Python __builtin__.all() Examples

def load(file):
    """
    Wrapper around cPickle.load which accepts either a file-like object or
    a filename.

    Note that the NumPy binary format is not based on pickle/cPickle anymore.
    For details on the preferred way of loading and saving files, see `load`
    and `save`.

    See Also
    --------
    load, save

    """
    if isinstance(file, type("")):
        file = open(file, "rb")
    return pickle.load(file)

# These are all essentially abbreviations
# These might wind up in a special abbreviations module 

def load(file):
    """
    Wrapper around cPickle.load which accepts either a file-like object or
    a filename.

    Note that the NumPy binary format is not based on pickle/cPickle anymore.
    For details on the preferred way of loading and saving files, see `load`
    and `save`.

    See Also
    --------
    load, save

    """
    if isinstance(file, type("")):
        file = open(file, "rb")
    return pickle.load(file)

# These are all essentially abbreviations
# These might wind up in a special abbreviations module 

def __init__(self, env=None):

        # Global scope
        self._globals = env if env is not None else {}

        # Local scope(s)
        self._locals = None
        self._locals_stack = []

        # Variables which are references to entries in global scope
        self._globalrefs = None
        self._globalrefs_stack = []

        # Current scope (globals + locals in all embedding and in current scope)
        self._scope = self._globals

        # Turn on restricted mode
        self._restrict_builtins() 

def __init__(self):
        # The tree, which should be built.
        self._tree = []

        # List of all open constructs
        self._open_blocks = []

        # Nodes to which the open blocks have to be appended when closed
        self._path = []

        # Nr. of open blocks when file was opened. Used for checking whether all
        # blocks have been closed, when file processing finishes.
        self._nr_prev_blocks = []

        # Current node, to which content should be added
        self._curnode = self._tree

        # Current file
        self._curfile = None 

def all(items):
        return reduce(operator.__and__, items) 

def array_equal(a1, a2):
    """
    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

    """
    try:
        a1, a2 = asarray(a1), asarray(a2)
    except:
        return False
    if a1.shape != a2.shape:
        return False
    return bool(asarray(a1 == a2).all()) 

def identity(n, 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.]])

    """
    from numpy import eye
    return eye(n, dtype=dtype) 

def _maketup(descr, val):
    dt = dtype(descr)
    # Place val in all scalar tuples:
    fields = dt.fields
    if fields is None:
        return val
    else:
        res = [_maketup(fields[name][0], val) for name in dt.names]
        return tuple(res) 

def _validate_axis(axis, ndim, argname):
    try:
        axis = [operator.index(axis)]
    except TypeError:
        axis = list(axis)
    axis = [a + ndim if a < 0 else a for a in axis]
    if not builtins.all(0 <= a < ndim for a in axis):
        raise ValueError('invalid axis for this array in `%s` argument' %
                         argname)
    if len(set(axis)) != len(axis):
        raise ValueError('repeated axis in `%s` argument' % argname)
    return axis 

def all(items):
        return reduce(operator.__and__, items)

# --- test if interpreter supports yield keyword --- 

def any(items):
        for item in items:
            if item:
                return True
        return False

# ---all() from Python 2.5 --- 

def _validate_axis(axis, ndim, argname):
    try:
        axis = [operator.index(axis)]
    except TypeError:
        axis = list(axis)
    axis = [a + ndim if a < 0 else a for a in axis]
    if not builtins.all(0 <= a < ndim for a in axis):
        raise ValueError('invalid axis for this array in `%s` argument' %
                         argname)
    if len(set(axis)) != len(axis):
        raise ValueError('repeated axis in `%s` argument' % argname)
    return axis 

def any(items):
        for item in items:
            if item:
                return True
        return False

# ---all() from Python 2.5 --- 

def all(items):
        return reduce(operator.__and__, items)

# --- test if interpreter supports yield keyword --- 

def any(items):
        for item in items:
            if item:
                return True
        return False

# ---all() from Python 2.5 --- 

def all(items):
        return reduce(operator.__and__, items)

# --- test if interpreter supports yield keyword --- 

def any(items):
        for item in items:
            if item:
                return True
        return False

# ---all() from Python 2.5 --- 

def array_equal(a1, a2):
    """
    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

    """
    try:
        a1, a2 = asarray(a1), asarray(a2)
    except:
        return False
    if a1.shape != a2.shape:
        return False
    return bool(asarray(a1 == a2).all()) 

def identity(n, 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.]])

    """
    from numpy import eye
    return eye(n, dtype=dtype) 

def _maketup(descr, val):
    dt = dtype(descr)
    # Place val in all scalar tuples:
    fields = dt.fields
    if fields is None:
        return val
    else:
        res = [_maketup(fields[name][0], val) for name in dt.names]
        return tuple(res) 

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 umath.geterrobj()[2] 

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.])

    """
    maskvalue = umath.geterrobj()[1]
    mask = 7
    res = {}
    val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
    res['divide'] = _errdict_rev[val]
    val = (maskvalue >> SHIFT_OVERFLOW) & mask
    res['over'] = _errdict_rev[val]
    val = (maskvalue >> SHIFT_UNDERFLOW) & mask
    res['under'] = _errdict_rev[val]
    val = (maskvalue >> SHIFT_INVALID) & mask
    res['invalid'] = _errdict_rev[val]
    return res 

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.])

    """
    maskvalue = umath.geterrobj()[1]
    mask = 7
    res = {}
    val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
    res['divide'] = _errdict_rev[val]
    val = (maskvalue >> SHIFT_OVERFLOW) & mask
    res['over'] = _errdict_rev[val]
    val = (maskvalue >> SHIFT_UNDERFLOW) & mask
    res['under'] = _errdict_rev[val]
    val = (maskvalue >> SHIFT_INVALID) & mask
    res['invalid'] = _errdict_rev[val]
    return res 

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 umath.geterrobj()[2]