def check_initial_values(self):
""" Make sure a viable set of initial values exists for each model. """
for par_array in self.permutations:
hyper_params = self._permutation_to_dict(par_array)
model = create_model(self.model_name,
config=self.config,
logger=self.logger)
model._get_initial_state(hyper_params=hyper_params)
def _sample(self, index, n_samples, burnin):
"""
"""
# Create the model instance
# Need to do this here so we don't run into pickle/multiprocessing problems
model = create_model(self.model_name,
config=self.config,
logger=self.logger)
# Package parameter permutation
par_array = self.permutations[index]
hyper_params = self._permutation_to_dict(par_array)
# Get filename for this permutation
filename = self._get_sample_filename(index)
# Sample the model for this parameter permutation
model.sample(hyper_params=hyper_params,
filename=filename,
n_samples=n_samples,
burnin=burnin)
self.logger.debug(
f"Finished grid index {index} of {self.n_permutations}.")
import os
import cProfile
from udgsizes.core import get_config
from udgsizes.model.utils import create_model
if __name__ == "__main__":
n_samples = 400
burnin = 100
model_name = "blue_sedgwick_shen"
config = get_config()
filename = os.path.join(config['directories']['data'], 'profile_sampling.prof')
model = create_model(model_name)
hyper_params = {"rec_phys_offset": {"alpha": 0.4}, "logmstar": {"a": -1.45}}
def func(): # Not sure if this is necessary
model.sample(n_samples=n_samples, hyper_params=hyper_params, burnin=burnin)
cProfile.run("func()", filename=filename)
# snakeviz profile_sampling.prof
import matplotlib.pyplot as plt
from udgsizes.model.utils import create_model
from udgsizes.fitting.utils.plotting import fit_summary_plot
if __name__ == "__main__":
n_samples = 500
burnin = 250
ignore_recov = False
model_name = "blue_sedgwick_shen"
model = create_model(model_name, ignore_recov=ignore_recov)
hyper_params = {
'rec_phys_offset': {
'alpha': 0.375
},
'logmstar': {
'a': -1.4750000000000003
}
}
# hyper_params = {'rec_phys_offset': {'alpha': 0.4}, 'logmstar': {'a': -1.45}}
df = model.sample(burnin=burnin,
n_samples=n_samples,
hyper_params=hyper_params)
if not ignore_recov:
cond = df["selected_jig"] == 1
df = df[cond].reset_index(drop=True)
plt.show(block=False)
if __name__ == "__main__":
n_samples = 10000
burnin = 1000
# Get best fitting hyper parameters
grid = ParameterGrid(MODEL_NAME)
# Get best fitting hyper parameters
hyper_params = grid.get_best_hyper_parameters()
# Sample the model with no recovery efficiency
model = create_model(UDG_MODEL_NAME, ignore_recov=True)
df = model.sample(burnin=burnin,
n_samples=n_samples,
hyper_params=hyper_params)
# Identify UDGs
cond = df["is_udg"].values == 1
df = df[cond].reset_index(drop=True)
# Get sizes
x = df["rec_phys"].values
# Make histogram
y, edges = np.histogram(np.log10(x), bins=10, density=True) # Log10 bins
centres = 0.5 * (edges[1:] + edges[:-1])