MaskedArray.__ror__ x.__ror__(y) <==> y|x

matrix.tolist() [source] 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]]

numpy.testing.Tester [source] alias of NoseTester

numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) [source] 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 : None or int or tuple of ints, optional Axis or axes along which the

numpy.polynomial.laguerre.lagval3d(x, y, z, c) [source] Evaluate a 3-D Laguerre series at points (x, y, z). This function returns the values: The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dimens

ndarray.__ixor__ x.__ixor__(y) <==> x^=y

numpy.mask_indices(n, mask_func, k=0) [source] 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 vali

numpy.iscomplexobj(x) [source] 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 >>&

MaskedArray.size Number of elements in the array. Equivalent to np.prod(a.shape), i.e., the product of the array?s dimensions. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30

numpy.testing.decorators.skipif(skip_condition, msg=None) [source] Make function raise SkipTest exception if a given condition is true. If the condition is a callable, it is used at runtime to dynamically make the decision. This is useful for tests that may require costly imports, to delay the cost until the test suite is actually executed. Parameters: skip_condition : bool or callable Flag to determine whether to skip the decorated test. msg : str, optional Message to give on raising a