uhammer offers a convenience layer for emcee.
Features: uhammer
offers a simplified API.
requires no code changes between running on multiple cores or with MPI.
fixes some issues with the MPI Pool from emcee / schwimmbad.
prints diagnostic messages when allocated nodes / cores do not fit well to specified number of walkers or other parallelization related settings.
can capture worker specific output to separate files.
implements persisting of sampler state and supports continuation of sampling at a later time.
can show an animated progress bar.
To use uhammer you need:
an instance of Parameters for declaring the
parameters you want to sample from.
a function, e.g. named lnprob, which takes a parameters object and possible
extra arguments. This function returns the logarithic value of the computed
posterior probability.
finally you call sample for running the sampler.
import time
import numpy as np
from uhammer import Parameters, sample
sigma = .5
def gen_data():
a0 = .5
b0 = .5
c0 = 1
x = np.linspace(-2, 2, 100)
y_measured = a0 + b0 * x + c0 * x ** 2 + sigma * np.random.randn(*x.shape)
return x, y_measured
p = Parameters()
p.add("a", (0, 1))
p.add("b", (0, 1))
p.add("c", (0, 2))
def lnprob(p, x, y_measured):
time.sleep(.0002)
y = p.a + p.b * x + p.c * x ** 2
diff = (y - y_measured) / sigma
return -np.dot(diff.T, diff) / 2
n_samples = 15000
n_walkers_per_param = 200
samples, lnprobs = sample(
lnprob,
p,
args=gen_data(),
n_walkers_per_param=n_walkers_per_param,
n_samples=n_samples,
show_progress=True,
show_output=False,
)
print()
print(samples[5000:].mean(axis=0))
$ python examples/sample_line_fit.py
uhammer: perform 25 steps of emcee sampler
✗ passed: 00:00:11.2 left: 00:00:00.0 - [∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣]
[0.52389808 0.53415134 1.01585175]
This package was created with Cookiecutter and the uweschmitt/cookiecutter-pypackage project template.