I added a new feature to the interval selection in select.py, which can select events on a signal based on its correlation with Gaussian functions characterized by the given parameters. The algorithm filters the signal, and it filters the Gaussians as well, then it calculates the correlation between them. At the end, it defines events, where the correlation is higher than the given treshold.
I also created a detailed test function in the flap_tests.py, where I generated some realistic signals using a 1kHz oscillation, some random placed and sized events, and a valid noise of an ABES channel in Wendelstein 7-X. This 1kHz mode is important, because the main importance of this method is that it can be used in such a frequency range which is close to a plasma mode. For the events, I used Gaussians, and ELM-like perturbations as well, just to show the fact that the algorithm is sufficient for non-Gaussian events as well.
I added a new feature to the interval selection in select.py, which can select events on a signal based on its correlation with Gaussian functions characterized by the given parameters. The algorithm filters the signal, and it filters the Gaussians as well, then it calculates the correlation between them. At the end, it defines events, where the correlation is higher than the given treshold.
I also created a detailed test function in the flap_tests.py, where I generated some realistic signals using a 1kHz oscillation, some random placed and sized events, and a valid noise of an ABES channel in Wendelstein 7-X. This 1kHz mode is important, because the main importance of this method is that it can be used in such a frequency range which is close to a plasma mode. For the events, I used Gaussians, and ELM-like perturbations as well, just to show the fact that the algorithm is sufficient for non-Gaussian events as well.