apply

esis.data.inversion.mart.antialias.apply(data, x_axis_index=-3, y_axis_index=-2, user_provided_kernel=False, kernel=<Quantity [0.25, 0.5, 0.25]>)

Apply the antialias kernel to the cube data, for use in MART related inversion problems. :type data: numpy.ndarray :param data: :type x_axis_index: int :param x_axis_index: axis in data that is the spatial x-axis :type y_axis_index: int :param y_axis_index: axis in data that is the spatial y-axis :type user_provided_kernel: bool :param user_provided_kernel: if True, do not use calc_kernel to calculate the convolution kernel, instead using a kernel provided by the user. :type kernel: astropy.units.Quantity :param kernel: 1-dimensional kernel to be given to calc_kernel to generate the convolution kernel, or, if user_provided_kernel True, this kernel is handed directly to the convolution :rtype: numpy.ndarray :return: antialiased version of data

Parameters
Return type

numpy.ndarray