# At this point we would call `b.run_solver(solver='emcee_solver', solution='resample_solution')` to start a new emcee run initialize from the distribution created from this top-branch from the previous run.
#
# Now let's imagine that we also want to include an additional parameter in our next emcee run. So far our initializing distribution contains the following:
# In[10]:
_ = b.plot_distribution_collection('resample_branch', show=True)
# If we wanted to add additional parameters, we need to define their initial distributions via [b.add_distribution](../api/phoebe.frontend.bundle.Bundle.add_distribution.md).
#
# By passing `distribution='resample_branch'`, they would be appended to the existing distribution collection, but we could also name then separately and combine them later. For example:
# In[11]:
b.add_distribution('requiv@secondary',
phoebe.gaussian_around(200),
distribution='init_from_new')
# Now we need to set `init_from` to **both** of these distributions.
# In[12]:
b.set_value('init_from',
solver='emcee_solver',
value=['resample_branch', 'init_from_new'])
# Whenever `init_from` contains more than one distribution, they will be combined according to `init_from_combine` (and similarly for `priors` and `priors_combine`).
# In[13]:
print(b.get_parameter('init_from_combine', solver='emcee_solver'))
# ### Initializing Distributions
#
# Instead of the `fit_parameters` list that was used for [optimizers](./nelder_mead.ipynb), emcee requires initializing distributions. Each of our `nwalkers` walkers will then draw from this distribution set to determine the starting position in parameter space.
#
# For this case, we'll sample over just a few parameters (the ones that we offset somewhat from the known true solution). In practice, you want to sample over as many of the most-sensitive parameters as possible to account for any correlations between the parameters... but each additional parameter adds a dimension to the parameter space that needs to be sampled and so increases the computational cost.
#
# If using `pblum_mode='dataset-scaled'` while optimizing, it is generally a good idea to disable this (set to 'component-coupled' or 'dataset-coupled' and sample over `pblum` to account for any correlations between the luminosities and your other sampled parameters. For more details, see the [pblum tutorial](./pblum.ipynb).
#
# If your observational uncertainties are not reliable, you may also want to sample over `sigmas_lnf` (the [emcee fitting a line tutorial](https://emcee.readthedocs.io/en/stable/tutorials/line/) has a nice overview on the mathematics, and the [inverse paper](http://phoebe-project.org/publications/2020Conroy+) describes the implementation within PHOEBE).
#
# See the [distributions tutorial](./distributions.ipynb) for more details on adding distributions.
# In[12]:
b.add_distribution({'sma@binary': phoebe.gaussian_around(0.1),
'incl@binary': phoebe.gaussian_around(5),
't0_supconj': phoebe.gaussian_around(0.001),
'requiv@primary': phoebe.gaussian_around(0.2),
'pblum@primary': phoebe.gaussian_around(0.2),
'sigmas_lnf@lc01': phoebe.uniform(-1e9, -1e4),
}, distribution='ball_around_guess')
# It is useful to make sure that the model-parameter space represented by this initializing distribution covers the observations themselves.
# In[13]:
b.run_compute(compute='fastcompute', sample_from='ball_around_guess',
sample_num=20, model='init_from_model')
print(b.compute_pblums(compute='fastcompute', dataset='lc01', pbflux=True))
# And although it doesn't really matter, let's marginalize over 'sma' and 'incl' instead of 'asini' and 'incl'.
# In[45]:
b.flip_constraint('sma@binary', solve_for='asini')
# We'll now create our initializing distribution, including gaussian "balls" around all of the optimized values and a uniform boxcar on `pblum@primary`.
# In[46]:
b.add_distribution(
{
'teffratio': phoebe.gaussian_around(0.1),
'requivsumfrac': phoebe.gaussian_around(0.1),
'incl@binary': phoebe.gaussian_around(3),
'sma@binary': phoebe.gaussian_around(2),
'q': phoebe.gaussian_around(0.1),
'ecc': phoebe.gaussian_around(0.05),
'per0': phoebe.gaussian_around(5),
'pblum': phoebe.uniform_around(0.5)
},
distribution='ball_around_optimized_solution')
# We can look at this combined set of distributions, which will be used to sample the initial values of our walkers in [emcee](../api/phoebe.parameters.solver.sampler.emcee.md).
# In[47]:
_ = b.plot_distribution_collection('ball_around_optimized_solution', show=True)
b.set_value('pblum_mode',
'component-coupled') #Could also set to 'dataset-coupled'
#Default distribution from example:
# b.add_distribution({'sma@binary': pb.gaussian_around(0.1),
# 'incl@binary': pb.gaussian_around(5),
# 't0_supconj': pb.gaussian_around(0.001),
# 'requiv@primary': pb.gaussian_around(0.2),
# 'pblum@primary': pb.gaussian_around(0.2),
# 'sigmas_lnf@lc01': pb.uniform(-1e9, -1e4),
# }, distribution='ball_around_guess')
b.add_distribution(
{
't0_supconj': pb.gaussian_around(0.001),
'incl@binary': pb.gaussian_around(5),
'q': pb.gaussian_around(1),
'ecc': pb.gaussian_around(0.005),
'per0': pb.gaussian_around(0.2),
'sigmas_lnf@lc01': pb.uniform(-1e9, -1e4),
},
distribution='ball_around_guess')
b.run_compute(model='EMCEE_Fit',
compute='fastcompute',
sample_from='ball_around_guess',
sample_num=20)
b.set_value('init_from', 'ball_around_guess')
# In[2]:
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
# The latex representations of parameters are mostly used while plotting [distributions](./distributions.ipynb)... so let's just create a few dummy distributions so that we can see how they're labeled when plotting.
# In[4]:
b = phoebe.default_binary()
b.add_distribution({
'teff@primary': phoebe.gaussian_around(100),
'teff@secondary': phoebe.gaussian_around(150),
'requiv@primary': phoebe.uniform_around(0.2)
})
# ## Default Representation
# By default, whenever parameters themselves are referenced in plotting (like when calling [b.plot_distribution_collection](../api/phoebe.frontend.bundle.Bundle.plot_distribution_collection.md), a latex representation of the parameter name, along with the component or dataset, when applicable, is used.
# In[5]:
_ = b.plot_distribution_collection(show=True)
# ## Overriding Component Labels
#
# By default, the component labels themselves are used within this latex representation. These labels can be changed internally with [b.rename_component](../api/phoebe.frontend.bundle.Bundle.rename_component.md). However, sometimes it is convenient to use a different naming convention for the latex representation.
legend=True,
s=0.01,
save='lightcurves/SolverFitted/WithOptimizer.pdf')
print('Optimizer Fit Plotted\n')
#Sampler
b.add_solver('sampler.emcee', solver='emcee_solver')
b.set_value('compute', value='fastcompute', solver='emcee_solver')
b.set_value('pblum_mode',
'component-coupled') #Could also set to 'dataset-coupled'
#Default distribution from example:
b.add_distribution(
{
'sma@binary': pb.gaussian_around(0.1),
'incl@binary': pb.gaussian_around(5),
't0_supconj': pb.gaussian_around(0.001),
'requiv@primary': pb.gaussian_around(0.2),
'pblum@primary': pb.gaussian_around(0.2),
'sigmas_lnf@lc01': pb.uniform(-1e9, -1e4),
},
distribution='ball_around_guess')
# b.add_distribution({'teffratio': pb.gaussian_around(0.1),
# 'requivsumfrac': pb.gaussian_around(5),
# 't0_supconj': pb.gaussian_around(0.001),
# 'ecc': pb.gaussian_around(0.2),
# 'per0': pb.gaussian_around(0.2),
# 'sigmas_lnf@lc01': pb.uniform(-1e9, -1e4),
# }, distribution='ball_around_guess')