A low-pass filter smooths the input data by applying a convolution filter. This filter is termed 'low-pass' because it allows low frequencies, or slow wavenumbers, to pass through to the output. Features in the data shorter than the cutoff wavelength will be removed.
The filter will be designed using Fraser's 1966 method. The default length will match the cutoff wavelength, minimizing side effects like Gibbs' phenomenon.
Filtered sensor values will be displayed as a solid line, with the original data shown as dashed lines after pressing the "Apply" button.
The "Undo" button will revert the values of selected sensor to their original state.
The "Apply to all" button will apply the same filter to all sensor values in files loaded with the same template as the current one.
Filtered data should be saved to either the same file or a new one using "Save to.."
A low-pass filter smooths the input data by applying a convolution filter. This filter is termed 'low-pass' because it allows low frequencies, or slow wavenumbers, to pass through to the output. Features in the data shorter than the cutoff wavelength will be removed.
The filter will be designed using Fraser's 1966 method. The default length will match the cutoff wavelength, minimizing side effects like Gibbs' phenomenon.
Filtered sensor values will be displayed as a solid line, with the original data shown as dashed lines after pressing the "Apply" button.
The "Undo" button will revert the values of selected sensor to their original state.
The "Apply to all" button will apply the same filter to all sensor values in files loaded with the same template as the current one.
Filtered data should be saved to either the same file or a new one using "Save to.."