# Plotting

`redback`

provides a few plotting functions to help you visualize your results/data.
These functions are implemented in the `redback.plotting`

module and available as methods in the `transient`

and `result`

class.

In particular, the five main plots are:

plot_data: Which plots all the data in one panel.

plot_multiband_data: Which plots the data in multiple panels, one panel per band.

plot_corner: Which plots the corner plot of the result.

plot_lightcurve: Which plots the lightcurve from the result on top of the plot_data. By default this plots the 100 randomly drawn lightcurves from the posterior. Alongside the maximum likelihood lightcurve.

plot_multiband_lightcurve: Which plots the lightcurve from the result on top of the plot_multiband_data. By default this plots the 100 randomly drawn lightcurves from the posterior for each band in a panel.

Using dependency injections, we can completely change the behaviour of plotting. However, we don’t think this is necessary as almost all aesthetic and functional changes can simply be modified using matplotlib and kwargs.

For example, all plotting methods have the functionality to return the matplotlib axes. The matplotlib axes can be used to change the labels/xscale/limits if so desired. We can also pass several kwargs directly to the plotting function. Such as a dictionary to replace the default colours that are plotted to one of your choice.

```
ax = result.plot_data(show=False, band_colors={'r':'red', 'g':'green'})
ax.set_xlabel("Modified Julian Date")
ax.set_ylabel("Flux")
ax.set_xscale('log')
```

We note that many of these properties can also be modified by passing in the relevant kwargs to the plotting function. Please look at the Plotting API for more details on kwargs that can be passed in and plotting functionality that can be modified.

Of course, thanks to the modularity, one can also call the model themselves and format the data how they want to and plot how they wish to.