PyNumDiff
PyNumDiff copied to clipboard
florisvb
Methods for numerical differentiation of noisy data in python
We've retained an ability to call differentiation methods with a `params` list and `options` dictionary, and we have kept around old method signatures like `meandiff` and `constant_velocity` that are really...
I've discovered yet another method in this paper https://ejde.math.txstate.edu/conf-proc/21/k3/knowles.pdf (citing their earlier work, https://link.springer.com/article/10.1007/s002110050111). The derivative of the smoothed function, which they call $g$, is the same as the derivative...
One of the papers Dennis Bernstein sent us, https://inria.hal.science/hal-02960923/file/Mojallizadeh_etal_022021.pdf (100+ pages), deals specifically with online (one-sided, real-time) differentiation. There is emphasis on a family that I'm generally referring to as...
Yet another smoothing method to take derivatives of noisy data. Seems like it might have nice properties, computing the derivative at multiple resolutions and working for aperiodic functions. The frequency...
In `numpy` we can often feed a method a multidimensional array and an `axis` parameter that specifies with direction we're operating along. The methods in this package are designed to...
Shouldn't be hard. It's essentially the same as what I've already done in the Kalman filter, taking matrix exponentials to step whatever distances and performing a check to step around...
In principle a polynomial fit can be performed to data spaced wherever, and NaNs can simply be ignored. But `np.polyfit` loses its mind and returns all NaNs if given a...
Taking derivatives of a noisy time series of angles is always a huge pain (mostly because unwrapping noisy data is very hard). It would be nice if we could provide...
