numpy.polynomial.hermite.hermval3d(x, y, z, c) [source] Evaluate a 3-D Hermite series 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 dimensi

classmethod Legendre.cast(series, domain=None, window=None) [source] Convert series to series of this class. The series is expected to be an instance of some polynomial series of one of the types supported by by the numpy.polynomial module, but could be some other class that supports the convert method. New in version 1.7.0. Parameters: series : series The series instance to be converted. domain : {None, array_like}, optional If given, the array must be of the form [beg, end], where b

dtype.isbuiltin Integer indicating how this dtype relates to the built-in dtypes. Read-only. 0 if this is a structured array type, with fields 1 if this is a dtype compiled into numpy (such as ints, floats etc) 2 if the dtype is for a user-defined numpy type A user-defined type uses the numpy C-API machinery to extend numpy to handle a new array type. See User-defined data-types in the Numpy manual. Examples >>> dt = np.dtype('i2') >>> dt.isbuiltin 1 >>> dt = np.d

ndarray.cumprod(axis=None, dtype=None, out=None) Return the cumulative product of the elements along the given axis. Refer to numpy.cumprod for full documentation. See also numpy.cumprod equivalent function

ndarray.flatten(order='C') Return a copy of the array collapsed into one dimension. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional ?C? means to flatten in row-major (C-style) order. ?F? means to flatten in column-major (Fortran- style) order. ?A? means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ?K? means to flatten a in the order the elements occur in memory. The default is ?C?. Returns: y : ndarray A copy of the input array,

matrix.clip(min=None, max=None, out=None) Return an array whose values are limited to [min, max]. One of max or min must be given. Refer to numpy.clip for full documentation. See also numpy.clip equivalent function

recarray.shape Tuple of array dimensions. Notes May be used to ?reshape? the array, as long as this would not require a change in the total number of elements Examples >>> x = np.array([1, 2, 3, 4]) >>> x.shape (4,) >>> y = np.zeros((2, 3, 4)) >>> y.shape (2, 3, 4) >>> y.shape = (3, 8) >>> y array([[ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.]])

generic.newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. The new_order code can be any from the following: ?S? - swap dtype from current to opposite endian {?<?, ?L?} - little endian {?>?, ?B?} - big endian {?=?, ?N?} - native order {?|?, ?I?} - ignore (no change to byte order) Parameters: new_order : str, optional Byte order to force; a value from the byte order specifications above. The

numpy.polynomial.polynomial.polymul(c1, c2) [source] Multiply one polynomial by another. Returns the product of two polynomials c1 * c2. The arguments are sequences of coefficients, from lowest order term to highest, e.g., [1,2,3] represents the polynomial 1 + 2*x + 3*x**2. Parameters: c1, c2 : array_like 1-D arrays of coefficients representing a polynomial, relative to the ?standard? basis, and ordered from lowest order term to highest. Returns: out : ndarray Of the coefficients of t

numpy.tril_indices_from(arr, k=0) [source] Return the indices for the lower-triangle of arr. See tril_indices for full details. Parameters: arr : array_like The indices will be valid for square arrays whose dimensions are the same as arr. k : int, optional Diagonal offset (see tril for details). See also tril_indices, tril Notes New in version 1.4.0.