numpy.geterrcall() [source] 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

record.itemsize length of one element in bytes

classmethod Polynomial.basis(deg, domain=None, window=None) [source] Series basis polynomial of degree deg. Returns the series representing the basis polynomial of degree deg. New in version 1.7.0. Parameters: deg : int Degree of the basis polynomial for the series. Must be >= 0. domain : {None, array_like}, optional If given, the array must be of the form [beg, end], where beg and end are the endpoints of the domain. If None is given then the class domain is used. The default is N

numpy.logical_or(x1, x2[, out]) = Compute the truth value of x1 OR x2 element-wise. 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 or 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, Fal

matrix.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 sc

ndarray.mean(axis=None, dtype=None, out=None, keepdims=False) Returns the average of the array elements along given axis. Refer to numpy.mean for full documentation. See also numpy.mean equivalent function

matrix.A 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]])

numpy.ma.harden_mask(self) = Force the mask to hard. Whether the mask of a masked array is hard or soft is determined by its hardmask property. harden_mask sets hardmask to True. See also hardmask

numpy.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 . 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 >

ndarray.__iand__ x.__iand__(y) <==> x&=y