Notebook

Searches: DynestyStatic¶

This example illustrates how to use the MCMC ensamble sampler algorithm Zeus.

Information about Zeus can be found at the following links:

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%matplotlib inline
from pyprojroot import here
workspace_path = str(here())
%cd $workspace_path
print(f"Working Directory has been set to `{workspace_path}`")

import matplotlib.pyplot as plt
import numpy as np
from os import path

import autofit as af

Data

This example fits a single 1D Gaussian, we therefore load and plot data containing one Gaussian.

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dataset_path = path.join("dataset", "example_1d", "gaussian_x1")
data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, "data.json"))
noise_map = af.util.numpy_array_from_json(
    file_path=path.join(dataset_path, "noise_map.json")
)

plt.errorbar(
    x=range(data.shape[0]),
    y=data,
    yerr=noise_map,
    color="k",
    ecolor="k",
    elinewidth=1,
    capsize=2,
)
plt.show()
plt.close()

Model + Analysis

We create the model and analysis, which in this example is a single Gaussian and therefore has dimensionality N=3.

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model = af.Model(af.ex.Gaussian)

model.centre = af.UniformPrior(lower_limit=0.0, upper_limit=100.0)
model.normalization = af.UniformPrior(lower_limit=1e-2, upper_limit=1e2)
model.sigma = af.UniformPrior(lower_limit=0.0, upper_limit=30.0)

analysis = af.ex.Analysis(data=data, noise_map=noise_map)

Search

We now create and run the Zeus object which acts as our non-linear search.

We manually specify all of the Zeus settings, descriptions of which are provided at the following webpage:

https://zeus-mcmc.readthedocs.io/en/latest/ https://zeus-mcmc.readthedocs.io/en/latest/api/sampler.html

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search = af.Zeus(
    path_prefix="searches",
    name="Zeus",
    nwalkers=30,
    nsteps=1001,
    initializer=af.InitializerBall(lower_limit=0.49, upper_limit=0.51),
    auto_correlations_settings=af.AutoCorrelationsSettings(
        check_for_convergence=True,
        check_size=100,
        required_length=50,
        change_threshold=0.01,
    ),
    tune=False,
    tolerance=0.05,
    patience=5,
    maxsteps=10000,
    mu=1.0,
    maxiter=10000,
    vectorize=False,
    check_walkers=True,
    shuffle_ensemble=True,
    light_mode=False,
    iterations_per_update=501,
    number_of_cores=1,
)

result = search.fit(model=model, analysis=analysis)

Result

The result object returned by the fit provides information on the results of the non-linear search. Lets use it to compare the maximum log likelihood Gaussian to the data.

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model_data = result.max_log_likelihood_instance.model_data_from(
    xvalues=np.arange(data.shape[0])
)

plt.errorbar(
    x=range(data.shape[0]),
    y=data,
    yerr=noise_map,
    linestyle="",
    color="k",
    ecolor="k",
    elinewidth=1,
    capsize=2,
)
plt.plot(range(data.shape[0]), model_data, color="r")
plt.title("DynestyStatic model fit to 1D Gaussian dataset.")
plt.xlabel("x values of profile")
plt.ylabel("Profile normalization")
plt.show()
plt.close()

Search Internal

The result also contains the internal representation of the non-linear search.

The internal representation of the non-linear search ensures that all sampling info is available in its native form. This can be passed to functions which take it as input, for example if the sampling package has bespoke visualization functions.

For Emcee, this is an instance of the Sampler object (from zeus import EnsembleSampler).

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search_internal = result.search_internal

print(search_internal)

The internal search is by default not saved to hard-disk, because it can often take up quite a lot of hard-disk space (significantly more than standard output files).

This means that the search internal will only be available the first time you run the search. If you rerun the code and the search is bypassed because the results already exist on hard-disk, the search internal will not be available.

If you are frequently using the search internal you can have it saved to hard-disk by changing the search_internal setting in output.yaml to True. The result will then have the search internal available as an attribute, irrespective of whether the search is re-run or not.

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