HermiteE.has_samewindow(other) [source] Check if windows match. New in version 1.6.0. Parameters: other : class instance The other class must have the window attribute. Returns: bool : boolean True if the windows are the same, False otherwise.

numpy.intersect1d(ar1, ar2, assume_unique=False) [source] Find the intersection of two arrays. Return the sorted, unique values that are in both of the input arrays. Parameters: ar1, ar2 : array_like Input arrays. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns: intersect1d : ndarray Sorted 1D array of common and unique elements. See also numpy.lib.arraysetops Module with a number of oth

numpy.correlate(a, v, mode='valid') [source] Cross-correlation of two 1-dimensional sequences. This function computes the correlation as generally defined in signal processing texts: c_{av}[k] = sum_n a[n+k] * conj(v[n]) with a and v sequences being zero-padded where necessary and conj being the conjugate. Parameters: a, v : array_like Input sequences. mode : {?valid?, ?same?, ?full?}, optional Refer to the convolve docstring. Note that the default is ?valid?, unlike convolve, which us

numpy.ma.row_stack(tup) = Stack arrays in sequence vertically (row wise). Take a sequence of arrays and stack them vertically to make a single array. Rebuild arrays divided by vsplit. Parameters: tup : sequence of ndarrays Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis. Returns: stacked : ndarray The array formed by stacking the given arrays. See also stack Join a sequence of arrays along a new axis. hstack Stack array

numpy.nper(rate, pmt, pv, fv=0, when='end') [source] Compute the number of periodic payments. Parameters: rate : array_like Rate of interest (per period) pmt : array_like Payment pv : array_like Present value fv : array_like, optional Future value when : {{?begin?, 1}, {?end?, 0}}, {string, int}, optional When payments are due (?begin? (1) or ?end? (0)) Notes The number of periods nper is computed by solving the equation: fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate*((1+rate)

numpy.vander(x, N=None, increasing=False) [source] Generate a Vandermonde matrix. The columns of the output matrix are powers of the input vector. The order of the powers is determined by the increasing boolean argument. Specifically, when increasing is False, the i-th output column is the input vector raised element-wise to the power of N - i - 1. Such a matrix with a geometric progression in each row is named for Alexandre- Theophile Vandermonde. Parameters: x : array_like 1-D input arr

numpy.polynomial.legendre.legtrim(c, tol=0) [source] Remove ?small? ?trailing? coefficients from a polynomial. ?Small? means ?small in absolute value? and is controlled by the parameter tol; ?trailing? means highest order coefficient(s), e.g., in [0, 1, 1, 0, 0] (which represents 0 + x + x**2 + 0*x**3 + 0*x**4) both the 3-rd and 4-th order coefficients would be ?trimmed.? Parameters: c : array_like 1-d array of coefficients, ordered from lowest order to highest. tol : number, optional T

numpy.polynomial.legendre.legadd(c1, c2) [source] Add one Legendre series to another. Returns the sum of two Legendre series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Legendre series coefficients ordered from low to high. Returns: out : ndarray Array representing the Legendre series of their sum. See also legsub, legmul, legdiv

numpy.polynomial.legendre.legroots(c) [source] Compute the roots of a Legendre 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, chebroots, lagroots, hermroots, hermeroots Notes The root estimates are obtained as the eigenvalues of the companion matrix, Roots fa

numpy.tile(A, reps) [source] Construct an array by repeating A the number of times given by reps. If reps has length d, the result will have dimension of max(d, A.ndim). If A.ndim < d, A is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote A to d-dimensions manually before calling this function. If A.ndim > d, reps is promoted to A.nd