spectrochempy.whittaker¶
- whittaker(dataset, lamb=1.0, order=2, **kwargs)[source]¶
Smooth the data using the Whittaker smoothing algorithm.
This implementation based on the work by Eilers [2003] uses sparse matrices enabling high-speed processing of large input vectors.
Copyright M. H. V. Werts, 2017 (see LICENSES/WITTAKER_SMOOTH_LICENSE.rst)
- Parameters
dataset (
NDDataset
or array-like of shape (n_observations
,n_features
)) – Input data, where n_observations is the number of observations and n_features is the number of features.lamb (
float
, optional, default: 1.0) – Smoothing/Regularization parameter. The largerlamb
, the smoother the data.order (
int
, optional, default=2) – The difference order of the penalized least-squares.**kwargs (keyword parameters, optional) – See Other Parameters.
- Returns
NDdataset
– Smoothed data.- Other Parameters
dim (
int
orstr
, optional, default: -1,) – Dimension along which the method is applied. By default, the method is applied to the last dimension. Ifdim
is specified as an integer it is equivalent to the usualaxis
numpy parameter.log_level (any of [
"INFO"
,"DEBUG"
,"WARNING"
,"ERROR"
], optional, default:"WARNING"
) – The log level at startup. It can be changed later on using theset_log_level
method or by changing thelog_level
attribute.
See also
Filter
Define and apply filters/smoothers using various algorithms.
smooth
Function to smooth data using various window filters.
savgol
Savitzky-Golay filter.
savgol_filter
Alias of
savgol