# Post Analyzer

This tool can analyze time series data, for example from the Data Browser, in various ways.

## Add Data

Get data into the Data Browser Plot, then open the Data Browser Config View and use the context menu of selected data browser PVs or formula items to send their data to the Post Analyzer.

## Algorithms

The following describes the available algorithms.

### Original Data

This algorithm simply displays the original data of the selected channel.

#### Crop

The "crop" operation removes all samples which are before or after the visible section of the time axis in the plot window. This can be used to restrict the algorithms to only subsections of the data that was originally imported.

#### Baseline Removal

The "baseline" operation turns the lower end of the "Y" (vertical) axis into the zero point of the data, i.e. it subtracts the value of the lower Y axis end from all samples.

### Line Fit

Performs a line fit to the data. The slope of the line reflects the change of the signal in one second.

### Exponential Fit

Attempts to fit the data to an exponential rise or decay.

Note that this only works if the signal really follows an exponential function `ampl * exp(-x/decay)`. If the data contains a constant baseline offset `baseline + ampl * exp(-x/decay)`, the fit will fail. One can sometimes remove this baseline offset by zooming such that the lower end of the "Y" axis is at that baseline, and then using the "Baseline" removal button.

### Gaussian Fit

Attempts to fit the data to a Gauss distribution.

### Correlation

Correlates two different channels, using one for the "X" and the other for the "Y" axis. When both channels are the same or closely related, the result is a diagonal line.

### FFT

Computes the Fourier transform, i.e. the frequency spectrum of the signal. The data in the initial "Original Data" example represented a valve position that cycled about every 4 minutes, i.e. every 240 seconds or 1/240 = 0.004 Hz. The FFT shows this 0.004 Hz frequency component in addition to the usually big "DC" component at 0 Hz and some others.

#### Aliasing, Nyquist Limit

The FFT upper spectrum limit is at the so called Nyquist frequency. Given data points every T seconds, the Nyquist frequency is 1/(2T) Hz. When sampling every 0.1 seconds, i.e. at 10 Hz, then the FFT includes components up to 5 Hz. A 1 Hz signal sampled like this...

... clearly shows up in the FFT:
A 6 Hz signal, however, cannot be properly detected in such an undersampled setup. The FFT will show an alias 4 Hz component instead of the true 6 Hz frequency.

#### Window Filters

While the sample period defines the upper limit of the FFT spectrum, the total sample count defines the granularity of the FFT. In most cases, frequencies of interest can lie between those spectrum points for which the FFT is computed. By applying a window filter to the original signal, such problems are reduced. See detailed FFT description.