from numpy import roll, ndarray, floor, iscomplexobj, round, any, isnan, nan_to_num from scipy.ndimage.measurements import maximum_position, center_of_mass from scipy.fftpack import fftn, fftshift, ifftn, ifftshift from warnings import warn from numpy.lib.stride_tricks import as_strided __all__ = ["shift_array", 'roll_to_pos', 'shifted_ifft', 'shifted_fft', 'tile_array'] def shift_array(arr: ndarray, dy: int, dx: int): """ Use numpy.roll to shift an array in the first and second dimensions :param arr: numpy array :param dy: shift in first dimension :param dx: shift in second dimension :return: array like arr """ temp = roll(arr, (dy, dx), (0, 1)) return temp def roll_to_pos(arr: ndarray, y: int = 0, x: int = 0, pos: tuple = None, move_maximum: bool = False, by_abs_val: bool = True) -> ndarray: """ Shift the center of mass of an array to the given position by cyclic permutation :param arr: 2d array, works best for well-centered feature with limited support :param y: position parameter :param x: position parameter for second dimension :param pos: tuple with the new position, overriding y,x values. should be used for higher-dimensional arrays :param move_maximum: if true, look only at max-value :param by_abs_val: take abs value for the determination of max-val or center-of-mass :return: array like original """ if move_maximum: if by_abs_val or iscomplexobj(arr): old = floor(maximum_position(abs(arr))) else: old = floor(maximum_position(arr)) else: if by_abs_val or iscomplexobj(arr): old = floor(center_of_mass(abs(arr))) else: old = floor(center_of_mass(arr)) if any(isnan(old)): old = nan_to_num(old) warn(Warning("Unexpected error in the calculation of the center of mass, casting NaNs to num")) if pos is not None: # dimension-independent method shifts = tuple([int(round(pos[i] - old[i])) for i in range(len(pos))]) dims = tuple([i for i in range(len(pos))]) temp = roll(arr, shift=shifts, axis=dims) else: # old method temp = shift_array(arr, int(y - old[0]), int(x - old[1])) if temp.shape != arr.shape: raise Exception('Non-matching input and output shapes') return temp def shifted_fft(arr, axes=None): """ Combined fftshift and fft routine, based on scipy.fftpack Args: arr: numpy array axes: identical to argument for scipy.fftpack.fft Returns: transformed array """ return ifftshift(fftn(fftshift(arr, axes=axes), axes=axes), axes=axes) def shifted_ifft(arr, axes=None): """ Combined fftshift and fft routine, based on scipy.fftpack Args: arr: numpy array axes: identical to argument for scipy.fftpack.fft Returns: transformed array """ return fftshift(ifftn(ifftshift(arr, axes=axes), axes=axes), axes=axes) def tile_array(a: ndarray, shape): """ Upsample an array by nearest-neighbour interpolation, i.e. [1,2] -> [1,1,2,2] :param a: numpy array, ndim = [2,3] :param shape: tile size, single integer for rectangular tiles, tuple for individual axes otherwise :return: resampled array """ if a.ndim == 2: try: b0, b1 = shape except TypeError: b0 = shape b1 = shape r, c = a.shape # number of rows/columns rs, cs = a.strides # row/column strides x = as_strided(a, (r, b0, c, b1), (rs, 0, cs, 0)) # view a as larger 4D array return x.reshape(r * b0, c * b1) # create new 2D array elif a.ndim == 3: try: b0, b1, b2 = shape except TypeError: b0 = shape b1 = shape b2 = shape x, y, z = a.shape xs, ys, zs = a.strides temp = as_strided(a, (x, b0, y, b1, z, b2), (xs, 0, ys, 0, zs, 0)) return temp.reshape((x * b0, y * b1, z * b2)) else: raise NotImplementedError("Arrays of dimensions other than 2 and 3 are not implemented yet")