apply¶
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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
data (numpy.ndarray) –
x_axis_index (int) –
y_axis_index (int) –
user_provided_kernel (bool) –
kernel (astropy.units.Quantity) –
- Return type