numpy.fft.ifft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional inverse discrete Fourier Transform. This function computes the inverse of the 2-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, ifft2(fft2(a)) == a to within numerical accuracy. By default, the inverse transform is computed over the last two axes of the input array. The input, analogously to ifft, shoul

matrix.tolist() [source] Return the matrix as a (possibly nested) list. See ndarray.tolist for full documentation. See also ndarray.tolist Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> x.tolist() [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]

numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) [source] Compute the standard deviation along the specified axis. Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. Parameters: a : array_like Calculate the standard deviation of these values. axis : None or int or tuple of ints, optional Axis or axes along which the

numpy.polynomial.laguerre.lagval3d(x, y, z, c) [source] Evaluate a 3-D Laguerre 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 dimens

ndarray.__ixor__ x.__ixor__(y) <==> x^=y

MaskedArray.size Number of elements in the array. Equivalent to np.prod(a.shape), i.e., the product of the array?s dimensions. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30

MaskedArray.__irshift__ x.__irshift__(y) <==> x>>=y

Matrix and vector products dot(a, b[, out]) Dot product of two arrays. vdot(a, b) Return the dot product of two vectors. inner(a, b) Inner product of two arrays. outer(a, b[, out]) Compute the outer product of two vectors. matmul(a, b[, out]) Matrix product of two arrays. tensordot(a, b[, axes]) Compute tensor dot product along specified axes for arrays >= 1-D. einsum(subscripts, *operands[, out, dtype, ...]) Evaluates the Einstein summation convention on the operands. linalg.matri

numpy.dsplit(ary, indices_or_sections) [source] Split array into multiple sub-arrays along the 3rd axis (depth). Please refer to the split documentation. dsplit is equivalent to split with axis=2, the array is always split along the third axis provided the array dimension is greater than or equal to 3. See also split Split an array into multiple sub-arrays of equal size. Examples >>> x = np.arange(16.0).reshape(2, 2, 4) >>> x array([[[ 0., 1., 2., 3.],

matrix.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) Copy of the array, cast to a specified type. Parameters: dtype : str or dtype Typecode or data-type to which the array is cast. order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout order of the result. ?C? means C order, ?F? means Fortran order, ?A? means ?F? order if all the arrays are Fortran contiguous, ?C? order otherwise, and ?K? means as close to the order the array elements appear in memory as p