IterativeNonlinearLSTSQPSF.compute_jacobian¶
giant.point_spread_functions.psf_meta
:
- abstract IterativeNonlinearLSTSQPSF.compute_jacobian(x, y, computed)[source]¶
This method computes the Jacobian of the PSF with respect to a change in the state.
Mathematically, it should return the nxm matrix
\[\mathbf{J} = \frac{\partial f(x, y)}{\partial \mathbf{t}}\]where \(f(x,y)\) is the function being fit, \(\mathbf{t}\) is a length m vector of the state parameters, and \(\mathbf{J}\) is the Jacobian matrix
- Parameters:
x (ndarray) – The x values to evaluate the Jacobian at as a length n array
y (ndarray) – The y values to evaluate the Jacobian at as a length n array
computed (ndarray) – \(f(x,y)\) evaluated at x and y as a length n array. This is provided for efficiency and convenience as the evaluated function is frequently needed in the computation of the Jacobian and it is definitely needed in the non-linear least squares. If not needed for computing the Jacobian this can safely be ignored.
- Returns:
The Jacobian matrix as a nxm numpy array, with n being the number of measurements and m being the number of state parameters being estimated
- Return type:
ndarray