numpy.polyval(p, x) [source] Evaluate a polynomial at specific values. If p is of length N, this function returns the value: p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1] If x is a sequence, then p(x) is returned for each element of x. If x is another polynomial then the composite polynomial p(x(t)) is returned. Parameters: p : array_like or poly1d object 1D array of polynomial coefficients (including coefficients equal to zero) from highest degree to the constant term, or an i

numpy.polynomial.polynomial.polyzero = array([0])

numpy.polysub(a1, a2) [source] Difference (subtraction) of two polynomials. Given two polynomials a1 and a2, returns a1 - a2. a1 and a2 can be either array_like sequences of the polynomials? coefficients (including coefficients equal to zero), or poly1d objects. Parameters: a1, a2 : array_like or poly1d Minuend and subtrahend polynomials, respectively. Returns: out : ndarray or poly1d Array or poly1d object of the difference polynomial?s coefficients. See also polyval, polydiv, pol

numpy.polynomial.polynomial.polyx = array([0, 1])

numpy.polynomial.polynomial.polyvander2d(x, y, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the powers of x and y. If V = polyvander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a 2-D coefficie

numpy.polynomial.polynomial.polyvander3d(x, y, z, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y, z). If l, m, n are the given degrees in x, y, z, then The pseudo-Vandermonde matrix is defined by where 0 <= i <= l, 0 <= j <= m, and 0 <= j <= n. The leading indices of V index the points (x, y, z) and the last index encodes the powers of x, y, and z. If V = polyvander3d(x, y, z, [xdeg,

numpy.polynomial.polynomial.polyval3d(x, y, z, c) [source] Evaluate a 3-D polynomial at points (x, y, z). This function returns the values: The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dimensio

numpy.polynomial.polynomial.polyvander(x, deg) [source] Vandermonde matrix of given degree. Returns the Vandermonde matrix of degree deg and sample points x. The 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 power of x. If c is a 1-D array of coefficients of length n + 1 and V is the matrix V = polyvander(x, n), then np.dot(V, c) and polyval(x, c) are the same up to roundoff. This equivalence is usef

numpy.polynomial.polynomial.polyval(x, c, tensor=True) [source] Evaluate a polynomial at points x. If c is of length n + 1, this function returns the value The parameter x is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either x or its elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array, then p(x) will have the same shape as x. If c is multidimensional, then t

numpy.polynomial.polynomial.polyval2d(x, y, c) [source] Evaluate a 2-D polynomial at points (x, y). This function returns the value The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than two dimensions, ones are i