numpy.testing.assert_array_less(x, y, err_msg='', verbose=True) [source] Raises an AssertionError if two array_like objects are not ordered by less than. Given two array_like objects, check that the shape is equal and all elements of the first object are strictly smaller than those of the second object. An exception is raised at shape mismatch or incorrectly ordered values. Shape mismatch does not raise if an object has zero dimension. In contrast to the standard usage in numpy, NaNs are co

ndarray.tobytes(order='C') Construct Python bytes containing the raw data bytes in the array. Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ?C? or ?Fortran?, or ?Any? order (the default is ?C?-order). ?Any? order means C-order unless the F_CONTIGUOUS flag in the array is set, in which case it means ?Fortran? order. New in version 1.9.0. Parameters: order : {?C?, ?F?, None}, optional Order of the data for multidimens

MaskedArray.ids() [source] Return the addresses of the data and mask areas. Parameters: None Examples >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1]) >>> x.ids() (166670640, 166659832) If the array has no mask, the address of nomask is returned. This address is typically not close to the data in memory: >>> x = np.ma.array([1, 2, 3]) >>> x.ids() (166691080, 3083169284L)

numpy.polynomial.laguerre.poly2lag(pol) [source] Convert a polynomial to a Laguerre series. Convert an array representing the coefficients of a polynomial (relative to the ?standard? basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Laguerre series, ordered from lowest to highest degree. Parameters: pol : array_like 1-D array containing the polynomial coefficients Returns: c : ndarray 1-D array containing the coefficients of the equivalent

New in version 1.4.0. Using the Convenience ClassesBasics Calculus Other Polynomial Constructors Fitting Polynomial Module (numpy.polynomial.polynomial)Polynomial Class Basics Fitting Calculus Algebra Miscellaneous Chebyshev Module (numpy.polynomial.chebyshev)Chebyshev Class Basics Fitting Calculus Algebra Quadrature Miscellaneous Legendre Module (numpy.polynomial.legendre)Legendre Class Basics Fitting Calculus Algebra Quadrature Miscellaneous Laguerre Module (numpy.polynomial.lagu

MaskedArray.product(axis=None, dtype=None, out=None) [source] 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 t

Hermite.convert(domain=None, kind=None, window=None) [source] Convert series to a different kind and/or domain and/or window. Parameters: domain : array_like, optional The domain of the converted series. If the value is None, the default domain of kind is used. kind : class, optional The polynomial series type class to which the current instance should be converted. If kind is None, then the class of the current instance is used. window : array_like, optional The window of the convert

numpy.random.permutation(x) Randomly permute a sequence, or return a permuted range. If x is a multi-dimensional array, it is only shuffled along its first index. Parameters: x : int or array_like If x is an integer, randomly permute np.arange(x). If x is an array, make a copy and shuffle the elements randomly. Returns: out : ndarray Permuted sequence or array range. Examples >>> np.random.permutation(10) array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) >>> np.random.permuta

MaskedArray.any(axis=None, out=None) [source] Check if any of the elements of a are true. Performs a logical_or over the given axis and returns the result. Masked values are considered as False during computation. Parameters: axis : {None, integer} Axis to perform the operation over. If None, perform over flattened array and return a scalar. out : {None, array}, optional Array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output

numpy.asanyarray(a, dtype=None, order=None) [source] Convert the input to an ndarray, but pass ndarray subclasses through. Parameters: a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {?C?, ?F?}, optional Whether to use row-major (C-style) or column-major (Fortran-