (default), otherwise
                the returned array will be forced to be a base-class array.
            copy : bool, optional
                By default, astype always returns a newly allocated array. If this
                is set to false, and the `dtype`, `order`, and `subok`
                requirements are satisfied, the input array is returned instead
                of a copy.
        
            Returns
            -------
            arr_t : ndarray
                Unless `copy` is False and the other conditions for returning the input
                array are satisfied (see description for `copy` input paramter), `arr_t`
                is a new array of the same shape as the input array, with dtype, order
                given by `dtype`, `order`.
        
            Raises
            ------
            ComplexWarning
                When casting from complex to float or int. To avoid this,
                one should use ``a.real.astype(t)``.
        
            Examples
            --------
            >>> x = np.array([1, 2, 2.5])
            >>> x
            array([ 1. ,  2. ,  2.5])
        
            >>> x.astype(int)
            array([1, 2, 2])"""
        return ndarray()
    base = getset_descriptor()
    def byteswap(self, inplace):
        """a.byteswap(inplace)
        
            Swap the bytes of the array elements
        
            Toggle between low-endian and big-endian data representation by
            returning a byteswapped array, optionally swapped in-place.
        
            Parameters
            ----------
            inplace : bool, optional
                If ``True``, swap bytes in-place, default is ``False``.
        
            Returns
            -------
            out : ndarray
                The byteswapped array. If `inplace` is ``True``, this is
                a view to self.
        
            Examples
            --------
            >>> A = np.array([1, 256, 8755], dtype=np.int16)
            >>> map(hex, A)
            ['0x1', '0x100', '0x2233']
            >>> A.byteswap(True)
            array([  256,     1, 13090], dtype=int16)
            >>> map(hex, A)
            ['0x100', '0x1', '0x3322']
        
            Arrays of strings are not swapped
        
            >>> A = np.array(['ceg', 'fac'])
            >>> A.byteswap()
            array(['ceg', 'fac'],
                  dtype='|S3')"""
        return ndarray()
    def choose(self, choices, out=None, mode=_raise):
        """a.choose(choices, out=None, mode='raise')
        
            Use an index array to construct a new array from a set of choices.
        
            Refer to `numpy.choose` for full documentation.
        
            See Also
            --------
            numpy.choose : equivalent function"""
        return None
    def clip(self, a_min, a_max, out=None):
        """a.clip(a_min, a_max, out=None)
        
            Return an array whose values are limited to ``[a_min, a_max]``.
        
            Refer to `numpy.clip` for full documentation.
        
            See Also
            --------
            numpy.clip : equivalent function"""
        return None
    def compress(self, condition, axis=None, out=None):
        """a.compress(condition, axis=None, out=None)
        
            Return selected slices of this array along given axis.
        
            Refer to `numpy.compress` for full documentation.
        
            See Also
            --------
            numpy.compress : equivalent function"""
        return None
    def conj(self, _):
        """a.conj()
        
            Complex-conjugate all elements.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def conjugate(self, _):
        """a.conjugate()
        
            Return the complex conjugate, element-wise.
        
            Refer to `numpy.conjugate` for full documentation.
        
            See Also
            --------
            numpy.conjugate : equivalent function"""
        return None
    def copy(self, order):
        """a.copy(order='C')
        
            Return a copy of the array.
        
            Parameters
            ----------
            order : {'C', 'F', 'A', 'K'}, optional
                Controls the memory layout of the copy. 'C' means C-order,
                'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
                'C' otherwise. 'K' means match the layout of `a` as closely
                as possible. (Note that this function and :func:numpy.copy are very
                similar, but have different default values for their order=
                arguments.)
        
            See also
            --------
            numpy.copy
            numpy.copyto
        
            Examples
            --------
            >>> x = np.array([[1,2,3],[4,5,6]], order='F')
        
            >>> y = x.copy()
        
            >>> x.fill(0)
        
            >>> x
            array([[0, 0, 0],
                   [0, 0, 0]])
        
            >>> y
            array([[1, 2, 3],
                   [4, 5, 6]])
        
            >>> y.flags['C_CONTIGUOUS']
            True"""
        return None
    ctypes = getset_descriptor()
    def cumprod(self, axis=None, dtype=None, out=None):
        """a.cumprod(axis=None, dtype=None, out=None)
        
            Return the cumulative product of the elements along the given axis.
        
            Refer to `numpy.cumprod` for full documentation.
        
            See Also
            --------
            numpy.cumprod : equivalent function"""
        return None
    def cumsum(self, axis=None, dtype=None, out=None):
        """a.cumsum(axis=None, dtype=None, out=None)
        
            Return the cumulative sum of the elements along the given axis.
        
            Refer to `numpy.cumsum` for full documentation.
        
            See Also
            --------
            numpy.cumsum : equivalent function"""
        return None
    data = getset_descriptor()
    def diagonal(self, offset=0, axis1=0, axis2=1):
        """a.diagonal(offset=0, axis1=0, axis2=1)
        
            Return specified diagonals.
        
            Refer to :func:`numpy.diagonal` for full documentation.
        
            See Also
            --------
            numpy.diagonal : equivalent function"""
        return None
    def dot(self, b, out=None):
        """a.dot(b, out=None)
        
            Dot product of two arrays.
        
            Refer to `numpy.dot` for full documentation.
        
            See Also
            --------
            numpy.dot : equivalent function
        
            Examples
            --------
            >>> a = np.eye(2)
            >>> b = np.ones((2, 2)) * 2
            >>> a.dot(b)
            array([[ 2.,  2.],
                   [ 2.,  2.]])
        
            This array method can be conveniently chained:
        
            >>> a.dot(b).dot(b)
            array([[ 8.,  8.],
                   [ 8.,  8.]])"""
        return None
    dtype = getset_descriptor()
    def dump(self, file):
        """a.dump(file)
        
            Dump a pickle of the array to the specified file.
            The array can be read back with pickle.load or numpy.load.
        
            Parameters
            ----------
            file : str
                A string naming the dump file."""
        return None
    def dumps(self, _):
        """a.dumps()
        
            Returns the pickle of the array as a string.
            pickle.loads or numpy.loads will convert the string back to an array.
        
            Parameters
            ----------
            None"""
        return None
    def field(self, attr=None, val=None):
        """None"""
        return None
    def fill(self, value):
        """a.fill(value)
        
            Fill the array with a scalar value.
        
            Parameters
            ----------
            value : scalar
                All elements of `a` will be assigned this value.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.fill(0)
            >>> a
            array([0, 0])
            >>> a = np.empty(2)
            >>> a.fill(1)
            >>> a
            array([ 1.,  1.])"""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def flatten(self, order):
        """a.flatten(order='C')
        
            Return a copy of the array collapsed into one dimension.
        
            Parameters
            ----------
            order : {'C', 'F', 'A'}, optional
                Whether to flatten in C (row-major), Fortran (column-major) order,
                or preserve the C/Fortran ordering from `a`.
                The default is 'C'.
        
            Returns
            -------
            y : ndarray
                A copy of the input array, flattened to one dimension.
        
            See Also
            --------
            ravel : Return a flattened array.
            flat : A 1-D flat iterator over the array.
        
            Examples
            --------
            >>> a = np.array([[1,2], [3,4]])
            >>> a.flatten()
            array([1, 2, 3, 4])
            >>> a.flatten('F')
            array([1, 3, 2, 4])"""
        return ndarray()
    def getfield(self, dtype, offset):
        """a.getfield(dtype, offset=0)
        
            Returns a field of the given array as a certain type.
        
            A field is a view of the array data with a given data-type. The values in
            the view are determined by the given type and the offset into the current
            array in bytes. The offset needs to be such that the view dtype fits in the
            array dtype; for example an array of dtype complex128 has 16-byte elements.
            If taking a view with a 32-bit integer (4 bytes), the offset needs to be
            between 0 and 12 bytes.
        
            Parameters
            ----------
            dtype : str or dtype
                The data type of the view. The dtype size of the view can not be larger
                than that of the array itself.
            offset : int
                Number of bytes to skip before beginning the element view.
        
            Examples
            --------
            >>> x = np.diag([1.+1.j]*2)
            >>> x[1, 1] = 2 + 4.j
            >>> x
            array([[ 1.+1.j,  0.+0.j],
                   [ 0.+0.j,  2.+4.j]])
            >>> x.getfield(np.float64)
            array([[ 1.,  0.],
                   [ 0.,  2.]])
        
            By choosing an offset of 8 bytes we can select the complex part of the
            array for our view:
        
            >>> x.getfield(np.float64, offset=8)
            array([[ 1.,  0.],
               [ 0.,  4.]])"""
        return array()
    imag = getset_descriptor()
    def item(self, ESCargs):
        """a.item(*args)
        
            Copy an element of an array to a standard Python scalar and return it.
        
            Parameters
            ----------
            \*args : Arguments (variable number and type)
        
                * none: in this case, the method only works for arrays
                  with one element (`a.size == 1`), which element is
                  copied into a standard Python scalar object and returned.
        
                * int_type: this argument is interpreted as a flat index into
                  the array, specifying which element to copy and return.
        
                * tuple of int_types: functions as does a single int_type argument,
                  except that the argument is interpreted as an nd-index into the
                  array.
        
            Returns
            -------
            z : Standard Python scalar object
                A copy of the specified element of the array as a suitable
                Python scalar
        
            Notes
            -----
            When the data type of `a` is longdouble or clongdouble, item() returns
            a scalar array object because there is no available Python scalar that
            would not lose information. Void arrays return a buffer object for item(),
            unless fields are defined, in which case a tuple is returned.
        
            `item` is very similar to a[args], except, instead of an array scalar,
            a standard Python scalar is returned. This can be useful for speeding up
            access to elements of the array and doing arithmetic on elements of the
            array using Python's optimized math.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.item(3)
            2
            >>> x.item(7)
            5
            >>> x.item((0, 1))
            1
            >>> x.item((2, 2))
            3"""
        return Standard()
    def itemset(self, ESCargs):
        """a.itemset(*args)
        
            Insert scalar into an array (scalar is cast to array's dtype, if possible)
        
            There must be at least 1 argument, and define the last argument
            as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
            than ``a[args] = item``.  The item should be a scalar value and `args`
            must select a single item in the array `a`.
        
            Parameters
            ----------
            \*args : Arguments
                If one argument: a scalar, only used in case `a` is of size 1.
                If two arguments: the last argument is the value to be set
                and must be a scalar, the first argument specifies a single array
                element location. It is either an int or a tuple.
        
            Notes
            -----
            Compared to indexing syntax, `itemset` provides some speed increase
            for placing a scalar into a particular location in an `ndarray`,
            if you must do this.  However, generally this is discouraged:
            among other problems, it complicates the appearance of the code.
            Also, when using `itemset` (and `item`) inside a loop, be sure
            to assign the methods to a local variable to avoid the attribute
            look-up at each loop iteration.
        
            Examples
            --------
            >>> x = np.random.randint(9, size=(3, 3))
            >>> x
            array([[3, 1, 7],
                   [2, 8, 3],
                   [8, 5, 3]])
            >>> x.itemset(4, 0)
            >>> x.itemset((2, 2), 9)
            >>> x
            array([[3, 1, 7],
                   [2, 0, 3],
                   [8, 5, 9]])"""
        return None
    itemsize = getset_descriptor()
    def max(self, axis=None, out=None):
        """a.max(axis=None, out=None)
        
            Return the maximum along a given axis.
        
            Refer to `numpy.amax` for full documentation.
        
            See Also
            --------
            numpy.amax : equivalent function"""
        return None
    def mean(self, axis=None, dtype=None, out=None):
        """a.mean(axis=None, dtype=None, out=None)
        
            Returns the average of the array elements along given axis.
        
            Refer to `numpy.mean` for full documentation.
        
            See Also
            --------
            numpy.mean : equivalent function"""
        return None
    def min(self, axis=None, out=None):
        """a.min(axis=None, out=None)
        
            Return the minimum along a given axis.
        
            Refer to `numpy.amin` for full documentation.
        
            See Also
            --------
            numpy.amin : equivalent function"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """arr.newbyteorder(new_order='S')
        
            Return the array with the same data viewed with a different byte order.
        
            Equivalent to::
        
                arr.view(arr.dtype.newbytorder(new_order))
        
            Changes are also made in all fields and sub-arrays of the array data
            type.
        
        
        
            Parameters
            ----------
            new_order : string, optional
                Byte order to force; a value from the byte order specifications
                above. `new_order` codes can be any of::
        
                 * 'S' - swap dtype from current to opposite endian
                 * {'<', 'L'} - little endian
                 * {'>', 'B'} - big endian
                 * {'=', 'N'} - native order
                 * {'|', 'I'} - ignore (no change to byte order)
        
                The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_arr : array
                New array object with the dtype reflecting given change to the
                byte order."""
        return array()
    def nonzero(self, _):
        """a.nonzero()
        
            Return the indices of the elements that are non-zero.
        
            Refer to `numpy.nonzero` for full documentation.
        
            See Also
            --------
            numpy.nonzero : equivalent function"""
        return None
    def partition(self, kth, axis, kind, order):
        """a.partition(kth, axis=-1, kind='introselect', order=None)
        
            Rearranges the elements in the array in such a way that value of the
            element in kth position is in the position it would be in a sorted array.
            All elements smaller than the kth element are moved before this element and
            all equal or greater are moved behind it. The ordering of the elements in
            the two partitions is undefined.
        
            .. versionadded:: 1.8.0
        
            Parameters
            ----------
            kth : int or sequence of ints
                Element index to partition by. The kth element value will be in its
                final sorted position and all smaller elements will be moved before it
                and all equal or greater elements behind it.
                The order all elements in the partitions is undefined.
                If provided with a sequence of kth it will partition all elements
                indexed by kth of them into their sorted position at once.
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'introselect'}, optional
                Selection algorithm. Default is 'introselect'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.partition : Return a parititioned copy of an array.
            argpartition : Indirect partition.
            sort : Full sort.
        
            Notes
            -----
            See ``np.partition`` for notes on the different algorithms.
        
            Examples
            --------
            >>> a = np.array([3, 4, 2, 1])
            >>> a.partition(a, 3)
            >>> a
            array([2, 1, 3, 4])
        
            >>> a.partition((1, 3))
            array([1, 2, 3, 4])"""
        return None
    def prod(self, axis=None, dtype=None, out=None):
        """a.prod(axis=None, dtype=None, out=None)
        
            Return the product of the array elements over the given axis
        
            Refer to `numpy.prod` for full documentation.
        
            See Also
            --------
            numpy.prod : equivalent function"""
        return None
    def ptp(self, axis=None, out=None):
        """a.ptp(axis=None, out=None)
        
            Peak to peak (maximum - minimum) value along a given axis.
        
            Refer to `numpy.ptp` for full documentation.
        
            See Also
            --------
            numpy.ptp : equivalent function"""
        return None
    def put(self, indices, values, mode=_raise):
        """a.put(indices, values, mode='raise')
        
            Set ``a.flat[n] = values[n]`` for all `n` in indices.
        
            Refer to `numpy.put` for full documentation.
        
            See Also
            --------
            numpy.put : equivalent function"""
        return None
    def ravel(self, order):
        """a.ravel([order])
        
            Return a flattened array.
        
            Refer to `numpy.ravel` for full documentation.
        
            See Also
            --------
            numpy.ravel : equivalent function
        
            ndarray.flat : a flat iterator on the array."""
        return None
    real = getset_descriptor()
    def repeat(self, repeats, axis=None):
        """a.repeat(repeats, axis=None)
        
            Repeat elements of an array.
        
            Refer to `numpy.repeat` for full documentation.
        
            See Also
            --------
            numpy.repeat : equivalent function"""
        return None
    def reshape(self, shape, order=C):
        """a.reshape(shape, order='C')
        
            Returns an array containing the same data with a new shape.
        
            Refer to `numpy.reshape` for full documentation.
        
            See Also
            --------
            numpy.reshape : equivalent function"""
        return None
    def resize(self, new_shape, refcheck):
        """a.resize(new_shape, refcheck=True)
        
            Change shape and size of array in-place.
        
            Parameters
            ----------
            new_shape : tuple of ints, or `n` ints
                Shape of resized array.
            refcheck : bool, optional
                If False, reference count will not be checked. Default is True.
        
            Returns
            -------
            None
        
            Raises
            ------
            ValueError
                If `a` does not own its own data or references or views to it exist,
                and the data memory must be changed.
        
            SystemError
                If the `order` keyword argument is specified. This behaviour is a
                bug in NumPy.
        
            See Also
            --------
            resize : Return a new array with the specified shape.
        
            Notes
            -----
            This reallocates space for the data area if necessary.
        
            Only contiguous arrays (data elements consecutive in memory) can be
            resized.
        
            The purpose of the reference count check is to make sure you
            do not use this array as a buffer for another Python object and then
            reallocate the memory. However, reference counts can increase in
            other ways so if you are sure that you have not shared the memory
            for this array with another Python object, then you may safely set
            `refcheck` to False.
        
            Examples
            --------
            Shrinking an array: array is flattened (in the order that the data are
            stored in memory), resized, and reshaped:
        
            >>> a = np.array([[0, 1], [2, 3]], order='C')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [1]])
        
            >>> a = np.array([[0, 1], [2, 3]], order='F')
            >>> a.resize((2, 1))
            >>> a
            array([[0],
                   [2]])
        
            Enlarging an array: as above, but missing entries are filled with zeros:
        
            >>> b = np.array([[0, 1], [2, 3]])
            >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
            >>> b
            array([[0, 1, 2],
                   [3, 0, 0]])
        
            Referencing an array prevents resizing...
        
            >>> c = a
            >>> a.resize((1, 1))
            Traceback (most recent call last):
            ...
            ValueError: cannot resize an array that has been referenced ...
        
            Unless `refcheck` is False:
        
            >>> a.resize((1, 1), refcheck=False)
            >>> a
            array([[0]])
            >>> c
            array([[0]])"""
        return None
    def round(self, decimals=0, out=None):
        """a.round(decimals=0, out=None)
        
            Return `a` with each element rounded to the given number of decimals.
        
            Refer to `numpy.around` for full documentation.
        
            See Also
            --------
            numpy.around : equivalent function"""
        return None
    def searchsorted(self, v, side=left, sorter=None):
        """a.searchsorted(v, side='left', sorter=None)
        
            Find indices where elements of v should be inserted in a to maintain order.
        
            For full documentation, see `numpy.searchsorted`
        
            See Also
            --------
            numpy.searchsorted : equivalent function"""
        return None
    def setfield(self, val, dtype, offset):
        """a.setfield(val, dtype, offset=0)
        
            Put a value into a specified place in a field defined by a data-type.
        
            Place `val` into `a`'s field defined by `dtype` and beginning `offset`
            bytes into the field.
        
            Parameters
            ----------
            val : object
                Value to be placed in field.
            dtype : dtype object
                Data-type of the field in which to place `val`.
            offset : int, optional
                The number of bytes into the field at which to place `val`.
        
            Returns
            -------
            None
        
            See Also
            --------
            getfield
        
            Examples
            --------
            >>> x = np.eye(3)
            >>> x.getfield(np.float64)
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])
            >>> x.setfield(3, np.int32)
            >>> x.getfield(np.int32)
            array([[3, 3, 3],
                   [3, 3, 3],
                   [3, 3, 3]])
            >>> x
            array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
                   [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
                   [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
            >>> x.setfield(np.eye(3), np.int32)
            >>> x
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])"""
        return None
    def setflags(self, write, align, uic):
        """a.setflags(write=None, align=None, uic=None)
        
            Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
        
            These Boolean-valued flags affect how numpy interprets the memory
            area used by `a` (see Notes below). The ALIGNED flag can only
            be set to True if the data is actually aligned according to the type.
            The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE
            can only be set to True if the array owns its own memory, or the
            ultimate owner of the memory exposes a writeable buffer interface,
            or is a string. (The exception for string is made so that unpickling
            can be done without copying memory.)
        
            Parameters
            ----------
            write : bool, optional
                Describes whether or not `a` can be written to.
            align : bool, optional
                Describes whether or not `a` is aligned properly for its type.
            uic : bool, optional
                Describes whether or not `a` is a copy of another "base" array.
        
            Notes
            -----
            Array flags provide information about how the memory area used
            for the array is to be interpreted. There are 6 Boolean flags
            in use, only three of which can be changed by the user:
            UPDATEIFCOPY, WRITEABLE, and ALIGNED.
        
            WRITEABLE (W) the data area can be written to;
        
            ALIGNED (A) the data and strides are aligned appropriately for the hardware
            (as determined by the compiler);
        
            UPDATEIFCOPY (U) this array is a copy of some other array (referenced
            by .base). When this array is deallocated, the base array will be
            updated with the contents of this array.
        
            All flags can be accessed using their first (upper case) letter as well
            as the full name.
        
            Examples
            --------
            >>> y
            array([[3, 1, 7],
                   [2, 0, 0],
                   [8, 5, 9]])
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : True
              ALIGNED : True
              UPDATEIFCOPY : False
            >>> y.setflags(write=0, align=0)
            >>> y.flags
              C_CONTIGUOUS : True
              F_CONTIGUOUS : False
              OWNDATA : True
              WRITEABLE : False
              ALIGNED : False
              UPDATEIFCOPY : False
            >>> y.setflags(uic=1)
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ValueError: cannot set UPDATEIFCOPY flag to True"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def sort(self, axis, kind, order):
        """a.sort(axis=-1, kind='quicksort', order=None)
        
            Sort an array, in-place.
        
            Parameters
            ----------
            axis : int, optional
                Axis along which to sort. Default is -1, which means sort along the
                last axis.
            kind : {'quicksort', 'mergesort', 'heapsort'}, optional
                Sorting algorithm. Default is 'quicksort'.
            order : list, optional
                When `a` is an array with fields defined, this argument specifies
                which fields to compare first, second, etc.  Not all fields need be
                specified.
        
            See Also
            --------
            numpy.sort : Return a sorted copy of an array.
            argsort : Indirect sort.
            lexsort : Indirect stable sort on multiple keys.
            searchsorted : Find elements in sorted array.
            partition: Partial sort.
        
            Notes
            -----
            See ``sort`` for notes on the different sorting algorithms.
        
            Examples
            --------
            >>> a = np.array([[1,4], [3,1]])
            >>> a.sort(axis=1)
            >>> a
            array([[1, 4],
                   [1, 3]])
            >>> a.sort(axis=0)
            >>> a
            array([[1, 3],
                   [1, 4]])
        
            Use the `order` keyword to specify a field to use when sorting a
            structured array:
        
            >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
            >>> a.sort(order='y')
            >>> a
            array([('c', 1), ('a', 2)],
                  dtype=[('x', '|S1'), ('y', '<i4')])"""
        return None
    def squeeze(self, axis=None):
        """a.squeeze(axis=None)
        
            Remove single-dimensional entries from the shape of `a`.
        
            Refer to `numpy.squeeze` for full documentation.
        
            See Also
            --------
            numpy.squeeze : equivalent function"""
        return None
    def std(self, axis=None, dtype=None, out=None, ddof=0):
        """a.std(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the standard deviation of the array elements along given axis.
        
            Refer to `numpy.std` for full documentation.
        
            See Also
            --------
            numpy.std : equivalent function"""
        return None
    strides = getset_descriptor()
    def sum(self, axis=None, dtype=None, out=None):
        """a.sum(axis=None, dtype=None, out=None)
        
            Return the sum of the array elements over the given axis.
        
            Refer to `numpy.sum` for full documentation.
        
            See Also
            --------
            numpy.sum : equivalent function"""
        return None
    def swapaxes(self, axis1, axis2):
        """a.swapaxes(axis1, axis2)
        
            Return a view of the array with `axis1` and `axis2` interchanged.
        
            Refer to `numpy.swapaxes` for full documentation.
        
            See Also
            --------
            numpy.swapaxes : equivalent function"""
        return None
    def take(self, indices, axis=None, out=None, mode=_raise):
        """a.take(indices, axis=None, out=None, mode='raise')
        
            Return an array formed from the elements of `a` at the given indices.
        
            Refer to `numpy.take` for full documentation.
        
            See Also
            --------
            numpy.take : equivalent function"""
        return None
    def tofile(self, fid, sep, format):
        """a.tofile(fid, sep="", format="%s")
        
            Write array to a file as text or binary (default).
        
            Data is always written in 'C' order, independent of the order of `a`.
            The data produced by this method can be recovered using the function
            fromfile().
        
            Parameters
            ----------
            fid : file or str
                An open file object, or a string containing a filename.
            sep : str
                Separator between array items for text output.
                If "" (empty), a binary file is written, equivalent to
                ``file.write(a.tostring())``.
            format : str
                Format string for text file output.
                Each entry in the array is formatted to text by first converting
                it to the closest Python type, and then using "format" % item.
        
            Notes
            -----
            This is a convenience function for quick storage of array data.
            Information on endianness and precision is lost, so this method is not a
            good choice for files intended to archive data or transport data between
            machines with different endianness. Some of these problems can be overcome
            by outputting the data as text files, at the expense of speed and file
            size."""
        return None
    def tolist(self, _):
        """a.tolist()
        
            Return the array as a (possibly nested) list.
        
            Return a copy of the array data as a (nested) Python list.
            Data items are converted to the nearest compatible Python type.
        
            Parameters
            ----------
            none
        
            Returns
            -------
            y : list
                The possibly nested list of array elements.
        
            Notes
            -----
            The array may be recreated, ``a = np.array(a.tolist())``.
        
            Examples
            --------
            >>> a = np.array([1, 2])
            >>> a.tolist()
            [1, 2]
            >>> a = np.array([[1, 2], [3, 4]])
            >>> list(a)
            [array([1, 2]), array([3, 4])]
            >>> a.tolist()
            [[1, 2], [3, 4]]"""
        return list()
    def tostring(self, order):
        """a.tostring(order='C')
        
            Construct a Python string containing the raw data bytes in the array.
        
            Constructs a Python string showing a copy of the raw contents of
            data memory. The string can be produced in either 'C' or 'Fortran',
            or 'Any' order (the default is 'C'-order). 'Any' order means C-order
            unless the F_CONTIGUOUS flag in the array is set, in which case it
            means 'Fortran' order.
        
            Parameters
            ----------
            order : {'C', 'F', None}, optional
                Order of the data for multidimensional arrays:
                C, Fortran, or the same as for the original array.
        
            Returns
            -------
            s : str
                A Python string exhibiting a copy of `a`'s raw data.
        
            Examples
            --------
            >>> x = np.array([[0, 1], [2, 3]])
            >>> x.tostring()
            '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
            >>> x.tostring('C') == x.tostring()
            True
            >>> x.tostring('F')
            '\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'"""
        return str()
    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
        """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
        
            Return the sum along diagonals of the array.
        
            Refer to `numpy.trace` for full documentation.
        
            See Also
            --------
            numpy.trace : equivalent function"""
        return None
    def transpose(self, axes):
        """a.transpose(*axes)
        
            Returns a view of the array with axes transposed.
        
            For a 1-D array, this has no effect. (To change between column and
            row vectors, first cast the 1-D array into a matrix object.)
            For a 2-D array, this is the usual matrix transpose.
            For an n-D array, if axes are given, their order indicates how the
            axes are permuted (see Examples). If axes are not provided and
            ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
            ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
        
            Parameters
            ----------
            axes : None, tuple of ints, or `n` ints
        
             * None or no argument: reverses the order of the axes.
        
             * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
               `i`-th axis becomes `a.transpose()`'s `j`-th axis.
        
             * `n` ints: same as an n-tuple of the same ints (this form is
               intended simply as a "convenience" alternative to the tuple form)
        
            Returns
            -------
            out : ndarray
                View of `a`, with axes suitably permuted.
        
            See Also
            --------
            ndarray.T : Array property returning the array transposed.
        
            Examples
            --------
            >>> a = np.array([[1, 2], [3, 4]])
            >>> a
            array([[1, 2],
                   [3, 4]])
            >>> a.transpose()
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose((1, 0))
            array([[1, 3],
                   [2, 4]])
            >>> a.transpose(1, 0)
            array([[1, 3],
                   [2, 4]])"""
        return ndarray()
    def var(self, axis=None, dtype=None, out=None, ddof=0):
        """a.var(axis=None, dtype=None, out=None, ddof=0)
        
            Returns the variance of the array elements, along given axis.
        
            Refer to `numpy.var` for full documentation.
        
            See Also
            --------
            numpy.var : equivalent function"""
        return None
    def view(self=None, dtype=None, type=None):
        """None"""
        return None
def rec_fromcsv(fnamekwargs):
    """
        Load ASCII data stored in a comma-separated file.
    
        The returned array is a record array (if ``usemask=False``, see
        `recarray`) or a masked record array (if ``usemask=True``,
        see `ma.mrecords.MaskedRecords`).
    
        Parameters
        ----------
        fname, kwargs : For a description of input parameters, see `genfromtxt`.
    
        See Also
        --------
        numpy.genfromtxt : generic function to load ASCII data.
    
        """
    return None
def rec_fromtxt(fnamekwargs):
    """
        Load ASCII data from a file and return it in a record array.
    
        If ``usemask=False`` a standard `recarray` is returned,
        if ``usemask=True`` a MaskedRecords array is returned.
    
        Parameters
        ----------
        fname, kwargs : For a description of input parameters, see `genfromtxt`.
    
        See Also
        --------
        numpy.genfromtxt : generic function
    
        Notes
        -----
        By default, `dtype` is None, which means that the data-type of the output
        array will be determined from the data.
    
        """
    return None
def reciprocal(x, out=None):
    """reciprocal(x[, out])
    
    Return the reciprocal of the argument, element-wise.
    
    Calculates ``1/x``.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    y : ndarray
        Return array.
    
    Notes
    -----
    .. note::
        This function is not designed to work with integers.
    
    For integer arguments with absolute value larger than 1 the result is
    always zero because of the way Python handles integer division.
    For integer zero the result is an overflow.
    
    Examples
    --------
    >>> np.reciprocal(2.)
    0.5
    >>> np.reciprocal([1, 2., 3.33])
    array([ 1.       ,  0.5      ,  0.3003003])"""
    return ndarray()
class record:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    def getfield(self, _):
        """None"""
        return None
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    def pprint(self, _):
        """Pretty-print all fields."""
        return None
    real = getset_descriptor()
    def setfield(self, _):
        """None"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def remainder(x1, x2, out):
    """remainder(x1, x2[, out])
    
    Return element-wise remainder of division.
    
    Computes ``x1 - floor(x1 / x2) * x2``.
    
    Parameters
    ----------
    x1 : array_like
        Dividend array.
    x2 : array_like
        Divisor array.
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved and it
        must be of the right shape to hold the output. See doc.ufuncs.
    
    Returns
    -------
    y : ndarray
        The remainder of the quotient ``x1/x2``, element-wise. Returns a scalar
        if both  `x1` and `x2` are scalars.
    
    See Also
    --------
    divide, floor
    
    Notes
    -----
    Returns 0 when `x2` is 0 and both `x1` and `x2` are (arrays of) integers.
    
    Examples
    --------
    >>> np.remainder([4, 7], [2, 3])
    array([0, 1])
    >>> np.remainder(np.arange(7), 5)
    array([0, 1, 2, 3, 4, 0, 1])"""
    return ndarray()
def repeat(a, repeats=None, axis=None):
    """
        Repeat elements of an array.
    
        Parameters
        ----------
        a : array_like
            Input array.
        repeats : {int, array of ints}
            The number of repetitions for each element.  `repeats` is broadcasted
            to fit the shape of the given axis.
        axis : int, optional
            The axis along which to repeat values.  By default, use the
            flattened input array, and return a flat output array.
    
        Returns
        -------
        repeated_array : ndarray
            Output array which has the same shape as `a`, except along
            the given axis.
    
        See Also
        --------
        tile : Tile an array.
    
        Examples
        --------
        >>> x = np.array([[1,2],[3,4]])
        >>> np.repeat(x, 2)
        array([1, 1, 2, 2, 3, 3, 4, 4])
        >>> np.repeat(x, 3, axis=1)
        array([[1, 1, 1, 2, 2, 2],
               [3, 3, 3, 4, 4, 4]])
        >>> np.repeat(x, [1, 2], axis=0)
        array([[1, 2],
               [3, 4],
               [3, 4]])
    
        """
    return ndarray()
def require(a=None, dtype=None, requirements=None):
    """
        Return an ndarray of the provided type that satisfies requirements.
    
        This function is useful to be sure that an array with the correct flags
        is returned for passing to compiled code (perhaps through ctypes).
    
        Parameters
        ----------
        a : array_like
           The object to be converted to a type-and-requirement-satisfying array.
        dtype : data-type
           The required data-type, the default data-type is float64).
        requirements : str or list of str
           The requirements list can be any of the following
    
           * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
           * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
           * 'ALIGNED' ('A')      - ensure a data-type aligned array
           * 'WRITEABLE' ('W')    - ensure a writable array
           * 'OWNDATA' ('O')      - ensure an array that owns its own data
    
        See Also
        --------
        asarray : Convert input to an ndarray.
        asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
        ascontiguousarray : Convert input to a contiguous array.
        asfortranarray : Convert input to an ndarray with column-major
                         memory order.
        ndarray.flags : Information about the memory layout of the array.
    
        Notes
        -----
        The returned array will be guaranteed to have the listed requirements
        by making a copy if needed.
    
        Examples
        --------
        >>> x = np.arange(6).reshape(2,3)
        >>> x.flags
          C_CONTIGUOUS : True
          F_CONTIGUOUS : False
          OWNDATA : False
          WRITEABLE : True
          ALIGNED : True
          UPDATEIFCOPY : False
    
        >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
        >>> y.flags
          C_CONTIGUOUS : False
          F_CONTIGUOUS : True
          OWNDATA : True
          WRITEABLE : True
          ALIGNED : True
          UPDATEIFCOPY : False
    
        """
    return None
def reshape(a, newshape="C", order="C"):
    """
        Gives a new shape to an array without changing its data.
    
        Parameters
        ----------
        a : array_like
            Array to be reshaped.
        newshape : int or tuple of ints
            The new shape should be compatible with the original shape. If
            an integer, then the result will be a 1-D array of that length.
            One shape dimension can be -1. In this case, the value is inferred
            from the length of the array and remaining dimensions.
        order : {'C', 'F', 'A'}, optional
            Read the elements of `a` using this index order, and place the elements
            into the reshaped array using this index order.  'C' means to
            read / write the elements using C-like index order, with the last axis index
            changing fastest, back to the first axis index changing slowest.  'F'
            means to read / write the elements using Fortran-like index order, with
            the first index changing fastest, and the last index changing slowest.
            Note that the 'C' and 'F' options take no account of the memory layout
            of the underlying array, and only refer to the order of indexing.  'A'
            means to read / write the elements in Fortran-like index order if `a` is
            Fortran *contiguous* in memory, C-like order otherwise.
    
        Returns
        -------
        reshaped_array : ndarray
            This will be a new view object if possible; otherwise, it will
            be a copy.  Note there is no guarantee of the *memory layout* (C- or
            Fortran- contiguous) of the returned array.
    
        See Also
        --------
        ndarray.reshape : Equivalent method.
    
        Notes
        -----
        It is not always possible to change the shape of an array without
        copying the data. If you want an error to be raise if the data is copied,
        you should assign the new shape to the shape attribute of the array::
    
         >>> a = np.zeros((10, 2))
         # A transpose make the array non-contiguous
         >>> b = a.T
         # Taking a view makes it possible to modify the shape without modifying the
         # initial object.
         >>> c = b.view()
         >>> c.shape = (20)
         AttributeError: incompatible shape for a non-contiguous array
    
        The `order` keyword gives the index ordering both for *fetching* the values
        from `a`, and then *placing* the values into the output array.  For example,
        let's say you have an array:
    
        >>> a = np.arange(6).reshape((3, 2))
        >>> a
        array([[0, 1],
               [2, 3],
               [4, 5]])
    
        You can think of reshaping as first raveling the array (using the given
        index order), then inserting the elements from the raveled array into the
        new array using the same kind of index ordering as was used for the
        raveling.
    
        >>> np.reshape(a, (2, 3)) # C-like index ordering
        array([[0, 1, 2],
               [3, 4, 5]])
        >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
        array([[0, 1, 2],
               [3, 4, 5]])
        >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
        array([[0, 4, 3],
               [2, 1, 5]])
        >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
        array([[0, 4, 3],
               [2, 1, 5]])
    
        Examples
        --------
        >>> a = np.array([[1,2,3], [4,5,6]])
        >>> np.reshape(a, 6)
        array([1, 2, 3, 4, 5, 6])
        >>> np.reshape(a, 6, order='F')
        array([1, 4, 2, 5, 3, 6])
    
        >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
        array([[1, 2],
               [3, 4],
               [5, 6]])
        """
    return ndarray()
def resize(a, new_shape):
    """
        Return a new array with the specified shape.
    
        If the new array is larger than the original array, then the new
        array is filled with repeated copies of `a`.  Note that this behavior
        is different from a.resize(new_shape) which fills with zeros instead
        of repeated copies of `a`.
    
        Parameters
        ----------
        a : array_like
            Array to be resized.
    
        new_shape : int or tuple of int
            Shape of resized array.
    
        Returns
        -------
        reshaped_array : ndarray
            The new array is formed from the data in the old array, repeated
            if necessary to fill out the required number of elements.  The
            data are repeated in the order that they are stored in memory.
    
        See Also
        --------
        ndarray.resize : resize an array in-place.
    
        Examples
        --------
        >>> a=np.array([[0,1],[2,3]])
        >>> np.resize(a,(1,4))
        array([[0, 1, 2, 3]])
        >>> np.resize(a,(2,4))
        array([[0, 1, 2, 3],
               [0, 1, 2, 3]])
    
        """
    return ndarray()
def restoredot():
    """Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS
        implementations.
    
        Typically, the user will only need to call this when troubleshooting and
        installation problem, reproducing the conditions of a build without an
        accelerated BLAS, or when being very careful about benchmarking linear
        algebra operations.
    
        See Also
        --------
        alterdot : `restoredot` undoes the effects of `alterdot`."""
    return None
def result_type(arrays_and_dtypes):
    """result_type(*arrays_and_dtypes)
    
        Returns the type that results from applying the NumPy
        type promotion rules to the arguments.
    
        Type promotion in NumPy works similarly to the rules in languages
        like C++, with some slight differences.  When both scalars and
        arrays are used, the array's type takes precedence and the actual value
        of the scalar is taken into account.
    
        For example, calculating 3*a, where a is an array of 32-bit floats,
        intuitively should result in a 32-bit float output.  If the 3 is a
        32-bit integer, the NumPy rules indicate it can't convert losslessly
        into a 32-bit float, so a 64-bit float should be the result type.
        By examining the value of the constant, '3', we see that it fits in
        an 8-bit integer, which can be cast losslessly into the 32-bit float.
    
        Parameters
        ----------
        arrays_and_dtypes : list of arrays and dtypes
            The operands of some operation whose result type is needed.
    
        Returns
        -------
        out : dtype
            The result type.
    
        See also
        --------
        dtype, promote_types, min_scalar_type, can_cast
    
        Notes
        -----
        .. versionadded:: 1.6.0
    
        The specific algorithm used is as follows.
    
        Categories are determined by first checking which of boolean,
        integer (int/uint), or floating point (float/complex) the maximum
        kind of all the arrays and the scalars are.
    
        If there are only scalars or the maximum category of the scalars
        is higher than the maximum category of the arrays,
        the data types are combined with :func:`promote_types`
        to produce the return value.
    
        Otherwise, `min_scalar_type` is called on each array, and
        the resulting data types are all combined with :func:`promote_types`
        to produce the return value.
    
        The set of int values is not a subset of the uint values for types
        with the same number of bits, something not reflected in
        :func:`min_scalar_type`, but handled as a special case in `result_type`.
    
        Examples
        --------
        >>> np.result_type(3, np.arange(7, dtype='i1'))
        dtype('int8')
    
        >>> np.result_type('i4', 'c8')
        dtype('complex128')
    
        >>> np.result_type(3.0, -2)
        dtype('float64')"""
    return dtype()
def right_shift(x1, x2, out=None):
    """right_shift(x1, x2[, out])
    
    Shift the bits of an integer to the right.
    
    Bits are shifted to the right by removing `x2` bits at the right of `x1`.
    Since the internal representation of numbers is in binary format, this
    operation is equivalent to dividing `x1` by ``2**x2``.
    
    Parameters
    ----------
    x1 : array_like, int
        Input values.
    x2 : array_like, int
        Number of bits to remove at the right of `x1`.
    
    Returns
    -------
    out : ndarray, int
        Return `x1` with bits shifted `x2` times to the right.
    
    See Also
    --------
    left_shift : Shift the bits of an integer to the left.
    binary_repr : Return the binary representation of the input number
        as a string.
    
    Examples
    --------
    >>> np.binary_repr(10)
    '1010'
    >>> np.right_shift(10, 1)
    5
    >>> np.binary_repr(5)
    '101'
    
    >>> np.right_shift(10, [1,2,3])
    array([5, 2, 1])"""
    return ndarray()
def rint(x, out=None):
    """rint(x[, out])
    
    Round elements of the array to the nearest integer.
    
    Parameters
    ----------
    x : array_like
        Input array.
    
    Returns
    -------
    out : {ndarray, scalar}
        Output array is same shape and type as `x`.
    
    See Also
    --------
    ceil, floor, trunc
    
    Examples
    --------
    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
    >>> np.rint(a)
    array([-2., -2., -0.,  0.,  2.,  2.,  2.])"""
    return ndarray()
def roll(a, shift=None, axis=None):
    """
        Roll array elements along a given axis.
    
        Elements that roll beyond the last position are re-introduced at
        the first.
    
        Parameters
        ----------
        a : array_like
            Input array.
        shift : int
            The number of places by which elements are shifted.
        axis : int, optional
            The axis along which elements are shifted.  By default, the array
            is flattened before shifting, after which the original
            shape is restored.
    
        Returns
        -------
        res : ndarray
            Output array, with the same shape as `a`.
    
        See Also
        --------
        rollaxis : Roll the specified axis backwards, until it lies in a
                   given position.
    
        Examples
        --------
        >>> x = np.arange(10)
        >>> np.roll(x, 2)
        array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])
    
        >>> x2 = np.reshape(x, (2,5))
        >>> x2
        array([[0, 1, 2, 3, 4],
               [5, 6, 7, 8, 9]])
        >>> np.roll(x2, 1)
        array([[9, 0, 1, 2, 3],
               [4, 5, 6, 7, 8]])
        >>> np.roll(x2, 1, axis=0)
        array([[5, 6, 7, 8, 9],
               [0, 1, 2, 3, 4]])
        >>> np.roll(x2, 1, axis=1)
        array([[4, 0, 1, 2, 3],
               [9, 5, 6, 7, 8]])
    
        """
    return ndarray()
def rollaxis(a, axis=0, start=0):
    """
        Roll the specified axis backwards, until it lies in a given position.
    
        Parameters
        ----------
        a : ndarray
            Input array.
        axis : int
            The axis to roll backwards.  The positions of the other axes do not
            change relative to one another.
        start : int, optional
            The axis is rolled until it lies before this position.  The default,
            0, results in a "complete" roll.
    
        Returns
        -------
        res : ndarray
            Output array.
    
        See Also
        --------
        roll : Roll the elements of an array by a number of positions along a
            given axis.
    
        Examples
        --------
        >>> a = np.ones((3,4,5,6))
        >>> np.rollaxis(a, 3, 1).shape
        (3, 6, 4, 5)
        >>> np.rollaxis(a, 2).shape
        (5, 3, 4, 6)
        >>> np.rollaxis(a, 1, 4).shape
        (3, 5, 6, 4)
    
        """
    return ndarray()
def roots(p):
    """
        Return the roots of a polynomial with coefficients given in p.
    
        The values in the rank-1 array `p` are coefficients of a polynomial.
        If the length of `p` is n+1 then the polynomial is described by::
    
          p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]
    
        Parameters
        ----------
        p : array_like
            Rank-1 array of polynomial coefficients.
    
        Returns
        -------
        out : ndarray
            An array containing the complex roots of the polynomial.
    
        Raises
        ------
        ValueError
            When `p` cannot be converted to a rank-1 array.
    
        See also
        --------
        poly : Find the coefficients of a polynomial with a given sequence
               of roots.
        polyval : Evaluate a polynomial at a point.
        polyfit : Least squares polynomial fit.
        poly1d : A one-dimensional polynomial class.
    
        Notes
        -----
        The algorithm relies on computing the eigenvalues of the
        companion matrix [1]_.
    
        References
        ----------
        .. [1] R. A. Horn & C. R. Johnson, *Matrix Analysis*.  Cambridge, UK:
            Cambridge University Press, 1999, pp. 146-7.
    
        Examples
        --------
        >>> coeff = [3.2, 2, 1]
        >>> np.roots(coeff)
        array([-0.3125+0.46351241j, -0.3125-0.46351241j])
    
        """
    return ndarray()
def rot90(m=1, k=1):
    """
        Rotate an array by 90 degrees in the counter-clockwise direction.
    
        The first two dimensions are rotated; therefore, the array must be at
        least 2-D.
    
        Parameters
        ----------
        m : array_like
            Array of two or more dimensions.
        k : integer
            Number of times the array is rotated by 90 degrees.
    
        Returns
        -------
        y : ndarray
            Rotated array.
    
        See Also
        --------
        fliplr : Flip an array horizontally.
        flipud : Flip an array vertically.
    
        Examples
        --------
        >>> m = np.array([[1,2],[3,4]], int)
        >>> m
        array([[1, 2],
               [3, 4]])
        >>> np.rot90(m)
        array([[2, 4],
               [1, 3]])
        >>> np.rot90(m, 2)
        array([[4, 3],
               [2, 1]])
    
        """
    return ndarray()
def round_(a=None, decimals=0, out=None):
    """
        Round an array to the given number of decimals.
    
        Refer to `around` for full documentation.
    
        See Also
        --------
        around : equivalent function
    
        """
    return None
def round_(a=None, decimals=0, out=None):
    """
        Round an array to the given number of decimals.
    
        Refer to `around` for full documentation.
    
        See Also
        --------
        around : equivalent function
    
        """
    return None
def vstack(tup):
    """
        Stack arrays in sequence vertically (row wise).
    
        Take a sequence of arrays and stack them vertically to make a single
        array. Rebuild arrays divided by `vsplit`.
    
        Parameters
        ----------
        tup : sequence of ndarrays
            Tuple containing arrays to be stacked. The arrays must have the same
            shape along all but the first axis.
    
        Returns
        -------
        stacked : ndarray
            The array formed by stacking the given arrays.
    
        See Also
        --------
        hstack : Stack arrays in sequence horizontally (column wise).
        dstack : Stack arrays in sequence depth wise (along third dimension).
        concatenate : Join a sequence of arrays together.
        vsplit : Split array into a list of multiple sub-arrays vertically.
    
        Notes
        -----
        Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that
        are at least 2-dimensional.
    
        Examples
        --------
        >>> a = np.array([1, 2, 3])
        >>> b = np.array([2, 3, 4])
        >>> np.vstack((a,b))
        array([[1, 2, 3],
               [2, 3, 4]])
    
        >>> a = np.array([[1], [2], [3]])
        >>> b = np.array([[2], [3], [4]])
        >>> np.vstack((a,b))
        array([[1],
               [2],
               [3],
               [2],
               [3],
               [4]])
    
        """
    return ndarray()
s_ = IndexExpression()
def safe_eval(source):
    """
        Protected string evaluation.
    
        Evaluate a string containing a Python literal expression without
        allowing the execution of arbitrary non-literal code.
    
        Parameters
        ----------
        source : str
            The string to evaluate.
    
        Returns
        -------
        obj : object
           The result of evaluating `source`.
    
        Raises
        ------
        SyntaxError
            If the code has invalid Python syntax, or if it contains non-literal
            code.
    
        Examples
        --------
        >>> np.safe_eval('1')
        1
        >>> np.safe_eval('[1, 2, 3]')
        [1, 2, 3]
        >>> np.safe_eval('{"foo": ("bar", 10.0)}')
        {'foo': ('bar', 10.0)}
    
        >>> np.safe_eval('import os')
        Traceback (most recent call last):
          ...
        SyntaxError: invalid syntax
    
        >>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()')
        Traceback (most recent call last):
          ...
        SyntaxError: Unsupported source construct: compiler.ast.CallFunc
    
        """
    return object()
def save(file, arr):
    """
        Save an array to a binary file in NumPy ``.npy`` format.
    
        Parameters
        ----------
        file : file or str
            File or filename to which the data is saved.  If file is a file-object,
            then the filename is unchanged.  If file is a string, a ``.npy``
            extension will be appended to the file name if it does not already
            have one.
        arr : array_like
            Array data to be saved.
    
        See Also
        --------
        savez : Save several arrays into a ``.npz`` archive
        savetxt, load
    
        Notes
        -----
        For a description of the ``.npy`` format, see `format`.
    
        Examples
        --------
        >>> from tempfile import TemporaryFile
        >>> outfile = TemporaryFile()
    
        >>> x = np.arange(10)
        >>> np.save(outfile, x)
    
        >>> outfile.seek(0) # Only needed here to simulate closing & reopening file
        >>> np.load(outfile)
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    
        """
    return None
def savetxt(fname, X="# ", fmt="%.18e", delimiter=" ", newline=" ", header="", footer="", comments="# "):
    """
        Save an array to a text file.
    
        Parameters
        ----------
        fname : filename or file handle
            If the filename ends in ``.gz``, the file is automatically saved in
            compressed gzip format.  `loadtxt` understands gzipped files
            transparently.
        X : array_like
            Data to be saved to a text file.
        fmt : str or sequence of strs, optional
            A single format (%10.5f), a sequence of formats, or a
            multi-format string, e.g. 'Iteration %d -- %10.5f', in which
            case `delimiter` is ignored. For complex `X`, the legal options
            for `fmt` are:
                a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted
                    like `' (%s+%sj)' % (fmt, fmt)`
                b) a full string specifying every real and imaginary part, e.g.
                    `' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns
                c) a list of specifiers, one per column - in this case, the real
                    and imaginary part must have separate specifiers,
                    e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns
        delimiter : str, optional
            Character separating columns.
        newline : str, optional
            .. versionadded:: 1.5.0
        header : str, optional
            String that will be written at the beginning of the file.
            .. versionadded:: 1.7.0
        footer : str, optional
            String that will be written at the end of the file.
            .. versionadded:: 1.7.0
        comments : str, optional
            String that will be prepended to the ``header`` and ``footer`` strings,
            to mark them as comments. Default: '# ',  as expected by e.g.
            ``numpy.loadtxt``.
            .. versionadded:: 1.7.0
    
            Character separating lines.
    
        See Also
        --------
        save : Save an array to a binary file in NumPy ``.npy`` format
        savez : Save several arrays into a ``.npz`` compressed archive
    
        Notes
        -----
        Further explanation of the `fmt` parameter
        (``%[flag]width[.precision]specifier``):
    
        flags:
            ``-`` : left justify
    
            ``+`` : Forces to preceed result with + or -.
    
            ``0`` : Left pad the number with zeros instead of space (see width).
    
        width:
            Minimum number of characters to be printed. The value is not truncated
            if it has more characters.
    
        precision:
            - For integer specifiers (eg. ``d,i,o,x``), the minimum number of
              digits.
            - For ``e, E`` and ``f`` specifiers, the number of digits to print
              after the decimal point.
            - For ``g`` and ``G``, the maximum number of significant digits.
            - For ``s``, the maximum number of characters.
    
        specifiers:
            ``c`` : character
    
            ``d`` or ``i`` : signed decimal integer
    
            ``e`` or ``E`` : scientific notation with ``e`` or ``E``.
    
            ``f`` : decimal floating point
    
            ``g,G`` : use the shorter of ``e,E`` or ``f``
    
            ``o`` : signed octal
    
            ``s`` : string of characters
    
            ``u`` : unsigned decimal integer
    
            ``x,X`` : unsigned hexadecimal integer
    
        This explanation of ``fmt`` is not complete, for an exhaustive
        specification see [1]_.
    
        References
        ----------
        .. [1] `Format Specification Mini-Language
               <http://docs.python.org/library/string.html#
               format-specification-mini-language>`_, Python Documentation.
    
        Examples
        --------
        >>> x = y = z = np.arange(0.0,5.0,1.0)
        >>> np.savetxt('test.out', x, delimiter=',')   # X is an array
        >>> np.savetxt('test.out', (x,y,z))   # x,y,z equal sized 1D arrays
        >>> np.savetxt('test.out', x, fmt='%1.4e')   # use exponential notation
    
        """
    return None
def savez(file, args, kwds):
    """
        Save several arrays into a single file in uncompressed ``.npz`` format.
    
        If arguments are passed in with no keywords, the corresponding variable
        names, in the .npz file, are 'arr_0', 'arr_1', etc. If keyword arguments
        are given, the corresponding variable names, in the ``.npz`` file will
        match the keyword names.
    
        Parameters
        ----------
        file : str or file
            Either the file name (string) or an open file (file-like object)
            where the data will be saved. If file is a string, the ``.npz``
            extension will be appended to the file name if it is not already there.
        args : Arguments, optional
            Arrays to save to the file. Since it is not possible for Python to
            know the names of the arrays outside `savez`, the arrays will be saved
            with names "arr_0", "arr_1", and so on. These arguments can be any
            expression.
        kwds : Keyword arguments, optional
            Arrays to save to the file. Arrays will be saved in the file with the
            keyword names.
    
        Returns
        -------
        None
    
        See Also
        --------
        save : Save a single array to a binary file in NumPy format.
        savetxt : Save an array to a file as plain text.
        savez_compressed : Save several arrays into a compressed .npz file format
    
        Notes
        -----
        The ``.npz`` file format is a zipped archive of files named after the
        variables they contain.  The archive is not compressed and each file
        in the archive contains one variable in ``.npy`` format. For a
        description of the ``.npy`` format, see `format`.
    
        When opening the saved ``.npz`` file with `load` a `NpzFile` object is
        returned. This is a dictionary-like object which can be queried for
        its list of arrays (with the ``.files`` attribute), and for the arrays
        themselves.
    
        Examples
        --------
        >>> from tempfile import TemporaryFile
        >>> outfile = TemporaryFile()
        >>> x = np.arange(10)
        >>> y = np.sin(x)
    
        Using `savez` with \*args, the arrays are saved with default names.
    
        >>> np.savez(outfile, x, y)
        >>> outfile.seek(0) # Only needed here to simulate closing & reopening file
        >>> npzfile = np.load(outfile)
        >>> npzfile.files
        ['arr_1', 'arr_0']
        >>> npzfile['arr_0']
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    
        Using `savez` with \**kwds, the arrays are saved with the keyword names.
    
        >>> outfile = TemporaryFile()
        >>> np.savez(outfile, x=x, y=y)
        >>> outfile.seek(0)
        >>> npzfile = np.load(outfile)
        >>> npzfile.files
        ['y', 'x']
        >>> npzfile['x']
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    
        """
    return None
def savez_compressed(file, args, kwds):
    """
        Save several arrays into a single file in compressed ``.npz`` format.
    
        If keyword arguments are given, then filenames are taken from the keywords.
        If arguments are passed in with no keywords, then stored file names are
        arr_0, arr_1, etc.
    
        Parameters
        ----------
        file : str
            File name of .npz file.
        args : Arguments
            Function arguments.
        kwds : Keyword arguments
            Keywords.
    
        See Also
        --------
        numpy.savez : Save several arrays into an uncompressed .npz file format
    
        """
    return None
def sctype2char(sctype):
    """
        Return the string representation of a scalar dtype.
    
        Parameters
        ----------
        sctype : scalar dtype or object
            If a scalar dtype, the corresponding string character is
            returned. If an object, `sctype2char` tries to infer its scalar type
            and then return the corresponding string character.
    
        Returns
        -------
        typechar : str
            The string character corresponding to the scalar type.
    
        Raises
        ------
        ValueError
            If `sctype` is an object for which the type can not be inferred.
    
        See Also
        --------
        obj2sctype, issctype, issubsctype, mintypecode
    
        Examples
        --------
        >>> for sctype in [np.int32, np.float, np.complex, np.string_, np.ndarray]:
        ...     print np.sctype2char(sctype)
        l
        d
        D
        S
        O
    
        >>> x = np.array([1., 2-1.j])
        >>> np.sctype2char(x)
        'D'
        >>> np.sctype2char(list)
        'O'
    
        """
    return str()
sctypeDict = dict()
sctypeNA = dict()
sctypes = dict()
def searchsorted(a, v=None, side="left", sorter=None):
    """
        Find indices where elements should be inserted to maintain order.
    
        Find the indices into a sorted array `a` such that, if the
        corresponding elements in `v` were inserted before the indices, the
        order of `a` would be preserved.
    
        Parameters
        ----------
        a : 1-D array_like
            Input array. If `sorter` is None, then it must be sorted in
            ascending order, otherwise `sorter` must be an array of indices
            that sort it.
        v : array_like
            Values to insert into `a`.
        side : {'left', 'right'}, optional
            If 'left', the index of the first suitable location found is given.
            If 'right', return the last such index.  If there is no suitable
            index, return either 0 or N (where N is the length of `a`).
        sorter : 1-D array_like, optional
            .. versionadded:: 1.7.0
            Optional array of integer indices that sort array a into ascending
            order. They are typically the result of argsort.
    
        Returns
        -------
        indices : array of ints
            Array of insertion points with the same shape as `v`.
    
        See Also
        --------
        sort : Return a sorted copy of an array.
        histogram : Produce histogram from 1-D data.
    
        Notes
        -----
        Binary search is used to find the required insertion points.
    
        As of Numpy 1.4.0 `searchsorted` works with real/complex arrays containing
        `nan` values. The enhanced sort order is documented in `sort`.
    
        Examples
        --------
        >>> np.searchsorted([1,2,3,4,5], 3)
        2
        >>> np.searchsorted([1,2,3,4,5], 3, side='right')
        3
        >>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
        array([0, 5, 1, 2])
    
        """
    return array()
def select(condlist, choicelist=0, default=0):
    """
        Return an array drawn from elements in choicelist, depending on conditions.
    
        Parameters
        ----------
        condlist : list of bool ndarrays
            The list of conditions which determine from which array in `choicelist`
            the output elements are taken. When multiple conditions are satisfied,
            the first one encountered in `condlist` is used.
        choicelist : list of ndarrays
            The list of arrays from which the output elements are taken. It has
            to be of the same length as `condlist`.
        default : scalar, optional
            The element inserted in `output` when all conditions evaluate to False.
    
        Returns
        -------
        output : ndarray
            The output at position m is the m-th element of the array in
            `choicelist` where the m-th element of the corresponding array in
            `condlist` is True.
    
        See Also
        --------
        where : Return elements from one of two arrays depending on condition.
        take, choose, compress, diag, diagonal
    
        Examples
        --------
        >>> x = np.arange(10)
        >>> condlist = [x<3, x>5]
        >>> choicelist = [x, x**2]
        >>> np.select(condlist, choicelist)
        array([ 0,  1,  2,  0,  0,  0, 36, 49, 64, 81])
    
        """
    return ndarray()
def set_numeric_ops(op1=func1, op2=func2, more_args=None):
    """set_numeric_ops(op1=func1, op2=func2, ...)
    
        Set numerical operators for array objects.
    
        Parameters
        ----------
        op1, op2, ... : callable
            Each ``op = func`` pair describes an operator to be replaced.
            For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace
            addition by modulus 5 addition.
    
        Returns
        -------
        saved_ops : list of callables
            A list of all operators, stored before making replacements.
    
        Notes
        -----
        .. WARNING::
           Use with care!  Incorrect usage may lead to memory errors.
    
        A function replacing an operator cannot make use of that operator.
        For example, when replacing add, you may not use ``+``.  Instead,
        directly call ufuncs.
    
        Examples
        --------
        >>> def add_mod5(x, y):
        ...     return np.add(x, y) % 5
        ...
        >>> old_funcs = np.set_numeric_ops(add=add_mod5)
    
        >>> x = np.arange(12).reshape((3, 4))
        >>> x + x
        array([[0, 2, 4, 1],
               [3, 0, 2, 4],
               [1, 3, 0, 2]])
    
        >>> ignore = np.set_numeric_ops(**old_funcs) # restore operators"""
    return list()
def set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None):
    """
        Set printing options.
    
        These options determine the way floating point numbers, arrays and
        other NumPy objects are displayed.
    
        Parameters
        ----------
        precision : int, optional
            Number of digits of precision for floating point output (default 8).
        threshold : int, optional
            Total number of array elements which trigger summarization
            rather than full repr (default 1000).
        edgeitems : int, optional
            Number of array items in summary at beginning and end of
            each dimension (default 3).
        linewidth : int, optional
            The number of characters per line for the purpose of inserting
            line breaks (default 75).
        suppress : bool, optional
            Whether or not suppress printing of small floating point values
            using scientific notation (default False).
        nanstr : str, optional
            String representation of floating point not-a-number (default nan).
        infstr : str, optional
            String representation of floating point infinity (default inf).
        formatter : dict of callables, optional
            If not None, the keys should indicate the type(s) that the respective
            formatting function applies to.  Callables should return a string.
            Types that are not specified (by their corresponding keys) are handled
            by the default formatters.  Individual types for which a formatter
            can be set are::
    
                - 'bool'
                - 'int'
                - 'timedelta' : a `numpy.timedelta64`
                - 'datetime' : a `numpy.datetime64`
                - 'float'
                - 'longfloat' : 128-bit floats
                - 'complexfloat'
                - 'longcomplexfloat' : composed of two 128-bit floats
                - 'numpy_str' : types `numpy.string_` and `numpy.unicode_`
                - 'str' : all other strings
    
            Other keys that can be used to set a group of types at once are::
    
                - 'all' : sets all types
                - 'int_kind' : sets 'int'
                - 'float_kind' : sets 'float' and 'longfloat'
                - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
                - 'str_kind' : sets 'str' and 'numpystr'
    
        See Also
        --------
        get_printoptions, set_string_function, array2string
    
        Notes
        -----
        `formatter` is always reset with a call to `set_printoptions`.
    
        Examples
        --------
        Floating point precision can be set:
    
        >>> np.set_printoptions(precision=4)
        >>> print np.array([1.123456789])
        [ 1.1235]
    
        Long arrays can be summarised:
    
        >>> np.set_printoptions(threshold=5)
        >>> print np.arange(10)
        [0 1 2 ..., 7 8 9]
    
        Small results can be suppressed:
    
        >>> eps = np.finfo(float).eps
        >>> x = np.arange(4.)
        >>> x**2 - (x + eps)**2
        array([ -4.9304e-32,  -4.4409e-16,   0.0000e+00,   0.0000e+00])
        >>> np.set_printoptions(suppress=True)
        >>> x**2 - (x + eps)**2
        array([-0., -0.,  0.,  0.])
    
        A custom formatter can be used to display array elements as desired:
    
        >>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
        >>> x = np.arange(3)
        >>> x
        array([int: 0, int: -1, int: -2])
        >>> np.set_printoptions()  # formatter gets reset
        >>> x
        array([0, 1, 2])
    
        To put back the default options, you can use:
    
        >>> np.set_printoptions(edgeitems=3,infstr='inf',
        ... linewidth=75, nanstr='nan', precision=8,
        ... suppress=False, threshold=1000, formatter=None)
        """
    return None
def set_string_function(f=True, repr=True):
    """
        Set a Python function to be used when pretty printing arrays.
    
        Parameters
        ----------
        f : function or None
            Function to be used to pretty print arrays. The function should expect
            a single array argument and return a string of the representation of
            the array. If None, the function is reset to the default NumPy function
            to print arrays.
        repr : bool, optional
            If True (default), the function for pretty printing (``__repr__``)
            is set, if False the function that returns the default string
            representation (``__str__``) is set.
    
        See Also
        --------
        set_printoptions, get_printoptions
    
        Examples
        --------
        >>> def pprint(arr):
        ...     return 'HA! - What are you going to do now?'
        ...
        >>> np.set_string_function(pprint)
        >>> a = np.arange(10)
        >>> a
        HA! - What are you going to do now?
        >>> print a
        [0 1 2 3 4 5 6 7 8 9]
    
        We can reset the function to the default:
    
        >>> np.set_string_function(None)
        >>> a
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    
        `repr` affects either pretty printing or normal string representation.
        Note that ``__repr__`` is still affected by setting ``__str__``
        because the width of each array element in the returned string becomes
        equal to the length of the result of ``__str__()``.
    
        >>> x = np.arange(4)
        >>> np.set_string_function(lambda x:'random', repr=False)
        >>> x.__str__()
        'random'
        >>> x.__repr__()
        'array([     0,      1,      2,      3])'
    
        """
    return None
def setbufsize():
    """
        Set the size of the buffer used in ufuncs.
    
        Parameters
        ----------
        size : int
            Size of buffer.
    
        """
    return None
def setdiff1d(ar1, ar2=False, assume_unique=False):
    """
        Find the set difference of two arrays.
    
        Return the sorted, unique values in `ar1` that are not in `ar2`.
    
        Parameters
        ----------
        ar1 : array_like
            Input array.
        ar2 : array_like
            Input comparison array.
        assume_unique : bool
            If True, the input arrays are both assumed to be unique, which
            can speed up the calculation.  Default is False.
    
        Returns
        -------
        setdiff1d : ndarray
            Sorted 1D array of values in `ar1` that are not in `ar2`.
    
        See Also
        --------
        numpy.lib.arraysetops : Module with a number of other functions for
                                performing set operations on arrays.
    
        Examples
        --------
        >>> a = np.array([1, 2, 3, 2, 4, 1])
        >>> b = np.array([3, 4, 5, 6])
        >>> np.setdiff1d(a, b)
        array([1, 2])
    
        """
    return ndarray()
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
    """
        Set how floating-point errors are handled.
    
        Note that operations on integer scalar types (such as `int16`) are
        handled like floating point, and are affected by these settings.
    
        Parameters
        ----------
        all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
            Set treatment for all types of floating-point errors at once:
    
            - ignore: Take no action when the exception occurs.
            - warn: Print a `RuntimeWarning` (via the Python `warnings` module).
            - raise: Raise a `FloatingPointError`.
            - call: Call a function specified using the `seterrcall` function.
            - print: Print a warning directly to ``stdout``.
            - log: Record error in a Log object specified by `seterrcall`.
    
            The default is not to change the current behavior.
        divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
            Treatment for division by zero.
        over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
            Treatment for floating-point overflow.
        under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
            Treatment for floating-point underflow.
        invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
            Treatment for invalid floating-point operation.
    
        Returns
        -------
        old_settings : dict
            Dictionary containing the old settings.
    
        See also
        --------
        seterrcall : Set a callback function for the 'call' mode.
        geterr, geterrcall, errstate
    
        Notes
        -----
        The floating-point exceptions are defined in the IEEE 754 standard [1]:
    
        - Division by zero: infinite result obtained from finite numbers.
        - Overflow: result too large to be expressed.
        - Underflow: result so close to zero that some precision
          was lost.
        - Invalid operation: result is not an expressible number, typically
          indicates that a NaN was produced.
    
        .. [1] http://en.wikipedia.org/wiki/IEEE_754
    
        Examples
        --------
        >>> old_settings = np.seterr(all='ignore')  #seterr to known value
        >>> np.seterr(over='raise')
        {'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore',
         'under': 'ignore'}
        >>> np.seterr(all='ignore')  # reset to default
        {'over': 'raise', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'}
    
        >>> np.int16(32000) * np.int16(3)
        30464
        >>> old_settings = np.seterr(all='warn', over='raise')
        >>> np.int16(32000) * np.int16(3)
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        FloatingPointError: overflow encountered in short_scalars
    
        >>> old_settings = np.seterr(all='print')
        >>> np.geterr()
        {'over': 'print', 'divide': 'print', 'invalid': 'print', 'under': 'print'}
        >>> np.int16(32000) * np.int16(3)
        Warning: overflow encountered in short_scalars
        30464
    
        """
    return dict()
def seterrcall(func):
    """
        Set the floating-point error callback function or log object.
    
        There are two ways to capture floating-point error messages.  The first
        is to set the error-handler to 'call', using `seterr`.  Then, set
        the function to call using this function.
    
        The second is to set the error-handler to 'log', using `seterr`.
        Floating-point errors then trigger a call to the 'write' method of
        the provided object.
    
        Parameters
        ----------
        func : callable f(err, flag) or object with write method
            Function to call upon floating-point errors ('call'-mode) or
            object whose 'write' method is used to log such message ('log'-mode).
    
            The call function takes two arguments. The first is the
            type of error (one of "divide", "over", "under", or "invalid"),
            and the second is the status flag.  The flag is a byte, whose
            least-significant bits indicate the status::
    
              [0 0 0 0 invalid over under invalid]
    
            In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
    
            If an object is provided, its write method should take one argument,
            a string.
    
        Returns
        -------
        h : callable, log instance or None
            The old error handler.
    
        See Also
        --------
        seterr, geterr, geterrcall
    
        Examples
        --------
        Callback upon error:
    
        >>> def err_handler(type, flag):
        ...     print "Floating point error (%s), with flag %s" % (type, flag)
        ...
    
        >>> saved_handler = np.seterrcall(err_handler)
        >>> save_err = np.seterr(all='call')
    
        >>> np.array([1, 2, 3]) / 0.0
        Floating point error (divide by zero), with flag 1
        array([ Inf,  Inf,  Inf])
    
        >>> np.seterrcall(saved_handler)
        <function err_handler at 0x...>
        >>> np.seterr(**save_err)
        {'over': 'call', 'divide': 'call', 'invalid': 'call', 'under': 'call'}
    
        Log error message:
    
        >>> class Log(object):
        ...     def write(self, msg):
        ...         print "LOG: %s" % msg
        ...
    
        >>> log = Log()
        >>> saved_handler = np.seterrcall(log)
        >>> save_err = np.seterr(all='log')
    
        >>> np.array([1, 2, 3]) / 0.0
        LOG: Warning: divide by zero encountered in divide
        <BLANKLINE>
        array([ Inf,  Inf,  Inf])
    
        >>> np.seterrcall(saved_handler)
        <__main__.Log object at 0x...>
        >>> np.seterr(**save_err)
        {'over': 'log', 'divide': 'log', 'invalid': 'log', 'under': 'log'}
    
        """
    return callable() if False else None()
def seterrobj(errobj):
    """seterrobj(errobj)
    
        Set the object that defines floating-point error handling.
    
        The error object contains all information that defines the error handling
        behavior in Numpy. `seterrobj` is used internally by the other
        functions that set error handling behavior (`seterr`, `seterrcall`).
    
        Parameters
        ----------
        errobj : list
            The error object, a list containing three elements:
            [internal numpy buffer size, error mask, error callback function].
    
            The error mask is a single integer that holds the treatment information
            on all four floating point errors. The information for each error type
            is contained in three bits of the integer. If we print it in base 8, we
            can see what treatment is set for "invalid", "under", "over", and
            "divide" (in that order). The printed string can be interpreted with
    
            * 0 : 'ignore'
            * 1 : 'warn'
            * 2 : 'raise'
            * 3 : 'call'
            * 4 : 'print'
            * 5 : 'log'
    
        See Also
        --------
        geterrobj, seterr, geterr, seterrcall, geterrcall
        getbufsize, setbufsize
    
        Notes
        -----
        For complete documentation of the types of floating-point exceptions and
        treatment options, see `seterr`.
    
        Examples
        --------
        >>> old_errobj = np.geterrobj()  # first get the defaults
        >>> old_errobj
        [10000, 0, None]
    
        >>> def err_handler(type, flag):
        ...     print "Floating point error (%s), with flag %s" % (type, flag)
        ...
        >>> new_errobj = [20000, 12, err_handler]
        >>> np.seterrobj(new_errobj)
        >>> np.base_repr(12, 8)  # int for divide=4 ('print') and over=1 ('warn')
        '14'
        >>> np.geterr()
        {'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}
        >>> np.geterrcall() is err_handler
        True"""
    return None
def setxor1d(ar1, ar2=False, assume_unique=False):
    """
        Find the set exclusive-or of two arrays.
    
        Return the sorted, unique values that are in only one (not both) of the
        input arrays.
    
        Parameters
        ----------
        ar1, ar2 : array_like
            Input arrays.
        assume_unique : bool
            If True, the input arrays are both assumed to be unique, which
            can speed up the calculation.  Default is False.
    
        Returns
        -------
        setxor1d : ndarray
            Sorted 1D array of unique values that are in only one of the input
            arrays.
    
        Examples
        --------
        >>> a = np.array([1, 2, 3, 2, 4])
        >>> b = np.array([2, 3, 5, 7, 5])
        >>> np.setxor1d(a,b)
        array([1, 4, 5, 7])
    
        """
    return ndarray()
def shape(a):
    """
        Return the shape of an array.
    
        Parameters
        ----------
        a : array_like
            Input array.
    
        Returns
        -------
        shape : tuple of ints
            The elements of the shape tuple give the lengths of the
            corresponding array dimensions.
    
        See Also
        --------
        alen
        ndarray.shape : Equivalent array method.
    
        Examples
        --------
        >>> np.shape(np.eye(3))
        (3, 3)
        >>> np.shape([[1, 2]])
        (1, 2)
        >>> np.shape([0])
        (1,)
        >>> np.shape(0)
        ()
    
        >>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
        >>> np.shape(a)
        (2,)
        >>> a.shape
        (2,)
    
        """
    return tuple()
class int16:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def show():
    """None"""
    return None
def sign(x, out=None):
    """sign(x[, out])
    
    Returns an element-wise indication of the sign of a number.
    
    The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``.
    
    Parameters
    ----------
    x : array_like
      Input values.
    
    Returns
    -------
    y : ndarray
      The sign of `x`.
    
    Examples
    --------
    >>> np.sign([-5., 4.5])
    array([-1.,  1.])
    >>> np.sign(0)
    0"""
    return ndarray()
def signbit(x, out):
    """signbit(x[, out])
    
    Returns element-wise True where signbit is set (less than zero).
    
    Parameters
    ----------
    x : array_like
        The input value(s).
    out : ndarray, optional
        Array into which the output is placed. Its type is preserved
        and it must be of the right shape to hold the output.
        See `doc.ufuncs`.
    
    Returns
    -------
    result : ndarray of bool
        Output array, or reference to `out` if that was supplied.
    
    Examples
    --------
    >>> np.signbit(-1.2)
    True
    >>> np.signbit(np.array([1, -2.3, 2.1]))
    array([False,  True, False], dtype=bool)"""
    return ndarray()
class signedinteger:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def sin(x, out=None):
    """sin(x[, out])
    
    Trigonometric sine, element-wise.
    
    Parameters
    ----------
    x : array_like
        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
    
    Returns
    -------
    y : array_like
        The sine of each element of x.
    
    See Also
    --------
    arcsin, sinh, cos
    
    Notes
    -----
    The sine is one of the fundamental functions of trigonometry
    (the mathematical study of triangles).  Consider a circle of radius
    1 centered on the origin.  A ray comes in from the :math:`+x` axis,
    makes an angle at the origin (measured counter-clockwise from that
    axis), and departs from the origin.  The :math:`y` coordinate of
    the outgoing ray's intersection with the unit circle is the sine
    of that angle.  It ranges from -1 for :math:`x=3\pi / 2` to
    +1 for :math:`\pi / 2.`  The function has zeroes where the angle is
    a multiple of :math:`\pi`.  Sines of angles between :math:`\pi` and
    :math:`2\pi` are negative.  The numerous properties of the sine and
    related functions are included in any standard trigonometry text.
    
    Examples
    --------
    Print sine of one angle:
    
    >>> np.sin(np.pi/2.)
    1.0
    
    Print sines of an array of angles given in degrees:
    
    >>> np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180. )
    array([ 0.        ,  0.5       ,  0.70710678,  0.8660254 ,  1.        ])
    
    Plot the sine function:
    
    >>> import matplotlib.pylab as plt
    >>> x = np.linspace(-np.pi, np.pi, 201)
    >>> plt.plot(x, np.sin(x))
    >>> plt.xlabel('Angle [rad]')
    >>> plt.ylabel('sin(x)')
    >>> plt.axis('tight')
    >>> plt.show()"""
    return ndarray()
def sinc(x):
    """
        Return the sinc function.
    
        The sinc function is :math:`\sin(\pi x)/(\pi x)`.
    
        Parameters
        ----------
        x : ndarray
            Array (possibly multi-dimensional) of values for which to to
            calculate ``sinc(x)``.
    
        Returns
        -------
        out : ndarray
            ``sinc(x)``, which has the same shape as the input.
    
        Notes
        -----
        ``sinc(0)`` is the limit value 1.
    
        The name sinc is short for "sine cardinal" or "sinus cardinalis".
    
        The sinc function is used in various signal processing applications,
        including in anti-aliasing, in the construction of a
        Lanczos resampling filter, and in interpolation.
    
        For bandlimited interpolation of discrete-time signals, the ideal
        interpolation kernel is proportional to the sinc function.
    
        References
        ----------
        .. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web
               Resource. http://mathworld.wolfram.com/SincFunction.html
        .. [2] Wikipedia, "Sinc function",
               http://en.wikipedia.org/wiki/Sinc_function
    
        Examples
        --------
        >>> x = np.linspace(-4, 4, 41)
        >>> np.sinc(x)
        array([ -3.89804309e-17,  -4.92362781e-02,  -8.40918587e-02,
                -8.90384387e-02,  -5.84680802e-02,   3.89804309e-17,
                 6.68206631e-02,   1.16434881e-01,   1.26137788e-01,
                 8.50444803e-02,  -3.89804309e-17,  -1.03943254e-01,
                -1.89206682e-01,  -2.16236208e-01,  -1.55914881e-01,
                 3.89804309e-17,   2.33872321e-01,   5.04551152e-01,
                 7.56826729e-01,   9.35489284e-01,   1.00000000e+00,
                 9.35489284e-01,   7.56826729e-01,   5.04551152e-01,
                 2.33872321e-01,   3.89804309e-17,  -1.55914881e-01,
                -2.16236208e-01,  -1.89206682e-01,  -1.03943254e-01,
                -3.89804309e-17,   8.50444803e-02,   1.26137788e-01,
                 1.16434881e-01,   6.68206631e-02,   3.89804309e-17,
                -5.84680802e-02,  -8.90384387e-02,  -8.40918587e-02,
                -4.92362781e-02,  -3.89804309e-17])
    
        >>> plt.plot(x, np.sinc(x))
        [<matplotlib.lines.Line2D object at 0x...>]
        >>> plt.title("Sinc Function")
        <matplotlib.text.Text object at 0x...>
        >>> plt.ylabel("Amplitude")
        <matplotlib.text.Text object at 0x...>
        >>> plt.xlabel("X")
        <matplotlib.text.Text object at 0x...>
        >>> plt.show()
    
        It works in 2-D as well:
    
        >>> x = np.linspace(-4, 4, 401)
        >>> xx = np.outer(x, x)
        >>> plt.imshow(np.sinc(xx))
        <matplotlib.image.AxesImage object at 0x...>
    
        """
    return ndarray()
class float32:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class complex64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = str()
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def sinh(x, out):
    """sinh(x[, out])
    
    Hyperbolic sine, element-wise.
    
    Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or
    ``-1j * np.sin(1j*x)``.
    
    Parameters
    ----------
    x : array_like
        Input array.
    out : ndarray, optional
        Output array of same shape as `x`.
    
    Returns
    -------
    y : ndarray
        The corresponding hyperbolic sine values.
    
    Raises
    ------
    ValueError: invalid return array shape
        if `out` is provided and `out.shape` != `x.shape` (See Examples)
    
    Notes
    -----
    If `out` is provided, the function writes the result into it,
    and returns a reference to `out`.  (See Examples)
    
    References
    ----------
    M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
    New York, NY: Dover, 1972, pg. 83.
    
    Examples
    --------
    >>> np.sinh(0)
    0.0
    >>> np.sinh(np.pi*1j/2)
    1j
    >>> np.sinh(np.pi*1j) # (exact value is 0)
    1.2246063538223773e-016j
    >>> # Discrepancy due to vagaries of floating point arithmetic.
    
    >>> # Example of providing the optional output parameter
    >>> out2 = np.sinh([0.1], out1)
    >>> out2 is out1
    True
    
    >>> # Example of ValueError due to provision of shape mis-matched `out`
    >>> np.sinh(np.zeros((3,3)),np.zeros((2,2)))
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ValueError: invalid return array shape"""
    return ndarray()
def size(a=None, axis=None):
    """
        Return the number of elements along a given axis.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which the elements are counted.  By default, give
            the total number of elements.
    
        Returns
        -------
        element_count : int
            Number of elements along the specified axis.
    
        See Also
        --------
        shape : dimensions of array
        ndarray.shape : dimensions of array
        ndarray.size : number of elements in array
    
        Examples
        --------
        >>> a = np.array([[1,2,3],[4,5,6]])
        >>> np.size(a)
        6
        >>> np.size(a,1)
        3
        >>> np.size(a,0)
        2
    
        """
    return int()
def sometrue(a=False, axis=None, out=None, keepdims=False):
    """
        Check whether some values are true.
    
        Refer to `any` for full documentation.
    
        See Also
        --------
        any : equivalent function
    
        """
    return None
def sort(a=None, axis=-1, kind="quicksort", order=None):
    """
        Return a sorted copy of an array.
    
        Parameters
        ----------
        a : array_like
            Array to be sorted.
        axis : int or None, optional
            Axis along which to sort. If None, the array is flattened before
            sorting. The default is -1, which sorts along the last axis.
        kind : {'quicksort', 'mergesort', 'heapsort'}, optional
            Sorting algorithm. Default is 'quicksort'.
        order : list, optional
            When `a` is a structured array, this argument specifies which fields
            to compare first, second, and so on.  This list does not need to
            include all of the fields.
    
        Returns
        -------
        sorted_array : ndarray
            Array of the same type and shape as `a`.
    
        See Also
        --------
        ndarray.sort : Method to sort an array in-place.
        argsort : Indirect sort.
        lexsort : Indirect stable sort on multiple keys.
        searchsorted : Find elements in a sorted array.
        partition : Partial sort.
    
        Notes
        -----
        The various sorting algorithms are characterized by their average speed,
        worst case performance, work space size, and whether they are stable. A
        stable sort keeps items with the same key in the same relative
        order. The three available algorithms have the following
        properties:
    
        =========== ======= ============= ============ =======
           kind      speed   worst case    work space  stable
        =========== ======= ============= ============ =======
        'quicksort'    1     O(n^2)            0          no
        'mergesort'    2     O(n*log(n))      ~n/2        yes
        'heapsort'     3     O(n*log(n))       0          no
        =========== ======= ============= ============ =======
    
        All the sort algorithms make temporary copies of the data when
        sorting along any but the last axis.  Consequently, sorting along
        the last axis is faster and uses less space than sorting along
        any other axis.
    
        The sort order for complex numbers is lexicographic. If both the real
        and imaginary parts are non-nan then the order is determined by the
        real parts except when they are equal, in which case the order is
        determined by the imaginary parts.
    
        Previous to numpy 1.4.0 sorting real and complex arrays containing nan
        values led to undefined behaviour. In numpy versions >= 1.4.0 nan
        values are sorted to the end. The extended sort order is:
    
          * Real: [R, nan]
          * Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]
    
        where R is a non-nan real value. Complex values with the same nan
        placements are sorted according to the non-nan part if it exists.
        Non-nan values are sorted as before.
    
        Examples
        --------
        >>> a = np.array([[1,4],[3,1]])
        >>> np.sort(a)                # sort along the last axis
        array([[1, 4],
               [1, 3]])
        >>> np.sort(a, axis=None)     # sort the flattened array
        array([1, 1, 3, 4])
        >>> np.sort(a, axis=0)        # sort along the first axis
        array([[1, 1],
               [3, 4]])
    
        Use the `order` keyword to specify a field to use when sorting a
        structured array:
    
        >>> dtype = [('name', 'S10'), ('height', float), ('age', int)]
        >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
        ...           ('Galahad', 1.7, 38)]
        >>> a = np.array(values, dtype=dtype)       # create a structured array
        >>> np.sort(a, order='height')                        # doctest: +SKIP
        array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
               ('Lancelot', 1.8999999999999999, 38)],
              dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
    
        Sort by age, then height if ages are equal:
    
        >>> np.sort(a, order=['age', 'height'])               # doctest: +SKIP
        array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),
               ('Arthur', 1.8, 41)],
              dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
    
        """
    return ndarray()
def sort_complex(a):
    """
        Sort a complex array using the real part first, then the imaginary part.
    
        Parameters
        ----------
        a : array_like
            Input array
    
        Returns
        -------
        out : complex ndarray
            Always returns a sorted complex array.
    
        Examples
        --------
        >>> np.sort_complex([5, 3, 6, 2, 1])
        array([ 1.+0.j,  2.+0.j,  3.+0.j,  5.+0.j,  6.+0.j])
    
        >>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
        array([ 1.+2.j,  2.-1.j,  3.-3.j,  3.-2.j,  3.+5.j])
    
        """
    return complex()
def source(object="<open file '../../documentation_files/numpy.py', mode 'w' at 0x7ff32be959c0>", output="<open file '../../documentation_files/numpy.py', mode 'w' at 0x7ff32be959c0>"):
    """
        Print or write to a file the source code for a Numpy object.
    
        The source code is only returned for objects written in Python. Many
        functions and classes are defined in C and will therefore not return
        useful information.
    
        Parameters
        ----------
        object : numpy object
            Input object. This can be any object (function, class, module, ...).
        output : file object, optional
            If `output` not supplied then source code is printed to screen
            (sys.stdout).  File object must be created with either write 'w' or
            append 'a' modes.
    
        See Also
        --------
        lookfor, info
    
        Examples
        --------
        >>> np.source(np.interp)                        #doctest: +SKIP
        In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py
        def interp(x, xp, fp, left=None, right=None):
            ___.... (full docstring printed)___
            if isinstance(x, (float, int, number)):
                return compiled_interp([x], xp, fp, left, right).item()
            else:
                return compiled_interp(x, xp, fp, left, right)
    
        The source code is only returned for objects written in Python.
    
        >>> np.source(np.array)                         #doctest: +SKIP
        Not available for this object.
    
        """
    return None
def spacing(x, out=None):
    """spacing(x[, out])
    
    Return the distance between x and the nearest adjacent number.
    
    Parameters
    ----------
    x1 : array_like
        Values to find the spacing of.
    
    Returns
    -------
    out : array_like
        The spacing of values of `x1`.
    
    Notes
    -----
    It can be considered as a generalization of EPS:
    ``spacing(np.float64(1)) == np.finfo(np.float64).eps``, and there
    should not be any representable number between ``x + spacing(x)`` and
    x for any finite x.
    
    Spacing of +- inf and nan is nan.
    
    Examples
    --------
    >>> np.spacing(1) == np.finfo(np.float64).eps
    True"""
    return ndarray()
def split(ary, indices_or_sections=0, axis=0):
    """
        Split an array into multiple sub-arrays.
    
        Parameters
        ----------
        ary : ndarray
            Array to be divided into sub-arrays.
        indices_or_sections : int or 1-D array
            If `indices_or_sections` is an integer, N, the array will be divided
            into N equal arrays along `axis`.  If such a split is not possible,
            an error is raised.
    
            If `indices_or_sections` is a 1-D array of sorted integers, the entries
            indicate where along `axis` the array is split.  For example,
            ``[2, 3]`` would, for ``axis=0``, result in
    
              - ary[:2]
              - ary[2:3]
              - ary[3:]
    
            If an index exceeds the dimension of the array along `axis`,
            an empty sub-array is returned correspondingly.
        axis : int, optional
            The axis along which to split, default is 0.
    
        Returns
        -------
        sub-arrays : list of ndarrays
            A list of sub-arrays.
    
        Raises
        ------
        ValueError
            If `indices_or_sections` is given as an integer, but
            a split does not result in equal division.
    
        See Also
        --------
        array_split : Split an array into multiple sub-arrays of equal or
                      near-equal size.  Does not raise an exception if
                      an equal division cannot be made.
        hsplit : Split array into multiple sub-arrays horizontally (column-wise).
        vsplit : Split array into multiple sub-arrays vertically (row wise).
        dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
        concatenate : Join arrays together.
        hstack : Stack arrays in sequence horizontally (column wise).
        vstack : Stack arrays in sequence vertically (row wise).
        dstack : Stack arrays in sequence depth wise (along third dimension).
    
        Examples
        --------
        >>> x = np.arange(9.0)
        >>> np.split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.,  8.])]
    
        >>> x = np.arange(8.0)
        >>> np.split(x, [3, 5, 6, 10])
        [array([ 0.,  1.,  2.]),
         array([ 3.,  4.]),
         array([ 5.]),
         array([ 6.,  7.]),
         array([], dtype=float64)]
    
        """
    return list()
def sqrt(x, out):
    """sqrt(x[, out])
    
    Return the positive square-root of an array, element-wise.
    
    Parameters
    ----------
    x : array_like
        The values whose square-roots are required.
    out : ndarray, optional
        Alternate array object in which to put the result; if provided, it
        must have the same shape as `x`
    
    Returns
    -------
    y : ndarray
        An array of the same shape as `x`, containing the positive
        square-root of each element in `x`.  If any element in `x` is
        complex, a complex array is returned (and the square-roots of
        negative reals are calculated).  If all of the elements in `x`
        are real, so is `y`, with negative elements returning ``nan``.
        If `out` was provided, `y` is a reference to it.
    
    See Also
    --------
    lib.scimath.sqrt
        A version which returns complex numbers when given negative reals.
    
    Notes
    -----
    *sqrt* has--consistent with common convention--as its branch cut the
    real "interval" [`-inf`, 0), and is continuous from above on it.
    (A branch cut is a curve in the complex plane across which a given
    complex function fails to be continuous.)
    
    Examples
    --------
    >>> np.sqrt([1,4,9])
    array([ 1.,  2.,  3.])
    
    >>> np.sqrt([4, -1, -3+4J])
    array([ 2.+0.j,  0.+1.j,  1.+2.j])
    
    >>> np.sqrt([4, -1, numpy.inf])
    array([  2.,  NaN,  Inf])"""
    return ndarray()
def square(x, out=None):
    """square(x[, out])
    
    Return the element-wise square of the input.
    
    Parameters
    ----------
    x : array_like
        Input data.
    
    Returns
    -------
    out : ndarray
        Element-wise `x*x`, of the same shape and dtype as `x`.
        Returns scalar if `x` is a scalar.
    
    See Also
    --------
    numpy.linalg.matrix_power
    sqrt
    power
    
    Examples
    --------
    >>> np.square([-1j, 1])
    array([-1.-0.j,  1.+0.j])"""
    return ndarray()
def squeeze(a=None, axis=None):
    """
        Remove single-dimensional entries from the shape of an array.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : None or int or tuple of ints, optional
            .. versionadded:: 1.7.0
    
            Selects a subset of the single-dimensional entries in the
            shape. If an axis is selected with shape entry greater than
            one, an error is raised.
    
        Returns
        -------
        squeezed : ndarray
            The input array, but with with all or a subset of the
            dimensions of length 1 removed. This is always `a` itself
            or a view into `a`.
    
        Examples
        --------
        >>> x = np.array([[[0], [1], [2]]])
        >>> x.shape
        (1, 3, 1)
        >>> np.squeeze(x).shape
        (3,)
        >>> np.squeeze(x, axis=(2,)).shape
        (1, 3)
    
        """
    return ndarray()
def std(a=False, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    """
        Compute the standard deviation along the specified axis.
    
        Returns the standard deviation, a measure of the spread of a distribution,
        of the array elements. The standard deviation is computed for the
        flattened array by default, otherwise over the specified axis.
    
        Parameters
        ----------
        a : array_like
            Calculate the standard deviation of these values.
        axis : int, optional
            Axis along which the standard deviation is computed. The default is
            to compute the standard deviation of the flattened array.
        dtype : dtype, optional
            Type to use in computing the standard deviation. For arrays of
            integer type the default is float64, for arrays of float types it is
            the same as the array type.
        out : ndarray, optional
            Alternative output array in which to place the result. It must have
            the same shape as the expected output but the type (of the calculated
            values) will be cast if necessary.
        ddof : int, optional
            Means Delta Degrees of Freedom.  The divisor used in calculations
            is ``N - ddof``, where ``N`` represents the number of elements.
            By default `ddof` is zero.
        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 original `arr`.
    
        Returns
        -------
        standard_deviation : ndarray, see dtype parameter above.
            If `out` is None, return a new array containing the standard deviation,
            otherwise return a reference to the output array.
    
        See Also
        --------
        var, mean, nanmean, nanstd, nanvar
        numpy.doc.ufuncs : Section "Output arguments"
    
        Notes
        -----
        The standard deviation is the square root of the average of the squared
        deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.
    
        The average squared deviation is normally calculated as
        ``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is specified,
        the divisor ``N - ddof`` is used instead. In standard statistical
        practice, ``ddof=1`` provides an unbiased estimator of the variance
        of the infinite population. ``ddof=0`` provides a maximum likelihood
        estimate of the variance for normally distributed variables. The
        standard deviation computed in this function is the square root of
        the estimated variance, so even with ``ddof=1``, it will not be an
        unbiased estimate of the standard deviation per se.
    
        Note that, for complex numbers, `std` takes the absolute
        value before squaring, so that the result is always real and nonnegative.
    
        For floating-point input, the *std* is computed using the same
        precision the input has. Depending on the input data, this can cause
        the results to be inaccurate, especially for float32 (see example below).
        Specifying a higher-accuracy accumulator using the `dtype` keyword can
        alleviate this issue.
    
        Examples
        --------
        >>> a = np.array([[1, 2], [3, 4]])
        >>> np.std(a)
        1.1180339887498949
        >>> np.std(a, axis=0)
        array([ 1.,  1.])
        >>> np.std(a, axis=1)
        array([ 0.5,  0.5])
    
        In single precision, std() can be inaccurate:
    
        >>> a = np.zeros((2,512*512), dtype=np.float32)
        >>> a[0,:] = 1.0
        >>> a[1,:] = 0.1
        >>> np.std(a)
        0.45172946707416706
    
        Computing the standard deviation in float64 is more accurate:
    
        >>> np.std(a, dtype=np.float64)
        0.44999999925552653
    
        """
    return ndarray()
class str:
    __doc__ = str()
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    def capitalize(self, _):
        """S.capitalize() -> string
        
        Return a copy of the string S with only its first character
        capitalized."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> string
        
        Return S centered in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        string S[start:end].  Optional arguments start and end are interpreted
        as in slice notation."""
        return None
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> object
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> object
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that is able to handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> string
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> string
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. uppercase characters may only follow uncased
        characters and lowercase characters only cased ones. Return False
        otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def join(self, iterable):
        """S.join(iterable) -> string
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> string
        
        Return S left-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> string
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> string or unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> string
        
        Return a copy of string S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> string
        
        Return S right-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string, starting at the end of the string and working
        to the front.  If maxsplit is given, at most maxsplit splits are
        done. If sep is not specified or is None, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> string or unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are removed
        from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    def strip(self, chars):
        """S.strip([chars]) -> string or unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> string
        
        Return a copy of the string S with uppercase characters
        converted to lowercase and vice versa."""
        return None
    def title(self, _):
        """S.title() -> string
        
        Return a titlecased version of S, i.e. words start with uppercase
        characters, all remaining cased characters have lowercase."""
        return None
    def translate(self, table, deletechars):
        """S.translate(table [,deletechars]) -> string
        
        Return a copy of the string S, where all characters occurring
        in the optional argument deletechars are removed, and the
        remaining characters have been mapped through the given
        translation table, which must be a string of length 256 or None.
        If the table argument is None, no translation is applied and
        the operation simply removes the characters in deletechars."""
        return None
    def upper(self, _):
        """S.upper() -> string
        
        Return a copy of the string S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> string
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width.  The string S is never truncated."""
        return None
class string_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    base = getset_descriptor()
    def capitalize(self, _):
        """S.capitalize() -> string
        
        Return a copy of the string S with only its first character
        capitalized."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> string
        
        Return S centered in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def conj(self, _):
        """None"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        string S[start:end].  Optional arguments start and end are interpreted
        as in slice notation."""
        return None
    data = getset_descriptor()
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> object
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    dtype = getset_descriptor()
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> object
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that is able to handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> string
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> string
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    imag = getset_descriptor()
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. uppercase characters may only follow uncased
        characters and lowercase characters only cased ones. Return False
        otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    itemsize = getset_descriptor()
    def join(self, iterable):
        """S.join(iterable) -> string
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> string
        
        Return S left-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> string
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> string or unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    real = getset_descriptor()
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> string
        
        Return a copy of string S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> string
        
        Return S right-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string, starting at the end of the string and working
        to the front.  If maxsplit is given, at most maxsplit splits are
        done. If sep is not specified or is None, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> string or unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are removed
        from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    strides = getset_descriptor()
    def strip(self, chars):
        """S.strip([chars]) -> string or unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> string
        
        Return a copy of the string S with uppercase characters
        converted to lowercase and vice versa."""
        return None
    def title(self, _):
        """S.title() -> string
        
        Return a titlecased version of S, i.e. words start with uppercase
        characters, all remaining cased characters have lowercase."""
        return None
    def translate(self, table, deletechars):
        """S.translate(table [,deletechars]) -> string
        
        Return a copy of the string S, where all characters occurring
        in the optional argument deletechars are removed, and the
        remaining characters have been mapped through the given
        translation table, which must be a string of length 256 or None.
        If the table argument is None, no translation is applied and
        the operation simply removes the characters in deletechars."""
        return None
    def upper(self, _):
        """S.upper() -> string
        
        Return a copy of the string S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> string
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width.  The string S is never truncated."""
        return None
class string_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    base = getset_descriptor()
    def capitalize(self, _):
        """S.capitalize() -> string
        
        Return a copy of the string S with only its first character
        capitalized."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> string
        
        Return S centered in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def conj(self, _):
        """None"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        string S[start:end].  Optional arguments start and end are interpreted
        as in slice notation."""
        return None
    data = getset_descriptor()
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> object
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    dtype = getset_descriptor()
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> object
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that is able to handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> string
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> string
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    imag = getset_descriptor()
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. uppercase characters may only follow uncased
        characters and lowercase characters only cased ones. Return False
        otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    itemsize = getset_descriptor()
    def join(self, iterable):
        """S.join(iterable) -> string
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> string
        
        Return S left-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> string
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> string or unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    real = getset_descriptor()
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> string
        
        Return a copy of string S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> string
        
        Return S right-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string, starting at the end of the string and working
        to the front.  If maxsplit is given, at most maxsplit splits are
        done. If sep is not specified or is None, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> string or unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are removed
        from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    strides = getset_descriptor()
    def strip(self, chars):
        """S.strip([chars]) -> string or unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> string
        
        Return a copy of the string S with uppercase characters
        converted to lowercase and vice versa."""
        return None
    def title(self, _):
        """S.title() -> string
        
        Return a titlecased version of S, i.e. words start with uppercase
        characters, all remaining cased characters have lowercase."""
        return None
    def translate(self, table, deletechars):
        """S.translate(table [,deletechars]) -> string
        
        Return a copy of the string S, where all characters occurring
        in the optional argument deletechars are removed, and the
        remaining characters have been mapped through the given
        translation table, which must be a string of length 256 or None.
        If the table argument is None, no translation is applied and
        the operation simply removes the characters in deletechars."""
        return None
    def upper(self, _):
        """S.upper() -> string
        
        Return a copy of the string S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> string
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width.  The string S is never truncated."""
        return None
class string_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    base = getset_descriptor()
    def capitalize(self, _):
        """S.capitalize() -> string
        
        Return a copy of the string S with only its first character
        capitalized."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> string
        
        Return S centered in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def conj(self, _):
        """None"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        string S[start:end].  Optional arguments start and end are interpreted
        as in slice notation."""
        return None
    data = getset_descriptor()
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> object
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    dtype = getset_descriptor()
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> object
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that is able to handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> string
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> string
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    imag = getset_descriptor()
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. uppercase characters may only follow uncased
        characters and lowercase characters only cased ones. Return False
        otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    itemsize = getset_descriptor()
    def join(self, iterable):
        """S.join(iterable) -> string
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> string
        
        Return S left-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> string
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> string or unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    real = getset_descriptor()
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> string
        
        Return a copy of string S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> string
        
        Return S right-justified in a string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string, starting at the end of the string and working
        to the front.  If maxsplit is given, at most maxsplit splits are
        done. If sep is not specified or is None, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> string or unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in the string S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are removed
        from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    strides = getset_descriptor()
    def strip(self, chars):
        """S.strip([chars]) -> string or unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is unicode, S will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> string
        
        Return a copy of the string S with uppercase characters
        converted to lowercase and vice versa."""
        return None
    def title(self, _):
        """S.title() -> string
        
        Return a titlecased version of S, i.e. words start with uppercase
        characters, all remaining cased characters have lowercase."""
        return None
    def translate(self, table, deletechars):
        """S.translate(table [,deletechars]) -> string
        
        Return a copy of the string S, where all characters occurring
        in the optional argument deletechars are removed, and the
        remaining characters have been mapped through the given
        translation table, which must be a string of length 256 or None.
        If the table argument is None, no translation is applied and
        the operation simply removes the characters in deletechars."""
        return None
    def upper(self, _):
        """S.upper() -> string
        
        Return a copy of the string S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> string
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width.  The string S is never truncated."""
        return None
def subtract(x1, x2, out=None):
    """subtract(x1, x2[, out])
    
    Subtract arguments, element-wise.
    
    Parameters
    ----------
    x1, x2 : array_like
        The arrays to be subtracted from each other.
    
    Returns
    -------
    y : ndarray
        The difference of `x1` and `x2`, element-wise.  Returns a scalar if
        both  `x1` and `x2` are scalars.
    
    Notes
    -----
    Equivalent to ``x1 - x2`` in terms of array broadcasting.
    
    Examples
    --------
    >>> np.subtract(1.0, 4.0)
    -3.0
    
    >>> x1 = np.arange(9.0).reshape((3, 3))
    >>> x2 = np.arange(3.0)
    >>> np.subtract(x1, x2)
    array([[ 0.,  0.,  0.],
           [ 3.,  3.,  3.],
           [ 6.,  6.,  6.]])"""
    return ndarray()
def sum(a=False, axis=None, dtype=None, out=None, keepdims=False):
    """
        Sum of array elements over a given axis.
    
        Parameters
        ----------
        a : array_like
            Elements to sum.
        axis : None or int or tuple of ints, optional
            Axis or axes along which a sum is performed.
            The default (`axis` = `None`) is perform a sum 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 sum is performed on multiple
            axes, instead of a single axis or all the axes as before.
        dtype : dtype, optional
            The type of the returned array and of the accumulator in which
            the elements are summed.  By default, the dtype of `a` is used.
            An exception is when `a` has an integer type with less precision
            than the default platform integer.  In that case, the default
            platform integer is used instead.
        out : ndarray, optional
            Array into which the output is placed.  By default, a new array is
            created.  If `out` is given, it must be of the appropriate shape
            (the shape of `a` with `axis` removed, i.e.,
            ``numpy.delete(a.shape, axis)``).  Its type is preserved. See
            `doc.ufuncs` (Section "Output arguments") 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 original `arr`.
    
        Returns
        -------
        sum_along_axis : ndarray
            An array with the same shape as `a`, with the specified
            axis removed.   If `a` is a 0-d array, or if `axis` is None, a scalar
            is returned.  If an output array is specified, a reference to
            `out` is returned.
    
        See Also
        --------
        ndarray.sum : Equivalent method.
    
        cumsum : Cumulative sum of array elements.
    
        trapz : Integration of array values using the composite trapezoidal rule.
    
        mean, average
    
        Notes
        -----
        Arithmetic is modular when using integer types, and no error is
        raised on overflow.
    
        Examples
        --------
        >>> np.sum([0.5, 1.5])
        2.0
        >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
        1
        >>> np.sum([[0, 1], [0, 5]])
        6
        >>> np.sum([[0, 1], [0, 5]], axis=0)
        array([0, 6])
        >>> np.sum([[0, 1], [0, 5]], axis=1)
        array([1, 5])
    
        If the accumulator is too small, overflow occurs:
    
        >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
        -128
    
        """
    return ndarray()
def swapaxes(a, axis1, axis2):
    """
        Interchange two axes of an array.
    
        Parameters
        ----------
        a : array_like
            Input array.
        axis1 : int
            First axis.
        axis2 : int
            Second axis.
    
        Returns
        -------
        a_swapped : ndarray
            If `a` is an ndarray, then a view of `a` is returned; otherwise
            a new array is created.
    
        Examples
        --------
        >>> x = np.array([[1,2,3]])
        >>> np.swapaxes(x,0,1)
        array([[1],
               [2],
               [3]])
    
        >>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
        >>> x
        array([[[0, 1],
                [2, 3]],
               [[4, 5],
                [6, 7]]])
    
        >>> np.swapaxes(x,0,2)
        array([[[0, 4],
                [2, 6]],
               [[1, 5],
                [3, 7]]])
    
        """
    return ndarray()
def take(a, indices="raise", axis=None, out=None, mode="raise"):
    """
        Take elements from an array along an axis.
    
        This function does the same thing as "fancy" indexing (indexing arrays
        using arrays); however, it can be easier to use if you need elements
        along a given axis.
    
        Parameters
        ----------
        a : array_like
            The source array.
        indices : array_like
            The indices of the values to extract.
    
            .. versionadded:: 1.8.0
    
            Also allow scalars for indices.
        axis : int, optional
            The axis over which to select values. By default, the flattened
            input array is used.
        out : ndarray, optional
            If provided, the result will be placed in this array. It should
            be of the appropriate shape and dtype.
        mode : {'raise', 'wrap', 'clip'}, optional
            Specifies how out-of-bounds indices will behave.
    
            * 'raise' -- raise an error (default)
            * 'wrap' -- wrap around
            * 'clip' -- clip to the range
    
            'clip' mode means that all indices that are too large are replaced
            by the index that addresses the last element along that axis. Note
            that this disables indexing with negative numbers.
    
        Returns
        -------
        subarray : ndarray
            The returned array has the same type as `a`.
    
        See Also
        --------
        ndarray.take : equivalent method
    
        Examples
        --------
        >>> a = [4, 3, 5, 7, 6, 8]
        >>> indices = [0, 1, 4]
        >>> np.take(a, indices)
        array([4, 3, 6])
    
        In this example if `a` is an ndarray, "fancy" indexing can be used.
    
        >>> a = np.array(a)
        >>> a[indices]
        array([4, 3, 6])
    
        If `indices` is not one dimensional, the output also has these dimensions.
    
        >>> np.take(a, [[0, 1], [2, 3]])
        array([[4, 3],
               [5, 7]])
        """
    return ndarray()
def tan(x, out):
    """tan(x[, out])
    
    Compute tangent element-wise.
    
    Equivalent to ``np.sin(x)/np.cos(x)`` element-wise.
    
    Parameters
    ----------
    x : array_like
      Input array.
    out : ndarray, optional
        Output array of same shape as `x`.
    
    Returns
    -------
    y : ndarray
      The corresponding tangent values.
    
    Raises
    ------
    ValueError: invalid return array shape
        if `out` is provided and `out.shape` != `x.shape` (See Examples)
    
    Notes
    -----
    If `out` is provided, the function writes the result into it,
    and returns a reference to `out`.  (See Examples)
    
    References
    ----------
    M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
    New York, NY: Dover, 1972.
    
    Examples
    --------
    >>> from math import pi
    >>> np.tan(np.array([-pi,pi/2,pi]))
    array([  1.22460635e-16,   1.63317787e+16,  -1.22460635e-16])
    >>>
    >>> # Example of providing the optional output parameter illustrating
    >>> # that what is returned is a reference to said parameter
    >>> out2 = np.cos([0.1], out1)
    >>> out2 is out1
    True
    >>>
    >>> # Example of ValueError due to provision of shape mis-matched `out`
    >>> np.cos(np.zeros((3,3)),np.zeros((2,2)))
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ValueError: invalid return array shape"""
    return ndarray()
def tanh(x, out):
    """tanh(x[, out])
    
    Compute hyperbolic tangent element-wise.
    
    Equivalent to ``np.sinh(x)/np.cosh(x)`` or
    ``-1j * np.tan(1j*x)``.
    
    Parameters
    ----------
    x : array_like
        Input array.
    out : ndarray, optional
        Output array of same shape as `x`.
    
    Returns
    -------
    y : ndarray
        The corresponding hyperbolic tangent values.
    
    Raises
    ------
    ValueError: invalid return array shape
        if `out` is provided and `out.shape` != `x.shape` (See Examples)
    
    Notes
    -----
    If `out` is provided, the function writes the result into it,
    and returns a reference to `out`.  (See Examples)
    
    References
    ----------
    .. [1] M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
           New York, NY: Dover, 1972, pg. 83.
           http://www.math.sfu.ca/~cbm/aands/
    
    .. [2] Wikipedia, "Hyperbolic function",
           http://en.wikipedia.org/wiki/Hyperbolic_function
    
    Examples
    --------
    >>> np.tanh((0, np.pi*1j, np.pi*1j/2))
    array([ 0. +0.00000000e+00j,  0. -1.22460635e-16j,  0. +1.63317787e+16j])
    
    >>> # Example of providing the optional output parameter illustrating
    >>> # that what is returned is a reference to said parameter
    >>> out2 = np.tanh([0.1], out1)
    >>> out2 is out1
    True
    
    >>> # Example of ValueError due to provision of shape mis-matched `out`
    >>> np.tanh(np.zeros((3,3)),np.zeros((2,2)))
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ValueError: invalid return array shape"""
    return ndarray()
def tensordot(a, b=2, axes=2):
    """
        Compute tensor dot product along specified axes for arrays >= 1-D.
    
        Given two tensors (arrays of dimension greater than or equal to one),
        `a` and `b`, and an array_like object containing two array_like
        objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s
        elements (components) over the axes specified by ``a_axes`` and
        ``b_axes``. The third argument can be a single non-negative
        integer_like scalar, ``N``; if it is such, then the last ``N``
        dimensions of `a` and the first ``N`` dimensions of `b` are summed
        over.
    
        Parameters
        ----------
        a, b : array_like, len(shape) >= 1
            Tensors to "dot".
        axes : variable type
            * integer_like scalar
              Number of axes to sum over (applies to both arrays); or
            * (2,) array_like, both elements array_like of the same length
              List of axes to be summed over, first sequence applying to `a`,
              second to `b`.
    
        See Also
        --------
        dot, einsum
    
        Notes
        -----
        When there is more than one axis to sum over - and they are not the last
        (first) axes of `a` (`b`) - the argument `axes` should consist of
        two sequences of the same length, with the first axis to sum over given
        first in both sequences, the second axis second, and so forth.
    
        Examples
        --------
        A "traditional" example:
    
        >>> a = np.arange(60.).reshape(3,4,5)
        >>> b = np.arange(24.).reshape(4,3,2)
        >>> c = np.tensordot(a,b, axes=([1,0],[0,1]))
        >>> c.shape
        (5, 2)
        >>> c
        array([[ 4400.,  4730.],
               [ 4532.,  4874.],
               [ 4664.,  5018.],
               [ 4796.,  5162.],
               [ 4928.,  5306.]])
        >>> # A slower but equivalent way of computing the same...
        >>> d = np.zeros((5,2))
        >>> for i in range(5):
        ...   for j in range(2):
        ...     for k in range(3):
        ...       for n in range(4):
        ...         d[i,j] += a[k,n,i] * b[n,k,j]
        >>> c == d
        array([[ True,  True],
               [ True,  True],
               [ True,  True],
               [ True,  True],
               [ True,  True]], dtype=bool)
    
        An extended example taking advantage of the overloading of + and \*:
    
        >>> a = np.array(range(1, 9))
        >>> a.shape = (2, 2, 2)
        >>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)
        >>> A.shape = (2, 2)
        >>> a; A
        array([[[1, 2],
                [3, 4]],
               [[5, 6],
                [7, 8]]])
        array([[a, b],
               [c, d]], dtype=object)
    
        >>> np.tensordot(a, A) # third argument default is 2
        array([abbcccdddd, aaaaabbbbbbcccccccdddddddd], dtype=object)
    
        >>> np.tensordot(a, A, 1)
        array([[[acc, bdd],
                [aaacccc, bbbdddd]],
               [[aaaaacccccc, bbbbbdddddd],
                [aaaaaaacccccccc, bbbbbbbdddddddd]]], dtype=object)
    
        >>> np.tensordot(a, A, 0) # "Left for reader" (result too long to incl.)
        array([[[[[a, b],
                  [c, d]],
                  ...
    
        >>> np.tensordot(a, A, (0, 1))
        array([[[abbbbb, cddddd],
                [aabbbbbb, ccdddddd]],
               [[aaabbbbbbb, cccddddddd],
                [aaaabbbbbbbb, ccccdddddddd]]], dtype=object)
    
        >>> np.tensordot(a, A, (2, 1))
        array([[[abb, cdd],
                [aaabbbb, cccdddd]],
               [[aaaaabbbbbb, cccccdddddd],
                [aaaaaaabbbbbbbb, cccccccdddddddd]]], dtype=object)
    
        >>> np.tensordot(a, A, ((0, 1), (0, 1)))
        array([abbbcccccddddddd, aabbbbccccccdddddddd], dtype=object)
    
        >>> np.tensordot(a, A, ((2, 1), (1, 0)))
        array([acccbbdddd, aaaaacccccccbbbbbbdddddddd], dtype=object)
    
        """
    return None
def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
    """
            Run tests for module using nose.
    
            Parameters
            ----------
            label : {'fast', 'full', '', attribute identifier}, optional
                Identifies the tests to run. This can be a string to pass to
                the nosetests executable with the '-A' option, or one of several
                special values.  Special values are:
                * 'fast' - the default - which corresponds to the ``nosetests -A``
                  option of 'not slow'.
                * 'full' - fast (as above) and slow tests as in the
                  'no -A' option to nosetests - this is the same as ''.
                * None or '' - run all tests.
                attribute_identifier - string passed directly to nosetests as '-A'.
            verbose : int, optional
                Verbosity value for test outputs, in the range 1-10. Default is 1.
            extra_argv : list, optional
                List with any extra arguments to pass to nosetests.
            doctests : bool, optional
                If True, run doctests in module. Default is False.
            coverage : bool, optional
                If True, report coverage of NumPy code. Default is False.
                (This requires the `coverage module:
                 <http://nedbatchelder.com/code/modules/coverage.html>`_).
            raise_warnings : str or sequence of warnings, optional
                This specifies which warnings to configure as 'raise' instead
                of 'warn' during the test execution.  Valid strings are:
    
                  - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                  - "release" : equals ``()``, don't raise on any warnings.
    
            Returns
            -------
            result : object
                Returns the result of running the tests as a
                ``nose.result.TextTestResult`` object.
    
            Notes
            -----
            Each NumPy module exposes `test` in its namespace to run all tests for it.
            For example, to run all tests for numpy.lib:
    
            >>> np.lib.test() #doctest: +SKIP
    
            Examples
            --------
            >>> result = np.lib.test() #doctest: +SKIP
            Running unit tests for numpy.lib
            ...
            Ran 976 tests in 3.933s
    
            OK
    
            >>> result.errors #doctest: +SKIP
            []
            >>> result.knownfail #doctest: +SKIP
            []
            """
    return object()
def tile(A, reps):
    """
        Construct an array by repeating A the number of times given by reps.
    
        If `reps` has length ``d``, the result will have dimension of
        ``max(d, A.ndim)``.
    
        If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
        axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
        or shape (1, 1, 3) for 3-D replication. If this is not the desired
        behavior, promote `A` to d-dimensions manually before calling this
        function.
    
        If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
        Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
        (1, 1, 2, 2).
    
        Parameters
        ----------
        A : array_like
            The input array.
        reps : array_like
            The number of repetitions of `A` along each axis.
    
        Returns
        -------
        c : ndarray
            The tiled output array.
    
        See Also
        --------
        repeat : Repeat elements of an array.
    
        Examples
        --------
        >>> a = np.array([0, 1, 2])
        >>> np.tile(a, 2)
        array([0, 1, 2, 0, 1, 2])
        >>> np.tile(a, (2, 2))
        array([[0, 1, 2, 0, 1, 2],
               [0, 1, 2, 0, 1, 2]])
        >>> np.tile(a, (2, 1, 2))
        array([[[0, 1, 2, 0, 1, 2]],
               [[0, 1, 2, 0, 1, 2]]])
    
        >>> b = np.array([[1, 2], [3, 4]])
        >>> np.tile(b, 2)
        array([[1, 2, 1, 2],
               [3, 4, 3, 4]])
        >>> np.tile(b, (2, 1))
        array([[1, 2],
               [3, 4],
               [1, 2],
               [3, 4]])
    
        """
    return ndarray()
class timedelta64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def trace(a=None, offset=0, axis1=0, axis2=1, dtype=None, out=None):
    """
        Return the sum along diagonals of the array.
    
        If `a` is 2-D, the sum along its diagonal with the given offset
        is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
    
        If `a` has more than two dimensions, then the axes specified by axis1 and
        axis2 are used to determine the 2-D sub-arrays whose traces are returned.
        The shape of the resulting array is the same as that of `a` with `axis1`
        and `axis2` removed.
    
        Parameters
        ----------
        a : array_like
            Input array, from which the diagonals are taken.
        offset : int, optional
            Offset of the diagonal from the main diagonal. Can be both positive
            and negative. Defaults to 0.
        axis1, axis2 : int, optional
            Axes to be used as the first and second axis of the 2-D sub-arrays
            from which the diagonals should be taken. Defaults are the first two
            axes of `a`.
        dtype : dtype, optional
            Determines the data-type of the returned array and of the accumulator
            where the elements are summed. If dtype has the value None and `a` is
            of integer type of precision less than the default integer
            precision, then the default integer precision is used. Otherwise,
            the precision is the same as that of `a`.
        out : ndarray, optional
            Array into which the output is placed. Its type is preserved and
            it must be of the right shape to hold the output.
    
        Returns
        -------
        sum_along_diagonals : ndarray
            If `a` is 2-D, the sum along the diagonal is returned.  If `a` has
            larger dimensions, then an array of sums along diagonals is returned.
    
        See Also
        --------
        diag, diagonal, diagflat
    
        Examples
        --------
        >>> np.trace(np.eye(3))
        3.0
        >>> a = np.arange(8).reshape((2,2,2))
        >>> np.trace(a)
        array([6, 8])
    
        >>> a = np.arange(24).reshape((2,2,2,3))
        >>> np.trace(a).shape
        (2, 3)
    
        """
    return ndarray()
def transpose(a=None, axes=None):
    """
        Permute the dimensions of an array.
    
        Parameters
        ----------
        a : array_like
            Input array.
        axes : list of ints, optional
            By default, reverse the dimensions, otherwise permute the axes
            according to the values given.
    
        Returns
        -------
        p : ndarray
            `a` with its axes permuted.  A view is returned whenever
            possible.
    
        See Also
        --------
        rollaxis
    
        Examples
        --------
        >>> x = np.arange(4).reshape((2,2))
        >>> x
        array([[0, 1],
               [2, 3]])
    
        >>> np.transpose(x)
        array([[0, 2],
               [1, 3]])
    
        >>> x = np.ones((1, 2, 3))
        >>> np.transpose(x, (1, 0, 2)).shape
        (2, 1, 3)
    
        """
    return ndarray()
def trapz(y=-1, x=None, dx=1.0, axis=-1):
    """
        Integrate along the given axis using the composite trapezoidal rule.
    
        Integrate `y` (`x`) along given axis.
    
        Parameters
        ----------
        y : array_like
            Input array to integrate.
        x : array_like, optional
            If `x` is None, then spacing between all `y` elements is `dx`.
        dx : scalar, optional
            If `x` is None, spacing given by `dx` is assumed. Default is 1.
        axis : int, optional
            Specify the axis.
    
        Returns
        -------
        trapz : float
            Definite integral as approximated by trapezoidal rule.
    
        See Also
        --------
        sum, cumsum
    
        Notes
        -----
        Image [2]_ illustrates trapezoidal rule -- y-axis locations of points will
        be taken from `y` array, by default x-axis distances between points will be
        1.0, alternatively they can be provided with `x` array or with `dx` scalar.
        Return value will be equal to combined area under the red lines.
    
    
        References
        ----------
        .. [1] Wikipedia page: http://en.wikipedia.org/wiki/Trapezoidal_rule
    
        .. [2] Illustration image:
               http://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
    
        Examples
        --------
        >>> np.trapz([1,2,3])
        4.0
        >>> np.trapz([1,2,3], x=[4,6,8])
        8.0
        >>> np.trapz([1,2,3], dx=2)
        8.0
        >>> a = np.arange(6).reshape(2, 3)
        >>> a
        array([[0, 1, 2],
               [3, 4, 5]])
        >>> np.trapz(a, axis=0)
        array([ 1.5,  2.5,  3.5])
        >>> np.trapz(a, axis=1)
        array([ 2.,  8.])
    
        """
    return float()
def tri(N=typefloat(), M=None, k=0, dtype=typefloat()):
    """
        An array with ones at and below the given diagonal and zeros elsewhere.
    
        Parameters
        ----------
        N : int
            Number of rows in the array.
        M : int, optional
            Number of columns in the array.
            By default, `M` is taken equal to `N`.
        k : int, optional
            The sub-diagonal at and below which the array is filled.
            `k` = 0 is the main diagonal, while `k` < 0 is below it,
            and `k` > 0 is above.  The default is 0.
        dtype : dtype, optional
            Data type of the returned array.  The default is float.
    
        Returns
        -------
        tri : ndarray of shape (N, M)
            Array with its lower triangle filled with ones and zero elsewhere;
            in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise.
    
        Examples
        --------
        >>> np.tri(3, 5, 2, dtype=int)
        array([[1, 1, 1, 0, 0],
               [1, 1, 1, 1, 0],
               [1, 1, 1, 1, 1]])
    
        >>> np.tri(3, 5, -1)
        array([[ 0.,  0.,  0.,  0.,  0.],
               [ 1.,  0.,  0.,  0.,  0.],
               [ 1.,  1.,  0.,  0.,  0.]])
    
        """
    return ndarray()
def tril(m=0, k=0):
    """
        Lower triangle of an array.
    
        Return a copy of an array with elements above the `k`-th diagonal zeroed.
    
        Parameters
        ----------
        m : array_like, shape (M, N)
            Input array.
        k : int, optional
            Diagonal above which to zero elements.  `k = 0` (the default) is the
            main diagonal, `k < 0` is below it and `k > 0` is above.
    
        Returns
        -------
        tril : ndarray, shape (M, N)
            Lower triangle of `m`, of same shape and data-type as `m`.
    
        See Also
        --------
        triu : same thing, only for the upper triangle
    
        Examples
        --------
        >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
        array([[ 0,  0,  0],
               [ 4,  0,  0],
               [ 7,  8,  0],
               [10, 11, 12]])
    
        """
    return ndarray()
def tril_indices(n=0, k=0):
    """
        Return the indices for the lower-triangle of an (n, n) array.
    
        Parameters
        ----------
        n : int
            The row dimension of the square arrays for which the returned
            indices will be valid.
        k : int, optional
            Diagonal offset (see `tril` for details).
    
        Returns
        -------
        inds : tuple of arrays
            The indices for the triangle. The returned tuple contains two arrays,
            each with the indices along one dimension of the array.
    
        See also
        --------
        triu_indices : similar function, for upper-triangular.
        mask_indices : generic function accepting an arbitrary mask function.
        tril, triu
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        Examples
        --------
        Compute two different sets of indices to access 4x4 arrays, one for the
        lower triangular part starting at the main diagonal, and one starting two
        diagonals further right:
    
        >>> il1 = np.tril_indices(4)
        >>> il2 = np.tril_indices(4, 2)
    
        Here is how they can be used with a sample array:
    
        >>> a = np.arange(16).reshape(4, 4)
        >>> a
        array([[ 0,  1,  2,  3],
               [ 4,  5,  6,  7],
               [ 8,  9, 10, 11],
               [12, 13, 14, 15]])
    
        Both for indexing:
    
        >>> a[il1]
        array([ 0,  4,  5,  8,  9, 10, 12, 13, 14, 15])
    
        And for assigning values:
    
        >>> a[il1] = -1
        >>> a
        array([[-1,  1,  2,  3],
               [-1, -1,  6,  7],
               [-1, -1, -1, 11],
               [-1, -1, -1, -1]])
    
        These cover almost the whole array (two diagonals right of the main one):
    
        >>> a[il2] = -10
        >>> a
        array([[-10, -10, -10,   3],
               [-10, -10, -10, -10],
               [-10, -10, -10, -10],
               [-10, -10, -10, -10]])
    
        """
    return tuple()
def tril_indices__from(arr=0, k=0):
    """
        Return the indices for the lower-triangle of arr.
    
        See `tril_indices` for full details.
    
        Parameters
        ----------
        arr : array_like
            The indices will be valid for square arrays whose dimensions are
            the same as arr.
        k : int, optional
            Diagonal offset (see `tril` for details).
    
        See Also
        --------
        tril_indices, tril
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        """
    return None
def trim_zeros(filt="fb", trim="fb"):
    """
        Trim the leading and/or trailing zeros from a 1-D array or sequence.
    
        Parameters
        ----------
        filt : 1-D array or sequence
            Input array.
        trim : str, optional
            A string with 'f' representing trim from front and 'b' to trim from
            back. Default is 'fb', trim zeros from both front and back of the
            array.
    
        Returns
        -------
        trimmed : 1-D array or sequence
            The result of trimming the input. The input data type is preserved.
    
        Examples
        --------
        >>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
        >>> np.trim_zeros(a)
        array([1, 2, 3, 0, 2, 1])
    
        >>> np.trim_zeros(a, 'b')
        array([0, 0, 0, 1, 2, 3, 0, 2, 1])
    
        The input data type is preserved, list/tuple in means list/tuple out.
    
        >>> np.trim_zeros([0, 1, 2, 0])
        [1, 2]
    
        """
    return _1_D() if False else sequence()
def triu(m=0, k=0):
    """
        Upper triangle of an array.
    
        Return a copy of a matrix with the elements below the `k`-th diagonal
        zeroed.
    
        Please refer to the documentation for `tril` for further details.
    
        See Also
        --------
        tril : lower triangle of an array
    
        Examples
        --------
        >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
        array([[ 1,  2,  3],
               [ 4,  5,  6],
               [ 0,  8,  9],
               [ 0,  0, 12]])
    
        """
    return None
def triu_indices(n=0, k=0):
    """
        Return the indices for the upper-triangle of an (n, n) array.
    
        Parameters
        ----------
        n : int
            The size of the arrays for which the returned indices will
            be valid.
        k : int, optional
            Diagonal offset (see `triu` for details).
    
        Returns
        -------
        inds : tuple, shape(2) of ndarrays, shape(`n`)
            The indices for the triangle. The returned tuple contains two arrays,
            each with the indices along one dimension of the array.  Can be used
            to slice a ndarray of shape(`n`, `n`).
    
        See also
        --------
        tril_indices : similar function, for lower-triangular.
        mask_indices : generic function accepting an arbitrary mask function.
        triu, tril
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        Examples
        --------
        Compute two different sets of indices to access 4x4 arrays, one for the
        upper triangular part starting at the main diagonal, and one starting two
        diagonals further right:
    
        >>> iu1 = np.triu_indices(4)
        >>> iu2 = np.triu_indices(4, 2)
    
        Here is how they can be used with a sample array:
    
        >>> a = np.arange(16).reshape(4, 4)
        >>> a
        array([[ 0,  1,  2,  3],
               [ 4,  5,  6,  7],
               [ 8,  9, 10, 11],
               [12, 13, 14, 15]])
    
        Both for indexing:
    
        >>> a[iu1]
        array([ 0,  1,  2,  3,  5,  6,  7, 10, 11, 15])
    
        And for assigning values:
    
        >>> a[iu1] = -1
        >>> a
        array([[-1, -1, -1, -1],
               [ 4, -1, -1, -1],
               [ 8,  9, -1, -1],
               [12, 13, 14, -1]])
    
        These cover only a small part of the whole array (two diagonals right
        of the main one):
    
        >>> a[iu2] = -10
        >>> a
        array([[ -1,  -1, -10, -10],
               [  4,  -1,  -1, -10],
               [  8,   9,  -1,  -1],
               [ 12,  13,  14,  -1]])
    
        """
    return tuple()
def triu_indices__from(arr=0, k=0):
    """
        Return the indices for the upper-triangle of a (N, N) array.
    
        See `triu_indices` for full details.
    
        Parameters
        ----------
        arr : ndarray, shape(N, N)
            The indices will be valid for square arrays.
        k : int, optional
            Diagonal offset (see `triu` for details).
    
        Returns
        -------
        triu_indices_from : tuple, shape(2) of ndarray, shape(N)
            Indices for the upper-triangle of `arr`.
    
        See Also
        --------
        triu_indices, triu
    
        Notes
        -----
        .. versionadded:: 1.4.0
    
        """
    return tuple()
def true_divide(x1, x2, out=None):
    """true_divide(x1, x2[, out])
    
    Returns a true division of the inputs, element-wise.
    
    Instead of the Python traditional 'floor division', this returns a true
    division.  True division adjusts the output type to present the best
    answer, regardless of input types.
    
    Parameters
    ----------
    x1 : array_like
        Dividend array.
    x2 : array_like
        Divisor array.
    
    Returns
    -------
    out : ndarray
        Result is scalar if both inputs are scalar, ndarray otherwise.
    
    Notes
    -----
    The floor division operator ``//`` was added in Python 2.2 making ``//``
    and ``/`` equivalent operators.  The default floor division operation of
    ``/`` can be replaced by true division with
    ``from __future__ import division``.
    
    In Python 3.0, ``//`` is the floor division operator and ``/`` the
    true division operator.  The ``true_divide(x1, x2)`` function is
    equivalent to true division in Python.
    
    Examples
    --------
    >>> x = np.arange(5)
    >>> np.true_divide(x, 4)
    array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])
    
    >>> x/4
    array([0, 0, 0, 0, 1])
    >>> x//4
    array([0, 0, 0, 0, 1])
    
    >>> from __future__ import division
    >>> x/4
    array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])
    >>> x//4
    array([0, 0, 0, 0, 1])"""
    return ndarray()
def trunc(x, out=None):
    """trunc(x[, out])
    
    Return the truncated value of the input, element-wise.
    
    The truncated value of the scalar `x` is the nearest integer `i` which
    is closer to zero than `x` is. In short, the fractional part of the
    signed number `x` is discarded.
    
    Parameters
    ----------
    x : array_like
        Input data.
    
    Returns
    -------
    y : {ndarray, scalar}
        The truncated value of each element in `x`.
    
    See Also
    --------
    ceil, floor, rint
    
    Notes
    -----
    .. versionadded:: 1.3.0
    
    Examples
    --------
    >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
    >>> np.trunc(a)
    array([-1., -1., -0.,  0.,  1.,  1.,  2.])"""
    return ndarray()
typeDict = dict()
typeNA = dict()
typecodes = dict()
def typename(char):
    """
        Return a description for the given data type code.
    
        Parameters
        ----------
        char : str
            Data type code.
    
        Returns
        -------
        out : str
            Description of the input data type code.
    
        See Also
        --------
        dtype, typecodes
    
        Examples
        --------
        >>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
        ...              'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
        >>> for typechar in typechars:
        ...     print typechar, ' : ', np.typename(typechar)
        ...
        S1  :  character
        ?  :  bool
        B  :  unsigned char
        D  :  complex double precision
        G  :  complex long double precision
        F  :  complex single precision
        I  :  unsigned integer
        H  :  unsigned short
        L  :  unsigned long integer
        O  :  object
        Q  :  unsigned long long integer
        S  :  string
        U  :  unicode
        V  :  void
        b  :  signed char
        d  :  double precision
        g  :  long precision
        f  :  single precision
        i  :  integer
        h  :  short
        l  :  long integer
        q  :  long long integer
    
        """
    return str()
class uint8:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class ufunc:
    __doc__ = str()
    __name__ = str()
    def accumulate(self, array, axis, dtype, out):
        """accumulate(array, axis=0, dtype=None, out=None)
        
            Accumulate the result of applying the operator to all elements.
        
            For a one-dimensional array, accumulate produces results equivalent to::
        
              r = np.empty(len(A))
              t = op.identity        # op = the ufunc being applied to A's  elements
              for i in range(len(A)):
                  t = op(t, A[i])
                  r[i] = t
              return r
        
            For example, add.accumulate() is equivalent to np.cumsum().
        
            For a multi-dimensional array, accumulate is applied along only one
            axis (axis zero by default; see Examples below) so repeated use is
            necessary if one wants to accumulate over multiple axes.
        
            Parameters
            ----------
            array : array_like
                The array to act on.
            axis : int, optional
                The axis along which to apply the accumulation; default is zero.
            dtype : data-type code, optional
                The data-type used to represent the intermediate results. Defaults
                to the data-type of the output array if such is provided, or the
                the data-type of the input array if no output array is provided.
            out : ndarray, optional
                A location into which the result is stored. If not provided a
                freshly-allocated array is returned.
        
            Returns
            -------
            r : ndarray
                The accumulated values. If `out` was supplied, `r` is a reference to
                `out`.
        
            Examples
            --------
            1-D array examples:
        
            >>> np.add.accumulate([2, 3, 5])
            array([ 2,  5, 10])
            >>> np.multiply.accumulate([2, 3, 5])
            array([ 2,  6, 30])
        
            2-D array examples:
        
            >>> I = np.eye(2)
            >>> I
            array([[ 1.,  0.],
                   [ 0.,  1.]])
        
            Accumulate along axis 0 (rows), down columns:
        
            >>> np.add.accumulate(I, 0)
            array([[ 1.,  0.],
                   [ 1.,  1.]])
            >>> np.add.accumulate(I) # no axis specified = axis zero
            array([[ 1.,  0.],
                   [ 1.,  1.]])
        
            Accumulate along axis 1 (columns), through rows:
        
            >>> np.add.accumulate(I, 1)
            array([[ 1.,  1.],
                   [ 0.,  1.]])"""
        return ndarray()
    def at(self, a, indices, b):
        """at(a, indices, b=None)
        
            Performs unbuffered in place operation on operand 'a' for elements
            specified by 'indices'. For addition ufunc, this method is equivalent to
            `a[indices] += b`, except that results are accumulated for elements that
            are indexed more than once. For example, `a[[0,0]] += 1` will only
            increment the first element once because of buffering, whereas
            `add.at(a, [0,0], 1)` will increment the first element twice.
        
            Parameters
            ----------
            a : array_like
                The array to perform in place operation on.
            indices : array_like or tuple
                Array like index object or slice object for indexing into first
                operand. If first operand has multiple dimensions, indices can be a
                tuple of array like index objects or slice objects.
            b : array_like
                Second operand for ufuncs requiring two operands. Operand must be
                broadcastable over first operand after indexing or slicing.
        
            Examples
            --------
            Set items 0 and 1 to their negative values:
        
            >>> a = np.array([1, 2, 3, 4])
            >>> np.negative.at(a, [0, 1])
            >>> print(a)
            array([-1, -2, 3, 4])
        
            ::
        
            Increment items 0 and 1, and increment item 2 twice:
        
            >>> a = np.array([1, 2, 3, 4])
            >>> np.add.at(a, [0, 1, 2, 2], 1)
            >>> print(a)
            array([2, 3, 5, 4])
        
            ::
        
            Add items 0 and 1 in first array to second array,
            and store results in first array:
        
            >>> a = np.array([1, 2, 3, 4])
            >>> b = np.array([1, 2])
            >>> np.add.at(a, [0, 1], b)
            >>> print(a)
            array([2, 4, 3, 4])"""
        return None
    identity = getset_descriptor()
    nargs = getset_descriptor()
    nin = getset_descriptor()
    nout = getset_descriptor()
    ntypes = getset_descriptor()
    def outer(self, A, B):
        """outer(A, B)
        
            Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.
        
            Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
            ``op.outer(A, B)`` is an array of dimension M + N such that:
        
            .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
               op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])
        
            For `A` and `B` one-dimensional, this is equivalent to::
        
              r = empty(len(A),len(B))
              for i in range(len(A)):
                  for j in range(len(B)):
                      r[i,j] = op(A[i], B[j]) # op = ufunc in question
        
            Parameters
            ----------
            A : array_like
                First array
            B : array_like
                Second array
        
            Returns
            -------
            r : ndarray
                Output array
        
            See Also
            --------
            numpy.outer
        
            Examples
            --------
            >>> np.multiply.outer([1, 2, 3], [4, 5, 6])
            array([[ 4,  5,  6],
                   [ 8, 10, 12],
                   [12, 15, 18]])
        
            A multi-dimensional example:
        
            >>> A = np.array([[1, 2, 3], [4, 5, 6]])
            >>> A.shape
            (2, 3)
            >>> B = np.array([[1, 2, 3, 4]])
            >>> B.shape
            (1, 4)
            >>> C = np.multiply.outer(A, B)
            >>> C.shape; C
            (2, 3, 1, 4)
            array([[[[ 1,  2,  3,  4]],
                    [[ 2,  4,  6,  8]],
                    [[ 3,  6,  9, 12]]],
                   [[[ 4,  8, 12, 16]],
                    [[ 5, 10, 15, 20]],
                    [[ 6, 12, 18, 24]]]])"""
        return ndarray()
    def reduce(self, a, axis, dtype, out, keepdims):
        """reduce(a, axis=0, dtype=None, out=None, keepdims=False)
        
            Reduces `a`'s dimension by one, by applying ufunc along one axis.
        
            Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then
            :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
            the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
            ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
            For a one-dimensional array, reduce produces results equivalent to:
            ::
        
             r = op.identity # op = ufunc
             for i in range(len(A)):
               r = op(r, A[i])
             return r
        
            For example, add.reduce() is equivalent to sum().
        
            Parameters
            ----------
            a : array_like
                The array to act on.
            axis : None or int or tuple of ints, optional
                Axis or axes along which a reduction is performed.
                The default (`axis` = 0) is perform a reduction over the first
                dimension 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 `None`, a reduction is performed over all the axes.
                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.
        
                For operations which are either not commutative or not associative,
                doing a reduction over multiple axes is not well-defined. The
                ufuncs do not currently raise an exception in this case, but will
                likely do so in the future.
            dtype : data-type code, optional
                The type used to represent the intermediate results. Defaults
                to the data-type of the output array if this is provided, or
                the data-type of the input array if no output array is provided.
            out : ndarray, optional
                A location into which the result is stored. If not provided, a
                freshly-allocated array is returned.
            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 original `arr`.
        
            Returns
            -------
            r : ndarray
                The reduced array. If `out` was supplied, `r` is a reference to it.
        
            Examples
            --------
            >>> np.multiply.reduce([2,3,5])
            30
        
            A multi-dimensional array example:
        
            >>> X = np.arange(8).reshape((2,2,2))
            >>> X
            array([[[0, 1],
                    [2, 3]],
                   [[4, 5],
                    [6, 7]]])
            >>> np.add.reduce(X, 0)
            array([[ 4,  6],
                   [ 8, 10]])
            >>> np.add.reduce(X) # confirm: default axis value is 0
            array([[ 4,  6],
                   [ 8, 10]])
            >>> np.add.reduce(X, 1)
            array([[ 2,  4],
                   [10, 12]])
            >>> np.add.reduce(X, 2)
            array([[ 1,  5],
                   [ 9, 13]])"""
        return ndarray()
    def reduceat(self, a, indices, axis, dtype, out):
        """reduceat(a, indices, axis=0, dtype=None, out=None)
        
            Performs a (local) reduce with specified slices over a single axis.
        
            For i in ``range(len(indices))``, `reduceat` computes
            ``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th
            generalized "row" parallel to `axis` in the final result (i.e., in a
            2-D array, for example, if `axis = 0`, it becomes the i-th row, but if
            `axis = 1`, it becomes the i-th column).  There are two exceptions to this:
        
              * when ``i = len(indices) - 1`` (so for the last index),
                ``indices[i+1] = a.shape[axis]``.
              * if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is
                simply ``a[indices[i]]``.
        
            The shape of the output depends on the size of `indices`, and may be
            larger than `a` (this happens if ``len(indices) > a.shape[axis]``).
        
            Parameters
            ----------
            a : array_like
                The array to act on.
            indices : array_like
                Paired indices, comma separated (not colon), specifying slices to
                reduce.
            axis : int, optional
                The axis along which to apply the reduceat.
            dtype : data-type code, optional
                The type used to represent the intermediate results. Defaults
                to the data type of the output array if this is provided, or
                the data type of the input array if no output array is provided.
            out : ndarray, optional
                A location into which the result is stored. If not provided a
                freshly-allocated array is returned.
        
            Returns
            -------
            r : ndarray
                The reduced values. If `out` was supplied, `r` is a reference to
                `out`.
        
            Notes
            -----
            A descriptive example:
        
            If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as
            ``ufunc.reduceat(a, indices)[::2]`` where `indices` is
            ``range(len(array) - 1)`` with a zero placed
            in every other element:
            ``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.
        
            Don't be fooled by this attribute's name: `reduceat(a)` is not
            necessarily smaller than `a`.
        
            Examples
            --------
            To take the running sum of four successive values:
        
            >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
            array([ 6, 10, 14, 18])
        
            A 2-D example:
        
            >>> x = np.linspace(0, 15, 16).reshape(4,4)
            >>> x
            array([[  0.,   1.,   2.,   3.],
                   [  4.,   5.,   6.,   7.],
                   [  8.,   9.,  10.,  11.],
                   [ 12.,  13.,  14.,  15.]])
        
            ::
        
             # reduce such that the result has the following five rows:
             # [row1 + row2 + row3]
             # [row4]
             # [row2]
             # [row3]
             # [row1 + row2 + row3 + row4]
        
            >>> np.add.reduceat(x, [0, 3, 1, 2, 0])
            array([[ 12.,  15.,  18.,  21.],
                   [ 12.,  13.,  14.,  15.],
                   [  4.,   5.,   6.,   7.],
                   [  8.,   9.,  10.,  11.],
                   [ 24.,  28.,  32.,  36.]])
        
            ::
        
             # reduce such that result has the following two columns:
             # [col1 * col2 * col3, col4]
        
            >>> np.multiply.reduceat(x, [0, 3], 1)
            array([[    0.,     3.],
                   [  120.,     7.],
                   [  720.,    11.],
                   [ 2184.,    15.]])"""
        return ndarray()
    signature = getset_descriptor()
    types = getset_descriptor()
class uint64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint16:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint32:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint8:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint32:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class uint64:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class unicode:
    __doc__ = str()
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    def capitalize(self, _):
        """S.capitalize() -> unicode
        
        Return a capitalized version of S, i.e. make the first character
        have upper case and the rest lower case."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> unicode
        
        Return S centered in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        Unicode string S[start:end].  Optional arguments start and end are
        interpreted as in slice notation."""
        return None
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> string or unicode
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> string or unicode
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that can handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> unicode
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> unicode
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdecimal(self, _):
        """S.isdecimal() -> bool
        
        Return True if there are only decimal characters in S,
        False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isnumeric(self, _):
        """S.isnumeric() -> bool
        
        Return True if there are only numeric characters in S,
        False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. upper- and titlecase characters may only
        follow uncased characters and lowercase characters only cased ones.
        Return False otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def join(self, iterable):
        """S.join(iterable) -> unicode
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> int
        
        Return S left-justified in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> unicode
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> unicode
        
        Return a copy of S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> unicode
        
        Return S right-justified in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in S, using sep as the
        delimiter string, starting at the end of the string and
        working to the front.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are
        removed from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    def strip(self, chars):
        """S.strip([chars]) -> unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> unicode
        
        Return a copy of S with uppercase characters converted to lowercase
        and vice versa."""
        return None
    def title(self, _):
        """S.title() -> unicode
        
        Return a titlecased version of S, i.e. words start with title case
        characters, all remaining cased characters have lower case."""
        return None
    def translate(self, table):
        """S.translate(table) -> unicode
        
        Return a copy of the string S, where all characters have been mapped
        through the given translation table, which must be a mapping of
        Unicode ordinals to Unicode ordinals, Unicode strings or None.
        Unmapped characters are left untouched. Characters mapped to None
        are deleted."""
        return None
    def upper(self, _):
        """S.upper() -> unicode
        
        Return a copy of S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> unicode
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width. The string S is never truncated."""
        return None
class unicode_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    base = getset_descriptor()
    def capitalize(self, _):
        """S.capitalize() -> unicode
        
        Return a capitalized version of S, i.e. make the first character
        have upper case and the rest lower case."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> unicode
        
        Return S centered in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def conj(self, _):
        """None"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        Unicode string S[start:end].  Optional arguments start and end are
        interpreted as in slice notation."""
        return None
    data = getset_descriptor()
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> string or unicode
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    dtype = getset_descriptor()
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> string or unicode
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that can handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> unicode
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> unicode
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    imag = getset_descriptor()
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdecimal(self, _):
        """S.isdecimal() -> bool
        
        Return True if there are only decimal characters in S,
        False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isnumeric(self, _):
        """S.isnumeric() -> bool
        
        Return True if there are only numeric characters in S,
        False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. upper- and titlecase characters may only
        follow uncased characters and lowercase characters only cased ones.
        Return False otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    itemsize = getset_descriptor()
    def join(self, iterable):
        """S.join(iterable) -> unicode
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> int
        
        Return S left-justified in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> unicode
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    real = getset_descriptor()
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> unicode
        
        Return a copy of S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> unicode
        
        Return S right-justified in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in S, using sep as the
        delimiter string, starting at the end of the string and
        working to the front.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are
        removed from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    strides = getset_descriptor()
    def strip(self, chars):
        """S.strip([chars]) -> unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> unicode
        
        Return a copy of S with uppercase characters converted to lowercase
        and vice versa."""
        return None
    def title(self, _):
        """S.title() -> unicode
        
        Return a titlecased version of S, i.e. words start with title case
        characters, all remaining cased characters have lower case."""
        return None
    def translate(self, table):
        """S.translate(table) -> unicode
        
        Return a copy of the string S, where all characters have been mapped
        through the given translation table, which must be a mapping of
        Unicode ordinals to Unicode ordinals, Unicode strings or None.
        Unmapped characters are left untouched. Characters mapped to None
        are deleted."""
        return None
    def upper(self, _):
        """S.upper() -> unicode
        
        Return a copy of S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> unicode
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width. The string S is never truncated."""
        return None
class unicode_:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    def _formatter_field_name_split(self, _):
        """None"""
        return None
    def _formatter_parser(self, _):
        """None"""
        return None
    base = getset_descriptor()
    def capitalize(self, _):
        """S.capitalize() -> unicode
        
        Return a capitalized version of S, i.e. make the first character
        have upper case and the rest lower case."""
        return None
    def center(self, width, fillchar):
        """S.center(width[, fillchar]) -> unicode
        
        Return S centered in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)"""
        return None
    def conj(self, _):
        """None"""
        return None
    def count(self, sub, start, end):
        """S.count(sub[, start[, end]]) -> int
        
        Return the number of non-overlapping occurrences of substring sub in
        Unicode string S[start:end].  Optional arguments start and end are
        interpreted as in slice notation."""
        return None
    data = getset_descriptor()
    def decode(self, encoding, errors):
        """S.decode([encoding[,errors]]) -> string or unicode
        
        Decodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
        as well as any other name registered with codecs.register_error that is
        able to handle UnicodeDecodeErrors."""
        return None
    dtype = getset_descriptor()
    def encode(self, encoding, errors):
        """S.encode([encoding[,errors]]) -> string or unicode
        
        Encodes S using the codec registered for encoding. encoding defaults
        to the default encoding. errors may be given to set a different error
        handling scheme. Default is 'strict' meaning that encoding errors raise
        a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
        'xmlcharrefreplace' as well as any other name registered with
        codecs.register_error that can handle UnicodeEncodeErrors."""
        return None
    def endswith(self, suffix, start, end):
        """S.endswith(suffix[, start[, end]]) -> bool
        
        Return True if S ends with the specified suffix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        suffix can also be a tuple of strings to try."""
        return None
    def expandtabs(self, tabsize):
        """S.expandtabs([tabsize]) -> unicode
        
        Return a copy of S where all tab characters are expanded using spaces.
        If tabsize is not given, a tab size of 8 characters is assumed."""
        return None
    def find(self, sub, start, end):
        """S.find(sub [,start [,end]]) -> int
        
        Return the lowest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    flags = getset_descriptor()
    flat = getset_descriptor()
    def format(self, args, kwargs):
        """S.format(*args, **kwargs) -> unicode
        
        Return a formatted version of S, using substitutions from args and kwargs.
        The substitutions are identified by braces ('{' and '}')."""
        return None
    imag = getset_descriptor()
    def index(self, sub, start, end):
        """S.index(sub [,start [,end]]) -> int
        
        Like S.find() but raise ValueError when the substring is not found."""
        return None
    def isalnum(self, _):
        """S.isalnum() -> bool
        
        Return True if all characters in S are alphanumeric
        and there is at least one character in S, False otherwise."""
        return None
    def isalpha(self, _):
        """S.isalpha() -> bool
        
        Return True if all characters in S are alphabetic
        and there is at least one character in S, False otherwise."""
        return None
    def isdecimal(self, _):
        """S.isdecimal() -> bool
        
        Return True if there are only decimal characters in S,
        False otherwise."""
        return None
    def isdigit(self, _):
        """S.isdigit() -> bool
        
        Return True if all characters in S are digits
        and there is at least one character in S, False otherwise."""
        return None
    def islower(self, _):
        """S.islower() -> bool
        
        Return True if all cased characters in S are lowercase and there is
        at least one cased character in S, False otherwise."""
        return None
    def isnumeric(self, _):
        """S.isnumeric() -> bool
        
        Return True if there are only numeric characters in S,
        False otherwise."""
        return None
    def isspace(self, _):
        """S.isspace() -> bool
        
        Return True if all characters in S are whitespace
        and there is at least one character in S, False otherwise."""
        return None
    def istitle(self, _):
        """S.istitle() -> bool
        
        Return True if S is a titlecased string and there is at least one
        character in S, i.e. upper- and titlecase characters may only
        follow uncased characters and lowercase characters only cased ones.
        Return False otherwise."""
        return None
    def isupper(self, _):
        """S.isupper() -> bool
        
        Return True if all cased characters in S are uppercase and there is
        at least one cased character in S, False otherwise."""
        return None
    itemsize = getset_descriptor()
    def join(self, iterable):
        """S.join(iterable) -> unicode
        
        Return a string which is the concatenation of the strings in the
        iterable.  The separator between elements is S."""
        return None
    def ljust(self, width, fillchar):
        """S.ljust(width[, fillchar]) -> int
        
        Return S left-justified in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def lower(self, _):
        """S.lower() -> unicode
        
        Return a copy of the string S converted to lowercase."""
        return None
    def lstrip(self, chars):
        """S.lstrip([chars]) -> unicode
        
        Return a copy of the string S with leading whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    def partition(self, sep):
        """S.partition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, and return the part before it,
        the separator itself, and the part after it.  If the separator is not
        found, return S and two empty strings."""
        return None
    real = getset_descriptor()
    def replace(self, old, new, count):
        """S.replace(old, new[, count]) -> unicode
        
        Return a copy of S with all occurrences of substring
        old replaced by new.  If the optional argument count is
        given, only the first count occurrences are replaced."""
        return None
    def rfind(self, sub, start, end):
        """S.rfind(sub [,start [,end]]) -> int
        
        Return the highest index in S where substring sub is found,
        such that sub is contained within S[start:end].  Optional
        arguments start and end are interpreted as in slice notation.
        
        Return -1 on failure."""
        return None
    def rindex(self, sub, start, end):
        """S.rindex(sub [,start [,end]]) -> int
        
        Like S.rfind() but raise ValueError when the substring is not found."""
        return None
    def rjust(self, width, fillchar):
        """S.rjust(width[, fillchar]) -> unicode
        
        Return S right-justified in a Unicode string of length width. Padding is
        done using the specified fill character (default is a space)."""
        return None
    def rpartition(self, sep):
        """S.rpartition(sep) -> (head, sep, tail)
        
        Search for the separator sep in S, starting at the end of S, and return
        the part before it, the separator itself, and the part after it.  If the
        separator is not found, return two empty strings and S."""
        return None
    def rsplit(self, sep, maxsplit):
        """S.rsplit([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in S, using sep as the
        delimiter string, starting at the end of the string and
        working to the front.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified, any whitespace string
        is a separator."""
        return None
    def rstrip(self, chars):
        """S.rstrip([chars]) -> unicode
        
        Return a copy of the string S with trailing whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    def split(self, sep, maxsplit):
        """S.split([sep [,maxsplit]]) -> list of strings
        
        Return a list of the words in S, using sep as the
        delimiter string.  If maxsplit is given, at most maxsplit
        splits are done. If sep is not specified or is None, any
        whitespace string is a separator and empty strings are
        removed from the result."""
        return None
    def splitlines(self, keepends=False):
        """S.splitlines(keepends=False) -> list of strings
        
        Return a list of the lines in S, breaking at line boundaries.
        Line breaks are not included in the resulting list unless keepends
        is given and true."""
        return None
    def startswith(self, prefix, start, end):
        """S.startswith(prefix[, start[, end]]) -> bool
        
        Return True if S starts with the specified prefix, False otherwise.
        With optional start, test S beginning at that position.
        With optional end, stop comparing S at that position.
        prefix can also be a tuple of strings to try."""
        return None
    strides = getset_descriptor()
    def strip(self, chars):
        """S.strip([chars]) -> unicode
        
        Return a copy of the string S with leading and trailing
        whitespace removed.
        If chars is given and not None, remove characters in chars instead.
        If chars is a str, it will be converted to unicode before stripping"""
        return None
    def swapcase(self, _):
        """S.swapcase() -> unicode
        
        Return a copy of S with uppercase characters converted to lowercase
        and vice versa."""
        return None
    def title(self, _):
        """S.title() -> unicode
        
        Return a titlecased version of S, i.e. words start with title case
        characters, all remaining cased characters have lower case."""
        return None
    def translate(self, table):
        """S.translate(table) -> unicode
        
        Return a copy of the string S, where all characters have been mapped
        through the given translation table, which must be a mapping of
        Unicode ordinals to Unicode ordinals, Unicode strings or None.
        Unmapped characters are left untouched. Characters mapped to None
        are deleted."""
        return None
    def upper(self, _):
        """S.upper() -> unicode
        
        Return a copy of S converted to uppercase."""
        return None
    def zfill(self, width):
        """S.zfill(width) -> unicode
        
        Pad a numeric string S with zeros on the left, to fill a field
        of the specified width. The string S is never truncated."""
        return None
def union1d(ar1ar2):
    """
        Find the union of two arrays.
    
        Return the unique, sorted array of values that are in either of the two
        input arrays.
    
        Parameters
        ----------
        ar1, ar2 : array_like
            Input arrays. They are flattened if they are not already 1D.
    
        Returns
        -------
        union1d : ndarray
            Unique, sorted union of the input arrays.
    
        See Also
        --------
        numpy.lib.arraysetops : Module with a number of other functions for
                                performing set operations on arrays.
    
        Examples
        --------
        >>> np.union1d([-1, 0, 1], [-2, 0, 2])
        array([-2, -1,  0,  1,  2])
    
        """
    return ndarray()
def unique(ar=False, return_index=False, return_inverse=False):
    """
        Find the unique elements of an array.
    
        Returns the sorted unique elements of an array. There are two optional
        outputs in addition to the unique elements: the indices of the input array
        that give the unique values, and the indices of the unique array that
        reconstruct the input array.
    
        Parameters
        ----------
        ar : array_like
            Input array. This will be flattened if it is not already 1-D.
        return_index : bool, optional
            If True, also return the indices of `ar` that result in the unique
            array.
        return_inverse : bool, optional
            If True, also return the indices of the unique array that can be used
            to reconstruct `ar`.
    
        Returns
        -------
        unique : ndarray
            The sorted unique values.
        unique_indices : ndarray, optional
            The indices of the first occurrences of the unique values in the
            (flattened) original array. Only provided if `return_index` is True.
        unique_inverse : ndarray, optional
            The indices to reconstruct the (flattened) original array from the
            unique array. Only provided if `return_inverse` is True.
    
        See Also
        --------
        numpy.lib.arraysetops : Module with a number of other functions for
                                performing set operations on arrays.
    
        Examples
        --------
        >>> np.unique([1, 1, 2, 2, 3, 3])
        array([1, 2, 3])
        >>> a = np.array([[1, 1], [2, 3]])
        >>> np.unique(a)
        array([1, 2, 3])
    
        Return the indices of the original array that give the unique values:
    
        >>> a = np.array(['a', 'b', 'b', 'c', 'a'])
        >>> u, indices = np.unique(a, return_index=True)
        >>> u
        array(['a', 'b', 'c'],
               dtype='|S1')
        >>> indices
        array([0, 1, 3])
        >>> a[indices]
        array(['a', 'b', 'c'],
               dtype='|S1')
    
        Reconstruct the input array from the unique values:
    
        >>> a = np.array([1, 2, 6, 4, 2, 3, 2])
        >>> u, indices = np.unique(a, return_inverse=True)
        >>> u
        array([1, 2, 3, 4, 6])
        >>> indices
        array([0, 1, 4, 3, 1, 2, 1])
        >>> u[indices]
        array([1, 2, 6, 4, 2, 3, 2])
    
        """
    return ndarray()
def unpackbits(myarray, axis):
    """unpackbits(myarray, axis=None)
    
        Unpacks elements of a uint8 array into a binary-valued output array.
    
        Each element of `myarray` represents a bit-field that should be unpacked
        into a binary-valued output array. The shape of the output array is either
        1-D (if `axis` is None) or the same shape as the input array with unpacking
        done along the axis specified.
    
        Parameters
        ----------
        myarray : ndarray, uint8 type
           Input array.
        axis : int, optional
           Unpacks along this axis.
    
        Returns
        -------
        unpacked : ndarray, uint8 type
           The elements are binary-valued (0 or 1).
    
        See Also
        --------
        packbits : Packs the elements of a binary-valued array into bits in a uint8
                   array.
    
        Examples
        --------
        >>> a = np.array([[2], [7], [23]], dtype=np.uint8)
        >>> a
        array([[ 2],
               [ 7],
               [23]], dtype=uint8)
        >>> b = np.unpackbits(a, axis=1)
        >>> b
        array([[0, 0, 0, 0, 0, 0, 1, 0],
               [0, 0, 0, 0, 0, 1, 1, 1],
               [0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)"""
    return ndarray()
def unravel_index(indices, dims, order):
    """unravel_index(indices, dims, order='C')
    
        Converts a flat index or array of flat indices into a tuple
        of coordinate arrays.
    
        Parameters
        ----------
        indices : array_like
            An integer array whose elements are indices into the flattened
            version of an array of dimensions ``dims``. Before version 1.6.0,
            this function accepted just one index value.
        dims : tuple of ints
            The shape of the array to use for unraveling ``indices``.
        order : {'C', 'F'}, optional
            .. versionadded:: 1.6.0
    
            Determines whether the indices should be viewed as indexing in
            C (row-major) order or FORTRAN (column-major) order.
    
        Returns
        -------
        unraveled_coords : tuple of ndarray
            Each array in the tuple has the same shape as the ``indices``
            array.
    
        See Also
        --------
        ravel_multi_index
    
        Examples
        --------
        >>> np.unravel_index([22, 41, 37], (7,6))
        (array([3, 6, 6]), array([4, 5, 1]))
        >>> np.unravel_index([31, 41, 13], (7,6), order='F')
        (array([3, 6, 6]), array([4, 5, 1]))
    
        >>> np.unravel_index(1621, (6,7,8,9))
        (3, 1, 4, 1)"""
    return tuple()
class unsignedinteger:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def unwrap(p=-1, discont=3.14159265359, axis=-1):
    """
        Unwrap by changing deltas between values to 2*pi complement.
    
        Unwrap radian phase `p` by changing absolute jumps greater than
        `discont` to their 2*pi complement along the given axis.
    
        Parameters
        ----------
        p : array_like
            Input array.
        discont : float, optional
            Maximum discontinuity between values, default is ``pi``.
        axis : int, optional
            Axis along which unwrap will operate, default is the last axis.
    
        Returns
        -------
        out : ndarray
            Output array.
    
        See Also
        --------
        rad2deg, deg2rad
    
        Notes
        -----
        If the discontinuity in `p` is smaller than ``pi``, but larger than
        `discont`, no unwrapping is done because taking the 2*pi complement
        would only make the discontinuity larger.
    
        Examples
        --------
        >>> phase = np.linspace(0, np.pi, num=5)
        >>> phase[3:] += np.pi
        >>> phase
        array([ 0.        ,  0.78539816,  1.57079633,  5.49778714,  6.28318531])
        >>> np.unwrap(phase)
        array([ 0.        ,  0.78539816,  1.57079633, -0.78539816,  0.        ])
    
        """
    return ndarray()
class uint16:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def vander(x=None, N=None):
    """
        Generate a Van der Monde matrix.
    
        The columns of the output matrix are decreasing powers of the input
        vector.  Specifically, the `i`-th output column is the input vector
        raised element-wise to the power of ``N - i - 1``.  Such a matrix with
        a geometric progression in each row is named for Alexandre-Theophile
        Vandermonde.
    
        Parameters
        ----------
        x : array_like
            1-D input array.
        N : int, optional
            Order of (number of columns in) the output.  If `N` is not specified,
            a square array is returned (``N = len(x)``).
    
        Returns
        -------
        out : ndarray
            Van der Monde matrix of order `N`.  The first column is ``x^(N-1)``,
            the second ``x^(N-2)`` and so forth.
    
        Examples
        --------
        >>> x = np.array([1, 2, 3, 5])
        >>> N = 3
        >>> np.vander(x, N)
        array([[ 1,  1,  1],
               [ 4,  2,  1],
               [ 9,  3,  1],
               [25,  5,  1]])
    
        >>> np.column_stack([x**(N-1-i) for i in range(N)])
        array([[ 1,  1,  1],
               [ 4,  2,  1],
               [ 9,  3,  1],
               [25,  5,  1]])
    
        >>> x = np.array([1, 2, 3, 5])
        >>> np.vander(x)
        array([[  1,   1,   1,   1],
               [  8,   4,   2,   1],
               [ 27,   9,   3,   1],
               [125,  25,   5,   1]])
    
        The determinant of a square Vandermonde matrix is the product
        of the differences between the values of the input vector:
    
        >>> np.linalg.det(np.vander(x))
        48.000000000000043
        >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)
        48
    
        """
    return ndarray()
def var(a=False, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    """
        Compute the variance along the specified axis.
    
        Returns the variance of the array elements, a measure of the spread of a
        distribution.  The variance is computed for the flattened array by
        default, otherwise over the specified axis.
    
        Parameters
        ----------
        a : array_like
            Array containing numbers whose variance is desired.  If `a` is not an
            array, a conversion is attempted.
        axis : int, optional
            Axis along which the variance is computed.  The default is to compute
            the variance of the flattened array.
        dtype : data-type, optional
            Type to use in computing the variance.  For arrays of integer type
            the default is `float32`; for arrays of float types it is the same as
            the array type.
        out : ndarray, optional
            Alternate output array in which to place the result.  It must have
            the same shape as the expected output, but the type is cast if
            necessary.
        ddof : int, optional
            "Delta Degrees of Freedom": the divisor used in the calculation is
            ``N - ddof``, where ``N`` represents the number of elements. By
            default `ddof` is zero.
        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 original `arr`.
    
        Returns
        -------
        variance : ndarray, see dtype parameter above
            If ``out=None``, returns a new array containing the variance;
            otherwise, a reference to the output array is returned.
    
        See Also
        --------
        std , mean, nanmean, nanstd, nanvar
        numpy.doc.ufuncs : Section "Output arguments"
    
        Notes
        -----
        The variance is the average of the squared deviations from the mean,
        i.e.,  ``var = mean(abs(x - x.mean())**2)``.
    
        The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
        If, however, `ddof` is specified, the divisor ``N - ddof`` is used
        instead.  In standard statistical practice, ``ddof=1`` provides an
        unbiased estimator of the variance of a hypothetical infinite population.
        ``ddof=0`` provides a maximum likelihood estimate of the variance for
        normally distributed variables.
    
        Note that for complex numbers, the absolute value is taken before
        squaring, so that the result is always real and nonnegative.
    
        For floating-point input, the variance is computed using the same
        precision the input has.  Depending on the input data, this can cause
        the results to be inaccurate, especially for `float32` (see example
        below).  Specifying a higher-accuracy accumulator using the ``dtype``
        keyword can alleviate this issue.
    
        Examples
        --------
        >>> a = np.array([[1,2],[3,4]])
        >>> np.var(a)
        1.25
        >>> np.var(a, axis=0)
        array([ 1.,  1.])
        >>> np.var(a, axis=1)
        array([ 0.25,  0.25])
    
        In single precision, var() can be inaccurate:
    
        >>> a = np.zeros((2,512*512), dtype=np.float32)
        >>> a[0,:] = 1.0
        >>> a[1,:] = 0.1
        >>> np.var(a)
        0.20405951142311096
    
        Computing the variance in float64 is more accurate:
    
        >>> np.var(a, dtype=np.float64)
        0.20249999932997387
        >>> ((1-0.55)**2 + (0.1-0.55)**2)/2
        0.20250000000000001
    
        """
    return ndarray()
def vdot(a, b):
    """vdot(a, b)
    
        Return the dot product of two vectors.
    
        The vdot(`a`, `b`) function handles complex numbers differently than
        dot(`a`, `b`).  If the first argument is complex the complex conjugate
        of the first argument is used for the calculation of the dot product.
    
        Note that `vdot` handles multidimensional arrays differently than `dot`:
        it does *not* perform a matrix product, but flattens input arguments
        to 1-D vectors first. Consequently, it should only be used for vectors.
    
        Parameters
        ----------
        a : array_like
            If `a` is complex the complex conjugate is taken before calculation
            of the dot product.
        b : array_like
            Second argument to the dot product.
    
        Returns
        -------
        output : ndarray
            Dot product of `a` and `b`.  Can be an int, float, or
            complex depending on the types of `a` and `b`.
    
        See Also
        --------
        dot : Return the dot product without using the complex conjugate of the
              first argument.
    
        Examples
        --------
        >>> a = np.array([1+2j,3+4j])
        >>> b = np.array([5+6j,7+8j])
        >>> np.vdot(a, b)
        (70-8j)
        >>> np.vdot(b, a)
        (70+8j)
    
        Note that higher-dimensional arrays are flattened!
    
        >>> a = np.array([[1, 4], [5, 6]])
        >>> b = np.array([[4, 1], [2, 2]])
        >>> np.vdot(a, b)
        30
        >>> np.vdot(b, a)
        30
        >>> 1*4 + 4*1 + 5*2 + 6*2
        30"""
    return ndarray()
class vectorize:
    __dict__ = dictproxy()
    __doc__ = str()
    __module__ = str()
    __weakref__ = getset_descriptor()
    def _get_ufunc_and_otypes(self, ufunc, otypes):
        """Return (ufunc, otypes)."""
        return None
    def _vectorize_call(self, _):
        """Vectorized call to `func` over positional `args`."""
        return None
class void:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    def getfield(self, _):
        """None"""
        return None
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    def setfield(self, _):
        """None"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
class void:
    T = getset_descriptor()
    __array_interface__ = getset_descriptor()
    __array_priority__ = getset_descriptor()
    __array_struct__ = getset_descriptor()
    __doc__ = None
    base = getset_descriptor()
    def conj(self, _):
        """None"""
        return None
    data = getset_descriptor()
    dtype = getset_descriptor()
    flags = getset_descriptor()
    flat = getset_descriptor()
    def getfield(self, _):
        """None"""
        return None
    imag = getset_descriptor()
    itemsize = getset_descriptor()
    nbytes = getset_descriptor()
    ndim = getset_descriptor()
    def newbyteorder(self, new_order):
        """newbyteorder(new_order='S')
        
            Return a new `dtype` with a different byte order.
        
            Changes are also made in all fields and sub-arrays of the data type.
        
            The `new_order` code can be any from the following:
        
            * {'<', 'L'} - little endian
            * {'>', 'B'} - big endian
            * {'=', 'N'} - native order
            * 'S' - swap dtype from current to opposite endian
            * {'|', 'I'} - ignore (no change to byte order)
        
            Parameters
            ----------
            new_order : str, optional
                Byte order to force; a value from the byte order specifications
                above.  The default value ('S') results in swapping the current
                byte order. The code does a case-insensitive check on the first
                letter of `new_order` for the alternatives above.  For example,
                any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
        
        
            Returns
            -------
            new_dtype : dtype
                New `dtype` object with the given change to the byte order."""
        return dtype()
    real = getset_descriptor()
    def setfield(self, _):
        """None"""
        return None
    shape = getset_descriptor()
    size = getset_descriptor()
    strides = getset_descriptor()
def vsplit():
    """
        Split an array into multiple sub-arrays vertically (row-wise).
    
        Please refer to the ``split`` documentation.  ``vsplit`` is equivalent
        to ``split`` with `axis=0` (default), the array is always split along the
        first axis regardless of the array dimension.
    
        See Also
        --------
        split : Split an array into multiple sub-arrays of equal size.
    
        Examples
        --------
        >>> x = np.arange(16.0).reshape(4, 4)
        >>> x
        array([[  0.,   1.,   2.,   3.],
               [  4.,   5.,   6.,   7.],
               [  8.,   9.,  10.,  11.],
               [ 12.,  13.,  14.,  15.]])
        >>> np.vsplit(x, 2)
        [array([[ 0.,  1.,  2.,  3.],
               [ 4.,  5.,  6.,  7.]]),
         array([[  8.,   9.,  10.,  11.],
               [ 12.,  13.,  14.,  15.]])]
        >>> np.vsplit(x, np.array([3, 6]))
        [array([[  0.,   1.,   2.,   3.],
               [  4.,   5.,   6.,   7.],
               [  8.,   9.,  10.,  11.]]),
         array([[ 12.,  13.,  14.,  15.]]),
         array([], dtype=float64)]
    
        With a higher dimensional array the split is still along the first axis.
    
        >>> x = np.arange(8.0).reshape(2, 2, 2)
        >>> x
        array([[[ 0.,  1.],
                [ 2.,  3.]],
               [[ 4.,  5.],
                [ 6.,  7.]]])
        >>> np.vsplit(x, 2)
        [array([[[ 0.,  1.],
                [ 2.,  3.]]]),
         array([[[ 4.,  5.],
                [ 6.,  7.]]])]
    
        """
    return None
def vstack(tup):
    """
        Stack arrays in sequence vertically (row wise).
    
        Take a sequence of arrays and stack them vertically to make a single
        array. Rebuild arrays divided by `vsplit`.
    
        Parameters
        ----------
        tup : sequence of ndarrays
            Tuple containing arrays to be stacked. The arrays must have the same
            shape along all but the first axis.
    
        Returns
        -------
        stacked : ndarray
            The array formed by stacking the given arrays.
    
        See Also
        --------
        hstack : Stack arrays in sequence horizontally (column wise).
        dstack : Stack arrays in sequence depth wise (along third dimension).
        concatenate : Join a sequence of arrays together.
        vsplit : Split array into a list of multiple sub-arrays vertically.
    
        Notes
        -----
        Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that
        are at least 2-dimensional.
    
        Examples
        --------
        >>> a = np.array([1, 2, 3])
        >>> b = np.array([2, 3, 4])
        >>> np.vstack((a,b))
        array([[1, 2, 3],
               [2, 3, 4]])
    
        >>> a = np.array([[1], [2], [3]])
        >>> b = np.array([[2], [3], [4]])
        >>> np.vstack((a,b))
        array([[1],
               [2],
               [3],
               [2],
               [3],
               [4]])
    
        """
    return ndarray()
def where(condition, x=None, y=None):
    """where(condition, [x, y])
    
        Return elements, either from `x` or `y`, depending on `condition`.
    
        If only `condition` is given, return ``condition.nonzero()``.
    
        Parameters
        ----------
        condition : array_like, bool
            When True, yield `x`, otherwise yield `y`.
        x, y : array_like, optional
            Values from which to choose. `x` and `y` need to have the same
            shape as `condition`.
    
        Returns
        -------
        out : ndarray or tuple of ndarrays
            If both `x` and `y` are specified, the output array contains
            elements of `x` where `condition` is True, and elements from
            `y` elsewhere.
    
            If only `condition` is given, return the tuple
            ``condition.nonzero()``, the indices where `condition` is True.
    
        See Also
        --------
        nonzero, choose
    
        Notes
        -----
        If `x` and `y` are given and input arrays are 1-D, `where` is
        equivalent to::
    
            [xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
    
        Examples
        --------
        >>> np.where([[True, False], [True, True]],
        ...          [[1, 2], [3, 4]],
        ...          [[9, 8], [7, 6]])
        array([[1, 8],
               [3, 4]])
    
        >>> np.where([[0, 1], [1, 0]])
        (array([0, 1]), array([1, 0]))
    
        >>> x = np.arange(9.).reshape(3, 3)
        >>> np.where( x > 5 )
        (array([2, 2, 2]), array([0, 1, 2]))
        >>> x[np.where( x > 3.0 )]               # Note: result is 1D.
        array([ 4.,  5.,  6.,  7.,  8.])
        >>> np.where(x < 5, x, -1)               # Note: broadcasting.
        array([[ 0.,  1.,  2.],
               [ 3.,  4., -1.],
               [-1., -1., -1.]])
    
        Find the indices of elements of `x` that are in `goodvalues`.
    
        >>> goodvalues = [3, 4, 7]
        >>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape)
        >>> ix
        array([[False, False, False],
               [ True,  True, False],
               [False,  True, False]], dtype=bool)
        >>> np.where(ix)
        (array([1, 1, 2]), array([0, 1, 1]))"""
    return ndarray() if False else tuple()
def who(vardict=None):
    """
        Print the Numpy arrays in the given dictionary.
    
        If there is no dictionary passed in or `vardict` is None then returns
        Numpy arrays in the globals() dictionary (all Numpy arrays in the
        namespace).
    
        Parameters
        ----------
        vardict : dict, optional
            A dictionary possibly containing ndarrays.  Default is globals().
    
        Returns
        -------
        out : None
            Returns 'None'.
    
        Notes
        -----
        Prints out the name, shape, bytes and type of all of the ndarrays present
        in `vardict`.
    
        Examples
        --------
        >>> a = np.arange(10)
        >>> b = np.ones(20)
        >>> np.who()
        Name            Shape            Bytes            Type
        ===========================================================
        a               10               40               int32
        b               20               160              float64
        Upper bound on total bytes  =       200
    
        >>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
        ... 'idx':5}
        >>> np.who(d)
        Name            Shape            Bytes            Type
        ===========================================================
        y               3                24               float64
        x               2                16               float64
        Upper bound on total bytes  =       40
    
        """
    return None()
def zeros(shape, dtype, order):
    """zeros(shape, dtype=float, order='C')
    
        Return a new array of given shape and type, filled with zeros.
    
        Parameters
        ----------
        shape : int or sequence of ints
            Shape of the new array, e.g., ``(2, 3)`` or ``2``.
        dtype : data-type, optional
            The desired data-type for the array, e.g., `numpy.int8`.  Default is
            `numpy.float64`.
        order : {'C', 'F'}, optional
            Whether to store multidimensional data in C- or Fortran-contiguous
            (row- or column-wise) order in memory.
    
        Returns
        -------
        out : ndarray
            Array of zeros with the given shape, dtype, and order.
    
        See Also
        --------
        zeros_like : Return an array of zeros with shape and type of input.
        ones_like : Return an array of ones with shape and type of input.
        empty_like : Return an empty array with shape and type of input.
        ones : Return a new array setting values to one.
        empty : Return a new uninitialized array.
    
        Examples
        --------
        >>> np.zeros(5)
        array([ 0.,  0.,  0.,  0.,  0.])
    
        >>> np.zeros((5,), dtype=numpy.int)
        array([0, 0, 0, 0, 0])
    
        >>> np.zeros((2, 1))
        array([[ 0.],
               [ 0.]])
    
        >>> s = (2,2)
        >>> np.zeros(s)
        array([[ 0.,  0.],
               [ 0.,  0.]])
    
        >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
        array([(0, 0), (0, 0)],
              dtype=[('x', '<i4'), ('y', '<i4')])"""
    return ndarray()
def zeros_like(a=True, dtype=None, order="K", subok=True):
    """
        Return an array of zeros with the same shape and type as a given array.
    
        Parameters
        ----------
        a : array_like
            The shape and data-type of `a` define these same attributes of
            the returned array.
        dtype : data-type, optional
            .. versionadded:: 1.6.0
            Overrides the data type of the result.
        order : {'C', 'F', 'A', or 'K'}, optional
            .. versionadded:: 1.6.0
            Overrides the memory layout of the result. 'C' means C-order,
            'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
            'C' otherwise. 'K' means match the layout of `a` as closely
            as possible.
        subok : bool, optional.
            If True, then the newly created array will use the sub-class
            type of 'a', otherwise it will be a base-class array. Defaults
            to True.
    
        Returns
        -------
        out : ndarray
            Array of zeros with the same shape and type as `a`.
    
        See Also
        --------
        ones_like : Return an array of ones with shape and type of input.
        empty_like : Return an empty array with shape and type of input.
        zeros : Return a new array setting values to zero.
        ones : Return a new array setting values to one.
        empty : Return a new uninitialized array.
    
        Examples
        --------
        >>> x = np.arange(6)
        >>> x = x.reshape((2, 3))
        >>> x
        array([[0, 1, 2],
               [3, 4, 5]])
        >>> np.zeros_like(x)
        array([[0, 0, 0],
               [0, 0, 0]])
    
        >>> y = np.arange(3, dtype=np.float)
        >>> y
        array([ 0.,  1.,  2.])
        >>> np.zeros_like(y)
        array([ 0.,  0.,  0.])
    
        """
    return ndarray()
class ctypeslib:
    class PyCArrayType:
        __bases__ = tuple()
        __basicsize__ = int()
        __dict__ = dictproxy()
        __dictoffset__ = int()
        __doc__ = str()
        __flags__ = int()
        __itemsize__ = int()
        __module__ = str()
        __mro__ = tuple()
        __name__ = str()
        __weakrefoffset__ = int()
        def _from_address(self, integer):
            """C.from_address(integer) -> C instance
            access a C instance at the specified address"""
            return None
        def _from_buffer(self, object, offset=0):
            """C.from_buffer(object, offset=0) -> C instance
            create a C instance from a writeable buffer"""
            return None
        def _from_buffer_copy(self, object, offset=0):
            """C.from_buffer_copy(object, offset=0) -> C instance
            create a C instance from a readable buffer"""
            return None
        def _from_param(self, _):
            """Convert a Python object into a function call parameter."""
            return None
        def in_dll(self, dll, name):
            """C.in_dll(dll, name) -> C instance
            access a C instance in a dll"""
            return None
        def mro(self, _):
            """mro() -> list
            return a type's method resolution order"""
            return None
    __all__ = list()
    __builtins__ = dict()
    __doc__ = str()
    __file__ = str()
    __name__ = str()
    __package__ = None
    class dtype:
        __doc__ = str()
        alignment = member_descriptor()
        base = getset_descriptor()
        byteorder = member_descriptor()
        char = member_descriptor()
        descr = getset_descriptor()
        fields = getset_descriptor()
        flags = member_descriptor()
        hasobject = getset_descriptor()
        isalignedstruct = getset_descriptor()
        isbuiltin = getset_descriptor()
        isnative = getset_descriptor()
        itemsize = member_descriptor()
        kind = member_descriptor()
        metadata = getset_descriptor()
        name = getset_descriptor()
        names = getset_descriptor()
        def newbyteorder(self, new_order):
            """newbyteorder(new_order='S')
            
                Return a new dtype with a different byte order.
            
                Changes are also made in all fields and sub-arrays of the data type.
            
                Parameters
                ----------
                new_order : string, optional
                    Byte order to force; a value from the byte order
                    specifications below.  The default value ('S') results in
                    swapping the current byte order.
                    `new_order` codes can be any of::
            
                     * 'S' - swap dtype from current to opposite endian
                     * {'<', 'L'} - little endian
                     * {'>', 'B'} - big endian
                     * {'=', 'N'} - native order
                     * {'|', 'I'} - ignore (no change to byte order)
            
                    The code does a case-insensitive check on the first letter of
                    `new_order` for these alternatives.  For example, any of '>'
                    or 'B' or 'b' or 'brian' are valid to specify big-endian.
            
                Returns
                -------
                new_dtype : dtype
                    New dtype object with the given change to the byte order.
            
                Notes
                -----
                Changes are also made in all fields and sub-arrays of the data type.
            
                Examples
                --------
                >>> import sys
                >>> sys_is_le = sys.byteorder == 'little'
                >>> native_code = sys_is_le and '<' or '>'
                >>> swapped_code = sys_is_le and '>' or '<'
                >>> native_dt = np.dtype(native_code+'i2')
                >>> swapped_dt = np.dtype(swapped_code+'i2')
                >>> native_dt.newbyteorder('S') == swapped_dt
                True
                >>> native_dt.newbyteorder() == swapped_dt
                True
                >>> native_dt == swapped_dt.newbyteorder('S')
                True
                >>> native_dt == swapped_dt.newbyteorder('=')
                True
                >>> native_dt == swapped_dt.newbyteorder('N')
                True
                >>> native_dt == native_dt.newbyteorder('|')
                True
                >>> np.dtype('<i2') == native_dt.newbyteorder('<')
                True
                >>> np.dtype('<i2') == native_dt.newbyteorder('L')
                True
                >>> np.dtype('>i2') == native_dt.newbyteorder('>')
                True
                >>> np.dtype('>i2') == native_dt.newbyteorder('B')
                True"""
            return dtype()
        num = member_descriptor()
        shape = getset_descriptor()
        str = getset_descriptor()
        subdtype = getset_descriptor()
        type = member_descriptor()
    _flagdict = dict()
    _flagnames = list()
    def _flags__fromnum(self, _):
        """None"""
        return None
    def _ndptr(self, _):
        """None"""
        return None
    def c_void_p(self, _):
        """None"""
        return None
    def _num__fromflags(self, _):
        """None"""
        return None
    _pointer_type_cache = dict()
    _typecodes = dict()
    absolute_import = instance()
    def array(self, object, dtype, copy, order, subok, ndmin):
        """array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)
        
            Create an array.
        
            Parameters
            ----------
            object : array_like
                An array, any object exposing the array interface, an
                object whose __array__ method returns an array, or any
                (nested) sequence.
            dtype : data-type, optional
                The desired data-type for the array.  If not given, then
                the type will be determined as the minimum type required
                to hold the objects in the sequence.  This argument can only
                be used to 'upcast' the array.  For downcasting, use the
                .astype(t) method.
            copy : bool, optional
                If true (default), then the object is copied.  Otherwise, a copy
                will only be made if __array__ returns a copy, if obj is a
                nested sequence, or if a copy is needed to satisfy any of the other
                requirements (`dtype`, `order`, etc.).
            order : {'C', 'F', 'A'}, optional
                Specify the order of the array.  If order is 'C' (default), then the
                array will be in C-contiguous order (last-index varies the
                fastest).  If order is 'F', then the returned array
                will be in Fortran-contiguous order (first-index varies the
                fastest).  If order is 'A', then the returned array may
                be in any order (either C-, Fortran-contiguous, or even
                discontiguous).
            subok : bool, optional
                If True, then sub-classes will be passed-through, otherwise
                the returned array will be forced to be a base-class array (default).
            ndmin : int, optional
                Specifies the minimum number of dimensions that the resulting
                array should have.  Ones will be pre-pended to the shape as
                needed to meet this requirement.
        
            Returns
            -------
            out : ndarray
                An array object satisfying the specified requirements.
        
            See Also
            --------
            empty, empty_like, zeros, zeros_like, ones, ones_like, fill
        
            Examples
            --------
            >>> np.array([1, 2, 3])
            array([1, 2, 3])
        
            Upcasting:
        
            >>> np.array([1, 2, 3.0])
            array([ 1.,  2.,  3.])
        
            More than one dimension:
        
            >>> np.array([[1, 2], [3, 4]])
            array([[1, 2],
                   [3, 4]])
        
            Minimum dimensions 2:
        
            >>> np.array([1, 2, 3], ndmin=2)
            array([[1, 2, 3]])
        
            Type provided:
        
            >>> np.array([1, 2, 3], dtype=complex)
            array([ 1.+0.j,  2.+0.j,  3.+0.j])
        
            Data-type consisting of more than one element:
        
            >>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
            >>> x['a']
            array([1, 3])
        
            Creating an array from sub-classes:
        
            >>> np.array(np.mat('1 2; 3 4'))
            array([[1, 2],
                   [3, 4]])
        
            >>> np.array(np.mat('1 2; 3 4'), subok=True)
            matrix([[1, 2],
                    [3, 4]])"""
        return ndarray()
    def as_array(self, obj=None, shape=None):
        """Create a numpy array from a ctypes array or a ctypes POINTER.
                The numpy array shares the memory with the ctypes object.
        
                The size parameter must be given if converting from a ctypes POINTER.
                The size parameter is ignored if converting from a ctypes array
                """
        return None
    def as_ctypes(self, _):
        """Create and return a ctypes object from a numpy array.  Actually
                anything that exposes the __array_interface__ is accepted."""
        return None
    def c_long(self, _):
        """None"""
        return None
    code = str()
    def ctypes_load_library(self):
        """`ctypes_load_library` is deprecated, use `load_library` instead!"""
        return None
    def deprecate(self):
        """
            Issues a DeprecationWarning, adds warning to `old_name`'s
            docstring, rebinds ``old_name.__name__`` and returns the new
            function object.
        
            This function may also be used as a decorator.
        
            Parameters
            ----------
            func : function
                The function to be deprecated.
            old_name : str, optional
                The name of the function to be deprecated. Default is None, in which
                case the name of `func` is used.
            new_name : str, optional
                The new name for the function. Default is None, in which case
                the deprecation message is that `old_name` is deprecated. If given,
                the deprecation message is that `old_name` is deprecated and `new_name`
                should be used instead.
            message : str, optional
                Additional explanation of the deprecation.  Displayed in the docstring
                after the warning.
        
            Returns
            -------
            old_func : function
                The deprecated function.
        
            Examples
            --------
            Note that ``olduint`` returns a value after printing Deprecation Warning:
        
            >>> olduint = np.deprecate(np.uint)
            >>> olduint(6)
            /usr/lib/python2.5/site-packages/numpy/lib/utils.py:114:
            DeprecationWarning: uint32 is deprecated
              warnings.warn(str1, DeprecationWarning)
            6
        
            """
        return None
    division = instance()
    class flagsobj:
        __doc__ = None
        aligned = getset_descriptor()
        behaved = getset_descriptor()
        c_contiguous = getset_descriptor()
        carray = getset_descriptor()
        contiguous = getset_descriptor()
        f_contiguous = getset_descriptor()
        farray = getset_descriptor()
        fnc = getset_descriptor()
        forc = getset_descriptor()
        fortran = getset_descriptor()
        num = getset_descriptor()
        owndata = getset_descriptor()
        updateifcopy = getset_descriptor()
        writeable = getset_descriptor()
    class integer:
        T = getset_descriptor()
        __array_interface__ = getset_descriptor()
        __array_priority__ = getset_descriptor()
        __array_struct__ = getset_descriptor()
        __doc__ = None
        base = getset_descriptor()
        def conj(self, _):
            """None"""
            return None
        data = getset_descriptor()
        dtype = getset_descriptor()
        flags = getset_descriptor()
        flat = getset_descriptor()
        imag = getset_descriptor()
        itemsize = getset_descriptor()
        nbytes = getset_descriptor()
        ndim = getset_descriptor()
        def newbyteorder(self, new_order):
            """newbyteorder(new_order='S')
            
                Return a new `dtype` with a different byte order.
            
                Changes are also made in all fields and sub-arrays of the data type.
            
                The `new_order` code can be any from the following:
            
                * {'<', 'L'} - little endian
                * {'>', 'B'} - big endian
                * {'=', 'N'} - native order
                * 'S' - swap dtype from current to opposite endian
                * {'|', 'I'} - ignore (no change to byte order)
            
                Parameters
                ----------
                new_order : str, optional
                    Byte order to force; a value from the byte order specifications
                    above.  The default value ('S') results in swapping the current
                    byte order. The code does a case-insensitive check on the first
                    letter of `new_order` for the alternatives above.  For example,
                    any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
            
            
                Returns
                -------
                new_dtype : dtype
                    New `dtype` object with the given change to the byte order."""
            return None
        real = getset_descriptor()
        shape = getset_descriptor()
        size = getset_descriptor()
        strides = getset_descriptor()
    def load_library(self, _):
        """None"""
        return None
    class ndarray:
        T = getset_descriptor()
        __array_finalize__ = getset_descriptor()
        __array_interface__ = getset_descriptor()
        __array_priority__ = getset_descriptor()
        __array_struct__ = getset_descriptor()
        __doc__ = str()
        def all(self, axis=None, out=None):
            """a.all(axis=None, out=None)
            
                Returns True if all elements evaluate to True.
            
                Refer to `numpy.all` for full documentation.
            
                See Also
                --------
                numpy.all : equivalent function"""
            return None
        def any(self, axis=None, out=None):
            """a.any(axis=None, out=None)
            
                Returns True if any of the elements of `a` evaluate to True.
            
                Refer to `numpy.any` for full documentation.
            
                See Also
                --------
                numpy.any : equivalent function"""
            return None
        def argmax(self, axis=None, out=None):
            """a.argmax(axis=None, out=None)
            
                Return indices of the maximum values along the given axis.
            
                Refer to `numpy.argmax` for full documentation.
            
                See Also
                --------
                numpy.argmax : equivalent function"""
            return None
        def argmin(self, axis=None, out=None):
            """a.argmin(axis=None, out=None)
            
                Return indices of the minimum values along the given axis of `a`.
            
                Refer to `numpy.argmin` for detailed documentation.
            
                See Also
                --------
                numpy.argmin : equivalent function"""
            return None
        def argpartition(self, kth, axis=_1, kind=quickselect, order=None):
            """a.argpartition(kth, axis=-1, kind='quickselect', order=None)
            
                Returns the indices that would partition this array.
            
                Refer to `numpy.argpartition` for full documentation.
            
                .. versionadded:: 1.8.0
            
                See Also
                --------
                numpy.argpartition : equivalent function"""
            return None
        def argsort(self, axis=_1, kind=quicksort, order=None):
            """a.argsort(axis=-1, kind='quicksort', order=None)
            
                Returns the indices that would sort this array.
            
                Refer to `numpy.argsort` for full documentation.
            
                See Also
                --------
                numpy.argsort : equivalent function"""
            return None
        def astype(self, dtype, order, casting, subok, copy):
            """a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
            
                Copy of the array, cast to a specified type.
            
                Parameters
                ----------
                dtype : str or dtype
                    Typecode or data-type to which the array is cast.
                order : {'C', 'F', 'A', 'K'}, optional
                    Controls the memory layout order of the result.
                    'C' means C order, 'F' means Fortran order, 'A'
                    means 'F' order if all the arrays are Fortran contiguous,
                    'C' order otherwise, and 'K' means as close to the
                    order the array elements appear in memory as possible.
                    Default is 'K'.
                casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
                    Controls what kind of data casting may occur. Defaults to 'unsafe'
                    for backwards compatibility.
            
                      * 'no' means the data types should not be cast at all.
                      * 'equiv' means only byte-order changes are allowed.
                      * 'safe' means only casts which can preserve values are allowed.
                      * 'same_kind' means only safe casts or casts within a kind,
                        like float64 to float32, are allowed.
                      * 'unsafe' means any data conversions may be done.
                subok : bool, optional
                    If True, then sub-classes will be passed-through (default), otherwise
                    the returned array will be forced to be a base-class array.
                copy : bool, optional
                    By default, astype always returns a newly allocated array. If this
                    is set to false, and the `dtype`, `order`, and `subok`
                    requirements are satisfied, the input array is returned instead
                    of a copy.
            
                Returns
                -------
                arr_t : ndarray
                    Unless `copy` is False and the other conditions for returning the input
                    array are satisfied (see description for `copy` input paramter), `arr_t`
                    is a new array of the same shape as the input array, with dtype, order
                    given by `dtype`, `order`.
            
                Raises
                ------
                ComplexWarning
                    When casting from complex to float or int. To avoid this,
                    one should use ``a.real.astype(t)``.
            
                Examples
                --------
                >>> x = np.array([1, 2, 2.5])
                >>> x
                array([ 1. ,  2. ,  2.5])
            
                >>> x.astype(int)
                array([1, 2, 2])"""
            return ndarray()
        base = getset_descriptor()
        def byteswap(self, inplace):
            """a.byteswap(inplace)
            
                Swap the bytes of the array elements
            
                Toggle between low-endian and big-endian data representation by
                returning a byteswapped array, optionally swapped in-place.
            
                Parameters
                ----------
                inplace : bool, optional
                    If ``True``, swap bytes in-place, default is ``False``.
            
                Returns
                -------
                out : ndarray
                    The byteswapped array. If `inplace` is ``True``, this is
                    a view to self.
            
                Examples
                --------
                >>> A = np.array([1, 256, 8755], dtype=np.int16)
                >>> map(hex, A)
                ['0x1', '0x100', '0x2233']
                >>> A.byteswap(True)
                array([  256,     1, 13090], dtype=int16)
                >>> map(hex, A)
                ['0x100', '0x1', '0x3322']
            
                Arrays of strings are not swapped
            
                >>> A = np.array(['ceg', 'fac'])
                >>> A.byteswap()
                array(['ceg', 'fac'],
                      dtype='|S3')"""
            return ndarray()
        def choose(self, choices, out=None, mode=_raise):
            """a.choose(choices, out=None, mode='raise')
            
                Use an index array to construct a new array from a set of choices.
            
                Refer to `numpy.choose` for full documentation.
            
                See Also
                --------
                numpy.choose : equivalent function"""
            return None
        def clip(self, a_min, a_max, out=None):
            """a.clip(a_min, a_max, out=None)
            
                Return an array whose values are limited to ``[a_min, a_max]``.
            
                Refer to `numpy.clip` for full documentation.
            
                See Also
                --------
                numpy.clip : equivalent function"""
            return None
        def compress(self, condition, axis=None, out=None):
            """a.compress(condition, axis=None, out=None)
            
                Return selected slices of this array along given axis.
            
                Refer to `numpy.compress` for full documentation.
            
                See Also
                --------
                numpy.compress : equivalent function"""
            return None
        def conj(self, _):
            """a.conj()
            
                Complex-conjugate all elements.
            
                Refer to `numpy.conjugate` for full documentation.
            
                See Also
                --------
                numpy.conjugate : equivalent function"""
            return None
        def conjugate(self, _):
            """a.conjugate()
            
                Return the complex conjugate, element-wise.
            
                Refer to `numpy.conjugate` for full documentation.
            
                See Also
                --------
                numpy.conjugate : equivalent function"""
            return None
        def copy(self, order):
            """a.copy(order='C')
            
                Return a copy of the array.
            
                Parameters
                ----------
                order : {'C', 'F', 'A', 'K'}, optional
                    Controls the memory layout of the copy. 'C' means C-order,
                    'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
                    'C' otherwise. 'K' means match the layout of `a` as closely
                    as possible. (Note that this function and :func:numpy.copy are very
                    similar, but have different default values for their order=
                    arguments.)
            
                See also
                --------
                numpy.copy
                numpy.copyto
            
                Examples
                --------
                >>> x = np.array([[1,2,3],[4,5,6]], order='F')
            
                >>> y = x.copy()
            
                >>> x.fill(0)
            
                >>> x
                array([[0, 0, 0],
                       [0, 0, 0]])
            
                >>> y
                array([[1, 2, 3],
                       [4, 5, 6]])
            
                >>> y.flags['C_CONTIGUOUS']
                True"""
            return None
        ctypes = getset_descriptor()
        def cumprod(self, axis=None, dtype=None, out=None):
            """a.cumprod(axis=None, dtype=None, out=None)
            
                Return the cumulative product of the elements along the given axis.
            
                Refer to `numpy.cumprod` for full documentation.
            
                See Also
                --------
                numpy.cumprod : equivalent function"""
            return None
        def cumsum(self, axis=None, dtype=None, out=None):
            """a.cumsum(axis=None, dtype=None, out=None)
            
                Return the cumulative sum of the elements along the given axis.
            
                Refer to `numpy.cumsum` for full documentation.
            
                See Also
                --------
                numpy.cumsum : equivalent function"""
            return None
        data = getset_descriptor()
        def diagonal(self, offset=0, axis1=0, axis2=1):
            """a.diagonal(offset=0, axis1=0, axis2=1)
            
                Return specified diagonals.
            
                Refer to :func:`numpy.diagonal` for full documentation.
            
                See Also
                --------
                numpy.diagonal : equivalent function"""
            return None
        def dot(self, b, out=None):
            """a.dot(b, out=None)
            
                Dot product of two arrays.
            
                Refer to `numpy.dot` for full documentation.
            
                See Also
                --------
                numpy.dot : equivalent function
            
                Examples
                --------
                >>> a = np.eye(2)
                >>> b = np.ones((2, 2)) * 2
                >>> a.dot(b)
                array([[ 2.,  2.],
                       [ 2.,  2.]])
            
                This array method can be conveniently chained:
            
                >>> a.dot(b).dot(b)
                array([[ 8.,  8.],
                       [ 8.,  8.]])"""
            return None
        dtype = getset_descriptor()
        def dump(self, file):
            """a.dump(file)
            
                Dump a pickle of the array to the specified file.
                The array can be read back with pickle.load or numpy.load.
            
                Parameters
                ----------
                file : str
                    A string naming the dump file."""
            return None
        def dumps(self, _):
            """a.dumps()
            
                Returns the pickle of the array as a string.
                pickle.loads or numpy.loads will convert the string back to an array.
            
                Parameters
                ----------
                None"""
            return None
        def fill(self, value):
            """a.fill(value)
            
                Fill the array with a scalar value.
            
                Parameters
                ----------
                value : scalar
                    All elements of `a` will be assigned this value.
            
                Examples
                --------
                >>> a = np.array([1, 2])
                >>> a.fill(0)
                >>> a
                array([0, 0])
                >>> a = np.empty(2)
                >>> a.fill(1)
                >>> a
                array([ 1.,  1.])"""
            return None
        flags = getset_descriptor()
        flat = getset_descriptor()
        def flatten(self, order):
            """a.flatten(order='C')
            
                Return a copy of the array collapsed into one dimension.
            
                Parameters
                ----------
                order : {'C', 'F', 'A'}, optional
                    Whether to flatten in C (row-major), Fortran (column-major) order,
                    or preserve the C/Fortran ordering from `a`.
                    The default is 'C'.
            
                Returns
                -------
                y : ndarray
                    A copy of the input array, flattened to one dimension.
            
                See Also
                --------
                ravel : Return a flattened array.
                flat : A 1-D flat iterator over the array.
            
                Examples
                --------
                >>> a = np.array([[1,2], [3,4]])
                >>> a.flatten()
                array([1, 2, 3, 4])
                >>> a.flatten('F')
                array([1, 3, 2, 4])"""
            return ndarray()
        def getfield(self, dtype, offset):
            """a.getfield(dtype, offset=0)
            
                Returns a field of the given array as a certain type.
            
                A field is a view of the array data with a given data-type. The values in
                the view are determined by the given type and the offset into the current
                array in bytes. The offset needs to be such that the view dtype fits in the
                array dtype; for example an array of dtype complex128 has 16-byte elements.
                If taking a view with a 32-bit integer (4 bytes), the offset needs to be
                between 0 and 12 bytes.
            
                Parameters
                ----------
                dtype : str or dtype
                    The data type of the view. The dtype size of the view can not be larger
                    than that of the array itself.
                offset : int
                    Number of bytes to skip before beginning the element view.
            
                Examples
                --------
                >>> x = np.diag([1.+1.j]*2)
                >>> x[1, 1] = 2 + 4.j
                >>> x
                array([[ 1.+1.j,  0.+0.j],
                       [ 0.+0.j,  2.+4.j]])
                >>> x.getfield(np.float64)
                array([[ 1.,  0.],
                       [ 0.,  2.]])
            
                By choosing an offset of 8 bytes we can select the complex part of the
                array for our view:
            
                >>> x.getfield(np.float64, offset=8)
                array([[ 1.,  0.],
                   [ 0.,  4.]])"""
            return array()
        imag = getset_descriptor()
        def item(self, ESCargs):
            """a.item(*args)
            
                Copy an element of an array to a standard Python scalar and return it.
            
                Parameters
                ----------
                \*args : Arguments (variable number and type)
            
                    * none: in this case, the method only works for arrays
                      with one element (`a.size == 1`), which element is
                      copied into a standard Python scalar object and returned.
            
                    * int_type: this argument is interpreted as a flat index into
                      the array, specifying which element to copy and return.
            
                    * tuple of int_types: functions as does a single int_type argument,
                      except that the argument is interpreted as an nd-index into the
                      array.
            
                Returns
                -------
                z : Standard Python scalar object
                    A copy of the specified element of the array as a suitable
                    Python scalar
            
                Notes
                -----
                When the data type of `a` is longdouble or clongdouble, item() returns
                a scalar array object because there is no available Python scalar that
                would not lose information. Void arrays return a buffer object for item(),
                unless fields are defined, in which case a tuple is returned.
            
                `item` is very similar to a[args], except, instead of an array scalar,
                a standard Python scalar is returned. This can be useful for speeding up
                access to elements of the array and doing arithmetic on elements of the
                array using Python's optimized math.
            
                Examples
                --------
                >>> x = np.random.randint(9, size=(3, 3))
                >>> x
                array([[3, 1, 7],
                       [2, 8, 3],
                       [8, 5, 3]])
                >>> x.item(3)
                2
                >>> x.item(7)
                5
                >>> x.item((0, 1))
                1
                >>> x.item((2, 2))
                3"""
            return Standard()
        def itemset(self, ESCargs):
            """a.itemset(*args)
            
                Insert scalar into an array (scalar is cast to array's dtype, if possible)
            
                There must be at least 1 argument, and define the last argument
                as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
                than ``a[args] = item``.  The item should be a scalar value and `args`
                must select a single item in the array `a`.
            
                Parameters
                ----------
                \*args : Arguments
                    If one argument: a scalar, only used in case `a` is of size 1.
                    If two arguments: the last argument is the value to be set
                    and must be a scalar, the first argument specifies a single array
                    element location. It is either an int or a tuple.
            
                Notes
                -----
                Compared to indexing syntax, `itemset` provides some speed increase
                for placing a scalar into a particular location in an `ndarray`,
                if you must do this.  However, generally this is discouraged:
                among other problems, it complicates the appearance of the code.
                Also, when using `itemset` (and `item`) inside a loop, be sure
                to assign the methods to a local variable to avoid the attribute
                look-up at each loop iteration.
            
                Examples
                --------
                >>> x = np.random.randint(9, size=(3, 3))
                >>> x
                array([[3, 1, 7],
                       [2, 8, 3],
                       [8, 5, 3]])
                >>> x.itemset(4, 0)
                >>> x.itemset((2, 2), 9)
                >>> x
                array([[3, 1, 7],
                       [2, 0, 3],
                       [8, 5, 9]])"""
            return None
        itemsize = getset_descriptor()
        def max(self, axis=None, out=None):
            """a.max(axis=None, out=None)
            
                Return the maximum along a given axis.
            
                Refer to `numpy.amax` for full documentation.
            
                See Also
                --------
                numpy.amax : equivalent function"""
            return None
        def mean(self, axis=None, dtype=None, out=None):
            """a.mean(axis=None, dtype=None, out=None)
            
                Returns the average of the array elements along given axis.
            
                Refer to `numpy.mean` for full documentation.
            
                See Also
                --------
                numpy.mean : equivalent function"""
            return None
        def min(self, axis=None, out=None):
            """a.min(axis=None, out=None)
            
                Return the minimum along a given axis.
            
                Refer to `numpy.amin` for full documentation.
            
                See Also
                --------
                numpy.amin : equivalent function"""
            return None
        nbytes = getset_descriptor()
        ndim = getset_descriptor()
        def newbyteorder(self, new_order):
            """arr.newbyteorder(new_order='S')
            
                Return the array with the same data viewed with a different byte order.
            
                Equivalent to::
            
                    arr.view(arr.dtype.newbytorder(new_order))
            
                Changes are also made in all fields and sub-arrays of the array data
                type.
            
            
            
                Parameters
                ----------
                new_order : string, optional
                    Byte order to force; a value from the byte order specifications
                    above. `new_order` codes can be any of::
            
                     * 'S' - swap dtype from current to opposite endian
                     * {'<', 'L'} - little endian
                     * {'>', 'B'} - big endian
                     * {'=', 'N'} - native order
                     * {'|', 'I'} - ignore (no change to byte order)
            
                    The default value ('S') results in swapping the current
                    byte order. The code does a case-insensitive check on the first
                    letter of `new_order` for the alternatives above.  For example,
                    any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
            
            
                Returns
                -------
                new_arr : array
                    New array object with the dtype reflecting given change to the
                    byte order."""
            return array()
        def nonzero(self, _):
            """a.nonzero()
            
                Return the indices of the elements that are non-zero.
            
                Refer to `numpy.nonzero` for full documentation.
            
                See Also
                --------
                numpy.nonzero : equivalent function"""
            return None
        def partition(self, kth, axis, kind, order):
            """a.partition(kth, axis=-1, kind='introselect', order=None)
            
                Rearranges the elements in the array in such a way that value of the
                element in kth position is in the position it would be in a sorted array.
                All elements smaller than the kth element are moved before this element and
                all equal or greater are moved behind it. The ordering of the elements in
                the two partitions is undefined.
            
                .. versionadded:: 1.8.0
            
                Parameters
                ----------
                kth : int or sequence of ints
                    Element index to partition by. The kth element value will be in its
                    final sorted position and all smaller elements will be moved before it
                    and all equal or greater elements behind it.
                    The order all elements in the partitions is undefined.
                    If provided with a sequence of kth it will partition all elements
                    indexed by kth of them into their sorted position at once.
                axis : int, optional
                    Axis along which to sort. Default is -1, which means sort along the
                    last axis.
                kind : {'introselect'}, optional
                    Selection algorithm. Default is 'introselect'.
                order : list, optional
                    When `a` is an array with fields defined, this argument specifies
                    which fields to compare first, second, etc.  Not all fields need be
                    specified.
            
                See Also
                --------
                numpy.partition : Return a parititioned copy of an array.
                argpartition : Indirect partition.
                sort : Full sort.
            
                Notes
                -----
                See ``np.partition`` for notes on the different algorithms.
            
                Examples
                --------
                >>> a = np.array([3, 4, 2, 1])
                >>> a.partition(a, 3)
                >>> a
                array([2, 1, 3, 4])
            
                >>> a.partition((1, 3))
                array([1, 2, 3, 4])"""
            return None
        def prod(self, axis=None, dtype=None, out=None):
            """a.prod(axis=None, dtype=None, out=None)
            
                Return the product of the array elements over the given axis
            
                Refer to `numpy.prod` for full documentation.
            
                See Also
                --------
                numpy.prod : equivalent function"""
            return None
        def ptp(self, axis=None, out=None):
            """a.ptp(axis=None, out=None)
            
                Peak to peak (maximum - minimum) value along a given axis.
            
                Refer to `numpy.ptp` for full documentation.
            
                See Also
                --------
                numpy.ptp : equivalent function"""
            return None
        def put(self, indices, values, mode=_raise):
            """a.put(indices, values, mode='raise')
            
                Set ``a.flat[n] = values[n]`` for all `n` in indices.
            
                Refer to `numpy.put` for full documentation.
            
                See Also
                --------
                numpy.put : equivalent function"""
            return None
        def ravel(self, order):
            """a.ravel([order])
            
                Return a flattened array.
            
                Refer to `numpy.ravel` for full documentation.
            
                See Also
                --------
                numpy.ravel : equivalent function
            
                ndarray.flat : a flat iterator on the array."""
            return None
        real = getset_descriptor()
        def repeat(self, repeats, axis=None):
            """a.repeat(repeats, axis=None)
            
                Repeat elements of an array.
            
                Refer to `numpy.repeat` for full documentation.
            
                See Also
                --------
                numpy.repeat : equivalent function"""
            return None
        def reshape(self, shape, order=C):
            """a.reshape(shape, order='C')
            
                Returns an array containing the same data with a new shape.
            
                Refer to `numpy.reshape` for full documentation.
            
                See Also
                --------
                numpy.reshape : equivalent function"""
            return None
        def resize(self, new_shape, refcheck):
            """a.resize(new_shape, refcheck=True)
            
                Change shape and size of array in-place.
            
                Parameters
                ----------
                new_shape : tuple of ints, or `n` ints
                    Shape of resized array.
                refcheck : bool, optional
                    If False, reference count will not be checked. Default is True.
            
                Returns
                -------
                None
            
                Raises
                ------
                ValueError
                    If `a` does not own its own data or references or views to it exist,
                    and the data memory must be changed.
            
                SystemError
                    If the `order` keyword argument is specified. This behaviour is a
                    bug in NumPy.
            
                See Also
                --------
                resize : Return a new array with the specified shape.
            
                Notes
                -----
                This reallocates space for the data area if necessary.
            
                Only contiguous arrays (data elements consecutive in memory) can be
                resized.
            
                The purpose of the reference count check is to make sure you
                do not use this array as a buffer for another Python object and then
                reallocate the memory. However, reference counts can increase in
                other ways so if you are sure that you have not shared the memory
                for this array with another Python object, then you may safely set
                `refcheck` to False.
            
                Examples
                --------
                Shrinking an array: array is flattened (in the order that the data are
                stored in memory), resized, and reshaped:
            
                >>> a = np.array([[0, 1], [2, 3]], order='C')
                >>> a.resize((2, 1))
                >>> a
                array([[0],
                       [1]])
            
                >>> a = np.array([[0, 1], [2, 3]], order='F')
                >>> a.resize((2, 1))
                >>> a
                array([[0],
                       [2]])
            
                Enlarging an array: as above, but missing entries are filled with zeros:
            
                >>> b = np.array([[0, 1], [2, 3]])
                >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
                >>> b
                array([[0, 1, 2],
                       [3, 0, 0]])
            
                Referencing an array prevents resizing...
            
                >>> c = a
                >>> a.resize((1, 1))
                Traceback (most recent call last):
                ...
                ValueError: cannot resize an array that has been referenced ...
            
                Unless `refcheck` is False:
            
                >>> a.resize((1, 1), refcheck=False)
                >>> a
                array([[0]])
                >>> c
                array([[0]])"""
            return None
        def round(self, decimals=0, out=None):
            """a.round(decimals=0, out=None)
            
                Return `a` with each element rounded to the given number of decimals.
            
                Refer to `numpy.around` for full documentation.
            
                See Also
                --------
                numpy.around : equivalent function"""
            return None
        def searchsorted(self, v, side=left, sorter=None):
            """a.searchsorted(v, side='left', sorter=None)
            
                Find indices where elements of v should be inserted in a to maintain order.
            
                For full documentation, see `numpy.searchsorted`
            
                See Also
                --------
                numpy.searchsorted : equivalent function"""
            return None
        def setfield(self, val, dtype, offset):
            """a.setfield(val, dtype, offset=0)
            
                Put a value into a specified place in a field defined by a data-type.
            
                Place `val` into `a`'s field defined by `dtype` and beginning `offset`
                bytes into the field.
            
                Parameters
                ----------
                val : object
                    Value to be placed in field.
                dtype : dtype object
                    Data-type of the field in which to place `val`.
                offset : int, optional
                    The number of bytes into the field at which to place `val`.
            
                Returns
                -------
                None
            
                See Also
                --------
                getfield
            
                Examples
                --------
                >>> x = np.eye(3)
                >>> x.getfield(np.float64)
                array([[ 1.,  0.,  0.],
                       [ 0.,  1.,  0.],
                       [ 0.,  0.,  1.]])
                >>> x.setfield(3, np.int32)
                >>> x.getfield(np.int32)
                array([[3, 3, 3],
                       [3, 3, 3],
                       [3, 3, 3]])
                >>> x
                array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
                       [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
                       [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
                >>> x.setfield(np.eye(3), np.int32)
                >>> x
                array([[ 1.,  0.,  0.],
                       [ 0.,  1.,  0.],
                       [ 0.,  0.,  1.]])"""
            return None
        def setflags(self, write, align, uic):
            """a.setflags(write=None, align=None, uic=None)
            
                Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
            
                These Boolean-valued flags affect how numpy interprets the memory
                area used by `a` (see Notes below). The ALIGNED flag can only
                be set to True if the data is actually aligned according to the type.
                The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE
                can only be set to True if the array owns its own memory, or the
                ultimate owner of the memory exposes a writeable buffer interface,
                or is a string. (The exception for string is made so that unpickling
                can be done without copying memory.)
            
                Parameters
                ----------
                write : bool, optional
                    Describes whether or not `a` can be written to.
                align : bool, optional
                    Describes whether or not `a` is aligned properly for its type.
                uic : bool, optional
                    Describes whether or not `a` is a copy of another "base" array.
            
                Notes
                -----
                Array flags provide information about how the memory area used
                for the array is to be interpreted. There are 6 Boolean flags
                in use, only three of which can be changed by the user:
                UPDATEIFCOPY, WRITEABLE, and ALIGNED.
            
                WRITEABLE (W) the data area can be written to;
            
                ALIGNED (A) the data and strides are aligned appropriately for the hardware
                (as determined by the compiler);
            
                UPDATEIFCOPY (U) this array is a copy of some other array (referenced
                by .base). When this array is deallocated, the base array will be
                updated with the contents of this array.
            
                All flags can be accessed using their first (upper case) letter as well
                as the full name.
            
                Examples
                --------
                >>> y
                array([[3, 1, 7],
                       [2, 0, 0],
                       [8, 5, 9]])
                >>> y.flags
                  C_CONTIGUOUS : True
                  F_CONTIGUOUS : False
                  OWNDATA : True
                  WRITEABLE : True
                  ALIGNED : True
                  UPDATEIFCOPY : False
                >>> y.setflags(write=0, align=0)
                >>> y.flags
                  C_CONTIGUOUS : True
                  F_CONTIGUOUS : False
                  OWNDATA : True
                  WRITEABLE : False
                  ALIGNED : False
                  UPDATEIFCOPY : False
                >>> y.setflags(uic=1)
                Traceback (most recent call last):
                  File "<stdin>", line 1, in <module>
                ValueError: cannot set UPDATEIFCOPY flag to True"""
            return None
        shape = getset_descriptor()
        size = getset_descriptor()
        def sort(self, axis, kind, order):
            """a.sort(axis=-1, kind='quicksort', order=None)
            
                Sort an array, in-place.
            
                Parameters
                ----------
                axis : int, optional
                    Axis along which to sort. Default is -1, which means sort along the
                    last axis.
                kind : {'quicksort', 'mergesort', 'heapsort'}, optional
                    Sorting algorithm. Default is 'quicksort'.
                order : list, optional
                    When `a` is an array with fields defined, this argument specifies
                    which fields to compare first, second, etc.  Not all fields need be
                    specified.
            
                See Also
                --------
                numpy.sort : Return a sorted copy of an array.
                argsort : Indirect sort.
                lexsort : Indirect stable sort on multiple keys.
                searchsorted : Find elements in sorted array.
                partition: Partial sort.
            
                Notes
                -----
                See ``sort`` for notes on the different sorting algorithms.
            
                Examples
                --------
                >>> a = np.array([[1,4], [3,1]])
                >>> a.sort(axis=1)
                >>> a
                array([[1, 4],
                       [1, 3]])
                >>> a.sort(axis=0)
                >>> a
                array([[1, 3],
                       [1, 4]])
            
                Use the `order` keyword to specify a field to use when sorting a
                structured array:
            
                >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
                >>> a.sort(order='y')
                >>> a
                array([('c', 1), ('a', 2)],
                      dtype=[('x', '|S1'), ('y', '<i4')])"""
            return None
        def squeeze(self, axis=None):
            """a.squeeze(axis=None)
            
                Remove single-dimensional entries from the shape of `a`.
            
                Refer to `numpy.squeeze` for full documentation.
            
                See Also
                --------
                numpy.squeeze : equivalent function"""
            return None
        def std(self, axis=None, dtype=None, out=None, ddof=0):
            """a.std(axis=None, dtype=None, out=None, ddof=0)
            
                Returns the standard deviation of the array elements along given axis.
            
                Refer to `numpy.std` for full documentation.
            
                See Also
                --------
                numpy.std : equivalent function"""
            return None
        strides = getset_descriptor()
        def sum(self, axis=None, dtype=None, out=None):
            """a.sum(axis=None, dtype=None, out=None)
            
                Return the sum of the array elements over the given axis.
            
                Refer to `numpy.sum` for full documentation.
            
                See Also
                --------
                numpy.sum : equivalent function"""
            return None
        def swapaxes(self, axis1, axis2):
            """a.swapaxes(axis1, axis2)
            
                Return a view of the array with `axis1` and `axis2` interchanged.
            
                Refer to `numpy.swapaxes` for full documentation.
            
                See Also
                --------
                numpy.swapaxes : equivalent function"""
            return None
        def take(self, indices, axis=None, out=None, mode=_raise):
            """a.take(indices, axis=None, out=None, mode='raise')
            
                Return an array formed from the elements of `a` at the given indices.
            
                Refer to `numpy.take` for full documentation.
            
                See Also
                --------
                numpy.take : equivalent function"""
            return None
        def tofile(self, fid, sep, format):
            """a.tofile(fid, sep="", format="%s")
            
                Write array to a file as text or binary (default).
            
                Data is always written in 'C' order, independent of the order of `a`.
                The data produced by this method can be recovered using the function
                fromfile().
            
                Parameters
                ----------
                fid : file or str
                    An open file object, or a string containing a filename.
                sep : str
                    Separator between array items for text output.
                    If "" (empty), a binary file is written, equivalent to
                    ``file.write(a.tostring())``.
                format : str
                    Format string for text file output.
                    Each entry in the array is formatted to text by first converting
                    it to the closest Python type, and then using "format" % item.
            
                Notes
                -----
                This is a convenience function for quick storage of array data.
                Information on endianness and precision is lost, so this method is not a
                good choice for files intended to archive data or transport data between
                machines with different endianness. Some of these problems can be overcome
                by outputting the data as text files, at the expense of speed and file
                size."""
            return None
        def tolist(self, _):
            """a.tolist()
            
                Return the array as a (possibly nested) list.
            
                Return a copy of the array data as a (nested) Python list.
                Data items are converted to the nearest compatible Python type.
            
                Parameters
                ----------
                none
            
                Returns
                -------
                y : list
                    The possibly nested list of array elements.
            
                Notes
                -----
                The array may be recreated, ``a = np.array(a.tolist())``.
            
                Examples
                --------
                >>> a = np.array([1, 2])
                >>> a.tolist()
                [1, 2]
                >>> a = np.array([[1, 2], [3, 4]])
                >>> list(a)
                [array([1, 2]), array([3, 4])]
                >>> a.tolist()
                [[1, 2], [3, 4]]"""
            return list()
        def tostring(self, order):
            """a.tostring(order='C')
            
                Construct a Python string containing the raw data bytes in the array.
            
                Constructs a Python string showing a copy of the raw contents of
                data memory. The string can be produced in either 'C' or 'Fortran',
                or 'Any' order (the default is 'C'-order). 'Any' order means C-order
                unless the F_CONTIGUOUS flag in the array is set, in which case it
                means 'Fortran' order.
            
                Parameters
                ----------
                order : {'C', 'F', None}, optional
                    Order of the data for multidimensional arrays:
                    C, Fortran, or the same as for the original array.
            
                Returns
                -------
                s : str
                    A Python string exhibiting a copy of `a`'s raw data.
            
                Examples
                --------
                >>> x = np.array([[0, 1], [2, 3]])
                >>> x.tostring()
                '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
                >>> x.tostring('C') == x.tostring()
                True
                >>> x.tostring('F')
                '\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'"""
            return str()
        def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
            """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
            
                Return the sum along diagonals of the array.
            
                Refer to `numpy.trace` for full documentation.
            
                See Also
                --------
                numpy.trace : equivalent function"""
            return None
        def transpose(self, axes):
            """a.transpose(*axes)
            
                Returns a view of the array with axes transposed.
            
                For a 1-D array, this has no effect. (To change between column and
                row vectors, first cast the 1-D array into a matrix object.)
                For a 2-D array, this is the usual matrix transpose.
                For an n-D array, if axes are given, their order indicates how the
                axes are permuted (see Examples). If axes are not provided and
                ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
                ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
            
                Parameters
                ----------
                axes : None, tuple of ints, or `n` ints
            
                 * None or no argument: reverses the order of the axes.
            
                 * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
                   `i`-th axis becomes `a.transpose()`'s `j`-th axis.
            
                 * `n` ints: same as an n-tuple of the same ints (this form is
                   intended simply as a "convenience" alternative to the tuple form)
            
                Returns
                -------
                out : ndarray
                    View of `a`, with axes suitably permuted.
            
                See Also
                --------
                ndarray.T : Array property returning the array transposed.
            
                Examples
                --------
                >>> a = np.array([[1, 2], [3, 4]])
                >>> a
                array([[1, 2],
                       [3, 4]])
                >>> a.transpose()
                array([[1, 3],
                       [2, 4]])
                >>> a.transpose((1, 0))
                array([[1, 3],
                       [2, 4]])
                >>> a.transpose(1, 0)
                array([[1, 3],
                       [2, 4]])"""
            return ndarray()
        def var(self, axis=None, dtype=None, out=None, ddof=0):
            """a.var(axis=None, dtype=None, out=None, ddof=0)
            
                Returns the variance of the array elements, along given axis.
            
                Refer to `numpy.var` for full documentation.
            
                See Also
                --------
                numpy.var : equivalent function"""
            return None
        def view(self, dtype, type):
            """a.view(dtype=None, type=None)
            
                New view of array with the same data.
            
                Parameters
                ----------
                dtype : data-type or ndarray sub-class, optional
                    Data-type descriptor of the returned view, e.g., float32 or int16. The
                    default, None, results in the view having the same data-type as `a`.
                    This argument can also be specified as an ndarray sub-class, which
                    then specifies the type of the returned object (this is equivalent to
                    setting the ``type`` parameter).
                type : Python type, optional
                    Type of the returned view, e.g., ndarray or matrix.  Again, the
                    default None results in type preservation.
            
                Notes
                -----
                ``a.view()`` is used two different ways:
            
                ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
                of the array's memory with a different data-type.  This can cause a
                reinterpretation of the bytes of memory.
            
                ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
                returns an instance of `ndarray_subclass` that looks at the same array
                (same shape, dtype, etc.)  This does not cause a reinterpretation of the
                memory.
            
                For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
                bytes per entry than the previous dtype (for example, converting a
                regular array to a structured array), then the behavior of the view
                cannot be predicted just from the superficial appearance of ``a`` (shown
                by ``print(a)``). It also depends on exactly how ``a`` is stored in
                memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
                defined as a slice or transpose, etc., the view may give different
                results.
            
            
                Examples
                --------
                >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
            
                Viewing array data using a different type and dtype:
            
                >>> y = x.view(dtype=np.int16, type=np.matrix)
                >>> y
                matrix([[513]], dtype=int16)
                >>> print type(y)
                <class 'numpy.matrixlib.defmatrix.matrix'>
            
                Creating a view on a structured array so it can be used in calculations
            
                >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
                >>> xv = x.view(dtype=np.int8).reshape(-1,2)
                >>> xv
                array([[1, 2],
                       [3, 4]], dtype=int8)
                >>> xv.mean(0)
                array([ 2.,  3.])
            
                Making changes to the view changes the underlying array
            
                >>> xv[0,1] = 20
                >>> print x
                [(1, 20) (3, 4)]
            
                Using a view to convert an array to a record array:
            
                >>> z = x.view(np.recarray)
                >>> z.a
                array([1], dtype=int8)
            
                Views share data:
            
                >>> x[0] = (9, 10)
                >>> z[0]
                (9, 10)
            
                Views that change the dtype size (bytes per entry) should normally be
                avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
            
                >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
                >>> y = x[:, 0:2]
                >>> y
                array([[1, 2],
                       [4, 5]], dtype=int16)
                >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
                Traceback (most recent call last):
                  File "<stdin>", line 1, in <module>
                ValueError: new type not compatible with array.
                >>> z = y.copy()
                >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
                array([[(1, 2)],
                       [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])"""
            return None
    def ndpointer(self, dtype=None, ndim=None, shape=None, flags=None):
        """
            Array-checking restype/argtypes.
        
            An ndpointer instance is used to describe an ndarray in restypes
            and argtypes specifications.  This approach is more flexible than
            using, for example, ``POINTER(c_double)``, since several restrictions
            can be specified, which are verified upon calling the ctypes function.
            These include data type, number of dimensions, shape and flags.  If a
            given array does not satisfy the specified restrictions,
            a ``TypeError`` is raised.
        
            Parameters
            ----------
            dtype : data-type, optional
                Array data-type.
            ndim : int, optional
                Number of array dimensions.
            shape : tuple of ints, optional
                Array shape.
            flags : str or tuple of str
                Array flags; may be one or more of:
        
                  - C_CONTIGUOUS / C / CONTIGUOUS
                  - F_CONTIGUOUS / F / FORTRAN
                  - OWNDATA / O
                  - WRITEABLE / W
                  - ALIGNED / A
                  - UPDATEIFCOPY / U
        
            Returns
            -------
            klass : ndpointer type object
                A type object, which is an ``_ndtpr`` instance containing
                dtype, ndim, shape and flags information.
        
            Raises
            ------
            TypeError
                If a given array does not satisfy the specified restrictions.
        
            Examples
            --------
            >>> clib.somefunc.argtypes = [np.ctypeslib.ndpointer(dtype=np.float64,
            ...                                                  ndim=1,
            ...                                                  flags='C_CONTIGUOUS')]
            ... #doctest: +SKIP
            >>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64))
            ... #doctest: +SKIP
        
            """
        return ndpointer()
    def prep_array(self, _):
        """Given a ctypes array type, construct and attach an
                __array_interface__ property to it if it does not yet have one.
                """
        return None
    def prep_pointer(self, _):
        """Given a ctypes pointer object, construct and
                attach an __array_interface__ property to it if it does not
                yet have one.
                """
        return None
    def prep_simple(self, _):
        """Given a ctypes simple type, construct and attach an
                __array_interface__ property to it if it does not yet have one.
                """
        return None
    print_function = instance()
    simple_types = list()
    def c_double(self, _):
        """None"""
        return None
    types = tuple()
class lib:
    class scimath:
        __all__ = list()
        __builtins__ = dict()
        __doc__ = str()
        __file__ = str()
        __name__ = str()
        __package__ = None
        def _fix_int_lt_zero(self, x):
            """Convert `x` to double if it has real, negative components.
            
                Otherwise, output is just the array version of the input (via asarray).
            
                Parameters
                ----------
                x : array_like
            
                Returns
                -------
                array
            
                Examples
                --------
                >>> np.lib.scimath._fix_int_lt_zero([1,2])
                array([1, 2])
            
                >>> np.lib.scimath._fix_int_lt_zero([-1,2])
                array([-1.,  2.])
                """
            return None
        def _fix_real_abs_gt_1(self, x):
            """Convert `x` to complex if it has real components x_i with abs(x_i)>1.
            
                Otherwise, output is just the array version of the input (via asarray).
            
                Parameters
                ----------
                x : array_like
            
                Returns
                -------
                array
            
                Examples
                --------
                >>> np.lib.scimath._fix_real_abs_gt_1([0,1])
                array([0, 1])
            
                >>> np.lib.scimath._fix_real_abs_gt_1([0,2])
                array([ 0.+0.j,  2.+0.j])
                """
            return None
        def _fix_real_lt_zero(self, x):
            """Convert `x` to complex if it has real, negative components.
            
                Otherwise, output is just the array version of the input (via asarray).
            
                Parameters
                ----------
                x : array_like
            
                Returns
                -------
                array
            
                Examples
                --------
                >>> np.lib.scimath._fix_real_lt_zero([1,2])
                array([1, 2])
            
                >>> np.lib.scimath._fix_real_lt_zero([-1,2])
                array([-1.+0.j,  2.+0.j])
                """
            return None
        _ln2 = float64()
        def _tocomplex(self, arr):
            """Convert its input `arr` to a complex array.
            
                The input is returned as a complex array of the smallest type that will fit
                the original data: types like single, byte, short, etc. become csingle,
                while others become cdouble.
            
                A copy of the input is always made.
            
                Parameters
                ----------
                arr : array
            
                Returns
                -------
                array
                    An array with the same input data as the input but in complex form.
            
                Examples
                --------
            
                First, consider an input of type short:
            
                >>> a = np.array([1,2,3],np.short)
            
                >>> ac = np.lib.scimath._tocomplex(a); ac
                array([ 1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
            
                >>> ac.dtype
                dtype('complex64')
            
                If the input is of type double, the output is correspondingly of the
                complex double type as well:
            
                >>> b = np.array([1,2,3],np.double)
            
                >>> bc = np.lib.scimath._tocomplex(b); bc
                array([ 1.+0.j,  2.+0.j,  3.+0.j])
            
                >>> bc.dtype
                dtype('complex128')
            
                Note that even if the input was complex to begin with, a copy is still
                made, since the astype() method always copies:
            
                >>> c = np.array([1,2,3],np.csingle)
            
                >>> cc = np.lib.scimath._tocomplex(c); cc
                array([ 1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
            
                >>> c *= 2; c
                array([ 2.+0.j,  4.+0.j,  6.+0.j], dtype=complex64)
            
                >>> cc
                array([ 1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
                """
            return None
        absolute_import = instance()
        def any(self, a=False, axis=None, out=None, keepdims=False):
            """
                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 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 `doc.ufuncs` (Section "Output arguments") for 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 original `arr`.
            
                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], dtype=bool)
            
                >>> 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], dtype=bool), array([ True], dtype=bool))
                >>> # 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)
            
                """
            return bool() if False else ndarray()
        def arccos(self, x):
            """
                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])
            
                """
            return ndarray() if False else float()
        def arcsin(self, x):
            """
                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])
            
                """
            return ndarray() if False else float()
        def arctanh(self, x):
            """
                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)
            
                >>> np.emath.arctanh(np.matrix(np.eye(2)))
                array([[ Inf,   0.],
                       [  0.,  Inf]])
                >>> np.emath.arctanh([1j])
                array([ 0.+0.7854j])
            
                """
            return ndarray() if False else float()
        def asarray(self, a=None, dtype=None, order=None):
            """
                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') or column-major ('F' for FORTRAN)
                    memory representation.  Defaults to 'C'.
            
                Returns
                -------
                out : ndarray
                    Array interpretation of `a`.  No copy is performed if the input
                    is already an ndarray.  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.matrix, np.ndarray)
                True
                >>> a = np.matrix([[1, 2]])
                >>> np.asarray(a) is a
                False
                >>> np.asanyarray(a) is a
                True
            
                """
            return ndarray()
        division = instance()
        def isreal(self, 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 ndarray()
        def log(self, x):
            """
                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
            
                """
            return ndarray() if False else float()
        def log10(self, x):
            """
                Compute the logarithm base 10 of `x`.
            
                Return the "principal value" (for a description of this, see
                `numpy.log10`) of :math:`log_{10}(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    ])
            
                """
            return ndarray() if False else float()
        def log2(self, x):
            """
                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    ])
            
                """
            return ndarray() if False else float()
        def logn(self, n, x):
            """
                Take log base n of x.
            
                If `x` contains negative inputs, the answer is computed and returned in the
                complex domain.
            
                Parameters
                ----------
                n : int
                   The base 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 ndarray() if False else float()
        def power(self, x, p):
            """
                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
                    ``x[0]**p[0], x[1]**p[1], ...``.
            
                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])
            
                """
            return ndarray() if False else float()
        print_function = instance()
        def sqrt(self, 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])
            
                """
            return ndarray() if False else float()
class fft:
    class NoseTester:
        __dict__ = dictproxy()
        __doc__ = str()
        __module__ = str()
        __weakref__ = getset_descriptor()
        def _get_custom_doctester(self, _):
            """ Return instantiated plugin for doctests
            
                    Allows subclassing of this class to override doctester
            
                    A return value of None means use the nose builtin doctest plugin
                    """
            return None
        def _show_system_info(self, _):
            """None"""
            return None
        def _test_argv(self, label, verbose, extra_argv):
            """ Generate argv for nosetest command
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        see ``test`` docstring
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    argv : list
                        command line arguments that will be passed to nose
                    """
            return list()
        def bench(self=None, label="fast", verbose=1, extra_argv=None):
            """
                    Run benchmarks for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the benchmarks to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow benchmarks as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    success : bool
                        Returns True if running the benchmarks works, False if an error
                        occurred.
            
                    Notes
                    -----
                    Benchmarks are like tests, but have names starting with "bench" instead
                    of "test", and can be found under the "benchmarks" sub-directory of the
                    module.
            
                    Each NumPy module exposes `bench` in its namespace to run all benchmarks
                    for it.
            
                    Examples
                    --------
                    >>> success = np.lib.bench() #doctest: +SKIP
                    Running benchmarks for numpy.lib
                    ...
                    using 562341 items:
                    unique:
                    0.11
                    unique1d:
                    0.11
                    ratio: 1.0
                    nUnique: 56230 == 56230
                    ...
                    OK
            
                    >>> success #doctest: +SKIP
                    True
            
                    """
            return bool()
        excludes = list()
        def prepare_test_args(self=False, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False):
            """
                    Run tests for module using nose.
            
                    This method does the heavy lifting for the `test` method. It takes all
                    the same arguments, for details see `test`.
            
                    See Also
                    --------
                    test
            
                    """
            return None
        def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
            """
                    Run tests for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the tests to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow tests as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
                    doctests : bool, optional
                        If True, run doctests in module. Default is False.
                    coverage : bool, optional
                        If True, report coverage of NumPy code. Default is False.
                        (This requires the `coverage module:
                         <http://nedbatchelder.com/code/modules/coverage.html>`_).
                    raise_warnings : str or sequence of warnings, optional
                        This specifies which warnings to configure as 'raise' instead
                        of 'warn' during the test execution.  Valid strings are:
            
                          - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                          - "release" : equals ``()``, don't raise on any warnings.
            
                    Returns
                    -------
                    result : object
                        Returns the result of running the tests as a
                        ``nose.result.TextTestResult`` object.
            
                    Notes
                    -----
                    Each NumPy module exposes `test` in its namespace to run all tests for it.
                    For example, to run all tests for numpy.lib:
            
                    >>> np.lib.test() #doctest: +SKIP
            
                    Examples
                    --------
                    >>> result = np.lib.test() #doctest: +SKIP
                    Running unit tests for numpy.lib
                    ...
                    Ran 976 tests in 3.933s
            
                    OK
            
                    >>> result.errors #doctest: +SKIP
                    []
                    >>> result.knownfail #doctest: +SKIP
                    []
                    """
            return object()
    __builtins__ = dict()
    __doc__ = str()
    __file__ = str()
    __name__ = str()
    __package__ = str()
    __path__ = list()
    absolute_import = instance()
    def bench(self=None, label="fast", verbose=1, extra_argv=None):
        """
                Run benchmarks for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the benchmarks to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow benchmarks as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
        
                Returns
                -------
                success : bool
                    Returns True if running the benchmarks works, False if an error
                    occurred.
        
                Notes
                -----
                Benchmarks are like tests, but have names starting with "bench" instead
                of "test", and can be found under the "benchmarks" sub-directory of the
                module.
        
                Each NumPy module exposes `bench` in its namespace to run all benchmarks
                for it.
        
                Examples
                --------
                >>> success = np.lib.bench() #doctest: +SKIP
                Running benchmarks for numpy.lib
                ...
                using 562341 items:
                unique:
                0.11
                unique1d:
                0.11
                ratio: 1.0
                nUnique: 56230 == 56230
                ...
                OK
        
                >>> success #doctest: +SKIP
                True
        
                """
        return bool()
    division = instance()
    def fft(self, a=-1, n=None, axis=-1):
        """
            Compute the one-dimensional discrete Fourier Transform.
        
            This function computes the one-dimensional *n*-point discrete Fourier
            Transform (DFT) with the efficient Fast Fourier Transform (FFT)
            algorithm [CT].
        
            Parameters
            ----------
            a : array_like
                Input array, can be complex.
            n : int, optional
                Length of the transformed axis of the output.
                If `n` is smaller than the length of the input, the input is cropped.
                If it is larger, the input is padded with zeros.  If `n` is not given,
                the length of the input (along the axis specified by `axis`) is used.
            axis : int, optional
                Axis over which to compute the FFT.  If not given, the last axis is
                used.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axis
                indicated by `axis`, or the last one if `axis` is not specified.
        
            Raises
            ------
            IndexError
                if `axes` is larger than the last axis of `a`.
        
            See Also
            --------
            numpy.fft : for definition of the DFT and conventions used.
            ifft : The inverse of `fft`.
            fft2 : The two-dimensional FFT.
            fftn : The *n*-dimensional FFT.
            rfftn : The *n*-dimensional FFT of real input.
            fftfreq : Frequency bins for given FFT parameters.
        
            Notes
            -----
            FFT (Fast Fourier Transform) refers to a way the discrete Fourier
            Transform (DFT) can be calculated efficiently, by using symmetries in the
            calculated terms.  The symmetry is highest when `n` is a power of 2, and
            the transform is therefore most efficient for these sizes.
        
            The DFT is defined, with the conventions used in this implementation, in
            the documentation for the `numpy.fft` module.
        
            References
            ----------
            .. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
                    machine calculation of complex Fourier series," *Math. Comput.*
                    19: 297-301.
        
            Examples
            --------
            >>> np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
            array([ -3.44505240e-16 +1.14383329e-17j,
                     8.00000000e+00 -5.71092652e-15j,
                     2.33482938e-16 +1.22460635e-16j,
                     1.64863782e-15 +1.77635684e-15j,
                     9.95839695e-17 +2.33482938e-16j,
                     0.00000000e+00 +1.66837030e-15j,
                     1.14383329e-17 +1.22460635e-16j,
                     -1.64863782e-15 +1.77635684e-15j])
        
            >>> import matplotlib.pyplot as plt
            >>> t = np.arange(256)
            >>> sp = np.fft.fft(np.sin(t))
            >>> freq = np.fft.fftfreq(t.shape[-1])
            >>> plt.plot(freq, sp.real, freq, sp.imag)
            [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>]
            >>> plt.show()
        
            In this example, real input has an FFT which is Hermitian, i.e., symmetric
            in the real part and anti-symmetric in the imaginary part, as described in
            the `numpy.fft` documentation.
        
            """
        return complex()
    def fft2(self, a="(-2, -1)", s=None, axes="(-2, -1)"):
        """
            Compute the 2-dimensional discrete Fourier Transform
        
            This function computes the *n*-dimensional discrete Fourier Transform
            over any axes in an *M*-dimensional array by means of the
            Fast Fourier Transform (FFT).  By default, the transform is computed over
            the last two axes of the input array, i.e., a 2-dimensional FFT.
        
            Parameters
            ----------
            a : array_like
                Input array, can be complex
            s : sequence of ints, optional
                Shape (length of each transformed axis) of the output
                (`s[0]` refers to axis 0, `s[1]` to axis 1, etc.).
                This corresponds to `n` for `fft(x, n)`.
                Along each axis, if the given shape is smaller than that of the input,
                the input is cropped.  If it is larger, the input is padded with zeros.
                if `s` is not given, the shape of the input (along the axes specified
                by `axes`) is used.
            axes : sequence of ints, optional
                Axes over which to compute the FFT.  If not given, the last two
                axes are used.  A repeated index in `axes` means the transform over
                that axis is performed multiple times.  A one-element sequence means
                that a one-dimensional FFT is performed.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axes
                indicated by `axes`, or the last two axes if `axes` is not given.
        
            Raises
            ------
            ValueError
                If `s` and `axes` have different length, or `axes` not given and
                ``len(s) != 2``.
            IndexError
                If an element of `axes` is larger than than the number of axes of `a`.
        
            See Also
            --------
            numpy.fft : Overall view of discrete Fourier transforms, with definitions
                 and conventions used.
            ifft2 : The inverse two-dimensional FFT.
            fft : The one-dimensional FFT.
            fftn : The *n*-dimensional FFT.
            fftshift : Shifts zero-frequency terms to the center of the array.
                For two-dimensional input, swaps first and third quadrants, and second
                and fourth quadrants.
        
            Notes
            -----
            `fft2` is just `fftn` with a different default for `axes`.
        
            The output, analogously to `fft`, contains the term for zero frequency in
            the low-order corner of the transformed axes, the positive frequency terms
            in the first half of these axes, the term for the Nyquist frequency in the
            middle of the axes and the negative frequency terms in the second half of
            the axes, in order of decreasingly negative frequency.
        
            See `fftn` for details and a plotting example, and `numpy.fft` for
            definitions and conventions used.
        
        
            Examples
            --------
            >>> a = np.mgrid[:5, :5][0]
            >>> np.fft.fft2(a)
            array([[  0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
                   [  5.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
                   [ 10.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
                   [ 15.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
                   [ 20.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j]])
        
            """
        return complex()
    def fftfreq(self, n=1.0, d=1.0):
        """
            Return the Discrete Fourier Transform sample frequencies.
        
            The returned float array `f` contains the frequency bin centers in cycles
            per unit of the sample spacing (with zero at the start).  For instance, if
            the sample spacing is in seconds, then the frequency unit is cycles/second.
        
            Given a window length `n` and a sample spacing `d`::
        
              f = [0, 1, ...,   n/2-1,     -n/2, ..., -1] / (d*n)   if n is even
              f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n)   if n is odd
        
            Parameters
            ----------
            n : int
                Window length.
            d : scalar, optional
                Sample spacing (inverse of the sampling rate). Defaults to 1.
        
            Returns
            -------
            f : ndarray
                Array of length `n` containing the sample frequencies.
        
            Examples
            --------
            >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)
            >>> fourier = np.fft.fft(signal)
            >>> n = signal.size
            >>> timestep = 0.1
            >>> freq = np.fft.fftfreq(n, d=timestep)
            >>> freq
            array([ 0.  ,  1.25,  2.5 ,  3.75, -5.  , -3.75, -2.5 , -1.25])
        
            """
        return ndarray()
    def fftn(self, a=None, s=None, axes=None):
        """
            Compute the N-dimensional discrete Fourier Transform.
        
            This function computes the *N*-dimensional discrete Fourier Transform over
            any number of axes in an *M*-dimensional array by means of the Fast Fourier
            Transform (FFT).
        
            Parameters
            ----------
            a : array_like
                Input array, can be complex.
            s : sequence of ints, optional
                Shape (length of each transformed axis) of the output
                (`s[0]` refers to axis 0, `s[1]` to axis 1, etc.).
                This corresponds to `n` for `fft(x, n)`.
                Along any axis, if the given shape is smaller than that of the input,
                the input is cropped.  If it is larger, the input is padded with zeros.
                if `s` is not given, the shape of the input (along the axes specified
                by `axes`) is used.
            axes : sequence of ints, optional
                Axes over which to compute the FFT.  If not given, the last ``len(s)``
                axes are used, or all axes if `s` is also not specified.
                Repeated indices in `axes` means that the transform over that axis is
                performed multiple times.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axes
                indicated by `axes`, or by a combination of `s` and `a`,
                as explained in the parameters section above.
        
            Raises
            ------
            ValueError
                If `s` and `axes` have different length.
            IndexError
                If an element of `axes` is larger than than the number of axes of `a`.
        
            See Also
            --------
            numpy.fft : Overall view of discrete Fourier transforms, with definitions
                and conventions used.
            ifftn : The inverse of `fftn`, the inverse *n*-dimensional FFT.
            fft : The one-dimensional FFT, with definitions and conventions used.
            rfftn : The *n*-dimensional FFT of real input.
            fft2 : The two-dimensional FFT.
            fftshift : Shifts zero-frequency terms to centre of array
        
            Notes
            -----
            The output, analogously to `fft`, contains the term for zero frequency in
            the low-order corner of all axes, the positive frequency terms in the
            first half of all axes, the term for the Nyquist frequency in the middle
            of all axes and the negative frequency terms in the second half of all
            axes, in order of decreasingly negative frequency.
        
            See `numpy.fft` for details, definitions and conventions used.
        
            Examples
            --------
            >>> a = np.mgrid[:3, :3, :3][0]
            >>> np.fft.fftn(a, axes=(1, 2))
            array([[[  0.+0.j,   0.+0.j,   0.+0.j],
                    [  0.+0.j,   0.+0.j,   0.+0.j],
                    [  0.+0.j,   0.+0.j,   0.+0.j]],
                   [[  9.+0.j,   0.+0.j,   0.+0.j],
                    [  0.+0.j,   0.+0.j,   0.+0.j],
                    [  0.+0.j,   0.+0.j,   0.+0.j]],
                   [[ 18.+0.j,   0.+0.j,   0.+0.j],
                    [  0.+0.j,   0.+0.j,   0.+0.j],
                    [  0.+0.j,   0.+0.j,   0.+0.j]]])
            >>> np.fft.fftn(a, (2, 2), axes=(0, 1))
            array([[[ 2.+0.j,  2.+0.j,  2.+0.j],
                    [ 0.+0.j,  0.+0.j,  0.+0.j]],
                   [[-2.+0.j, -2.+0.j, -2.+0.j],
                    [ 0.+0.j,  0.+0.j,  0.+0.j]]])
        
            >>> import matplotlib.pyplot as plt
            >>> [X, Y] = np.meshgrid(2 * np.pi * np.arange(200) / 12,
            ...                      2 * np.pi * np.arange(200) / 34)
            >>> S = np.sin(X) + np.cos(Y) + np.random.uniform(0, 1, X.shape)
            >>> FS = np.fft.fftn(S)
            >>> plt.imshow(np.log(np.abs(np.fft.fftshift(FS))**2))
            <matplotlib.image.AxesImage object at 0x...>
            >>> plt.show()
        
            """
        return complex()
    def fftshift(self, x=None, axes=None):
        """
            Shift the zero-frequency component to the center of the spectrum.
        
            This function swaps half-spaces for all axes listed (defaults to all).
            Note that ``y[0]`` is the Nyquist component only if ``len(x)`` is even.
        
            Parameters
            ----------
            x : array_like
                Input array.
            axes : int or shape tuple, optional
                Axes over which to shift.  Default is None, which shifts all axes.
        
            Returns
            -------
            y : ndarray
                The shifted array.
        
            See Also
            --------
            ifftshift : The inverse of `fftshift`.
        
            Examples
            --------
            >>> freqs = np.fft.fftfreq(10, 0.1)
            >>> freqs
            array([ 0.,  1.,  2.,  3.,  4., -5., -4., -3., -2., -1.])
            >>> np.fft.fftshift(freqs)
            array([-5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
        
            Shift the zero-frequency component only along the second axis:
        
            >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
            >>> freqs
            array([[ 0.,  1.,  2.],
                   [ 3.,  4., -4.],
                   [-3., -2., -1.]])
            >>> np.fft.fftshift(freqs, axes=(1,))
            array([[ 2.,  0.,  1.],
                   [-4.,  3.,  4.],
                   [-1., -3., -2.]])
        
            """
        return ndarray()
    def hfft(self, a=-1, n=None, axis=-1):
        """
            Compute the FFT of a signal whose spectrum has Hermitian symmetry.
        
            Parameters
            ----------
            a : array_like
                The input array.
            n : int, optional
                The length of the FFT.
            axis : int, optional
                The axis over which to compute the FFT, assuming Hermitian symmetry
                of the spectrum. Default is the last axis.
        
            Returns
            -------
            out : ndarray
                The transformed input.
        
            See also
            --------
            rfft : Compute the one-dimensional FFT for real input.
            ihfft : The inverse of `hfft`.
        
            Notes
            -----
            `hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
            opposite case: here the signal is real in the frequency domain and has
            Hermite symmetry in the time domain. So here it's `hfft` for which
            you must supply the length of the result if it is to be odd:
            ``ihfft(hfft(a), len(a)) == a``, within numerical accuracy.
        
            Examples
            --------
            >>> signal = np.array([[1, 1.j], [-1.j, 2]])
            >>> np.conj(signal.T) - signal   # check Hermitian symmetry
            array([[ 0.-0.j,  0.+0.j],
                   [ 0.+0.j,  0.-0.j]])
            >>> freq_spectrum = np.fft.hfft(signal)
            >>> freq_spectrum
            array([[ 1.,  1.],
                   [ 2., -2.]])
        
            """
        return ndarray()
    def ifft(self, a=-1, n=None, axis=-1):
        """
            Compute the one-dimensional inverse discrete Fourier Transform.
        
            This function computes the inverse of the one-dimensional *n*-point
            discrete Fourier transform computed by `fft`.  In other words,
            ``ifft(fft(a)) == a`` to within numerical accuracy.
            For a general description of the algorithm and definitions,
            see `numpy.fft`.
        
            The input should be ordered in the same way as is returned by `fft`,
            i.e., ``a[0]`` should contain the zero frequency term,
            ``a[1:n/2+1]`` should contain the positive-frequency terms, and
            ``a[n/2+1:]`` should contain the negative-frequency terms, in order of
            decreasingly negative frequency.  See `numpy.fft` for details.
        
            Parameters
            ----------
            a : array_like
                Input array, can be complex.
            n : int, optional
                Length of the transformed axis of the output.
                If `n` is smaller than the length of the input, the input is cropped.
                If it is larger, the input is padded with zeros.  If `n` is not given,
                the length of the input (along the axis specified by `axis`) is used.
                See notes about padding issues.
            axis : int, optional
                Axis over which to compute the inverse DFT.  If not given, the last
                axis is used.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axis
                indicated by `axis`, or the last one if `axis` is not specified.
        
            Raises
            ------
            IndexError
                If `axes` is larger than the last axis of `a`.
        
            See Also
            --------
            numpy.fft : An introduction, with definitions and general explanations.
            fft : The one-dimensional (forward) FFT, of which `ifft` is the inverse
            ifft2 : The two-dimensional inverse FFT.
            ifftn : The n-dimensional inverse FFT.
        
            Notes
            -----
            If the input parameter `n` is larger than the size of the input, the input
            is padded by appending zeros at the end.  Even though this is the common
            approach, it might lead to surprising results.  If a different padding is
            desired, it must be performed before calling `ifft`.
        
            Examples
            --------
            >>> np.fft.ifft([0, 4, 0, 0])
            array([ 1.+0.j,  0.+1.j, -1.+0.j,  0.-1.j])
        
            Create and plot a band-limited signal with random phases:
        
            >>> import matplotlib.pyplot as plt
            >>> t = np.arange(400)
            >>> n = np.zeros((400,), dtype=complex)
            >>> n[40:60] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20,)))
            >>> s = np.fft.ifft(n)
            >>> plt.plot(t, s.real, 'b-', t, s.imag, 'r--')
            [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>]
            >>> plt.legend(('real', 'imaginary'))
            <matplotlib.legend.Legend object at 0x...>
            >>> plt.show()
        
            """
        return complex()
    def ifft2(self, a="(-2, -1)", s=None, axes="(-2, -1)"):
        """
            Compute the 2-dimensional inverse discrete Fourier Transform.
        
            This function computes the inverse of the 2-dimensional discrete Fourier
            Transform over any number of axes in an M-dimensional array by means of
            the Fast Fourier Transform (FFT).  In other words, ``ifft2(fft2(a)) == a``
            to within numerical accuracy.  By default, the inverse transform is
            computed over the last two axes of the input array.
        
            The input, analogously to `ifft`, should be ordered in the same way as is
            returned by `fft2`, i.e. it should have the term for zero frequency
            in the low-order corner of the two axes, the positive frequency terms in
            the first half of these axes, the term for the Nyquist frequency in the
            middle of the axes and the negative frequency terms in the second half of
            both axes, in order of decreasingly negative frequency.
        
            Parameters
            ----------
            a : array_like
                Input array, can be complex.
            s : sequence of ints, optional
                Shape (length of each axis) of the output (``s[0]`` refers to axis 0,
                ``s[1]`` to axis 1, etc.).  This corresponds to `n` for ``ifft(x, n)``.
                Along each axis, if the given shape is smaller than that of the input,
                the input is cropped.  If it is larger, the input is padded with zeros.
                if `s` is not given, the shape of the input (along the axes specified
                by `axes`) is used.  See notes for issue on `ifft` zero padding.
            axes : sequence of ints, optional
                Axes over which to compute the FFT.  If not given, the last two
                axes are used.  A repeated index in `axes` means the transform over
                that axis is performed multiple times.  A one-element sequence means
                that a one-dimensional FFT is performed.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axes
                indicated by `axes`, or the last two axes if `axes` is not given.
        
            Raises
            ------
            ValueError
                If `s` and `axes` have different length, or `axes` not given and
                ``len(s) != 2``.
            IndexError
                If an element of `axes` is larger than than the number of axes of `a`.
        
            See Also
            --------
            numpy.fft : Overall view of discrete Fourier transforms, with definitions
                 and conventions used.
            fft2 : The forward 2-dimensional FFT, of which `ifft2` is the inverse.
            ifftn : The inverse of the *n*-dimensional FFT.
            fft : The one-dimensional FFT.
            ifft : The one-dimensional inverse FFT.
        
            Notes
            -----
            `ifft2` is just `ifftn` with a different default for `axes`.
        
            See `ifftn` for details and a plotting example, and `numpy.fft` for
            definition and conventions used.
        
            Zero-padding, analogously with `ifft`, is performed by appending zeros to
            the input along the specified dimension.  Although this is the common
            approach, it might lead to surprising results.  If another form of zero
            padding is desired, it must be performed before `ifft2` is called.
        
            Examples
            --------
            >>> a = 4 * np.eye(4)
            >>> np.fft.ifft2(a)
            array([[ 1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],
                   [ 0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j],
                   [ 0.+0.j,  0.+0.j,  1.+0.j,  0.+0.j],
                   [ 0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j]])
        
            """
        return complex()
    def ifftn(self, a=None, s=None, axes=None):
        """
            Compute the N-dimensional inverse discrete Fourier Transform.
        
            This function computes the inverse of the N-dimensional discrete
            Fourier Transform over any number of axes in an M-dimensional array by
            means of the Fast Fourier Transform (FFT).  In other words,
            ``ifftn(fftn(a)) == a`` to within numerical accuracy.
            For a description of the definitions and conventions used, see `numpy.fft`.
        
            The input, analogously to `ifft`, should be ordered in the same way as is
            returned by `fftn`, i.e. it should have the term for zero frequency
            in all axes in the low-order corner, the positive frequency terms in the
            first half of all axes, the term for the Nyquist frequency in the middle
            of all axes and the negative frequency terms in the second half of all
            axes, in order of decreasingly negative frequency.
        
            Parameters
            ----------
            a : array_like
                Input array, can be complex.
            s : sequence of ints, optional
                Shape (length of each transformed axis) of the output
                (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
                This corresponds to ``n`` for ``ifft(x, n)``.
                Along any axis, if the given shape is smaller than that of the input,
                the input is cropped.  If it is larger, the input is padded with zeros.
                if `s` is not given, the shape of the input (along the axes specified
                by `axes`) is used.  See notes for issue on `ifft` zero padding.
            axes : sequence of ints, optional
                Axes over which to compute the IFFT.  If not given, the last ``len(s)``
                axes are used, or all axes if `s` is also not specified.
                Repeated indices in `axes` means that the inverse transform over that
                axis is performed multiple times.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axes
                indicated by `axes`, or by a combination of `s` or `a`,
                as explained in the parameters section above.
        
            Raises
            ------
            ValueError
                If `s` and `axes` have different length.
            IndexError
                If an element of `axes` is larger than than the number of axes of `a`.
        
            See Also
            --------
            numpy.fft : Overall view of discrete Fourier transforms, with definitions
                 and conventions used.
            fftn : The forward *n*-dimensional FFT, of which `ifftn` is the inverse.
            ifft : The one-dimensional inverse FFT.
            ifft2 : The two-dimensional inverse FFT.
            ifftshift : Undoes `fftshift`, shifts zero-frequency terms to beginning
                of array.
        
            Notes
            -----
            See `numpy.fft` for definitions and conventions used.
        
            Zero-padding, analogously with `ifft`, is performed by appending zeros to
            the input along the specified dimension.  Although this is the common
            approach, it might lead to surprising results.  If another form of zero
            padding is desired, it must be performed before `ifftn` is called.
        
            Examples
            --------
            >>> a = np.eye(4)
            >>> np.fft.ifftn(np.fft.fftn(a, axes=(0,)), axes=(1,))
            array([[ 1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],
                   [ 0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j],
                   [ 0.+0.j,  0.+0.j,  1.+0.j,  0.+0.j],
                   [ 0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j]])
        
        
            Create and plot an image with band-limited frequency content:
        
            >>> import matplotlib.pyplot as plt
            >>> n = np.zeros((200,200), dtype=complex)
            >>> n[60:80, 20:40] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20, 20)))
            >>> im = np.fft.ifftn(n).real
            >>> plt.imshow(im)
            <matplotlib.image.AxesImage object at 0x...>
            >>> plt.show()
        
            """
        return complex()
    def ifftshift(self, x=None, axes=None):
        """
            The inverse of fftshift.
        
            Parameters
            ----------
            x : array_like
                Input array.
            axes : int or shape tuple, optional
                Axes over which to calculate.  Defaults to None, which shifts all axes.
        
            Returns
            -------
            y : ndarray
                The shifted array.
        
            See Also
            --------
            fftshift : Shift zero-frequency component to the center of the spectrum.
        
            Examples
            --------
            >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
            >>> freqs
            array([[ 0.,  1.,  2.],
                   [ 3.,  4., -4.],
                   [-3., -2., -1.]])
            >>> np.fft.ifftshift(np.fft.fftshift(freqs))
            array([[ 0.,  1.,  2.],
                   [ 3.,  4., -4.],
                   [-3., -2., -1.]])
        
            """
        return ndarray()
    def ihfft(self, a=-1, n=None, axis=-1):
        """
            Compute the inverse FFT of a signal whose spectrum has Hermitian symmetry.
        
            Parameters
            ----------
            a : array_like
                Input array.
            n : int, optional
                Length of the inverse FFT.
            axis : int, optional
                Axis over which to compute the inverse FFT, assuming Hermitian
                symmetry of the spectrum. Default is the last axis.
        
            Returns
            -------
            out : ndarray
                The transformed input.
        
            See also
            --------
            hfft, irfft
        
            Notes
            -----
            `hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
            opposite case: here the signal is real in the frequency domain and has
            Hermite symmetry in the time domain. So here it's `hfft` for which
            you must supply the length of the result if it is to be odd:
            ``ihfft(hfft(a), len(a)) == a``, within numerical accuracy.
        
            """
        return ndarray()
    def irfft(self, a=-1, n=None, axis=-1):
        """
            Compute the inverse of the n-point DFT for real input.
        
            This function computes the inverse of the one-dimensional *n*-point
            discrete Fourier Transform of real input computed by `rfft`.
            In other words, ``irfft(rfft(a), len(a)) == a`` to within numerical
            accuracy. (See Notes below for why ``len(a)`` is necessary here.)
        
            The input is expected to be in the form returned by `rfft`, i.e. the
            real zero-frequency term followed by the complex positive frequency terms
            in order of increasing frequency.  Since the discrete Fourier Transform of
            real input is Hermite-symmetric, the negative frequency terms are taken
            to be the complex conjugates of the corresponding positive frequency terms.
        
            Parameters
            ----------
            a : array_like
                The input array.
            n : int, optional
                Length of the transformed axis of the output.
                For `n` output points, ``n//2+1`` input points are necessary.  If the
                input is longer than this, it is cropped.  If it is shorter than this,
                it is padded with zeros.  If `n` is not given, it is determined from
                the length of the input (along the axis specified by `axis`).
            axis : int, optional
                Axis over which to compute the inverse FFT.
        
            Returns
            -------
            out : ndarray
                The truncated or zero-padded input, transformed along the axis
                indicated by `axis`, or the last one if `axis` is not specified.
                The length of the transformed axis is `n`, or, if `n` is not given,
                ``2*(m-1)`` where `m` is the length of the transformed axis of the
                input. To get an odd number of output points, `n` must be specified.
        
            Raises
            ------
            IndexError
                If `axis` is larger than the last axis of `a`.
        
            See Also
            --------
            numpy.fft : For definition of the DFT and conventions used.
            rfft : The one-dimensional FFT of real input, of which `irfft` is inverse.
            fft : The one-dimensional FFT.
            irfft2 : The inverse of the two-dimensional FFT of real input.
            irfftn : The inverse of the *n*-dimensional FFT of real input.
        
            Notes
            -----
            Returns the real valued `n`-point inverse discrete Fourier transform
            of `a`, where `a` contains the non-negative frequency terms of a
            Hermite-symmetric sequence. `n` is the length of the result, not the
            input.
        
            If you specify an `n` such that `a` must be zero-padded or truncated, the
            extra/removed values will be added/removed at high frequencies. One can
            thus resample a series to `m` points via Fourier interpolation by:
            ``a_resamp = irfft(rfft(a), m)``.
        
        
            Examples
            --------
            >>> np.fft.ifft([1, -1j, -1, 1j])
            array([ 0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j])
            >>> np.fft.irfft([1, -1j, -1])
            array([ 0.,  1.,  0.,  0.])
        
            Notice how the last term in the input to the ordinary `ifft` is the
            complex conjugate of the second term, and the output has zero imaginary
            part everywhere.  When calling `irfft`, the negative frequencies are not
            specified, and the output array is purely real.
        
            """
        return ndarray()
    def irfft2(self, a="(-2, -1)", s=None, axes="(-2, -1)"):
        """
            Compute the 2-dimensional inverse FFT of a real array.
        
            Parameters
            ----------
            a : array_like
                The input array
            s : sequence of ints, optional
                Shape of the inverse FFT.
            axes : sequence of ints, optional
                The axes over which to compute the inverse fft.
                Default is the last two axes.
        
            Returns
            -------
            out : ndarray
                The result of the inverse real 2-D FFT.
        
            See Also
            --------
            irfftn : Compute the inverse of the N-dimensional FFT of real input.
        
            Notes
            -----
            This is really `irfftn` with different defaults.
            For more details see `irfftn`.
        
            """
        return ndarray()
    def irfftn(self, a=None, s=None, axes=None):
        """
            Compute the inverse of the N-dimensional FFT of real input.
        
            This function computes the inverse of the N-dimensional discrete
            Fourier Transform for real input over any number of axes in an
            M-dimensional array by means of the Fast Fourier Transform (FFT).  In
            other words, ``irfftn(rfftn(a), a.shape) == a`` to within numerical
            accuracy. (The ``a.shape`` is necessary like ``len(a)`` is for `irfft`,
            and for the same reason.)
        
            The input should be ordered in the same way as is returned by `rfftn`,
            i.e. as for `irfft` for the final transformation axis, and as for `ifftn`
            along all the other axes.
        
            Parameters
            ----------
            a : array_like
                Input array.
            s : sequence of ints, optional
                Shape (length of each transformed axis) of the output
                (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.). `s` is also the
                number of input points used along this axis, except for the last axis,
                where ``s[-1]//2+1`` points of the input are used.
                Along any axis, if the shape indicated by `s` is smaller than that of
                the input, the input is cropped.  If it is larger, the input is padded
                with zeros. If `s` is not given, the shape of the input (along the
                axes specified by `axes`) is used.
            axes : sequence of ints, optional
                Axes over which to compute the inverse FFT. If not given, the last
                `len(s)` axes are used, or all axes if `s` is also not specified.
                Repeated indices in `axes` means that the inverse transform over that
                axis is performed multiple times.
        
            Returns
            -------
            out : ndarray
                The truncated or zero-padded input, transformed along the axes
                indicated by `axes`, or by a combination of `s` or `a`,
                as explained in the parameters section above.
                The length of each transformed axis is as given by the corresponding
                element of `s`, or the length of the input in every axis except for the
                last one if `s` is not given.  In the final transformed axis the length
                of the output when `s` is not given is ``2*(m-1)`` where `m` is the
                length of the final transformed axis of the input.  To get an odd
                number of output points in the final axis, `s` must be specified.
        
            Raises
            ------
            ValueError
                If `s` and `axes` have different length.
            IndexError
                If an element of `axes` is larger than than the number of axes of `a`.
        
            See Also
            --------
            rfftn : The forward n-dimensional FFT of real input,
                    of which `ifftn` is the inverse.
            fft : The one-dimensional FFT, with definitions and conventions used.
            irfft : The inverse of the one-dimensional FFT of real input.
            irfft2 : The inverse of the two-dimensional FFT of real input.
        
            Notes
            -----
            See `fft` for definitions and conventions used.
        
            See `rfft` for definitions and conventions used for real input.
        
            Examples
            --------
            >>> a = np.zeros((3, 2, 2))
            >>> a[0, 0, 0] = 3 * 2 * 2
            >>> np.fft.irfftn(a)
            array([[[ 1.,  1.],
                    [ 1.,  1.]],
                   [[ 1.,  1.],
                    [ 1.,  1.]],
                   [[ 1.,  1.],
                    [ 1.,  1.]]])
        
            """
        return ndarray()
    print_function = instance()
    def rfft(self, a=-1, n=None, axis=-1):
        """
            Compute the one-dimensional discrete Fourier Transform for real input.
        
            This function computes the one-dimensional *n*-point discrete Fourier
            Transform (DFT) of a real-valued array by means of an efficient algorithm
            called the Fast Fourier Transform (FFT).
        
            Parameters
            ----------
            a : array_like
                Input array
            n : int, optional
                Number of points along transformation axis in the input to use.
                If `n` is smaller than the length of the input, the input is cropped.
                If it is larger, the input is padded with zeros. If `n` is not given,
                the length of the input (along the axis specified by `axis`) is used.
            axis : int, optional
                Axis over which to compute the FFT. If not given, the last axis is
                used.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axis
                indicated by `axis`, or the last one if `axis` is not specified.
                If `n` is even, the length of the transformed axis is ``(n/2)+1``.
                If `n` is odd, the length is ``(n+1)/2``.
        
            Raises
            ------
            IndexError
                If `axis` is larger than the last axis of `a`.
        
            See Also
            --------
            numpy.fft : For definition of the DFT and conventions used.
            irfft : The inverse of `rfft`.
            fft : The one-dimensional FFT of general (complex) input.
            fftn : The *n*-dimensional FFT.
            rfftn : The *n*-dimensional FFT of real input.
        
            Notes
            -----
            When the DFT is computed for purely real input, the output is
            Hermite-symmetric, i.e. the negative frequency terms are just the complex
            conjugates of the corresponding positive-frequency terms, and the
            negative-frequency terms are therefore redundant.  This function does not
            compute the negative frequency terms, and the length of the transformed
            axis of the output is therefore ``n//2+1``.
        
            When ``A = rfft(a)`` and fs is the sampling frequency, ``A[0]`` contains
            the zero-frequency term 0*fs, which is real due to Hermitian symmetry.
        
            If `n` is even, ``A[-1]`` contains the term representing both positive
            and negative Nyquist frequency (+fs/2 and -fs/2), and must also be purely
            real. If `n` is odd, there is no term at fs/2; ``A[-1]`` contains
            the largest positive frequency (fs/2*(n-1)/n), and is complex in the
            general case.
        
            If the input `a` contains an imaginary part, it is silently discarded.
        
            Examples
            --------
            >>> np.fft.fft([0, 1, 0, 0])
            array([ 1.+0.j,  0.-1.j, -1.+0.j,  0.+1.j])
            >>> np.fft.rfft([0, 1, 0, 0])
            array([ 1.+0.j,  0.-1.j, -1.+0.j])
        
            Notice how the final element of the `fft` output is the complex conjugate
            of the second element, for real input. For `rfft`, this symmetry is
            exploited to compute only the non-negative frequency terms.
        
            """
        return complex()
    def rfft2(self, a="(-2, -1)", s=None, axes="(-2, -1)"):
        """
            Compute the 2-dimensional FFT of a real array.
        
            Parameters
            ----------
            a : array
                Input array, taken to be real.
            s : sequence of ints, optional
                Shape of the FFT.
            axes : sequence of ints, optional
                Axes over which to compute the FFT.
        
            Returns
            -------
            out : ndarray
                The result of the real 2-D FFT.
        
            See Also
            --------
            rfftn : Compute the N-dimensional discrete Fourier Transform for real
                    input.
        
            Notes
            -----
            This is really just `rfftn` with different default behavior.
            For more details see `rfftn`.
        
            """
        return ndarray()
    def rfftfreq(self, n=1.0, d=1.0):
        """
            Return the Discrete Fourier Transform sample frequencies
            (for usage with rfft, irfft).
        
            The returned float array `f` contains the frequency bin centers in cycles
            per unit of the sample spacing (with zero at the start).  For instance, if
            the sample spacing is in seconds, then the frequency unit is cycles/second.
        
            Given a window length `n` and a sample spacing `d`::
        
              f = [0, 1, ...,     n/2-1,     n/2] / (d*n)   if n is even
              f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n)   if n is odd
        
            Unlike `fftfreq` (but like `scipy.fftpack.rfftfreq`)
            the Nyquist frequency component is considered to be positive.
        
            Parameters
            ----------
            n : int
                Window length.
            d : scalar, optional
                Sample spacing (inverse of the sampling rate). Defaults to 1.
        
            Returns
            -------
            f : ndarray
                Array of length ``n//2 + 1`` containing the sample frequencies.
        
            Examples
            --------
            >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5, -3, 4], dtype=float)
            >>> fourier = np.fft.rfft(signal)
            >>> n = signal.size
            >>> sample_rate = 100
            >>> freq = np.fft.fftfreq(n, d=1./sample_rate)
            >>> freq
            array([  0.,  10.,  20.,  30.,  40., -50., -40., -30., -20., -10.])
            >>> freq = np.fft.rfftfreq(n, d=1./sample_rate)
            >>> freq
            array([  0.,  10.,  20.,  30.,  40.,  50.])
        
            """
        return ndarray()
    def rfftn(self, a=None, s=None, axes=None):
        """
            Compute the N-dimensional discrete Fourier Transform for real input.
        
            This function computes the N-dimensional discrete Fourier Transform over
            any number of axes in an M-dimensional real array by means of the Fast
            Fourier Transform (FFT).  By default, all axes are transformed, with the
            real transform performed over the last axis, while the remaining
            transforms are complex.
        
            Parameters
            ----------
            a : array_like
                Input array, taken to be real.
            s : sequence of ints, optional
                Shape (length along each transformed axis) to use from the input.
                (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
                The final element of `s` corresponds to `n` for ``rfft(x, n)``, while
                for the remaining axes, it corresponds to `n` for ``fft(x, n)``.
                Along any axis, if the given shape is smaller than that of the input,
                the input is cropped.  If it is larger, the input is padded with zeros.
                if `s` is not given, the shape of the input (along the axes specified
                by `axes`) is used.
            axes : sequence of ints, optional
                Axes over which to compute the FFT.  If not given, the last ``len(s)``
                axes are used, or all axes if `s` is also not specified.
        
            Returns
            -------
            out : complex ndarray
                The truncated or zero-padded input, transformed along the axes
                indicated by `axes`, or by a combination of `s` and `a`,
                as explained in the parameters section above.
                The length of the last axis transformed will be ``s[-1]//2+1``,
                while the remaining transformed axes will have lengths according to
                `s`, or unchanged from the input.
        
            Raises
            ------
            ValueError
                If `s` and `axes` have different length.
            IndexError
                If an element of `axes` is larger than than the number of axes of `a`.
        
            See Also
            --------
            irfftn : The inverse of `rfftn`, i.e. the inverse of the n-dimensional FFT
                 of real input.
            fft : The one-dimensional FFT, with definitions and conventions used.
            rfft : The one-dimensional FFT of real input.
            fftn : The n-dimensional FFT.
            rfft2 : The two-dimensional FFT of real input.
        
            Notes
            -----
            The transform for real input is performed over the last transformation
            axis, as by `rfft`, then the transform over the remaining axes is
            performed as by `fftn`.  The order of the output is as for `rfft` for the
            final transformation axis, and as for `fftn` for the remaining
            transformation axes.
        
            See `fft` for details, definitions and conventions used.
        
            Examples
            --------
            >>> a = np.ones((2, 2, 2))
            >>> np.fft.rfftn(a)
            array([[[ 8.+0.j,  0.+0.j],
                    [ 0.+0.j,  0.+0.j]],
                   [[ 0.+0.j,  0.+0.j],
                    [ 0.+0.j,  0.+0.j]]])
        
            >>> np.fft.rfftn(a, axes=(2, 0))
            array([[[ 4.+0.j,  0.+0.j],
                    [ 4.+0.j,  0.+0.j]],
                   [[ 0.+0.j,  0.+0.j],
                    [ 0.+0.j,  0.+0.j]]])
        
            """
        return complex()
    def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
        """
                Run tests for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the tests to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow tests as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for test outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
                doctests : bool, optional
                    If True, run doctests in module. Default is False.
                coverage : bool, optional
                    If True, report coverage of NumPy code. Default is False.
                    (This requires the `coverage module:
                     <http://nedbatchelder.com/code/modules/coverage.html>`_).
                raise_warnings : str or sequence of warnings, optional
                    This specifies which warnings to configure as 'raise' instead
                    of 'warn' during the test execution.  Valid strings are:
        
                      - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                      - "release" : equals ``()``, don't raise on any warnings.
        
                Returns
                -------
                result : object
                    Returns the result of running the tests as a
                    ``nose.result.TextTestResult`` object.
        
                Notes
                -----
                Each NumPy module exposes `test` in its namespace to run all tests for it.
                For example, to run all tests for numpy.lib:
        
                >>> np.lib.test() #doctest: +SKIP
        
                Examples
                --------
                >>> result = np.lib.test() #doctest: +SKIP
                Running unit tests for numpy.lib
                ...
                Ran 976 tests in 3.933s
        
                OK
        
                >>> result.errors #doctest: +SKIP
                []
                >>> result.knownfail #doctest: +SKIP
                []
                """
        return object()
class linalg:
    class LinAlgError:
        __dict__ = dictproxy()
        __doc__ = str()
        __module__ = str()
        __weakref__ = getset_descriptor()
        args = getset_descriptor()
        message = getset_descriptor()
    class NoseTester:
        __dict__ = dictproxy()
        __doc__ = str()
        __module__ = str()
        __weakref__ = getset_descriptor()
        def _get_custom_doctester(self, _):
            """ Return instantiated plugin for doctests
            
                    Allows subclassing of this class to override doctester
            
                    A return value of None means use the nose builtin doctest plugin
                    """
            return None
        def _show_system_info(self, _):
            """None"""
            return None
        def _test_argv(self, label, verbose, extra_argv):
            """ Generate argv for nosetest command
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        see ``test`` docstring
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    argv : list
                        command line arguments that will be passed to nose
                    """
            return list()
        def bench(self=None, label="fast", verbose=1, extra_argv=None):
            """
                    Run benchmarks for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the benchmarks to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow benchmarks as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    success : bool
                        Returns True if running the benchmarks works, False if an error
                        occurred.
            
                    Notes
                    -----
                    Benchmarks are like tests, but have names starting with "bench" instead
                    of "test", and can be found under the "benchmarks" sub-directory of the
                    module.
            
                    Each NumPy module exposes `bench` in its namespace to run all benchmarks
                    for it.
            
                    Examples
                    --------
                    >>> success = np.lib.bench() #doctest: +SKIP
                    Running benchmarks for numpy.lib
                    ...
                    using 562341 items:
                    unique:
                    0.11
                    unique1d:
                    0.11
                    ratio: 1.0
                    nUnique: 56230 == 56230
                    ...
                    OK
            
                    >>> success #doctest: +SKIP
                    True
            
                    """
            return bool()
        excludes = list()
        def prepare_test_args(self=False, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False):
            """
                    Run tests for module using nose.
            
                    This method does the heavy lifting for the `test` method. It takes all
                    the same arguments, for details see `test`.
            
                    See Also
                    --------
                    test
            
                    """
            return None
        def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
            """
                    Run tests for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the tests to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow tests as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
                    doctests : bool, optional
                        If True, run doctests in module. Default is False.
                    coverage : bool, optional
                        If True, report coverage of NumPy code. Default is False.
                        (This requires the `coverage module:
                         <http://nedbatchelder.com/code/modules/coverage.html>`_).
                    raise_warnings : str or sequence of warnings, optional
                        This specifies which warnings to configure as 'raise' instead
                        of 'warn' during the test execution.  Valid strings are:
            
                          - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                          - "release" : equals ``()``, don't raise on any warnings.
            
                    Returns
                    -------
                    result : object
                        Returns the result of running the tests as a
                        ``nose.result.TextTestResult`` object.
            
                    Notes
                    -----
                    Each NumPy module exposes `test` in its namespace to run all tests for it.
                    For example, to run all tests for numpy.lib:
            
                    >>> np.lib.test() #doctest: +SKIP
            
                    Examples
                    --------
                    >>> result = np.lib.test() #doctest: +SKIP
                    Running unit tests for numpy.lib
                    ...
                    Ran 976 tests in 3.933s
            
                    OK
            
                    >>> result.errors #doctest: +SKIP
                    []
                    >>> result.knownfail #doctest: +SKIP
                    []
                    """
            return object()
    __builtins__ = dict()
    __doc__ = str()
    __file__ = str()
    __name__ = str()
    __package__ = str()
    __path__ = list()
    absolute_import = instance()
    def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
        """
                Run tests for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the tests to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow tests as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for test outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
                doctests : bool, optional
                    If True, run doctests in module. Default is False.
                coverage : bool, optional
                    If True, report coverage of NumPy code. Default is False.
                    (This requires the `coverage module:
                     <http://nedbatchelder.com/code/modules/coverage.html>`_).
                raise_warnings : str or sequence of warnings, optional
                    This specifies which warnings to configure as 'raise' instead
                    of 'warn' during the test execution.  Valid strings are:
        
                      - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                      - "release" : equals ``()``, don't raise on any warnings.
        
                Returns
                -------
                result : object
                    Returns the result of running the tests as a
                    ``nose.result.TextTestResult`` object.
        
                Notes
                -----
                Each NumPy module exposes `test` in its namespace to run all tests for it.
                For example, to run all tests for numpy.lib:
        
                >>> np.lib.test() #doctest: +SKIP
        
                Examples
                --------
                >>> result = np.lib.test() #doctest: +SKIP
                Running unit tests for numpy.lib
                ...
                Ran 976 tests in 3.933s
        
                OK
        
                >>> result.errors #doctest: +SKIP
                []
                >>> result.knownfail #doctest: +SKIP
                []
                """
        return object()
    def cholesky(self, a):
        """
            Cholesky decomposition.
        
            Return the Cholesky decomposition, `L * L.H`, of the square matrix `a`,
            where `L` is lower-triangular and .H is the conjugate transpose operator
            (which is the ordinary transpose if `a` is real-valued).  `a` must be
            Hermitian (symmetric if real-valued) and positive-definite.  Only `L` is
            actually returned.
        
            Parameters
            ----------
            a : (..., M, M) array_like
                Hermitian (symmetric if all elements are real), positive-definite
                input matrix.
        
            Returns
            -------
            L : (..., M, M) array_like
                Upper or lower-triangular Cholesky factor of `a`.  Returns a
                matrix object if `a` is a matrix object.
        
            Raises
            ------
            LinAlgError
               If the decomposition fails, for example, if `a` is not
               positive-definite.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            The Cholesky decomposition is often used as a fast way of solving
        
            .. math:: A \mathbf{x} = \mathbf{b}
        
            (when `A` is both Hermitian/symmetric and positive-definite).
        
            First, we solve for :math:`\mathbf{y}` in
        
            .. math:: L \mathbf{y} = \mathbf{b},
        
            and then for :math:`\mathbf{x}` in
        
            .. math:: L.H \mathbf{x} = \mathbf{y}.
        
            Examples
            --------
            >>> A = np.array([[1,-2j],[2j,5]])
            >>> A
            array([[ 1.+0.j,  0.-2.j],
                   [ 0.+2.j,  5.+0.j]])
            >>> L = np.linalg.cholesky(A)
            >>> L
            array([[ 1.+0.j,  0.+0.j],
                   [ 0.+2.j,  1.+0.j]])
            >>> np.dot(L, L.T.conj()) # verify that L * L.H = A
            array([[ 1.+0.j,  0.-2.j],
                   [ 0.+2.j,  5.+0.j]])
            >>> A = [[1,-2j],[2j,5]] # what happens if A is only array_like?
            >>> np.linalg.cholesky(A) # an ndarray object is returned
            array([[ 1.+0.j,  0.+0.j],
                   [ 0.+2.j,  1.+0.j]])
            >>> # But a matrix object is returned if A is a matrix object
            >>> LA.cholesky(np.matrix(A))
            matrix([[ 1.+0.j,  0.+0.j],
                    [ 0.+2.j,  1.+0.j]])
        
            """
        return more_args()
    def cond(self, x=None, p=None):
        """
            Compute the condition number of a matrix.
        
            This function is capable of returning the condition number using
            one of seven different norms, depending on the value of `p` (see
            Parameters below).
        
            Parameters
            ----------
            x : (M, N) array_like
                The matrix whose condition number is sought.
            p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional
                Order of the norm:
        
                =====  ============================
                p      norm for matrices
                =====  ============================
                None   2-norm, computed directly using the ``SVD``
                'fro'  Frobenius norm
                inf    max(sum(abs(x), axis=1))
                -inf   min(sum(abs(x), axis=1))
                1      max(sum(abs(x), axis=0))
                -1     min(sum(abs(x), axis=0))
                2      2-norm (largest sing. value)
                -2     smallest singular value
                =====  ============================
        
                inf means the numpy.inf object, and the Frobenius norm is
                the root-of-sum-of-squares norm.
        
            Returns
            -------
            c : {float, inf}
                The condition number of the matrix. May be infinite.
        
            See Also
            --------
            numpy.linalg.norm
        
            Notes
            -----
            The condition number of `x` is defined as the norm of `x` times the
            norm of the inverse of `x` [1]_; the norm can be the usual L2-norm
            (root-of-sum-of-squares) or one of a number of other matrix norms.
        
            References
            ----------
            .. [1] G. Strang, *Linear Algebra and Its Applications*, Orlando, FL,
                   Academic Press, Inc., 1980, pg. 285.
        
            Examples
            --------
            >>> from numpy import linalg as LA
            >>> a = np.array([[1, 0, -1], [0, 1, 0], [1, 0, 1]])
            >>> a
            array([[ 1,  0, -1],
                   [ 0,  1,  0],
                   [ 1,  0,  1]])
            >>> LA.cond(a)
            1.4142135623730951
            >>> LA.cond(a, 'fro')
            3.1622776601683795
            >>> LA.cond(a, np.inf)
            2.0
            >>> LA.cond(a, -np.inf)
            1.0
            >>> LA.cond(a, 1)
            2.0
            >>> LA.cond(a, -1)
            1.0
            >>> LA.cond(a, 2)
            1.4142135623730951
            >>> LA.cond(a, -2)
            0.70710678118654746
            >>> min(LA.svd(a, compute_uv=0))*min(LA.svd(LA.inv(a), compute_uv=0))
            0.70710678118654746
        
            """
        return None
    def det(self, a):
        """
            Compute the determinant of an array.
        
            Parameters
            ----------
            a : (..., M, M) array_like
                Input array to compute determinants for.
        
            Returns
            -------
            det : (...) array_like
                Determinant of `a`.
        
            See Also
            --------
            slogdet : Another way to representing the determinant, more suitable
              for large matrices where underflow/overflow may occur.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            The determinant is computed via LU factorization using the LAPACK
            routine z/dgetrf.
        
            Examples
            --------
            The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
        
            >>> a = np.array([[1, 2], [3, 4]])
            >>> np.linalg.det(a)
            -2.0
        
            Computing determinants for a stack of matrices:
        
            >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
            >>> a.shape
            (2, 2, 2
            >>> np.linalg.det(a)
            array([-2., -3., -8.])
        
            """
        return more_args()
    division = instance()
    def eig(self, a):
        """
            Compute the eigenvalues and right eigenvectors of a square array.
        
            Parameters
            ----------
            a : (..., M, M) array
                Matrices for which the eigenvalues and right eigenvectors will
                be computed
        
            Returns
            -------
            w : (..., M) array
                The eigenvalues, each repeated according to its multiplicity.
                The eigenvalues are not necessarily ordered. The resulting
                array will be always be of complex type. When `a` is real
                the resulting eigenvalues will be real (0 imaginary part) or
                occur in conjugate pairs
        
            v : (..., M, M) array
                The normalized (unit "length") eigenvectors, such that the
                column ``v[:,i]`` is the eigenvector corresponding to the
                eigenvalue ``w[i]``.
        
            Raises
            ------
            LinAlgError
                If the eigenvalue computation does not converge.
        
            See Also
            --------
            eigvalsh : eigenvalues of a symmetric or Hermitian (conjugate symmetric)
               array.
        
            eigvals : eigenvalues of a non-symmetric array.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            This is implemented using the _geev LAPACK routines which compute
            the eigenvalues and eigenvectors of general square arrays.
        
            The number `w` is an eigenvalue of `a` if there exists a vector
            `v` such that ``dot(a,v) = w * v``. Thus, the arrays `a`, `w`, and
            `v` satisfy the equations ``dot(a[:,:], v[:,i]) = w[i] * v[:,i]``
            for :math:`i \in \{0,...,M-1\}`.
        
            The array `v` of eigenvectors may not be of maximum rank, that is, some
            of the columns may be linearly dependent, although round-off error may
            obscure that fact. If the eigenvalues are all different, then theoretically
            the eigenvectors are linearly independent. Likewise, the (complex-valued)
            matrix of eigenvectors `v` is unitary if the matrix `a` is normal, i.e.,
            if ``dot(a, a.H) = dot(a.H, a)``, where `a.H` denotes the conjugate
            transpose of `a`.
        
            Finally, it is emphasized that `v` consists of the *right* (as in
            right-hand side) eigenvectors of `a`.  A vector `y` satisfying
            ``dot(y.T, a) = z * y.T`` for some number `z` is called a *left*
            eigenvector of `a`, and, in general, the left and right eigenvectors
            of a matrix are not necessarily the (perhaps conjugate) transposes
            of each other.
        
            References
            ----------
            G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, FL,
            Academic Press, Inc., 1980, Various pp.
        
            Examples
            --------
            >>> from numpy import linalg as LA
        
            (Almost) trivial example with real e-values and e-vectors.
        
            >>> w, v = LA.eig(np.diag((1, 2, 3)))
            >>> w; v
            array([ 1.,  2.,  3.])
            array([[ 1.,  0.,  0.],
                   [ 0.,  1.,  0.],
                   [ 0.,  0.,  1.]])
        
            Real matrix possessing complex e-values and e-vectors; note that the
            e-values are complex conjugates of each other.
        
            >>> w, v = LA.eig(np.array([[1, -1], [1, 1]]))
            >>> w; v
            array([ 1. + 1.j,  1. - 1.j])
            array([[ 0.70710678+0.j        ,  0.70710678+0.j        ],
                   [ 0.00000000-0.70710678j,  0.00000000+0.70710678j]])
        
            Complex-valued matrix with real e-values (but complex-valued e-vectors);
            note that a.conj().T = a, i.e., a is Hermitian.
        
            >>> a = np.array([[1, 1j], [-1j, 1]])
            >>> w, v = LA.eig(a)
            >>> w; v
            array([  2.00000000e+00+0.j,   5.98651912e-36+0.j]) # i.e., {2, 0}
            array([[ 0.00000000+0.70710678j,  0.70710678+0.j        ],
                   [ 0.70710678+0.j        ,  0.00000000+0.70710678j]])
        
            Be careful about round-off error!
        
            >>> a = np.array([[1 + 1e-9, 0], [0, 1 - 1e-9]])
            >>> # Theor. e-values are 1 +/- 1e-9
            >>> w, v = LA.eig(a)
            >>> w; v
            array([ 1.,  1.])
            array([[ 1.,  0.],
                   [ 0.,  1.]])
        
            """
        return None
    def eigh(self, a="L", UPLO="L"):
        """
            Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.
        
            Returns two objects, a 1-D array containing the eigenvalues of `a`, and
            a 2-D square array or matrix (depending on the input type) of the
            corresponding eigenvectors (in columns).
        
            Parameters
            ----------
            A : (..., M, M) array
                Hermitian/Symmetric matrices whose eigenvalues and
                eigenvectors are to be computed.
            UPLO : {'L', 'U'}, optional
                Specifies whether the calculation is done with the lower triangular
                part of `a` ('L', default) or the upper triangular part ('U').
        
            Returns
            -------
            w : (..., M) ndarray
                The eigenvalues, not necessarily ordered.
            v : {(..., M, M) ndarray, (..., M, M) matrix}
                The column ``v[:, i]`` is the normalized eigenvector corresponding
                to the eigenvalue ``w[i]``.  Will return a matrix object if `a` is
                a matrix object.
        
            Raises
            ------
            LinAlgError
                If the eigenvalue computation does not converge.
        
            See Also
            --------
            eigvalsh : eigenvalues of symmetric or Hermitian arrays.
            eig : eigenvalues and right eigenvectors for non-symmetric arrays.
            eigvals : eigenvalues of non-symmetric arrays.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            The eigenvalues/eigenvectors are computed using LAPACK routines _ssyevd,
            _heevd
        
            The eigenvalues of real symmetric or complex Hermitian matrices are
            always real. [1]_ The array `v` of (column) eigenvectors is unitary
            and `a`, `w`, and `v` satisfy the equations
            ``dot(a, v[:, i]) = w[i] * v[:, i]``.
        
            References
            ----------
            .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
                   FL, Academic Press, Inc., 1980, pg. 222.
        
            Examples
            --------
            >>> from numpy import linalg as LA
            >>> a = np.array([[1, -2j], [2j, 5]])
            >>> a
            array([[ 1.+0.j,  0.-2.j],
                   [ 0.+2.j,  5.+0.j]])
            >>> w, v = LA.eigh(a)
            >>> w; v
            array([ 0.17157288,  5.82842712])
            array([[-0.92387953+0.j        , -0.38268343+0.j        ],
                   [ 0.00000000+0.38268343j,  0.00000000-0.92387953j]])
        
            >>> np.dot(a, v[:, 0]) - w[0] * v[:, 0] # verify 1st e-val/vec pair
            array([2.77555756e-17 + 0.j, 0. + 1.38777878e-16j])
            >>> np.dot(a, v[:, 1]) - w[1] * v[:, 1] # verify 2nd e-val/vec pair
            array([ 0.+0.j,  0.+0.j])
        
            >>> A = np.matrix(a) # what happens if input is a matrix object
            >>> A
            matrix([[ 1.+0.j,  0.-2.j],
                    [ 0.+2.j,  5.+0.j]])
            >>> w, v = LA.eigh(A)
            >>> w; v
            array([ 0.17157288,  5.82842712])
            matrix([[-0.92387953+0.j        , -0.38268343+0.j        ],
                    [ 0.00000000+0.38268343j,  0.00000000-0.92387953j]])
        
            """
        return more_args()
    def eigvals(self, a):
        """
            Compute the eigenvalues of a general matrix.
        
            Main difference between `eigvals` and `eig`: the eigenvectors aren't
            returned.
        
            Parameters
            ----------
            a : (..., M, M) array_like
                A complex- or real-valued matrix whose eigenvalues will be computed.
        
            Returns
            -------
            w : (..., M,) ndarray
                The eigenvalues, each repeated according to its multiplicity.
                They are not necessarily ordered, nor are they necessarily
                real for real matrices.
        
            Raises
            ------
            LinAlgError
                If the eigenvalue computation does not converge.
        
            See Also
            --------
            eig : eigenvalues and right eigenvectors of general arrays
            eigvalsh : eigenvalues of symmetric or Hermitian arrays.
            eigh : eigenvalues and eigenvectors of symmetric/Hermitian arrays.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            This is implemented using the _geev LAPACK routines which compute
            the eigenvalues and eigenvectors of general square arrays.
        
            Examples
            --------
            Illustration, using the fact that the eigenvalues of a diagonal matrix
            are its diagonal elements, that multiplying a matrix on the left
            by an orthogonal matrix, `Q`, and on the right by `Q.T` (the transpose
            of `Q`), preserves the eigenvalues of the "middle" matrix.  In other words,
            if `Q` is orthogonal, then ``Q * A * Q.T`` has the same eigenvalues as
            ``A``:
        
            >>> from numpy import linalg as LA
            >>> x = np.random.random()
            >>> Q = np.array([[np.cos(x), -np.sin(x)], [np.sin(x), np.cos(x)]])
            >>> LA.norm(Q[0, :]), LA.norm(Q[1, :]), np.dot(Q[0, :],Q[1, :])
            (1.0, 1.0, 0.0)
        
            Now multiply a diagonal matrix by Q on one side and by Q.T on the other:
        
            >>> D = np.diag((-1,1))
            >>> LA.eigvals(D)
            array([-1.,  1.])
            >>> A = np.dot(Q, D)
            >>> A = np.dot(A, Q.T)
            >>> LA.eigvals(A)
            array([ 1., -1.])
        
            """
        return more_args()
    def eigvalsh(self, a="L", UPLO="L"):
        """
            Compute the eigenvalues of a Hermitian or real symmetric matrix.
        
            Main difference from eigh: the eigenvectors are not computed.
        
            Parameters
            ----------
            a : (..., M, M) array_like
                A complex- or real-valued matrix whose eigenvalues are to be
                computed.
            UPLO : {'L', 'U'}, optional
                Same as `lower`, with 'L' for lower and 'U' for upper triangular.
                Deprecated.
        
            Returns
            -------
            w : (..., M,) ndarray
                The eigenvalues, not necessarily ordered, each repeated according to
                its multiplicity.
        
            Raises
            ------
            LinAlgError
                If the eigenvalue computation does not converge.
        
            See Also
            --------
            eigh : eigenvalues and eigenvectors of symmetric/Hermitian arrays.
            eigvals : eigenvalues of general real or complex arrays.
            eig : eigenvalues and right eigenvectors of general real or complex
                  arrays.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            The eigenvalues are computed using LAPACK routines _ssyevd, _heevd
        
            Examples
            --------
            >>> from numpy import linalg as LA
            >>> a = np.array([[1, -2j], [2j, 5]])
            >>> LA.eigvalsh(a)
            array([ 0.17157288+0.j,  5.82842712+0.j])
        
            """
        return more_args()
    def inv(self, a):
        """
            Compute the (multiplicative) inverse of a matrix.
        
            Given a square matrix `a`, return the matrix `ainv` satisfying
            ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
        
            Parameters
            ----------
            a : (..., M, M) array_like
                Matrix to be inverted.
        
            Returns
            -------
            ainv : (..., M, M) ndarray or matrix
                (Multiplicative) inverse of the matrix `a`.
        
            Raises
            ------
            LinAlgError
                If `a` is not square or inversion fails.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            Examples
            --------
            >>> from numpy.linalg import inv
            >>> a = np.array([[1., 2.], [3., 4.]])
            >>> ainv = inv(a)
            >>> np.allclose(np.dot(a, ainv), np.eye(2))
            True
            >>> np.allclose(np.dot(ainv, a), np.eye(2))
            True
        
            If a is a matrix object, then the return value is a matrix as well:
        
            >>> ainv = inv(np.matrix(a))
            >>> ainv
            matrix([[-2. ,  1. ],
                    [ 1.5, -0.5]])
        
            Inverses of several matrices can be computed at once:
        
            >>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])
            >>> inv(a)
            array([[[-2. ,  1. ],
                    [ 1.5, -0.5]],
                   [[-5. ,  2. ],
                    [ 3. , -1. ]]])
        
            """
        return more_args() if False else matrix()
    def lstsq(self, a, b=-1, rcond=-1):
        """
            Return the least-squares solution to a linear matrix equation.
        
            Solves the equation `a x = b` by computing a vector `x` that
            minimizes the Euclidean 2-norm `|| b - a x ||^2`.  The equation may
            be under-, well-, or over- determined (i.e., the number of
            linearly independent rows of `a` can be less than, equal to, or
            greater than its number of linearly independent columns).  If `a`
            is square and of full rank, then `x` (but for round-off error) is
            the "exact" solution of the equation.
        
            Parameters
            ----------
            a : (M, N) array_like
                "Coefficient" matrix.
            b : {(M,), (M, K)} array_like
                Ordinate or "dependent variable" values. If `b` is two-dimensional,
                the least-squares solution is calculated for each of the `K` columns
                of `b`.
            rcond : float, optional
                Cut-off ratio for small singular values of `a`.
                Singular values are set to zero if they are smaller than `rcond`
                times the largest singular value of `a`.
        
            Returns
            -------
            x : {(N,), (N, K)} ndarray
                Least-squares solution. If `b` is two-dimensional,
                the solutions are in the `K` columns of `x`.
            residuals : {(), (1,), (K,)} ndarray
                Sums of residuals; squared Euclidean 2-norm for each column in
                ``b - a*x``.
                If the rank of `a` is < N or > M, this is an empty array.
                If `b` is 1-dimensional, this is a (1,) shape array.
                Otherwise the shape is (K,).
            rank : int
                Rank of matrix `a`.
            s : (min(M, N),) ndarray
                Singular values of `a`.
        
            Raises
            ------
            LinAlgError
                If computation does not converge.
        
            Notes
            -----
            If `b` is a matrix, then all array results are returned as matrices.
        
            Examples
            --------
            Fit a line, ``y = mx + c``, through some noisy data-points:
        
            >>> x = np.array([0, 1, 2, 3])
            >>> y = np.array([-1, 0.2, 0.9, 2.1])
        
            By examining the coefficients, we see that the line should have a
            gradient of roughly 1 and cut the y-axis at, more or less, -1.
        
            We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]``
            and ``p = [[m], [c]]``.  Now use `lstsq` to solve for `p`:
        
            >>> A = np.vstack([x, np.ones(len(x))]).T
            >>> A
            array([[ 0.,  1.],
                   [ 1.,  1.],
                   [ 2.,  1.],
                   [ 3.,  1.]])
        
            >>> m, c = np.linalg.lstsq(A, y)[0]
            >>> print m, c
            1.0 -0.95
        
            Plot the data along with the fitted line:
        
            >>> import matplotlib.pyplot as plt
            >>> plt.plot(x, y, 'o', label='Original data', markersize=10)
            >>> plt.plot(x, m*x + c, 'r', label='Fitted line')
            >>> plt.legend()
            >>> plt.show()
        
            """
        return N()
    def matrix_power(self, M, n):
        """
            Raise a square matrix to the (integer) power `n`.
        
            For positive integers `n`, the power is computed by repeated matrix
            squarings and matrix multiplications. If ``n == 0``, the identity matrix
            of the same shape as M is returned. If ``n < 0``, the inverse
            is computed and then raised to the ``abs(n)``.
        
            Parameters
            ----------
            M : ndarray or matrix object
                Matrix to be "powered."  Must be square, i.e. ``M.shape == (m, m)``,
                with `m` a positive integer.
            n : int
                The exponent can be any integer or long integer, positive,
                negative, or zero.
        
            Returns
            -------
            M**n : ndarray or matrix object
                The return value is the same shape and type as `M`;
                if the exponent is positive or zero then the type of the
                elements is the same as those of `M`. If the exponent is
                negative the elements are floating-point.
        
            Raises
            ------
            LinAlgError
                If the matrix is not numerically invertible.
        
            See Also
            --------
            matrix
                Provides an equivalent function as the exponentiation operator
                (``**``, not ``^``).
        
            Examples
            --------
            >>> from numpy import linalg as LA
            >>> i = np.array([[0, 1], [-1, 0]]) # matrix equiv. of the imaginary unit
            >>> LA.matrix_power(i, 3) # should = -i
            array([[ 0, -1],
                   [ 1,  0]])
            >>> LA.matrix_power(np.matrix(i), 3) # matrix arg returns matrix
            matrix([[ 0, -1],
                    [ 1,  0]])
            >>> LA.matrix_power(i, 0)
            array([[1, 0],
                   [0, 1]])
            >>> LA.matrix_power(i, -3) # should = 1/(-i) = i, but w/ f.p. elements
            array([[ 0.,  1.],
                   [-1.,  0.]])
        
            Somewhat more sophisticated example
        
            >>> q = np.zeros((4, 4))
            >>> q[0:2, 0:2] = -i
            >>> q[2:4, 2:4] = i
            >>> q # one of the three quarternion units not equal to 1
            array([[ 0., -1.,  0.,  0.],
                   [ 1.,  0.,  0.,  0.],
                   [ 0.,  0.,  0.,  1.],
                   [ 0.,  0., -1.,  0.]])
            >>> LA.matrix_power(q, 2) # = -np.eye(4)
            array([[-1.,  0.,  0.,  0.],
                   [ 0., -1.,  0.,  0.],
                   [ 0.,  0., -1.,  0.],
                   [ 0.,  0.,  0., -1.]])
        
            """
        return ndarray() if False else matrix()
    def matrix_rank(self, M=None, tol=None):
        """
            Return matrix rank of array using SVD method
        
            Rank of the array is the number of SVD singular values of the array that are
            greater than `tol`.
        
            Parameters
            ----------
            M : {(M,), (M, N)} array_like
                array of <=2 dimensions
            tol : {None, float}, optional
               threshold below which SVD values are considered zero. If `tol` is
               None, and ``S`` is an array with singular values for `M`, and
               ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
               set to ``S.max() * max(M.shape) * eps``.
        
            Notes
            -----
            The default threshold to detect rank deficiency is a test on the magnitude
            of the singular values of `M`.  By default, we identify singular values less
            than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
            the symbols defined above). This is the algorithm MATLAB uses [1].  It also
            appears in *Numerical recipes* in the discussion of SVD solutions for linear
            least squares [2].
        
            This default threshold is designed to detect rank deficiency accounting for
            the numerical errors of the SVD computation.  Imagine that there is a column
            in `M` that is an exact (in floating point) linear combination of other
            columns in `M`. Computing the SVD on `M` will not produce a singular value
            exactly equal to 0 in general: any difference of the smallest SVD value from
            0 will be caused by numerical imprecision in the calculation of the SVD.
            Our threshold for small SVD values takes this numerical imprecision into
            account, and the default threshold will detect such numerical rank
            deficiency.  The threshold may declare a matrix `M` rank deficient even if
            the linear combination of some columns of `M` is not exactly equal to
            another column of `M` but only numerically very close to another column of
            `M`.
        
            We chose our default threshold because it is in wide use.  Other thresholds
            are possible.  For example, elsewhere in the 2007 edition of *Numerical
            recipes* there is an alternative threshold of ``S.max() *
            np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
            this threshold as being based on "expected roundoff error" (p 71).
        
            The thresholds above deal with floating point roundoff error in the
            calculation of the SVD.  However, you may have more information about the
            sources of error in `M` that would make you consider other tolerance values
            to detect *effective* rank deficiency.  The most useful measure of the
            tolerance depends on the operations you intend to use on your matrix.  For
            example, if your data come from uncertain measurements with uncertainties
            greater than floating point epsilon, choosing a tolerance near that
            uncertainty may be preferable.  The tolerance may be absolute if the
            uncertainties are absolute rather than relative.
        
            References
            ----------
            .. [1] MATLAB reference documention, "Rank"
                   http://www.mathworks.com/help/techdoc/ref/rank.html
            .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
                   "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
                   page 795.
        
            Examples
            --------
            >>> from numpy.linalg import matrix_rank
            >>> matrix_rank(np.eye(4)) # Full rank matrix
            4
            >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
            >>> matrix_rank(I)
            3
            >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
            1
            >>> matrix_rank(np.zeros((4,)))
            0
            """
        return None
    def norm(self, x=None, ord=None, axis=None):
        """
            Matrix or vector norm.
        
            This function is able to return one of seven different matrix norms,
            or one of an infinite number of vector norms (described below), depending
            on the value of the ``ord`` parameter.
        
            Parameters
            ----------
            x : array_like
                Input array.  If `axis` is None, `x` must be 1-D or 2-D.
            ord : {non-zero int, inf, -inf, 'fro'}, optional
                Order of the norm (see table under ``Notes``). inf means numpy's
                `inf` object.
            axis : {int, 2-tuple of ints, None}, optional
                If `axis` is an integer, it specifies the axis of `x` along which to
                compute the vector norms.  If `axis` is a 2-tuple, it specifies the
                axes that hold 2-D matrices, and the matrix norms of these matrices
                are computed.  If `axis` is None then either a vector norm (when `x`
                is 1-D) or a matrix norm (when `x` is 2-D) is returned.
        
            Returns
            -------
            n : float or ndarray
                Norm of the matrix or vector(s).
        
            Notes
            -----
            For values of ``ord <= 0``, the result is, strictly speaking, not a
            mathematical 'norm', but it may still be useful for various numerical
            purposes.
        
            The following norms can be calculated:
        
            =====  ============================  ==========================
            ord    norm for matrices             norm for vectors
            =====  ============================  ==========================
            None   Frobenius norm                2-norm
            'fro'  Frobenius norm                --
            inf    max(sum(abs(x), axis=1))      max(abs(x))
            -inf   min(sum(abs(x), axis=1))      min(abs(x))
            0      --                            sum(x != 0)
            1      max(sum(abs(x), axis=0))      as below
            -1     min(sum(abs(x), axis=0))      as below
            2      2-norm (largest sing. value)  as below
            -2     smallest singular value       as below
            other  --                            sum(abs(x)**ord)**(1./ord)
            =====  ============================  ==========================
        
            The Frobenius norm is given by [1]_:
        
                :math:`||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
        
            References
            ----------
            .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
                   Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
        
            Examples
            --------
            >>> from numpy import linalg as LA
            >>> a = np.arange(9) - 4
            >>> a
            array([-4, -3, -2, -1,  0,  1,  2,  3,  4])
            >>> b = a.reshape((3, 3))
            >>> b
            array([[-4, -3, -2],
                   [-1,  0,  1],
                   [ 2,  3,  4]])
        
            >>> LA.norm(a)
            7.745966692414834
            >>> LA.norm(b)
            7.745966692414834
            >>> LA.norm(b, 'fro')
            7.745966692414834
            >>> LA.norm(a, np.inf)
            4
            >>> LA.norm(b, np.inf)
            9
            >>> LA.norm(a, -np.inf)
            0
            >>> LA.norm(b, -np.inf)
            2
        
            >>> LA.norm(a, 1)
            20
            >>> LA.norm(b, 1)
            7
            >>> LA.norm(a, -1)
            -4.6566128774142013e-010
            >>> LA.norm(b, -1)
            6
            >>> LA.norm(a, 2)
            7.745966692414834
            >>> LA.norm(b, 2)
            7.3484692283495345
        
            >>> LA.norm(a, -2)
            nan
            >>> LA.norm(b, -2)
            1.8570331885190563e-016
            >>> LA.norm(a, 3)
            5.8480354764257312
            >>> LA.norm(a, -3)
            nan
        
            Using the `axis` argument to compute vector norms:
        
            >>> c = np.array([[ 1, 2, 3],
            ...               [-1, 1, 4]])
            >>> LA.norm(c, axis=0)
            array([ 1.41421356,  2.23606798,  5.        ])
            >>> LA.norm(c, axis=1)
            array([ 3.74165739,  4.24264069])
            >>> LA.norm(c, ord=1, axis=1)
            array([6, 6])
        
            Using the `axis` argument to compute matrix norms:
        
            >>> m = np.arange(8).reshape(2,2,2)
            >>> LA.norm(m, axis=(1,2))
            array([  3.74165739,  11.22497216])
            >>> LA.norm(m[0, :, :]), LA.norm(m[1, :, :])
            (3.7416573867739413, 11.224972160321824)
        
            """
        return float() if False else ndarray()
    def pinv(self, a=1e-15, rcond=1e-15):
        """
            Compute the (Moore-Penrose) pseudo-inverse of a matrix.
        
            Calculate the generalized inverse of a matrix using its
            singular-value decomposition (SVD) and including all
            *large* singular values.
        
            Parameters
            ----------
            a : (M, N) array_like
              Matrix to be pseudo-inverted.
            rcond : float
              Cutoff for small singular values.
              Singular values smaller (in modulus) than
              `rcond` * largest_singular_value (again, in modulus)
              are set to zero.
        
            Returns
            -------
            B : (N, M) ndarray
              The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so
              is `B`.
        
            Raises
            ------
            LinAlgError
              If the SVD computation does not converge.
        
            Notes
            -----
            The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
            defined as: "the matrix that 'solves' [the least-squares problem]
            :math:`Ax = b`," i.e., if :math:`\bar{x}` is said solution, then
            :math:`A^+` is that matrix such that :math:`\bar{x} = A^+b`.
        
            It can be shown that if :math:`Q_1 \Sigma Q_2^T = A` is the singular
            value decomposition of A, then
            :math:`A^+ = Q_2 \Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are
            orthogonal matrices, :math:`\Sigma` is a diagonal matrix consisting
            of A's so-called singular values, (followed, typically, by
            zeros), and then :math:`\Sigma^+` is simply the diagonal matrix
            consisting of the reciprocals of A's singular values
            (again, followed by zeros). [1]_
        
            References
            ----------
            .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
                   FL, Academic Press, Inc., 1980, pp. 139-142.
        
            Examples
            --------
            The following example checks that ``a * a+ * a == a`` and
            ``a+ * a * a+ == a+``:
        
            >>> a = np.random.randn(9, 6)
            >>> B = np.linalg.pinv(a)
            >>> np.allclose(a, np.dot(a, np.dot(B, a)))
            True
            >>> np.allclose(B, np.dot(B, np.dot(a, B)))
            True
        
            """
        return N()
    print_function = instance()
    def qr(self, a="reduced", mode="reduced"):
        """
            Compute the qr factorization of a matrix.
        
            Factor the matrix `a` as *qr*, where `q` is orthonormal and `r` is
            upper-triangular.
        
            Parameters
            ----------
            a : array_like, shape (M, N)
                Matrix to be factored.
            mode : {'reduced', 'complete', 'r', 'raw', 'full', 'economic'}, optional
                If K = min(M, N), then
        
                'reduced'  : returns q, r with dimensions (M, K), (K, N) (default)
                'complete' : returns q, r with dimensions (M, M), (M, N)
                'r'        : returns r only with dimensions (K, N)
                'raw'      : returns h, tau with dimensions (N, M), (K,)
                'full'     : alias of 'reduced', deprecated
                'economic' : returns h from 'raw', deprecated.
        
                The options 'reduced', 'complete, and 'raw' are new in numpy 1.8,
                see the notes for more information. The default is 'reduced' and to
                maintain backward compatibility with earlier versions of numpy both
                it and the old default 'full' can be omitted. Note that array h
                returned in 'raw' mode is transposed for calling Fortran. The
                'economic' mode is deprecated.  The modes 'full' and 'economic' may
                be passed using only the first letter for backwards compatibility,
                but all others must be spelled out. See the Notes for more
                explanation.
        
        
            Returns
            -------
            q : ndarray of float or complex, optional
                A matrix with orthonormal columns. When mode = 'complete' the
                result is an orthogonal/unitary matrix depending on whether or not
                a is real/complex. The determinant may be either +/- 1 in that
                case.
            r : ndarray of float or complex, optional
                The upper-triangular matrix.
            (h, tau) : ndarrays of np.double or np.cdouble, optional
                The array h contains the Householder reflectors that generate q
                along with r. The tau array contains scaling factors for the
                reflectors. In the deprecated  'economic' mode only h is returned.
        
            Raises
            ------
            LinAlgError
                If factoring fails.
        
            Notes
            -----
            This is an interface to the LAPACK routines dgeqrf, zgeqrf,
            dorgqr, and zungqr.
        
            For more information on the qr factorization, see for example:
            http://en.wikipedia.org/wiki/QR_factorization
        
            Subclasses of `ndarray` are preserved except for the 'raw' mode. So if
            `a` is of type `matrix`, all the return values will be matrices too.
        
            New 'reduced', 'complete', and 'raw' options for mode were added in
            Numpy 1.8 and the old option 'full' was made an alias of 'reduced'.  In
            addition the options 'full' and 'economic' were deprecated.  Because
            'full' was the previous default and 'reduced' is the new default,
            backward compatibility can be maintained by letting `mode` default.
            The 'raw' option was added so that LAPACK routines that can multiply
            arrays by q using the Householder reflectors can be used. Note that in
            this case the returned arrays are of type np.double or np.cdouble and
            the h array is transposed to be FORTRAN compatible.  No routines using
            the 'raw' return are currently exposed by numpy, but some are available
            in lapack_lite and just await the necessary work.
        
            Examples
            --------
            >>> a = np.random.randn(9, 6)
            >>> q, r = np.linalg.qr(a)
            >>> np.allclose(a, np.dot(q, r))  # a does equal qr
            True
            >>> r2 = np.linalg.qr(a, mode='r')
            >>> r3 = np.linalg.qr(a, mode='economic')
            >>> np.allclose(r, r2)  # mode='r' returns the same r as mode='full'
            True
            >>> # But only triu parts are guaranteed equal when mode='economic'
            >>> np.allclose(r, np.triu(r3[:6,:6], k=0))
            True
        
            Example illustrating a common use of `qr`: solving of least squares
            problems
        
            What are the least-squares-best `m` and `y0` in ``y = y0 + mx`` for
            the following data: {(0,1), (1,0), (1,2), (2,1)}. (Graph the points
            and you'll see that it should be y0 = 0, m = 1.)  The answer is provided
            by solving the over-determined matrix equation ``Ax = b``, where::
        
              A = array([[0, 1], [1, 1], [1, 1], [2, 1]])
              x = array([[y0], [m]])
              b = array([[1], [0], [2], [1]])
        
            If A = qr such that q is orthonormal (which is always possible via
            Gram-Schmidt), then ``x = inv(r) * (q.T) * b``.  (In numpy practice,
            however, we simply use `lstsq`.)
        
            >>> A = np.array([[0, 1], [1, 1], [1, 1], [2, 1]])
            >>> A
            array([[0, 1],
                   [1, 1],
                   [1, 1],
                   [2, 1]])
            >>> b = np.array([1, 0, 2, 1])
            >>> q, r = LA.qr(A)
            >>> p = np.dot(q.T, b)
            >>> np.dot(LA.inv(r), p)
            array([  1.1e-16,   1.0e+00])
        
            """
        return ndarray() if False else complex()
    def slogdet(self, a):
        """
            Compute the sign and (natural) logarithm of the determinant of an array.
        
            If an array has a very small or very large determinant, than a call to
            `det` may overflow or underflow. This routine is more robust against such
            issues, because it computes the logarithm of the determinant rather than
            the determinant itself.
        
            Parameters
            ----------
            a : (..., M, M) array_like
                Input array, has to be a square 2-D array.
        
            Returns
            -------
            sign : (...) array_like
                A number representing the sign of the determinant. For a real matrix,
                this is 1, 0, or -1. For a complex matrix, this is a complex number
                with absolute value 1 (i.e., it is on the unit circle), or else 0.
            logdet : (...) array_like
                The natural log of the absolute value of the determinant.
        
            If the determinant is zero, then `sign` will be 0 and `logdet` will be
            -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
        
            See Also
            --------
            det
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            The determinant is computed via LU factorization using the LAPACK
            routine z/dgetrf.
        
            .. versionadded:: 1.6.0.
        
            Examples
            --------
            The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
        
            >>> a = np.array([[1, 2], [3, 4]])
            >>> (sign, logdet) = np.linalg.slogdet(a)
            >>> (sign, logdet)
            (-1, 0.69314718055994529)
            >>> sign * np.exp(logdet)
            -2.0
        
            Computing log-determinants for a stack of matrices:
        
            >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
            >>> a.shape
            (3, 2, 2)
            >>> sign, logdet = np.linalg.slogdet(a)
            >>> (sign, logdet)
            (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
            >>> sign * np.exp(logdet)
            array([-2., -3., -8.])
        
            This routine succeeds where ordinary `det` does not:
        
            >>> np.linalg.det(np.eye(500) * 0.1)
            0.0
            >>> np.linalg.slogdet(np.eye(500) * 0.1)
            (1, -1151.2925464970228)
        
            """
        return more_args()
    def solve(self, a, b):
        """
            Solve a linear matrix equation, or system of linear scalar equations.
        
            Computes the "exact" solution, `x`, of the well-determined, i.e., full
            rank, linear matrix equation `ax = b`.
        
            Parameters
            ----------
            a : (..., M, M) array_like
                Coefficient matrix.
            b : {(..., M,), (..., M, K)}, array_like
                Ordinate or "dependent variable" values.
        
            Returns
            -------
            x : {(..., M,), (..., M, K)} ndarray
                Solution to the system a x = b.  Returned shape is identical to `b`.
        
            Raises
            ------
            LinAlgError
                If `a` is singular or not square.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            The solutions are computed using LAPACK routine _gesv
        
            `a` must be square and of full-rank, i.e., all rows (or, equivalently,
            columns) must be linearly independent; if either is not true, use
            `lstsq` for the least-squares best "solution" of the
            system/equation.
        
            References
            ----------
            .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
                   FL, Academic Press, Inc., 1980, pg. 22.
        
            Examples
            --------
            Solve the system of equations ``3 * x0 + x1 = 9`` and ``x0 + 2 * x1 = 8``:
        
            >>> a = np.array([[3,1], [1,2]])
            >>> b = np.array([9,8])
            >>> x = np.linalg.solve(a, b)
            >>> x
            array([ 2.,  3.])
        
            Check that the solution is correct:
        
            >>> np.allclose(np.dot(a, x), b)
            True
        
            """
        return more_args()
    def svd(self, a=1, full_matrices=1, compute_uv=1):
        """
            Singular Value Decomposition.
        
            Factors the matrix `a` as ``u * np.diag(s) * v``, where `u` and `v`
            are unitary and `s` is a 1-d array of `a`'s singular values.
        
            Parameters
            ----------
            a : (..., M, N) array_like
                A real or complex matrix of shape (`M`, `N`) .
            full_matrices : bool, optional
                If True (default), `u` and `v` have the shapes (`M`, `M`) and
                (`N`, `N`), respectively.  Otherwise, the shapes are (`M`, `K`)
                and (`K`, `N`), respectively, where `K` = min(`M`, `N`).
            compute_uv : bool, optional
                Whether or not to compute `u` and `v` in addition to `s`.  True
                by default.
        
            Returns
            -------
            u : { (..., M, M), (..., M, K) } array
                Unitary matrices. The actual shape depends on the value of
                ``full_matrices``. Only returned when ``compute_uv`` is True.
            s : (..., K) array
                The singular values for every matrix, sorted in descending order.
            v : { (..., N, N), (..., K, N) } array
                Unitary matrices. The actual shape depends on the value of
                ``full_matrices``. Only returned when ``compute_uv`` is True.
        
            Raises
            ------
            LinAlgError
                If SVD computation does not converge.
        
            Notes
            -----
            Broadcasting rules apply, see the `numpy.linalg` documentation for
            details.
        
            The decomposition is performed using LAPACK routine _gesdd
        
            The SVD is commonly written as ``a = U S V.H``.  The `v` returned
            by this function is ``V.H`` and ``u = U``.
        
            If ``U`` is a unitary matrix, it means that it
            satisfies ``U.H = inv(U)``.
        
            The rows of `v` are the eigenvectors of ``a.H a``. The columns
            of `u` are the eigenvectors of ``a a.H``.  For row ``i`` in
            `v` and column ``i`` in `u`, the corresponding eigenvalue is
            ``s[i]**2``.
        
            If `a` is a `matrix` object (as opposed to an `ndarray`), then so
            are all the return values.
        
            Examples
            --------
            >>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6)
        
            Reconstruction based on full SVD:
        
            >>> U, s, V = np.linalg.svd(a, full_matrices=True)
            >>> U.shape, V.shape, s.shape
            ((9, 9), (6, 6), (6,))
            >>> S = np.zeros((9, 6), dtype=complex)
            >>> S[:6, :6] = np.diag(s)
            >>> np.allclose(a, np.dot(U, np.dot(S, V)))
            True
        
            Reconstruction based on reduced SVD:
        
            >>> U, s, V = np.linalg.svd(a, full_matrices=False)
            >>> U.shape, V.shape, s.shape
            ((9, 6), (6, 6), (6,))
            >>> S = np.diag(s)
            >>> np.allclose(a, np.dot(U, np.dot(S, V)))
            True
        
            """
        return None
    def tensorinv(self, a=2, ind=2):
        """
            Compute the 'inverse' of an N-dimensional array.
        
            The result is an inverse for `a` relative to the tensordot operation
            ``tensordot(a, b, ind)``, i. e., up to floating-point accuracy,
            ``tensordot(tensorinv(a), a, ind)`` is the "identity" tensor for the
            tensordot operation.
        
            Parameters
            ----------
            a : array_like
                Tensor to 'invert'. Its shape must be 'square', i. e.,
                ``prod(a.shape[:ind]) == prod(a.shape[ind:])``.
            ind : int, optional
                Number of first indices that are involved in the inverse sum.
                Must be a positive integer, default is 2.
        
            Returns
            -------
            b : ndarray
                `a`'s tensordot inverse, shape ``a.shape[:ind] + a.shape[ind:]``.
        
            Raises
            ------
            LinAlgError
                If `a` is singular or not 'square' (in the above sense).
        
            See Also
            --------
            tensordot, tensorsolve
        
            Examples
            --------
            >>> a = np.eye(4*6)
            >>> a.shape = (4, 6, 8, 3)
            >>> ainv = np.linalg.tensorinv(a, ind=2)
            >>> ainv.shape
            (8, 3, 4, 6)
            >>> b = np.random.randn(4, 6)
            >>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
            True
        
            >>> a = np.eye(4*6)
            >>> a.shape = (24, 8, 3)
            >>> ainv = np.linalg.tensorinv(a, ind=1)
            >>> ainv.shape
            (8, 3, 24)
            >>> b = np.random.randn(24)
            >>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
            True
        
            """
        return ndarray()
    def tensorsolve(self, a, b=None, axes=None):
        """
            Solve the tensor equation ``a x = b`` for x.
        
            It is assumed that all indices of `x` are summed over in the product,
            together with the rightmost indices of `a`, as is done in, for example,
            ``tensordot(a, x, axes=len(b.shape))``.
        
            Parameters
            ----------
            a : array_like
                Coefficient tensor, of shape ``b.shape + Q``. `Q`, a tuple, equals
                the shape of that sub-tensor of `a` consisting of the appropriate
                number of its rightmost indices, and must be such that
               ``prod(Q) == prod(b.shape)`` (in which sense `a` is said to be
               'square').
            b : array_like
                Right-hand tensor, which can be of any shape.
            axes : tuple of ints, optional
                Axes in `a` to reorder to the right, before inversion.
                If None (default), no reordering is done.
        
            Returns
            -------
            x : ndarray, shape Q
        
            Raises
            ------
            LinAlgError
                If `a` is singular or not 'square' (in the above sense).
        
            See Also
            --------
            tensordot, tensorinv, einsum
        
            Examples
            --------
            >>> a = np.eye(2*3*4)
            >>> a.shape = (2*3, 4, 2, 3, 4)
            >>> b = np.random.randn(2*3, 4)
            >>> x = np.linalg.tensorsolve(a, b)
            >>> x.shape
            (2, 3, 4)
            >>> np.allclose(np.tensordot(a, x, axes=3), b)
            True
        
            """
        return None
    def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
        """
                Run tests for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the tests to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow tests as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for test outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
                doctests : bool, optional
                    If True, run doctests in module. Default is False.
                coverage : bool, optional
                    If True, report coverage of NumPy code. Default is False.
                    (This requires the `coverage module:
                     <http://nedbatchelder.com/code/modules/coverage.html>`_).
                raise_warnings : str or sequence of warnings, optional
                    This specifies which warnings to configure as 'raise' instead
                    of 'warn' during the test execution.  Valid strings are:
        
                      - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                      - "release" : equals ``()``, don't raise on any warnings.
        
                Returns
                -------
                result : object
                    Returns the result of running the tests as a
                    ``nose.result.TextTestResult`` object.
        
                Notes
                -----
                Each NumPy module exposes `test` in its namespace to run all tests for it.
                For example, to run all tests for numpy.lib:
        
                >>> np.lib.test() #doctest: +SKIP
        
                Examples
                --------
                >>> result = np.lib.test() #doctest: +SKIP
                Running unit tests for numpy.lib
                ...
                Ran 976 tests in 3.933s
        
                OK
        
                >>> result.errors #doctest: +SKIP
                []
                >>> result.knownfail #doctest: +SKIP
                []
                """
        return object()
class testing:
    class IgnoreException:
        __dict__ = dictproxy()
        __doc__ = str()
        __module__ = str()
        __weakref__ = getset_descriptor()
        args = getset_descriptor()
        message = getset_descriptor()
    class TestCase:
        __dict__ = dictproxy()
        __doc__ = str()
        __module__ = str()
        __weakref__ = getset_descriptor()
        def _addSkip(self, _):
            """None"""
            return None
        def _baseAssertEqual(self, first, second=None, msg=None):
            """The default assertEqual implementation, not type specific."""
            return None
        _classSetupFailed = bool()
        def _deprecate(self, _):
            """None"""
            return None
        _diffThreshold = int()
        def _formatMessage(self, _):
            """Honour the longMessage attribute when generating failure messages.
                    If longMessage is False this means:
                    * Use only an explicit message if it is provided
                    * Otherwise use the standard message for the assert
            
                    If longMessage is True:
                    * Use the standard message
                    * If an explicit message is provided, plus ' : ' and the explicit message
                    """
            return None
        def _getAssertEqualityFunc(self, _):
            """Get a detailed comparison function for the types of the two args.
            
                    Returns: A callable accepting (first, second, msg=None) that will
                    raise a failure exception if first != second with a useful human
                    readable error message for those types.
                    """
            return None
        def _truncateMessage(self, _):
            """None"""
            return None
        def addCleanup(self, _):
            """Add a function, with arguments, to be called when the test is
                    completed. Functions added are called on a LIFO basis and are
                    called after tearDown on test failure or success.
            
                    Cleanup items are called even if setUp fails (unlike tearDown)."""
            return None
        def addTypeEqualityFunc(self, _):
            """Add a type specific assertEqual style function to compare a type.
            
                    This method is for use by TestCase subclasses that need to register
                    their own type equality functions to provide nicer error messages.
            
                    Args:
                        typeobj: The data type to call this function on when both values
                                are of the same type in assertEqual().
                        function: The callable taking two arguments and an optional
                                msg= argument that raises self.failureException with a
                                useful error message when the two arguments are not equal.
                    """
            return None
        def assertAlmostEqual(self, first, second=None, places=None, msg=None, delta=None):
            """Fail if the two objects are unequal as determined by their
                       difference rounded to the given number of decimal places
                       (default 7) and comparing to zero, or by comparing that the
                       between the two objects is more than the given delta.
            
                       Note that decimal places (from zero) are usually not the same
                       as significant digits (measured from the most signficant digit).
            
                       If the two objects compare equal then they will automatically
                       compare almost equal.
                    """
            return None
        def assertAlmostEqual(self, first, second=None, places=None, msg=None, delta=None):
            """Fail if the two objects are unequal as determined by their
                       difference rounded to the given number of decimal places
                       (default 7) and comparing to zero, or by comparing that the
                       between the two objects is more than the given delta.
            
                       Note that decimal places (from zero) are usually not the same
                       as significant digits (measured from the most signficant digit).
            
                       If the two objects compare equal then they will automatically
                       compare almost equal.
                    """
            return None
        def assertDictContainsSubset(self, expected, actual=None, msg=None):
            """Checks whether actual is a superset of expected."""
            return None
        def assertDictEqual(self, d1, d2=None, msg=None):
            """None"""
            return None
        def assertEqual(self, first, second=None, msg=None):
            """Fail if the two objects are unequal as determined by the '=='
                       operator.
                    """
            return None
        def assertEqual(self, first, second=None, msg=None):
            """Fail if the two objects are unequal as determined by the '=='
                       operator.
                    """
            return None
        def assertFalse(self, expr=None, msg=None):
            """Check that the expression is false."""
            return None
        def assertGreater(self, a, b=None, msg=None):
            """Just like self.assertTrue(a > b), but with a nicer default message."""
            return None
        def assertGreaterEqual(self, a, b=None, msg=None):
            """Just like self.assertTrue(a >= b), but with a nicer default message."""
            return None
        def assertIn(self, member, container=None, msg=None):
            """Just like self.assertTrue(a in b), but with a nicer default message."""
            return None
        def assertIs(self, expr1, expr2=None, msg=None):
            """Just like self.assertTrue(a is b), but with a nicer default message."""
            return None
        def assertIsInstance(self, obj, cls=None, msg=None):
            """Same as self.assertTrue(isinstance(obj, cls)), with a nicer
                    default message."""
            return None
        def assertIsNone(self, obj=None, msg=None):
            """Same as self.assertTrue(obj is None), with a nicer default message."""
            return None
        def assertIsNot(self, expr1, expr2=None, msg=None):
            """Just like self.assertTrue(a is not b), but with a nicer default message."""
            return None
        def assertIsNotNone(self, obj=None, msg=None):
            """Included for symmetry with assertIsNone."""
            return None
        def assertItemsEqual(self, expected_seq, actual_seq=None, msg=None):
            """An unordered sequence specific comparison. It asserts that
                    actual_seq and expected_seq have the same element counts.
                    Equivalent to::
            
                        self.assertEqual(Counter(iter(actual_seq)),
                                         Counter(iter(expected_seq)))
            
                    Asserts that each element has the same count in both sequences.
                    Example:
                        - [0, 1, 1] and [1, 0, 1] compare equal.
                        - [0, 0, 1] and [0, 1] compare unequal.
                    """
            return None
        def assertLess(self, a, b=None, msg=None):
            """Just like self.assertTrue(a < b), but with a nicer default message."""
            return None
        def assertLessEqual(self, a, b=None, msg=None):
            """Just like self.assertTrue(a <= b), but with a nicer default message."""
            return None
        def assertListEqual(self, list1, list2=None, msg=None):
            """A list-specific equality assertion.
            
                    Args:
                        list1: The first list to compare.
                        list2: The second list to compare.
                        msg: Optional message to use on failure instead of a list of
                                differences.
            
                    """
            return None
        def assertMultiLineEqual(self, first, second=None, msg=None):
            """Assert that two multi-line strings are equal."""
            return None
        def assertNotAlmostEqual(self, first, second=None, places=None, msg=None, delta=None):
            """Fail if the two objects are equal as determined by their
                       difference rounded to the given number of decimal places
                       (default 7) and comparing to zero, or by comparing that the
                       between the two objects is less than the given delta.
            
                       Note that decimal places (from zero) are usually not the same
                       as significant digits (measured from the most signficant digit).
            
                       Objects that are equal automatically fail.
                    """
            return None
        def assertNotAlmostEqual(self, first, second=None, places=None, msg=None, delta=None):
            """Fail if the two objects are equal as determined by their
                       difference rounded to the given number of decimal places
                       (default 7) and comparing to zero, or by comparing that the
                       between the two objects is less than the given delta.
            
                       Note that decimal places (from zero) are usually not the same
                       as significant digits (measured from the most signficant digit).
            
                       Objects that are equal automatically fail.
                    """
            return None
        def assertNotEqual(self, first, second=None, msg=None):
            """Fail if the two objects are equal as determined by the '!='
                       operator.
                    """
            return None
        def assertNotEqual(self, first, second=None, msg=None):
            """Fail if the two objects are equal as determined by the '!='
                       operator.
                    """
            return None
        def assertNotIn(self, member, container=None, msg=None):
            """Just like self.assertTrue(a not in b), but with a nicer default message."""
            return None
        def assertNotIsInstance(self, obj, cls=None, msg=None):
            """Included for symmetry with assertIsInstance."""
            return None
        def assertNotRegexpMatches(self, text, unexpected_regexp=None, msg=None):
            """Fail the test if the text matches the regular expression."""
            return None
        def assertRaises(self, excClass=None, callableObj=None):
            """Fail unless an exception of class excClass is raised
                       by callableObj when invoked with arguments args and keyword
                       arguments kwargs. If a different type of exception is
                       raised, it will not be caught, and the test case will be
                       deemed to have suffered an error, exactly as for an
                       unexpected exception.
            
                       If called with callableObj omitted or None, will return a
                       context object used like this::
            
                            with self.assertRaises(SomeException):
                                do_something()
            
                       The context manager keeps a reference to the exception as
                       the 'exception' attribute. This allows you to inspect the
                       exception after the assertion::
            
                           with self.assertRaises(SomeException) as cm:
                               do_something()
                           the_exception = cm.exception
                           self.assertEqual(the_exception.error_code, 3)
                    """
            return None
        def assertRaisesRegexp(self, expected_exception, expected_regexp=None, callable_obj=None):
            """Asserts that the message in a raised exception matches a regexp.
            
                    Args:
                        expected_exception: Exception class expected to be raised.
                        expected_regexp: Regexp (re pattern object or string) expected
                                to be found in error message.
                        callable_obj: Function to be called.
                        args: Extra args.
                        kwargs: Extra kwargs.
                    """
            return None
        def assertRegexpMatches(self, text, expected_regexp=None, msg=None):
            """Fail the test unless the text matches the regular expression."""
            return None
        def assertSequenceEqual(self, seq1, seq2=None, msg=None, seq_type=None):
            """An equality assertion for ordered sequences (like lists and tuples).
            
                    For the purposes of this function, a valid ordered sequence type is one
                    which can be indexed, has a length, and has an equality operator.
            
                    Args:
                        seq1: The first sequence to compare.
                        seq2: The second sequence to compare.
                        seq_type: The expected datatype of the sequences, or None if no
                                datatype should be enforced.
                        msg: Optional message to use on failure instead of a list of
                                differences.
                    """
            return None
        def assertSetEqual(self, set1, set2=None, msg=None):
            """A set-specific equality assertion.
            
                    Args:
                        set1: The first set to compare.
                        set2: The second set to compare.
                        msg: Optional message to use on failure instead of a list of
                                differences.
            
                    assertSetEqual uses ducktyping to support different types of sets, and
                    is optimized for sets specifically (parameters must support a
                    difference method).
                    """
            return None
        def assertTrue(self, expr=None, msg=None):
            """Check that the expression is true."""
            return None
        def assertTupleEqual(self, tuple1, tuple2=None, msg=None):
            """A tuple-specific equality assertion.
            
                    Args:
                        tuple1: The first tuple to compare.
                        tuple2: The second tuple to compare.
                        msg: Optional message to use on failure instead of a list of
                                differences.
                    """
            return None
        def assertTrue(self, expr=None, msg=None):
            """Check that the expression is true."""
            return None
        def countTestCases(self, _):
            """None"""
            return None
        def debug(self, _):
            """Run the test without collecting errors in a TestResult"""
            return None
        def defaultTestResult(self, _):
            """None"""
            return None
        def doCleanups(self, _):
            """Execute all cleanup functions. Normally called for you after
                    tearDown."""
            return None
        def fail(self=None, msg=None):
            """Fail immediately, with the given message."""
            return None
        def deprecated_func(self):
            """None"""
            return None
        def deprecated_func(self):
            """None"""
            return None
        def deprecated_func(self):
            """None"""
            return None
        def deprecated_func(self):
            """None"""
            return None
        def deprecated_func(self):
            """None"""
            return None
        def deprecated_func(self):
            """None"""
            return None
        def deprecated_func(self):
            """None"""
            return None
        def id(self, _):
            """None"""
            return None
        longMessage = bool()
        maxDiff = int()
        def run(self=None, result=None):
            """None"""
            return None
        def setUp(self, _):
            """Hook method for setting up the test fixture before exercising it."""
            return None
        def setUpClass(self, _):
            """Hook method for setting up class fixture before running tests in the class."""
            return None
        def shortDescription(self, _):
            """Returns a one-line description of the test, or None if no
                    description has been provided.
            
                    The default implementation of this method returns the first line of
                    the specified test method's docstring.
                    """
            return None
        def skipTest(self, _):
            """Skip this test."""
            return None
        def tearDown(self, _):
            """Hook method for deconstructing the test fixture after testing it."""
            return None
        def tearDownClass(self, _):
            """Hook method for deconstructing the class fixture after running all tests in the class."""
            return None
    class NoseTester:
        __dict__ = dictproxy()
        __doc__ = str()
        __module__ = str()
        __weakref__ = getset_descriptor()
        def _get_custom_doctester(self, _):
            """ Return instantiated plugin for doctests
            
                    Allows subclassing of this class to override doctester
            
                    A return value of None means use the nose builtin doctest plugin
                    """
            return None
        def _show_system_info(self, _):
            """None"""
            return None
        def _test_argv(self, label, verbose, extra_argv):
            """ Generate argv for nosetest command
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        see ``test`` docstring
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    argv : list
                        command line arguments that will be passed to nose
                    """
            return list()
        def bench(self=None, label="fast", verbose=1, extra_argv=None):
            """
                    Run benchmarks for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the benchmarks to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow benchmarks as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    success : bool
                        Returns True if running the benchmarks works, False if an error
                        occurred.
            
                    Notes
                    -----
                    Benchmarks are like tests, but have names starting with "bench" instead
                    of "test", and can be found under the "benchmarks" sub-directory of the
                    module.
            
                    Each NumPy module exposes `bench` in its namespace to run all benchmarks
                    for it.
            
                    Examples
                    --------
                    >>> success = np.lib.bench() #doctest: +SKIP
                    Running benchmarks for numpy.lib
                    ...
                    using 562341 items:
                    unique:
                    0.11
                    unique1d:
                    0.11
                    ratio: 1.0
                    nUnique: 56230 == 56230
                    ...
                    OK
            
                    >>> success #doctest: +SKIP
                    True
            
                    """
            return bool()
        excludes = list()
        def prepare_test_args(self=False, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False):
            """
                    Run tests for module using nose.
            
                    This method does the heavy lifting for the `test` method. It takes all
                    the same arguments, for details see `test`.
            
                    See Also
                    --------
                    test
            
                    """
            return None
        def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
            """
                    Run tests for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the tests to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow tests as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
                    doctests : bool, optional
                        If True, run doctests in module. Default is False.
                    coverage : bool, optional
                        If True, report coverage of NumPy code. Default is False.
                        (This requires the `coverage module:
                         <http://nedbatchelder.com/code/modules/coverage.html>`_).
                    raise_warnings : str or sequence of warnings, optional
                        This specifies which warnings to configure as 'raise' instead
                        of 'warn' during the test execution.  Valid strings are:
            
                          - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                          - "release" : equals ``()``, don't raise on any warnings.
            
                    Returns
                    -------
                    result : object
                        Returns the result of running the tests as a
                        ``nose.result.TextTestResult`` object.
            
                    Notes
                    -----
                    Each NumPy module exposes `test` in its namespace to run all tests for it.
                    For example, to run all tests for numpy.lib:
            
                    >>> np.lib.test() #doctest: +SKIP
            
                    Examples
                    --------
                    >>> result = np.lib.test() #doctest: +SKIP
                    Running unit tests for numpy.lib
                    ...
                    Ran 976 tests in 3.933s
            
                    OK
            
                    >>> result.errors #doctest: +SKIP
                    []
                    >>> result.knownfail #doctest: +SKIP
                    []
                    """
            return object()
    __builtins__ = dict()
    __doc__ = str()
    __file__ = str()
    __name__ = str()
    __package__ = str()
    __path__ = list()
    absolute_import = instance()
    def assert_(self, val="", msg=""):
        """
            Assert that works in release mode.
        
            The Python built-in ``assert`` does not work when executing code in
            optimized mode (the ``-O`` flag) - no byte-code is generated for it.
        
            For documentation on usage, refer to the Python documentation.
        
            """
        return None
    def assert_allclose(self, actual, desired=True, rtol=1e-07, atol=0, err_msg="", verbose=True):
        """
            Raise an assertion if two objects are not equal up to desired tolerance.
        
            The test is equivalent to ``allclose(actual, desired, rtol, atol)``.
            It compares the difference between `actual` and `desired` to
            ``atol + rtol * abs(desired)``.
        
            Parameters
            ----------
            actual : array_like
                Array obtained.
            desired : array_like
                Array desired.
            rtol : float, optional
                Relative tolerance.
            atol : float, optional
                Absolute tolerance.
            err_msg : str, optional
                The error message to be printed in case of failure.
            verbose : bool, optional
                If True, the conflicting values are appended to the error message.
        
            Raises
            ------
            AssertionError
                If actual and desired are not equal up to specified precision.
        
            See Also
            --------
            assert_array_almost_equal_nulp, assert_array_max_ulp
        
            Examples
            --------
            >>> x = [1e-5, 1e-3, 1e-1]
            >>> y = np.arccos(np.cos(x))
            >>> assert_allclose(x, y, rtol=1e-5, atol=0)
        
            """
        return None
    def assert_almost_equal(self, actual, desired=True, decimal=7, err_msg="", verbose=True):
        """
            Raise an assertion if two items are not equal up to desired precision.
        
            .. note:: It is recommended to use one of `assert_allclose`,
                      `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
                      instead of this function for more consistent floating point
                      comparisons.
        
            The test is equivalent to ``abs(desired-actual) < 0.5 * 10**(-decimal)``.
        
            Given two objects (numbers or ndarrays), check that all elements of these
            objects are almost equal. An exception is raised at conflicting values.
            For ndarrays this delegates to assert_array_almost_equal
        
            Parameters
            ----------
            actual : array_like
                The object to check.
            desired : array_like
                The expected object.
            decimal : int, optional
                Desired precision, default is 7.
            err_msg : str, optional
                The error message to be printed in case of failure.
            verbose : bool, optional
                If True, the conflicting values are appended to the error message.
        
            Raises
            ------
            AssertionError
              If actual and desired are not equal up to specified precision.
        
            See Also
            --------
            assert_allclose: Compare two array_like objects for equality with desired
                             relative and/or absolute precision.
            assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
        
            Examples
            --------
            >>> import numpy.testing as npt
            >>> npt.assert_almost_equal(2.3333333333333, 2.33333334)
            >>> npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
            ...
            <type 'exceptions.AssertionError'>:
            Items are not equal:
             ACTUAL: 2.3333333333333002
             DESIRED: 2.3333333399999998
        
            >>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]),
            ...                         np.array([1.0,2.33333334]), decimal=9)
            ...
            <type 'exceptions.AssertionError'>:
            Arrays are not almost equal
            <BLANKLINE>
            (mismatch 50.0%)
             x: array([ 1.        ,  2.33333333])
             y: array([ 1.        ,  2.33333334])
        
            """
        return None
    def assert_approx_equal(self, actual, desired=True, significant=7, err_msg="", verbose=True):
        """
            Raise an assertion if two items are not equal up to significant digits.
        
            .. note:: It is recommended to use one of `assert_allclose`,
                      `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
                      instead of this function for more consistent floating point
                      comparisons.
        
            Given two numbers, check that they are approximately equal.
            Approximately equal is defined as the number of significant digits
            that agree.
        
            Parameters
            ----------
            actual : scalar
                The object to check.
            desired : scalar
                The expected object.
            significant : int, optional
                Desired precision, default is 7.
            err_msg : str, optional
                The error message to be printed in case of failure.
            verbose : bool, optional
                If True, the conflicting values are appended to the error message.
        
            Raises
            ------
            AssertionError
              If actual and desired are not equal up to specified precision.
        
            See Also
            --------
            assert_allclose: Compare two array_like objects for equality with desired
                             relative and/or absolute precision.
            assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
        
            Examples
            --------
            >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)
            >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,
                                               significant=8)
            >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,
                                               significant=8)
            ...
            <type 'exceptions.AssertionError'>:
            Items are not equal to 8 significant digits:
             ACTUAL: 1.234567e-021
             DESIRED: 1.2345672000000001e-021
        
            the evaluated condition that raises the exception is
        
            >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
            True
        
            """
        return None
    def assert_array_almost_equal(self, x, y=True, decimal=6, err_msg="", verbose=True):
        """
            Raise an assertion if two objects are not equal up to desired precision.
        
            .. note:: It is recommended to use one of `assert_allclose`,
                      `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
                      instead of this function for more consistent floating point
                      comparisons.
        
            The test verifies identical shapes and verifies values with
            ``abs(desired-actual) < 0.5 * 10**(-decimal)``.
        
            Given two array_like objects, check that the shape is equal and all
            elements of these objects are almost equal. An exception is raised at
            shape mismatch or conflicting values. In contrast to the standard usage
            in numpy, NaNs are compared like numbers, no assertion is raised if
            both objects have NaNs in the same positions.
        
            Parameters
            ----------
            x : array_like
                The actual object to check.
            y : array_like
                The desired, expected object.
            decimal : int, optional
                Desired precision, default is 6.
            err_msg : str, optional
              The error message to be printed in case of failure.
            verbose : bool, optional
                If True, the conflicting values are appended to the error message.
        
            Raises
            ------
            AssertionError
                If actual and desired are not equal up to specified precision.
        
            See Also
            --------
            assert_allclose: Compare two array_like objects for equality with desired
                             relative and/or absolute precision.
            assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
        
            Examples
            --------
            the first assert does not raise an exception
        
            >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
                                                     [1.0,2.333,np.nan])
        
            >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
            ...                                      [1.0,2.33339,np.nan], decimal=5)
            ...
            <type 'exceptions.AssertionError'>:
            AssertionError:
            Arrays are not almost equal
            <BLANKLINE>
            (mismatch 50.0%)
             x: array([ 1.     ,  2.33333,      NaN])
             y: array([ 1.     ,  2.33339,      NaN])
        
            >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
            ...                                      [1.0,2.33333, 5], decimal=5)
            <type 'exceptions.ValueError'>:
            ValueError:
            Arrays are not almost equal
             x: array([ 1.     ,  2.33333,      NaN])
             y: array([ 1.     ,  2.33333,  5.     ])
        
            """
        return None
    def assert_array_almost_equal_nulp(self, x, y=1, nulp=1):
        """
            Compare two arrays relatively to their spacing.
        
            This is a relatively robust method to compare two arrays whose amplitude
            is variable.
        
            Parameters
            ----------
            x, y : array_like
                Input arrays.
            nulp : int, optional
                The maximum number of unit in the last place for tolerance (see Notes).
                Default is 1.
        
            Returns
            -------
            None
        
            Raises
            ------
            AssertionError
                If the spacing between `x` and `y` for one or more elements is larger
                than `nulp`.
        
            See Also
            --------
            assert_array_max_ulp : Check that all items of arrays differ in at most
                N Units in the Last Place.
            spacing : Return the distance between x and the nearest adjacent number.
        
            Notes
            -----
            An assertion is raised if the following condition is not met::
        
                abs(x - y) <= nulps * spacing(max(abs(x), abs(y)))
        
            Examples
            --------
            >>> x = np.array([1., 1e-10, 1e-20])
            >>> eps = np.finfo(x.dtype).eps
            >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)
        
            >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
            ------------------------------------------------------------
            Traceback (most recent call last):
              ...
            AssertionError: X and Y are not equal to 1 ULP (max is 2)
        
            """
        return None
    def assert_array_equal(self, x, y=True, err_msg="", verbose=True):
        """
            Raise an assertion if two array_like objects are not equal.
        
            Given two array_like objects, check that the shape is equal and all
            elements of these objects are equal. An exception is raised at
            shape mismatch or conflicting values. In contrast to the standard usage
            in numpy, NaNs are compared like numbers, no assertion is raised if
            both objects have NaNs in the same positions.
        
            The usual caution for verifying equality with floating point numbers is
            advised.
        
            Parameters
            ----------
            x : array_like
                The actual object to check.
            y : array_like
                The desired, expected object.
            err_msg : str, optional
                The error message to be printed in case of failure.
            verbose : bool, optional
                If True, the conflicting values are appended to the error message.
        
            Raises
            ------
            AssertionError
                If actual and desired objects are not equal.
        
            See Also
            --------
            assert_allclose: Compare two array_like objects for equality with desired
                             relative and/or absolute precision.
            assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
        
            Examples
            --------
            The first assert does not raise an exception:
        
            >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
            ...                               [np.exp(0),2.33333, np.nan])
        
            Assert fails with numerical inprecision with floats:
        
            >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
            ...                               [1, np.sqrt(np.pi)**2, np.nan])
            ...
            <type 'exceptions.ValueError'>:
            AssertionError:
            Arrays are not equal
            <BLANKLINE>
            (mismatch 50.0%)
             x: array([ 1.        ,  3.14159265,         NaN])
             y: array([ 1.        ,  3.14159265,         NaN])
        
            Use `assert_allclose` or one of the nulp (number of floating point values)
            functions for these cases instead:
        
            >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
            ...                            [1, np.sqrt(np.pi)**2, np.nan],
            ...                            rtol=1e-10, atol=0)
        
            """
        return None
    def assert_array_less(self, x, y=True, err_msg="", verbose=True):
        """
            Raise an assertion if two array_like objects are not ordered by less than.
        
            Given two array_like objects, check that the shape is equal and all
            elements of the first object are strictly smaller than those of the
            second object. An exception is raised at shape mismatch or incorrectly
            ordered values. Shape mismatch does not raise if an object has zero
            dimension. In contrast to the standard usage in numpy, NaNs are
            compared, no assertion is raised if both objects have NaNs in the same
            positions.
        
        
        
            Parameters
            ----------
            x : array_like
              The smaller object to check.
            y : array_like
              The larger object to compare.
            err_msg : string
              The error message to be printed in case of failure.
            verbose : bool
                If True, the conflicting values are appended to the error message.
        
            Raises
            ------
            AssertionError
              If actual and desired objects are not equal.
        
            See Also
            --------
            assert_array_equal: tests objects for equality
            assert_array_almost_equal: test objects for equality up to precision
        
        
        
            Examples
            --------
            >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
            >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan])
            ...
            <type 'exceptions.ValueError'>:
            Arrays are not less-ordered
            (mismatch 50.0%)
             x: array([  1.,   1.,  NaN])
             y: array([  1.,   2.,  NaN])
        
            >>> np.testing.assert_array_less([1.0, 4.0], 3)
            ...
            <type 'exceptions.ValueError'>:
            Arrays are not less-ordered
            (mismatch 50.0%)
             x: array([ 1.,  4.])
             y: array(3)
        
            >>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
            ...
            <type 'exceptions.ValueError'>:
            Arrays are not less-ordered
            (shapes (3,), (1,) mismatch)
             x: array([ 1.,  2.,  3.])
             y: array([4])
        
            """
        return None
    def assert_array_max_ulp(self, a, b=None, maxulp=1, dtype=None):
        """
            Check that all items of arrays differ in at most N Units in the Last Place.
        
            Parameters
            ----------
            a, b : array_like
                Input arrays to be compared.
            maxulp : int, optional
                The maximum number of units in the last place that elements of `a` and
                `b` can differ. Default is 1.
            dtype : dtype, optional
                Data-type to convert `a` and `b` to if given. Default is None.
        
            Returns
            -------
            ret : ndarray
                Array containing number of representable floating point numbers between
                items in `a` and `b`.
        
            Raises
            ------
            AssertionError
                If one or more elements differ by more than `maxulp`.
        
            See Also
            --------
            assert_array_almost_equal_nulp : Compare two arrays relatively to their
                spacing.
        
            Examples
            --------
            >>> a = np.linspace(0., 1., 100)
            >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))
        
            """
        return ndarray()
    def assert_equal(self, actual, desired=True, err_msg="", verbose=True):
        """
            Raise an assertion if two objects are not equal.
        
            Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
            check that all elements of these objects are equal. An exception is raised
            at the first conflicting values.
        
            Parameters
            ----------
            actual : array_like
                The object to check.
            desired : array_like
                The expected object.
            err_msg : str, optional
                The error message to be printed in case of failure.
            verbose : bool, optional
                If True, the conflicting values are appended to the error message.
        
            Raises
            ------
            AssertionError
                If actual and desired are not equal.
        
            Examples
            --------
            >>> np.testing.assert_equal([4,5], [4,6])
            ...
            <type 'exceptions.AssertionError'>:
            Items are not equal:
            item=1
             ACTUAL: 5
             DESIRED: 6
        
            """
        return None
    def assert_no_warnings(self, func, ESCargs, ESCESCkwargs):
        """
            Fail if the given callable produces any warnings.
        
            Parameters
            ----------
            func : callable
                The callable to test.
            \*args : Arguments
                Arguments passed to `func`.
            \*\*kwargs : Kwargs
                Keyword arguments passed to `func`.
        
            Returns
            -------
            The value returned by `func`.
        
            """
        return None
    def assert__raises(self):
        """
            assert_raises(exception_class, callable, *args, **kwargs)
        
            Fail unless an exception of class exception_class is thrown
            by callable when invoked with arguments args and keyword
            arguments kwargs. If a different type of exception is
            thrown, it will not be caught, and the test case will be
            deemed to have suffered an error, exactly as for an
            unexpected exception.
        
            """
        return None
    def assert_string_equal(self, actual, desired):
        """
            Test if two strings are equal.
        
            If the given strings are equal, `assert_string_equal` does nothing.
            If they are not equal, an AssertionError is raised, and the diff
            between the strings is shown.
        
            Parameters
            ----------
            actual : str
                The string to test for equality against the expected string.
            desired : str
                The expected string.
        
            Examples
            --------
            >>> np.testing.assert_string_equal('abc', 'abc')
            >>> np.testing.assert_string_equal('abc', 'abcd')
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
            ...
            AssertionError: Differences in strings:
            - abc+ abcd?    +
        
            """
        return None
    def assert_warns(self, warning__class, func, ESCargs, ESCESCkwargs):
        """
            Fail unless the given callable throws the specified warning.
        
            A warning of class warning_class should be thrown by the callable when
            invoked with arguments args and keyword arguments kwargs.
            If a different type of warning is thrown, it will not be caught, and the
            test case will be deemed to have suffered an error.
        
            Parameters
            ----------
            warning_class : class
                The class defining the warning that `func` is expected to throw.
            func : callable
                The callable to test.
            \*args : Arguments
                Arguments passed to `func`.
            \*\*kwargs : Kwargs
                Keyword arguments passed to `func`.
        
            Returns
            -------
            The value returned by `func`.
        
            """
        return None
    def build_err_msg(self, arrays, err_msg="('ACTUAL', 'DESIRED')", header="Items are not equal:", verbose=True, names="('ACTUAL', 'DESIRED')"):
        """None"""
        return None
    def decorate_methods(cls, decorator=None, testmatch=None):
        """
            Apply a decorator to all methods in a class matching a regular expression.
        
            The given decorator is applied to all public methods of `cls` that are
            matched by the regular expression `testmatch`
            (``testmatch.search(methodname)``). Methods that are private, i.e. start
            with an underscore, are ignored.
        
            Parameters
            ----------
            cls : class
                Class whose methods to decorate.
            decorator : function
                Decorator to apply to methods
            testmatch : compiled regexp or str, optional
                The regular expression. Default value is None, in which case the
                nose default (``re.compile(r'(?:^|[\b_\.%s-])[Tt]est' % os.sep)``)
                is used.
                If `testmatch` is a string, it is compiled to a regular expression
                first.
        
            """
        return None
    division = instance()
    def importall(self, _):
        """
            `importall` is DEPRECATED and will be removed in numpy 1.9.0
        
            Try recursively to import all subpackages under package.
            """
        return None
    def jiffies(self, _proc_pid_stat="/proc/405/stat", _load_time="[]"):
        """ Return number of jiffies (1/100ths of a second) that this
            process has been scheduled in user mode. See man 5 proc. """
        return None
    def measure(self, code_str=None, times=1, label=None):
        """
            Return elapsed time for executing code in the namespace of the caller.
        
            The supplied code string is compiled with the Python builtin ``compile``.
            The precision of the timing is 10 milli-seconds. If the code will execute
            fast on this timescale, it can be executed many times to get reasonable
            timing accuracy.
        
            Parameters
            ----------
            code_str : str
                The code to be timed.
            times : int, optional
                The number of times the code is executed. Default is 1. The code is
                only compiled once.
            label : str, optional
                A label to identify `code_str` with. This is passed into ``compile``
                as the second argument (for run-time error messages).
        
            Returns
            -------
            elapsed : float
                Total elapsed time in seconds for executing `code_str` `times` times.
        
            Examples
            --------
            >>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)',
            ...                            times=times)
            >>> print "Time for a single execution : ", etime / times, "s"
            Time for a single execution :  0.005 s
        
            """
        return float()
    def memusage(self, _proc_pid_stat="/proc/405/stat"):
        """ Return virtual memory size in bytes of the running python.
                """
        return None
    def print_assert_equal(self, test_string, actual, desired):
        """
            Test if two objects are equal, and print an error message if test fails.
        
            The test is performed with ``actual == desired``.
        
            Parameters
            ----------
            test_string : str
                The message supplied to AssertionError.
            actual : object
                The object to test for equality against `desired`.
            desired : object
                The expected result.
        
            Examples
            --------
            >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])
            >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])
            Traceback (most recent call last):
            ...
            AssertionError: Test XYZ of func xyz failed
            ACTUAL:
            [0, 1]
            DESIRED:
            [0, 2]
        
            """
        return None
    print_function = instance()
    def _raises(self):
        """None"""
        return None
    def rand(self):
        """Returns an array of random numbers with the given shape.
        
            This only uses the standard library, so it is useful for testing purposes.
            """
        return None
    def run_module_suite(self, file_to_run=None):
        """None"""
        return None
    def rundocs(self, filename=None, raise_on_error=True):
        """
            Run doctests found in the given file.
        
            By default `rundocs` raises an AssertionError on failure.
        
            Parameters
            ----------
            filename : str
                The path to the file for which the doctests are run.
            raise_on_error : bool
                Whether to raise an AssertionError when a doctest fails. Default is
                True.
        
            Notes
            -----
            The doctests can be run by the user/developer by adding the ``doctests``
            argument to the ``test()`` call. For example, to run all tests (including
            doctests) for `numpy.lib`:
        
            >>> np.lib.test(doctests=True) #doctest: +SKIP
            """
        return None
    def runstring(self, _):
        """None"""
        return None
    def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
        """
                Run tests for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the tests to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow tests as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for test outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
                doctests : bool, optional
                    If True, run doctests in module. Default is False.
                coverage : bool, optional
                    If True, report coverage of NumPy code. Default is False.
                    (This requires the `coverage module:
                     <http://nedbatchelder.com/code/modules/coverage.html>`_).
                raise_warnings : str or sequence of warnings, optional
                    This specifies which warnings to configure as 'raise' instead
                    of 'warn' during the test execution.  Valid strings are:
        
                      - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                      - "release" : equals ``()``, don't raise on any warnings.
        
                Returns
                -------
                result : object
                    Returns the result of running the tests as a
                    ``nose.result.TextTestResult`` object.
        
                Notes
                -----
                Each NumPy module exposes `test` in its namespace to run all tests for it.
                For example, to run all tests for numpy.lib:
        
                >>> np.lib.test() #doctest: +SKIP
        
                Examples
                --------
                >>> result = np.lib.test() #doctest: +SKIP
                Running unit tests for numpy.lib
                ...
                Ran 976 tests in 3.933s
        
                OK
        
                >>> result.errors #doctest: +SKIP
                []
                >>> result.knownfail #doctest: +SKIP
                []
                """
        return object()
    verbose = int()
class random:
    class RandomState:
        __doc__ = str()
        def beta(self, a, b, size):
            """
                    beta(a, b, size=None)
            
                    The Beta distribution over ``[0, 1]``.
            
                    The Beta distribution is a special case of the Dirichlet distribution,
                    and is related to the Gamma distribution.  It has the probability
                    distribution function
            
                    .. math:: f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1}
                                                                     (1 - x)^{\beta - 1},
            
                    where the normalisation, B, is the beta function,
            
                    .. math:: B(\alpha, \beta) = \int_0^1 t^{\alpha - 1}
                                                 (1 - t)^{\beta - 1} dt.
            
                    It is often seen in Bayesian inference and order statistics.
            
                    Parameters
                    ----------
                    a : float
                        Alpha, non-negative.
                    b : float
                        Beta, non-negative.
                    size : tuple of ints, optional
                        The number of samples to draw.  The output is packed according to
                        the size given.
            
                    Returns
                    -------
                    out : ndarray
                        Array of the given shape, containing values drawn from a
                        Beta distribution.
            
                    """
            return ndarray()
        def binomial(self, n, p, size):
            """
                    binomial(n, p, size=None)
            
                    Draw samples from a binomial distribution.
            
                    Samples are drawn from a Binomial distribution with specified
                    parameters, n trials and p probability of success where
                    n an integer >= 0 and p is in the interval [0,1]. (n may be
                    input as a float, but it is truncated to an integer in use)
            
                    Parameters
                    ----------
                    n : float (but truncated to an integer)
                            parameter, >= 0.
                    p : float
                            parameter, >= 0 and <=1.
                    size : {tuple, int}
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : {ndarray, scalar}
                              where the values are all integers in  [0, n].
            
                    See Also
                    --------
                    scipy.stats.distributions.binom : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Binomial distribution is
            
                    .. math:: P(N) = \binom{n}{N}p^N(1-p)^{n-N},
            
                    where :math:`n` is the number of trials, :math:`p` is the probability
                    of success, and :math:`N` is the number of successes.
            
                    When estimating the standard error of a proportion in a population by
                    using a random sample, the normal distribution works well unless the
                    product p*n <=5, where p = population proportion estimate, and n =
                    number of samples, in which case the binomial distribution is used
                    instead. For example, a sample of 15 people shows 4 who are left
                    handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
                    so the binomial distribution should be used in this case.
            
                    References
                    ----------
                    .. [1] Dalgaard, Peter, "Introductory Statistics with R",
                           Springer-Verlag, 2002.
                    .. [2] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
                           Fifth Edition, 2002.
                    .. [3] Lentner, Marvin, "Elementary Applied Statistics", Bogden
                           and Quigley, 1972.
                    .. [4] Weisstein, Eric W. "Binomial Distribution." From MathWorld--A
                           Wolfram Web Resource.
                           http://mathworld.wolfram.com/BinomialDistribution.html
                    .. [5] Wikipedia, "Binomial-distribution",
                           http://en.wikipedia.org/wiki/Binomial_distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> n, p = 10, .5 # number of trials, probability of each trial
                    >>> s = np.random.binomial(n, p, 1000)
                    # result of flipping a coin 10 times, tested 1000 times.
            
                    A real world example. A company drills 9 wild-cat oil exploration
                    wells, each with an estimated probability of success of 0.1. All nine
                    wells fail. What is the probability of that happening?
            
                    Let's do 20,000 trials of the model, and count the number that
                    generate zero positive results.
            
                    >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.
                    answer = 0.38885, or 38%.
            
                    """
            return ndarray()
        def bytes(self, length):
            """
                    bytes(length)
            
                    Return random bytes.
            
                    Parameters
                    ----------
                    length : int
                        Number of random bytes.
            
                    Returns
                    -------
                    out : str
                        String of length `length`.
            
                    Examples
                    --------
                    >>> np.random.bytes(10)
                    ' eh\x85\x022SZ\xbf\xa4' #random
            
                    """
            return str()
        def chisquare(self, df, size):
            """
                    chisquare(df, size=None)
            
                    Draw samples from a chi-square distribution.
            
                    When `df` independent random variables, each with standard normal
                    distributions (mean 0, variance 1), are squared and summed, the
                    resulting distribution is chi-square (see Notes).  This distribution
                    is often used in hypothesis testing.
            
                    Parameters
                    ----------
                    df : int
                         Number of degrees of freedom.
                    size : tuple of ints, int, optional
                         Size of the returned array.  By default, a scalar is
                         returned.
            
                    Returns
                    -------
                    output : ndarray
                        Samples drawn from the distribution, packed in a `size`-shaped
                        array.
            
                    Raises
                    ------
                    ValueError
                        When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)
                        is given.
            
                    Notes
                    -----
                    The variable obtained by summing the squares of `df` independent,
                    standard normally distributed random variables:
            
                    .. math:: Q = \sum_{i=0}^{\mathtt{df}} X^2_i
            
                    is chi-square distributed, denoted
            
                    .. math:: Q \sim \chi^2_k.
            
                    The probability density function of the chi-squared distribution is
            
                    .. math:: p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)}
                                     x^{k/2 - 1} e^{-x/2},
            
                    where :math:`\Gamma` is the gamma function,
            
                    .. math:: \Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt.
            
                    References
                    ----------
                    `NIST/SEMATECH e-Handbook of Statistical Methods
                    <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_
            
                    Examples
                    --------
                    >>> np.random.chisquare(2,4)
                    array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])
            
                    """
            return ndarray()
        def choice(self, a, size, replace, p):
            """
                    choice(a, size=None, replace=True, p=None)
            
                    Generates a random sample from a given 1-D array
            
                            .. versionadded:: 1.7.0
            
                    Parameters
                    -----------
                    a : 1-D array-like or int
                        If an ndarray, a random sample is generated from its elements.
                        If an int, the random sample is generated as if a was np.arange(n)
                    size : int or tuple of ints, optional
                        Output shape. Default is None, in which case a single value is
                        returned.
                    replace : boolean, optional
                        Whether the sample is with or without replacement
                    p : 1-D array-like, optional
                        The probabilities associated with each entry in a.
                        If not given the sample assumes a uniform distribtion over all
                        entries in a.
            
                    Returns
                    --------
                    samples : 1-D ndarray, shape (size,)
                        The generated random samples
            
                    Raises
                    -------
                    ValueError
                        If a is an int and less than zero, if a or p are not 1-dimensional,
                        if a is an array-like of size 0, if p is not a vector of
                        probabilities, if a and p have different lengths, or if
                        replace=False and the sample size is greater than the population
                        size
            
                    See Also
                    ---------
                    randint, shuffle, permutation
            
                    Examples
                    ---------
                    Generate a uniform random sample from np.arange(5) of size 3:
            
                    >>> np.random.choice(5, 3)
                    array([0, 3, 4])
                    >>> #This is equivalent to np.random.randint(0,5,3)
            
                    Generate a non-uniform random sample from np.arange(5) of size 3:
            
                    >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
                    array([3, 3, 0])
            
                    Generate a uniform random sample from np.arange(5) of size 3 without
                    replacement:
            
                    >>> np.random.choice(5, 3, replace=False)
                    array([3,1,0])
                    >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]
            
                    Generate a non-uniform random sample from np.arange(5) of size
                    3 without replacement:
            
                    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
                    array([2, 3, 0])
            
                    Any of the above can be repeated with an arbitrary array-like
                    instead of just integers. For instance:
            
                    >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
                    >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
                    array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],
                          dtype='|S11')
            
                    """
            return _1_D()
        def dirichlet(self, alpha, size):
            """
                    dirichlet(alpha, size=None)
            
                    Draw samples from the Dirichlet distribution.
            
                    Draw `size` samples of dimension k from a Dirichlet distribution. A
                    Dirichlet-distributed random variable can be seen as a multivariate
                    generalization of a Beta distribution. Dirichlet pdf is the conjugate
                    prior of a multinomial in Bayesian inference.
            
                    Parameters
                    ----------
                    alpha : array
                        Parameter of the distribution (k dimension for sample of
                        dimension k).
                    size : array
                        Number of samples to draw.
            
                    Returns
                    -------
                    samples : ndarray,
                        The drawn samples, of shape (alpha.ndim, size).
            
                    Notes
                    -----
                    .. math:: X \approx \prod_{i=1}^{k}{x^{\alpha_i-1}_i}
            
                    Uses the following property for computation: for each dimension,
                    draw a random sample y_i from a standard gamma generator of shape
                    `alpha_i`, then
                    :math:`X = \frac{1}{\sum_{i=1}^k{y_i}} (y_1, \ldots, y_n)` is
                    Dirichlet distributed.
            
                    References
                    ----------
                    .. [1] David McKay, "Information Theory, Inference and Learning
                           Algorithms," chapter 23,
                           http://www.inference.phy.cam.ac.uk/mackay/
                    .. [2] Wikipedia, "Dirichlet distribution",
                           http://en.wikipedia.org/wiki/Dirichlet_distribution
            
                    Examples
                    --------
                    Taking an example cited in Wikipedia, this distribution can be used if
                    one wanted to cut strings (each of initial length 1.0) into K pieces
                    with different lengths, where each piece had, on average, a designated
                    average length, but allowing some variation in the relative sizes of the
                    pieces.
            
                    >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()
            
                    >>> plt.barh(range(20), s[0])
                    >>> plt.barh(range(20), s[1], left=s[0], color='g')
                    >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')
                    >>> plt.title("Lengths of Strings")
            
                    """
            return ndarray()
        def exponential(self, scale, size):
            """
                    exponential(scale=1.0, size=None)
            
                    Exponential distribution.
            
                    Its probability density function is
            
                    .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
            
                    for ``x > 0`` and 0 elsewhere. :math:`\beta` is the scale parameter,
                    which is the inverse of the rate parameter :math:`\lambda = 1/\beta`.
                    The rate parameter is an alternative, widely used parameterization
                    of the exponential distribution [3]_.
            
                    The exponential distribution is a continuous analogue of the
                    geometric distribution.  It describes many common situations, such as
                    the size of raindrops measured over many rainstorms [1]_, or the time
                    between page requests to Wikipedia [2]_.
            
                    Parameters
                    ----------
                    scale : float
                        The scale parameter, :math:`\beta = 1/\lambda`.
                    size : tuple of ints
                        Number of samples to draw.  The output is shaped
                        according to `size`.
            
                    References
                    ----------
                    .. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and
                           Random Signal Principles", 4th ed, 2001, p. 57.
                    .. [2] "Poisson Process", Wikipedia,
                           http://en.wikipedia.org/wiki/Poisson_process
                    .. [3] "Exponential Distribution, Wikipedia,
                           http://en.wikipedia.org/wiki/Exponential_distribution
            
                    """
            return None
        def f(self, dfnum, dfden, size):
            """
                    f(dfnum, dfden, size=None)
            
                    Draw samples from a F distribution.
            
                    Samples are drawn from an F distribution with specified parameters,
                    `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom
                    in denominator), where both parameters should be greater than zero.
            
                    The random variate of the F distribution (also known as the
                    Fisher distribution) is a continuous probability distribution
                    that arises in ANOVA tests, and is the ratio of two chi-square
                    variates.
            
                    Parameters
                    ----------
                    dfnum : float
                        Degrees of freedom in numerator. Should be greater than zero.
                    dfden : float
                        Degrees of freedom in denominator. Should be greater than zero.
                    size : {tuple, int}, optional
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``,
                        then ``m * n * k`` samples are drawn. By default only one sample
                        is returned.
            
                    Returns
                    -------
                    samples : {ndarray, scalar}
                        Samples from the Fisher distribution.
            
                    See Also
                    --------
                    scipy.stats.distributions.f : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The F statistic is used to compare in-group variances to between-group
                    variances. Calculating the distribution depends on the sampling, and
                    so it is a function of the respective degrees of freedom in the
                    problem.  The variable `dfnum` is the number of samples minus one, the
                    between-groups degrees of freedom, while `dfden` is the within-groups
                    degrees of freedom, the sum of the number of samples in each group
                    minus the number of groups.
            
                    References
                    ----------
                    .. [1] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
                           Fifth Edition, 2002.
                    .. [2] Wikipedia, "F-distribution",
                           http://en.wikipedia.org/wiki/F-distribution
            
                    Examples
                    --------
                    An example from Glantz[1], pp 47-40.
                    Two groups, children of diabetics (25 people) and children from people
                    without diabetes (25 controls). Fasting blood glucose was measured,
                    case group had a mean value of 86.1, controls had a mean value of
                    82.2. Standard deviations were 2.09 and 2.49 respectively. Are these
                    data consistent with the null hypothesis that the parents diabetic
                    status does not affect their children's blood glucose levels?
                    Calculating the F statistic from the data gives a value of 36.01.
            
                    Draw samples from the distribution:
            
                    >>> dfnum = 1. # between group degrees of freedom
                    >>> dfden = 48. # within groups degrees of freedom
                    >>> s = np.random.f(dfnum, dfden, 1000)
            
                    The lower bound for the top 1% of the samples is :
            
                    >>> sort(s)[-10]
                    7.61988120985
            
                    So there is about a 1% chance that the F statistic will exceed 7.62,
                    the measured value is 36, so the null hypothesis is rejected at the 1%
                    level.
            
                    """
            return ndarray()
        def gamma(self, shape, scale, size):
            """
                    gamma(shape, scale=1.0, size=None)
            
                    Draw samples from a Gamma distribution.
            
                    Samples are drawn from a Gamma distribution with specified parameters,
                    `shape` (sometimes designated "k") and `scale` (sometimes designated
                    "theta"), where both parameters are > 0.
            
                    Parameters
                    ----------
                    shape : scalar > 0
                        The shape of the gamma distribution.
                    scale : scalar > 0, optional
                        The scale of the gamma distribution.  Default is equal to 1.
                    size : shape_tuple, optional
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    out : ndarray, float
                        Returns one sample unless `size` parameter is specified.
            
                    See Also
                    --------
                    scipy.stats.distributions.gamma : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Gamma distribution is
            
                    .. math:: p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},
            
                    where :math:`k` is the shape and :math:`\theta` the scale,
                    and :math:`\Gamma` is the Gamma function.
            
                    The Gamma distribution is often used to model the times to failure of
                    electronic components, and arises naturally in processes for which the
                    waiting times between Poisson distributed events are relevant.
            
                    References
                    ----------
                    .. [1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A
                           Wolfram Web Resource.
                           http://mathworld.wolfram.com/GammaDistribution.html
                    .. [2] Wikipedia, "Gamma-distribution",
                           http://en.wikipedia.org/wiki/Gamma-distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> shape, scale = 2., 2. # mean and dispersion
                    >>> s = np.random.gamma(shape, scale, 1000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> import scipy.special as sps
                    >>> count, bins, ignored = plt.hist(s, 50, normed=True)
                    >>> y = bins**(shape-1)*(np.exp(-bins/scale) /
                    ...                      (sps.gamma(shape)*scale**shape))
                    >>> plt.plot(bins, y, linewidth=2, color='r')
                    >>> plt.show()
            
                    """
            return ndarray()
        def geometric(self, p, size):
            """
                    geometric(p, size=None)
            
                    Draw samples from the geometric distribution.
            
                    Bernoulli trials are experiments with one of two outcomes:
                    success or failure (an example of such an experiment is flipping
                    a coin).  The geometric distribution models the number of trials
                    that must be run in order to achieve success.  It is therefore
                    supported on the positive integers, ``k = 1, 2, ...``.
            
                    The probability mass function of the geometric distribution is
            
                    .. math:: f(k) = (1 - p)^{k - 1} p
            
                    where `p` is the probability of success of an individual trial.
            
                    Parameters
                    ----------
                    p : float
                        The probability of success of an individual trial.
                    size : tuple of ints
                        Number of values to draw from the distribution.  The output
                        is shaped according to `size`.
            
                    Returns
                    -------
                    out : ndarray
                        Samples from the geometric distribution, shaped according to
                        `size`.
            
                    Examples
                    --------
                    Draw ten thousand values from the geometric distribution,
                    with the probability of an individual success equal to 0.35:
            
                    >>> z = np.random.geometric(p=0.35, size=10000)
            
                    How many trials succeeded after a single run?
            
                    >>> (z == 1).sum() / 10000.
                    0.34889999999999999 #random
            
                    """
            return ndarray()
        def get_state(self, _):
            """
                    get_state()
            
                    Return a tuple representing the internal state of the generator.
            
                    For more details, see `set_state`.
            
                    Returns
                    -------
                    out : tuple(str, ndarray of 624 uints, int, int, float)
                        The returned tuple has the following items:
            
                        1. the string 'MT19937'.
                        2. a 1-D array of 624 unsigned integer keys.
                        3. an integer ``pos``.
                        4. an integer ``has_gauss``.
                        5. a float ``cached_gaussian``.
            
                    See Also
                    --------
                    set_state
            
                    Notes
                    -----
                    `set_state` and `get_state` are not needed to work with any of the
                    random distributions in NumPy. If the internal state is manually altered,
                    the user should know exactly what he/she is doing.
            
                    """
            return None
        def gumbel(self, loc, scale, size):
            """
                    gumbel(loc=0.0, scale=1.0, size=None)
            
                    Gumbel distribution.
            
                    Draw samples from a Gumbel distribution with specified location and scale.
                    For more information on the Gumbel distribution, see Notes and References
                    below.
            
                    Parameters
                    ----------
                    loc : float
                        The location of the mode of the distribution.
                    scale : float
                        The scale parameter of the distribution.
                    size : tuple of ints
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    out : ndarray
                        The samples
            
                    See Also
                    --------
                    scipy.stats.gumbel_l
                    scipy.stats.gumbel_r
                    scipy.stats.genextreme
                        probability density function, distribution, or cumulative density
                        function, etc. for each of the above
                    weibull
            
                    Notes
                    -----
                    The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value
                    Type I) distribution is one of a class of Generalized Extreme Value (GEV)
                    distributions used in modeling extreme value problems.  The Gumbel is a
                    special case of the Extreme Value Type I distribution for maximums from
                    distributions with "exponential-like" tails.
            
                    The probability density for the Gumbel distribution is
            
                    .. math:: p(x) = \frac{e^{-(x - \mu)/ \beta}}{\beta} e^{ -e^{-(x - \mu)/
                              \beta}},
            
                    where :math:`\mu` is the mode, a location parameter, and :math:`\beta` is
                    the scale parameter.
            
                    The Gumbel (named for German mathematician Emil Julius Gumbel) was used
                    very early in the hydrology literature, for modeling the occurrence of
                    flood events. It is also used for modeling maximum wind speed and rainfall
                    rates.  It is a "fat-tailed" distribution - the probability of an event in
                    the tail of the distribution is larger than if one used a Gaussian, hence
                    the surprisingly frequent occurrence of 100-year floods. Floods were
                    initially modeled as a Gaussian process, which underestimated the frequency
                    of extreme events.
            
            
                    It is one of a class of extreme value distributions, the Generalized
                    Extreme Value (GEV) distributions, which also includes the Weibull and
                    Frechet.
            
                    The function has a mean of :math:`\mu + 0.57721\beta` and a variance of
                    :math:`\frac{\pi^2}{6}\beta^2`.
            
                    References
                    ----------
                    Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University
                    Press, 1958.
            
                    Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from
                    Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,
                    2001.
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> mu, beta = 0, 0.1 # location and scale
                    >>> s = np.random.gumbel(mu, beta, 1000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, 30, normed=True)
                    >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
                    ...          * np.exp( -np.exp( -(bins - mu) /beta) ),
                    ...          linewidth=2, color='r')
                    >>> plt.show()
            
                    Show how an extreme value distribution can arise from a Gaussian process
                    and compare to a Gaussian:
            
                    >>> means = []
                    >>> maxima = []
                    >>> for i in range(0,1000) :
                    ...    a = np.random.normal(mu, beta, 1000)
                    ...    means.append(a.mean())
                    ...    maxima.append(a.max())
                    >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)
                    >>> beta = np.std(maxima)*np.pi/np.sqrt(6)
                    >>> mu = np.mean(maxima) - 0.57721*beta
                    >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
                    ...          * np.exp(-np.exp(-(bins - mu)/beta)),
                    ...          linewidth=2, color='r')
                    >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))
                    ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),
                    ...          linewidth=2, color='g')
                    >>> plt.show()
            
                    """
            return ndarray()
        def hypergeometric(self, ngood, nbad, nsample, size):
            """
                    hypergeometric(ngood, nbad, nsample, size=None)
            
                    Draw samples from a Hypergeometric distribution.
            
                    Samples are drawn from a Hypergeometric distribution with specified
                    parameters, ngood (ways to make a good selection), nbad (ways to make
                    a bad selection), and nsample = number of items sampled, which is less
                    than or equal to the sum ngood + nbad.
            
                    Parameters
                    ----------
                    ngood : int or array_like
                        Number of ways to make a good selection.  Must be nonnegative.
                    nbad : int or array_like
                        Number of ways to make a bad selection.  Must be nonnegative.
                    nsample : int or array_like
                        Number of items sampled.  Must be at least 1 and at most
                        ``ngood + nbad``.
                    size : int or tuple of int
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : ndarray or scalar
                        The values are all integers in  [0, n].
            
                    See Also
                    --------
                    scipy.stats.distributions.hypergeom : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Hypergeometric distribution is
            
                    .. math:: P(x) = \frac{\binom{m}{n}\binom{N-m}{n-x}}{\binom{N}{n}},
            
                    where :math:`0 \le x \le m` and :math:`n+m-N \le x \le n`
            
                    for P(x) the probability of x successes, n = ngood, m = nbad, and
                    N = number of samples.
            
                    Consider an urn with black and white marbles in it, ngood of them
                    black and nbad are white. If you draw nsample balls without
                    replacement, then the Hypergeometric distribution describes the
                    distribution of black balls in the drawn sample.
            
                    Note that this distribution is very similar to the Binomial
                    distribution, except that in this case, samples are drawn without
                    replacement, whereas in the Binomial case samples are drawn with
                    replacement (or the sample space is infinite). As the sample space
                    becomes large, this distribution approaches the Binomial.
            
                    References
                    ----------
                    .. [1] Lentner, Marvin, "Elementary Applied Statistics", Bogden
                           and Quigley, 1972.
                    .. [2] Weisstein, Eric W. "Hypergeometric Distribution." From
                           MathWorld--A Wolfram Web Resource.
                           http://mathworld.wolfram.com/HypergeometricDistribution.html
                    .. [3] Wikipedia, "Hypergeometric-distribution",
                           http://en.wikipedia.org/wiki/Hypergeometric-distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> ngood, nbad, nsamp = 100, 2, 10
                    # number of good, number of bad, and number of samples
                    >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)
                    >>> hist(s)
                    #   note that it is very unlikely to grab both bad items
            
                    Suppose you have an urn with 15 white and 15 black marbles.
                    If you pull 15 marbles at random, how likely is it that
                    12 or more of them are one color?
            
                    >>> s = np.random.hypergeometric(15, 15, 15, 100000)
                    >>> sum(s>=12)/100000. + sum(s<=3)/100000.
                    #   answer = 0.003 ... pretty unlikely!
            
                    """
            return ndarray() if False else float()
        def laplace(self, loc, scale):
            """
                    laplace(loc=0.0, scale=1.0, size=None)
            
                    Draw samples from the Laplace or double exponential distribution with
                    specified location (or mean) and scale (decay).
            
                    The Laplace distribution is similar to the Gaussian/normal distribution,
                    but is sharper at the peak and has fatter tails. It represents the
                    difference between two independent, identically distributed exponential
                    random variables.
            
                    Parameters
                    ----------
                    loc : float
                        The position, :math:`\mu`, of the distribution peak.
                    scale : float
                        :math:`\lambda`, the exponential decay.
            
                    Notes
                    -----
                    It has the probability density function
            
                    .. math:: f(x; \mu, \lambda) = \frac{1}{2\lambda}
                                                   \exp\left(-\frac{|x - \mu|}{\lambda}\right).
            
                    The first law of Laplace, from 1774, states that the frequency of an error
                    can be expressed as an exponential function of the absolute magnitude of
                    the error, which leads to the Laplace distribution. For many problems in
                    Economics and Health sciences, this distribution seems to model the data
                    better than the standard Gaussian distribution
            
            
                    References
                    ----------
                    .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical
                           Functions with Formulas, Graphs, and Mathematical Tables, 9th
                           printing.  New York: Dover, 1972.
            
                    .. [2] The Laplace distribution and generalizations
                           By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,
                           Birkhauser, 2001.
            
                    .. [3] Weisstein, Eric W. "Laplace Distribution."
                           From MathWorld--A Wolfram Web Resource.
                           http://mathworld.wolfram.com/LaplaceDistribution.html
            
                    .. [4] Wikipedia, "Laplace distribution",
                           http://en.wikipedia.org/wiki/Laplace_distribution
            
                    Examples
                    --------
                    Draw samples from the distribution
            
                    >>> loc, scale = 0., 1.
                    >>> s = np.random.laplace(loc, scale, 1000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, 30, normed=True)
                    >>> x = np.arange(-8., 8., .01)
                    >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)
                    >>> plt.plot(x, pdf)
            
                    Plot Gaussian for comparison:
            
                    >>> g = (1/(scale * np.sqrt(2 * np.pi)) * 
                    ...      np.exp( - (x - loc)**2 / (2 * scale**2) ))
                    >>> plt.plot(x,g)
            
                    """
            return None
        def logistic(self, loc, scale, size):
            """
                    logistic(loc=0.0, scale=1.0, size=None)
            
                    Draw samples from a Logistic distribution.
            
                    Samples are drawn from a Logistic distribution with specified
                    parameters, loc (location or mean, also median), and scale (>0).
            
                    Parameters
                    ----------
                    loc : float
            
                    scale : float > 0.
            
                    size : {tuple, int}
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : {ndarray, scalar}
                              where the values are all integers in  [0, n].
            
                    See Also
                    --------
                    scipy.stats.distributions.logistic : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Logistic distribution is
            
                    .. math:: P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},
            
                    where :math:`\mu` = location and :math:`s` = scale.
            
                    The Logistic distribution is used in Extreme Value problems where it
                    can act as a mixture of Gumbel distributions, in Epidemiology, and by
                    the World Chess Federation (FIDE) where it is used in the Elo ranking
                    system, assuming the performance of each player is a logistically
                    distributed random variable.
            
                    References
                    ----------
                    .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme
                           Values, from Insurance, Finance, Hydrology and Other Fields,
                           Birkhauser Verlag, Basel, pp 132-133.
                    .. [2] Weisstein, Eric W. "Logistic Distribution." From
                           MathWorld--A Wolfram Web Resource.
                           http://mathworld.wolfram.com/LogisticDistribution.html
                    .. [3] Wikipedia, "Logistic-distribution",
                           http://en.wikipedia.org/wiki/Logistic-distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> loc, scale = 10, 1
                    >>> s = np.random.logistic(loc, scale, 10000)
                    >>> count, bins, ignored = plt.hist(s, bins=50)
            
                    #   plot against distribution
            
                    >>> def logist(x, loc, scale):
                    ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)
                    >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\
                    ... logist(bins, loc, scale).max())
                    >>> plt.show()
            
                    """
            return ndarray()
        def lognormal(self, mean, sigma, size):
            """
                    lognormal(mean=0.0, sigma=1.0, size=None)
            
                    Return samples drawn from a log-normal distribution.
            
                    Draw samples from a log-normal distribution with specified mean,
                    standard deviation, and array shape.  Note that the mean and standard
                    deviation are not the values for the distribution itself, but of the
                    underlying normal distribution it is derived from.
            
                    Parameters
                    ----------
                    mean : float
                        Mean value of the underlying normal distribution
                    sigma : float, > 0.
                        Standard deviation of the underlying normal distribution
                    size : tuple of ints
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : ndarray or float
                        The desired samples. An array of the same shape as `size` if given,
                        if `size` is None a float is returned.
            
                    See Also
                    --------
                    scipy.stats.lognorm : probability density function, distribution,
                        cumulative density function, etc.
            
                    Notes
                    -----
                    A variable `x` has a log-normal distribution if `log(x)` is normally
                    distributed.  The probability density function for the log-normal
                    distribution is:
            
                    .. math:: p(x) = \frac{1}{\sigma x \sqrt{2\pi}}
                                     e^{(-\frac{(ln(x)-\mu)^2}{2\sigma^2})}
            
                    where :math:`\mu` is the mean and :math:`\sigma` is the standard
                    deviation of the normally distributed logarithm of the variable.
                    A log-normal distribution results if a random variable is the *product*
                    of a large number of independent, identically-distributed variables in
                    the same way that a normal distribution results if the variable is the
                    *sum* of a large number of independent, identically-distributed
                    variables.
            
                    References
                    ----------
                    Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal Distributions
                    across the Sciences: Keys and Clues," *BioScience*, Vol. 51, No. 5,
                    May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf
            
                    Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,
                    Basel: Birkhauser Verlag, 2001, pp. 31-32.
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> mu, sigma = 3., 1. # mean and standard deviation
                    >>> s = np.random.lognormal(mu, sigma, 1000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')
            
                    >>> x = np.linspace(min(bins), max(bins), 10000)
                    >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
                    ...        / (x * sigma * np.sqrt(2 * np.pi)))
            
                    >>> plt.plot(x, pdf, linewidth=2, color='r')
                    >>> plt.axis('tight')
                    >>> plt.show()
            
                    Demonstrate that taking the products of random samples from a uniform
                    distribution can be fit well by a log-normal probability density function.
            
                    >>> # Generate a thousand samples: each is the product of 100 random
                    >>> # values, drawn from a normal distribution.
                    >>> b = []
                    >>> for i in range(1000):
                    ...    a = 10. + np.random.random(100)
                    ...    b.append(np.product(a))
            
                    >>> b = np.array(b) / np.min(b) # scale values to be positive
                    >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')
                    >>> sigma = np.std(np.log(b))
                    >>> mu = np.mean(np.log(b))
            
                    >>> x = np.linspace(min(bins), max(bins), 10000)
                    >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
                    ...        / (x * sigma * np.sqrt(2 * np.pi)))
            
                    >>> plt.plot(x, pdf, color='r', linewidth=2)
                    >>> plt.show()
            
                    """
            return ndarray() if False else float()
        def logseries(self, loc, scale, size):
            """
                    logseries(p, size=None)
            
                    Draw samples from a Logarithmic Series distribution.
            
                    Samples are drawn from a Log Series distribution with specified
                    parameter, p (probability, 0 < p < 1).
            
                    Parameters
                    ----------
                    loc : float
            
                    scale : float > 0.
            
                    size : {tuple, int}
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : {ndarray, scalar}
                              where the values are all integers in  [0, n].
            
                    See Also
                    --------
                    scipy.stats.distributions.logser : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Log Series distribution is
            
                    .. math:: P(k) = \frac{-p^k}{k \ln(1-p)},
            
                    where p = probability.
            
                    The Log Series distribution is frequently used to represent species
                    richness and occurrence, first proposed by Fisher, Corbet, and
                    Williams in 1943 [2].  It may also be used to model the numbers of
                    occupants seen in cars [3].
            
                    References
                    ----------
                    .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional
                           species diversity through the log series distribution of
                           occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,
                           Volume 5, Number 5, September 1999 , pp. 187-195(9).
                    .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The
                           relation between the number of species and the number of
                           individuals in a random sample of an animal population.
                           Journal of Animal Ecology, 12:42-58.
                    .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small
                           Data Sets, CRC Press, 1994.
                    .. [4] Wikipedia, "Logarithmic-distribution",
                           http://en.wikipedia.org/wiki/Logarithmic-distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> a = .6
                    >>> s = np.random.logseries(a, 10000)
                    >>> count, bins, ignored = plt.hist(s)
            
                    #   plot against distribution
            
                    >>> def logseries(k, p):
                    ...     return -p**k/(k*log(1-p))
                    >>> plt.plot(bins, logseries(bins, a)*count.max()/
                                 logseries(bins, a).max(), 'r')
                    >>> plt.show()
            
                    """
            return ndarray()
        def multinomial(self, n, pvals, size):
            """
                    multinomial(n, pvals, size=None)
            
                    Draw samples from a multinomial distribution.
            
                    The multinomial distribution is a multivariate generalisation of the
                    binomial distribution.  Take an experiment with one of ``p``
                    possible outcomes.  An example of such an experiment is throwing a dice,
                    where the outcome can be 1 through 6.  Each sample drawn from the
                    distribution represents `n` such experiments.  Its values,
                    ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome
                    was ``i``.
            
                    Parameters
                    ----------
                    n : int
                        Number of experiments.
                    pvals : sequence of floats, length p
                        Probabilities of each of the ``p`` different outcomes.  These
                        should sum to 1 (however, the last element is always assumed to
                        account for the remaining probability, as long as
                        ``sum(pvals[:-1]) <= 1)``.
                    size : tuple of ints
                        Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,
                        and the output shape becomes ``(M, N, K, p)``, since each sample
                        has shape ``(p,)``.
            
                    Examples
                    --------
                    Throw a dice 20 times:
            
                    >>> np.random.multinomial(20, [1/6.]*6, size=1)
                    array([[4, 1, 7, 5, 2, 1]])
            
                    It landed 4 times on 1, once on 2, etc.
            
                    Now, throw the dice 20 times, and 20 times again:
            
                    >>> np.random.multinomial(20, [1/6.]*6, size=2)
                    array([[3, 4, 3, 3, 4, 3],
                           [2, 4, 3, 4, 0, 7]])
            
                    For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,
                    we threw 2 times 1, 4 times 2, etc.
            
                    A loaded dice is more likely to land on number 6:
            
                    >>> np.random.multinomial(100, [1/7.]*5)
                    array([13, 16, 13, 16, 42])
            
                    """
            return None
        def multivariate_normal(self, mean, cov, size):
            """
                    multivariate_normal(mean, cov[, size])
            
                    Draw random samples from a multivariate normal distribution.
            
                    The multivariate normal, multinormal or Gaussian distribution is a
                    generalization of the one-dimensional normal distribution to higher
                    dimensions.  Such a distribution is specified by its mean and
                    covariance matrix.  These parameters are analogous to the mean
                    (average or "center") and variance (standard deviation, or "width,"
                    squared) of the one-dimensional normal distribution.
            
                    Parameters
                    ----------
                    mean : 1-D array_like, of length N
                        Mean of the N-dimensional distribution.
                    cov : 2-D array_like, of shape (N, N)
                        Covariance matrix of the distribution.  Must be symmetric and
                        positive semi-definite for "physically meaningful" results.
                    size : int or tuple of ints, optional
                        Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are
                        generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because
                        each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.
                        If no shape is specified, a single (`N`-D) sample is returned.
            
                    Returns
                    -------
                    out : ndarray
                        The drawn samples, of shape *size*, if that was provided.  If not,
                        the shape is ``(N,)``.
            
                        In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
                        value drawn from the distribution.
            
                    Notes
                    -----
                    The mean is a coordinate in N-dimensional space, which represents the
                    location where samples are most likely to be generated.  This is
                    analogous to the peak of the bell curve for the one-dimensional or
                    univariate normal distribution.
            
                    Covariance indicates the level to which two variables vary together.
                    From the multivariate normal distribution, we draw N-dimensional
                    samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix
                    element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.
                    The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its
                    "spread").
            
                    Instead of specifying the full covariance matrix, popular
                    approximations include:
            
                      - Spherical covariance (*cov* is a multiple of the identity matrix)
                      - Diagonal covariance (*cov* has non-negative elements, and only on
                        the diagonal)
            
                    This geometrical property can be seen in two dimensions by plotting
                    generated data-points:
            
                    >>> mean = [0,0]
                    >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis
            
                    >>> import matplotlib.pyplot as plt
                    >>> x,y = np.random.multivariate_normal(mean,cov,5000).T
                    >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()
            
                    Note that the covariance matrix must be non-negative definite.
            
                    References
                    ----------
                    Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,
                    3rd ed., New York: McGraw-Hill, 1991.
            
                    Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,
                    2nd ed., New York: Wiley, 2001.
            
                    Examples
                    --------
                    >>> mean = (1,2)
                    >>> cov = [[1,0],[1,0]]
                    >>> x = np.random.multivariate_normal(mean,cov,(3,3))
                    >>> x.shape
                    (3, 3, 2)
            
                    The following is probably true, given that 0.6 is roughly twice the
                    standard deviation:
            
                    >>> print list( (x[0,0,:] - mean) < 0.6 )
                    [True, True]
            
                    """
            return ndarray()
        def negative_binomial(self, n, p, size):
            """
                    negative_binomial(n, p, size=None)
            
                    Draw samples from a negative_binomial distribution.
            
                    Samples are drawn from a negative_Binomial distribution with specified
                    parameters, `n` trials and `p` probability of success where `n` is an
                    integer > 0 and `p` is in the interval [0, 1].
            
                    Parameters
                    ----------
                    n : int
                        Parameter, > 0.
                    p : float
                        Parameter, >= 0 and <=1.
                    size : int or tuple of ints
                        Output shape. If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : int or ndarray of ints
                        Drawn samples.
            
                    Notes
                    -----
                    The probability density for the Negative Binomial distribution is
            
                    .. math:: P(N;n,p) = \binom{N+n-1}{n-1}p^{n}(1-p)^{N},
            
                    where :math:`n-1` is the number of successes, :math:`p` is the probability
                    of success, and :math:`N+n-1` is the number of trials.
            
                    The negative binomial distribution gives the probability of n-1 successes
                    and N failures in N+n-1 trials, and success on the (N+n)th trial.
            
                    If one throws a die repeatedly until the third time a "1" appears, then the
                    probability distribution of the number of non-"1"s that appear before the
                    third "1" is a negative binomial distribution.
            
                    References
                    ----------
                    .. [1] Weisstein, Eric W. "Negative Binomial Distribution." From
                           MathWorld--A Wolfram Web Resource.
                           http://mathworld.wolfram.com/NegativeBinomialDistribution.html
                    .. [2] Wikipedia, "Negative binomial distribution",
                           http://en.wikipedia.org/wiki/Negative_binomial_distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    A real world example. A company drills wild-cat oil exploration wells, each
                    with an estimated probability of success of 0.1.  What is the probability
                    of having one success for each successive well, that is what is the
                    probability of a single success after drilling 5 wells, after 6 wells,
                    etc.?
            
                    >>> s = np.random.negative_binomial(1, 0.1, 100000)
                    >>> for i in range(1, 11):
                    ...    probability = sum(s<i) / 100000.
                    ...    print i, "wells drilled, probability of one success =", probability
            
                    """
            return int() if False else ndarray()
        def noncentral_chisquare(self, df, nonc, size):
            """
                    noncentral_chisquare(df, nonc, size=None)
            
                    Draw samples from a noncentral chi-square distribution.
            
                    The noncentral :math:`\chi^2` distribution is a generalisation of
                    the :math:`\chi^2` distribution.
            
                    Parameters
                    ----------
                    df : int
                        Degrees of freedom, should be >= 1.
                    nonc : float
                        Non-centrality, should be > 0.
                    size : int or tuple of ints
                        Shape of the output.
            
                    Notes
                    -----
                    The probability density function for the noncentral Chi-square distribution
                    is
            
                    .. math:: P(x;df,nonc) = \sum^{\infty}_{i=0}
                                           \frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),
            
                    where :math:`Y_{q}` is the Chi-square with q degrees of freedom.
            
                    In Delhi (2007), it is noted that the noncentral chi-square is useful in
                    bombing and coverage problems, the probability of killing the point target
                    given by the noncentral chi-squared distribution.
            
                    References
                    ----------
                    .. [1] Delhi, M.S. Holla, "On a noncentral chi-square distribution in the
                           analysis of weapon systems effectiveness", Metrika, Volume 15,
                           Number 1 / December, 1970.
                    .. [2] Wikipedia, "Noncentral chi-square distribution"
                           http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution
            
                    Examples
                    --------
                    Draw values from the distribution and plot the histogram
            
                    >>> import matplotlib.pyplot as plt
                    >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
                    ...                   bins=200, normed=True)
                    >>> plt.show()
            
                    Draw values from a noncentral chisquare with very small noncentrality,
                    and compare to a chisquare.
            
                    >>> plt.figure()
                    >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),
                    ...                   bins=np.arange(0., 25, .1), normed=True)
                    >>> values2 = plt.hist(np.random.chisquare(3, 100000),
                    ...                    bins=np.arange(0., 25, .1), normed=True)
                    >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')
                    >>> plt.show()
            
                    Demonstrate how large values of non-centrality lead to a more symmetric
                    distribution.
            
                    >>> plt.figure()
                    >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
                    ...                   bins=200, normed=True)
                    >>> plt.show()
            
                    """
            return None
        def noncentral_f(self, dfnum, dfden, nonc, size):
            """
                    noncentral_f(dfnum, dfden, nonc, size=None)
            
                    Draw samples from the noncentral F distribution.
            
                    Samples are drawn from an F distribution with specified parameters,
                    `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of
                    freedom in denominator), where both parameters > 1.
                    `nonc` is the non-centrality parameter.
            
                    Parameters
                    ----------
                    dfnum : int
                        Parameter, should be > 1.
                    dfden : int
                        Parameter, should be > 1.
                    nonc : float
                        Parameter, should be >= 0.
                    size : int or tuple of ints
                        Output shape. If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : scalar or ndarray
                        Drawn samples.
            
                    Notes
                    -----
                    When calculating the power of an experiment (power = probability of
                    rejecting the null hypothesis when a specific alternative is true) the
                    non-central F statistic becomes important.  When the null hypothesis is
                    true, the F statistic follows a central F distribution. When the null
                    hypothesis is not true, then it follows a non-central F statistic.
            
                    References
                    ----------
                    Weisstein, Eric W. "Noncentral F-Distribution." From MathWorld--A Wolfram
                    Web Resource.  http://mathworld.wolfram.com/NoncentralF-Distribution.html
            
                    Wikipedia, "Noncentral F distribution",
                    http://en.wikipedia.org/wiki/Noncentral_F-distribution
            
                    Examples
                    --------
                    In a study, testing for a specific alternative to the null hypothesis
                    requires use of the Noncentral F distribution. We need to calculate the
                    area in the tail of the distribution that exceeds the value of the F
                    distribution for the null hypothesis.  We'll plot the two probability
                    distributions for comparison.
            
                    >>> dfnum = 3 # between group deg of freedom
                    >>> dfden = 20 # within groups degrees of freedom
                    >>> nonc = 3.0
                    >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)
                    >>> NF = np.histogram(nc_vals, bins=50, normed=True)
                    >>> c_vals = np.random.f(dfnum, dfden, 1000000)
                    >>> F = np.histogram(c_vals, bins=50, normed=True)
                    >>> plt.plot(F[1][1:], F[0])
                    >>> plt.plot(NF[1][1:], NF[0])
                    >>> plt.show()
            
                    """
            return float() if False else ndarray()
        def normal(self, loc, scale, size):
            """
                    normal(loc=0.0, scale=1.0, size=None)
            
                    Draw random samples from a normal (Gaussian) distribution.
            
                    The probability density function of the normal distribution, first
                    derived by De Moivre and 200 years later by both Gauss and Laplace
                    independently [2]_, is often called the bell curve because of
                    its characteristic shape (see the example below).
            
                    The normal distributions occurs often in nature.  For example, it
                    describes the commonly occurring distribution of samples influenced
                    by a large number of tiny, random disturbances, each with its own
                    unique distribution [2]_.
            
                    Parameters
                    ----------
                    loc : float
                        Mean ("centre") of the distribution.
                    scale : float
                        Standard deviation (spread or "width") of the distribution.
                    size : tuple of ints
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    See Also
                    --------
                    scipy.stats.distributions.norm : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Gaussian distribution is
            
                    .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
                                     e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
            
                    where :math:`\mu` is the mean and :math:`\sigma` the standard deviation.
                    The square of the standard deviation, :math:`\sigma^2`, is called the
                    variance.
            
                    The function has its peak at the mean, and its "spread" increases with
                    the standard deviation (the function reaches 0.607 times its maximum at
                    :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
                    `numpy.random.normal` is more likely to return samples lying close to the
                    mean, rather than those far away.
            
                    References
                    ----------
                    .. [1] Wikipedia, "Normal distribution",
                           http://en.wikipedia.org/wiki/Normal_distribution
                    .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability, Random
                           Variables and Random Signal Principles", 4th ed., 2001,
                           pp. 51, 51, 125.
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> mu, sigma = 0, 0.1 # mean and standard deviation
                    >>> s = np.random.normal(mu, sigma, 1000)
            
                    Verify the mean and the variance:
            
                    >>> abs(mu - np.mean(s)) < 0.01
                    True
            
                    >>> abs(sigma - np.std(s, ddof=1)) < 0.01
                    True
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, 30, normed=True)
                    >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
                    ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
                    ...          linewidth=2, color='r')
                    >>> plt.show()
            
                    """
            return None
        def pareto(self, shape, size):
            """
                    pareto(a, size=None)
            
                    Draw samples from a Pareto II or Lomax distribution with specified shape.
            
                    The Lomax or Pareto II distribution is a shifted Pareto distribution. The
                    classical Pareto distribution can be obtained from the Lomax distribution
                    by adding the location parameter m, see below. The smallest value of the
                    Lomax distribution is zero while for the classical Pareto distribution it
                    is m, where the standard Pareto distribution has location m=1.
                    Lomax can also be considered as a simplified version of the Generalized
                    Pareto distribution (available in SciPy), with the scale set to one and
                    the location set to zero.
            
                    The Pareto distribution must be greater than zero, and is unbounded above.
                    It is also known as the "80-20 rule".  In this distribution, 80 percent of
                    the weights are in the lowest 20 percent of the range, while the other 20
                    percent fill the remaining 80 percent of the range.
            
                    Parameters
                    ----------
                    shape : float, > 0.
                        Shape of the distribution.
                    size : tuple of ints
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    See Also
                    --------
                    scipy.stats.distributions.lomax.pdf : probability density function,
                        distribution or cumulative density function, etc.
                    scipy.stats.distributions.genpareto.pdf : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Pareto distribution is
            
                    .. math:: p(x) = \frac{am^a}{x^{a+1}}
            
                    where :math:`a` is the shape and :math:`m` the location
            
                    The Pareto distribution, named after the Italian economist Vilfredo Pareto,
                    is a power law probability distribution useful in many real world problems.
                    Outside the field of economics it is generally referred to as the Bradford
                    distribution. Pareto developed the distribution to describe the
                    distribution of wealth in an economy.  It has also found use in insurance,
                    web page access statistics, oil field sizes, and many other problems,
                    including the download frequency for projects in Sourceforge [1].  It is
                    one of the so-called "fat-tailed" distributions.
            
            
                    References
                    ----------
                    .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of
                           Sourceforge projects.
                    .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.
                    .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme
                           Values, Birkhauser Verlag, Basel, pp 23-30.
                    .. [4] Wikipedia, "Pareto distribution",
                           http://en.wikipedia.org/wiki/Pareto_distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> a, m = 3., 1. # shape and mode
                    >>> s = np.random.pareto(a, 1000) + m
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')
                    >>> fit = a*m**a/bins**(a+1)
                    >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')
                    >>> plt.show()
            
                    """
            return None
        def permutation(self, x):
            """
                    permutation(x)
            
                    Randomly permute a sequence, or return a permuted range.
            
                    If `x` is a multi-dimensional array, it is only shuffled along its
                    first index.
            
                    Parameters
                    ----------
                    x : int or array_like
                        If `x` is an integer, randomly permute ``np.arange(x)``.
                        If `x` is an array, make a copy and shuffle the elements
                        randomly.
            
                    Returns
                    -------
                    out : ndarray
                        Permuted sequence or array range.
            
                    Examples
                    --------
                    >>> np.random.permutation(10)
                    array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])
            
                    >>> np.random.permutation([1, 4, 9, 12, 15])
                    array([15,  1,  9,  4, 12])
            
                    >>> arr = np.arange(9).reshape((3, 3))
                    >>> np.random.permutation(arr)
                    array([[6, 7, 8],
                           [0, 1, 2],
                           [3, 4, 5]])
            
                    """
            return ndarray()
        def poisson(self, lam, size):
            """
                    poisson(lam=1.0, size=None)
            
                    Draw samples from a Poisson distribution.
            
                    The Poisson distribution is the limit of the Binomial
                    distribution for large N.
            
                    Parameters
                    ----------
                    lam : float
                        Expectation of interval, should be >= 0.
                    size : int or tuple of ints, optional
                        Output shape. If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Notes
                    -----
                    The Poisson distribution
            
                    .. math:: f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}
            
                    For events with an expected separation :math:`\lambda` the Poisson
                    distribution :math:`f(k; \lambda)` describes the probability of
                    :math:`k` events occurring within the observed interval :math:`\lambda`.
            
                    Because the output is limited to the range of the C long type, a
                    ValueError is raised when `lam` is within 10 sigma of the maximum
                    representable value.
            
                    References
                    ----------
                    .. [1] Weisstein, Eric W. "Poisson Distribution." From MathWorld--A Wolfram
                           Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html
                    .. [2] Wikipedia, "Poisson distribution",
                       http://en.wikipedia.org/wiki/Poisson_distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> import numpy as np
                    >>> s = np.random.poisson(5, 10000)
            
                    Display histogram of the sample:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, 14, normed=True)
                    >>> plt.show()
            
                    """
            return None
        poisson_lam_max = float64()
        def power(self, a, size):
            """
                    power(a, size=None)
            
                    Draws samples in [0, 1] from a power distribution with positive
                    exponent a - 1.
            
                    Also known as the power function distribution.
            
                    Parameters
                    ----------
                    a : float
                        parameter, > 0
                    size : tuple of ints
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                                ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : {ndarray, scalar}
                        The returned samples lie in [0, 1].
            
                    Raises
                    ------
                    ValueError
                        If a<1.
            
                    Notes
                    -----
                    The probability density function is
            
                    .. math:: P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0.
            
                    The power function distribution is just the inverse of the Pareto
                    distribution. It may also be seen as a special case of the Beta
                    distribution.
            
                    It is used, for example, in modeling the over-reporting of insurance
                    claims.
            
                    References
                    ----------
                    .. [1] Christian Kleiber, Samuel Kotz, "Statistical size distributions
                           in economics and actuarial sciences", Wiley, 2003.
                    .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:
                           Dataplot Reference Manual, Volume 2: Let Subcommands and Library
                           Functions", National Institute of Standards and Technology Handbook
                           Series, June 2003.
                           http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> a = 5. # shape
                    >>> samples = 1000
                    >>> s = np.random.power(a, samples)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, bins=30)
                    >>> x = np.linspace(0, 1, 100)
                    >>> y = a*x**(a-1.)
                    >>> normed_y = samples*np.diff(bins)[0]*y
                    >>> plt.plot(x, normed_y)
                    >>> plt.show()
            
                    Compare the power function distribution to the inverse of the Pareto.
            
                    >>> from scipy import stats
                    >>> rvs = np.random.power(5, 1000000)
                    >>> rvsp = np.random.pareto(5, 1000000)
                    >>> xx = np.linspace(0,1,100)
                    >>> powpdf = stats.powerlaw.pdf(xx,5)
            
                    >>> plt.figure()
                    >>> plt.hist(rvs, bins=50, normed=True)
                    >>> plt.plot(xx,powpdf,'r-')
                    >>> plt.title('np.random.power(5)')
            
                    >>> plt.figure()
                    >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)
                    >>> plt.plot(xx,powpdf,'r-')
                    >>> plt.title('inverse of 1 + np.random.pareto(5)')
            
                    >>> plt.figure()
                    >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)
                    >>> plt.plot(xx,powpdf,'r-')
                    >>> plt.title('inverse of stats.pareto(5)')
            
                    """
            return ndarray()
        def rand(self, d0d1more_argsdn):
            """
                    rand(d0, d1, ..., dn)
            
                    Random values in a given shape.
            
                    Create an array of the given shape and propagate it with
                    random samples from a uniform distribution
                    over ``[0, 1)``.
            
                    Parameters
                    ----------
                    d0, d1, ..., dn : int, optional
                        The dimensions of the returned array, should all be positive.
                        If no argument is given a single Python float is returned.
            
                    Returns
                    -------
                    out : ndarray, shape ``(d0, d1, ..., dn)``
                        Random values.
            
                    See Also
                    --------
                    random
            
                    Notes
                    -----
                    This is a convenience function. If you want an interface that
                    takes a shape-tuple as the first argument, refer to
                    np.random.random_sample .
            
                    Examples
                    --------
                    >>> np.random.rand(3,2)
                    array([[ 0.14022471,  0.96360618],  #random
                           [ 0.37601032,  0.25528411],  #random
                           [ 0.49313049,  0.94909878]]) #random
            
                    """
            return None
        def randint(self, low, high, size):
            """
                    randint(low, high=None, size=None)
            
                    Return random integers from `low` (inclusive) to `high` (exclusive).
            
                    Return random integers from the "discrete uniform" distribution in the
                    "half-open" interval [`low`, `high`). If `high` is None (the default),
                    then results are from [0, `low`).
            
                    Parameters
                    ----------
                    low : int
                        Lowest (signed) integer to be drawn from the distribution (unless
                        ``high=None``, in which case this parameter is the *highest* such
                        integer).
                    high : int, optional
                        If provided, one above the largest (signed) integer to be drawn
                        from the distribution (see above for behavior if ``high=None``).
                    size : int or tuple of ints, optional
                        Output shape. Default is None, in which case a single int is
                        returned.
            
                    Returns
                    -------
                    out : int or ndarray of ints
                        `size`-shaped array of random integers from the appropriate
                        distribution, or a single such random int if `size` not provided.
            
                    See Also
                    --------
                    random.random_integers : similar to `randint`, only for the closed
                        interval [`low`, `high`], and 1 is the lowest value if `high` is
                        omitted. In particular, this other one is the one to use to generate
                        uniformly distributed discrete non-integers.
            
                    Examples
                    --------
                    >>> np.random.randint(2, size=10)
                    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
                    >>> np.random.randint(1, size=10)
                    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
            
                    Generate a 2 x 4 array of ints between 0 and 4, inclusive:
            
                    >>> np.random.randint(5, size=(2, 4))
                    array([[4, 0, 2, 1],
                           [3, 2, 2, 0]])
            
                    """
            return int() if False else ndarray()
        def randn(self, d0d1more_argsdn):
            """
                    randn(d0, d1, ..., dn)
            
                    Return a sample (or samples) from the "standard normal" distribution.
            
                    If positive, int_like or int-convertible arguments are provided,
                    `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
                    with random floats sampled from a univariate "normal" (Gaussian)
                    distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
                    floats, they are first converted to integers by truncation). A single
                    float randomly sampled from the distribution is returned if no
                    argument is provided.
            
                    This is a convenience function.  If you want an interface that takes a
                    tuple as the first argument, use `numpy.random.standard_normal` instead.
            
                    Parameters
                    ----------
                    d0, d1, ..., dn : int, optional
                        The dimensions of the returned array, should be all positive.
                        If no argument is given a single Python float is returned.
            
                    Returns
                    -------
                    Z : ndarray or float
                        A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
                        the standard normal distribution, or a single such float if
                        no parameters were supplied.
            
                    See Also
                    --------
                    random.standard_normal : Similar, but takes a tuple as its argument.
            
                    Notes
                    -----
                    For random samples from :math:`N(\mu, \sigma^2)`, use:
            
                    ``sigma * np.random.randn(...) + mu``
            
                    Examples
                    --------
                    >>> np.random.randn()
                    2.1923875335537315 #random
            
                    Two-by-four array of samples from N(3, 6.25):
            
                    >>> 2.5 * np.random.randn(2, 4) + 3
                    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
                           [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random
            
                    """
            return ndarray() if False else float()
        def random_integers(self, low, high, size):
            """
                    random_integers(low, high=None, size=None)
            
                    Return random integers between `low` and `high`, inclusive.
            
                    Return random integers from the "discrete uniform" distribution in the
                    closed interval [`low`, `high`].  If `high` is None (the default),
                    then results are from [1, `low`].
            
                    Parameters
                    ----------
                    low : int
                        Lowest (signed) integer to be drawn from the distribution (unless
                        ``high=None``, in which case this parameter is the *highest* such
                        integer).
                    high : int, optional
                        If provided, the largest (signed) integer to be drawn from the
                        distribution (see above for behavior if ``high=None``).
                    size : int or tuple of ints, optional
                        Output shape. Default is None, in which case a single int is returned.
            
                    Returns
                    -------
                    out : int or ndarray of ints
                        `size`-shaped array of random integers from the appropriate
                        distribution, or a single such random int if `size` not provided.
            
                    See Also
                    --------
                    random.randint : Similar to `random_integers`, only for the half-open
                        interval [`low`, `high`), and 0 is the lowest value if `high` is
                        omitted.
            
                    Notes
                    -----
                    To sample from N evenly spaced floating-point numbers between a and b,
                    use::
            
                      a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)
            
                    Examples
                    --------
                    >>> np.random.random_integers(5)
                    4
                    >>> type(np.random.random_integers(5))
                    <type 'int'>
                    >>> np.random.random_integers(5, size=(3.,2.))
                    array([[5, 4],
                           [3, 3],
                           [4, 5]])
            
                    Choose five random numbers from the set of five evenly-spaced
                    numbers between 0 and 2.5, inclusive (*i.e.*, from the set
                    :math:`{0, 5/8, 10/8, 15/8, 20/8}`):
            
                    >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.
                    array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])
            
                    Roll two six sided dice 1000 times and sum the results:
            
                    >>> d1 = np.random.random_integers(1, 6, 1000)
                    >>> d2 = np.random.random_integers(1, 6, 1000)
                    >>> dsums = d1 + d2
            
                    Display results as a histogram:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)
                    >>> plt.show()
            
                    """
            return int() if False else ndarray()
        def random_sample(self, size):
            """
                    random_sample(size=None)
            
                    Return random floats in the half-open interval [0.0, 1.0).
            
                    Results are from the "continuous uniform" distribution over the
                    stated interval.  To sample :math:`Unif[a, b), b > a` multiply
                    the output of `random_sample` by `(b-a)` and add `a`::
            
                      (b - a) * random_sample() + a
            
                    Parameters
                    ----------
                    size : int or tuple of ints, optional
                        Defines the shape of the returned array of random floats. If None
                        (the default), returns a single float.
            
                    Returns
                    -------
                    out : float or ndarray of floats
                        Array of random floats of shape `size` (unless ``size=None``, in which
                        case a single float is returned).
            
                    Examples
                    --------
                    >>> np.random.random_sample()
                    0.47108547995356098
                    >>> type(np.random.random_sample())
                    <type 'float'>
                    >>> np.random.random_sample((5,))
                    array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])
            
                    Three-by-two array of random numbers from [-5, 0):
            
                    >>> 5 * np.random.random_sample((3, 2)) - 5
                    array([[-3.99149989, -0.52338984],
                           [-2.99091858, -0.79479508],
                           [-1.23204345, -1.75224494]])
            
                    """
            return float() if False else ndarray()
        def rayleigh(self, scale, size):
            """
                    rayleigh(scale=1.0, size=None)
            
                    Draw samples from a Rayleigh distribution.
            
                    The :math:`\chi` and Weibull distributions are generalizations of the
                    Rayleigh.
            
                    Parameters
                    ----------
                    scale : scalar
                        Scale, also equals the mode. Should be >= 0.
                    size : int or tuple of ints, optional
                        Shape of the output. Default is None, in which case a single
                        value is returned.
            
                    Notes
                    -----
                    The probability density function for the Rayleigh distribution is
            
                    .. math:: P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}
            
                    The Rayleigh distribution arises if the wind speed and wind direction are
                    both gaussian variables, then the vector wind velocity forms a Rayleigh
                    distribution. The Rayleigh distribution is used to model the expected
                    output from wind turbines.
            
                    References
                    ----------
                    .. [1] Brighton Webs Ltd., Rayleigh Distribution,
                          http://www.brighton-webs.co.uk/distributions/rayleigh.asp
                    .. [2] Wikipedia, "Rayleigh distribution"
                          http://en.wikipedia.org/wiki/Rayleigh_distribution
            
                    Examples
                    --------
                    Draw values from the distribution and plot the histogram
            
                    >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)
            
                    Wave heights tend to follow a Rayleigh distribution. If the mean wave
                    height is 1 meter, what fraction of waves are likely to be larger than 3
                    meters?
            
                    >>> meanvalue = 1
                    >>> modevalue = np.sqrt(2 / np.pi) * meanvalue
                    >>> s = np.random.rayleigh(modevalue, 1000000)
            
                    The percentage of waves larger than 3 meters is:
            
                    >>> 100.*sum(s>3)/1000000.
                    0.087300000000000003
            
                    """
            return None
        def seed(self, seed):
            """
                    seed(seed=None)
            
                    Seed the generator.
            
                    This method is called when `RandomState` is initialized. It can be
                    called again to re-seed the generator. For details, see `RandomState`.
            
                    Parameters
                    ----------
                    seed : int or array_like, optional
                        Seed for `RandomState`.
            
                    See Also
                    --------
                    RandomState
            
                    """
            return None
        def set_state(self, state):
            """
                    set_state(state)
            
                    Set the internal state of the generator from a tuple.
            
                    For use if one has reason to manually (re-)set the internal state of the
                    "Mersenne Twister"[1]_ pseudo-random number generating algorithm.
            
                    Parameters
                    ----------
                    state : tuple(str, ndarray of 624 uints, int, int, float)
                        The `state` tuple has the following items:
            
                        1. the string 'MT19937', specifying the Mersenne Twister algorithm.
                        2. a 1-D array of 624 unsigned integers ``keys``.
                        3. an integer ``pos``.
                        4. an integer ``has_gauss``.
                        5. a float ``cached_gaussian``.
            
                    Returns
                    -------
                    out : None
                        Returns 'None' on success.
            
                    See Also
                    --------
                    get_state
            
                    Notes
                    -----
                    `set_state` and `get_state` are not needed to work with any of the
                    random distributions in NumPy. If the internal state is manually altered,
                    the user should know exactly what he/she is doing.
            
                    For backwards compatibility, the form (str, array of 624 uints, int) is
                    also accepted although it is missing some information about the cached
                    Gaussian value: ``state = ('MT19937', keys, pos)``.
            
                    References
                    ----------
                    .. [1] M. Matsumoto and T. Nishimura, "Mersenne Twister: A
                       623-dimensionally equidistributed uniform pseudorandom number
                       generator," *ACM Trans. on Modeling and Computer Simulation*,
                       Vol. 8, No. 1, pp. 3-30, Jan. 1998.
            
                    """
            return None
        def shuffle(self, x):
            """
                    shuffle(x)
            
                    Modify a sequence in-place by shuffling its contents.
            
                    Parameters
                    ----------
                    x : array_like
                        The array or list to be shuffled.
            
                    Returns
                    -------
                    None
            
                    Examples
                    --------
                    >>> arr = np.arange(10)
                    >>> np.random.shuffle(arr)
                    >>> arr
                    [1 7 5 2 9 4 3 6 0 8]
            
                    This function only shuffles the array along the first index of a
                    multi-dimensional array:
            
                    >>> arr = np.arange(9).reshape((3, 3))
                    >>> np.random.shuffle(arr)
                    >>> arr
                    array([[3, 4, 5],
                           [6, 7, 8],
                           [0, 1, 2]])
            
                    """
            return None
        def standard_cauchy(self, size):
            """
                    standard_cauchy(size=None)
            
                    Standard Cauchy distribution with mode = 0.
            
                    Also known as the Lorentz distribution.
            
                    Parameters
                    ----------
                    size : int or tuple of ints
                        Shape of the output.
            
                    Returns
                    -------
                    samples : ndarray or scalar
                        The drawn samples.
            
                    Notes
                    -----
                    The probability density function for the full Cauchy distribution is
            
                    .. math:: P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+
                              (\frac{x-x_0}{\gamma})^2 \bigr] }
            
                    and the Standard Cauchy distribution just sets :math:`x_0=0` and
                    :math:`\gamma=1`
            
                    The Cauchy distribution arises in the solution to the driven harmonic
                    oscillator problem, and also describes spectral line broadening. It
                    also describes the distribution of values at which a line tilted at
                    a random angle will cut the x axis.
            
                    When studying hypothesis tests that assume normality, seeing how the
                    tests perform on data from a Cauchy distribution is a good indicator of
                    their sensitivity to a heavy-tailed distribution, since the Cauchy looks
                    very much like a Gaussian distribution, but with heavier tails.
            
                    References
                    ----------
                    .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy
                          Distribution",
                          http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
                    .. [2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A
                          Wolfram Web Resource.
                          http://mathworld.wolfram.com/CauchyDistribution.html
                    .. [3] Wikipedia, "Cauchy distribution"
                          http://en.wikipedia.org/wiki/Cauchy_distribution
            
                    Examples
                    --------
                    Draw samples and plot the distribution:
            
                    >>> s = np.random.standard_cauchy(1000000)
                    >>> s = s[(s>-25) & (s<25)]  # truncate distribution so it plots well
                    >>> plt.hist(s, bins=100)
                    >>> plt.show()
            
                    """
            return ndarray() if False else float()
        def standard_exponential(self, size):
            """
                    standard_exponential(size=None)
            
                    Draw samples from the standard exponential distribution.
            
                    `standard_exponential` is identical to the exponential distribution
                    with a scale parameter of 1.
            
                    Parameters
                    ----------
                    size : int or tuple of ints
                        Shape of the output.
            
                    Returns
                    -------
                    out : float or ndarray
                        Drawn samples.
            
                    Examples
                    --------
                    Output a 3x8000 array:
            
                    >>> n = np.random.standard_exponential((3, 8000))
            
                    """
            return float() if False else ndarray()
        def standard_gamma(self, shape, size):
            """
                    standard_gamma(shape, size=None)
            
                    Draw samples from a Standard Gamma distribution.
            
                    Samples are drawn from a Gamma distribution with specified parameters,
                    shape (sometimes designated "k") and scale=1.
            
                    Parameters
                    ----------
                    shape : float
                        Parameter, should be > 0.
                    size : int or tuple of ints
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : ndarray or scalar
                        The drawn samples.
            
                    See Also
                    --------
                    scipy.stats.distributions.gamma : probability density function,
                        distribution or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Gamma distribution is
            
                    .. math:: p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},
            
                    where :math:`k` is the shape and :math:`\theta` the scale,
                    and :math:`\Gamma` is the Gamma function.
            
                    The Gamma distribution is often used to model the times to failure of
                    electronic components, and arises naturally in processes for which the
                    waiting times between Poisson distributed events are relevant.
            
                    References
                    ----------
                    .. [1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A
                           Wolfram Web Resource.
                           http://mathworld.wolfram.com/GammaDistribution.html
                    .. [2] Wikipedia, "Gamma-distribution",
                           http://en.wikipedia.org/wiki/Gamma-distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> shape, scale = 2., 1. # mean and width
                    >>> s = np.random.standard_gamma(shape, 1000000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> import scipy.special as sps
                    >>> count, bins, ignored = plt.hist(s, 50, normed=True)
                    >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \
                    ...                       (sps.gamma(shape) * scale**shape))
                    >>> plt.plot(bins, y, linewidth=2, color='r')
                    >>> plt.show()
            
                    """
            return ndarray() if False else float()
        def standard_normal(self, size):
            """
                    standard_normal(size=None)
            
                    Returns samples from a Standard Normal distribution (mean=0, stdev=1).
            
                    Parameters
                    ----------
                    size : int or tuple of ints, optional
                        Output shape. Default is None, in which case a single value is
                        returned.
            
                    Returns
                    -------
                    out : float or ndarray
                        Drawn samples.
            
                    Examples
                    --------
                    >>> s = np.random.standard_normal(8000)
                    >>> s
                    array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311, #random
                           -0.38672696, -0.4685006 ])                               #random
                    >>> s.shape
                    (8000,)
                    >>> s = np.random.standard_normal(size=(3, 4, 2))
                    >>> s.shape
                    (3, 4, 2)
            
                    """
            return float() if False else ndarray()
        def standard_t(self, df, size):
            """
                    standard_t(df, size=None)
            
                    Standard Student's t distribution with df degrees of freedom.
            
                    A special case of the hyperbolic distribution.
                    As `df` gets large, the result resembles that of the standard normal
                    distribution (`standard_normal`).
            
                    Parameters
                    ----------
                    df : int
                        Degrees of freedom, should be > 0.
                    size : int or tuple of ints, optional
                        Output shape. Default is None, in which case a single value is
                        returned.
            
                    Returns
                    -------
                    samples : ndarray or scalar
                        Drawn samples.
            
                    Notes
                    -----
                    The probability density function for the t distribution is
            
                    .. math:: P(x, df) = \frac{\Gamma(\frac{df+1}{2})}{\sqrt{\pi df}
                              \Gamma(\frac{df}{2})}\Bigl( 1+\frac{x^2}{df} \Bigr)^{-(df+1)/2}
            
                    The t test is based on an assumption that the data come from a Normal
                    distribution. The t test provides a way to test whether the sample mean
                    (that is the mean calculated from the data) is a good estimate of the true
                    mean.
            
                    The derivation of the t-distribution was forst published in 1908 by William
                    Gisset while working for the Guinness Brewery in Dublin. Due to proprietary
                    issues, he had to publish under a pseudonym, and so he used the name
                    Student.
            
                    References
                    ----------
                    .. [1] Dalgaard, Peter, "Introductory Statistics With R",
                           Springer, 2002.
                    .. [2] Wikipedia, "Student's t-distribution"
                           http://en.wikipedia.org/wiki/Student's_t-distribution
            
                    Examples
                    --------
                    From Dalgaard page 83 [1]_, suppose the daily energy intake for 11
                    women in Kj is:
            
                    >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \
                    ...                    7515, 8230, 8770])
            
                    Does their energy intake deviate systematically from the recommended
                    value of 7725 kJ?
            
                    We have 10 degrees of freedom, so is the sample mean within 95% of the
                    recommended value?
            
                    >>> s = np.random.standard_t(10, size=100000)
                    >>> np.mean(intake)
                    6753.636363636364
                    >>> intake.std(ddof=1)
                    1142.1232221373727
            
                    Calculate the t statistic, setting the ddof parameter to the unbiased
                    value so the divisor in the standard deviation will be degrees of
                    freedom, N-1.
            
                    >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))
                    >>> import matplotlib.pyplot as plt
                    >>> h = plt.hist(s, bins=100, normed=True)
            
                    For a one-sided t-test, how far out in the distribution does the t
                    statistic appear?
            
                    >>> >>> np.sum(s<t) / float(len(s))
                    0.0090699999999999999  #random
            
                    So the p-value is about 0.009, which says the null hypothesis has a
                    probability of about 99% of being true.
            
                    """
            return ndarray() if False else float()
        def tomaxint(self, size):
            """
                    tomaxint(size=None)
            
                    Random integers between 0 and ``sys.maxint``, inclusive.
            
                    Return a sample of uniformly distributed random integers in the interval
                    [0, ``sys.maxint``].
            
                    Parameters
                    ----------
                    size : tuple of ints, int, optional
                        Shape of output.  If this is, for example, (m,n,k), m*n*k samples
                        are generated.  If no shape is specified, a single sample is
                        returned.
            
                    Returns
                    -------
                    out : ndarray
                        Drawn samples, with shape `size`.
            
                    See Also
                    --------
                    randint : Uniform sampling over a given half-open interval of integers.
                    random_integers : Uniform sampling over a given closed interval of
                        integers.
            
                    Examples
                    --------
                    >>> RS = np.random.mtrand.RandomState() # need a RandomState object
                    >>> RS.tomaxint((2,2,2))
                    array([[[1170048599, 1600360186],
                            [ 739731006, 1947757578]],
                           [[1871712945,  752307660],
                            [1601631370, 1479324245]]])
                    >>> import sys
                    >>> sys.maxint
                    2147483647
                    >>> RS.tomaxint((2,2,2)) < sys.maxint
                    array([[[ True,  True],
                            [ True,  True]],
                           [[ True,  True],
                            [ True,  True]]], dtype=bool)
            
                    """
            return ndarray()
        def triangular(self, left, mode, right, size):
            """
                    triangular(left, mode, right, size=None)
            
                    Draw samples from the triangular distribution.
            
                    The triangular distribution is a continuous probability distribution with
                    lower limit left, peak at mode, and upper limit right. Unlike the other
                    distributions, these parameters directly define the shape of the pdf.
            
                    Parameters
                    ----------
                    left : scalar
                        Lower limit.
                    mode : scalar
                        The value where the peak of the distribution occurs.
                        The value should fulfill the condition ``left <= mode <= right``.
                    right : scalar
                        Upper limit, should be larger than `left`.
                    size : int or tuple of ints, optional
                        Output shape. Default is None, in which case a single value is
                        returned.
            
                    Returns
                    -------
                    samples : ndarray or scalar
                        The returned samples all lie in the interval [left, right].
            
                    Notes
                    -----
                    The probability density function for the Triangular distribution is
            
                    .. math:: P(x;l, m, r) = \begin{cases}
                              \frac{2(x-l)}{(r-l)(m-l)}& \text{for $l \leq x \leq m$},\\
                              \frac{2(m-x)}{(r-l)(r-m)}& \text{for $m \leq x \leq r$},\\
                              0& \text{otherwise}.
                              \end{cases}
            
                    The triangular distribution is often used in ill-defined problems where the
                    underlying distribution is not known, but some knowledge of the limits and
                    mode exists. Often it is used in simulations.
            
                    References
                    ----------
                    .. [1] Wikipedia, "Triangular distribution"
                          http://en.wikipedia.org/wiki/Triangular_distribution
            
                    Examples
                    --------
                    Draw values from the distribution and plot the histogram:
            
                    >>> import matplotlib.pyplot as plt
                    >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=200,
                    ...              normed=True)
                    >>> plt.show()
            
                    """
            return ndarray() if False else float()
        def uniform(self, low, high, size):
            """
                    uniform(low=0.0, high=1.0, size=1)
            
                    Draw samples from a uniform distribution.
            
                    Samples are uniformly distributed over the half-open interval
                    ``[low, high)`` (includes low, but excludes high).  In other words,
                    any value within the given interval is equally likely to be drawn
                    by `uniform`.
            
                    Parameters
                    ----------
                    low : float, optional
                        Lower boundary of the output interval.  All values generated will be
                        greater than or equal to low.  The default value is 0.
                    high : float
                        Upper boundary of the output interval.  All values generated will be
                        less than high.  The default value is 1.0.
                    size : int or tuple of ints, optional
                        Shape of output.  If the given size is, for example, (m,n,k),
                        m*n*k samples are generated.  If no shape is specified, a single sample
                        is returned.
            
                    Returns
                    -------
                    out : ndarray
                        Drawn samples, with shape `size`.
            
                    See Also
                    --------
                    randint : Discrete uniform distribution, yielding integers.
                    random_integers : Discrete uniform distribution over the closed
                                      interval ``[low, high]``.
                    random_sample : Floats uniformly distributed over ``[0, 1)``.
                    random : Alias for `random_sample`.
                    rand : Convenience function that accepts dimensions as input, e.g.,
                           ``rand(2,2)`` would generate a 2-by-2 array of floats,
                           uniformly distributed over ``[0, 1)``.
            
                    Notes
                    -----
                    The probability density function of the uniform distribution is
            
                    .. math:: p(x) = \frac{1}{b - a}
            
                    anywhere within the interval ``[a, b)``, and zero elsewhere.
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> s = np.random.uniform(-1,0,1000)
            
                    All values are within the given interval:
            
                    >>> np.all(s >= -1)
                    True
                    >>> np.all(s < 0)
                    True
            
                    Display the histogram of the samples, along with the
                    probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> count, bins, ignored = plt.hist(s, 15, normed=True)
                    >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
                    >>> plt.show()
            
                    """
            return ndarray()
        def vonmises(self, mu, kappa, size):
            """
                    vonmises(mu, kappa, size=None)
            
                    Draw samples from a von Mises distribution.
            
                    Samples are drawn from a von Mises distribution with specified mode
                    (mu) and dispersion (kappa), on the interval [-pi, pi].
            
                    The von Mises distribution (also known as the circular normal
                    distribution) is a continuous probability distribution on the unit
                    circle.  It may be thought of as the circular analogue of the normal
                    distribution.
            
                    Parameters
                    ----------
                    mu : float
                        Mode ("center") of the distribution.
                    kappa : float
                        Dispersion of the distribution, has to be >=0.
                    size : int or tuple of int
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    Returns
                    -------
                    samples : scalar or ndarray
                        The returned samples, which are in the interval [-pi, pi].
            
                    See Also
                    --------
                    scipy.stats.distributions.vonmises : probability density function,
                        distribution, or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the von Mises distribution is
            
                    .. math:: p(x) = \frac{e^{\kappa cos(x-\mu)}}{2\pi I_0(\kappa)},
            
                    where :math:`\mu` is the mode and :math:`\kappa` the dispersion,
                    and :math:`I_0(\kappa)` is the modified Bessel function of order 0.
            
                    The von Mises is named for Richard Edler von Mises, who was born in
                    Austria-Hungary, in what is now the Ukraine.  He fled to the United
                    States in 1939 and became a professor at Harvard.  He worked in
                    probability theory, aerodynamics, fluid mechanics, and philosophy of
                    science.
            
                    References
                    ----------
                    Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical
                    Functions*, New York: Dover, 1965.
            
                    von Mises, R., *Mathematical Theory of Probability and Statistics*,
                    New York: Academic Press, 1964.
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> mu, kappa = 0.0, 4.0 # mean and dispersion
                    >>> s = np.random.vonmises(mu, kappa, 1000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> import scipy.special as sps
                    >>> count, bins, ignored = plt.hist(s, 50, normed=True)
                    >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)
                    >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))
                    >>> plt.plot(x, y/max(y), linewidth=2, color='r')
                    >>> plt.show()
            
                    """
            return float() if False else ndarray()
        def wald(self, mean, scale, size):
            """
                    wald(mean, scale, size=None)
            
                    Draw samples from a Wald, or Inverse Gaussian, distribution.
            
                    As the scale approaches infinity, the distribution becomes more like a
                    Gaussian.
            
                    Some references claim that the Wald is an Inverse Gaussian with mean=1, but
                    this is by no means universal.
            
                    The Inverse Gaussian distribution was first studied in relationship to
                    Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian
                    because there is an inverse relationship between the time to cover a unit
                    distance and distance covered in unit time.
            
                    Parameters
                    ----------
                    mean : scalar
                        Distribution mean, should be > 0.
                    scale : scalar
                        Scale parameter, should be >= 0.
                    size : int or tuple of ints, optional
                        Output shape. Default is None, in which case a single value is
                        returned.
            
                    Returns
                    -------
                    samples : ndarray or scalar
                        Drawn sample, all greater than zero.
            
                    Notes
                    -----
                    The probability density function for the Wald distribution is
            
                    .. math:: P(x;mean,scale) = \sqrt{\frac{scale}{2\pi x^3}}e^
                                                \frac{-scale(x-mean)^2}{2\cdotp mean^2x}
            
                    As noted above the Inverse Gaussian distribution first arise from attempts
                    to model Brownian Motion. It is also a competitor to the Weibull for use in
                    reliability modeling and modeling stock returns and interest rate
                    processes.
            
                    References
                    ----------
                    .. [1] Brighton Webs Ltd., Wald Distribution,
                          http://www.brighton-webs.co.uk/distributions/wald.asp
                    .. [2] Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian
                          Distribution: Theory : Methodology, and Applications", CRC Press,
                          1988.
                    .. [3] Wikipedia, "Wald distribution"
                          http://en.wikipedia.org/wiki/Wald_distribution
            
                    Examples
                    --------
                    Draw values from the distribution and plot the histogram:
            
                    >>> import matplotlib.pyplot as plt
                    >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)
                    >>> plt.show()
            
                    """
            return ndarray() if False else float()
        def weibull(self, a, size):
            """
                    weibull(a, size=None)
            
                    Weibull distribution.
            
                    Draw samples from a 1-parameter Weibull distribution with the given
                    shape parameter `a`.
            
                    .. math:: X = (-ln(U))^{1/a}
            
                    Here, U is drawn from the uniform distribution over (0,1].
            
                    The more common 2-parameter Weibull, including a scale parameter
                    :math:`\lambda` is just :math:`X = \lambda(-ln(U))^{1/a}`.
            
                    Parameters
                    ----------
                    a : float
                        Shape of the distribution.
                    size : tuple of ints
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn.
            
                    See Also
                    --------
                    scipy.stats.distributions.weibull_max
                    scipy.stats.distributions.weibull_min
                    scipy.stats.distributions.genextreme
                    gumbel
            
                    Notes
                    -----
                    The Weibull (or Type III asymptotic extreme value distribution for smallest
                    values, SEV Type III, or Rosin-Rammler distribution) is one of a class of
                    Generalized Extreme Value (GEV) distributions used in modeling extreme
                    value problems.  This class includes the Gumbel and Frechet distributions.
            
                    The probability density for the Weibull distribution is
            
                    .. math:: p(x) = \frac{a}
                                     {\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},
            
                    where :math:`a` is the shape and :math:`\lambda` the scale.
            
                    The function has its peak (the mode) at
                    :math:`\lambda(\frac{a-1}{a})^{1/a}`.
            
                    When ``a = 1``, the Weibull distribution reduces to the exponential
                    distribution.
            
                    References
                    ----------
                    .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,
                           1939 "A Statistical Theory Of The Strength Of Materials",
                           Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,
                           Generalstabens Litografiska Anstalts Forlag, Stockholm.
                    .. [2] Waloddi Weibull, 1951 "A Statistical Distribution Function of Wide
                           Applicability",  Journal Of Applied Mechanics ASME Paper.
                    .. [3] Wikipedia, "Weibull distribution",
                           http://en.wikipedia.org/wiki/Weibull_distribution
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> a = 5. # shape
                    >>> s = np.random.weibull(a, 1000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> x = np.arange(1,100.)/50.
                    >>> def weib(x,n,a):
                    ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
            
                    >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))
                    >>> x = np.arange(1,100.)/50.
                    >>> scale = count.max()/weib(x, 1., 5.).max()
                    >>> plt.plot(x, weib(x, 1., 5.)*scale)
                    >>> plt.show()
            
                    """
            return None
        def zipf(self, a, size):
            """
                    zipf(a, size=None)
            
                    Draw samples from a Zipf distribution.
            
                    Samples are drawn from a Zipf distribution with specified parameter
                    `a` > 1.
            
                    The Zipf distribution (also known as the zeta distribution) is a
                    continuous probability distribution that satisfies Zipf's law: the
                    frequency of an item is inversely proportional to its rank in a
                    frequency table.
            
                    Parameters
                    ----------
                    a : float > 1
                        Distribution parameter.
                    size : int or tuple of int, optional
                        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                        ``m * n * k`` samples are drawn; a single integer is equivalent in
                        its result to providing a mono-tuple, i.e., a 1-D array of length
                        *size* is returned.  The default is None, in which case a single
                        scalar is returned.
            
                    Returns
                    -------
                    samples : scalar or ndarray
                        The returned samples are greater than or equal to one.
            
                    See Also
                    --------
                    scipy.stats.distributions.zipf : probability density function,
                        distribution, or cumulative density function, etc.
            
                    Notes
                    -----
                    The probability density for the Zipf distribution is
            
                    .. math:: p(x) = \frac{x^{-a}}{\zeta(a)},
            
                    where :math:`\zeta` is the Riemann Zeta function.
            
                    It is named for the American linguist George Kingsley Zipf, who noted
                    that the frequency of any word in a sample of a language is inversely
                    proportional to its rank in the frequency table.
            
                    References
                    ----------
                    Zipf, G. K., *Selected Studies of the Principle of Relative Frequency
                    in Language*, Cambridge, MA: Harvard Univ. Press, 1932.
            
                    Examples
                    --------
                    Draw samples from the distribution:
            
                    >>> a = 2. # parameter
                    >>> s = np.random.zipf(a, 1000)
            
                    Display the histogram of the samples, along with
                    the probability density function:
            
                    >>> import matplotlib.pyplot as plt
                    >>> import scipy.special as sps
                    Truncate s values at 50 so plot is interesting
                    >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)
                    >>> x = np.arange(1., 50.)
                    >>> y = x**(-a)/sps.zetac(a)
                    >>> plt.plot(x, y/max(y), linewidth=2, color='r')
                    >>> plt.show()
            
                    """
            return float() if False else ndarray()
    class NoseTester:
        __dict__ = dictproxy()
        __doc__ = str()
        __module__ = str()
        __weakref__ = getset_descriptor()
        def _get_custom_doctester(self, _):
            """ Return instantiated plugin for doctests
            
                    Allows subclassing of this class to override doctester
            
                    A return value of None means use the nose builtin doctest plugin
                    """
            return None
        def _show_system_info(self, _):
            """None"""
            return None
        def _test_argv(self, label, verbose, extra_argv):
            """ Generate argv for nosetest command
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        see ``test`` docstring
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    argv : list
                        command line arguments that will be passed to nose
                    """
            return list()
        def bench(self=None, label="fast", verbose=1, extra_argv=None):
            """
                    Run benchmarks for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the benchmarks to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow benchmarks as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
            
                    Returns
                    -------
                    success : bool
                        Returns True if running the benchmarks works, False if an error
                        occurred.
            
                    Notes
                    -----
                    Benchmarks are like tests, but have names starting with "bench" instead
                    of "test", and can be found under the "benchmarks" sub-directory of the
                    module.
            
                    Each NumPy module exposes `bench` in its namespace to run all benchmarks
                    for it.
            
                    Examples
                    --------
                    >>> success = np.lib.bench() #doctest: +SKIP
                    Running benchmarks for numpy.lib
                    ...
                    using 562341 items:
                    unique:
                    0.11
                    unique1d:
                    0.11
                    ratio: 1.0
                    nUnique: 56230 == 56230
                    ...
                    OK
            
                    >>> success #doctest: +SKIP
                    True
            
                    """
            return bool()
        excludes = list()
        def prepare_test_args(self=False, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False):
            """
                    Run tests for module using nose.
            
                    This method does the heavy lifting for the `test` method. It takes all
                    the same arguments, for details see `test`.
            
                    See Also
                    --------
                    test
            
                    """
            return None
        def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
            """
                    Run tests for module using nose.
            
                    Parameters
                    ----------
                    label : {'fast', 'full', '', attribute identifier}, optional
                        Identifies the tests to run. This can be a string to pass to
                        the nosetests executable with the '-A' option, or one of several
                        special values.  Special values are:
                        * 'fast' - the default - which corresponds to the ``nosetests -A``
                          option of 'not slow'.
                        * 'full' - fast (as above) and slow tests as in the
                          'no -A' option to nosetests - this is the same as ''.
                        * None or '' - run all tests.
                        attribute_identifier - string passed directly to nosetests as '-A'.
                    verbose : int, optional
                        Verbosity value for test outputs, in the range 1-10. Default is 1.
                    extra_argv : list, optional
                        List with any extra arguments to pass to nosetests.
                    doctests : bool, optional
                        If True, run doctests in module. Default is False.
                    coverage : bool, optional
                        If True, report coverage of NumPy code. Default is False.
                        (This requires the `coverage module:
                         <http://nedbatchelder.com/code/modules/coverage.html>`_).
                    raise_warnings : str or sequence of warnings, optional
                        This specifies which warnings to configure as 'raise' instead
                        of 'warn' during the test execution.  Valid strings are:
            
                          - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                          - "release" : equals ``()``, don't raise on any warnings.
            
                    Returns
                    -------
                    result : object
                        Returns the result of running the tests as a
                        ``nose.result.TextTestResult`` object.
            
                    Notes
                    -----
                    Each NumPy module exposes `test` in its namespace to run all tests for it.
                    For example, to run all tests for numpy.lib:
            
                    >>> np.lib.test() #doctest: +SKIP
            
                    Examples
                    --------
                    >>> result = np.lib.test() #doctest: +SKIP
                    Running unit tests for numpy.lib
                    ...
                    Ran 976 tests in 3.933s
            
                    OK
            
                    >>> result.errors #doctest: +SKIP
                    []
                    >>> result.knownfail #doctest: +SKIP
                    []
                    """
            return object()
    __all__ = list()
    __builtins__ = dict()
    __doc__ = str()
    __file__ = str()
    __name__ = str()
    __package__ = str()
    __path__ = list()
    __warningregistry__ = dict()
    absolute_import = instance()
    def bench(self=None, label="fast", verbose=1, extra_argv=None):
        """
                Run benchmarks for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the benchmarks to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow benchmarks as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
        
                Returns
                -------
                success : bool
                    Returns True if running the benchmarks works, False if an error
                    occurred.
        
                Notes
                -----
                Benchmarks are like tests, but have names starting with "bench" instead
                of "test", and can be found under the "benchmarks" sub-directory of the
                module.
        
                Each NumPy module exposes `bench` in its namespace to run all benchmarks
                for it.
        
                Examples
                --------
                >>> success = np.lib.bench() #doctest: +SKIP
                Running benchmarks for numpy.lib
                ...
                using 562341 items:
                unique:
                0.11
                unique1d:
                0.11
                ratio: 1.0
                nUnique: 56230 == 56230
                ...
                OK
        
                >>> success #doctest: +SKIP
                True
        
                """
        return bool()
    def beta(self, a, b, size):
        """
                beta(a, b, size=None)
        
                The Beta distribution over ``[0, 1]``.
        
                The Beta distribution is a special case of the Dirichlet distribution,
                and is related to the Gamma distribution.  It has the probability
                distribution function
        
                .. math:: f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1}
                                                                 (1 - x)^{\beta - 1},
        
                where the normalisation, B, is the beta function,
        
                .. math:: B(\alpha, \beta) = \int_0^1 t^{\alpha - 1}
                                             (1 - t)^{\beta - 1} dt.
        
                It is often seen in Bayesian inference and order statistics.
        
                Parameters
                ----------
                a : float
                    Alpha, non-negative.
                b : float
                    Beta, non-negative.
                size : tuple of ints, optional
                    The number of samples to draw.  The output is packed according to
                    the size given.
        
                Returns
                -------
                out : ndarray
                    Array of the given shape, containing values drawn from a
                    Beta distribution.
        
                """
        return ndarray()
    def binomial(self, n, p, size):
        """
                binomial(n, p, size=None)
        
                Draw samples from a binomial distribution.
        
                Samples are drawn from a Binomial distribution with specified
                parameters, n trials and p probability of success where
                n an integer >= 0 and p is in the interval [0,1]. (n may be
                input as a float, but it is truncated to an integer in use)
        
                Parameters
                ----------
                n : float (but truncated to an integer)
                        parameter, >= 0.
                p : float
                        parameter, >= 0 and <=1.
                size : {tuple, int}
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : {ndarray, scalar}
                          where the values are all integers in  [0, n].
        
                See Also
                --------
                scipy.stats.distributions.binom : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Binomial distribution is
        
                .. math:: P(N) = \binom{n}{N}p^N(1-p)^{n-N},
        
                where :math:`n` is the number of trials, :math:`p` is the probability
                of success, and :math:`N` is the number of successes.
        
                When estimating the standard error of a proportion in a population by
                using a random sample, the normal distribution works well unless the
                product p*n <=5, where p = population proportion estimate, and n =
                number of samples, in which case the binomial distribution is used
                instead. For example, a sample of 15 people shows 4 who are left
                handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
                so the binomial distribution should be used in this case.
        
                References
                ----------
                .. [1] Dalgaard, Peter, "Introductory Statistics with R",
                       Springer-Verlag, 2002.
                .. [2] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
                       Fifth Edition, 2002.
                .. [3] Lentner, Marvin, "Elementary Applied Statistics", Bogden
                       and Quigley, 1972.
                .. [4] Weisstein, Eric W. "Binomial Distribution." From MathWorld--A
                       Wolfram Web Resource.
                       http://mathworld.wolfram.com/BinomialDistribution.html
                .. [5] Wikipedia, "Binomial-distribution",
                       http://en.wikipedia.org/wiki/Binomial_distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> n, p = 10, .5 # number of trials, probability of each trial
                >>> s = np.random.binomial(n, p, 1000)
                # result of flipping a coin 10 times, tested 1000 times.
        
                A real world example. A company drills 9 wild-cat oil exploration
                wells, each with an estimated probability of success of 0.1. All nine
                wells fail. What is the probability of that happening?
        
                Let's do 20,000 trials of the model, and count the number that
                generate zero positive results.
        
                >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.
                answer = 0.38885, or 38%.
        
                """
        return ndarray()
    def bytes(self, length):
        """
                bytes(length)
        
                Return random bytes.
        
                Parameters
                ----------
                length : int
                    Number of random bytes.
        
                Returns
                -------
                out : str
                    String of length `length`.
        
                Examples
                --------
                >>> np.random.bytes(10)
                ' eh\x85\x022SZ\xbf\xa4' #random
        
                """
        return str()
    def chisquare(self, df, size):
        """
                chisquare(df, size=None)
        
                Draw samples from a chi-square distribution.
        
                When `df` independent random variables, each with standard normal
                distributions (mean 0, variance 1), are squared and summed, the
                resulting distribution is chi-square (see Notes).  This distribution
                is often used in hypothesis testing.
        
                Parameters
                ----------
                df : int
                     Number of degrees of freedom.
                size : tuple of ints, int, optional
                     Size of the returned array.  By default, a scalar is
                     returned.
        
                Returns
                -------
                output : ndarray
                    Samples drawn from the distribution, packed in a `size`-shaped
                    array.
        
                Raises
                ------
                ValueError
                    When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)
                    is given.
        
                Notes
                -----
                The variable obtained by summing the squares of `df` independent,
                standard normally distributed random variables:
        
                .. math:: Q = \sum_{i=0}^{\mathtt{df}} X^2_i
        
                is chi-square distributed, denoted
        
                .. math:: Q \sim \chi^2_k.
        
                The probability density function of the chi-squared distribution is
        
                .. math:: p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)}
                                 x^{k/2 - 1} e^{-x/2},
        
                where :math:`\Gamma` is the gamma function,
        
                .. math:: \Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt.
        
                References
                ----------
                `NIST/SEMATECH e-Handbook of Statistical Methods
                <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_
        
                Examples
                --------
                >>> np.random.chisquare(2,4)
                array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])
        
                """
        return ndarray()
    def choice(self, a, size, replace, p):
        """
                choice(a, size=None, replace=True, p=None)
        
                Generates a random sample from a given 1-D array
        
                        .. versionadded:: 1.7.0
        
                Parameters
                -----------
                a : 1-D array-like or int
                    If an ndarray, a random sample is generated from its elements.
                    If an int, the random sample is generated as if a was np.arange(n)
                size : int or tuple of ints, optional
                    Output shape. Default is None, in which case a single value is
                    returned.
                replace : boolean, optional
                    Whether the sample is with or without replacement
                p : 1-D array-like, optional
                    The probabilities associated with each entry in a.
                    If not given the sample assumes a uniform distribtion over all
                    entries in a.
        
                Returns
                --------
                samples : 1-D ndarray, shape (size,)
                    The generated random samples
        
                Raises
                -------
                ValueError
                    If a is an int and less than zero, if a or p are not 1-dimensional,
                    if a is an array-like of size 0, if p is not a vector of
                    probabilities, if a and p have different lengths, or if
                    replace=False and the sample size is greater than the population
                    size
        
                See Also
                ---------
                randint, shuffle, permutation
        
                Examples
                ---------
                Generate a uniform random sample from np.arange(5) of size 3:
        
                >>> np.random.choice(5, 3)
                array([0, 3, 4])
                >>> #This is equivalent to np.random.randint(0,5,3)
        
                Generate a non-uniform random sample from np.arange(5) of size 3:
        
                >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
                array([3, 3, 0])
        
                Generate a uniform random sample from np.arange(5) of size 3 without
                replacement:
        
                >>> np.random.choice(5, 3, replace=False)
                array([3,1,0])
                >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]
        
                Generate a non-uniform random sample from np.arange(5) of size
                3 without replacement:
        
                >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
                array([2, 3, 0])
        
                Any of the above can be repeated with an arbitrary array-like
                instead of just integers. For instance:
        
                >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
                >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
                array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],
                      dtype='|S11')
        
                """
        return _1_D()
    def dirichlet(self, alpha, size):
        """
                dirichlet(alpha, size=None)
        
                Draw samples from the Dirichlet distribution.
        
                Draw `size` samples of dimension k from a Dirichlet distribution. A
                Dirichlet-distributed random variable can be seen as a multivariate
                generalization of a Beta distribution. Dirichlet pdf is the conjugate
                prior of a multinomial in Bayesian inference.
        
                Parameters
                ----------
                alpha : array
                    Parameter of the distribution (k dimension for sample of
                    dimension k).
                size : array
                    Number of samples to draw.
        
                Returns
                -------
                samples : ndarray,
                    The drawn samples, of shape (alpha.ndim, size).
        
                Notes
                -----
                .. math:: X \approx \prod_{i=1}^{k}{x^{\alpha_i-1}_i}
        
                Uses the following property for computation: for each dimension,
                draw a random sample y_i from a standard gamma generator of shape
                `alpha_i`, then
                :math:`X = \frac{1}{\sum_{i=1}^k{y_i}} (y_1, \ldots, y_n)` is
                Dirichlet distributed.
        
                References
                ----------
                .. [1] David McKay, "Information Theory, Inference and Learning
                       Algorithms," chapter 23,
                       http://www.inference.phy.cam.ac.uk/mackay/
                .. [2] Wikipedia, "Dirichlet distribution",
                       http://en.wikipedia.org/wiki/Dirichlet_distribution
        
                Examples
                --------
                Taking an example cited in Wikipedia, this distribution can be used if
                one wanted to cut strings (each of initial length 1.0) into K pieces
                with different lengths, where each piece had, on average, a designated
                average length, but allowing some variation in the relative sizes of the
                pieces.
        
                >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()
        
                >>> plt.barh(range(20), s[0])
                >>> plt.barh(range(20), s[1], left=s[0], color='g')
                >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')
                >>> plt.title("Lengths of Strings")
        
                """
        return ndarray()
    division = instance()
    def exponential(self, scale, size):
        """
                exponential(scale=1.0, size=None)
        
                Exponential distribution.
        
                Its probability density function is
        
                .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
        
                for ``x > 0`` and 0 elsewhere. :math:`\beta` is the scale parameter,
                which is the inverse of the rate parameter :math:`\lambda = 1/\beta`.
                The rate parameter is an alternative, widely used parameterization
                of the exponential distribution [3]_.
        
                The exponential distribution is a continuous analogue of the
                geometric distribution.  It describes many common situations, such as
                the size of raindrops measured over many rainstorms [1]_, or the time
                between page requests to Wikipedia [2]_.
        
                Parameters
                ----------
                scale : float
                    The scale parameter, :math:`\beta = 1/\lambda`.
                size : tuple of ints
                    Number of samples to draw.  The output is shaped
                    according to `size`.
        
                References
                ----------
                .. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and
                       Random Signal Principles", 4th ed, 2001, p. 57.
                .. [2] "Poisson Process", Wikipedia,
                       http://en.wikipedia.org/wiki/Poisson_process
                .. [3] "Exponential Distribution, Wikipedia,
                       http://en.wikipedia.org/wiki/Exponential_distribution
        
                """
        return None
    def f(self, dfnum, dfden, size):
        """
                f(dfnum, dfden, size=None)
        
                Draw samples from a F distribution.
        
                Samples are drawn from an F distribution with specified parameters,
                `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom
                in denominator), where both parameters should be greater than zero.
        
                The random variate of the F distribution (also known as the
                Fisher distribution) is a continuous probability distribution
                that arises in ANOVA tests, and is the ratio of two chi-square
                variates.
        
                Parameters
                ----------
                dfnum : float
                    Degrees of freedom in numerator. Should be greater than zero.
                dfden : float
                    Degrees of freedom in denominator. Should be greater than zero.
                size : {tuple, int}, optional
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``,
                    then ``m * n * k`` samples are drawn. By default only one sample
                    is returned.
        
                Returns
                -------
                samples : {ndarray, scalar}
                    Samples from the Fisher distribution.
        
                See Also
                --------
                scipy.stats.distributions.f : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The F statistic is used to compare in-group variances to between-group
                variances. Calculating the distribution depends on the sampling, and
                so it is a function of the respective degrees of freedom in the
                problem.  The variable `dfnum` is the number of samples minus one, the
                between-groups degrees of freedom, while `dfden` is the within-groups
                degrees of freedom, the sum of the number of samples in each group
                minus the number of groups.
        
                References
                ----------
                .. [1] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
                       Fifth Edition, 2002.
                .. [2] Wikipedia, "F-distribution",
                       http://en.wikipedia.org/wiki/F-distribution
        
                Examples
                --------
                An example from Glantz[1], pp 47-40.
                Two groups, children of diabetics (25 people) and children from people
                without diabetes (25 controls). Fasting blood glucose was measured,
                case group had a mean value of 86.1, controls had a mean value of
                82.2. Standard deviations were 2.09 and 2.49 respectively. Are these
                data consistent with the null hypothesis that the parents diabetic
                status does not affect their children's blood glucose levels?
                Calculating the F statistic from the data gives a value of 36.01.
        
                Draw samples from the distribution:
        
                >>> dfnum = 1. # between group degrees of freedom
                >>> dfden = 48. # within groups degrees of freedom
                >>> s = np.random.f(dfnum, dfden, 1000)
        
                The lower bound for the top 1% of the samples is :
        
                >>> sort(s)[-10]
                7.61988120985
        
                So there is about a 1% chance that the F statistic will exceed 7.62,
                the measured value is 36, so the null hypothesis is rejected at the 1%
                level.
        
                """
        return ndarray()
    def gamma(self, shape, scale, size):
        """
                gamma(shape, scale=1.0, size=None)
        
                Draw samples from a Gamma distribution.
        
                Samples are drawn from a Gamma distribution with specified parameters,
                `shape` (sometimes designated "k") and `scale` (sometimes designated
                "theta"), where both parameters are > 0.
        
                Parameters
                ----------
                shape : scalar > 0
                    The shape of the gamma distribution.
                scale : scalar > 0, optional
                    The scale of the gamma distribution.  Default is equal to 1.
                size : shape_tuple, optional
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                out : ndarray, float
                    Returns one sample unless `size` parameter is specified.
        
                See Also
                --------
                scipy.stats.distributions.gamma : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Gamma distribution is
        
                .. math:: p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},
        
                where :math:`k` is the shape and :math:`\theta` the scale,
                and :math:`\Gamma` is the Gamma function.
        
                The Gamma distribution is often used to model the times to failure of
                electronic components, and arises naturally in processes for which the
                waiting times between Poisson distributed events are relevant.
        
                References
                ----------
                .. [1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A
                       Wolfram Web Resource.
                       http://mathworld.wolfram.com/GammaDistribution.html
                .. [2] Wikipedia, "Gamma-distribution",
                       http://en.wikipedia.org/wiki/Gamma-distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> shape, scale = 2., 2. # mean and dispersion
                >>> s = np.random.gamma(shape, scale, 1000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> import scipy.special as sps
                >>> count, bins, ignored = plt.hist(s, 50, normed=True)
                >>> y = bins**(shape-1)*(np.exp(-bins/scale) /
                ...                      (sps.gamma(shape)*scale**shape))
                >>> plt.plot(bins, y, linewidth=2, color='r')
                >>> plt.show()
        
                """
        return ndarray()
    def geometric(self, p, size):
        """
                geometric(p, size=None)
        
                Draw samples from the geometric distribution.
        
                Bernoulli trials are experiments with one of two outcomes:
                success or failure (an example of such an experiment is flipping
                a coin).  The geometric distribution models the number of trials
                that must be run in order to achieve success.  It is therefore
                supported on the positive integers, ``k = 1, 2, ...``.
        
                The probability mass function of the geometric distribution is
        
                .. math:: f(k) = (1 - p)^{k - 1} p
        
                where `p` is the probability of success of an individual trial.
        
                Parameters
                ----------
                p : float
                    The probability of success of an individual trial.
                size : tuple of ints
                    Number of values to draw from the distribution.  The output
                    is shaped according to `size`.
        
                Returns
                -------
                out : ndarray
                    Samples from the geometric distribution, shaped according to
                    `size`.
        
                Examples
                --------
                Draw ten thousand values from the geometric distribution,
                with the probability of an individual success equal to 0.35:
        
                >>> z = np.random.geometric(p=0.35, size=10000)
        
                How many trials succeeded after a single run?
        
                >>> (z == 1).sum() / 10000.
                0.34889999999999999 #random
        
                """
        return ndarray()
    def get_state(self, _):
        """
                get_state()
        
                Return a tuple representing the internal state of the generator.
        
                For more details, see `set_state`.
        
                Returns
                -------
                out : tuple(str, ndarray of 624 uints, int, int, float)
                    The returned tuple has the following items:
        
                    1. the string 'MT19937'.
                    2. a 1-D array of 624 unsigned integer keys.
                    3. an integer ``pos``.
                    4. an integer ``has_gauss``.
                    5. a float ``cached_gaussian``.
        
                See Also
                --------
                set_state
        
                Notes
                -----
                `set_state` and `get_state` are not needed to work with any of the
                random distributions in NumPy. If the internal state is manually altered,
                the user should know exactly what he/she is doing.
        
                """
        return None
    def gumbel(self, loc, scale, size):
        """
                gumbel(loc=0.0, scale=1.0, size=None)
        
                Gumbel distribution.
        
                Draw samples from a Gumbel distribution with specified location and scale.
                For more information on the Gumbel distribution, see Notes and References
                below.
        
                Parameters
                ----------
                loc : float
                    The location of the mode of the distribution.
                scale : float
                    The scale parameter of the distribution.
                size : tuple of ints
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                out : ndarray
                    The samples
        
                See Also
                --------
                scipy.stats.gumbel_l
                scipy.stats.gumbel_r
                scipy.stats.genextreme
                    probability density function, distribution, or cumulative density
                    function, etc. for each of the above
                weibull
        
                Notes
                -----
                The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value
                Type I) distribution is one of a class of Generalized Extreme Value (GEV)
                distributions used in modeling extreme value problems.  The Gumbel is a
                special case of the Extreme Value Type I distribution for maximums from
                distributions with "exponential-like" tails.
        
                The probability density for the Gumbel distribution is
        
                .. math:: p(x) = \frac{e^{-(x - \mu)/ \beta}}{\beta} e^{ -e^{-(x - \mu)/
                          \beta}},
        
                where :math:`\mu` is the mode, a location parameter, and :math:`\beta` is
                the scale parameter.
        
                The Gumbel (named for German mathematician Emil Julius Gumbel) was used
                very early in the hydrology literature, for modeling the occurrence of
                flood events. It is also used for modeling maximum wind speed and rainfall
                rates.  It is a "fat-tailed" distribution - the probability of an event in
                the tail of the distribution is larger than if one used a Gaussian, hence
                the surprisingly frequent occurrence of 100-year floods. Floods were
                initially modeled as a Gaussian process, which underestimated the frequency
                of extreme events.
        
        
                It is one of a class of extreme value distributions, the Generalized
                Extreme Value (GEV) distributions, which also includes the Weibull and
                Frechet.
        
                The function has a mean of :math:`\mu + 0.57721\beta` and a variance of
                :math:`\frac{\pi^2}{6}\beta^2`.
        
                References
                ----------
                Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University
                Press, 1958.
        
                Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from
                Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,
                2001.
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> mu, beta = 0, 0.1 # location and scale
                >>> s = np.random.gumbel(mu, beta, 1000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, 30, normed=True)
                >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
                ...          * np.exp( -np.exp( -(bins - mu) /beta) ),
                ...          linewidth=2, color='r')
                >>> plt.show()
        
                Show how an extreme value distribution can arise from a Gaussian process
                and compare to a Gaussian:
        
                >>> means = []
                >>> maxima = []
                >>> for i in range(0,1000) :
                ...    a = np.random.normal(mu, beta, 1000)
                ...    means.append(a.mean())
                ...    maxima.append(a.max())
                >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)
                >>> beta = np.std(maxima)*np.pi/np.sqrt(6)
                >>> mu = np.mean(maxima) - 0.57721*beta
                >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
                ...          * np.exp(-np.exp(-(bins - mu)/beta)),
                ...          linewidth=2, color='r')
                >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))
                ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),
                ...          linewidth=2, color='g')
                >>> plt.show()
        
                """
        return ndarray()
    def hypergeometric(self, ngood, nbad, nsample, size):
        """
                hypergeometric(ngood, nbad, nsample, size=None)
        
                Draw samples from a Hypergeometric distribution.
        
                Samples are drawn from a Hypergeometric distribution with specified
                parameters, ngood (ways to make a good selection), nbad (ways to make
                a bad selection), and nsample = number of items sampled, which is less
                than or equal to the sum ngood + nbad.
        
                Parameters
                ----------
                ngood : int or array_like
                    Number of ways to make a good selection.  Must be nonnegative.
                nbad : int or array_like
                    Number of ways to make a bad selection.  Must be nonnegative.
                nsample : int or array_like
                    Number of items sampled.  Must be at least 1 and at most
                    ``ngood + nbad``.
                size : int or tuple of int
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : ndarray or scalar
                    The values are all integers in  [0, n].
        
                See Also
                --------
                scipy.stats.distributions.hypergeom : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Hypergeometric distribution is
        
                .. math:: P(x) = \frac{\binom{m}{n}\binom{N-m}{n-x}}{\binom{N}{n}},
        
                where :math:`0 \le x \le m` and :math:`n+m-N \le x \le n`
        
                for P(x) the probability of x successes, n = ngood, m = nbad, and
                N = number of samples.
        
                Consider an urn with black and white marbles in it, ngood of them
                black and nbad are white. If you draw nsample balls without
                replacement, then the Hypergeometric distribution describes the
                distribution of black balls in the drawn sample.
        
                Note that this distribution is very similar to the Binomial
                distribution, except that in this case, samples are drawn without
                replacement, whereas in the Binomial case samples are drawn with
                replacement (or the sample space is infinite). As the sample space
                becomes large, this distribution approaches the Binomial.
        
                References
                ----------
                .. [1] Lentner, Marvin, "Elementary Applied Statistics", Bogden
                       and Quigley, 1972.
                .. [2] Weisstein, Eric W. "Hypergeometric Distribution." From
                       MathWorld--A Wolfram Web Resource.
                       http://mathworld.wolfram.com/HypergeometricDistribution.html
                .. [3] Wikipedia, "Hypergeometric-distribution",
                       http://en.wikipedia.org/wiki/Hypergeometric-distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> ngood, nbad, nsamp = 100, 2, 10
                # number of good, number of bad, and number of samples
                >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)
                >>> hist(s)
                #   note that it is very unlikely to grab both bad items
        
                Suppose you have an urn with 15 white and 15 black marbles.
                If you pull 15 marbles at random, how likely is it that
                12 or more of them are one color?
        
                >>> s = np.random.hypergeometric(15, 15, 15, 100000)
                >>> sum(s>=12)/100000. + sum(s<=3)/100000.
                #   answer = 0.003 ... pretty unlikely!
        
                """
        return ndarray() if False else float()
    def laplace(self, loc, scale):
        """
                laplace(loc=0.0, scale=1.0, size=None)
        
                Draw samples from the Laplace or double exponential distribution with
                specified location (or mean) and scale (decay).
        
                The Laplace distribution is similar to the Gaussian/normal distribution,
                but is sharper at the peak and has fatter tails. It represents the
                difference between two independent, identically distributed exponential
                random variables.
        
                Parameters
                ----------
                loc : float
                    The position, :math:`\mu`, of the distribution peak.
                scale : float
                    :math:`\lambda`, the exponential decay.
        
                Notes
                -----
                It has the probability density function
        
                .. math:: f(x; \mu, \lambda) = \frac{1}{2\lambda}
                                               \exp\left(-\frac{|x - \mu|}{\lambda}\right).
        
                The first law of Laplace, from 1774, states that the frequency of an error
                can be expressed as an exponential function of the absolute magnitude of
                the error, which leads to the Laplace distribution. For many problems in
                Economics and Health sciences, this distribution seems to model the data
                better than the standard Gaussian distribution
        
        
                References
                ----------
                .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical
                       Functions with Formulas, Graphs, and Mathematical Tables, 9th
                       printing.  New York: Dover, 1972.
        
                .. [2] The Laplace distribution and generalizations
                       By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,
                       Birkhauser, 2001.
        
                .. [3] Weisstein, Eric W. "Laplace Distribution."
                       From MathWorld--A Wolfram Web Resource.
                       http://mathworld.wolfram.com/LaplaceDistribution.html
        
                .. [4] Wikipedia, "Laplace distribution",
                       http://en.wikipedia.org/wiki/Laplace_distribution
        
                Examples
                --------
                Draw samples from the distribution
        
                >>> loc, scale = 0., 1.
                >>> s = np.random.laplace(loc, scale, 1000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, 30, normed=True)
                >>> x = np.arange(-8., 8., .01)
                >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)
                >>> plt.plot(x, pdf)
        
                Plot Gaussian for comparison:
        
                >>> g = (1/(scale * np.sqrt(2 * np.pi)) * 
                ...      np.exp( - (x - loc)**2 / (2 * scale**2) ))
                >>> plt.plot(x,g)
        
                """
        return None
    def logistic(self, loc, scale, size):
        """
                logistic(loc=0.0, scale=1.0, size=None)
        
                Draw samples from a Logistic distribution.
        
                Samples are drawn from a Logistic distribution with specified
                parameters, loc (location or mean, also median), and scale (>0).
        
                Parameters
                ----------
                loc : float
        
                scale : float > 0.
        
                size : {tuple, int}
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : {ndarray, scalar}
                          where the values are all integers in  [0, n].
        
                See Also
                --------
                scipy.stats.distributions.logistic : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Logistic distribution is
        
                .. math:: P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},
        
                where :math:`\mu` = location and :math:`s` = scale.
        
                The Logistic distribution is used in Extreme Value problems where it
                can act as a mixture of Gumbel distributions, in Epidemiology, and by
                the World Chess Federation (FIDE) where it is used in the Elo ranking
                system, assuming the performance of each player is a logistically
                distributed random variable.
        
                References
                ----------
                .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme
                       Values, from Insurance, Finance, Hydrology and Other Fields,
                       Birkhauser Verlag, Basel, pp 132-133.
                .. [2] Weisstein, Eric W. "Logistic Distribution." From
                       MathWorld--A Wolfram Web Resource.
                       http://mathworld.wolfram.com/LogisticDistribution.html
                .. [3] Wikipedia, "Logistic-distribution",
                       http://en.wikipedia.org/wiki/Logistic-distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> loc, scale = 10, 1
                >>> s = np.random.logistic(loc, scale, 10000)
                >>> count, bins, ignored = plt.hist(s, bins=50)
        
                #   plot against distribution
        
                >>> def logist(x, loc, scale):
                ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)
                >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\
                ... logist(bins, loc, scale).max())
                >>> plt.show()
        
                """
        return ndarray()
    def lognormal(self, mean, sigma, size):
        """
                lognormal(mean=0.0, sigma=1.0, size=None)
        
                Return samples drawn from a log-normal distribution.
        
                Draw samples from a log-normal distribution with specified mean,
                standard deviation, and array shape.  Note that the mean and standard
                deviation are not the values for the distribution itself, but of the
                underlying normal distribution it is derived from.
        
                Parameters
                ----------
                mean : float
                    Mean value of the underlying normal distribution
                sigma : float, > 0.
                    Standard deviation of the underlying normal distribution
                size : tuple of ints
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : ndarray or float
                    The desired samples. An array of the same shape as `size` if given,
                    if `size` is None a float is returned.
        
                See Also
                --------
                scipy.stats.lognorm : probability density function, distribution,
                    cumulative density function, etc.
        
                Notes
                -----
                A variable `x` has a log-normal distribution if `log(x)` is normally
                distributed.  The probability density function for the log-normal
                distribution is:
        
                .. math:: p(x) = \frac{1}{\sigma x \sqrt{2\pi}}
                                 e^{(-\frac{(ln(x)-\mu)^2}{2\sigma^2})}
        
                where :math:`\mu` is the mean and :math:`\sigma` is the standard
                deviation of the normally distributed logarithm of the variable.
                A log-normal distribution results if a random variable is the *product*
                of a large number of independent, identically-distributed variables in
                the same way that a normal distribution results if the variable is the
                *sum* of a large number of independent, identically-distributed
                variables.
        
                References
                ----------
                Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal Distributions
                across the Sciences: Keys and Clues," *BioScience*, Vol. 51, No. 5,
                May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf
        
                Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,
                Basel: Birkhauser Verlag, 2001, pp. 31-32.
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> mu, sigma = 3., 1. # mean and standard deviation
                >>> s = np.random.lognormal(mu, sigma, 1000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')
        
                >>> x = np.linspace(min(bins), max(bins), 10000)
                >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
                ...        / (x * sigma * np.sqrt(2 * np.pi)))
        
                >>> plt.plot(x, pdf, linewidth=2, color='r')
                >>> plt.axis('tight')
                >>> plt.show()
        
                Demonstrate that taking the products of random samples from a uniform
                distribution can be fit well by a log-normal probability density function.
        
                >>> # Generate a thousand samples: each is the product of 100 random
                >>> # values, drawn from a normal distribution.
                >>> b = []
                >>> for i in range(1000):
                ...    a = 10. + np.random.random(100)
                ...    b.append(np.product(a))
        
                >>> b = np.array(b) / np.min(b) # scale values to be positive
                >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')
                >>> sigma = np.std(np.log(b))
                >>> mu = np.mean(np.log(b))
        
                >>> x = np.linspace(min(bins), max(bins), 10000)
                >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
                ...        / (x * sigma * np.sqrt(2 * np.pi)))
        
                >>> plt.plot(x, pdf, color='r', linewidth=2)
                >>> plt.show()
        
                """
        return ndarray() if False else float()
    def logseries(self, loc, scale, size):
        """
                logseries(p, size=None)
        
                Draw samples from a Logarithmic Series distribution.
        
                Samples are drawn from a Log Series distribution with specified
                parameter, p (probability, 0 < p < 1).
        
                Parameters
                ----------
                loc : float
        
                scale : float > 0.
        
                size : {tuple, int}
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : {ndarray, scalar}
                          where the values are all integers in  [0, n].
        
                See Also
                --------
                scipy.stats.distributions.logser : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Log Series distribution is
        
                .. math:: P(k) = \frac{-p^k}{k \ln(1-p)},
        
                where p = probability.
        
                The Log Series distribution is frequently used to represent species
                richness and occurrence, first proposed by Fisher, Corbet, and
                Williams in 1943 [2].  It may also be used to model the numbers of
                occupants seen in cars [3].
        
                References
                ----------
                .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional
                       species diversity through the log series distribution of
                       occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,
                       Volume 5, Number 5, September 1999 , pp. 187-195(9).
                .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The
                       relation between the number of species and the number of
                       individuals in a random sample of an animal population.
                       Journal of Animal Ecology, 12:42-58.
                .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small
                       Data Sets, CRC Press, 1994.
                .. [4] Wikipedia, "Logarithmic-distribution",
                       http://en.wikipedia.org/wiki/Logarithmic-distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> a = .6
                >>> s = np.random.logseries(a, 10000)
                >>> count, bins, ignored = plt.hist(s)
        
                #   plot against distribution
        
                >>> def logseries(k, p):
                ...     return -p**k/(k*log(1-p))
                >>> plt.plot(bins, logseries(bins, a)*count.max()/
                             logseries(bins, a).max(), 'r')
                >>> plt.show()
        
                """
        return ndarray()
    def multinomial(self, n, pvals, size):
        """
                multinomial(n, pvals, size=None)
        
                Draw samples from a multinomial distribution.
        
                The multinomial distribution is a multivariate generalisation of the
                binomial distribution.  Take an experiment with one of ``p``
                possible outcomes.  An example of such an experiment is throwing a dice,
                where the outcome can be 1 through 6.  Each sample drawn from the
                distribution represents `n` such experiments.  Its values,
                ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome
                was ``i``.
        
                Parameters
                ----------
                n : int
                    Number of experiments.
                pvals : sequence of floats, length p
                    Probabilities of each of the ``p`` different outcomes.  These
                    should sum to 1 (however, the last element is always assumed to
                    account for the remaining probability, as long as
                    ``sum(pvals[:-1]) <= 1)``.
                size : tuple of ints
                    Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,
                    and the output shape becomes ``(M, N, K, p)``, since each sample
                    has shape ``(p,)``.
        
                Examples
                --------
                Throw a dice 20 times:
        
                >>> np.random.multinomial(20, [1/6.]*6, size=1)
                array([[4, 1, 7, 5, 2, 1]])
        
                It landed 4 times on 1, once on 2, etc.
        
                Now, throw the dice 20 times, and 20 times again:
        
                >>> np.random.multinomial(20, [1/6.]*6, size=2)
                array([[3, 4, 3, 3, 4, 3],
                       [2, 4, 3, 4, 0, 7]])
        
                For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,
                we threw 2 times 1, 4 times 2, etc.
        
                A loaded dice is more likely to land on number 6:
        
                >>> np.random.multinomial(100, [1/7.]*5)
                array([13, 16, 13, 16, 42])
        
                """
        return None
    def multivariate_normal(self, mean, cov, size):
        """
                multivariate_normal(mean, cov[, size])
        
                Draw random samples from a multivariate normal distribution.
        
                The multivariate normal, multinormal or Gaussian distribution is a
                generalization of the one-dimensional normal distribution to higher
                dimensions.  Such a distribution is specified by its mean and
                covariance matrix.  These parameters are analogous to the mean
                (average or "center") and variance (standard deviation, or "width,"
                squared) of the one-dimensional normal distribution.
        
                Parameters
                ----------
                mean : 1-D array_like, of length N
                    Mean of the N-dimensional distribution.
                cov : 2-D array_like, of shape (N, N)
                    Covariance matrix of the distribution.  Must be symmetric and
                    positive semi-definite for "physically meaningful" results.
                size : int or tuple of ints, optional
                    Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are
                    generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because
                    each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.
                    If no shape is specified, a single (`N`-D) sample is returned.
        
                Returns
                -------
                out : ndarray
                    The drawn samples, of shape *size*, if that was provided.  If not,
                    the shape is ``(N,)``.
        
                    In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
                    value drawn from the distribution.
        
                Notes
                -----
                The mean is a coordinate in N-dimensional space, which represents the
                location where samples are most likely to be generated.  This is
                analogous to the peak of the bell curve for the one-dimensional or
                univariate normal distribution.
        
                Covariance indicates the level to which two variables vary together.
                From the multivariate normal distribution, we draw N-dimensional
                samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix
                element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.
                The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its
                "spread").
        
                Instead of specifying the full covariance matrix, popular
                approximations include:
        
                  - Spherical covariance (*cov* is a multiple of the identity matrix)
                  - Diagonal covariance (*cov* has non-negative elements, and only on
                    the diagonal)
        
                This geometrical property can be seen in two dimensions by plotting
                generated data-points:
        
                >>> mean = [0,0]
                >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis
        
                >>> import matplotlib.pyplot as plt
                >>> x,y = np.random.multivariate_normal(mean,cov,5000).T
                >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()
        
                Note that the covariance matrix must be non-negative definite.
        
                References
                ----------
                Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,
                3rd ed., New York: McGraw-Hill, 1991.
        
                Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,
                2nd ed., New York: Wiley, 2001.
        
                Examples
                --------
                >>> mean = (1,2)
                >>> cov = [[1,0],[1,0]]
                >>> x = np.random.multivariate_normal(mean,cov,(3,3))
                >>> x.shape
                (3, 3, 2)
        
                The following is probably true, given that 0.6 is roughly twice the
                standard deviation:
        
                >>> print list( (x[0,0,:] - mean) < 0.6 )
                [True, True]
        
                """
        return ndarray()
    def negative_binomial(self, n, p, size):
        """
                negative_binomial(n, p, size=None)
        
                Draw samples from a negative_binomial distribution.
        
                Samples are drawn from a negative_Binomial distribution with specified
                parameters, `n` trials and `p` probability of success where `n` is an
                integer > 0 and `p` is in the interval [0, 1].
        
                Parameters
                ----------
                n : int
                    Parameter, > 0.
                p : float
                    Parameter, >= 0 and <=1.
                size : int or tuple of ints
                    Output shape. If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : int or ndarray of ints
                    Drawn samples.
        
                Notes
                -----
                The probability density for the Negative Binomial distribution is
        
                .. math:: P(N;n,p) = \binom{N+n-1}{n-1}p^{n}(1-p)^{N},
        
                where :math:`n-1` is the number of successes, :math:`p` is the probability
                of success, and :math:`N+n-1` is the number of trials.
        
                The negative binomial distribution gives the probability of n-1 successes
                and N failures in N+n-1 trials, and success on the (N+n)th trial.
        
                If one throws a die repeatedly until the third time a "1" appears, then the
                probability distribution of the number of non-"1"s that appear before the
                third "1" is a negative binomial distribution.
        
                References
                ----------
                .. [1] Weisstein, Eric W. "Negative Binomial Distribution." From
                       MathWorld--A Wolfram Web Resource.
                       http://mathworld.wolfram.com/NegativeBinomialDistribution.html
                .. [2] Wikipedia, "Negative binomial distribution",
                       http://en.wikipedia.org/wiki/Negative_binomial_distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                A real world example. A company drills wild-cat oil exploration wells, each
                with an estimated probability of success of 0.1.  What is the probability
                of having one success for each successive well, that is what is the
                probability of a single success after drilling 5 wells, after 6 wells,
                etc.?
        
                >>> s = np.random.negative_binomial(1, 0.1, 100000)
                >>> for i in range(1, 11):
                ...    probability = sum(s<i) / 100000.
                ...    print i, "wells drilled, probability of one success =", probability
        
                """
        return int() if False else ndarray()
    def noncentral_chisquare(self, df, nonc, size):
        """
                noncentral_chisquare(df, nonc, size=None)
        
                Draw samples from a noncentral chi-square distribution.
        
                The noncentral :math:`\chi^2` distribution is a generalisation of
                the :math:`\chi^2` distribution.
        
                Parameters
                ----------
                df : int
                    Degrees of freedom, should be >= 1.
                nonc : float
                    Non-centrality, should be > 0.
                size : int or tuple of ints
                    Shape of the output.
        
                Notes
                -----
                The probability density function for the noncentral Chi-square distribution
                is
        
                .. math:: P(x;df,nonc) = \sum^{\infty}_{i=0}
                                       \frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),
        
                where :math:`Y_{q}` is the Chi-square with q degrees of freedom.
        
                In Delhi (2007), it is noted that the noncentral chi-square is useful in
                bombing and coverage problems, the probability of killing the point target
                given by the noncentral chi-squared distribution.
        
                References
                ----------
                .. [1] Delhi, M.S. Holla, "On a noncentral chi-square distribution in the
                       analysis of weapon systems effectiveness", Metrika, Volume 15,
                       Number 1 / December, 1970.
                .. [2] Wikipedia, "Noncentral chi-square distribution"
                       http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution
        
                Examples
                --------
                Draw values from the distribution and plot the histogram
        
                >>> import matplotlib.pyplot as plt
                >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
                ...                   bins=200, normed=True)
                >>> plt.show()
        
                Draw values from a noncentral chisquare with very small noncentrality,
                and compare to a chisquare.
        
                >>> plt.figure()
                >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),
                ...                   bins=np.arange(0., 25, .1), normed=True)
                >>> values2 = plt.hist(np.random.chisquare(3, 100000),
                ...                    bins=np.arange(0., 25, .1), normed=True)
                >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')
                >>> plt.show()
        
                Demonstrate how large values of non-centrality lead to a more symmetric
                distribution.
        
                >>> plt.figure()
                >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
                ...                   bins=200, normed=True)
                >>> plt.show()
        
                """
        return None
    def noncentral_f(self, dfnum, dfden, nonc, size):
        """
                noncentral_f(dfnum, dfden, nonc, size=None)
        
                Draw samples from the noncentral F distribution.
        
                Samples are drawn from an F distribution with specified parameters,
                `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of
                freedom in denominator), where both parameters > 1.
                `nonc` is the non-centrality parameter.
        
                Parameters
                ----------
                dfnum : int
                    Parameter, should be > 1.
                dfden : int
                    Parameter, should be > 1.
                nonc : float
                    Parameter, should be >= 0.
                size : int or tuple of ints
                    Output shape. If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : scalar or ndarray
                    Drawn samples.
        
                Notes
                -----
                When calculating the power of an experiment (power = probability of
                rejecting the null hypothesis when a specific alternative is true) the
                non-central F statistic becomes important.  When the null hypothesis is
                true, the F statistic follows a central F distribution. When the null
                hypothesis is not true, then it follows a non-central F statistic.
        
                References
                ----------
                Weisstein, Eric W. "Noncentral F-Distribution." From MathWorld--A Wolfram
                Web Resource.  http://mathworld.wolfram.com/NoncentralF-Distribution.html
        
                Wikipedia, "Noncentral F distribution",
                http://en.wikipedia.org/wiki/Noncentral_F-distribution
        
                Examples
                --------
                In a study, testing for a specific alternative to the null hypothesis
                requires use of the Noncentral F distribution. We need to calculate the
                area in the tail of the distribution that exceeds the value of the F
                distribution for the null hypothesis.  We'll plot the two probability
                distributions for comparison.
        
                >>> dfnum = 3 # between group deg of freedom
                >>> dfden = 20 # within groups degrees of freedom
                >>> nonc = 3.0
                >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)
                >>> NF = np.histogram(nc_vals, bins=50, normed=True)
                >>> c_vals = np.random.f(dfnum, dfden, 1000000)
                >>> F = np.histogram(c_vals, bins=50, normed=True)
                >>> plt.plot(F[1][1:], F[0])
                >>> plt.plot(NF[1][1:], NF[0])
                >>> plt.show()
        
                """
        return float() if False else ndarray()
    def normal(self, loc, scale, size):
        """
                normal(loc=0.0, scale=1.0, size=None)
        
                Draw random samples from a normal (Gaussian) distribution.
        
                The probability density function of the normal distribution, first
                derived by De Moivre and 200 years later by both Gauss and Laplace
                independently [2]_, is often called the bell curve because of
                its characteristic shape (see the example below).
        
                The normal distributions occurs often in nature.  For example, it
                describes the commonly occurring distribution of samples influenced
                by a large number of tiny, random disturbances, each with its own
                unique distribution [2]_.
        
                Parameters
                ----------
                loc : float
                    Mean ("centre") of the distribution.
                scale : float
                    Standard deviation (spread or "width") of the distribution.
                size : tuple of ints
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                See Also
                --------
                scipy.stats.distributions.norm : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Gaussian distribution is
        
                .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
                                 e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
        
                where :math:`\mu` is the mean and :math:`\sigma` the standard deviation.
                The square of the standard deviation, :math:`\sigma^2`, is called the
                variance.
        
                The function has its peak at the mean, and its "spread" increases with
                the standard deviation (the function reaches 0.607 times its maximum at
                :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
                `numpy.random.normal` is more likely to return samples lying close to the
                mean, rather than those far away.
        
                References
                ----------
                .. [1] Wikipedia, "Normal distribution",
                       http://en.wikipedia.org/wiki/Normal_distribution
                .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability, Random
                       Variables and Random Signal Principles", 4th ed., 2001,
                       pp. 51, 51, 125.
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> mu, sigma = 0, 0.1 # mean and standard deviation
                >>> s = np.random.normal(mu, sigma, 1000)
        
                Verify the mean and the variance:
        
                >>> abs(mu - np.mean(s)) < 0.01
                True
        
                >>> abs(sigma - np.std(s, ddof=1)) < 0.01
                True
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, 30, normed=True)
                >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
                ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
                ...          linewidth=2, color='r')
                >>> plt.show()
        
                """
        return None
    def pareto(self, shape, size):
        """
                pareto(a, size=None)
        
                Draw samples from a Pareto II or Lomax distribution with specified shape.
        
                The Lomax or Pareto II distribution is a shifted Pareto distribution. The
                classical Pareto distribution can be obtained from the Lomax distribution
                by adding the location parameter m, see below. The smallest value of the
                Lomax distribution is zero while for the classical Pareto distribution it
                is m, where the standard Pareto distribution has location m=1.
                Lomax can also be considered as a simplified version of the Generalized
                Pareto distribution (available in SciPy), with the scale set to one and
                the location set to zero.
        
                The Pareto distribution must be greater than zero, and is unbounded above.
                It is also known as the "80-20 rule".  In this distribution, 80 percent of
                the weights are in the lowest 20 percent of the range, while the other 20
                percent fill the remaining 80 percent of the range.
        
                Parameters
                ----------
                shape : float, > 0.
                    Shape of the distribution.
                size : tuple of ints
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                See Also
                --------
                scipy.stats.distributions.lomax.pdf : probability density function,
                    distribution or cumulative density function, etc.
                scipy.stats.distributions.genpareto.pdf : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Pareto distribution is
        
                .. math:: p(x) = \frac{am^a}{x^{a+1}}
        
                where :math:`a` is the shape and :math:`m` the location
        
                The Pareto distribution, named after the Italian economist Vilfredo Pareto,
                is a power law probability distribution useful in many real world problems.
                Outside the field of economics it is generally referred to as the Bradford
                distribution. Pareto developed the distribution to describe the
                distribution of wealth in an economy.  It has also found use in insurance,
                web page access statistics, oil field sizes, and many other problems,
                including the download frequency for projects in Sourceforge [1].  It is
                one of the so-called "fat-tailed" distributions.
        
        
                References
                ----------
                .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of
                       Sourceforge projects.
                .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.
                .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme
                       Values, Birkhauser Verlag, Basel, pp 23-30.
                .. [4] Wikipedia, "Pareto distribution",
                       http://en.wikipedia.org/wiki/Pareto_distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> a, m = 3., 1. # shape and mode
                >>> s = np.random.pareto(a, 1000) + m
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')
                >>> fit = a*m**a/bins**(a+1)
                >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')
                >>> plt.show()
        
                """
        return None
    def permutation(self, x):
        """
                permutation(x)
        
                Randomly permute a sequence, or return a permuted range.
        
                If `x` is a multi-dimensional array, it is only shuffled along its
                first index.
        
                Parameters
                ----------
                x : int or array_like
                    If `x` is an integer, randomly permute ``np.arange(x)``.
                    If `x` is an array, make a copy and shuffle the elements
                    randomly.
        
                Returns
                -------
                out : ndarray
                    Permuted sequence or array range.
        
                Examples
                --------
                >>> np.random.permutation(10)
                array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])
        
                >>> np.random.permutation([1, 4, 9, 12, 15])
                array([15,  1,  9,  4, 12])
        
                >>> arr = np.arange(9).reshape((3, 3))
                >>> np.random.permutation(arr)
                array([[6, 7, 8],
                       [0, 1, 2],
                       [3, 4, 5]])
        
                """
        return ndarray()
    def poisson(self, lam, size):
        """
                poisson(lam=1.0, size=None)
        
                Draw samples from a Poisson distribution.
        
                The Poisson distribution is the limit of the Binomial
                distribution for large N.
        
                Parameters
                ----------
                lam : float
                    Expectation of interval, should be >= 0.
                size : int or tuple of ints, optional
                    Output shape. If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Notes
                -----
                The Poisson distribution
        
                .. math:: f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}
        
                For events with an expected separation :math:`\lambda` the Poisson
                distribution :math:`f(k; \lambda)` describes the probability of
                :math:`k` events occurring within the observed interval :math:`\lambda`.
        
                Because the output is limited to the range of the C long type, a
                ValueError is raised when `lam` is within 10 sigma of the maximum
                representable value.
        
                References
                ----------
                .. [1] Weisstein, Eric W. "Poisson Distribution." From MathWorld--A Wolfram
                       Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html
                .. [2] Wikipedia, "Poisson distribution",
                   http://en.wikipedia.org/wiki/Poisson_distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> import numpy as np
                >>> s = np.random.poisson(5, 10000)
        
                Display histogram of the sample:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, 14, normed=True)
                >>> plt.show()
        
                """
        return None
    def power(self, a, size):
        """
                power(a, size=None)
        
                Draws samples in [0, 1] from a power distribution with positive
                exponent a - 1.
        
                Also known as the power function distribution.
        
                Parameters
                ----------
                a : float
                    parameter, > 0
                size : tuple of ints
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                            ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : {ndarray, scalar}
                    The returned samples lie in [0, 1].
        
                Raises
                ------
                ValueError
                    If a<1.
        
                Notes
                -----
                The probability density function is
        
                .. math:: P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0.
        
                The power function distribution is just the inverse of the Pareto
                distribution. It may also be seen as a special case of the Beta
                distribution.
        
                It is used, for example, in modeling the over-reporting of insurance
                claims.
        
                References
                ----------
                .. [1] Christian Kleiber, Samuel Kotz, "Statistical size distributions
                       in economics and actuarial sciences", Wiley, 2003.
                .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:
                       Dataplot Reference Manual, Volume 2: Let Subcommands and Library
                       Functions", National Institute of Standards and Technology Handbook
                       Series, June 2003.
                       http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> a = 5. # shape
                >>> samples = 1000
                >>> s = np.random.power(a, samples)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, bins=30)
                >>> x = np.linspace(0, 1, 100)
                >>> y = a*x**(a-1.)
                >>> normed_y = samples*np.diff(bins)[0]*y
                >>> plt.plot(x, normed_y)
                >>> plt.show()
        
                Compare the power function distribution to the inverse of the Pareto.
        
                >>> from scipy import stats
                >>> rvs = np.random.power(5, 1000000)
                >>> rvsp = np.random.pareto(5, 1000000)
                >>> xx = np.linspace(0,1,100)
                >>> powpdf = stats.powerlaw.pdf(xx,5)
        
                >>> plt.figure()
                >>> plt.hist(rvs, bins=50, normed=True)
                >>> plt.plot(xx,powpdf,'r-')
                >>> plt.title('np.random.power(5)')
        
                >>> plt.figure()
                >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)
                >>> plt.plot(xx,powpdf,'r-')
                >>> plt.title('inverse of 1 + np.random.pareto(5)')
        
                >>> plt.figure()
                >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)
                >>> plt.plot(xx,powpdf,'r-')
                >>> plt.title('inverse of stats.pareto(5)')
        
                """
        return ndarray()
    print_function = instance()
    def rand(self, d0d1more_argsdn):
        """
                rand(d0, d1, ..., dn)
        
                Random values in a given shape.
        
                Create an array of the given shape and propagate it with
                random samples from a uniform distribution
                over ``[0, 1)``.
        
                Parameters
                ----------
                d0, d1, ..., dn : int, optional
                    The dimensions of the returned array, should all be positive.
                    If no argument is given a single Python float is returned.
        
                Returns
                -------
                out : ndarray, shape ``(d0, d1, ..., dn)``
                    Random values.
        
                See Also
                --------
                random
        
                Notes
                -----
                This is a convenience function. If you want an interface that
                takes a shape-tuple as the first argument, refer to
                np.random.random_sample .
        
                Examples
                --------
                >>> np.random.rand(3,2)
                array([[ 0.14022471,  0.96360618],  #random
                       [ 0.37601032,  0.25528411],  #random
                       [ 0.49313049,  0.94909878]]) #random
        
                """
        return None
    def randint(self, low, high, size):
        """
                randint(low, high=None, size=None)
        
                Return random integers from `low` (inclusive) to `high` (exclusive).
        
                Return random integers from the "discrete uniform" distribution in the
                "half-open" interval [`low`, `high`). If `high` is None (the default),
                then results are from [0, `low`).
        
                Parameters
                ----------
                low : int
                    Lowest (signed) integer to be drawn from the distribution (unless
                    ``high=None``, in which case this parameter is the *highest* such
                    integer).
                high : int, optional
                    If provided, one above the largest (signed) integer to be drawn
                    from the distribution (see above for behavior if ``high=None``).
                size : int or tuple of ints, optional
                    Output shape. Default is None, in which case a single int is
                    returned.
        
                Returns
                -------
                out : int or ndarray of ints
                    `size`-shaped array of random integers from the appropriate
                    distribution, or a single such random int if `size` not provided.
        
                See Also
                --------
                random.random_integers : similar to `randint`, only for the closed
                    interval [`low`, `high`], and 1 is the lowest value if `high` is
                    omitted. In particular, this other one is the one to use to generate
                    uniformly distributed discrete non-integers.
        
                Examples
                --------
                >>> np.random.randint(2, size=10)
                array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
                >>> np.random.randint(1, size=10)
                array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
        
                Generate a 2 x 4 array of ints between 0 and 4, inclusive:
        
                >>> np.random.randint(5, size=(2, 4))
                array([[4, 0, 2, 1],
                       [3, 2, 2, 0]])
        
                """
        return int() if False else ndarray()
    def randn(self, d0d1more_argsdn):
        """
                randn(d0, d1, ..., dn)
        
                Return a sample (or samples) from the "standard normal" distribution.
        
                If positive, int_like or int-convertible arguments are provided,
                `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
                with random floats sampled from a univariate "normal" (Gaussian)
                distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
                floats, they are first converted to integers by truncation). A single
                float randomly sampled from the distribution is returned if no
                argument is provided.
        
                This is a convenience function.  If you want an interface that takes a
                tuple as the first argument, use `numpy.random.standard_normal` instead.
        
                Parameters
                ----------
                d0, d1, ..., dn : int, optional
                    The dimensions of the returned array, should be all positive.
                    If no argument is given a single Python float is returned.
        
                Returns
                -------
                Z : ndarray or float
                    A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
                    the standard normal distribution, or a single such float if
                    no parameters were supplied.
        
                See Also
                --------
                random.standard_normal : Similar, but takes a tuple as its argument.
        
                Notes
                -----
                For random samples from :math:`N(\mu, \sigma^2)`, use:
        
                ``sigma * np.random.randn(...) + mu``
        
                Examples
                --------
                >>> np.random.randn()
                2.1923875335537315 #random
        
                Two-by-four array of samples from N(3, 6.25):
        
                >>> 2.5 * np.random.randn(2, 4) + 3
                array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
                       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random
        
                """
        return ndarray() if False else float()
    def random_sample(self, size):
        """
                random_sample(size=None)
        
                Return random floats in the half-open interval [0.0, 1.0).
        
                Results are from the "continuous uniform" distribution over the
                stated interval.  To sample :math:`Unif[a, b), b > a` multiply
                the output of `random_sample` by `(b-a)` and add `a`::
        
                  (b - a) * random_sample() + a
        
                Parameters
                ----------
                size : int or tuple of ints, optional
                    Defines the shape of the returned array of random floats. If None
                    (the default), returns a single float.
        
                Returns
                -------
                out : float or ndarray of floats
                    Array of random floats of shape `size` (unless ``size=None``, in which
                    case a single float is returned).
        
                Examples
                --------
                >>> np.random.random_sample()
                0.47108547995356098
                >>> type(np.random.random_sample())
                <type 'float'>
                >>> np.random.random_sample((5,))
                array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])
        
                Three-by-two array of random numbers from [-5, 0):
        
                >>> 5 * np.random.random_sample((3, 2)) - 5
                array([[-3.99149989, -0.52338984],
                       [-2.99091858, -0.79479508],
                       [-1.23204345, -1.75224494]])
        
                """
        return float() if False else ndarray()
    def random_integers(self, low, high, size):
        """
                random_integers(low, high=None, size=None)
        
                Return random integers between `low` and `high`, inclusive.
        
                Return random integers from the "discrete uniform" distribution in the
                closed interval [`low`, `high`].  If `high` is None (the default),
                then results are from [1, `low`].
        
                Parameters
                ----------
                low : int
                    Lowest (signed) integer to be drawn from the distribution (unless
                    ``high=None``, in which case this parameter is the *highest* such
                    integer).
                high : int, optional
                    If provided, the largest (signed) integer to be drawn from the
                    distribution (see above for behavior if ``high=None``).
                size : int or tuple of ints, optional
                    Output shape. Default is None, in which case a single int is returned.
        
                Returns
                -------
                out : int or ndarray of ints
                    `size`-shaped array of random integers from the appropriate
                    distribution, or a single such random int if `size` not provided.
        
                See Also
                --------
                random.randint : Similar to `random_integers`, only for the half-open
                    interval [`low`, `high`), and 0 is the lowest value if `high` is
                    omitted.
        
                Notes
                -----
                To sample from N evenly spaced floating-point numbers between a and b,
                use::
        
                  a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)
        
                Examples
                --------
                >>> np.random.random_integers(5)
                4
                >>> type(np.random.random_integers(5))
                <type 'int'>
                >>> np.random.random_integers(5, size=(3.,2.))
                array([[5, 4],
                       [3, 3],
                       [4, 5]])
        
                Choose five random numbers from the set of five evenly-spaced
                numbers between 0 and 2.5, inclusive (*i.e.*, from the set
                :math:`{0, 5/8, 10/8, 15/8, 20/8}`):
        
                >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.
                array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])
        
                Roll two six sided dice 1000 times and sum the results:
        
                >>> d1 = np.random.random_integers(1, 6, 1000)
                >>> d2 = np.random.random_integers(1, 6, 1000)
                >>> dsums = d1 + d2
        
                Display results as a histogram:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)
                >>> plt.show()
        
                """
        return int() if False else ndarray()
    def random_sample(self, size):
        """
                random_sample(size=None)
        
                Return random floats in the half-open interval [0.0, 1.0).
        
                Results are from the "continuous uniform" distribution over the
                stated interval.  To sample :math:`Unif[a, b), b > a` multiply
                the output of `random_sample` by `(b-a)` and add `a`::
        
                  (b - a) * random_sample() + a
        
                Parameters
                ----------
                size : int or tuple of ints, optional
                    Defines the shape of the returned array of random floats. If None
                    (the default), returns a single float.
        
                Returns
                -------
                out : float or ndarray of floats
                    Array of random floats of shape `size` (unless ``size=None``, in which
                    case a single float is returned).
        
                Examples
                --------
                >>> np.random.random_sample()
                0.47108547995356098
                >>> type(np.random.random_sample())
                <type 'float'>
                >>> np.random.random_sample((5,))
                array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])
        
                Three-by-two array of random numbers from [-5, 0):
        
                >>> 5 * np.random.random_sample((3, 2)) - 5
                array([[-3.99149989, -0.52338984],
                       [-2.99091858, -0.79479508],
                       [-1.23204345, -1.75224494]])
        
                """
        return float() if False else ndarray()
    def random_sample(self, size):
        """
                random_sample(size=None)
        
                Return random floats in the half-open interval [0.0, 1.0).
        
                Results are from the "continuous uniform" distribution over the
                stated interval.  To sample :math:`Unif[a, b), b > a` multiply
                the output of `random_sample` by `(b-a)` and add `a`::
        
                  (b - a) * random_sample() + a
        
                Parameters
                ----------
                size : int or tuple of ints, optional
                    Defines the shape of the returned array of random floats. If None
                    (the default), returns a single float.
        
                Returns
                -------
                out : float or ndarray of floats
                    Array of random floats of shape `size` (unless ``size=None``, in which
                    case a single float is returned).
        
                Examples
                --------
                >>> np.random.random_sample()
                0.47108547995356098
                >>> type(np.random.random_sample())
                <type 'float'>
                >>> np.random.random_sample((5,))
                array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])
        
                Three-by-two array of random numbers from [-5, 0):
        
                >>> 5 * np.random.random_sample((3, 2)) - 5
                array([[-3.99149989, -0.52338984],
                       [-2.99091858, -0.79479508],
                       [-1.23204345, -1.75224494]])
        
                """
        return float() if False else ndarray()
    def rayleigh(self, scale, size):
        """
                rayleigh(scale=1.0, size=None)
        
                Draw samples from a Rayleigh distribution.
        
                The :math:`\chi` and Weibull distributions are generalizations of the
                Rayleigh.
        
                Parameters
                ----------
                scale : scalar
                    Scale, also equals the mode. Should be >= 0.
                size : int or tuple of ints, optional
                    Shape of the output. Default is None, in which case a single
                    value is returned.
        
                Notes
                -----
                The probability density function for the Rayleigh distribution is
        
                .. math:: P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}
        
                The Rayleigh distribution arises if the wind speed and wind direction are
                both gaussian variables, then the vector wind velocity forms a Rayleigh
                distribution. The Rayleigh distribution is used to model the expected
                output from wind turbines.
        
                References
                ----------
                .. [1] Brighton Webs Ltd., Rayleigh Distribution,
                      http://www.brighton-webs.co.uk/distributions/rayleigh.asp
                .. [2] Wikipedia, "Rayleigh distribution"
                      http://en.wikipedia.org/wiki/Rayleigh_distribution
        
                Examples
                --------
                Draw values from the distribution and plot the histogram
        
                >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)
        
                Wave heights tend to follow a Rayleigh distribution. If the mean wave
                height is 1 meter, what fraction of waves are likely to be larger than 3
                meters?
        
                >>> meanvalue = 1
                >>> modevalue = np.sqrt(2 / np.pi) * meanvalue
                >>> s = np.random.rayleigh(modevalue, 1000000)
        
                The percentage of waves larger than 3 meters is:
        
                >>> 100.*sum(s>3)/1000000.
                0.087300000000000003
        
                """
        return None
    def random_sample(self, size):
        """
                random_sample(size=None)
        
                Return random floats in the half-open interval [0.0, 1.0).
        
                Results are from the "continuous uniform" distribution over the
                stated interval.  To sample :math:`Unif[a, b), b > a` multiply
                the output of `random_sample` by `(b-a)` and add `a`::
        
                  (b - a) * random_sample() + a
        
                Parameters
                ----------
                size : int or tuple of ints, optional
                    Defines the shape of the returned array of random floats. If None
                    (the default), returns a single float.
        
                Returns
                -------
                out : float or ndarray of floats
                    Array of random floats of shape `size` (unless ``size=None``, in which
                    case a single float is returned).
        
                Examples
                --------
                >>> np.random.random_sample()
                0.47108547995356098
                >>> type(np.random.random_sample())
                <type 'float'>
                >>> np.random.random_sample((5,))
                array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])
        
                Three-by-two array of random numbers from [-5, 0):
        
                >>> 5 * np.random.random_sample((3, 2)) - 5
                array([[-3.99149989, -0.52338984],
                       [-2.99091858, -0.79479508],
                       [-1.23204345, -1.75224494]])
        
                """
        return float() if False else ndarray()
    def seed(self, seed):
        """
                seed(seed=None)
        
                Seed the generator.
        
                This method is called when `RandomState` is initialized. It can be
                called again to re-seed the generator. For details, see `RandomState`.
        
                Parameters
                ----------
                seed : int or array_like, optional
                    Seed for `RandomState`.
        
                See Also
                --------
                RandomState
        
                """
        return None
    def set_state(self, state):
        """
                set_state(state)
        
                Set the internal state of the generator from a tuple.
        
                For use if one has reason to manually (re-)set the internal state of the
                "Mersenne Twister"[1]_ pseudo-random number generating algorithm.
        
                Parameters
                ----------
                state : tuple(str, ndarray of 624 uints, int, int, float)
                    The `state` tuple has the following items:
        
                    1. the string 'MT19937', specifying the Mersenne Twister algorithm.
                    2. a 1-D array of 624 unsigned integers ``keys``.
                    3. an integer ``pos``.
                    4. an integer ``has_gauss``.
                    5. a float ``cached_gaussian``.
        
                Returns
                -------
                out : None
                    Returns 'None' on success.
        
                See Also
                --------
                get_state
        
                Notes
                -----
                `set_state` and `get_state` are not needed to work with any of the
                random distributions in NumPy. If the internal state is manually altered,
                the user should know exactly what he/she is doing.
        
                For backwards compatibility, the form (str, array of 624 uints, int) is
                also accepted although it is missing some information about the cached
                Gaussian value: ``state = ('MT19937', keys, pos)``.
        
                References
                ----------
                .. [1] M. Matsumoto and T. Nishimura, "Mersenne Twister: A
                   623-dimensionally equidistributed uniform pseudorandom number
                   generator," *ACM Trans. on Modeling and Computer Simulation*,
                   Vol. 8, No. 1, pp. 3-30, Jan. 1998.
        
                """
        return None
    def shuffle(self, x):
        """
                shuffle(x)
        
                Modify a sequence in-place by shuffling its contents.
        
                Parameters
                ----------
                x : array_like
                    The array or list to be shuffled.
        
                Returns
                -------
                None
        
                Examples
                --------
                >>> arr = np.arange(10)
                >>> np.random.shuffle(arr)
                >>> arr
                [1 7 5 2 9 4 3 6 0 8]
        
                This function only shuffles the array along the first index of a
                multi-dimensional array:
        
                >>> arr = np.arange(9).reshape((3, 3))
                >>> np.random.shuffle(arr)
                >>> arr
                array([[3, 4, 5],
                       [6, 7, 8],
                       [0, 1, 2]])
        
                """
        return None
    def standard_cauchy(self, size):
        """
                standard_cauchy(size=None)
        
                Standard Cauchy distribution with mode = 0.
        
                Also known as the Lorentz distribution.
        
                Parameters
                ----------
                size : int or tuple of ints
                    Shape of the output.
        
                Returns
                -------
                samples : ndarray or scalar
                    The drawn samples.
        
                Notes
                -----
                The probability density function for the full Cauchy distribution is
        
                .. math:: P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+
                          (\frac{x-x_0}{\gamma})^2 \bigr] }
        
                and the Standard Cauchy distribution just sets :math:`x_0=0` and
                :math:`\gamma=1`
        
                The Cauchy distribution arises in the solution to the driven harmonic
                oscillator problem, and also describes spectral line broadening. It
                also describes the distribution of values at which a line tilted at
                a random angle will cut the x axis.
        
                When studying hypothesis tests that assume normality, seeing how the
                tests perform on data from a Cauchy distribution is a good indicator of
                their sensitivity to a heavy-tailed distribution, since the Cauchy looks
                very much like a Gaussian distribution, but with heavier tails.
        
                References
                ----------
                .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy
                      Distribution",
                      http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
                .. [2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A
                      Wolfram Web Resource.
                      http://mathworld.wolfram.com/CauchyDistribution.html
                .. [3] Wikipedia, "Cauchy distribution"
                      http://en.wikipedia.org/wiki/Cauchy_distribution
        
                Examples
                --------
                Draw samples and plot the distribution:
        
                >>> s = np.random.standard_cauchy(1000000)
                >>> s = s[(s>-25) & (s<25)]  # truncate distribution so it plots well
                >>> plt.hist(s, bins=100)
                >>> plt.show()
        
                """
        return ndarray() if False else float()
    def standard_exponential(self, size):
        """
                standard_exponential(size=None)
        
                Draw samples from the standard exponential distribution.
        
                `standard_exponential` is identical to the exponential distribution
                with a scale parameter of 1.
        
                Parameters
                ----------
                size : int or tuple of ints
                    Shape of the output.
        
                Returns
                -------
                out : float or ndarray
                    Drawn samples.
        
                Examples
                --------
                Output a 3x8000 array:
        
                >>> n = np.random.standard_exponential((3, 8000))
        
                """
        return float() if False else ndarray()
    def standard_gamma(self, shape, size):
        """
                standard_gamma(shape, size=None)
        
                Draw samples from a Standard Gamma distribution.
        
                Samples are drawn from a Gamma distribution with specified parameters,
                shape (sometimes designated "k") and scale=1.
        
                Parameters
                ----------
                shape : float
                    Parameter, should be > 0.
                size : int or tuple of ints
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : ndarray or scalar
                    The drawn samples.
        
                See Also
                --------
                scipy.stats.distributions.gamma : probability density function,
                    distribution or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Gamma distribution is
        
                .. math:: p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},
        
                where :math:`k` is the shape and :math:`\theta` the scale,
                and :math:`\Gamma` is the Gamma function.
        
                The Gamma distribution is often used to model the times to failure of
                electronic components, and arises naturally in processes for which the
                waiting times between Poisson distributed events are relevant.
        
                References
                ----------
                .. [1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A
                       Wolfram Web Resource.
                       http://mathworld.wolfram.com/GammaDistribution.html
                .. [2] Wikipedia, "Gamma-distribution",
                       http://en.wikipedia.org/wiki/Gamma-distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> shape, scale = 2., 1. # mean and width
                >>> s = np.random.standard_gamma(shape, 1000000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> import scipy.special as sps
                >>> count, bins, ignored = plt.hist(s, 50, normed=True)
                >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \
                ...                       (sps.gamma(shape) * scale**shape))
                >>> plt.plot(bins, y, linewidth=2, color='r')
                >>> plt.show()
        
                """
        return ndarray() if False else float()
    def standard_normal(self, size):
        """
                standard_normal(size=None)
        
                Returns samples from a Standard Normal distribution (mean=0, stdev=1).
        
                Parameters
                ----------
                size : int or tuple of ints, optional
                    Output shape. Default is None, in which case a single value is
                    returned.
        
                Returns
                -------
                out : float or ndarray
                    Drawn samples.
        
                Examples
                --------
                >>> s = np.random.standard_normal(8000)
                >>> s
                array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311, #random
                       -0.38672696, -0.4685006 ])                               #random
                >>> s.shape
                (8000,)
                >>> s = np.random.standard_normal(size=(3, 4, 2))
                >>> s.shape
                (3, 4, 2)
        
                """
        return float() if False else ndarray()
    def standard_t(self, df, size):
        """
                standard_t(df, size=None)
        
                Standard Student's t distribution with df degrees of freedom.
        
                A special case of the hyperbolic distribution.
                As `df` gets large, the result resembles that of the standard normal
                distribution (`standard_normal`).
        
                Parameters
                ----------
                df : int
                    Degrees of freedom, should be > 0.
                size : int or tuple of ints, optional
                    Output shape. Default is None, in which case a single value is
                    returned.
        
                Returns
                -------
                samples : ndarray or scalar
                    Drawn samples.
        
                Notes
                -----
                The probability density function for the t distribution is
        
                .. math:: P(x, df) = \frac{\Gamma(\frac{df+1}{2})}{\sqrt{\pi df}
                          \Gamma(\frac{df}{2})}\Bigl( 1+\frac{x^2}{df} \Bigr)^{-(df+1)/2}
        
                The t test is based on an assumption that the data come from a Normal
                distribution. The t test provides a way to test whether the sample mean
                (that is the mean calculated from the data) is a good estimate of the true
                mean.
        
                The derivation of the t-distribution was forst published in 1908 by William
                Gisset while working for the Guinness Brewery in Dublin. Due to proprietary
                issues, he had to publish under a pseudonym, and so he used the name
                Student.
        
                References
                ----------
                .. [1] Dalgaard, Peter, "Introductory Statistics With R",
                       Springer, 2002.
                .. [2] Wikipedia, "Student's t-distribution"
                       http://en.wikipedia.org/wiki/Student's_t-distribution
        
                Examples
                --------
                From Dalgaard page 83 [1]_, suppose the daily energy intake for 11
                women in Kj is:
        
                >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \
                ...                    7515, 8230, 8770])
        
                Does their energy intake deviate systematically from the recommended
                value of 7725 kJ?
        
                We have 10 degrees of freedom, so is the sample mean within 95% of the
                recommended value?
        
                >>> s = np.random.standard_t(10, size=100000)
                >>> np.mean(intake)
                6753.636363636364
                >>> intake.std(ddof=1)
                1142.1232221373727
        
                Calculate the t statistic, setting the ddof parameter to the unbiased
                value so the divisor in the standard deviation will be degrees of
                freedom, N-1.
        
                >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))
                >>> import matplotlib.pyplot as plt
                >>> h = plt.hist(s, bins=100, normed=True)
        
                For a one-sided t-test, how far out in the distribution does the t
                statistic appear?
        
                >>> >>> np.sum(s<t) / float(len(s))
                0.0090699999999999999  #random
        
                So the p-value is about 0.009, which says the null hypothesis has a
                probability of about 99% of being true.
        
                """
        return ndarray() if False else float()
    def test(self=None, label="fast", verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None):
        """
                Run tests for module using nose.
        
                Parameters
                ----------
                label : {'fast', 'full', '', attribute identifier}, optional
                    Identifies the tests to run. This can be a string to pass to
                    the nosetests executable with the '-A' option, or one of several
                    special values.  Special values are:
                    * 'fast' - the default - which corresponds to the ``nosetests -A``
                      option of 'not slow'.
                    * 'full' - fast (as above) and slow tests as in the
                      'no -A' option to nosetests - this is the same as ''.
                    * None or '' - run all tests.
                    attribute_identifier - string passed directly to nosetests as '-A'.
                verbose : int, optional
                    Verbosity value for test outputs, in the range 1-10. Default is 1.
                extra_argv : list, optional
                    List with any extra arguments to pass to nosetests.
                doctests : bool, optional
                    If True, run doctests in module. Default is False.
                coverage : bool, optional
                    If True, report coverage of NumPy code. Default is False.
                    (This requires the `coverage module:
                     <http://nedbatchelder.com/code/modules/coverage.html>`_).
                raise_warnings : str or sequence of warnings, optional
                    This specifies which warnings to configure as 'raise' instead
                    of 'warn' during the test execution.  Valid strings are:
        
                      - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
                      - "release" : equals ``()``, don't raise on any warnings.
        
                Returns
                -------
                result : object
                    Returns the result of running the tests as a
                    ``nose.result.TextTestResult`` object.
        
                Notes
                -----
                Each NumPy module exposes `test` in its namespace to run all tests for it.
                For example, to run all tests for numpy.lib:
        
                >>> np.lib.test() #doctest: +SKIP
        
                Examples
                --------
                >>> result = np.lib.test() #doctest: +SKIP
                Running unit tests for numpy.lib
                ...
                Ran 976 tests in 3.933s
        
                OK
        
                >>> result.errors #doctest: +SKIP
                []
                >>> result.knownfail #doctest: +SKIP
                []
                """
        return object()
    def triangular(self, left, mode, right, size):
        """
                triangular(left, mode, right, size=None)
        
                Draw samples from the triangular distribution.
        
                The triangular distribution is a continuous probability distribution with
                lower limit left, peak at mode, and upper limit right. Unlike the other
                distributions, these parameters directly define the shape of the pdf.
        
                Parameters
                ----------
                left : scalar
                    Lower limit.
                mode : scalar
                    The value where the peak of the distribution occurs.
                    The value should fulfill the condition ``left <= mode <= right``.
                right : scalar
                    Upper limit, should be larger than `left`.
                size : int or tuple of ints, optional
                    Output shape. Default is None, in which case a single value is
                    returned.
        
                Returns
                -------
                samples : ndarray or scalar
                    The returned samples all lie in the interval [left, right].
        
                Notes
                -----
                The probability density function for the Triangular distribution is
        
                .. math:: P(x;l, m, r) = \begin{cases}
                          \frac{2(x-l)}{(r-l)(m-l)}& \text{for $l \leq x \leq m$},\\
                          \frac{2(m-x)}{(r-l)(r-m)}& \text{for $m \leq x \leq r$},\\
                          0& \text{otherwise}.
                          \end{cases}
        
                The triangular distribution is often used in ill-defined problems where the
                underlying distribution is not known, but some knowledge of the limits and
                mode exists. Often it is used in simulations.
        
                References
                ----------
                .. [1] Wikipedia, "Triangular distribution"
                      http://en.wikipedia.org/wiki/Triangular_distribution
        
                Examples
                --------
                Draw values from the distribution and plot the histogram:
        
                >>> import matplotlib.pyplot as plt
                >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=200,
                ...              normed=True)
                >>> plt.show()
        
                """
        return ndarray() if False else float()
    def uniform(self, low, high, size):
        """
                uniform(low=0.0, high=1.0, size=1)
        
                Draw samples from a uniform distribution.
        
                Samples are uniformly distributed over the half-open interval
                ``[low, high)`` (includes low, but excludes high).  In other words,
                any value within the given interval is equally likely to be drawn
                by `uniform`.
        
                Parameters
                ----------
                low : float, optional
                    Lower boundary of the output interval.  All values generated will be
                    greater than or equal to low.  The default value is 0.
                high : float
                    Upper boundary of the output interval.  All values generated will be
                    less than high.  The default value is 1.0.
                size : int or tuple of ints, optional
                    Shape of output.  If the given size is, for example, (m,n,k),
                    m*n*k samples are generated.  If no shape is specified, a single sample
                    is returned.
        
                Returns
                -------
                out : ndarray
                    Drawn samples, with shape `size`.
        
                See Also
                --------
                randint : Discrete uniform distribution, yielding integers.
                random_integers : Discrete uniform distribution over the closed
                                  interval ``[low, high]``.
                random_sample : Floats uniformly distributed over ``[0, 1)``.
                random : Alias for `random_sample`.
                rand : Convenience function that accepts dimensions as input, e.g.,
                       ``rand(2,2)`` would generate a 2-by-2 array of floats,
                       uniformly distributed over ``[0, 1)``.
        
                Notes
                -----
                The probability density function of the uniform distribution is
        
                .. math:: p(x) = \frac{1}{b - a}
        
                anywhere within the interval ``[a, b)``, and zero elsewhere.
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> s = np.random.uniform(-1,0,1000)
        
                All values are within the given interval:
        
                >>> np.all(s >= -1)
                True
                >>> np.all(s < 0)
                True
        
                Display the histogram of the samples, along with the
                probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> count, bins, ignored = plt.hist(s, 15, normed=True)
                >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
                >>> plt.show()
        
                """
        return ndarray()
    def vonmises(self, mu, kappa, size):
        """
                vonmises(mu, kappa, size=None)
        
                Draw samples from a von Mises distribution.
        
                Samples are drawn from a von Mises distribution with specified mode
                (mu) and dispersion (kappa), on the interval [-pi, pi].
        
                The von Mises distribution (also known as the circular normal
                distribution) is a continuous probability distribution on the unit
                circle.  It may be thought of as the circular analogue of the normal
                distribution.
        
                Parameters
                ----------
                mu : float
                    Mode ("center") of the distribution.
                kappa : float
                    Dispersion of the distribution, has to be >=0.
                size : int or tuple of int
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                Returns
                -------
                samples : scalar or ndarray
                    The returned samples, which are in the interval [-pi, pi].
        
                See Also
                --------
                scipy.stats.distributions.vonmises : probability density function,
                    distribution, or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the von Mises distribution is
        
                .. math:: p(x) = \frac{e^{\kappa cos(x-\mu)}}{2\pi I_0(\kappa)},
        
                where :math:`\mu` is the mode and :math:`\kappa` the dispersion,
                and :math:`I_0(\kappa)` is the modified Bessel function of order 0.
        
                The von Mises is named for Richard Edler von Mises, who was born in
                Austria-Hungary, in what is now the Ukraine.  He fled to the United
                States in 1939 and became a professor at Harvard.  He worked in
                probability theory, aerodynamics, fluid mechanics, and philosophy of
                science.
        
                References
                ----------
                Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical
                Functions*, New York: Dover, 1965.
        
                von Mises, R., *Mathematical Theory of Probability and Statistics*,
                New York: Academic Press, 1964.
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> mu, kappa = 0.0, 4.0 # mean and dispersion
                >>> s = np.random.vonmises(mu, kappa, 1000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> import scipy.special as sps
                >>> count, bins, ignored = plt.hist(s, 50, normed=True)
                >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)
                >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))
                >>> plt.plot(x, y/max(y), linewidth=2, color='r')
                >>> plt.show()
        
                """
        return float() if False else ndarray()
    def wald(self, mean, scale, size):
        """
                wald(mean, scale, size=None)
        
                Draw samples from a Wald, or Inverse Gaussian, distribution.
        
                As the scale approaches infinity, the distribution becomes more like a
                Gaussian.
        
                Some references claim that the Wald is an Inverse Gaussian with mean=1, but
                this is by no means universal.
        
                The Inverse Gaussian distribution was first studied in relationship to
                Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian
                because there is an inverse relationship between the time to cover a unit
                distance and distance covered in unit time.
        
                Parameters
                ----------
                mean : scalar
                    Distribution mean, should be > 0.
                scale : scalar
                    Scale parameter, should be >= 0.
                size : int or tuple of ints, optional
                    Output shape. Default is None, in which case a single value is
                    returned.
        
                Returns
                -------
                samples : ndarray or scalar
                    Drawn sample, all greater than zero.
        
                Notes
                -----
                The probability density function for the Wald distribution is
        
                .. math:: P(x;mean,scale) = \sqrt{\frac{scale}{2\pi x^3}}e^
                                            \frac{-scale(x-mean)^2}{2\cdotp mean^2x}
        
                As noted above the Inverse Gaussian distribution first arise from attempts
                to model Brownian Motion. It is also a competitor to the Weibull for use in
                reliability modeling and modeling stock returns and interest rate
                processes.
        
                References
                ----------
                .. [1] Brighton Webs Ltd., Wald Distribution,
                      http://www.brighton-webs.co.uk/distributions/wald.asp
                .. [2] Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian
                      Distribution: Theory : Methodology, and Applications", CRC Press,
                      1988.
                .. [3] Wikipedia, "Wald distribution"
                      http://en.wikipedia.org/wiki/Wald_distribution
        
                Examples
                --------
                Draw values from the distribution and plot the histogram:
        
                >>> import matplotlib.pyplot as plt
                >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)
                >>> plt.show()
        
                """
        return ndarray() if False else float()
    def weibull(self, a, size):
        """
                weibull(a, size=None)
        
                Weibull distribution.
        
                Draw samples from a 1-parameter Weibull distribution with the given
                shape parameter `a`.
        
                .. math:: X = (-ln(U))^{1/a}
        
                Here, U is drawn from the uniform distribution over (0,1].
        
                The more common 2-parameter Weibull, including a scale parameter
                :math:`\lambda` is just :math:`X = \lambda(-ln(U))^{1/a}`.
        
                Parameters
                ----------
                a : float
                    Shape of the distribution.
                size : tuple of ints
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn.
        
                See Also
                --------
                scipy.stats.distributions.weibull_max
                scipy.stats.distributions.weibull_min
                scipy.stats.distributions.genextreme
                gumbel
        
                Notes
                -----
                The Weibull (or Type III asymptotic extreme value distribution for smallest
                values, SEV Type III, or Rosin-Rammler distribution) is one of a class of
                Generalized Extreme Value (GEV) distributions used in modeling extreme
                value problems.  This class includes the Gumbel and Frechet distributions.
        
                The probability density for the Weibull distribution is
        
                .. math:: p(x) = \frac{a}
                                 {\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},
        
                where :math:`a` is the shape and :math:`\lambda` the scale.
        
                The function has its peak (the mode) at
                :math:`\lambda(\frac{a-1}{a})^{1/a}`.
        
                When ``a = 1``, the Weibull distribution reduces to the exponential
                distribution.
        
                References
                ----------
                .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,
                       1939 "A Statistical Theory Of The Strength Of Materials",
                       Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,
                       Generalstabens Litografiska Anstalts Forlag, Stockholm.
                .. [2] Waloddi Weibull, 1951 "A Statistical Distribution Function of Wide
                       Applicability",  Journal Of Applied Mechanics ASME Paper.
                .. [3] Wikipedia, "Weibull distribution",
                       http://en.wikipedia.org/wiki/Weibull_distribution
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> a = 5. # shape
                >>> s = np.random.weibull(a, 1000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> x = np.arange(1,100.)/50.
                >>> def weib(x,n,a):
                ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
        
                >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))
                >>> x = np.arange(1,100.)/50.
                >>> scale = count.max()/weib(x, 1., 5.).max()
                >>> plt.plot(x, weib(x, 1., 5.)*scale)
                >>> plt.show()
        
                """
        return None
    def zipf(self, a, size):
        """
                zipf(a, size=None)
        
                Draw samples from a Zipf distribution.
        
                Samples are drawn from a Zipf distribution with specified parameter
                `a` > 1.
        
                The Zipf distribution (also known as the zeta distribution) is a
                continuous probability distribution that satisfies Zipf's law: the
                frequency of an item is inversely proportional to its rank in a
                frequency table.
        
                Parameters
                ----------
                a : float > 1
                    Distribution parameter.
                size : int or tuple of int, optional
                    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                    ``m * n * k`` samples are drawn; a single integer is equivalent in
                    its result to providing a mono-tuple, i.e., a 1-D array of length
                    *size* is returned.  The default is None, in which case a single
                    scalar is returned.
        
                Returns
                -------
                samples : scalar or ndarray
                    The returned samples are greater than or equal to one.
        
                See Also
                --------
                scipy.stats.distributions.zipf : probability density function,
                    distribution, or cumulative density function, etc.
        
                Notes
                -----
                The probability density for the Zipf distribution is
        
                .. math:: p(x) = \frac{x^{-a}}{\zeta(a)},
        
                where :math:`\zeta` is the Riemann Zeta function.
        
                It is named for the American linguist George Kingsley Zipf, who noted
                that the frequency of any word in a sample of a language is inversely
                proportional to its rank in the frequency table.
        
                References
                ----------
                Zipf, G. K., *Selected Studies of the Principle of Relative Frequency
                in Language*, Cambridge, MA: Harvard Univ. Press, 1932.
        
                Examples
                --------
                Draw samples from the distribution:
        
                >>> a = 2. # parameter
                >>> s = np.random.zipf(a, 1000)
        
                Display the histogram of the samples, along with
                the probability density function:
        
                >>> import matplotlib.pyplot as plt
                >>> import scipy.special as sps
                Truncate s values at 50 so plot is interesting
                >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)
                >>> x = np.arange(1., 50.)
                >>> y = x**(-a)/sps.zetac(a)
                >>> plt.plot(x, y/max(y), linewidth=2, color='r')
                >>> plt.show()
        
                """
        return float() if False else ndarray()
fromfile = _fromfile
frombuffer = _frombuffer
fromstring = _fromstring