updated readme

Author: pavelkomarov

pynumdiff/README.md

@@ -1,7 +1,8 @@
-- `finite_difference` contains basic first and second order finite differencing methods. The first order method supports iterative application.
-- `kalman_smooth` contains Kalman filtering and smoothing methods, currently constant-derivative methods up to 3rd order (jerk) and a classic linear Kalman Filter based on known dynamics.
-- `linear_model` is a bit of a miscellaneous module, containing methods which work linearly: `lineardiff`, `polydiff`, `savgoldiff`, and `spectraldiff`.
-- `optimize` contains code to find best parameter settings for methods, tuned using Nelder-Mead according to the paper "Numerical differentiation of noisy data: A unifying multi-objective optimization framework"
+- `finite_difference` contains a method to do iterative 1st, 2nd or 4th order finite differencing.
+- `kalman_smooth` contains classic linear Kalman filter and RTS smoothing code, as well as a constant-derivative naive-model smoothing method for 1st (velocity), 2nd (acceleration), or 3rd (jerk) order.
+- `linear_model` contains `lineardiff`, which tries to fit a linear model on a sliding window, and `spectraldiff`, based on Fourier transforms.
+- `optimize` contains code to find best parameter settings for methods, tuned using Nelder-Mead according to the paper "Numerical differentiation of noisy data: A unifying multi-objective optimization framework", as well as a method suggestion metamethod.
+- `polynomial_fit` contains `splinediff`, `polydiff`, and `savgoldiff`, which all explicitly or implicitly fit polynomials over pieces of the data.
 - `smooth_finite_difference` contains methods which do a smoothing step followed by simple finite difference.
 - `tests` contains `pytest` unit tests of
 	1. all the differentiation methods, checking their results against a suite of known analytic functions (including an ability to plot if the `--plot` command is passed to `pytest`, see `conftest.py`)