The bug came from how x and y designate rows and columns in matplotlib.pyplot.imshow vs. numpy.array. Here is an example that was previously failing, and now you can see that the blue point on the map corresponds to the blue spectrum, which has the highest flux.
I also generalized what points/spectra can be shown in this plot. Users can now pass the keyword argument points_on_map to the function gen_lightcurves.create_lightcurves_with_starry. points_on_map is a list of (x,y) tuples for each point that will be plotted. The plot above was generated with the default values: points_on_map = [(-0.4, 0.4), (-0.4, -0.4), (0.4, 0.4), (0.4, -0.4), (0.0, 0.0)]. Note that (0,0) is the center of the planetary disk (substellar point) at secondary eclipse, with values given in units the planet radius.
The bug came from how x and y designate rows and columns in
I also generalized what points/spectra can be shown in this plot. Users can now pass the keyword argument
matplotlib.pyplot.imshowvs.numpy.array. Here is an example that was previously failing, and now you can see that the blue point on the map corresponds to the blue spectrum, which has the highest flux.points_on_mapto the functiongen_lightcurves.create_lightcurves_with_starry.points_on_mapis a list of (x,y) tuples for each point that will be plotted. The plot above was generated with the default values:points_on_map = [(-0.4, 0.4), (-0.4, -0.4), (0.4, 0.4), (0.4, -0.4), (0.0, 0.0)]. Note that (0,0) is the center of the planetary disk (substellar point) at secondary eclipse, with values given in units the planet radius.