numpy.polynomial.hermite_e.hermeder(c, m=1, scl=1, axis=0) [source] Differentiate a Hermite_e series. Returns the series coefficients c differentiated m times along axis. At each iteration the result is multiplied by scl (the scaling factor is for use in a linear change of variable). The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series 1*He_0 + 2*He_1 + 3*He_2 while [[1,2],[1,2]] represents 1*He_0(x)*He_0(y) + 1*He_1(x)*He_0

numpy.ma.choose(indices, choices, out=None, mode='raise') [source] Use an index array to construct a new array from a set of choices. Given an array of integers and a set of n choice arrays, this method will create a new array that merges each of the choice arrays. Where a value in a is i, the new array will have the value that choices[i] contains in the same place. Parameters: a : ndarray of ints This array must contain integers in [0, n-1], where n is the number of choices. choices : s

numpy.fromiter(iterable, dtype, count=-1) Create a new 1-dimensional array from an iterable object. Parameters: iterable : iterable object An iterable object providing data for the array. dtype : data-type The data-type of the returned array. count : int, optional The number of items to read from iterable. The default is -1, which means all data is read. Returns: out : ndarray The output array. Notes Specify count to improve performance. It allows fromiter to pre-allocate the ou

numpy.promote_types(type1, type2) Returns the data type with the smallest size and smallest scalar kind to which both type1 and type2 may be safely cast. The returned data type is always in native byte order. This function is symmetric and associative. Parameters: type1 : dtype or dtype specifier First data type. type2 : dtype or dtype specifier Second data type. Returns: out : dtype The promoted data type. See also result_type, dtype, can_cast Notes New in version 1.6.0. Star

exception numpy.linalg.LinAlgError [source] Generic Python-exception-derived object raised by linalg functions. General purpose exception class, derived from Python?s exception.Exception class, programmatically raised in linalg functions when a Linear Algebra-related condition would prevent further correct execution of the function. Parameters: None Examples >>> from numpy import linalg as LA >>> LA.inv(np.zeros((2,2))) Traceback (most recent call last): File "<stdin&

matrix.A1 Return self as a flattened ndarray. Equivalent to np.asarray(x).ravel() Parameters: None Returns: ret : ndarray self, 1-D, 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.getA1() array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

numpy.kron(a, b) [source] Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters: a, b : array_like Returns: out : ndarray See also outer The outer product Notes The function assumes that the number of dimensions of a and b are the same, if necessary prepending the smallest with ones. If a.shape = (r0,r1,..,rN) and b.shape = (s0,s1,...,sN), the Kronecker product has shape (r0*s0, r1*s1, ...

numpy.maximum(x1, x2[, out]) = Element-wise maximum of array elements. Compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated. Parameters: x1, x2 : array_like T

numpy.polynomial.laguerre.laggauss(deg) [source] Gauss-Laguerre quadrature. Computes the sample points and weights for Gauss-Laguerre quadrature. These sample points and weights will correctly integrate polynomials of degree or less over the interval with the weight function . Parameters: deg : int Number of sample points and weights. It must be >= 1. Returns: x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes The resul

numpy.unpackbits(myarray, axis=None) Unpacks elements of a uint8 array into a binary-valued output array. Each element of myarray represents a bit-field that should be unpacked into a binary-valued output array. The shape of the output array is either 1-D (if axis is None) or the same shape as the input array with unpacking done along the axis specified. Parameters: myarray : ndarray, uint8 type Input array. axis : int, optional Unpacks along this axis. Returns: unpacked : ndarray, u