spectrochempy.Filter

class Filter(log_level='WARNING', *, cval=0.0, delta=1.0, deriv=0, lamb=1.0, method='savgol', mode='interp', order=2, size=5)[source]

Filters/smoothers processor.

The filters can be applied to 1D datasets consisting in a single row with n_features or to a 2D dataset with shape (n_observations, n_features).

Various filters/smoothers can be applied to the data. The currently available filters are:

Moving average (avg)
Convolution filters (han, hamming, bartlett, blackman)
Savitzky-Golay filter (savgol)
Whittaker-Eilers filter (whittaker)

Parameters:

log_level (any of ["INFO", "DEBUG", "WARNING", "ERROR"], optional, default: "WARNING") – The log level at startup. It can be changed later on using the set_log_level method or by changing the log_level attribute.
cval (float, optional, default: 0.0) – Value to fill past the edges of the input if mode is ‘constant’.
delta (float, optional, default: 1.0) – The spacing of the samples to which the filter will be applied. This is only used if deriv > 0.
deriv (int, optional, default: 0) – The order of the derivative to compute in the case of the Savitzky-Golay (savgol) filter. This must be a non-negative integer. The default is 0, which means to filter the data without differentiating.
lamb (float, optional, default: 1.0) – Smoothing/Regularization parameter. The larger lamb, the smoother the data.
method (any value of [ 'avg' , 'han' , 'hamming' , 'bartlett' , 'blackman' , 'median' , 'savgol' , 'whittaker' ], optional, default: 'savgol') – The filter method to be applied. By default, the Savitzky-Golay (savgol) filter is applied.
mode (any value of [ 'mirror' , 'constant' , 'nearest' , 'wrap' , 'interp' ], optional, default: 'interp') – The type of extension to use for the padded signal to which the filter is applied.
- When mode is ‘constant’, the padding value is given by cval.
- When the ‘interp’ mode is selected (the default), no extension is used. Instead, a polynomial of degree order is fit to the last size values of the edges, and this polynomial is used to evaluate the last window_length // 2 output values.
- When mode is ‘nearest’, the last size values are repeated.
- When mode is ‘mirror’, the padding is created by reflecting the signal about the end of the signal.
- When mode is ‘wrap’, the signal is wrapped around on itself to create the padding.
See scipy.signal.savgol_filter for more details on ‘mirror’, ‘constant’, ‘wrap’, and ‘nearest’.
order (int, optional, default: 2) – The order of the polynomial used to fit the datain the case of the Savitzky-Golay (savgol) filter. order must be less than size. In the case of the Whittaker-Eilers filter, order is the difference order of the penalized least squares.
size (positiveoddinteger, optional, default: 5) – The size of the filter window.size must be a positive odd integer.

See also

smooth: Function to smooth data using various window filters.
savgol: Savitzky-Golay filter.
savgol_filter: Alias of savgol
whittaker: Whittaker-Eilers filter.

Initialize the BaseConfigurable class.

Parameters:

log_level (int, optional) – The log level at startup. Default is logging.WARNING.
**kwargs (dict) – Additional keyword arguments for configuration.

Attributes Summary

`config`	`traitlets.config.Config` object.
`cval`	Value to fill past the edges of the input if `mode` is ‘constant’.
`delta`	The spacing of the samples to which the filter will be applied.
`deriv`	The order of the derivative to compute in the case of the Savitzky-Golay (savgol) filter.
`lamb`	Smoothing/Regularization parameter.
`log`	Return `log` output.
`method`	The filter method to be applied.
`mode`	The type of extension to use for the padded signal to which the filter is applied.
`name`	Object name
`order`	The order of the polynomial used to fit the datain the case of the Savitzky-Golay (savgol) filter.
`size`	The size of the filter window.size must be a positive odd integer.

Methods Summary

`parameters`([replace, removed, default])	Alias for `params` method.
`params`([default])	Return current or default configuration values.
`reset`()	Reset configuration parameters to their default values.
`to_dict`()	Return config value in a dict form.
`transform`(dataset[, dim])	Transform the input dataset X using the current model.

Attributes Documentation

config: traitlets.config.Config object.

cval: Value to fill past the edges of the input if mode is ‘constant’.

delta: The spacing of the samples to which the filter will be applied. This is only used if deriv > 0.

deriv: The order of the derivative to compute in the case of the Savitzky-Golay (savgol) filter. This must be a non-negative integer. The default is 0, which means to filter the data without differentiating.

lamb: Smoothing/Regularization parameter. The larger lamb, the smoother the data.

log: Return log output.

method: The filter method to be applied. By default, the Savitzky-Golay (savgol) filter is applied.

mode

The type of extension to use for the padded signal to which the filter is applied.

When mode is ‘constant’, the padding value is given by cval.
When the ‘interp’ mode is selected (the default), no extension is used. Instead, a polynomial of degree order is fit to the last size values of the edges, and this polynomial is used to evaluate the last window_length // 2 output values.
When mode is ‘nearest’, the last size values are repeated.
When mode is ‘mirror’, the padding is created by reflecting the signal about the end of the signal.
When mode is ‘wrap’, the signal is wrapped around on itself to create the padding.

See scipy.signal.savgol_filter for more details on ‘mirror’, ‘constant’, ‘wrap’, and ‘nearest’.

name: Object name

order: The order of the polynomial used to fit the datain the case of the Savitzky-Golay (savgol) filter. order must be less than size. In the case of the Whittaker-Eilers filter, order is the difference order of the penalized least squares.

size: The size of the filter window.size must be a positive odd integer.

Methods Documentation

parameters(replace="params", removed="0.8.0") def parameters(self, default=False)[source]: Alias for params method.

Deprecated since version 0.8.0: Use params instead.

params(default=False)[source]

Return current or default configuration values.

Parameters:: default (bool, optional, default: False) – If default is True, the default parameters are returned, else the current values.
Returns:: dict – Current or default configuration values.

reset()[source]: Reset configuration parameters to their default values.

to_dict()[source]

Return config value in a dict form.

Returns:: dict – A regular dictionary.

transform(dataset, dim=-1)

Transform the input dataset X using the current model.

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.
dim (int or str, optional, default: -1,) – Dimension along which the method is applied. By default, the method is applied to the last dimension. If dim is specified as an integer it is equivalent to the usual axis numpy parameter.

Returns:

NDDataset – The transformed dataset.

Examples using spectrochempy.Filter

Savitky-Golay and Whittaker-Eilers smoothing of a Raman spectrum