@@ -20,13 +20,13 @@ Moreover, it compares the power distribution expected (a $\chi^2$ distribution w
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, even after correcting them or the underlying light curve for orbital motions: by dividing the PSs by half the average power at high frequencies (returned by the function as `HalfMeanPow`), they can be treated as normally done for the purpose of power significance and sensitivity estimation.
The function takes five arguments as input (NyqFreq, LC, Binned, PSname, Noisename) and returns three (extrVoverE, CrossTalkProb, HalfMeanPow).
The function takes six arguments as input (NyqFreq, LC, Binned, PSname, Noisename, PowDisname) and returns three (extrVoverE, CrossTalkProb, HalfMeanPow).
#### Input arguments:
-`NyqFreq` is the Nyquist frequency in Hz (we suggest to use at least 1500 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`, `Noisename` and `PowDisname` are the filenames (or complete paths) of graphs to be saved: the first figure will contain the power spectra (PSs) and light curve, the second will compare the counts distribution with the one estimated through assumptions on the crosstalk distributions, while the third contains the comparison of a $\chi^2$ distribution vs our power distribution, both with "input" powers and the PS renormalized by `HalfMeanPow`. 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
-`PSname`, `Noisename`, and `PowDisname` are the filenames (or complete paths) of graphs to be saved: the first figure will contain the power spectra (PSs) and light curve, the second will compare the counts distribution with the one estimated through assumptions on the crosstalk distributions, while the third contains the comparison of a $\chi^2$ distribution vs our power distribution, both with "input" powers and the PS renormalized by `HalfMeanPow`. 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:
-`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
