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.chebyshev.chebval(x, c, tensor=True) [source] Evaluate a Chebyshev series 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,

ndarray.__array_wrap__(obj) ? Object of same type as ndarray object a.

ndarray.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

record.shape tuple of array dimensions

ndarray.conj() Complex-conjugate all elements. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function

numpy.linalg.cholesky(a) [source] Cholesky decomposition. Return the Cholesky decomposition, L * L.H, of the square matrix a, where L is lower-triangular and .H is the conjugate transpose operator (which is the ordinary transpose if a is real-valued). a must be Hermitian (symmetric if real-valued) and positive-definite. Only L is actually returned. Parameters: a : (..., M, M) array_like Hermitian (symmetric if all elements are real), positive-definite input matrix. Returns: L : (..., M

numpy.core.defchararray.center(a, width, fillchar=' ') [source] Return a copy of a with its elements centered in a string of length width. Calls str.center element-wise. Parameters: a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The padding character to use (default is space). Returns: out : ndarray Output array of str or unicode, depending on input types See also str.center

ndarray.argmin(axis=None, out=None) Return indices of the minimum values along the given axis of a. Refer to numpy.argmin for detailed documentation. See also numpy.argmin equivalent function

MaskedArray.get_fill_value() [source] Return the filling value of the masked array. Returns: fill_value : scalar The filling value. Examples >>> for dt in [np.int32, np.int64, np.float64, np.complex128]: ... np.ma.array([0, 1], dtype=dt).get_fill_value() ... 999999 999999 1e+20 (1e+20+0j) >>> x = np.ma.array([0, 1.], fill_value=-np.inf) >>> x.get_fill_value() -inf