Savitzky-golay filter

EOkit.smoothers.sav_golay.multiple_sav_golays(y_inputs, window_size, order, deriv=0, delta=1, n_threads=-1)

Run many Savitzky-golay smoothers on 1D data in a multithread manner.

This runs an identical algorithm to the single_sav_golay function. However, this functions takes a list of y_inputs. Rust is then used under the hood to multithread each Savitzky-golay smoother as a task leading to faster computation for pixel-based problems!

I have used a list here as apposed to a 2-D array so that arrays of different lengths can be supplied.

Parameters:

  • y_inputs (list of ndarrays of type float, size (N)) – A list of numpy arrays containing the values to be smoothed.

  • window_size (int) – The size of the sliding window. Generally, the larger the window of data points, the smoother the resultant data.

  • order (int) – Order of polynomial to fit the data with. Needs to be less than window_size - 1.

  • deriv (int, optional) – Order of the derivative to smooth, by default 0

  • delta (int, optional) – The spacing of the samples to which the filter is applied, by default 1

  • n_threads (int, optional) – Amount of worker threads spawned to complete the task. The default is -1 which uses all logical processor cores. To tone this down, use something between 1 and the number of processor cores you have. Setting this value to a number larger than the amount of logical cores you have will most likely degreade performance, by default -1

Returns:

A list of numpy arrays containing the smoothed data at y_inputs.

Return type:

list of ndarrays of type float, size (N)

References

EOkit.smoothers.sav_golay.single_sav_golay(y_input, window_size, order, deriv=0, delta=1)

Run a single Savitzky-golay filter on 1D data.

The Savitzky-golay smoother fits a polynomial to sliding windows of data using a least squares fit. The implementation I have used is similar to that found in the SciPy cookbook: https://scipy.github.io/old-wiki/pages/Cookbook/SavitzkyGolay.

Parameters:

  • y_input (ndarray of type float, size (N)) – The inputs that are to be smoothed.

  • window_size (int) – The size of the sliding window. Generally, the larger the window of data points, the smoother the resultant data.

  • order (int) – Order of polynomial to fit the data with. Needs to be less than window_size - 1.

  • deriv (int, optional) – Order of the derivative to smooth, by default 0

  • delta (int, optional) – The spacing of the samples to which the filter is applied, by default 1

Returns:

Smoothed data at y inputs.

Return type:

ndarray of type float, size (N)

Examples

Below is a simple example of how to use the Savitzky-golay smoother.

>>> data_len = 1000
>>> vci = (np.sin(np.arange(0, data_len, 1., dtype=float))
>>>           + np.random.standard_normal(data_len) * 2))
>>> rust_smoothed_data = sav_golay.single_sav_golay(vci, 7, 2, 0, 1)

References