NOTE: PHOEBE 1.0 legacy is an alternate backend and is not installed with PHOEBE 2.0. In order to run this backend, you'll need to have PHOEBE 1.0 installed and manually build the python bindings in the phoebe-py directory.
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
import numpy as np
import matplotlib.pyplot as plt
phoebe.devel_on() # needed to use WD-style meshing, which isn't fully supported yet
logger = phoebe.logger()
b = phoebe.default_binary()
b['q'] = 0.7
b['requiv@secondary'] = 0.7
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b.add_dataset('lc', times=np.linspace(0,1,101), dataset='lc01')
b.add_dataset('rv', times=np.linspace(0,1,101), dataset='rvdyn')
b.add_dataset('rv', times=np.linspace(0,1,101), dataset='rvnum')
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Let's add compute options for phoebe using both the new (marching) method for creating meshes as well as the WD method which imitates the format of the mesh used within legacy.
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b.add_compute(compute='phoebe2marching', irrad_method='none', mesh_method='marching')
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b.add_compute(compute='phoebe2wd', irrad_method='none', mesh_method='wd', eclipse_method='graham')
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Now we add compute options for the 'legacy' backend.
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b.add_compute('legacy', compute='phoebe1', irrad_method='none')
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And set the two RV datasets to use the correct methods (for both compute options)
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b.set_value_all('rv_method', dataset='rvdyn', value='dynamical')
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b.set_value_all('rv_method', dataset='rvnum', value='flux-weighted')
Let's use the external atmospheres available for both phoebe1 and phoebe2
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b.set_value_all('atm', 'extern_planckint')
Let's make sure both 'phoebe1' and 'phoebe2wd' use the same value for gridsize
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b.set_value_all('gridsize', 30)
Let's also disable other special effect such as heating, gravity, and light-time effects.
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b.set_value_all('ld_func', 'logarithmic')
b.set_value_all('ld_coeffs', [0.,0.])
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b.set_value_all('rv_grav', False)
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b.set_value_all('ltte', False)
Finally, let's compute all of our models
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b.run_compute(compute='phoebe2marching', model='phoebe2marchingmodel')
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b.run_compute(compute='phoebe2wd', model='phoebe2wdmodel')
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b.run_compute(compute='phoebe1', model='phoebe1model')
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colors = {'phoebe2marchingmodel': 'g', 'phoebe2wdmodel': 'b', 'phoebe1model': 'r'}
afig, mplfig = b['lc01'].plot(c=colors, legend=True, show=True)
Now let's plot the residuals between these two models
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artist, = plt.plot(b.get_value('fluxes@lc01@phoebe2marchingmodel') - b.get_value('fluxes@lc01@phoebe1model'), 'g-')
artist, = plt.plot(b.get_value('fluxes@lc01@phoebe2wdmodel') - b.get_value('fluxes@lc01@phoebe1model'), 'b-')
artist = plt.axhline(0.0, linestyle='dashed', color='k')
ylim = plt.ylim(-0.003, 0.003)
Since the dynamical RVs don't depend on the mesh, there should be no difference between the 'phoebe2marching' and 'phoebe2wd' synthetic models. Here we'll just choose one to plot.
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afig, mplfig = b.filter(dataset='rvdyn', model=['phoebe2wdmodel', 'phoebe1model']).plot(c=colors, legend=True, show=True)
And also plot the residuals of both the primary and secondary RVs (notice the scale on the y-axis)
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artist, = plt.plot(b.get_value('rvs@rvdyn@primary@phoebe2wdmodel') - b.get_value('rvs@rvdyn@primary@phoebe1model'), color='b', ls=':')
artist, = plt.plot(b.get_value('rvs@rvdyn@secondary@phoebe2wdmodel') - b.get_value('rvs@rvdyn@secondary@phoebe1model'), color='b', ls='-.')
artist = plt.axhline(0.0, linestyle='dashed', color='k')
ylim = plt.ylim(-1.5e-12, 1.5e-12)
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afig, mplfig = b.filter(dataset='rvnum').plot(c=colors, show=True)
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artist, = plt.plot(b.get_value('rvs@rvnum@primary@phoebe2marchingmodel', ) - b.get_value('rvs@rvnum@primary@phoebe1model'), color='g', ls=':')
artist, = plt.plot(b.get_value('rvs@rvnum@secondary@phoebe2marchingmodel') - b.get_value('rvs@rvnum@secondary@phoebe1model'), color='g', ls='-.')
artist, = plt.plot(b.get_value('rvs@rvnum@primary@phoebe2wdmodel', ) - b.get_value('rvs@rvnum@primary@phoebe1model'), color='b', ls=':')
artist, = plt.plot(b.get_value('rvs@rvnum@secondary@phoebe2wdmodel') - b.get_value('rvs@rvnum@secondary@phoebe1model'), color='b', ls='-.')
artist = plt.axhline(0.0, linestyle='dashed', color='k')
ylim = plt.ylim(-1e-2, 1e-2)
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