我们从Python开源项目中，提取了以下41个代码示例，用于说明如何使用__builtin__.all()。
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): # 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 __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 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)
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 _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 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 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 all(items): return reduce(operator.__and__, items) # --- test if interpreter supports yield keyword ---
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]
