Let's first make sure we have the latest version of PHOEBE 2.2 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).
In [ ]:
!pip install -I "phoebe>=2.2,<2.3"
As always, let's do imports and initialize a logger and a new Bundle. See the building a system tutorial for more details.
In [1]:
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
And we'll attach some dummy datasets. See the datasets tutorial for more details.
In [2]:
b.add_dataset(phoebe.dataset.orb, compute_times=np.linspace(0,10,10), dataset='orb01', component=['primary', 'secondary'])
times, fluxes, sigmas = np.loadtxt('test.lc.in', unpack=True)
# test.lc.in has 1000 datapoints... let's use every 10 just for brevity
times, fluxes, sigmas = times[:10], fluxes[:10], sigmas[:10]
b.add_dataset(phoebe.dataset.lc, times=times, fluxes=fluxes, sigmas=sigmas, dataset='lc01')
Out[2]:
In [3]:
print(b.computes)
In [4]:
print(b.filter(context='compute'))
In [5]:
b.set_value('irrad_method', 'none')
In other cases, we may want to manually add additional sets of compute options. This syntax should look very familiar by now, it takes a function (or the name of a recognized function in phoebe.parameters.compute) and then any kwargs to set in that ParameterSet, passed to b.add_compute.
Let's say that we want to create two sets of compute options - in this example, we'll create one called 'preview' which will cut some corners to quickly get us a model, and one called 'detailed' which will get a much more precise model but likely take longer. As with other tags, the string you provide for the compute tag is up to you (so long as it doesn't raise an error because it conflicts with other tags).
In [6]:
b.add_compute(phoebe.compute.phoebe, compute='preview', irrad_method='none')
Out[6]:
In [7]:
print(b['preview@compute'])
In [8]:
b.add_compute('phoebe', compute='detailed', irrad_method='wilson')
Out[8]:
In [9]:
print(b.get_compute('detailed'))
Most of the parameters in the compute options are specific to the backend being used. Here, of course, we're using the PHOEBE 2.0 backend - but for details on other backends see the Alternate Backends Tutorial.
The PHOEBE 2.0 compute options are described in the tutorial on their relevant dataset types:
By default, synthetic models will be created for all datasets in the Bundle when run_compute is called. But you can disable a dataset to have run_compute ignore that dataset. This is handled by a BoolParameter with the qualifier 'enabled' - and has a copy that lives in each set of compute options
Let's say we wanted to compute the orbit but not light curve - so we want to set enabled@lc01:
In [10]:
print(b['enabled@lc01'])
as you can see, there is a copy for both of our compute options ('preview' and 'detailed').
If we know which set of compute options we'll be using, or only want to enable/disable for a given set, then we can do that:
In [11]:
b['enabled@lc01@preview'] = False
print(b['enabled@lc01'])
or to enable/disable a dataset for all sets of compute options, we can use the set_value_all method:
In [12]:
b.set_value_all('enabled@lc01', True)
print(b['enabled@lc01'])
If the enabled parameter is missing for a set of compute options - it is likely that that particular backend does not support that dataset type.
run_compute takes arguments for the compute tag as well as the model tag for the resulting synthetic model(s).
You do not need to provide the compute tag if only 0 or 1 set of compute options exist in the Bundle. If there are no compute options, the default PHOEBE 2.0 options will be added on your behalf and used. If there is a single set of compute options, those will be assumed. In our case, we have two compute options in the Bundle (with tags 'preview' and 'detailed') so we must provide an argument for compute.
If you do not provide a tag for the model, one will be created for you called 'latest'. Note that the 'latest' model will be overwritten without throwing any errors, whereas other named models can only be overwritten if you pass overwrite=True (see the run_compute API docs for details). In general, though, if you want to maintain the results from previous calls to run_compute, you must provide a NEW model tag.
In [13]:
b.run_compute(compute='preview')
Out[13]:
In [14]:
print(b.models)
In [15]:
b.set_value('incl@orbit', 90)
b.run_compute(compute='preview', model='run_with_incl_90')
Out[15]:
In [16]:
b.set_value('incl@orbit', 85)
b.run_compute(compute='preview', model='run_with_incl_85')
Out[16]:
In [17]:
b.set_value('incl@orbit', 80)
b.run_compute(compute='preview', model='run_with_incl_80')
Out[17]:
We will now have three new sets of synthetics which can be compared, plotted, or removed.
In [18]:
print(b.models)
To remove a model, call remove_model.
In [19]:
b.remove_model('latest')
Out[19]:
In [20]:
print(b.models)
In [21]:
b.run_compute(compute='preview',
times=[0,0.1,0.2],
model='override_times')
Out[21]:
In [22]:
print("dataset times: {}\nmodel times: {}".format(
b.get_value('times', dataset='lc01', context='dataset'),
b.get_value('times', dataset='lc01', model='override_times')))
NEW in PHOEBE 2.2
Alternatively, you can override the times (or phases) per-dataset at which the model should be computed. This is particularly useful if you have attached real data (with a large number of data points) but want the model computed at only a subset of times.
Note that the method above (sending times as a keyword argument to run_compute) will take precedence over compute_times.
In [23]:
b.set_value('compute_times', dataset='lc01', value=[0, 0.2, 0.4])
In [24]:
b.run_compute(compute='preview',
model='override_compute_times')
Out[24]:
In [25]:
print("dataset times: {}\ndataset compute_times: {}\ndataset compute_phases: {}\n model times: {}".format(
b.get_value('times', dataset='lc01', context='dataset'),
b.get_value('compute_times', dataset='lc01', context='dataset'),
b.get_value('compute_phases', dataset='lc01', context='dataset'),
b.get_value('times', dataset='lc01', model='override_compute_times')))
for more details, see the advanced: compute times & phases tutorial.
So far we've seen how setting up different sets of compute options can be handy - 'preview' vs 'detailed', for example. But there could also be situations where you want to use different sets of options per dataset. Perhaps you have a high-precision follow-up light curve of an eclipse along with a lower-precision light curve over a longer time baseline. So here you'd want to run 'detailed' on the high-precision light curve, but 'preview' on the lower-precision light curve.
You could of course call run_compute twice and create two separate models - but that isn't always convenient and will be a problem in the future when we want to fit data.
Instead we can send a list of compute options to run_compute.
A given dataset can only be enabled in up to 1 of the compute options we're sending to run_compute. So let's take care of that first (if we don't, we'd get an error when trying to call run_compute):
In [26]:
print(b['enabled@orb01'])
In [27]:
b.set_value_all('enabled@orb01@detailed', False)
b.set_value_all('enabled@orb01@preview', True)
print(b['enabled@orb01'])
We probably have the same problem with 'lc01', but just didn't get far enough to raise the error. So let's fix that as well
In [28]:
print(b['enabled@lc01'])
In [29]:
b.set_value_all('enabled@lc01@detailed', True)
b.set_value_all('enabled@lc01@preview', False)
print(b['enabled@lc01'])
So in this case, 'lc01' will be computed using the options in 'detailed' while 'orb01' will use the options in 'preview'.
In [30]:
b.run_compute(compute=['detailed', 'preview'], model='multiplecompute')
Out[30]:
In [31]:
print(b.models)
In [32]:
b['run_with_incl_90']
Out[32]:
In [33]:
b['primary@run_with_incl_90']
Out[33]:
In [34]:
b['us@primary@run_with_incl_90']
Out[34]:
or of course through method access:
In [35]:
print(b.get_value(qualifier='us', dataset='orb01', component='primary', model='run_with_incl_90')[:10])
For more details about the resulting Parameters in the model context, see the tutorial on the relevant dataset types:
Next up: let's start plotting our synthetic model.
In [ ]: