Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
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!pip install -I "phoebe>=2.1,<2.2"
As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
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%matplotlib inline
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import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
Line profiles will be computed for each component in which the wavelengths are provided. If we wanted to expose the line profile for the binary as a whole, we'd set the wavelenghts for wavelengths@binary. If instead we wanted to expose per-star line profiles, we could set the wavelengths for both wavelengths@primary and wavelengths@secondary.
If you're passing wavelengths to the b.add_dataset call, it will default to filling the wavelengths at the system-level. To override this, pass components=['primary', 'secondary'], as well. For example: b.add_dataset('lp', wavelengths=np.linspace(549,551,101), components=['primary', 'secondary']).
Line profiles have an extra dimension than LC and RV datasets which have times as their independent variable. For that reason, the parameters in the LP dataset are tagged with individual times instead of having a separate times array. This allows the flux_densities and sigmas to be per-time. Because of this, times is not a variable, but instead must be passed when you call b.add_dataset. At that point, in order to change the times you would need to remove and re-add the dataset.
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b.add_dataset('lp', times=[0,1,2], wavelengths=np.linspace(549, 551, 101))
print b.filter(kind='lp')
Here we see that there are three wavelengths Parameters, with the wavelengths@primary being filled with the input array (since we didn't override the components or manually pass a dictionary). Because of this, the flux_densities and sigmas are only visible for the binary component as well. (If we were to fill either of the two other arrays, the corresponding Parameters would become visible as well). We can see, however, that there is an entry for flux_densities and sigmas for each of the times we passed.
In addition, there are some Parameters in the dataset not related directly to observations. These include information about the line profile, as well as passband-dependent parameters.
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print b.filter(kind='lp_dep')
For information on the passband-dependent parameters, see the section on the lc dataset (these are used only to compute fluxes when rv_method=='flux-weighted')
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print b.filter('wavelengths')
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print b.get_parameter('wavelengths', component='binary')
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print b.filter('flux_densities')
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print b.get_parameter('flux_densities', time=0)
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print b.filter('sigmas')
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print b.get_parameter('sigmas', time=0)
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print b.get_parameter('profile_func')
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print b.get_parameter('profile_rest')
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print b.get_parameter('profile_sv')
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b.run_compute(irrad_method='none')
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b['lp@model'].twigs
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The model for a line profile dataset will expose flux-densities at each time and for each component where the corresponding wavelengths Parameter was not empty. Here since we used the default and exposed line-profiles for the entire system, we have a single entry per-time.
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print b.filter('flux_densities', context='model')
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print b.get_parameter('flux_densities', context='model', time=0)
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afig, mplfig = b.filter(dataset='lp01', context='model', time=0).plot(show=True)
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b.add_dataset('mesh', times=[0], dataset='mesh01')
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print b['columns'].choices
Since line profiles are passband-dependent, we get all passband-dependent mesh quantities as options. Additionally, we get rvs@lp01 (which under-the-hood is being used to determine the doppler shift of the line profile per-element and then summed over the star). To avoid large amounts of data being stored in the mesh with an extra dimension, the per-element line profiles are never stored, and therefore not able to be exposed to the user.