Legendre.trim(tol=0) [source] Remove trailing coefficients Remove trailing coefficients until a coefficient is reached whose absolute value greater than tol or the beginning of the series is reached. If all the coefficients would be removed the series is set to [0]. A new series instance is returned with the new coefficients. The current instance remains unchanged. Parameters: tol : non-negative number. All trailing coefficients less than tol will be removed. Returns: new_series : seri

Hermite.truncate(size) [source] Truncate series to length size. Reduce the series to length size by discarding the high degree terms. The value of size must be a positive integer. This can be useful in least squares where the coefficients of the high degree terms may be very small. Parameters: size : positive int The series is reduced to length size by discarding the high degree terms. The value of size must be a positive integer. Returns: new_series : series New instance of series wi

numpy.polynomial.legendre.legvander(x, deg) [source] Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the Legendre polynomial. If c is a 1-D array of coefficients of length n + 1 and V is the array V = legvander(x, n), then np.dot(V, c) and legval(x, c) are the same up

numpy.greater_equal(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, less_equal, equal, not_equal Examples >>> np.greater_equal([4, 2, 1], [2, 2, 2]) array([ True, True,

numpy.polynomial.hermite.hermroots(c) [source] Compute the roots of a Hermite series. Return the roots (a.k.a. ?zeros?) of the polynomial Parameters: c : 1-D array_like 1-D array of coefficients. Returns: out : ndarray Array of the roots of the series. If all the roots are real, then out is also real, otherwise it is complex. See also polyroots, legroots, lagroots, chebroots, hermeroots Notes The root estimates are obtained as the eigenvalues of the companion matrix, Roots far

MaskedArray.sum(axis=None, dtype=None, out=None) [source] Return the sum of the array elements over the given axis. Masked elements are set to 0 internally. Parameters: axis : {None, -1, int}, optional Axis along which the sum is computed. The default (axis = None) is to compute over the flattened array. dtype : {None, dtype}, optional Determines the type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and the type of a is an integ

HermiteE.mapparms() [source] Return the mapping parameters. The returned values define a linear map off + scl*x that is applied to the input arguments before the series is evaluated. The map depends on the domain and window; if the current domain is equal to the window the resulting map is the identity. If the coefficients of the series instance are to be used by themselves outside this class, then the linear function must be substituted for the x in the standard representation of the base

Laguerre.roots() [source] Return the roots of the series polynomial. Compute the roots for the series. Note that the accuracy of the roots decrease the further outside the domain they lie. Returns: roots : ndarray Array containing the roots of the series.

dtype.kind A character code (one of ?biufcmMOSUV?) identifying the general kind of data. b boolean i signed integer u unsigned integer f floating-point c complex floating-point m timedelta M datetime O object S (byte-)string U Unicode V void

numpy.multiply(x1, x2[, out]) = Multiply arguments element-wise. Parameters: x1, x2 : array_like Input arrays to be multiplied. Returns: y : ndarray The product of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars. Notes Equivalent to x1 * x2 in terms of array broadcasting. Examples >>> np.multiply(2.0, 4.0) 8.0 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.multiply(x1, x2) array([[ 0., 1., 4.]