numpy.fft.fftn(a, s=None, axes=None, norm=None) [source] Compute the N-dimensional discrete Fourier Transform. This function computes the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). Parameters: a : array_like Input array, can be complex. s : sequence of ints, optional Shape (length of each transformed axis) of the output (s[0] refers to axis 0, s[1] to axis 1, etc.). This corresponds to n for ff

numpy.fft.fftfreq(n, d=1.0) [source] Return the Discrete Fourier Transform sample frequencies. The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second. Given a window length n and a sample spacing d: f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is o

numpy.fft.fft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional discrete Fourier Transform This function computes the n-dimensional discrete Fourier Transform over any axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). By default, the transform is computed over the last two axes of the input array, i.e., a 2-dimensional FFT. Parameters: a : array_like Input array, can be complex s : sequence of ints, optional Shape (length of each transfo

numpy.fft.fft(a, n=None, axis=-1, norm=None) [source] Compute the one-dimensional discrete Fourier Transform. This function computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm [CT]. Parameters: a : array_like Input array, can be complex. n : int, optional Length of the transformed axis of the output. If n is smaller than the length of the input, the input is cropped. If it is larger, the input is padded with zer

numpy.fabs(x[, out]) = Compute the absolute values element-wise. This function returns the absolute values (positive magnitude) of the data in x. Complex values are not handled, use absolute to find the absolute values of complex data. Parameters: x : array_like The array of numbers for which the absolute values are required. If x is a scalar, the result y will also be a scalar. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the r

numpy.eye(N, M=None, k=0, dtype=) [source] Return a 2-D array with ones on the diagonal and zeros elsewhere. Parameters: N : int Number of rows in the output. M : int, optional Number of columns in the output. If None, defaults to N. k : int, optional Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal. dtype : data-type, optional Data-type of the returned array. Returns: I : nd

numpy.extract(condition, arr) [source] Return the elements of an array that satisfy some condition. This is equivalent to np.compress(ravel(condition), ravel(arr)). If condition is boolean np.extract is equivalent to arr[condition]. Note that place does the exact opposite of extract. Parameters: condition : array_like An array whose nonzero or True entries indicate the elements of arr to extract. arr : array_like Input array of the same size as condition. Returns: extract : ndarray

numpy.expm1(x[, out]) = Calculate exp(x) - 1 for all elements in the array. Parameters: x : array_like Input values. Returns: out : ndarray Element-wise exponential minus one: out = exp(x) - 1. See also log1p log(1 + x), the inverse of expm1. Notes This function provides greater precision than exp(x) - 1 for small values of x. Examples The true value of exp(1e-10) - 1 is 1.00000000005e-10 to about 32 significant digits. This example shows the superiority of expm1 in this case

numpy.expand_dims(a, axis) [source] Expand the shape of an array. Insert a new axis, corresponding to a given position in the array shape. Parameters: a : array_like Input array. axis : int Position (amongst axes) where new axis is to be inserted. Returns: res : ndarray Output array. The number of dimensions is one greater than that of the input array. See also doc.indexing, atleast_1d, atleast_2d, atleast_3d Examples >>> x = np.array([1,2]) >>> x.shape (2,) T

numpy.exp2(x[, out]) = Calculate 2**p for all p in the input array. Parameters: x : array_like Input values. out : ndarray, optional Array to insert results into. Returns: out : ndarray Element-wise 2 to the power x. See also power Notes New in version 1.3.0. Examples >>> np.exp2([2, 3]) array([ 4., 8.])