Tutorial 5: Customising 2D plots¶
In this tutorial, we will go back to looking at staging options, but for 2D spectra instead of 1D spectra.
Contour levels and colours¶
Let’s revisit the original 2D plot that we did back in Tutorial 2: Plotting data. As before, it’s probably a good idea to move this into a script (or notebook).
import penguins as pg
data_2d = pg.read(".", 2, 1)
data_2d.stage()
pg.mkplot()
pg.show()
The most obvious problem is that the contour levels chosen are too low: that is, we are plotting quite a bit of noise along with the peaks. penguins tries to choose a reasonable contour level (using the same algorithm as TopSpin), but you will find that for nearly every spectrum you plot you will have to specify the contour levels manually.
This can be done using the levels parameter.
Passing a float here will lead to that float being used as the base level, i.e. the lowest level for which contours are drawn.
The smaller this number, the more noise you will pick up.
Finding a good number usually requires some experimentation.
data_2d.stage(levels=3e5)
pg.mkplot()
pg.show()
Contours are always shown for both positive and negative levels.
If you want to disable negative contours, the best way is currently to set the negative contour colour to be the same as the background.
Contour colours are a 2-tuple of strings (positive colour and negative colour), passed as the colors parameter (note the American English spelling!).
# contour levels reduced so that the negative ones are more visible
data_2d.stage(levels=1e5, colors=("yellowgreen", "purple"))
pg.mkplot()
pg.show()
Bounds¶
If you are already familiar with specifying bounds for 1D spectra, then 2D ones will be easy.
Instead of a single bounds parameter, you can now pass f1_bounds and/or f2_bounds.
The syntax is exactly the same, so either a string or a tuple can be passed:
data_2d.stage(levels=3e5, f1_bounds="0.3..7", f2_bounds=(0.3, 7))
pg.mkplot()
pg.show()
