Pre-packaged plotting routines

The plotting module of Magνs draws several of the standard figures of the field, each with a single call; Table The plotting functions lists them, and the sections that follow use them. Drawn directly in matplotlib, the same figure takes 25–40 lines of grid specification, tick locators, legend keywords, and savefig calls.

The plotting functions
Function (`plot_`)	Draws
`probability_vs_energy`	Probability against energy
`probability_vs_baseline`	Probability against baseline
`curves`	Any swept variable, optional error subpanel
`curves_stacked`	One panel per case, over a shared abscissa
`oscillogram`	Probability over energy and arrival direction
`biprobability`	Antineutrino against neutrino probability
`probability_with_average`	Oscillating probability, with its average
`probability_with_profile`	Density profile, above the probabilities along it

The plotting functions of magnus.plotting, each named with the prefix plot_. Each returns the matplotlib figure it built and its axes.

Most of these functions take the abscissa first, then a list of curves, each a dictionary with its data, y, and its label. For instance, the probability of \(\nu_\mu \to \nu_e\) against energy, in vacuum and at constant density, with Es, L, and P_scan the 200 energies, the baseline, and the scan of Listing Vacuum and constant density, three ways, is drawn by

import magnus.plotting as plotting

P_vac = oscprob.osc_prob_3nu_vacuum(Es, L)
fig, ax = plotting.\
    plot_probability_vs_energy(
    Es/gd.UNIT_GEV,
    [dict(y=P_vac[:, gd.NUMU, gd.NUE],
          label='Vacuum'),
     dict(y=P_scan[:, gd.NUMU, gd.NUE],
          label='Matter')],
    nu_i=gd.NUMU, nu_f=gd.NUE)

The flavor indices nu_i and nu_f only label the vertical axis, here \(P_{\nu_\mu \to \nu_e}\); the curves are the data passed. The API documentation [Bus] gives the arguments of every plotting function.

Each figure comes out in a preset style, so a set of figures is consistent without any styling passed. Since each function returns the matplotlib figure and its axes, the style can be changed afterwards. For instance, the figure above is resized, given a grid, and saved by

fig.set_size_inches(6, 3)
ax.grid(True)
fig.savefig('numu_to_nue.pdf')