@@ -19,13 +19,13 @@ It plots the light curve together with a simple Leahy-normalized power spectrum
By checking that the high-frequency PSs are flat (i.e., the noise tends to white noise), and that the alignment between at least one of the models and the data count distribution is good, users can safely just renormalize the powers in their PSs then by dividing them by half the average power at high frequencies.
The function takes five arguments as input (NyqFreq,LC,Binned,PSname,Noisename) and returns three (extrVoverE,CrossTalkProb,HalfMeanPow). \
Input arguments:
#### Input arguments:
-`NyqFreq` is the Nyquist frequency in Hz
-`LC` (light curve) is the vector of either binned counts, or photon times of arrival (ToAs)
- If `Binned` is true, we assume uniform binning and dt = 1/(2*NyqFreq), otherwise we assume the elements of LC are actually the ToAs, and the function bins the data with the aforementioned dt
-`PSname` and `Noisename` are the filenames (or complete paths) of graphs to be saved: the first figure will contain the power spectra (PSs) and light curve, and the second will compare the counts distribution with the one estimated through assumptions on the crosstalk distributions. Any of the [types supported](https://it.mathworks.com/help/matlab/ref/exportgraphics.html#mw_3db70293-26d3-4e3f-9abf-6b96488e4dde) by MATLAB's exportgraphics function should be fine, but formatting tests were carried out with pdfs
Output arguments:
#### Output arguments:
-`extrVoverE` is a three-element vector containing the ratios of Variance over Expected value calculated assuming a Poissonian process modified by, respectively, 8-pixel, 4-pixel, and 1-pixel crosstalk probability distributions
-`CrossTalkProb` is the total crosstalk probability (it only assumes underlying poissonian noise and is independent of the number of pixels that can be fired by a single photon)
-`HalfMeanPow` is half the average power at frequencies higher than 1000 Hz in the power spectrum (this is what extrVoverE has to be compared to)
