You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Great tool! A couple of suggestions for the plotting tools:
It would be awesome if the phase_plot could plot the phase-folded RV curves for signals sorted by the number of samples of the corresponding periodicity instead of being sorted by RV semi-amplitude as it is now.
It would be great to add an option for the plot_posterior_period function to include the periods of the most prominent peaks. Currently, the option show_peaks is only available if kde=True. It is quite simple to implement it for the other case. simply adding this:
if show_peaks and find_peaks:
peaks, _ = find_peaks(y, prominence=5*np.nanmedian(y))
for peak in peaks:
s = r'P$\simeq$%.2f' % xx[peak]
ax.text(xx[peak], y[peak], s, ha='left')
Note that here I changed to the default prominence=0.1 to a value dependent on the "noise floor" of the samples. But of course feel free to modify this if you are interested in adding this to the code.
The text was updated successfully, but these errors were encountered:
It would be awesome if the phase_plot could plot the phase-folded RV curves for signals sorted by the number of samples of the corresponding periodicity instead of being sorted by RV semi-amplitude as it is now.
This would be possible, but I think it would require binning the period posterior and/or deciding what samples are "close enough" to a particular period so they would be counted. I'm guessing you are searching for a way to sort the signals based on significance?
It would be great to add an option for the plot_posterior_period function to include the periods of the most prominent peaks.
Indeed, this would be helpful. I will try to add something like this to the plot.
Great tool! A couple of suggestions for the plotting tools:
Note that here I changed to the default prominence=0.1 to a value dependent on the "noise floor" of the samples. But of course feel free to modify this if you are interested in adding this to the code.
The text was updated successfully, but these errors were encountered: