Make backers public #143
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Make backers public #143
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pavelkomarov
commented
Aug 11, 2025
| """This module implements some common finite difference schemes | ||
| """ | ||
| from ._finite_difference import first_order, second_order, fourth_order | ||
| from ._finite_difference import finite_difference, first_order, second_order, fourth_order |
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There are simply more public methods now. The docstring makes reference to the fact other methods in the module really just call the backing one.
pavelkomarov
commented
Aug 11, 2025
| def _finite_difference(x, dt, num_iterations, order): | ||
| """Helper for all finite difference methods, since their iteration structure is all the same. | ||
| def finite_difference(x, dt, num_iterations, order): | ||
| """Perform iterated finite difference of a given order. This serves as the common backing function for |
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Had to add more proper docstrings.
…rward and backward difference, it doesn't do as well as just plain first order at the edges
pavelkomarov
commented
Aug 11, 2025
| """ | ||
| xhat0 = np.zeros(A.shape[0]); xhat0[0] = x[0] # See #110 for why this choice of xhat0 | ||
| q = 10**int(np.log10(qr_ratio)/2) # even-ish split of the powers across 0 | ||
| r = q/qr_ratio |
…referred order rather than alphabetically
This was referenced Aug 12, 2025
pavelkomarov
commented
Aug 12, 2025
| """ | ||
| from ._kalman_smooth import constant_velocity, constant_acceleration, constant_jerk, known_dynamics | ||
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| __all__ = ['constant_velocity', 'constant_acceleration', 'constant_jerk', 'known_dynamics'] # So these get treated as direct members of the module by sphinx |
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For modules where I manually reference methods in the .rst, there is no need to list out __all__ anymore.
pavelkomarov
commented
Aug 12, 2025
| method_params_and_bounds = { | ||
| spectraldiff: ({'even_extension': [True, False], | ||
| 'pad_to_zero_dxdt': [True, False], | ||
| spectraldiff: ({'even_extension': (True, False), # give boolean or numerical params in a list to scipy.optimize over them |
pavelkomarov
commented
Aug 12, 2025
| :param np.array[float] dxdt_truth: actual time series of the derivative of x, if known | ||
| :param float tvgamma: Only used if :code:`dxdt_truth` is given. Regularization value used to select for parameters | ||
| that yield a smooth derivative. Larger value results in a smoother derivative. | ||
| :param dict search_space_updates: At the top of :code:`_optimize.py`, each method has a search space of parameters |
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I renamed this param, because the old name was a little too grandiose for what it is.
pavelkomarov
commented
Aug 12, 2025
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| methods = [second_order, fourth_order, mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff, | ||
| splinediff, spectraldiff, polydiff, savgoldiff, constant_velocity, constant_acceleration, constant_jerk] | ||
| methods = [finite_difference, mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff, |
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shorter list now
pavelkomarov
commented
Aug 12, 2025
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| def metrics(x, dt, x_hat, dxdt_hat, x_truth=None, dxdt_truth=None, padding=0): | ||
| """Evaluate x_hat based on various metrics, depending on whether dxdt_truth and x_truth are known or not. | ||
| def rmse(x, dt, x_hat, dxdt_hat, x_truth=None, dxdt_truth=None, padding=0): |
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I decided to rename this one, because all the metrics it's calculating are RMSE-related.
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I decided to take on a bit of #138 with a "yes and". The old methods all live where they lived, but I've exposed the common backing methods of TVR, Kalman, and Iterated FD. In the process of the RTS smoothing/Kalman one, I took on #139, because I couldn't help myself.
Of course that then entailed some changes to the tests and to the optimization code. Most profound, I realized Iterated FD has numerical order, but only some are implemented, so the search space of that parameter needs to be categorical. Handling categoricals is a challenge I chose to leave alone last time I worked on the optimizer, but happy to say I've successfully wrestled that bear.