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,

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

MaskedArray.byteswap(inplace) Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Parameters: inplace : bool, optional If True, swap bytes in-place, default is False. Returns: out : ndarray The byteswapped array. If inplace is True, this is a view to self. Examples >>> A = np.array([1, 256, 8755], dtype=np.int16) >>> map(hex, A) ['0x1', '0x100', '0x2233'] &g

numpy.polynomial.legendre.legzero = array([0])

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

ndindex.ndincr() [source] Increment the multi-dimensional index by one. This method is for backward compatibility only: do not use.

generic.__array_interface__ Array protocol: Python side

MaskedArray.__pow__(other) [source] Raise self to the power other, masking the potential NaNs/Infs

numpy.ma.compress_rowcols(x, axis=None) [source] Suppress the rows and/or columns of a 2-D array that contain masked values. The suppression behavior is selected with the axis parameter. If axis is None, both rows and columns are suppressed. If axis is 0, only rows are suppressed. If axis is 1 or -1, only columns are suppressed. Parameters: x : array_like, MaskedArray The array to operate on. If not a MaskedArray instance (or if no array elements are masked), x is interpreted as a MaskedA

numpy.ma.all(self, axis=None, out=None) = Check if all of the elements of a are true. Performs a logical_and over the given axis and returns the result. Masked values are considered as True during computation. For convenience, the output array is masked where ALL the values along the current axis are masked: if the output would have been a scalar and that all the values are masked, then the output is masked. Parameters: axis : {None, integer} Axis to perform the operation over. If None,