numpy.nanmax(a, axis=None, out=None, keepdims=False) [source] Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and NaN is returned for that slice. Parameters: a : array_like Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the maximum is computed. The default is to compute the maximum of the flattened array. o

numpy.ma.getdata(a, subok=True) [source] Return the data of a masked array as an ndarray. Return the data of a (if any) as an ndarray if a is a MaskedArray, else return a as a ndarray or subclass (depending on subok) if not. Parameters: a : array_like Input MaskedArray, alternatively a ndarray or a subclass thereof. subok : bool Whether to force the output to be a pure ndarray (False) or to return a subclass of ndarray if appropriate (True, default). See also getmask Return the ma

classmethod Laguerre.cast(series, domain=None, window=None) [source] Convert series to series of this class. The series is expected to be an instance of some polynomial series of one of the types supported by by the numpy.polynomial module, but could be some other class that supports the convert method. New in version 1.7.0. Parameters: series : series The series instance to be converted. domain : {None, array_like}, optional If given, the array must be of the form [beg, end], where b

Laguerre.has_samecoef(other) [source] Check if coefficients match. New in version 1.6.0. Parameters: other : class instance The other class must have the coef attribute. Returns: bool : boolean True if the coefficients are the same, False otherwise.

numpy.less(x1, x2[, out]) = Return the truth value of (x1 < x2) element-wise. Parameters: x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: out : bool or ndarray of bool Array of bools, or a single bool if x1 and x2 are scalars. See also greater, less_equal, greater_equal, equal, not_equal Examples >>> np.less([1, 2], [2, 2]) array([ True, False], dtype=bool)

classmethod Legendre.fromroots(roots, domain=[], window=None) [source] Return series instance that has the specified roots. Returns a series representing the product (x - r[0])*(x - r[1])*...*(x - r[n-1]), where r is a list of roots. Parameters: roots : array_like List of roots. domain : {[], None, array_like}, optional Domain for the resulting series. If None the domain is the interval from the smallest root to the largest. If [] the domain is the class domain. The default is []. wind

numpy.sqrt(x[, out]) = Return the positive square-root of an array, element-wise. Parameters: x : array_like The values whose square-roots are required. out : ndarray, optional Alternate array object in which to put the result; if provided, it must have the same shape as x Returns: y : ndarray An array of the same shape as x, containing the positive square-root of each element in x. If any element in x is complex, a complex array is returned (and the square-roots of negative reals

Chebyshev.has_samedomain(other) [source] Check if domains match. New in version 1.6.0. Parameters: other : class instance The other class must have the domain attribute. Returns: bool : boolean True if the domains are the same, False otherwise.

numpy.logical_not(x[, out]) = Compute the truth value of NOT x element-wise. Parameters: x : array_like Logical NOT is applied to the elements of x. Returns: y : bool or ndarray of bool Boolean result with the same shape as x of the NOT operation on elements of x. See also logical_and, logical_or, logical_xor Examples >>> np.logical_not(3) False >>> np.logical_not([True, False, 0, 1]) array([False, True, True, False], dtype=bool) >>> x = np.arange(5)

numpy.polynomial.hermite.hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Hermite series. Returns the Hermite series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable. (?Buyer beware?: note that, depending on what one is doing, one may want scl to be the reciprocal of what one might expect; for more informat