Can we use a different color map that the matplotlib default in the plot_noise() and plot_responses() methods of the solution class?
Apologies for the green-ness of everything I'm about to say.
When @christopher.wipf uses LISO (and thus zero) to model the LIGO suspension coil drivers in suspensions/ligo/electronics/models/coildriver, some of the noise budget plots, where I assume he's using the plot_noise() method of the Solutions class (using words that I hope are correct from zero's help plotting documentation,
Plotting in analysis scripts In scripts, plots are generated by the call to the solution’s plot_responses() or plot_noise() methods. These support many display options, as listed below. The return value from these methods is the Figure, which can be further modified.
After calling either plot_responses() or plot_noise(), you can show the generated plots with show(). This method is called separately to allow you to show a number of plots simultaneously.
create plots like this:
The problem is that even if one ignores all the sub-dominant noise components, one can't identify what the dominant noise component is because the colormap loops over after ~10 curves or so, the components in orange could be the dominant or the subdominant.
A different colormap might solve this. - maybe user defined, or you, zero, don't want to be so insistent as to chose one for everyone to solve all problems, or - maybe, if we want to be smart about it, the plot_noise() methods could only plot the top 10 most dominant terms, and add all of the subdominant terms in quadrature, lumping them in to one subdominant curve, so they don't consume so many legend colors. - maybe, once the feature request Issue 49 / Node Graphs milestone gets implemented / hit, then this point will be come moot.
Thanks! And sorry again if I've used all the wrong language to describe my problem and ideas for a solution!