ndarray.any(axis=None, out=None, keepdims=False) Returns True if any of the elements of a evaluate to True. Refer to numpy.any for full documentation. See also numpy.any equivalent function

numpy.copy(a, order='K') [source] Return an array copy of the given object. Parameters: a : array_like Input data. order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout of the copy. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous, ?C? otherwise. ?K? means match the layout of a as closely as possible. (Note that this function and :meth:ndarray.copy are very similar, but have different default values for their order= arguments.) Returns: arr :

ndarray.argpartition(kth, axis=-1, kind='introselect', order=None) Returns the indices that would partition this array. Refer to numpy.argpartition for full documentation. New in version 1.8.0. See also numpy.argpartition equivalent function

numpy.arctan2(x1, x2[, out]) = Element-wise arc tangent of x1/x2 choosing the quadrant correctly. The quadrant (i.e., branch) is chosen so that arctan2(x1, x2) is the signed angle in radians between the ray ending at the origin and passing through the point (1,0), and the ray ending at the origin and passing through the point (x2, x1). (Note the role reversal: the ?y-coordinate? is the first function parameter, the ?x-coordinate? is the second.) By IEEE convention, this function is defined

The convenience classes provided by the polynomial package are: Name Provides Polynomial Power series Chebyshev Chebyshev series Legendre Legendre series Laguerre Laguerre series Hermite Hermite series HermiteE HermiteE series The series in this context are finite sums of the corresponding polynomial basis functions multiplied by coefficients. For instance, a power series looks like and has coefficients . The Chebyshev series with the same coefficients looks like and more generally

numpy.append(arr, values, axis=None) [source] Append values to the end of an array. Parameters: arr : array_like Values are appended to a copy of this array. values : array_like These values are appended to a copy of arr. It must be of the correct shape (the same shape as arr, excluding axis). If axis is not specified, values can be any shape and will be flattened before use. axis : int, optional The axis along which values are appended. If axis is not given, both arr and values are f

numpy.s_ = A nicer way to build up index tuples for arrays. Note Use one of the two predefined instances index_exp or s_ rather than directly using IndexExpression. For any index combination, including slicing and axis insertion, a[indices] is the same as a[np.index_exp[indices]] for any array a. However, np.index_exp[indices] can be used anywhere in Python code and returns a tuple of slice objects that can be used in the construction of complex index expressions. Parameters: maketuple

numpy.ma.flatnotmasked_edges(a) [source] Find the indices of the first and last unmasked values. Expects a 1-D MaskedArray, returns None if all values are masked. Parameters: a : array_like Input 1-D MaskedArray Returns: edges : ndarray or None The indices of first and last non-masked value in the array. Returns None if all values are masked. See also flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked Notes Only accepts 1-D arrays. Exampl

numpy.polynomial.hermite_e.hermecompanion(c) [source] Return the scaled companion matrix of c. The basis polynomials are scaled so that the companion matrix is symmetric when c is an HermiteE basis polynomial. This provides better eigenvalue estimates than the unscaled case and for basis polynomials the eigenvalues are guaranteed to be real if numpy.linalg.eigvalsh is used to obtain them. Parameters: c : array_like 1-D array of HermiteE series coefficients ordered from low to high degree.

matrix.dot(b, out=None) Dot product of two arrays. Refer to numpy.dot for full documentation. See also numpy.dot equivalent function Examples >>> a = np.eye(2) >>> b = np.ones((2, 2)) * 2 >>> a.dot(b) array([[ 2., 2.], [ 2., 2.]]) This array method can be conveniently chained: >>> a.dot(b).dot(b) array([[ 8., 8.], [ 8., 8.]])