numpy.fft.ifftn(a, s=None, axes=None, norm=None) [source] Compute the N-dimensional inverse discrete Fourier Transform. This function computes the inverse of the N-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, ifftn(fftn(a)) == a to within numerical accuracy. For a description of the definitions and conventions used, see numpy.fft. The input, analogously to ifft, should be ordered in the

MaskedArray.__array_wrap__(obj, context=None) [source] Special hook for ufuncs. Wraps the numpy array and sets the mask according to context.

numpy.empty(shape, dtype=float, order='C') Return a new array of given shape and type, without initializing entries. Parameters: shape : int or tuple of int Shape of the empty array dtype : data-type, optional Desired output data-type. order : {?C?, ?F?}, optional Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Returns: out : ndarray Array of uninitialized (arbitrary) data of the given shape, dtype, and order. Object

Python defines only one type of a particular data class (there is only one integer type, one floating-point type, etc.). This can be convenient in applications that don?t need to be concerned with all the ways data can be represented in a computer. For scientific computing, however, more control is often needed. In NumPy, there are 24 new fundamental Python types to describe different types of scalars. These type descriptors are mostly based on the types available in the C language that CPytho

numpy.invert(x[, out]) = Compute bit-wise inversion, or bit-wise NOT, element-wise. Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ~. For signed integer inputs, the two?s complement is returned. In a two?s-complement system negative numbers are represented by the two?s complement of the absolute value. This is the most common method of representing signed integers on computers [R32]. A N-bit

MaskedArray.__xor__ x.__xor__(y) <==> x^y

dtype.metadata

A data type object (an instance of numpy.dtype class) describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted. It describes the following aspects of the data: Type of the data (integer, float, Python object, etc.) Size of the data (how many bytes is in e.g. the integer) Byte order of the data (little-endian or big-endian) If the data type is structured, an aggregate of other data types, (e.g., describing an array item consisting of an inte

nditer.next x.next() -> the next value, or raise StopIteration

chararray.put(indices, values, mode='raise') Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation. See also numpy.put equivalent function