Legendre.copy() [source] Return a copy. Returns: new_series : series Copy of self.

classmethod HermiteE.identity(domain=None, window=None) [source] Identity function. If p is the returned series, then p(x) == x for all values of x. Parameters: 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 None. window : {None, array_like}, optional If given, the resulting array must be if the form [beg, end], where beg and end ar

numpy.testing.assert_warns(warning_class, *args, **kwargs) [source] 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. If called with all arguments other than the warning class omitted, may be used as a context manager: with asser

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

numpy.core.defchararray.mod(a, values) [source] Return (a % i), that is pre-Python 2.6 string formatting (iterpolation), element-wise for a pair of array_likes of str or unicode. Parameters: a : array_like of str or unicode values : array_like of values These values will be element-wise interpolated into the string. Returns: out : ndarray Output array of str or unicode, depending on input types See also str.__mod__

matrix.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

HermiteE.deriv(m=1) [source] Differentiate. Return a series instance of that is the derivative of the current series. Parameters: m : non-negative int Find the derivative of order m. Returns: new_series : series A new series representing the derivative. The domain is the same as the domain of the differentiated series.

MaskedArray.all(axis=None, out=None) [source] Check if all of the elements of a are true. Performs a logical_and over the given axis and returns the result. Masked values are considered as True during computation. For convenience, the output array is masked where ALL the values along the current axis are masked: if the output would have been a scalar and that all the values are masked, then the output is masked. Parameters: axis : {None, integer} Axis to perform the operation over. If Non

Legendre.deriv(m=1) [source] Differentiate. Return a series instance of that is the derivative of the current series. Parameters: m : non-negative int Find the derivative of order m. Returns: new_series : series A new series representing the derivative. The domain is the same as the domain of the differentiated series.

numpy.ma.prod(self, axis=None, dtype=None, out=None) = Return the product of the array elements over the given axis. Masked elements are set to 1 internally for computation. Parameters: axis : {None, int}, optional Axis over which the product is taken. If None is used, then the product is over all the array elements. dtype : {None, dtype}, optional Determines the type of the returned array and of the accumulator where the elements are multiplied. If dtype has the value None and the typ