numpy.fft.irfftn(a, s=None, axes=None, norm=None) [source] Compute the inverse of the N-dimensional FFT of real input. This function computes the inverse of the N-dimensional discrete Fourier Transform for real input over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, irfftn(rfftn(a), a.shape) == a to within numerical accuracy. (The a.shape is necessary like len(a) is for irfft, and for the same reason.) The input should be ordered

numpy.asfarray(a, dtype=) [source] Return an array converted to a float type. Parameters: a : array_like The input array. dtype : str or dtype object, optional Float type code to coerce input array a. If dtype is one of the ?int? dtypes, it is replaced with float64. Returns: out : ndarray The input a as a float ndarray. Examples >>> np.asfarray([2, 3]) array([ 2., 3.]) >>> np.asfarray([2, 3], dtype='float') array([ 2., 3.]) >>> np.asfarray([2, 3], dtyp

numpy.triu_indices_from(arr, k=0) [source] Return the indices for the upper-triangle of arr. See triu_indices for full details. Parameters: arr : ndarray, shape(N, N) The indices will be valid for square arrays. k : int, optional Diagonal offset (see triu for details). Returns: triu_indices_from : tuple, shape(2) of ndarray, shape(N) Indices for the upper-triangle of arr. See also triu_indices, triu Notes New in version 1.4.0.

matrix.dumps() Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array. Parameters: None

numpy.pad(array, pad_width, mode, **kwargs) [source] Pads an array. Parameters: array : array_like of rank N Input array pad_width : {sequence, array_like, int} Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes. mode : str or function One of the following string values

An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape, which is a tuple of N positive integers that specify the sizes of each dimension. The type of items in the array is specified by a separate data-type object (dtype), one of which is associated with each ndarray. As with other container objects in Python, the contents of an ndarray can be accessed and modified by indexing or

RandomState.f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters should be greater than zero. The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates. Parameters: dfnum

MaskedArray.__ne__(other) [source] Check whether other doesn?t equal self elementwise

MaskedArray.__ifloordiv__(other) [source] Floor divide self by other in-place.

numpy.linalg.eigh(a, UPLO='L') [source] Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix. Returns two objects, a 1-D array containing the eigenvalues of a, and a 2-D square array or matrix (depending on the input type) of the corresponding eigenvectors (in columns). Parameters: a : (..., M, M) array Hermitian/Symmetric matrices whose eigenvalues and eigenvectors are to be computed. UPLO : {?L?, ?U?}, optional Specifies whether the calculation is done with the l