def test_21(verbose=False, plot=False):
b = phoebe.load(os.path.join(dir, '21_export.phoebe'))
b.run_compute()
if plot:
b.plot(show=True, time=0)
return b
#!pip install -I "phoebe>=2.3,<2.4"
# In[2]:
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger('error')
# We'll then start with the bundle from the end of the [emcee tutorial](./emcee.ipynb). If you're running this notebook locally, you will need to run that first to create the `emcee_advanced_tutorials.bundle` file that we will use here.
# In[3]:
b = phoebe.load('emcee_advanced_tutorials.bundle')
# ## init_from parameter
#
# In cases where [continuing emcee from a previous run](./emcee_continue_from.ipynb) is insufficient, it may be ncessary to resample a new emcee run from the results of a previous run. This can be useful in several scenarios:
#
# * change the number of walkers
# * resample from the "best" branch when other branches are stuck (NOTE: this is making some assumptions and should be used with caution)
# * change the parameters being sampled (in this case, any NEW parameter will need to have initializing distributions created manually or from some other source)
# * "merge" the results from multiple emcee runs (if RV- and LC-sensitive parameters were sampled independently but now you want to resample simultaneously)
#
# In all of these cases, we simply need to set the `init_from` parameter to a distribution that was created from the results of a previous emcee solution.
# In[4]:
print(b.get_parameter(qualifier='init_from', solver='emcee_solver'))
log_level='warning',
pause=True)
# In[1]:
# only needed if starting script from here
import matplotlib.pyplot as plt
plt.rc('font', family='serif', size=14, serif='STIXGeneral')
plt.rc('mathtext', fontset='stix')
import phoebe
import numpy as np
logger = phoebe.logger('error')
b = phoebe.load('inverse_paper_examples_before_emcee.bundle')
# In[2]:
# NOTE: append .progress to view any of the following plots before the run has completed
b.import_solution('inverse_paper_examples_run_emcee.py.out',
solution='emcee_sol')
# To get as "clean" of posterior distributions as possible, we'll override the proposed thinning value and set it to 1 (effectively disabling thinning).
# In[3]:
print(b.get_value('thin', solution='emcee_sol'))
# In[4]: